Latent Space

By now we’re pretty used to LS Pod guests going on to great success, but today’s news is fast for even us - Nat and Alex at MSL have execuhired Dreamer just days after we shipped their pod, barely 11 days after we recorded with them:

We’re surprised, but not at all disappointed. If you can’t tell from the pod, we were immediately in love with the tech and the polish, but it was always going to be a long slog to build any consumer AI business and it is a very nice thing indeed to have Team Zuck on your side to push consumer distribution.

This is also approximately the 9 month anniversary of the MSL “Personal Superintelligence” Manifesto from Zuck, which reads:

As profound as the abundance produced by AI may one day be, an even more meaningful impact on our lives will likely come from everyone having a personal superintelligence that helps you achieve your goals, create what you want to see in the world, experience any adventure, be a better friend to those you care about, and grow to become the person you aspire to be.

and,

If trends continue, then you’d expect people to spend less time in productivity software, and more time creating and connecting. Personal superintelligence that knows us deeply, understands our goals, and can help us achieve them will be by far the most useful.

Rewatch the Dreamer walkthrough and observe how Sidekick is your personal intelligent agent-of-agents whose main job is the latter sentence:

This execuhire (our term for these licensing+hire-but-not-acquire deals) comes after the $2B Manus acquisition in December, also done in a matter of 10 days, which had similarly impressive tech and decent distribution, though perhaps with less of an “OS” and ecosystem heavy emphasis as Dreamer.

Combining the two teams makes for one of the most formidable consumer agent labs on Earth, and it is pretty clear what kind of talent Nat Friedman is in the market for (if you give him a pass for Vibes). If you are savvy enough… you should be able to tell what other kinds of companies might be up next. (register your predictions in the comments!)

AI News for 3/20/2026-3/23/2026. We checked 12 subreddits, 544 Twitters and no further Discords. AINews’ website lets you search all past issues. As a reminder, AINews is now a section of Latent Space. You can opt in/out of email frequencies!

Claude Computer Use, Agent Harnesses, and the Shift From “Codegen” to Full Workflow Automation

• Anthropic pushed computer use onto the desktop: Claude can now control the mouse, keyboard, and screen to operate arbitrary apps in a macOS research preview via Claude Cowork and Claude Code, a notable widening of the agent surface beyond APIs and browser sandboxes. The launch landed alongside strong community reactions about not needing a laptop for many tasks anymore and why Anthropic may have skipped acquiring broader external agent stacks in favor of owning the full “do anything on your computer” loop (Claude announcement, Felix Rieseberg, Yuchen Jin, Alex Albert).

• The agent stack is converging on long-running, parallel, tool-rich workflows: multiple tweets pointed to a maturing harness layer around coding and ops agents: Hermes Agent momentum and ecosystem curation (awesome-hermes-agent, Teknium tips, open-source vibe shift); T3 Code adding integrated browser and terminal capabilities (T3 Code browser integration, Theo on open-sourcing T3 Code); Command Center and similar orchestration tools for many-agent parallel execution from one workspace (Jimmy Koppel); and Parchi / BYOK workflows for very long-running autonomous tasks (0xSero, Qwen3.5-REAP in Parchi).

• Operational reality is now the bottleneck, not just model IQ: several practitioners complained that newer top models can be too eager, over-agentic, or delegated to weaker subagents, hurting real coding workflows; this showed up in complaints about GPT-5.2 Pro subagents, Claude browser/computer use fragility, and the broader critique that superficial parallelization often becomes “slop theater” rather than throughput gains (Mikhail Parakhin, Sarana, Jeremy Howard, bentlegen). A recurring theme: the winning products will likely be those that close the loop with traces, evals, incidents, and production feedback, not just generate code (LangSmith “close the loop”, PlayerZero summary).

Research on Self-Improving Agents, RL Post-Training, and Benchmark Generation

• Meta-affiliated work on self-improvement advanced beyond fixed meta-procedures: Hyperagents / DGM-H extends the Darwin Gödel Machine idea by allowing agents to improve not only task behavior but also the procedure that generates future improvements. The claim is that these meta-level improvements transfer across domains including coding, paper review, robotics reward design, and Olympiad grading, addressing a key limitation of prior self-improving systems that kept the self-improvement loop itself hand-authored (Jenny Zhang).

• Meta also presented a broader RL post-training unification story: RLLM = RL + LM-as-RM trains a language-model reward model on-policy from the policy’s own outputs, aiming to unify post-training over easy-to-verify, hard-to-verify, and non-verifiable tasks. The notable claim is that using a generative LM reward model can improve reward quality across task classes compared with more brittle bespoke reward setups (Jase Weston).

• Benchmark and environment generation is scaling up fast: WebArena-Infinity claims a dramatic reduction in browser environment construction cost—from months of grad-student labor to under 10 hours and <$100 per environment—while producing harder, verifiable browser-use tasks where strong open-source models now score below 50% despite doing much better on legacy WebArena/OSWorld. This matters because RL for agents increasingly needs automatically generated, high-authenticity environments rather than a handful of handcrafted testbeds (Shuyan Zhou).

• Topical RL synthesis remained popular, though less novel: a high-engagement overview from The Turing Post catalogued 16 RL variants spanning RLHF, RLAIF, RLVR, process rewards, self-feedback, and critique-based methods—useful as a taxonomy, but the more technically significant tweets this cycle were about how RL environments and reward models are being industrialized (Turing Post RL list).

World Models, JEPA, Mechanistic Interpretability, and Emerging Training Theory

• JEPA/world-model work had one of the stronger technical showings of the day: LeWorldModel claims stable end-to-end JEPA training directly from pixels with no teacher-student tricks, no EMA, and no heavy heuristics: 15M params, 1 GPU, and <1 second planning, with follow-on summaries emphasizing ~48–50× planning speedups and competitive performance against prior world-model baselines. This attracted attention because JEPA-style methods have often been seen as fragile or trick-heavy; these results argue for a much simpler training recipe (Lucas Maes, Randall Balestriero, RobotsDigest).

• Mechanistic interpretability continues to mature from “vibes” into reverse engineering: a thread summarizing Anthropic’s “On the Biology of a Large Language Model” framed current mech interp as uncovering circuits and internal features with a level of specificity that would have sounded implausible a decade ago, while also cautioning that traced circuits need not correspond to what the model can explicitly verbalize about its own reasoning (summary thread).

• Training theory and optimizer scaling also got attention: Antonio Orvieto’s thread argued that optimization theory for adaptive methods explains much of known LLM hyperparameter scaling and can suggest transfer rules without brute-force sweeps, while follow-up discussion highlighted optimizer dependence and implications for Muon-style setups (Orvieto, giffmana reaction, leloykun follow-up). This is one of the more useful undercurrents of the day: people are trying to replace empirical scaling folklore with derivations.

Document Parsing, Retrieval, and Search Infrastructure Became More “Agent-Native”

• Document parsing is becoming a serious systems layer, not a side utility: Google Devs and LlamaIndex highlighted a workflow combining LlamaParse + Gemini 3.1 Pro for extracting structured data from difficult financial PDFs, claiming roughly 15% accuracy gains on brokerage statements and complex tables. Separately, LlamaIndex’s new LiteParse targets a lighter-weight parsing path with URL and stream support and no VLM dependency, specifically pitched as something agents can call cheaply and quickly (Google Devs, Jerry Liu, LiteParse).

• Search/retrieval infra for coding agents improved materially: Cursor shipped Instant Grep, advertising regex search over millions of files in milliseconds, with a technical writeup on the indexing/algorithm tradeoffs. For agentic coding this kind of primitive matters more than another tiny model gain; search latency directly shapes whether agents can iterate over large repos fast enough to be useful (Cursor announcement, blog link).

• Late interaction / multi-vector retrieval is having a moment: the Weaviate/LightOn discussion argued that late interaction systems finally look practical for broader deployment, especially for code and reasoning-heavy retrieval. The core argument: token-level multi-vector representations can still be cheaper and more reusable than full cross-encoders, while materially improving recall and ranking quality for agentic workloads (Connor Shorten podcast, softwaredoug, Amélie Chatelain).

Model and Product Releases: Sakana Chat, MiniMax Plans, Luma Uni-1, NVIDIA Kimodo, and More

• Sakana AI made the biggest concrete product launch in the set: it launched Sakana Chat for Japanese users, backed by a new Namazu alpha model family, described as post-trained open models tuned to reduce upstream bias and better reflect Japanese context and values. Sakana positioned this as both a consumer product and a demonstration of culturally localized post-training; the supporting technical blog also tied into its prior work using ensembles plus novelty search to extract narratives from 1.1M social posts in a Yomiuri collaboration on information operations analysis (Sakana Chat, Namazu alpha, Hardmaru on the OSINT workflow).

• MiniMax continued to push productization hard: it introduced a flat-rate “Token Plan” covering text, speech, music, video, and image APIs under one subscription, explicitly pitching predictable all-modality billing and compatibility with third-party harnesses. This is notable not because subscription packaging is flashy, but because multimodal API consumption has become operationally annoying enough that simplifying pricing is itself product differentiation (MiniMax Token Plan).

• Generative media shipped notable artifacts: Luma’s Uni-1 was pitched as a model that “thinks and generates pixels simultaneously,” while NVIDIA’s Kimodo drew strong engagement as a promptable motion/timeline model trained on 700 hours of mocap, supporting both human and robot skeletons and available on Hugging Face (Luma Uni-1, Kimodo).

• Other release notes worth flagging: Hugging Face Kernels 0.12.3 added support for Flash-Attention 4 via cutlass.cute kernels (Sayak Paul); TRL v1.0.0 claimed up to 44× VRAM savings for long-sequence training with AsyncGRPO on the way (Amine Dirhoussi); and AI2’s MolmoPoint GUI targeted VLM-based GUI automation with grounding tokens rather than coordinate regression, reporting 61.1 on ScreenSpotPro (HuggingPapers).

Top Tweets (by engagement, filtered for technical relevance)

• Claude computer use launch: Anthropic’s desktop control feature was the most consequential product release in the set and one of the clearest signs that mainstream assistants are moving from “answering” to operating software directly (announcement).

• Cursor Instant Grep: highly engaged because it addressed a real systems bottleneck for coding agents—repo-scale search latency—not just another benchmark increment (Cursor).

• Luma Uni-1: major engagement around a model that collapses reasoning and image generation into one product surface, though details remain sparse in the tweet itself (Luma Labs).

• Sakana’s narrative intelligence / OSINT workflow: one of the more substantial applied-AI posts, combining LLM ensembles, novelty search, hypothesis generation, and human verification over 1.1M posts (Sakana).

• JEPA / LeWorldModel: strong engagement for a compact world model recipe that is much simpler and faster than many expected, and thus potentially more reproducible by ordinary labs (LeWorldModel).

• Hyperagents / DGM-H: among the most technically interesting research posts because it targets meta-level self-improvement, not just better task execution (Hyperagents).

Mar 23 update for Latent Spacenauts: this episode was recorded before the Dreamer team announced they were joining Meta Superintelligence Labs, and it turned out to be the last interview they did before the news became public. Consider this a snapshot from just before the transition!

In 2024, David Singleton left Stripe and joined forces with Hugo Barra for a buzzy stealth startup named /dev/agents. This month they emerged out as Dreamer, a consumer-first platform to discover, build, and use AI agents and agentic apps, centered on a personal “Sidekick” that helps users customize experiences via natural language.

Sidekick is nothing less than an “agent that builds agents”, with all the complexity that that entails:

You’ve seen many many website builder, app builder, and even agent builder startups by now, but our favorite detail is the sheer amount of work that has gone into the “full stack” nature of the platform, including shipping their own SDK, logging, database, prompt management, serverless functions, and so on. Most platforms restrict the tech stack you can use just to get off the ground — Dreamer does it “right” by letting you push whatever arbitrary code you want to their VMs.

Of course former leaders of Stripe and Android would not stop at just building the tools, but also building the ecosystem. Dreamer is deeply aware of the 4 sided network effect it has going on and is ready to fund all of it.

It’s time to Dream!

on youtube.

[00:00:00] swyx: Okay, we’re here in the studio with David Singleton. Welcome.

[00:00:08] David Singleton: Hey, Wix. It’s great to be here.

[00:00:09] swyx: It’s great to have you. Uh, we have very sympa that your company color is the same as Lean Spaces color.

[00:00:15] David Singleton: That’s right. Dreamer Purple.

[00:00:17] swyx: It used to be Devrel agents, which I thought was very cool. It’s like you call back to Devrel Payments.

[00:00:22] David Singleton: Yeah.

[00:00:22] swyx: And you were obviously CTO Stripe. And talk to me about just the origin or thinking process behind Dreamer. Yeah. And maybe, maybe start with like, what, what is Dreamer?

[00:00:31] David Singleton: Yeah.

[00:00:31] David Singleton: So Dreamer is a new product, uh, which everyone can come and play with today. Um, it’s a place where everyone, literally, everyone can discover, build, and enjoy and use AI agents and agenda apps.

[00:00:45] And we really did design it for consumers, for folks who are not necessarily. Uh, have any kind of technical background. It’s really aimed at everyone. I think often of my sister, she’s very smart. She’s not in the slightest bit technical. She has lots of problems in her life that [00:01:00] she would like to be able to have great software and intelligent software to solve.

[00:01:04] But you know, even with the rise of tools like Cloud Code and so forth, she’s got no way to get started. And Dreamer is a place where she can come in, grab some intelligent apps that other people in the community have built, start using them right away, and solve real problems in her life.

[00:01:19] David Singleton: And at the core, we have a personal agent called the Sidekick.

[00:01:24] Um, you can give your sidekick a name, you can give it its own personality, and it really helps you across your entire day, your life. It helps you use all of the agents on the platform, and it also helps you build anything you want. And we’ve been working in this for a little while. We recently launched in beta.

[00:01:41] So anyone can go to dreamer.com, join the wait list. Um, and we have many, many, many people in the community now who are building really fun, really powerful, really useful. Agents and the agentic apps for themselves.

[00:01:54] swyx: I think we’re gonna go right into a demo. Yeah. I just wanna make an observation that, uh, you, you, [00:02:00] you put discover first before build.

[00:02:02] Mm-hmm. But actually, at least for the engineers in the audience. ‘cause we are primarily engineers and you’re primarily targeting consumers, right?

[00:02:08] David Singleton: Yeah.

[00:02:08] swyx: For engineers. Like, there’s a huge full stack of stuff, which we’re gonna dive into. Let’s write. It’s so impressive. I’m like, holy shit, this, this is what I’ve always wanted.

[00:02:16] Cool. Uh, so, so I think that’s really good and I’ve, in some ways, I think given your background given, uh, Hugo’s, is it Hugo? Hugo.

[00:02:24] David Singleton: Hugo. Hugo Bar. Yeah.

[00:02:25] swyx: Hugo, it’s not surprising that you can basically kind of build an app store Yeah. For agents.

[00:02:30] David Singleton: Yeah. So Hugo was my co-founder. Yeah. Um, Hugo and I met with our other co-founder Nicholas Checkoff in the very early days of Android at Google, where we were building Google’s first mobile apps.

[00:02:41] Uh, we then contributed to very core pieces of Android itself. And you’re right, we were really excited about building two things. One, solving a bunch of problems. That this breakthrough technology here I’m talking about mobile needed to have solved in order to make it work for real people at scale. And then secondly, building this ecosystem, um, [00:03:00] of third party developers using the Play Store, um, and able to deliver way more value on the platform than we could have delivered on our own.

[00:03:08] And we think about Dreamer in exactly the same way. So I was working at Stripe, as you mentioned, and we had the opportunity to put some of the very first AI agent systems in the world into production. And from the moment we did the first of those, I was just struck with a strong sense of conviction that this is breakthrough technology that’s gonna change how all of us work with computers and phones and so forth, all of the, the technology in our lives, but.

[00:03:34] There’s a lot of problems to be solved, for real people to be able to make this approachable. Um, and it really is kind of a direct analog for what we were solving back in the early days of mobile apps at Google and, and Android. So it’s, it’s been fun to bring that to life.

[00:03:47] swyx: Yeah. Uh, let’s look at it.

[00:03:48] David Singleton: Yeah, let’s take a look.

[00:03:49] David Singleton: So, uh, dreamer.com, this is our homepage. This is where you can come and, uh, watch some videos about what is here and sign up for the wait list. Once

[00:03:57] swyx: you, I, I just wanna say for those listening, ‘cause we have a lot, you [00:04:00] know, switch to YouTube, look at the animations. So much care.

[00:04:03] David Singleton: We, we really care about, uh, this product being fun.

[00:04:07] Uh, and, and interesting to use. Obviously a lot of people are using it to do real important stuff. You can do real work, uh, here, uh, but also you can build fun things too. Once you get off of our wait list, you’ll come into the product. The first thing that happens is you’ll have a conversation with your side cake, which is this little friendly, uh, character here.

[00:04:27] And psychic will seek to get to know you and understand you. What do you care about? And will help you discover and build your first AI agents or agentic apps. After that, you’re, you’re gonna have a dashboard. This is my dashboard. Everyone’s is different. Um, you can see I have a few things here. I have a feed.

[00:04:42] So a lot of our agents do things in the background when you’re not looking and the feed is how they let you know what they’ve been up to. I have, uh, some widgets, uh, from apps that I have built. Uh, this one is called Calendar Hero. Uh, this is something that I installed from the gallery. Uh, so built by someone in our community.

[00:04:59] It’s a [00:05:00] really powerful calendar app because for each of my meetings, if it’s with someone I don’t already know, well it’ll actually go off and research it, um, and give me both a history of my interactions with those people and also a bunch of, you know, public useful information to, to get started. One of the things I love about this particular app is that every day it generates a podcast, um, a daily briefing.

[00:05:24] And one of the things that we’ve done with the platform is we’ve made it possible for all the things that agents do to show up in places that you care about. So if you look over here, this is the screen in my phone, and if I go ahead and open my Apple Podcasts, you can see right here. Your Daily briefing podcast is ready.

[00:05:39] This was produced by an agent running in my Dreamer account, and it was very easy by scanning a QR code to connect it to my Apple podcast. That’s what I listened to in the car now every morning. Yeah. On my way to work.

[00:05:50] swyx: It, it

[00:05:50] David Singleton: preps me for, for my day.

[00:05:52] swyx: So one additional bit of context. I asked you immediately after seeing this was like, what, what about, I wanna talk back to my agent and you said you actually started with voice and then you went to [00:06:00] podcasts.

[00:06:00] ‘cause it’s nice to have it pre downloaded

[00:06:02] David Singleton: that, right? That’s right. Um, yeah, we, you, you can talk to your sidekick. So, you know, on mobile we have, uh, a dreamer app and you can talk to the sidekick right here. Um, but we’ve actually found that making things, uh, show up in the other apps that you already use in your life is incredibly powerful.

[00:06:19] So let’s take a look at what’s kind of under the hood here.

[00:06:21] David Singleton: So I already mentioned that we have a gallery, so this is where you’ll find a lot of agents from our community. Uh, there’s. Many at this point, hundreds. And they are solving all kinds of, uh, use cases. I’d say the the top use cases are on personal productivity, but also a lot of information management that can range from personal information like docs and so forth, managing your emails.

[00:06:42] It also ranges out to public information that you might be interested in, but you need something to help manage the, the kind of fire hose of stuff that’s coming at you. For instance, I have, um, an agent which looks at all the AI news, um, all the time. There’s a lot of it and it finds the stuff that I would actually be [00:07:00] interested in, um, and I find it incredibly useful.

[00:07:03] So these are agents that you can install that other people have built. Anything that you install on Dreamer, you can actually just say, I wanna start making some changes, and we’ll look at that in a second. But in natural language, with the sidekicks help, you can change any of these experiences to work just the way you want them.

[00:07:18] But the base layer of the system are tools. So you know, as well as anyone swyx, that any AI system is only as good as the quality of data that it can pull in and the quality of action it can take. So before we launched our beta, we worked very hard to make sure that we seeded our tools with a bunch of very high quality and powerful integrations.

[00:07:39] So, you know, for instance, this is real Google search, this is actual Gmail. Um, and you can do very useful things with those. But also this is a platform for everyone. And as we got started talking to people in our alpha community, a whole bunch of sports use cases popped out and we realized if you want to build something cool for sports with ai, you need really high quality live data.

[00:07:58] So look at these [00:08:00] Formula one M-L-B-N-F-L, uh, these are tools, uh, that we’ve built. We’ve done a, these are not data scraped off the web. This is a, a direct data feed integration. And because it’s live and ‘cause it’s high quality, you can build really powerful stuff. But tools is not something that we are just going to kind of control ourselves.

[00:08:19] The platform is open for tool Builders to contribute tools that anyone on Dreamer can use. So, um, this is actually the place in the platform where I think software engineers, um, well number one, would love for you to come and play with it. Uh, but software engineers are really gonna build, um, a lot of powerful stuff into the system.

[00:08:38] And we are actually sharing something for the first time on this podcast, which there is, uh, tool builders on Dreamer get paid. So if you publish a tool to the platform and a lot of agents use it, you’ll actually get paid, uh, in proportion to their usage. And we’d love for folks to come and give this a try.

[00:08:54] We’ve got good docs that help you get started and you can build things that, you know, scratch your own itch. For instance, someone built this [00:09:00] Ski Bum tool, which provides live snow conditions for a bunch of, uh, ski resorts. I’d love to show you how I’ve used that in a second. And also we have some tools, partners where the tools themselves are paper use.

[00:09:12] So for instance, parallel web systems is a premium tool. Uh, you can do really cool stuff with it. Um, it’s a a, an agentic web research tool. And that one, because it’s expensive to operate, is paid on a, on a per usage basis. But if you’re coming in to build agents on the platform, even the premium tools, you get a free trial.

[00:09:29] So you get a chance to actually try them out, make sure that the use case is good for you before you decide to, to to sign up. So that’s tools. So we have the gallery, we have tools, and then the sidekick helps us put all of this together to build agents. We do that in the agents studio. You can also do this on your phone, but if I open up Agent Studio here on Desktop psychic’s, just gonna start a conversation about what you want to build together.

[00:09:51] I’d love to show you one that I made recently.

[00:09:53] swyx: Let’s do

[00:09:53] David Singleton: it.

[00:09:53] David Singleton: Um, let’s look at something that hopefully is kind of near and dear to your heart. So one of the things I love about Dreamer and this kind of moment in technology is that if you think about it. There are all these things in your life where, have you ever gone to a conference?

[00:10:09] I know you have. Right? And, uh, big conferences have apps. Um, and these apps are usually built by agencies and they’re, they’re usually actually quite expensive to build. I’ve been involved in running some of these myself. And how many conferences have you been to where the app was good? Zero. Honestly.

[00:10:23] swyx: Exactly. Zero,

[00:10:24] David Singleton: maybe one. I, I’ve, I’ve been to one conference. That was pretty good. Wait, wait session sessions. Um, but, but the point is, they’re rarely great pieces of software. Right. And they’re also expensive to build, but they’re, they’re interesting ‘cause they’re episodic, they last for this one thing. Um, and then they’re, they’re not relevant anymore.

[00:10:43] Um,

[00:10:43] swyx: and so it’s the worst feeling to invest in them because, you know, it’s like, it’s got a limited. Date?

[00:10:48] David Singleton: Absolutely. So I decided to build, uh, a conference app for your AI engineer conference. Amazing. Uh, on Dreamer. One of the things that Swix has done, uh, which I [00:11:00] thought was very forward-looking, is actually put a whole bunch of data about the conference on the webpage in an LLM readable way.

[00:11:06] There’s an LLMs txt file, there’s a feed of all of the sessions in js, ON. So I used the data from your conference last year and built this intelligent app, uh, just by talking to our sidekick, uh, in Dreamer. So just to give you a quick tour, this is my Dream Conference app. What I always wanna do for conferences is I wanna be able to search for speakers.

[00:11:28] I’m usually there because, uh, there, uh, is a speaker I care about. So, you know, SWIX, you’re the speaker I care about. I can actually see here who you’re on stage with. So here’s, here’s Greg Brockman. You’ve read even ai, uh, and this is his session. And look Greg and Swix for the speaker. So let’s add that to my schedule.

[00:11:45] Great. And then maybe there’s a couple others I might see here. Like on day two, I remember there were some keynotes. So, uh, building the open agenda web, that sounds fun. So I add that to my schedule.

[00:11:55] swyx: She’s now CEO of Xbox.

[00:11:56] David Singleton: Awesome.

[00:11:57] swyx: Which is interesting. So cool. So,

[00:11:59] David Singleton: so I’ve [00:12:00] gone through and picked out a couple of sessions that I cared about.

[00:12:03] That’s as far as I usually get with any conference app. But of course you’ve got the whole of the rest of the conference to figure out what to do. So here is where the native intelligence of, of these things you build on Dreamer can come in. So I’m gonna click guide me. So Dreamers sidekick actually parsed out the whole schedule and figured out what some of the themes are and I can choose what I’m interested in here.

[00:12:23] I’m definitely interested in agents. Uh, I’m definitely interested in code generation and also reasoning in rl. So now I’m gonna say build my schedule. So what this is doing is. It’s going across every time slot for the conference. And it’s choosing among the things I could go to, which one it thinks is best for me based on my interests.

[00:12:41] It also uses its own memory of me that’s part of Dreamer, uh, to understand what I might like best. And you know, there’s an LLM prompt running for each one of these time slots. So this is, it’s not super fast, but it’ll be done in about 30 or 40 seconds. And I’m gonna have a special custom schedule for the conference.

[00:12:57] This, like I said, is my [00:13:00] dream conference app is exactly what I’ve always wanted and I was able to build this yesterday morning. Um, I did it between some meetings. I think I spent a total of 25 minutes of wall clock time on it. I did it over the course of a couple of hours. And, uh, here is my schedule for the conference.

[00:13:15] I can see it in a calendar view. This is what I should do on Tuesday, this is what I should do on Wednesday. Oof, no conflicts, but, you know, I may not go to every single thing. And there you have it built in, you know, dreamer. So let’s take a look at what the building experience actually looks like. So this is the, the actual account that I made it on.

[00:13:32] Oh, of course I should say anything you build on Dreamer also works on your phone. So, uh, here is my AI engineer conference app right here on my phone. Got all the same functionality, and of course this is the best place to jump into my schedule.

[00:13:46] swyx: Yeah.

[00:13:46] David Singleton: Um,

[00:13:46] swyx: so you could generate a podcast about it just completely multimodal, absolute thing, right?

[00:13:51] To me, I mean, this is why I outsource, I mean, well, I, I posted the L-M-T-X-T, the JSON because you cannot run an engineer conference in 2025 [00:14:00] and not let engineers. Do whatever they want.

[00:14:02] David Singleton: Yeah.

[00:14:03] swyx: And since all conference apps suck, I’m just gonna put up a ba minimum viable app and just let people do whatever they want.

[00:14:09] David Singleton: Totally. And the cool thing about this on Bremer is I published this to the gallery and you can use it so you’ve got one that’s built to my taste of conference apps. I think it’s pretty cool. But you might want something different. Yeah. In which case you just start telling the sidekick how to change it.

[00:14:23] So let’s just very quickly look

[00:14:24] swyx: at our, what sports grid is also, you can fork it, right? That I can publish. That’s right. I can publish your one and go, this is the base starter. It’s, it’s got good defaults, but go customize, whatever.

[00:14:32] David Singleton: That’s right. That’s right.

[00:14:33] swyx: Yeah.

[00:14:33] David Singleton: So let’s take a look at how I actually built this.

[00:14:34] This is real. So I’m gonna say make changes. This experience we’re looking at now is our, uh, agent development studio. Um, like I said, you can do this on your phone as well. And in fact, this one I started out on desktop. Let’s look at my actual prompts. I said, let’s make an agent called AI Engineer Schedule Planner should be a custom schedule planner for the AI engineer conference.

[00:14:53] I’m not gonna read this all up. You get, you get the point and it told it where to get the data from. So that was the first prompt. And actually after I gave it that [00:15:00] prompt, I actually had a simple version of this app working, um, after the sidekick took one turn. So the Sidekick is a, like a professional software engineer, and we’ve worked very hard to make this work and build functional apps for folks that might not have any engineering experience whatsoever.

[00:15:14] So, you know, done here we have build logs that are technical, but you can hide those away. And sidekick, as it is building, will actually translate everything that is coming out of, uh, of the, the harness into English that you can actually read. And by the way, this English is in the personality of your sidekick, which is fun.

[00:15:32] Um. And the way that we build agents and agent apps, it’s a little different to what you might have seen in some other platforms for a couple of reasons. One, just the build process. The very first thing that Sidekick does, it understands all the agents you’ve got set up. It understands all the tools and it will come up with a plan for how to realize your goal, how to make sure it actually has the data and the capabilities to complete it.

[00:15:54] It will occasionally refuse. If it can’t do what you’re asking, it will tell you I can’t do that. It needs another tool. And that’s a good [00:16:00] jumping off point for any of the tool builders out there to build a new tool. So it’ll fi first figure out how, then it will build it, and then it will actually test it.

[00:16:07] So it will actually make sure that the thing that it has generated is realizing your goal. And you probably know as well as anybody that anytime you can get any. Modern state-of-the-art coding model into a loop where it can make changes and perceive its own output and then fix bugs. Magic happens. So these builds, the first build will often take 10 to 15 minutes on Dreamer, which is a little bit longer than you might’ve seen on some other platforms.

[00:16:31] But the first thing that it creates will work most of the time. And then of course, as you start making smaller changes, you can like ask it to tweak the UI in any way that you like. Those are much faster. And just to give you a sense, uh, for this one, here’s something I asked. Put a logo, I gave it a logo file in static files.

[00:16:48] Use that as the title. So for folks that actually really want to dig, uh, into a bit more detail, we’ve provided a powerful IDE here. So I can actually see here’s the code that was generated and some pieces of the [00:17:00] code are more accessible than others, like the prompts. So this is the prompt that’s used by a powerful LLM in order to do that schedule picking.

[00:17:08] And I can actually read it here directly. I can edit it without having to ask the sidekick if I want to do that.

[00:17:12] swyx: So this is very nice.

[00:17:13] David Singleton: This is for the more, the more, uh, sophisticated users.

[00:17:16] swyx: Yeah. This is other people’s entire startup is prop management.

[00:17:21] David Singleton: This is true. The other thing that is different about Dreamer is once you’ve built something here, it’s ready to go.

[00:17:28] We host it. So you don’t have to worry about getting a database from a database provider signing up, getting API keys. You don’t have to worry about your LLM provider tokens. All of that is hosted on the platform. And you can use it yourself. You can share it to the gallery for other people to, to riff on it.

[00:17:46] You can also share it with your friends and coworkers to use your instance of the agent or agentic app. And we’re seeing that happen a lot in our community. We’ve seen a whole bunch of folks who built little applications for their personal life [00:18:00] and shared them with their significant other. We’ve seen people who are building little productivity apps for their team at work and sharing it, uh, among them.

[00:18:07] And we actually do this a lot inside of the company. So at this point we, we pretty much run the company on Dreamer agents for all kinds of important things. Uh, maybe a good example of that is, um, our wait list. People are signing up every time someone signs up for our wait list. A dreamer agent will actually research, uh, that person.

[00:18:25] And we’re looking for folks who are builders, not super technical to build agents and come in, uh, and give us a lot of feedback and we’re prioritized bringing those people off of the wait list First,

[00:18:35] swyx: just a quick question on that one is there’s, it may not come up again. Do you find enrichment APIs to be useful like the ZoomInfo?

[00:18:42] Uh, clear bit

[00:18:43] David Singleton: enrichment is a very, uh, common use case. Um, on dreamer. Any application on Dreamer can kick off a sub-agent to do a particular task. Um, so this actually is a powerful agentic harness that runs inside of its own [00:19:00] vm. Uh, we call them sidekick tasks ‘cause they actually run in the context of the sidekick.

[00:19:04] I’ll talk more about Sidekick in a second and. Enrichment is a very common use case. And the cool thing about a sidekick task is that it has access to all the tools on the platform, but also public data as well. And so very frequently enrichment on our platform happens using public data that it can be found in the web.

[00:19:24] There are some tools for getting people data, uh, from, uh, from various bespoke systems. And so that works pretty well. But actually, you’d be surprised. I mean, we would love if someone out there would like to build a ZoomInfo tool, we don’t have one today. We’d love to see that on the platform, and I’m sure it’ll be very powerful.

[00:19:39] But we’re also seeing that this powerful agent harness can pull a lot of data in on that note of tools that make experiences better, we’re constantly adding more tools because people in the community are building them and publishing them. We review the tools carefully and then they go live for everybody.

[00:19:54] Yesterday we added granola. And that was pretty cool. So I was talking to actually, uh, Sarah on my team was [00:20:00] talking to, uh, someone building on the platform this morning and they actually, they have an agentic app that they built, which is a kind of magic to-do list. So they put stuff on their to-do list and for each thing it kicks off one of these, uh, sidekick tasks to figure out how to move the ball forward thing.

[00:20:14] Sometimes it’ll complete it

[00:20:15] swyx: entirely. Yeah.

[00:20:16] David Singleton: Often by calling another agent on the platform and sometimes it just kind of researches it and helps ‘em take the first step.

[00:20:21] swyx: Yeah. Do you know, this is Sam Altman’s number one, ask for an AI app. It’s the self-completing to-do list.

[00:20:26] David Singleton: Yeah. The self-completing to-do list is something that a lot of people have built on Dreamer and are getting a lot of use out of.

[00:20:32] Yeah. And, and finding it actually genuinely I shouldn’t, I should, I should try that. Mm-hmm. Please do. And you’ll even find some in the gallery that you can remix. So he was saying this morning that he’s, he built this self completing to-do list, uh, on Dreamer already. But he connected the granola tool yesterday and now something really magical happens, which is when he says in meetings that he’s gonna do a thing, it magically shows up on his to-do list and then it can magically get completed.

[00:20:56] And then, as I mentioned, all the agents, all the [00:21:00] apps on Dreamer can actually work together. So our coding agent, as it builds them, does something very special where it exposes the internals of each of the experiences to the system. And then Sidekick can manipulate those to get stuff done. So he has built another agent, which he uses for recruiting.

[00:21:18] It kind of keeps track of candidates and also it’s got a kinda mini CRM function, so he’s able to introduce candidates to each other. He told us this morning that something he’d committed to do in a meeting that was recorded on granola yesterday showed up in his magic to-do list and his magic to-do list.

[00:21:34] It was like introduce a person for recruiting, used his recruiting agent to get it done.

[00:21:39] swyx: Ah,

[00:21:39] David Singleton: um, and this is, this is the dream. This is why we started the company. It really is the case that you can build and use these very powerful, bespoke experiences that can automate your life by working together. And I’d love to talk a little bit about how they work together.

[00:21:55] David Singleton: So obviously it’s really cool to have [00:22:00] software that will work on your behalf, but it’s only useful if you can trust it, right? So privacy and security is very important to us making these things accessible and. While also being trustworthy is hard. So the model that we have, which is working very well, is that the sidekick is at the core of everything here.

[00:22:22] So it is both your companion, your helper, but it’s also the traffic cup in the system. So when, when one agent wants to work with another agent and dreamer, it doesn’t do it directly, it does it via the sidekick, well ask the sidekick to do the thing. And the sidekick understands both everything, all the expectations that have been set with me as a user about what agents can do, which tools I’ve given them permission to use.

[00:22:45] And it will make sure that whatever is is going on is actually aligned with my own interests. And you know, that’s part of the background that I bring to this problem domain. I’ve. Worked for years, uh, keeping very important information, safe and secure. And [00:23:00] so as we started to think about this problem, we realized that we actually had to build something that’s a bit like an operating system.

[00:23:06] You know, the sidekicks, like the kernel, the agents and apps are like users. Yeah. Different rings. Exactly. Because if you try to pick off just one piece of this, you can’t actually make it work for people at scale. Uh, because you could build little vibe coded apps, but they’re gonna grab all your data willy-nilly.

[00:23:23] They won’t be able to work together. You actually have to invest in the fundamental core in order to make it work well for people. And that’s what we’ve been doing and it’s, uh, it’s been a lot of fun. One other thing I wanted to mention is, um, I’ve obviously talked about two things, tools and agentic apps.

[00:23:42] We really designed Dreamer to be an ecosystem and a platform, and one of my favorite quotes about platforms, I think it’s from Bill Gates, is that you can only be a platform. If you create more value for the folks participating and using the platform than, than the platform itself creates. [00:24:00] And that’s our goal here.

[00:24:01] So we at every step have been thinking about how do we make sure that other people are deriving even more value from Dreamer than we are? So in that vein, I already mentioned tool builders get paid and people can build agents that solve their needs and share them with others, and we are already thinking about ways that they can actually monetize those as well.

[00:24:24] Against that backdrop, one of the things that we are launching today is our Builders in Residence program. So there are tons of people building really cool stuff and contributing it to the gallery already, but we’ve been really inspired by programs we’ve seen at other companies where artists might be in residence, people that are very creative.

[00:24:43] And might have ideas outside of what the, the folks at the company or in the ecosystem already have. And so we are looking for creative people who have fun ideas and, you know, want to really figure out how to apply their creativity at the cutting edge [00:25:00] of technology today to come and work with us. So, uh, if you go to dreamer.com/latent space, you’ll find, ooh, well, we love Latent space.

[00:25:09] Uh, you’ll find a link both to, uh, our tool Builder information and our builder in residence program. And for builders and residents, we’ll let you in off the wait list quickly, build an agent, and then for a small number of, of the most creative folks, we’re going to pay you to build agents. Uh, you can work directly with our team.

[00:25:29] You know, this is like building Legos. So, you know, we’ve got some of the basic blocks together already, but if you need a Ron steering wheel and we don’t have one already, like we’ll build it for you. Yeah. Um, we really want to be inspired by, by these, uh, these builders in residence.

[00:25:43] swyx: This Legos thing is pretty common as an analogy.

[00:25:46] And there’s a, there’s a thing I call the master builder. Uh, we, the actual Lego company has master builders that they employ Yeah. To inspire people and post on socials.

[00:25:56] David Singleton: That is exactly what inspired us as well. Honestly, we talked about the Lego Master [00:26:00] Builder program, so that’s our builder in residence program.

[00:26:02] swyx: Yeah.

[00:26:03] David Singleton: Um, and then, uh, finally back on, on tools. Like I said, anyone can come in and build tools today. If you follow the latent space link dreamer.com/latent space, again, we’ll get you off. Directly off the wait list. So you can build right away, you can monetize by publishing onto the platform. That’s for everyone, the very best tool that gets added to the platform by mid-April.

[00:26:23] Uh, we have a $10,000 prize that we want to give out really, because we just want to seed the creativity of everyone out there. So we’re excited to do that.

[00:26:31] swyx: Yeah. And you know, uh, this is completely a flywheel, right? Like the more tools, the more builders, the more the third thing agents, you know, it just feeds into each other.

[00:26:39] David Singleton: That’s right.

[00:26:39] swyx: Yeah. Just on the payments thing, because we probably won’t touch on that again, but I have to ask the former CTO Stripe on payments as presumably you’re using Stripe Connect.

[00:26:48] David Singleton: Yeah.

[00:26:48] swyx: Um. Any pain points that you’re, people are very interested in agent commerce and micropayment and all these things.

[00:26:55] Presumably stable coins get into a conversation at some point, but maybe not now.

[00:26:58] David Singleton: Yeah, we are [00:27:00] really, really excited about e agent commerce. The first step we are taking is help people in the world who have never been able to build these kind of experiences and software before to build stuff that meets their passions, share it with the world and get paid.

[00:27:14] So that’s all commerce that happens on our platform, and so we don’t need anything new to facilitate that. Stripe Connect has existed for quite a while and is the perfect solution for this kind of stuff, so, um, we we’re excited about that. First and foremost, however. A lot of the things that people are already doing on Dreamer, we just talked about a self-completing to-do list.

[00:27:34] A lot of the ways that you want to complete to-dos is by actually closing the loop in the real world, and that’s going to involve the exchange of value. So we have some folks that are building tools already that actually do have money move in order to, to complete that, that loop. So far, we just want to be open and agnostic to all the protocols out there.

[00:27:54] I honestly think this moment in time is a little bit like the early web. So I personally started coding as a kid [00:28:00] and I think I got access to the internet in about 19 95, 19 96. And back then, uh, the web existed, you know, HTTP was a protocol, but there were also other protocols I was using all the time, like Gopher and UUCP and uh, various others.

[00:28:15] So the point is like the web, HTTP and HTML. Was just one among many protocols. And of course it became the winner and it’s awesome. Yeah. Um, but the others were also kind of interesting and viable at the time as well. And I think the world of agentic commerce is like this right now. Also,

[00:28:30] swyx: acp.

[00:28:31] David Singleton: Acp, exactly.

[00:28:32] All the, all the cps, you know, on Dreamer. We hope that folks will build tools that kinda make use of all of these things, but I’m sure that at a certain point. One or two will emerge as the winners, and then we’ll be able to build like really deep support in,

[00:28:44] swyx: yeah. This is like maybe a complete tangent, but I do think about how a lot of these companies in AI companies in particular have to switch from c based to usage based because of course, but then, then they end up, end up having to sort of [00:29:00] obscure the margins a little bit and then they inventing end up inventing their equivalent of rob robots.

[00:29:04] David Singleton: Mm-hmm.

[00:29:04] swyx: Uh, where they’re like, well, okay, well every company should have their own currency. And it’s, it’s like very short lead to a token.

[00:29:11] David Singleton: Yeah.

[00:29:11] swyx: Or, and I’m like, okay, well where does this end? I can’t really play out the next step as to like, is this chaos? Is this,

[00:29:18] David Singleton: yeah.

[00:29:18] swyx: Okay.

[00:29:18] David Singleton: Well, I think it is kind of like the wild west.

[00:29:21] I don’t mean that in a completely, it’s all completely disorganized way, but there’s just so many things that could happen from here. The Overton window is very wide, right? Not far how this might land. And I’m just very excited to be building a platform that can take advantage of all of those opportunities and we’re just gonna be there.

[00:29:36] Uh, working for our users to make sure that things that emerge work,

[00:29:39] swyx: you’re gonna own the consumers, you’re gonna be up the OS for the app store for everything.

[00:29:43] David Singleton: So one of the ways to think about this is, um, dreamer actually uses all of the state-of-the-art models as a user. You don’t have to think about should I be using, you know, Opus four six, or should I be using the five four model from [00:30:00] OpenAI?

[00:30:00] We are continually doing evals and so forth to make sure that the best things are there for you. You can just build on the platform and know that as the world ships around, you’re gonna get the right stuff for you. Um, and I think that’s something that is needed to actually have folks take advantage of this technology at scale.

[00:30:19] I’d love to show you another example of something I built.

[00:30:21] swyx: Let’s do it.

[00:30:22] David Singleton: This is another example of software that just lasts for a certain moment in time. So recently I went on a ski trip with a bunch of friends,

[00:30:31] ski

[00:30:31] David Singleton: Bum. Uh, so it uses ski bum. Yes. I went on a ski trip to Big Sky. I’d never been there before.

[00:30:38] And I made this little intelligent app for us. And you can see it says it’s loading big sky conditions. So it’s actually calling the Ski Bum tool that I just showed you, which is, uh, published in our, uh, in our gallery. So what is this? This is a little app that was just for our weekend trip. It shows the current status of all the lifts of Big Sky.

[00:30:54] Using that tool from the ecosystem, it shows the forecast for the upcoming weekend. It shows our [00:31:00] accommodation. This is just like where my group was staying. This is just for us and also a bunch of dining information that one of our friends, uh, put together who, who’s an expert on Big Sky. So I was able to take this app, share the link with my friends.

[00:31:12] They weren’t on Dreamer yet, just send it to them on iMessage and they get a version they can use on their phone. And of course, here’s the real kicker. So I’ve been on ski trips before and other weekend adventures with my friends. Yeah, people pay for different things and at the end of the weekend it’s always a pain to figure out who needs to pay, who to settle up.

[00:31:29] So we use this during the weekend. We added all of our expenses in here. Uh, too close are it’s drill data. It’s only too closely. And then at the end of the trip, we press split. And we’re, we settled up and we’re done. So there’s another dreamer. This was all through dreamer. So the, the actual payment? No, no.

[00:31:47] We, it happened because, because we paid for stuff in the real world, it was like, okay, this person needs to pay that person 20 bucks. Right? Right. This person already paid in that. Right. So it just helped us all settle up. We didn’t move the money on Dreamer. You could do that. And in fact, if you’re a tool builder [00:32:00] thinking about this and getting excited, like come build a tool to do that stuff.

[00:32:02] We really think of our tool builders as design partners.

[00:32:05] swyx: Yeah. I got, I got the tool. Uh, what, like, I hate, I use Bank of America. I hate bank, I hate the app. Mm-hmm. I hate the web. All banking websites just horrible.

[00:32:13] David Singleton: Yeah.

[00:32:13] swyx: So just build me, like build a thing on top of Plaid.

[00:32:15] David Singleton: Yeah. Right. And then just So

[00:32:17] swyx: five code by banking app,

[00:32:18] David Singleton: there’s already a tool for that.

[00:32:20] Oh. So, um, attain Finance is a tool, a builder in our community built. Okay. Um, and it uses a secure system like Plaid. To access your, uh, financial data and you can build powerful personal finance agents on Dreamer today using this tool. And like I said, we review tools carefully. So when bringing Attain Finance onto the platform, we did actually quite a detailed security review with that company to make sure that if folks build stuff with it, it’s, it’s gonna work well.

[00:32:49] So yeah, check that out. I think, uh, I’m, I’m pretty certain it connects to Bank of America. So you’ll be able to build the, the app that you wanted already?

[00:32:55] swyx: Yeah. There’s a couple of points I wanted to sort of dive in on, maybe highlight to folks, [00:33:00] because I, obviously, I spent more time with Dreamers. So we’re making a point where you choose on behalf of your users because they’re meant to be consumers.

[00:33:07] So maybe less technical,

[00:33:08] David Singleton: right?

[00:33:08] swyx: But obviously people can, how users can override. If you read that’s, but it’s not just lms, it is also the, the transcription. It, it’s like all, like there’s, there’s a first party curated set of here’s the house opinion. That’s right. On what?

[00:33:21] David Singleton: That’s

[00:33:21] swyx: right. The thing is, that’s right.

[00:33:22] Is what’s the list? Is there like,

[00:33:24] David Singleton: yeah, so actually if you look in the tool gallery, the first party kind of curated set are all the ones that have these grayscale icons. So we have a built in tool for image understanding, for image generation, for RSS, exploration, text to speech and so forth.

[00:33:38] swyx: Recipes.

[00:33:39] David Singleton: Uh, we actually do have a built in recipes tool.

[00:33:41] It turns out that a lot of people in our alpha wanted to do stuff for cooking. Yeah. Um, and you know, you can scrape the web to get good recipes, but we were able to quite quickly find a good repository of recipes. It works great here. Yeah.

[00:33:55] David Singleton: So the point behind these though is that we’ll keep the interfaces stable, so they’ll always work.

[00:34:00] But you know, the best translation model and, you know, there are people using this translation tool to translate Chinese podcasts into English. It’s, it’s pretty powerful. It can deal with very long text, but the best translation tool today might be different from the best translation tool sometime next year.

[00:34:15] And we’re just gonna make sure that that translation tool is always pretty close to state of the art. So you can build something and you know it’s gonna continue to work well. Of course, some of our tools are branded. You may actually have a preferred way of buying groceries, like maybe you prefer Instacart and that’s great.

[00:34:29] You can use the Instacart tool specifically.

[00:34:31] swyx: Yeah.

[00:34:32] swyx: Your partnerships, uh, I mean, I don’t know if you ever hit of partnerships, but this is gonna be a bonanza for anyone on to do deals.

[00:34:38] David Singleton: We have an amazing person who, uh, works on all of our partnerships. Um, and it’s part of what you have to do to build a platform like this that’s gonna work for people.

[00:34:46] Like, we’ve gone and done that. Schlep has a lot of work, one talks lots of different companies, um, in order to make sure that you’ve got good tools at the core.

[00:34:54] swyx: Yeah.

[00:34:54] David Singleton: And then of course, because we’re open to tool builders contributing to the platform, this is only gonna get better and better and [00:35:00] better.

[00:35:00] swyx: Yeah.

[00:35:01] swyx: One observation I have this, this is gonna master a thesis I’ve been pursuing, which is, uh, what I’ve been calling an agent lab

[00:35:05] David Singleton: mm-hmm.

[00:35:06] swyx: Where you sort of different than a model lab in, in, in the sense that you never train your own models, but you are the router evaluation layer, ex subject domain expert for choosing between, uh, models.

[00:35:18] David Singleton: Yeah.

[00:35:18] swyx: And you’re explicitly doing these things. And so like in my sort of construction, every agent lab does some version of this where like, here’s the image understanding endpoint and we will route for you and don’t worry about it. Yeah. Sally, I think it’s kind of cool.

[00:35:32] David Singleton: I, I think it makes total sense. Um, and again, to make this work for folks that don’t follow the AI news every day, it’s an actually, it’s a, it’s a really important thing to do.

[00:35:42] Yeah. And it, it’s been, it’s been a real pleasure. I mean, I’m a, I’m personally a total geek for this stuff. I love it. And being able to go and dive into all those details in order to make it work well for other people. It’s a true pleasure. I cannot imagine working at anything else right now. It’s just so much fun.

[00:35:56] swyx: The tricky part is multimodality when some of these things do [00:36:00] merge.

[00:36:00] David Singleton: Mm-hmm.

[00:36:01] swyx: And you are, you’re sort of, this is your imposing structure on things that fundamentally don’t want to be structured. And so sometimes that might work against you, but for 99% of these cases, this is fine.

[00:36:10] David Singleton: Yeah. I mean, I think it’s gonna be very interesting to see how the, the, the world matures because a lot of the power of dreamer is the ability to kick off these subagents, so these powerful agent harnesses, which can actually change how they work based on the data.

[00:36:25] I actually think that we will be able to. Kind of keep up with and stay at the forefront of the changing landscape of how tools and systems work together. And that’s, that’s new. You know, software didn’t used to work like this and now it does. Um, so even, even just figuring out how to design the right pri to make that possible has itself be a lot of fun.

[00:36:44] swyx: This is, is a sort of maybe two part question that why can’t streamer make its own tools? And then why don’t you let you builders maybe stand up their own routing group? I call this a routing group, right? Like where it’s like collect Yeah. Things.

[00:36:58] David Singleton: So two things, to [00:37:00] some extent, dreamer does make its own tools in that agents appear to the system as tools.

[00:37:05] So they can be, they can be used to accomplish things. So you can build an agent that is essentially a tool. Yeah. Um, and it it,

[00:37:12] swyx: which is to me very useful for reuse.

[00:37:14] David Singleton: Right.

[00:37:14] swyx: Right. Exactly. ‘cause I, I like, this is the way I like it. Now my next five apps, I don’t want to do this whole series of back and forth again.

[00:37:20] David Singleton: Right.

[00:37:21] swyx: Yeah.

[00:37:21] David Singleton: Um. Then at the tool layer of the system, it’s open to anyone. So it’s actually quite powerful and flexible. So if you wanted to add a tool, which was, uh, imagine that you were training your own foundation model, Swyx. That might be fun. And imagine you wanted people to be able to play with, I don’t know, maybe you make like, you know, nano chat or whatever and you want to Yeah.

[00:37:42] Let people play with your own nano chat and see how I change themselves.

[00:37:44] swyx: Now.

[00:37:45] David Singleton: You could, you could publish a tool that is Nano Chat and it nano image generation behind a tool, and it could be your own writer if you wanted to. I see. And honestly, if that’s the kind of thing that gets you excited as a builder, please come and do it.

[00:37:57] Like we, we really are [00:38:00] believers in this idea that we aren’t going to figure out every single detail ourselves. We’re gonna make sure it’s a safe and fun place to build this stuff, but we’re really open to these ideas coming from other people. Um, and so I’d like nothing more than you come in and build a tool that does some of that cool stuff that you, that you have in mind.

[00:38:15] swyx: Yeah. Awesome.

[00:38:16] David Singleton: And just as a reminder, if you’d like to do that, the way to find the links is dreamer.com/latent space. Um, and for a limited time on that page, um, anyone who’s listening to this podcast will also get directly off of our wait list. Uh, it’s quite long right now. We are working hard to bring Zika.

[00:38:32] Wait, so skip the wait list.

[00:38:33] swyx: You know, I think, I think that’s fantastic. I, I think it’s, it is really sort of probuild way to do it. I wanted to jump back to the, the bar. Yeah. You know, you know, I get excited about this.

[00:38:41] David Singleton: Yes. Okay. Let’s set it back in there.

[00:38:43] swyx: Like, let’s, you know, this is the engineer podcast that’s get

[00:38:46] David Singleton: Yeah.

[00:38:46] swyx: As technical as you can.

[00:38:47] David Singleton: Yeah.

[00:38:47] swyx: On everything you’ve built, like have a show off.

[00:38:50] David Singleton: Yeah. Okay.

[00:38:51] David Singleton: So let’s go wild in the aisles in the Asian studio. So as you can see, over on the left here is a conversation with the sidekick where you ask it what to do and it will explain in English that anyone can understand what’s going on.

[00:39:03] But, um, if you want to pull back the covers and look under the hood, um, if you’re, uh, an engineer like me, then we have this, uh, this kind of debug drawer at the bottom. So you can see the full build logs here, but you can actually also dig in and see the files and prompts that have been generated. Uh, you can upload files from your computer in static files.

[00:39:24] Um,

[00:39:24] swyx: very important,

[00:39:25] David Singleton: uh, indeed. You can actually read the prompts that have been generated for you. We intentionally put an example in here just that you can see what the format looks like. And then, you know, we already looked at this one that was generated for this particular, um, app, but if you actually want to bring the code out of Dreamer and work on your own local machine, you can.

[00:39:45] So at the core of everything here is an SDK with a powerful command line interface and we built that first. It’s actually possible to build agents on Dreamer without talking to the sidekick. You can write code with your fingers on a keyboard if you want to. I know that’s very [00:40:00] antiquated, not, but actually this can be a lot of fun.

[00:40:02] So if you wanna pull it out onto your laptop, you can use our, our CLI and, uh, you can edit it in cursor or in cloud code. You know, you don’t have to use our sidekick. And the CLI actually has full access to the rest of the platform with you as the user. So, you know, obviously it is, uh, secure and privacy sensitive, and this is a way that, um, some of our most technical builders do build stuff on the platform.

[00:40:24] The really cool thing is the side cake. When it’s in coding mode, it uses exactly the same CLI. So the way it. Build stuff on Dreamer is using the same tools that you might as an engineer. Um, and that’s actually a very powerful abstraction because it turns out that the right way to give a lot of context to agents to use CLIs is to write great documentation.

[00:40:46] Make sure that all of the things that you could do are actually possible. And guess what? That makes it a delightful developer experience for real heroes as well.

[00:40:53] swyx: Yeah. So that’s pretty cool. We’ve been telling developers to do this and they ignore this until now they have to for content.

[00:40:58] David Singleton: I, I’ve been saying this for a [00:41:00] long time.

[00:41:00] Uh, we actually Stripe docs.

[00:41:02] swyx: I mean, come on. Absolutely. Come on.

[00:41:03] David Singleton: Absolutely. But actually, I was chatting with folks at Stripe last week and saying, Hey, you gotta make the Stripe CLI actually tell agents what they can do on Stripe because that way they’re gonna use more stuff on Stripe. I think this is a real trend for the entire industry.

[00:41:16] swyx: Yeah.

[00:41:16] David Singleton: So we, we’ve been doing that.

[00:41:17] swyx: To me, this, this download and, uh, GI push mm-hmm. Everything is complete confidence in that you’re not hacking it. Right. Because there’s other, let’s call them AI builder platforms that impose their stack on you and if you, if you, and so therefore they don’t allow you to do this because they cannot.

[00:41:34] Right. ‘cause they, they impose some degrees of freedom, uh, restrictions so that they can get it to work. Yours is a fully general like VM running the full code. Correct. Do whatever you want. Correct. Any language you want. Correct. Yeah.

[00:41:46] David Singleton: Correct. Well, in terms of language, if you use the SDK, you could build stuff in other languages.

[00:41:51] We’ve actually found that TypeScript is the best language for building these experiences. Yes. Because it’s strongly tight. So you find out at compile time if you’ve made mistakes [00:42:00] and there’s nothing better than getting in. A coding agent in a loop where it can see its mistakes and ask them. So TypeScript is the language that everything gets built in by default here.

[00:42:08] swyx: Did And did you see that TypeScript overtook Python? I did. I did. Yeah.

[00:42:12] David Singleton: And for what it’s worth, when we started the company, we started writing stuff in Python, and I love Python. Um, if I do, uh, a vendor code, I always write it in Python. It’s my favorite language as a developer with my fingers on the keyboard.

[00:42:23] Um, but TypeScript is an amazing language for AI because there’s tons of training data in the models, um, and it’s strongly tight. And actually at the company we built most of the stack in TypeScript, and we have this amazing property, which is, we have type safety all the way from the database to the front end.

[00:42:40] And there’s nothing better for working with coding agents than being able to have them check their correctness, compile time. So the same ideas behind building the company’s code base, we’ve put into the agent SDK here as well.

[00:42:51] swyx: Yeah. Do you know if you’d use one of those tools, like Prisma or whatever, or is it Tool Lab for you?

[00:42:55] David Singleton: We, we actually have crafted most of our own tools. Um. For [00:43:00] instance, we had LLM Driven Code Review, uh, before the thing that got published from philanthropic this week. You know, we, we’ve been doing this stuff, uh, on our own bat

[00:43:07] swyx: email, we’ll pay $25 per review.

[00:43:09] David Singleton: We, we pay a lot less than that. However, I hear that those reviews are excellent and possibly worth $25.

[00:43:14] swyx: Yeah. You know, it’s an option. Right. It’s good, good to have it.

[00:43:17] David Singleton: Just to give you a tour of some other stuff here. So, um, I can also see all the versions. Yeah. Um, this is not gi, this is not gi, this is built into dreamer. I can see all the versions that have been pushed before. Why is it

[00:43:27] swyx: not gi?

[00:43:28] David Singleton: It’s not gi because we can make it work more efficiently than Git.

[00:43:32] And we actually, we do some work behind the scenes to kind of understand what’s in each of these versions. Yeah. Um,

[00:43:37] swyx: so one of the things I’m pursuing, and I have a lot of thesis, right? Mm-hmm. One of the thesis is like, does GI go away? Does GitHub go away? And like, what, what is the active reinvent

[00:43:46] David Singleton: you for, for what it’s worth to some extent.

[00:43:48] And anything you build, there’s a lot of path dependency. If we started over, we might make this gi There’s, uh, you know, within the company we use, uh. For our, you know, platform source code. And we like it and it [00:44:00] works well with coding agents as well. The very first versions of this, we wanted to be able to make it possible for the sidekick to manipulate it easily.

[00:44:06] Um, and this, this was an expedient way to do it.

[00:44:08] swyx: Yeah.

[00:44:08] David Singleton: Um, you can also see all the activity that has happened in the workflows that you build. A lot of agents, you’ll build on Dreamer, do things in the background, so they run on triggers. These are stimuli from the outside to kick them off, and this is a nice way to see all of the things that might have kicked off your agent.

[00:44:24] You know, you can have an agent that kicks off on a webhook, so you can plug it into external systems. You can have an agent that runs when you receive certain emails that match filters, including LLM filters. And so here you can see, oh, when did it run? What did it do? You know, if I open up one of these guide me prompts or guide me, uh, events.

[00:44:41] Oh my can see God. Well, I told you it was calling an LLM for every one of those time slots. Here’s all of the LLM calls, here’s the actual prompts.

[00:44:49] swyx: And you don’t mind exposing all of this, right?

[00:44:51] David Singleton: No. We want builders to see what’s going on under the hood. It’s haiku to,

[00:44:53] swyx: okay. Yeah. So,

[00:44:54] David Singleton: okay. Right now that one was haiku.

[00:44:56] Like I said, we work with all the models and sidekick will actually pick the best one [00:45:00] for the job. And you saw that was pretty high quality and pretty fast. So Haiku four five is the one that it picked for that job. Exactly. Uh, we also have logs, as I mentioned, there’s a database spun up on demand for every, uh, agent.

[00:45:12] You don’t have to go and figure out how to do your own hosting. This is a SQL Light. This is a SQL Light database. Yeah. Um, it’s a multi-user SQL light database. And then, uh, but, but each one is you, you get a database that is unique to this agent. But then if you share the agent with multiple people, we take care of like who are the owners in each row?

[00:45:31] And all of that stuff is just there outta the box. Um,

[00:45:34] swyx: and again, in-house?

[00:45:35] David Singleton: In-house.

[00:45:36] swyx: Oh my God.

[00:45:37] David Singleton: Yeah. Um, well we do work with a bunch of infrastructure providers, but the technology for how to manipulate this is in-house. Fun fact. We actually did a lot of our own infrastructure development early on at the company and realized we need to spend our energy in the stuff that we’re uniquely doing in the world.

[00:45:53] So we’re very delighted to partner with a bunch of great designer and some of this stuff. And then finally, um, I mentioned that agentic apps agents [00:46:00] expose all of their internals to the system so the psychic can manipulate them and use them just like a user can. So you can see how it’s decided to break this problem up into functions.

[00:46:09] Some of the functions, the ones with the little I here are exported. That means that there’s probably the visible from outside. Exactly. And others are internal. And if you want to, you can dig right in here and call individual functions and see what happens. But mostly. You don’t need to think about that at all.

[00:46:24] Yeah. Uh, you can keep that little drawer closed and you can talk to your sidekick and build really powerful and enchanting experiences.

[00:46:30] swyx: Yeah. I mean, to me, like showing this gives the engineer a complete mental model of what you’ve done and what you can do with it. Yeah. For example, the first thing I, I, I look for.

[00:46:39] A mental checklist of things, right? Like is off in the database, off looks like it’s not right. So that’s a separate layer. That’s probably me means it’s hard to do multi-user apps on the same app, right?

[00:46:50] David Singleton: So you actually, we’ve solved that. So, um, see, yes, the platform builds in off, so you as a user sign into the platform, if you’re using an [00:47:00] agent that was published by someone else, then your identity is, is kind of taken care of by the system.

[00:47:05] And when you query the database, you’re gonna get the stuff that is for you. Unless the builder specifically said, this is public data that everyone should see. So they, they actually get a chance to think about that. And again, sidekick can guide you through building, uh, agents and apps that work that way.

[00:47:19] So you’re right, that’s another thing that people have to think about when they’re trying to figure out how to build software experiences on Dreamer. You, it’s built in. You talk to the sidekick as if it were a human being about what you want and that’s what you get. So, you know, my, my Big Sky app that I just showed you that was designed for multiple people to use it.

[00:47:38] And of course the things that we were putting in as expenses were supposed to be visible to everybody, and I just told the sidekick that’s the way I wanted it. Uh, but by default, if I built an app like that, the data from each user would not been visible to the others.

[00:47:49] swyx: Yeah. Yeah. Uh, this is, I presume this is a mood question, but basically you’ve had to build your own coding agent, right?

[00:47:55] Which is sidekick slash whatever is in Inside Psychic. Obviously there’s a lot of [00:48:00] people with a lot of desire for cloud code and Code X and attachment to it. Mm-hmm. I know under the hood data basically reduced to a loop, but like, would you let people use cloud coding and Code X or is the harness too specialized?

[00:48:12] David Singleton: Yeah. If you, if you want to use, um, cloud code and Code X, then you go down here. Yeah. Hit get the S St K. And we even say this right here, edits your heart’s content Z cursor code.

[00:48:22] swyx: Like people want to use it inside of Ick, right? Yeah. They want to switch the engine.

[00:48:26] David Singleton: Yeah.

[00:48:26] swyx: That’s the coding engine.

[00:48:27] David Singleton: Yeah. We are not doing that right now.

[00:48:29] Um, you know, again, the goal really is abstract the complexity. Yeah. Um, because the real target for. Building agentic apps is folks who can’t do this already today. I can’t tell you how many users in our community I’ve spoken to who are like Dreamer has changed my life because I used to have all these ideas.

[00:48:50] If only I could find an engineer to help me implement them, I’d be able to get them done. They’re free, and now I can talk to my sidekick and, and get it built. I think that’s like really how we think [00:49:00] about the people that should get a ton of value and fun, um, out of the platform. And so they’re not asking to be able to plug in their their own, you know, coding agent.

[00:49:11] And for those folks, the opportunity is massive. If you’ve never been able to do stuff in code, now you can build stuff for you, for your friends, for your family, for your coworkers. And also there’s a huge opportunity for folks who do build stuff in code to actually contribute to this ecosystem. So that’s how we think about it.

[00:49:28] swyx: Yeah. Amazing.

[00:49:28] swyx: That’s most of what I wanted to cover Dreamer wise. I think personalization and memory yeah. Is probably like the single most important job of, uh, of the os. Maybe we could talk about that and then I’ll, I wanted to zoom out on company building stuff.

[00:49:40] David Singleton: Yeah, yeah. Sounds good.

[00:49:41] swyx: Yeah. So how do you handle memory?

[00:49:43] What, yeah, what have you found? What have you tried and failed?

[00:49:45] David Singleton: Yeah. Okay. So, uh, first of all, at the core of dreamer is the sidekick. The sidekick gets to know you and it builds up a memory about you over time, and that turns out to be very important. So Dreamer, that’s your moat. That’s Dreamer gets better the more you use it.[00:50:00]

[00:50:00] For instance, a lot of agents in the platform, when you start using them, the first thing that they’ll show you, here’s what I think is relevant to you for this particular use case. Uh, a very popular kind of agent on Dreamer is a weekend activity planner. So, um,

[00:50:14] swyx: like, just tell me what to do.

[00:50:15] David Singleton: Well, tell me what to do, especially if I’ve got kids, right?

[00:50:17] So I have two kids and a dog, and my wife and I often spend a lot of time trying to figure out what are we gonna do with the crew this weekend. And, you know, we have interests that are very consistent. It actually can take a ton of work during the week to figure this out. So there is an agent on Dreamer called Weekend Activity Planner, and it helps me find things to do with, with the family of the weekend.

[00:50:39] In fact, this morning I got a message from my weekend activity planner telling me about the St. Patrick’s Day parade on Saturday. Oh, at Civic Center. I’m Irish. My kids are technically Irish as well. I mean, they, they, they have multiple citizenships, but you know, they’re, they’re Irish. Um, what a better thing to do than take them to the St.

[00:50:56] Patrick’s Day parade. Why did that get recommended to me? Because in the [00:51:00] profile that we can, activity Planner knows about me. It knows that I’m Irish, right? So all of that comes from the memory that Psychic builds up about me over time. We have invested in this a bunch. We will continue to invest in this more.

[00:51:11] We’ve tried actually many different techniques. As, you know, the, the kind of, um, cutting edge of a agentic memory has changed over time. You know, very early on we were putting lots of facts into a vector database and, uh, and doing embeddings and pulling them back out, um, using, you know, reverse lookup of embeddings rag that actually worked, but turned out to be much more complexity than was actually required.

[00:51:33] So, you know, today we’ve replaced it with a different system. Uh, I think we use a system that’s pretty similar to what you’ll find in lots of other products, but it’s an area that we’re actively, uh, investing in. Like, there’s, there’s. More than one person at the company specifically working on memory. And so expect us to just continue to make it better.

[00:51:50] swyx: Did you try knowledge graphs?

[00:51:51] David Singleton: We’ve tried knowledge graphs. The system that we have now is not a knowledge graph. Yeah. Um, but we’ve probably implemented most of the papers you’ve seen out there on agent [00:52:00] memory and the current system is working pretty well.

[00:52:02] swyx: Yeah. Excellent. Zooming out just on the company stuff.

[00:52:06] Mm-hmm. Um, uh, this is your first time in the CEO seat. Correct. You were CTO before. Correct. What’s different?

[00:52:11] David Singleton: Yeah. The difference between being a CEO and A CTO really is just. Like making sure you’re looking across everything. So, um, I have run products before, so for instance, Android wear, you’re basically a CEO

[00:52:25] swyx: of

[00:52:25] David Singleton: that product.

[00:52:26] I, I, I was running that as a general manager.

[00:52:28] swyx: Yeah.

[00:52:29] David Singleton: However, when you do it for your own company and the buck truly stops with you, it definitely kind of raises the temperature a little bit. Um, but it’s been a lot of fun for me to think about a lot of go to market topics. Um, I spend a lot of my time these days meeting users, uh, talking to folks about what they could do on the platform, being very active on X and LinkedIn, uh, which by the way is a huge pleasure.

[00:52:51] It is so much fun to be able to engage with users and potential users directly and understand what they would like to do. Um, and that’s the biggest difference [00:53:00] between this role and being the CTO, um, of, uh, of a company. At the same time, I am someone who always likes to look for why are we doing this?

[00:53:10] Who are the people that. Benefit from it. So, you know, even as A-C-T-O-I was always paying a lot of attention to topics across the company. So I feel very grateful for all I learned in my previous roles that kind of got me ready to, to do this at this kind of scale.

[00:53:24] swyx: Yeah.

[00:53:24] swyx: To me this is like the natural lead into when I went into your office.

[00:53:27] Yeah. It’s surprisingly small.

[00:53:28] David Singleton: Yes.

[00:53:29] swyx: So, and I have a, another thesis I’m pursuing for latent space, which is the emergence of tiny teams. Yeah. Where, uh, you know, the, the classic sort of image is that teams with more millions in revenue than employees, right? Yeah. So you, that’s revenue efficiency definition.

[00:53:43] But I do think as a CEO, you are going to run a smaller team than you used to.

[00:53:46] David Singleton: Yeah. So I believe very strongly in the power of small teams. So the more people you add to a team, the more communication overhead there is. And it doesn’t even grow linearly. If you think about it, the more people you add, everyone cares [00:54:00] about getting to know everybody else.

[00:54:01] And sharing what they’re doing with everybody else. And that’s great. I’m not saying they shouldn’t, right? The very, like, I wanna work in teams that are fun, where people are talking to each other and, and sharing ideas and so forth. But, you know, there’s just a kind of gravitational weight that comes from larger and larger teams.

[00:54:16] So just inherently large organizations are less nimble than small ones. And if you run a large organization, you have to keep thinking about how do I kinda like prune things so that it can act like a small team. So a dreamer, the, the core team that built everything I just showed you was, was honestly about six people.

[00:54:34] Uh, we’re larger than I we’re about 17 people at the company now because as, but

[00:54:38] swyx: still, uh, for everything you just showed,

[00:54:40] David Singleton: it’s, it’s still a small team, which is great. Very, very high talent density team. We’ve been very, very careful and kind of obsessed as we grew to make sure that everyone that’s joining the company is joining a team that they’re gonna get a lot of, uh, learning out of, but also they’re actually going to kind of.

[00:54:57] Help everyone else a lot as well. There’s something very [00:55:00] special about that too. You know, every single person at our company I would be delighted to do any project with at any time because, uh, they’re just all great. And, you know, the smaller you keep the team, the easier it is to make sure that, that that talent density is there as well.

[00:55:14] Of course, it’s a real luxury to be building a company. We started this company in late 24, but it’s a real luxury to be building a company today because we can build with agents. So we’re using coding agents.

[00:55:26] swyx: Yeah,

[00:55:26] David Singleton: we’re using Dreamer marketing agents. All of our operations. We’re looking at how we can, can actually accelerate what we’re doing, uh, using our own tools.

[00:55:36] swyx: Um, any, actually any agents that you don’t build that you wanna shout out? Just that, that you love?

[00:55:41] David Singleton: Yeah. Is it

[00:55:41] swyx: other people’s

[00:55:42] David Singleton: agents that we built for the

[00:55:43] swyx: company? No, no, no. Other people’s, uh, stuff like you shout out granola.

[00:55:46] David Singleton: Yeah. So I showed you Attain finance. Uh, attain Finance has an agent as well, which like helps you manage your money.

[00:55:53] I find this really amazing. So, um, I always have this like lingering feeling that I’ve got a whole bunch of [00:56:00] subscriptions that if I just had a bit of time to go across them, I could, you know, figure out how to consolidate them. And the person who built Attain Finance doesn’t work at our company. What they were part of the early Alpha group.

[00:56:10] So they gotta kind of look at how all this stuff works pretty early on. And they built this really amazing experience that actually helps you. Like, save a lot of money because it will kind of help you analyze your purchases. It’s almost like a kind of a financial fitness coach. He’s called Andrew, uh, who, who built it.

[00:56:26] He came and showed it to us and the first thing it did was it recommended that he should buy fewer burritos. And, uh, he was like, it’s true. Like that is actually how I could save the most money. So, uh, that’s a, that’s a pretty cool example.

[00:56:38] swyx: Uh, I noticed he was first. Because he’s order alphabetical order.

[00:56:43] So I’m, I’m wondering how come there are no like Avar? Uh,

[00:56:46] David Singleton: yeah. Well if you’re a builder right there and you’re wondering how do I seo o myself on the Dreamer platform, Swyx suggest you name your tool Avar. In all seriousness though, those are the tools I have connected. So they’re in alphabetical order.

[00:56:58] If you haven’t yet connected them, we actually [00:57:00] kind of put them in the right order for you. So if Sidekick understands you and puts in the right order, uh, but I’d say a arc is gonna come before, uh, anything else,

[00:57:06] swyx: right? Yeah, exactly. Um, and, and then I, I think how has hiring changed? Yeah. You’ve hired plenty of self engineers in your life.

[00:57:14] David Singleton: Mm-hmm.

[00:57:14] swyx: I assume something’s changed.

[00:57:15] David Singleton: Yeah, absolutely. So one of the main things that I look for now when hiring engineers is. How well do you work with coding agents? Our team actually is quite experienced a good number. Everyone at Dreamer, other than, well, I guess I write a lot of code too. Everyone’s an ic, an individual contributor.

[00:57:32] Many of the folks that work on the team have previously been managers. And it turns out being an engineering manager, as long as you stay very close to the code and are able to continue to craft it yourself, is actually a great skill profile for being able to make agents work for you and for your team in this, uh, in, in this age.

[00:57:50] And so that’s definitely something that we look for quite intently when hiring engineers. And, um, we still have folks write some code like with their fingers. It’s just important to know [00:58:00] that the kind of core of the craft is there. But the vast majority of what we spend time doing is building quite significant and elaborate stuff together in a fun, collaborative environment with coding agents.

[00:58:09] swyx: Right.

[00:58:09] David Singleton: Um,

[00:58:10] swyx: so what, what is the interview loop like? Sit there with Codex, do something.

[00:58:13] David Singleton: Yeah, I mean, our interview loop is one a coding. Screen to make sure that the, the base is there. And then we actually do a couple of short projects, uh, with an engineer on our team and whoever is thinking about joining, where we’ll actually put out a very fully formed product idea, we’ll riff on it together and make sure that we can test product sense a little bit and we’ll actually try to build the whole thing with x or cloud code or whatever, uh, whatever the person is most familiar with.

[00:58:39] Um, and watching how someone thinks about prompting the agents, what they do while the agent is working. ‘cause you know, you can actually, this is a kind of interesting, uh, dynamic in the industry. Anytime I’m working on code these days, I always have more than one agent going at the same time because while one agent is going and reviewing the output of the next one, and if you [00:59:00] get them in a nice round robin, you can be very, very productive.

[00:59:02] You can also chain agents together. You can have one agent producing code, another agent reviewing it. And actually just seeing how folks have adapted their workflow, um, is a big part of what we’re we’re looking for in our interview process.

[00:59:13] swyx: Amazing. I guess last question, but also open to you to bring up any topics that I haven’t touched on, have you wanted LLMs to do that they still cannot do today?

[00:59:23] David Singleton: That’s a great question. Um, and it’s amazing ‘cause the capabilities of LLM just, just advanced so quickly. You know, if you’d asked me a year ago, I would’ve said, well, you know, music generation, I, I like music. Um, and Suno is amazing by the way. And, but previous generations i’d, yeah, I can kind of tell that that’s AI generated today.

[00:59:42] I listened to the latest tracks made by Suno. I’m like, that’s, that’s pretty impressive. If we went back six months, I’d be asking for better image generation. The latest nano banana, uh, which by the way is a tool on the platform that you can use on Dreamer is producing spectacular infographics.

[00:59:58] Spectacular [01:00:00] painterly images when I ask for those as well. So, so that’s quite impressive. I still think I, so I think as we go forward into the future, there is still a lot of room for human creativity and so that’s also maybe where I’m going to have to say that LLMs are most lacking. So I think that when you think about building software, the thing that’s really important and that we all need to bring is taste.

[01:00:24] Mm-hmm. Right? You have to like actually truly understand people, their motivations. How do I build something that’s really delightful? So, you know, we had to do a lot of work on Dreamer to make it possible for the experiences that we build to not look like AI generic slop.

[01:00:43] swyx: Right? We go,

[01:00:44] David Singleton: um. And we’ve done that by putting a lot of our own taste into the templates and the prompts and the, the harness.

[01:00:52] Um, so I hope you have fun playing with it. I, I, I think Dreamer today generates experiences that don’t feel super generic, but that’s a ton of [01:01:00] work, right? The LMS do not do that by default. And in fact, I, if I see a, if you ask for a simple like to-do list app or something, uh, built by the models, I can tell which model built it just by kind of how it looks.

[01:01:10] So, um, taste, creativity, sense of individuality is still something that I think the LLMs are not producing out of the box. And I think that’s gonna be an interesting frontier. What do you think?

[01:01:21] swyx: Usually that’s, this is by, uh, from builder to researcher question. ‘cause uh, we do have researchers listening.

[01:01:27] Yeah. And I’m just like, well, that’s it. But like soft taste for me please is, is like a very broad topic. Uh, what do I think? I mean, I agree. I just think that it’s too big of a topic to break down. Mm-hmm. Particularly because there’s a lot of, I’ll know it when I see it type, uh, eval, which is unverifiable for, for researchers to do so.

[01:01:45] David Singleton: Yeah, I mean I, I do talk to researchers quite often and, uh, we talk about this topic and I think most people agree

[01:01:51] swyx: uhhuh

[01:01:52] David Singleton: that, you know, one of the great things about building models to generate code was just, you know, it’s so verifiable. So Yeah. Um, you know, it’s [01:02:00] very tractable and they agree that the next problem is how do you kind of step up that hierarchy of needs and get into these taste questions.

[01:02:08] And quantifying taste is hard, but I’m actually kind of excited that some people are gonna start doing this. And you know, that’s when I think that some of the really iconic companies in the world will start to become places where, you know, folks really try to like. Debug and understand the creative process.

[01:02:23] And I get pretty excited about that.

[01:02:25] swyx: Yeah. Uh, I, I think we are slowly uncovering what intelligence really means and, and the, the benchmarks that we adopt and then abandon because they’re solved is, is basically us evolving the machine intelligence in the way that we, the different way than we evolved, but we are slowly understanding what it means to be intelligent.

[01:02:44] Right. And, uh, and it’s, it’s interesting. I wonder how they suppress us in the future, but like, we’re not even there yet. We’re just like, get, get it to a place where we like what we get. Mm-hmm. From the machinist sometimes. You know, it used to be 30%, now it’s like 95%, but still there’s that 5%. [01:03:00] That’s right.

[01:03:00] Yeah. Any other topics we should have touched on?

[01:03:02] David Singleton: No, I think we’ve covered everything, but I wanna remind everyone,

[01:03:06] swyx: ct

[01:03:06] David Singleton: dreamer.com/latent space.

[01:03:09] swyx: Yes. No, it’s a, it’s a very good deal. I mean, like, come on. Like, yeah. So thank you for offering that.

[01:03:14] David Singleton: Cool. Well Swyx, thank you so much. This was fun.

[01:03:16] swyx: Yeah, thank you.

[01:03:17] Uh, we, we’ll get Alejandro to put like flashing neon signs on the, on the YouTube. Cool. Wonderful. Um, alright. Thanks. So my cool,

[01:03:23] David Singleton: awesome, thank you.

The news today of OpenAI acquiring Astral completes a loop first opened by GDM when they bought what became the Antigravity team last July, and then Anthropic’s purchase of Bun last December. Astral joins OpenClaw and (to a lesser extent) gpt-oss and Whisper in OpenAI’s growing list of top tier open source AI projects.

This comes against the backdrop of Fidji Simo explicitly dropping “side quests” like Shopping (with key partner Walmart reporting awful conversion about 1/3 of click-outs) and prioritizing Enterprise (Frontier Alliances) and Coding (Astral), and now unifying ChatGPT and Codex apps into one “superapp” — something we have predicted but is now explicitly being prioritized at the highest levels.

If we got one thing wrong in Rise of the AI Engineer 3 years ago, it is the importance of the role of code. Back then we framed the “1+2=3” thesis - that LLM-powered software would be capable of much more than either LLMs or software would alone - essentially presaging what would now be called harness engineering. But we almost completely missed the importance of the recursive nature of agentic coding improving agent/LLM training, which has been called out all over from Claude Code to MiniMax 2.7 as a key element of acceleration and importance in the labs. (we did end with a poignant “As human Engineers learn to harness AI, AIs will increasingly do Engineering as well, until a distant future when we look up one day and can no longer tell the difference.” — perhaps a half point there, but negative points on underestimating the importance and immediacy). Thankfully by the time the first AIE Summit came around, I was fully allocating 1/3 of the weight of AI Engineering to agentic coding:

AI News for 3/18/2026-3/19/2026. We checked 12 subreddits, 544 Twitters and no further Discords. AINews’ website lets you search all past issues. As a reminder, AINews is now a section of Latent Space. You can opt in/out of email frequencies!

AI Coding Agents, Developer Tooling, and the Race to Own the IDE

• Cursor’s Composer 2 looks like the day’s biggest developer-model launch: @cursor_ai released Composer 2, positioning it as a frontier-class coding model with major cost reductions. Cursor says quality gains came from its first continued pretraining run feeding a stronger base into RL (details). Third-party reactions emphasized both price/perf and benchmark competitiveness: @kimmonismus highlighted $0.50/M input and $2.50/M output with reported scores of 61.3 on CursorBench, 61.7 on Terminal-Bench 2.0, and 73.7 on SWE-bench Multilingual; @mntruell framed Cursor as a new kind of company combining API models with domain-specific in-house models. The launch also included an early alpha UI at Glass, with commentary from @theo that the industry will likely converge on this more agent-native UX. Several engineers also noted the training/infra story: @ellev3n11 said the RL run was distributed across 3–4 clusters worldwide, and @amanrsanger said the ~40-person team is focused exclusively on software engineering tasks.

• OpenAI moves down-stack with Astral; Anthropic expands Claude Code’s surface area: @charliermarsh announced that Astral—the team behind uv, ruff, and ty—is joining OpenAI’s Codex team; @gdb confirmed the deal from OpenAI’s side. The acquisition was broadly read as OpenAI strengthening its developer platform moat through ownership of foundational Python tooling; see @Yuchenj_UW and Simon Willison’s commentary. In parallel, Anthropic expanded Claude Code with channels so developers can interact via messaging apps, starting in research preview (announcement, docs). The product direction is notable: both OpenAI and Anthropic are pushing beyond “model API” toward persistent developer workflows and ambient agent access.

Agents, Multi-Agent Runtimes, and Enterprise Agent Control Planes

• The center of gravity is shifting from single agents to managed fleets, runtimes, and agent operating systems: @LangChain launched LangSmith Fleet, an enterprise workspace for creating and managing a fleet of agents with memory, tools, permissions, and channel integrations; repeated themes across the launch were agent identity, credential management, sharing controls, Slack exposure, and auditability (overview, additional framing). This lines up with broader discourse that “agent” is no longer a useful abstraction by itself: @YuvalinTheDeep argued the right metaphor is an AI operating system that allocates work, resources, and execution contexts. Complementary launches reinforced this stack-level view: @cognition added teams of Devins, where Devin decomposes work and delegates to parallel Devins in separate VMs; @lvwerra released AgentUI, a multi-agent interface coordinating code, search, and multimodal specialists; and @hrishioa argued long-horizon agentic work now requires a dedicated runtime with checkpointing, rollback, provider-specific harness switching, and execution repair.

• Security and permissions are becoming first-class design constraints for agent systems: a recurring thread across launches was that production agent deployment is bottlenecked less by “can the model do it?” and more by permissions, blast radius control, and observability. @swyx highlighted identity-based authorization as the emerging consensus for AI security, and @baseten described NemoClaw as NVIDIA’s answer to OpenClaw-style safety concerns with zero permissions by default, sandboxed subagents, and infra-enforced private inference. LangChain’s Fleet launch also heavily emphasized permissioning and audit trails. The throughline: agent stacks are maturing into something much closer to enterprise software infrastructure than chatbot wrappers.

Model Releases, Benchmarks, and Retrieval/Reasoning Results

• MiniMax M2.7 is being positioned as a practical agent model rather than a pure “frontier giant”: MiniMax teased a deeper technical livestream with OpenClaw around self-evolution and infrastructure for 100,000 running clusters (announcement), while early usage reports stressed improved emotional intelligence, character consistency, and strong agentic workflows (MiniMax note). More technical third-party evaluation from ZhihuFrontier said M2.7 keeps overall performance roughly on par with the previous generation but upgrades instruction following, context hallucination handling, and large-code / multi-round dialogue behavior, albeit with slightly worse hard reasoning and higher token consumption. Integration momentum was immediate: @Teknium added M2.7 to Hermes Agent, and users reported better long-running agent behavior than OpenClaw in some workflows (example).

• Qwen 3.5 Max Preview and retrieval-centric systems posted notable leaderboard movement: @arena reported Qwen 3.5 Max Preview reaching #3 in Math, Top 10 in Arena Expert, and Top 15 overall, with particularly large gains versus prior Max variants in text, writing, and math (breakdown); @Alibaba_Qwen confirmed more optimization is coming. Meanwhile, one of the most technically interesting result clusters was around late interaction retrieval: @antoine_chaffin claimed BrowseComp-Plus is now near 90% solved using Reason-ModernColBERT, a 150M model that outperformed systems up to 54× larger on deep research-style retrieval. Multiple follow-ups from @lateinteraction and others argued this is not a one-off but another strong signal that multi-vector / late-interaction retrieval is systematically outperforming dense single-vector approaches in reasoning-intensive search.

Multimodal Models, OCR, Document Parsing, and Creative Tools

• A strong crop of document/OCR tooling shipped, spanning model-based and model-free approaches: @nathanhabib1011 flagged Chandra OCR 2 as a new SOTA OCR release with 85.9% on olmOCR bench, 90+ languages, a 4B parameter model, and support for handwriting, math, forms, tables, and image caption extraction. Separately, @skalskip92 highlighted GLM-OCR 0.9B as a small OCR model reportedly beating Gemini on OCR benchmarks. On the parsing side, LlamaIndex open-sourced LiteParse, a local, layout-aware parser for PDFs, Office docs, and images with zero Python dependencies, built-in OCR options, spatial layout preservation, and explicit targeting at agent pipelines (launch, expanded post). This is a useful split in the stack: high-end OCR/VLMs for difficult pages, plus lightweight local parsers for the common case.

• Image/video and world-model work keeps accelerating, but the interesting part is latency and deployability: Google rolled out a significantly upgraded AI Studio “vibe coding” experience with a new Antigravity coding agent plus Firebase integrations, enabling multiplayer apps, backend services, auth, and persistent builds (Google AI Studio post, Google summary). On imaging, Microsoft launched MAI-Image-2, which debuted at #5 on the Image Arena and posted large subcategory gains over MAI-Image-1, especially in text rendering and portraits (arena ranking, Microsoft announcement). For vision/video understanding, @skalskip92 showed MolmoPoint doing point-based multi-object tracking directly from a VLM, distinct from segmentation-first approaches like SAM. And @kimmonismus made a useful systems point: sub-100ms prompt-to-output loops in generative media may matter more than raw model quality for real production workflows.

Training, Architectures, Inference, and Systems Research

• Continued pretraining and RL environment quality are re-emerging as core competitive levers: Composer 2’s team explicitly attributed gains to continued pretraining before RL (Cursor), and several researchers argued this pattern will become more common for specialized models (@code_star, @cwolferesearch). Relatedly, @pratyushmaini introduced the “Finetuner’s Fallacy”: early training data leaves a durable imprint on model representations that later finetuning struggles to undo. On the systems side, @skypilot_org scaled Karpathy-style autoresearch over a K8s GPU cluster, running ~910 experiments in 8 hours instead of ~96 sequentially, an example of infrastructure directly changing the shape of automated research loops.

• Architecture exploration remains lively beyond standard transformers: @MayankMish98 released M²RNN, revisiting non-linear recurrence with matrix-valued states for scalable language modeling; @tri_dao noted nonlinear RNN layers appear to add something distinct from attention and linear SSMs. NVIDIA’s Nemotron 3 stack also drew attention for mixing Transformer + Mamba 2, MoE/LatentMoE, multi-token prediction, and NVFP4 precision in service of lower inference costs and long-context agent workloads (summary). At the infra layer, @rachpradhan reported TurboAPI reaching 150k req/s, claiming 22× FastAPI throughput after a day of optimization, while @baseten launched the Baseten Delivery Network to reduce large-model cold starts by 2–3×.

Top tweets (by engagement)

• OpenAI acquires Astral: @charliermarsh announced Astral joining OpenAI’s Codex team, one of the clearest signals that AI labs now see ownership of core developer tooling as strategic.

• Cursor Composer 2 launch: @cursor_ai had the highest-engagement technical product launch in the set, reflecting how central coding-model price/performance has become.

• Google AI Studio’s upgraded vibe coding stack: @GoogleAIStudio and @OfficialLoganK drove major engagement around full-stack app generation with persistent builds, multiplayer, and backend integrations.

• LlamaIndex LiteParse: @jerryjliu0 resonated strongly, suggesting continued demand for practical, local-first parsing infrastructure for agent pipelines.

• Late interaction retrieval on BrowseComp-Plus: @antoine_chaffin posted one of the more important benchmark results of the day: a 150M late-interaction retriever pushing a hard deep-research benchmark toward 90%.

• MiniMax-M2.7 Announced! (Activity: 1078): The image presents a comparative analysis of the newly announced MiniMax-M2.7 model against other models like M2.5, Gemini 31 Pro, Sonnet 4.6, Opus 4.6, and GPT 5.4 across various benchmarks such as SWE Bench Pro, VIBE-Pro, and MM-ClawBench. MiniMax-M2.7 is highlighted in red and demonstrates superior performance in several categories. The model’s development emphasizes autonomous iteration, where it optimizes its performance through iterative cycles of analysis, planning, modification, and evaluation, achieving a 30% performance improvement on internal evaluation sets. This process includes optimizing sampling parameters and enhancing workflow guidelines, indicating a shift towards fully automated AI self-evolution. One commenter highlights the importance of real-world usability over benchmark performance, expressing skepticism about models that excel in evaluations but may not perform well in practical applications. Another comment humorously notes the rapid pace of new model releases, expressing excitement and anticipation for future developments.

  • Recoil42 highlights the autonomous iteration capabilities of the MiniMax-M2.7 model, which can optimize its own performance through iterative cycles. The model autonomously analyzes failure paths, plans changes, modifies code, and evaluates results, achieving a 30% performance improvement on internal evaluation sets. This process includes optimizing sampling parameters and enhancing workflow guidelines, indicating a move towards fully automated AI self-evolution.

  • Specialist_Sun_7819 raises a critical point about the discrepancy between benchmark performance and real-world usability. They emphasize that many models excel in evaluations but struggle with tasks that deviate from the training distribution. This comment underscores the importance of user testing to validate the practical effectiveness of models like MiniMax-M2.7.

  • Lowkey_LokiSN expresses concern about the model’s quantization resistance, referencing issues with the previous M2.5 model’s UD-Q4_K_XL variant. Quantization can affect model performance, and improvements in this area would be crucial for maintaining the integrity of MiniMax-M2.7’s capabilities when deployed in resource-constrained environments.

• Omnicoder-Claude-4.6-Opus-Uncensored-GGUF (Activity: 397): The post introduces the OmniClaw model, crafted from real Claude Code / Codex sessions using the DataClaw dataset, and available on Hugging Face. The Omnicoder model, distilled by Claude Opus, and the OmniRP model for creative writing, are also presented. All models are uncensored and use Q8_0 quantization due to quality issues with other quants. The models were merged using a Python script available on Pastebin, maintaining GGUF header and metadata for compatibility. The Omnicoder model was created by merging several models, including Jackrong’s and HauhauCS’s Qwen 3.5 9B models, Tesslate’s Omnicoder, and Bartowski’s Qwen 3.5-9B as a base. The OmniClaw and OmniRP models were further merged with models from empero-ai and nbeerbower, respectively. The post claims these models represent the best in Uncensored General Intelligence (UGI) for small 9B models based on the Qwen 3.5 9B architecture. A comment highlights a benchmark test on the Omnicoder 9B model, noting a 5.3% pass@1 and 29.3% pass@2 success rate on the Aider benchmark, with a runtime of 402 seconds per problem, suggesting skepticism about the effectiveness of Claude distillation in improving Omnicoder’s performance.

  • grumd provides a detailed benchmark comparison between Qwen3.5 35B-A3B and Omnicoder 9B using the Aider benchmark, which consists of 225 hard coding problems. Qwen3.5 35B-A3B achieved a 26.7% pass@1 and 54.7% pass@2, taking 95 seconds per problem on average. In contrast, Omnicoder 9B, after completing 75 problems, has a 5.3% pass@1 and 29.3% pass@2, with a significantly longer average time of 402 seconds per problem. This highlights a substantial performance gap between the models, particularly in efficiency and accuracy.

  • grumd expresses skepticism about the potential for Claude distillation to resolve Omnicoder’s performance issues, suggesting that the current results are not promising. The comparison with Qwen3.5 9B is anticipated to provide further insights into whether the performance issues are inherent to Omnicoder or if they can be mitigated through model adjustments or distillation techniques.

  • jack-in-the-sack raises a question about model interchangeability, specifically whether Claude Code can be replaced with Omnicoder. This reflects a common concern in the community about the compatibility and performance trade-offs when switching between different AI models, especially in specialized tasks like coding.

• My company just handed me a 2x H200 (282GB VRAM) rig. Help me pick the “Intelligence” ceiling. (Activity: 854): The user has access to a server with dual Nvidia H200 GPUs, each with 141GB HBM3e, totaling 282GB VRAM. They are tasked with testing large language models (LLMs) for local coding tasks, including code completion, generation, and reviews. A suggested model is Qwen 3.5 397B using vLLM for efficient context handling at Q4 quantization. It’s recommended to avoid models like ollama or llama.cpp due to their poor handling of batched inference, which is crucial for concurrent coding tasks. Instead, vLLM or sglang are suggested for better stability and performance in multi-user environments. One commenter emphasizes the importance of defining clear goals and outcomes before experimenting to ensure continued access to the hardware. Another shares a negative experience with ollama, citing instability and poor performance, and recommends vLLM for its stability and suitability for multi-user environments.

  • Zyj suggests using vLLM with the Qwen 3.5 397B model, which should allow for a significant context window at Q4 precision. This recommendation is based on the available VRAM and the need to balance model size with context capabilities.

  • TUBlender advises against using ollama or llama.cpp for setups requiring batched inference due to their poor handling of concurrent requests. They share personal experience with ollama serving qwen2.5 72b, which resulted in instability and crashes, recommending vllm or sglang as more stable alternatives for multi-user environments.

  • Mikolai007 warns against using models that max out the GPU’s VRAM, emphasizing the importance of maintaining a healthy context window. They recommend Minimax M2.5 and Qwen 3.5 as optimal choices, noting that GLM 5 is too large at 800b despite its capabilities.

• Two weeks ago, I posted here to see if people would be interested in an open-source local AI 3D model generator (Activity: 366): The post introduces a beta version of an open-source desktop application designed to generate 3D meshes from images, currently supporting the Hunyuan3D 2 Mini model. The app is modular, built around an extension system, and the developer is seeking feedback on features, file export extensions, and additional model support. The GitHub repository is available here. Commenters suggest features such as multi-image input, text-based editing, checkpoint saving, and support for formats like glTF. They also recommend supporting Trellis 2 for state-of-the-art open 3D model generation and propose a ggml backend for non-CUDA GPUs. Additional features like custom mesh import, texture generation, and basic editing tools are also discussed.

  • New_Comfortable7240 outlines a comprehensive feature set for a local AI 3D model generator, emphasizing the need for a user-friendly interface that allows for the addition of images and text to create initial meshes. They suggest implementing a chat interface for iterative editing, saving checkpoints, and ensuring compatibility with the glTF format through a healing function. The comment also highlights the importance of node renaming in glTF to avoid confusion and proposes optional features like texture generation, animations, and Level of Detail (LOD) management.

  • Nota_ReAlperson mentions Trellis 2 as the state-of-the-art for free open 3D model generation and suggests supporting it. They also propose the challenging task of developing a ggml backend for non-CUDA GPUs, which would broaden accessibility for users without high-end hardware. This highlights the importance of considering diverse hardware capabilities in the development of the model generator.

  • ArtifartX emphasizes the necessity of importing custom meshes and generating textures for them, suggesting enhancements like blending and basic brush tools. They reference a past project using SDXL and ControlNet with custom shaders for projection, indicating the potential for advanced texture manipulation features. The comment also advises focusing on commonly used file formats such as OBJ, FBX, GLTF, and USD for export options.

/r/Singularity, /r/Oobabooga, /r/MachineLearning, /r/OpenAI, /r/ClaudeAI, /r/StableDiffusion, /r/ChatGPT, /r/ChatGPTCoding, /r/aivideo, /r/aivideo

• Harmonic unleashes Aristotle, the world’s first formal mathematician agent for free (Activity: 446): The image announces the release of the “Aristotle Agent” by Harmonic, touted as the world’s first autonomous mathematician agent, available for free. This agent is notable for its ability to solve and formalize complex mathematical problems, distinguishing itself from other AI math tools by providing formal verification of proofs, which ensures correctness without human intervention. This is in contrast to other AI systems like DeepMind’s AlphaProof, which remains proprietary. The tool has been linked to recent attempts to solve the Erdős problem, highlighting its potential in tackling significant mathematical challenges. Commenters highlight the significance of the formal verification feature, which ensures that proofs are correct by construction, eliminating the need for human verification. There is curiosity about its capability to handle complex open problems beyond textbook-level challenges.

  • ikkiho highlights the significance of formal verification in Harmonic’s Aristotle, contrasting it with other AI math tools. Unlike LLMs that generate proofs in natural language, which may be incorrect, Aristotle’s use of Lean proofs ensures correctness by construction, eliminating the need for human verification. This approach is particularly notable as it is offered for free, unlike DeepMind’s proprietary AlphaProof.

  • ikkiho also raises a question about the current capabilities of Aristotle, wondering if it has been tested on challenging open problems or if it is primarily solving textbook-level mathematics. This inquiry points to the potential of Aristotle to tackle more complex mathematical challenges in the future.

  • omegahustle expresses hope that Aristotle remains free and is used responsibly, emphasizing the importance of its availability for those who can utilize it effectively. This comment underscores the potential impact of free access to advanced mathematical tools on the research community.

• A new version of the Gemini app was just released. (Activity: 425): The image announces an update to the Google Gemini app, version 1.2026.1062300, which introduces a ‘Personal Intelligence’ feature for free users in the US. This feature aims to enhance connectivity across Google apps, providing personalized responses. The update also includes UI improvements and bug fixes, with a download size of 196.2 MB. This suggests a significant enhancement in user experience and integration capabilities within the Google ecosystem. Commenters express concerns about privacy, particularly regarding the potential for government access to personal data through the ‘Personal Intelligence’ feature. There is also skepticism about the necessity of the Gemini app, with some users viewing it as redundant to existing Google app functionalities.

  • Technical_Train_9821 raises concerns about data privacy with the Gemini app, highlighting the potential risks of allowing the app to access and connect personal data. They suggest that if the government were to gain access, it could make an individual’s entire online presence searchable, posing significant privacy issues.

  • brandeded shares practical use cases for the Gemini app, emphasizing its ability to integrate with other services and perform complex tasks. They describe scenarios where the app can create calendar appointments based on email content, search for specific financial transactions, and retrieve information from Google Drive, showcasing its utility in managing personal data efficiently.

• Basically Official: Qwen Image 2.0 Not Open-Sourcing (Activity: 495): The image in the Reddit post is an announcement for the launch of Qwen-Image-2.0, a next-generation image generation model by Alibaba. Initially tagged as “Open-Source” on the Qwen research page, it has now been reclassified as “Release,” indicating it will not be open-sourced. This change aligns with recent internal shifts at Alibaba, including the departure of key engineers and a strategic pivot away from open-source models due to revenue concerns. The model features professional typography rendering, support for 1k-token instructions, and native 2K resolution, aimed at creating detailed infographics and comics. Commenters express confusion and disappointment over Alibaba’s decision not to open-source Qwen-Image-2.0, arguing that its value diminishes when closed-source, especially given the competitive landscape with models like Midjourney. Additionally, it’s noted that Alibaba’s CEO has shown dissatisfaction with the lack of revenue from open-source models, influencing this strategic shift.

  • Skystunt highlights a critical issue with Qwen Image 2.0’s closed-source approach, emphasizing that its competitive edge is diminished when compared to other models like Midjourney or Nano Banana, which offer more mature UIs and open-source benefits. The model’s closed nature, combined with data privacy concerns, makes it less appealing despite its technical capabilities as a 7B parameter model.

  • BreakingGood provides context on Alibaba’s strategic shift away from open-sourcing, citing the CEO’s dissatisfaction with the lack of revenue from open models. This has led to significant internal changes, including the departure of key engineers, suggesting a future where Alibaba may not release open-source models, impacting the community’s access to cutting-edge technology.

  • LeKhang98 comments on the perception of model release frequency, noting that while some feel overwhelmed by new models, the actual release rate is relatively low, with only 2-3 significant models per year. This perspective suggests that the community should appreciate the current pace and availability of new models, despite potential slowdowns in releases.

• An Australian ML researcher, used ChatGPT+AlphaFold to shrink 75% of his life-threatened dog’s MCT cancerous tumor, developing a personalized mRNA vaccine in just two months - after sequencing his dog’s DNA for $2,000 (Activity: 498): An Australian machine learning researcher, Paul Conyngham, utilized ChatGPT and AlphaFold to develop a personalized mRNA vaccine for his dog, Rosie, who had a life-threatening mast cell tumor. By sequencing the tumor’s DNA for approximately $2,000, Conyngham identified neoantigens using ChatGPT and predicted protein structures with AlphaFold. Collaborating with Martin Smith from UNSW for genome sequencing and Pall Thordarson for mRNA synthesis, he successfully shrank the tumor by 75% within two months, despite having no formal background in biology or medicine. This case highlights the potential of AI in personalized medicine and rapid vaccine development (source). Commenters are debating the implications of this case, questioning whether it represents a significant shift in healthcare democratization or if it’s overhyped. Some suggest that regulatory barriers are hindering medical progress, as demonstrated by the rapid development achieved in this instance.

  • DepartmentDapper9823 argues that this case illustrates how regulatory bodies may impede medical progress. They suggest that when these barriers are bypassed, advancements can occur more rapidly, as evidenced by the quick development of a personalized mRNA vaccine for the dog using ChatGPT and AlphaFold.

  • AngleAccomplished865 calls for expert opinions to assess the broader implications of this case, questioning whether it represents a significant shift in democratized healthcare or if it’s merely hype. They highlight the need for professional insights to determine the true impact of using AI tools like ChatGPT and AlphaFold in medical research.

  • 682463435465 raises a concern that individuals with cancer might attempt to replicate this approach on themselves, indicating a potential risk of self-experimentation without proper medical guidance. This underscores the need for careful consideration of the ethical and safety implications of using AI in personalized medicine.

• Built an open source tool that can find precise coordinates of any picture (Activity: 837): Netryx is an open-source tool developed by a college student, designed to determine precise geographic coordinates from street-level photos using visual clues and a custom machine learning pipeline. The tool is available on GitHub and aims to connect with developers and companies interested in geolocation technologies. The tool’s capabilities are demonstrated through a custom web version that geolocates events like the Qatar strikes, although the core pipeline remains consistent across versions. Commenters express mixed feelings about the tool’s potential uses, noting it could be both beneficial and harmful. There is also curiosity about its reliance on existing data sources like Google Street View for functionality.

• I built a Claude skill that writes accurate prompts for any AI tool. To stop burning credits on bad prompts. We just hit 600 stars on GitHub‼️ (Activity: 728): The prompt-master is a Claude skill designed to optimize prompt generation for various AI tools, achieving over 600 stars on GitHub. It intelligently detects the target AI tool and applies specific strategies, such as extracting 9 dimensions from user input and identifying 35 common prompt issues, to enhance prompt accuracy and efficiency. The tool supports a wide range of platforms including Claude, ChatGPT, Midjourney, and Eleven Labs, and is open-source, allowing for community-driven improvements. The latest version, v1.4, incorporates user feedback and plans for v1.5 are underway, focusing on agent-based enhancements. GitHub Repository. Commenters highlight the tool’s ability to tailor prompts to specific AI models, such as Midjourney and Claude Code, as a key differentiator from generic prompt tools. There is interest in its compatibility with open-source models, suggesting potential for broader application.

  • The tool’s ability to perform tool-specific routing is highlighted as a key feature, making it more effective than generic prompt enhancers. This is crucial because different AI tools like Midjourney and Claude Code require distinct prompt structures, which most general tools fail to address.

  • A user inquires about the compatibility of the tool with open-source models, specifically mentioning running it locally with ComfyUI on a 5090 GPU. This suggests interest in leveraging the tool’s capabilities beyond proprietary models, potentially expanding its utility in diverse AI environments.

  • Another user notes that while similar tools have been attempted, they often require manual tweaking of prompts. However, if this tool effectively manages tool-specific nuances, such as differences between Cursor and Claude Code, it could significantly enhance usability and efficiency.

• I got tired of manually prompting every single clip for my AI music videos, so I built a 100% local open-source (LTX Video desktop + Gradio) app to automate it, meet - Synesthesia (Activity: 306): Synesthesia is an open-source application designed to automate the creation of AI-generated music videos by integrating with local LLMs like Qwen3.5-9b. It processes three input files: an isolated vocal stem, a full band performance, and text lyrics, to generate a shot list that alternates between vocal and story segments. The app interfaces with LTX-Desktop for video generation, achieving a first-pass render of a 3-minute video in under an hour on a 5090 GPU at 540p resolution. Users can adjust the shot list manually or let it run automatically, and select multiple takes per shot for final editing. The project is hosted on GitHub. One commenter suggests adding LoRA support for consistent character representation, while another criticizes the automation, arguing that it cannot replace the creative process of manual prompting.

  • Loose_Object_8311 suggests that the app could benefit from LoRA support to maintain consistent character appearances across clips. LoRA (Low-Rank Adaptation) is a technique used to fine-tune models efficiently, which could enhance the app’s ability to generate consistent visual elements in AI-generated music videos.

  • InternationalBid831 inquires about compatibility with Wan2GP running LTX2 instead of LTX Desktop, particularly for users with a 5070ti GPU. This suggests a need for the app to support different hardware configurations and possibly different versions of the LTX software to accommodate a wider range of users.

  • Diadra_Underwood proposes adding a styles drop-down menu to the app, highlighting the potential for users to easily switch between different visual styles such as claymation, puppets, or CGI. This feature could enhance user experience by allowing for quick experimentation with various artistic styles in AI-generated content.

• The dictionaries are suing OpenAI for “massive” copyright infringement, and say ChatGPT is starving publishers of revenue (Activity: 718): Britannica and Merriam-Webster have filed a lawsuit against OpenAI in the Southern District of New York, alleging that OpenAI’s ChatGPT has infringed on their copyrights by using their researched content without permission. The lawsuit claims that ChatGPT’s ability to provide direct answers from absorbed content is depriving publishers of web traffic and ad revenue, which are crucial for their survival. This case adds to ongoing legal debates about AI’s use of online content and the boundaries of public knowledge versus proprietary information. Read more. Commenters are questioning the implications of allowing companies to own definitions and the broader impact on information accessibility. There’s a satirical tone regarding the monetization of word usage, reflecting skepticism about the lawsuit’s premise.

• CEO Asks ChatGPT How to Void $250 Million Contract, Ignores His Lawyers, Loses Terribly in Court (Activity: 465): In a recent legal debacle, Krafton CEO Changhan Kim attempted to void a $250 million contract with Unknown Worlds Entertainment by consulting ChatGPT instead of his legal team. The court decisively ruled against him, emphasizing the dangers of using AI for intricate legal strategies without professional oversight. The case underscores that while AI can assist in legal preparation by stress-testing arguments and summarizing precedents, it lacks the liability and contextual understanding necessary for direct legal action. For more details, see the 404 Media report. Commenters highlight the misuse of AI as a replacement for professional judgment, noting that AI should be used to enhance legal strategies rather than replace them. They emphasize the importance of human oversight, especially in complex legal matters, and suggest using AI to identify potential challenges rather than as a direct source of legal advice.

  • RobinWood_AI highlights the misuse of AI in legal contexts, emphasizing that AI should be used to enhance legal strategies rather than replace professional judgment. AI can assist in stress-testing arguments and drafting frameworks but lacks the liability and context of a human lawyer. The CEO’s mistake was using AI to directly void a contract without legal oversight, illustrating the gap between AI as a tool and a liability.

  • chiqu3n discusses the limitations of AI in understanding specific legal contexts, noting that general AI models like ChatGPT may not account for special legislation that could affect contract terms. They compare this with a specialized legal LLM, ‘justicio’, which provided a more nuanced and legally accurate response, highlighting the importance of human expert review in critical legal matters.

  • Dailan_Grace points out the issue of AI’s authoritative tone, which can mislead users into trusting incorrect information. AI models often present information confidently without hedging, which can be problematic if the user lacks the expertise to identify errors. This overconfidence in AI outputs may have contributed to the CEO’s poor decision-making.

• Jeremy O. Harris drunkenly called OpenAI’s Sam Altman a Nazi at the Vanity Fair Oscar party (Activity: 650): At the Vanity Fair Oscar party, playwright Jeremy O. Harris confronted Sam Altman, CEO of OpenAI, accusing him of being akin to a Nazi figure due to OpenAI’s new deal with the Department of War. Harris later clarified his statement, comparing Altman to Friedrich Flick, a German industrialist convicted of war crimes, rather than Joseph Goebbels. This incident highlights ongoing ethical debates surrounding AI and its military applications. The comments reflect skepticism about the appropriateness of the Nazi comparison, noting Altman’s Jewish background, and include some off-topic humor.

Unfortunately, Discord shut down our access today. We will not bring it back in this form but we will be shipping the new AINews soon. Thanks for reading to here, it was a good run.

Not 2 months after their IPO and first public quarter, MiniMax is back in the news with MiniMax 2.7, a nice bright spot in Chinese Open Models after the changeover in Qwen. They match Z.ai’s GLM-5 SOTA open model from last month, but the story is efficiency here (see green quadrant in Artificial Analysis’ chart):

The team calls out “Early Echoes of Self-Evolution”, calling it “our first model deeply participating in its own evolution.”, recalling Karpathy’s Autoresearch, although they only claim that “M2.7 is capable of handling 30%-50% of the workflow.”:

They also report some work on multi-agent collaboration (“Agent Teams”) as well as follow Anthropic and OpenAI’s lead in applying their models for finance usecases. Finally, they launch OpenRoom, an open source demo for entertainment usecases.

AI News for 3/18/2026-3/19/2026. We checked 12 subreddits, 544 Twitters and no further Discords. AINews’ website lets you search all past issues. As a reminder, AINews is now a section of Latent Space. You can opt in/out of email frequencies!

MiniMax M2.7, Xiaomi MiMo-V2-Pro, and the expanding “self-evolving agent” model class

• MiniMax M2.7 is the headline model release: MiniMax positioned M2.7 as its first model that “deeply participated in its own evolution,” claiming 56.22% on SWE-Pro, 57.0% on Terminal Bench 2, 97% skill adherence across 40+ skills, and parity with Sonnet 4.6 in OpenClaw. A follow-up says the internal harness also recursively improved itself—collecting feedback, building eval sets, and iterating on skills/MCP, memory, and architecture (thread). Third-party coverage broadly echoed the “self-evolving” framing, including TestingCatalog and kimmonismus.

• Artificial Analysis places M2.7 on the cost/performance frontier: Artificial Analysis reports 50 on its Intelligence Index, matching GLM-5 (Reasoning) while costing $176 to run the full index at $0.30/$1.20 per 1M input/output tokens—less than one-third of GLM-5’s cost. They also report GDPval-AA Elo 1494, ahead of MiMo-V2-Pro (1426), GLM-5 (1406), and Kimi K2.5 (1283), plus a large hallucination reduction vs M2.5. Distribution was immediate: Ollama cloud, Trae, Yupp, OpenRouter, Vercel, Zo, opencode, and kilocode.

• Xiaomi’s MiMo-V2-Pro looks like a serious Chinese API-only reasoning entrant: Artificial Analysis scores it at 49 on the Intelligence Index, with 1M context, $1/$3 per 1M tokens pricing, and GDPval-AA Elo 1426. Notably, they call out stronger token efficiency than peers and a relatively favorable AA-Omniscience score (+5) driven by lower hallucination. This follows Xiaomi’s earlier open-weight MiMo-V2-Flash (309B total / 15B active, MIT); V2-Pro itself is API-only for now.

• Mamba-3 is out and immediately being viewed through the hybrid-architecture lens: Cartesia announced Mamba-3 as an SSM optimized for an inference-heavy world, with Albert Gu noting Cartesia-backed testing and support (link). Early technical reactions focused less on standalone SSMs and more on plugging Mamba-3 into transformer hybrids: rasbt explicitly called out replacing Gated DeltaNet in next-gen hybrids like Qwen3.5 / Kimi Linear, while JG_Barthelemy highlighted hybrid integration and “unlocking Muon for SSMs.”

Agent harnesses, skills, MCP, and the shift from “prompting” to systems design

• The strongest recurring theme is that harness engineering is becoming the real differentiator: Multiple posts argued that the bottleneck is no longer just the base model, but the surrounding execution environment. The Turing Post’s interview with Michael Bolin frames coding agents as a problem of tools, repo legibility, constraints, and feedback loops—what many now call harness engineering. dbreunig made a similar point about why teams stick with DSPy, and nickbaumann_ argued GPT-5.4 mini matters specifically because cheap, fast subagents change what is worth delegating.

• Skills are solidifying into a shared abstraction across agent stacks: A practical thread from mstockton lays out real usage patterns for SKILLS: progressive disclosure, trace inspection, session distillation, CI-triggered skills, and self-improving skills. RhysSullivan suggests distributing skills via MCP resources may solve staleness/versioning. Anthropic’s Claude Code account clarifies that a skill is not just a text snippet but a folder with scripts/assets/data, and that the key description field should specify when to trigger it (tweet).

• Open agent stacks are converging on model + runtime + harness: Harrison Chase published a walkthrough framing Claude Code, OpenClaw, Manus, etc. as the same decomposition: open model + runtime + harness, using Nemotron 3, NVIDIA’s OpenShell, and DeepAgents. Related infrastructure releases include LangSmith Sandboxes for secure code execution, LangSmith Polly GA as an in-product debugging/improvement assistant, and a new LangChain guide on production observability for agents.

• MCP momentum continues, but there’s pushback: Useful MCP-related launches included Google Colab’s open-source MCP server, enabling local agents to drive Colab GPU runtimes, and Google’s Gemini API update allowing built-in tools plus custom functions in one call. At the same time, there’s visible skepticism: skirano bluntly said “MCP was a mistake. Long live CLIs.” and denisyarats joked about “model cli protocol.”

• A parallel trend: agent-native enterprise apps and “headless SaaS”: ivanburazin describes an emerging category of headless SaaS—traditional software rebuilt as agent-first APIs with no human UI. That idea lines up with product launches like Rippling’s AI analyst, Anthropic’s Claude for Excel/PowerPoint webinar, and the notion that meeting-notes apps are really becoming broader AI context/data apps (zachtratar).

Infra, kernels, and model-system co-design

• Attention Residual became a case study in infra-model co-design: Several posts unpacked Kimi/Moonshot’s AttnRes work as more than a novelty architecture. bigeagle_xd emphasized co-design across model research and infra, linking to an inference-infra writeup; ZhihuFrontier summarized why full attention residual strains pipeline parallelism due to asymmetric comms/memory patterns, and how Block Attention Residual plus cross-stage caching can restore symmetry. YyWangCS17122 reinforced the theme: kernel optimization, algorithm-system co-design, and numerical rigor as the path to production-worthy large models.

• Custom kernel packaging is getting easier: ariG23498 highlighted Hugging Face’s new kernels library, which aims to make custom kernels more shareable and easier to integrate via the Hub. The pitch is straightforward: lower the pain of writing and distributing fused/custom kernels without requiring every model team to hand-roll installation and integration logic.

• Inference optimization remains a first-class topic: The same thread on kernels reiterates the familiar optimization stack—close idle gaps between kernel launches, fuse ops with torch.compile, and only fall back to custom kernels where needed. On the hardware side, Stas Bekman noted that NVLink’s marketed bandwidth can be misleading because it is not duplex in the way many assume.

• Compute bottlenecks are still upstream of everything else: kimmonismus argues that ASML EUV machines and their narrow supply chains may cap production at roughly 100 machines/year by 2030, making lithography an important ceiling on AI scaling over this decade.

Documents, OCR, retrieval, and context engineering for real workflows

• Document AI is trending toward end-to-end multimodal parsers with grounding: Baidu introduced Qianfan-OCR, a 4B end-to-end document intelligence model that collapses table extraction, formula recognition, chart understanding, and KIE into a single pass. Vik Paruchuri open-sourced Chandra OCR 2, claiming 85.9% on olmOCR bench, 90+ language support, and stronger layout, handwriting, math, form, and table support in a smaller 4B model. On the platform side, LlamaIndex and jerryjliu0 emphasized that production document agents need not just markdown conversion but layout detection, segmentation, metadata context, and visual grounding to support human-auditable document workflows.

• Late-interaction retrieval continues to push on the memory/quality tradeoff: victorialslocum summarized MUVERA, which compresses multi-vector retrieval into fixed-dimensional encodings, reporting about 70% memory reduction and much smaller HNSW graphs at some recall/query-throughput cost. lateinteraction used the thread to reiterate the limitations of single-vector retrieval on harder OOD settings.

• Context engineering is becoming a product category: llama_index explicitly frames context engineering as the successor to prompt engineering, with structured parsing/extraction as a core lever. This pairs with Hugging Face’s new support for serving Markdown paper views to agents and a Paper Pages skill for searching and reading papers more token-efficiently (Clement Delangue, Niels Rogge, mishig25).

Evals, training methodology, and benchmarks worth watching

• LLM-as-judge reproducibility is under fire again: a1zhang showed a model scoring 10% under GPT-5.2-as-judge vs 43.5% under GPT-5.1-as-judge, despite a paper reporting 34%—a stark reminder that judge choice can swamp conclusions. torchcompiled distilled the takeaway: don’t use LLM-as-judge without validating human correlation or tuning for it.

• Pretraining data composition is re-emerging as a major lever: rosinality highlighted work showing that mixing SFT data during pretraining can outperform the standard pretrain-then-finetune pipeline, with a scaling law for the ratio under a token budget. Related posts from arimorcos, pratyushmaini, and Christina Baek all argue that domain adaptation often benefits more from earlier data mixing or even repeating small high-quality datasets 10–50x during pretraining than from naive finetuning alone.

• Benchmarks are shifting toward “unsolved and useful”: Ofir Press points to a future where improving on a benchmark means solving previously unsolved tasks that matter in the world, not just memorizing exam-like datasets. He also notes AssistantBench remains unsolved 1.5 years later. New benchmark/tooling drops include ScreenSpot-Pro on Hugging Face for GUI agents and Arena’s academic partnerships funding eval work.

Top tweets (by engagement, filtered for technical relevance)

• OpenAI’s Parameter Golf challenge: OpenAI launched Parameter Golf, a training challenge to fit the best LM in a 16MB artifact trained in under 10 minutes on 8×H100s, with $1M in compute behind it. Good talent-pipeline energy, and a nice complement to the NanoGPT speedrun culture (details via scaling01).

• Anthropic’s 81k-user study: Anthropic says it used Claude to interview 80,508 people in one week about hopes and fears around AI—the company calls it the largest qualitative study of its kind (announcement). The research is interesting both as social measurement and as a signal that model-mediated interviewing may become a standing product/research capability.

• Runway’s real-time video generation preview: Runway shared a research preview developed with NVIDIA showing HD video generation with time-to-first-frame under 100ms on Vera Rubin hardware (tweet). If it generalizes, this is a qualitatively different interaction loop for video models.

• Hugging Face on agent-facing research interfaces: The platform change to serve Markdown paper views to agents and the companion paper skill is small but important infrastructure for agentic research workflows (Clement Delangue).

• VS Code integrated browser debugging: Microsoft’s latest VS Code release adds integrated browser debugging for end-to-end web app workflows—useful in its own right, and likely to matter even more as coding agents are asked to operate against live browser state.

• MiniMax-M2.7 Announced! (Activity: 947): The image presents a comparative analysis of the newly announced MiniMax-M2.7 model against other models like Gemini 3.1 Pro, Sonnet 4.6, Opus 4.6, and GPT 5.4 across various benchmarks such as SWE Bench Pro, VIBE-Pro, and MM-ClawBench. MiniMax-M2.7 is highlighted in red, indicating its performance metrics, which are crucial for understanding its capabilities relative to existing models. The model’s autonomous iteration capabilities are emphasized, showcasing its ability to optimize software engineering tasks through iterative cycles, leading to a 30% performance improvement on internal evaluations. This highlights the model’s potential for self-evolution and automation in AI development. Commenters express skepticism about the practical usability of models that perform well on benchmarks but may not generalize well to real-world tasks. There is anticipation for user testing to validate the model’s effectiveness beyond controlled evaluations.

  • Recoil42 highlights the autonomous iteration capabilities of the MiniMax-M2.7 model, which can optimize its own performance through iterative cycles. The model autonomously analyzes failure paths, plans changes, modifies code, and evaluates results, achieving a 30% performance improvement on internal evaluation sets by optimizing sampling parameters and workflow guidelines.

  • Specialist_Sun_7819 raises a critical point about the discrepancy between benchmark performance and real-world usability. They emphasize the importance of user testing to assess how models perform on tasks that deviate from their training distribution, suggesting that many models excel in evaluations but struggle with off-distribution tasks.

  • Lowkey_LokiSN expresses concern about the model’s quantization resistance, referencing issues with the previous M2.5 model’s UD-Q4_K_XL variant. This highlights the importance of maintaining model performance post-quantization, which can be a challenge for large models when reducing precision for deployment.

• MiniMax M2.7 Is On The Way (Activity: 329): The image is a tweet from MiniMax announcing their participation in the NVIDIA GTC event where they plan to discuss their upcoming model, MiniMax M2.7, along with multimodal systems and AI products. This suggests that MiniMax M2.7 might incorporate multimodal capabilities, potentially handling multiple types of data inputs like text, images, and audio. The mention of multimodal systems aligns with current trends in AI development, where models are increasingly designed to process and integrate various data forms for more comprehensive outputs. A comment highlights the desire for a smaller version of the model, indicating user interest in more accessible or resource-efficient versions. Another comment praises the performance of MiniMax 2.5, noting its speed and tooling capabilities, but points out the lack of image and audio input support, which could be addressed in the upcoming M2.7 model.

  • z_3454_pfk highlights the performance of MiniMax 2.5, noting its efficiency with tooling and retrieval-augmented generation (RAG). The model is praised for its speed, though it currently lacks support for image and audio inputs, which could be a limitation for some applications.

  • Dismal-Effect-1914 emphasizes the compactness and efficiency of MiniMax 2.5, stating it is the best model available that fits under approximately 150 GB when using 4-bit quantization. This suggests a strong balance between performance and resource usage, making it suitable for environments with limited storage capacity.

Note: AIE Europe is ~sold out! Tickets and limited sponsorships for AIE Miami are next — as you can see from online buzz, speakers are excited and prepping. We’ll be there!

By total coincidence, today’s main pod guest also released today’s title story:

swyx: Does remote control work for Claude Cowork yet? No. Right.

Felix: Excellent question.

swyx: Coming soon.

And today, here it is:

Multiple people, from SimonW to Ethan Mollick, are comparing it (favorably) to OpenClaw. As Jensen said yesterday, every company needs an OpenClaw strategy. Now Anthropic, which famously “fumbled” the Clawdbot relationship, has an answer, and it’s a pretty pretty good one.

Tune in to the full pod to get the full origin story, usecases, and design thinking (particularly around the technical choices of sandboxing and Electron), on today’s pod.

AI News for 3/14/2026-3/16/2026. We checked 12 subreddits, 544 Twitters and no further Discords. AINews’ website lets you search all past issues. As a reminder, AINews is now a section of Latent Space. You can opt in/out of email frequencies!

OpenAI’s GPT-5.4 Mini/Nano Release and the Shift to Small, Coding-Optimized Models

• GPT-5.4 mini and nano shipped across API, ChatGPT, and Codex: OpenAI launched GPT-5.4 mini and GPT-5.4 nano, positioning them as its most capable small models yet. Per @OpenAIDevs, GPT-5.4 mini is more than 2x faster than GPT-5 mini, targets coding, computer use, multimodal understanding, and subagents, and offers a 400k context window in the API. OpenAI also claims mini approaches larger GPT-5.4 performance on evaluations including SWE-Bench Pro and OSWorld-Verified, while using only 30% of GPT-5.4 Codex quota, making it the new default for many background coding workflows and subagent fan-out.

• Early reception focused on coding value, but also on pricing and truthfulness tradeoffs: Developers immediately highlighted mini’s utility for subagents in Codex, computer-use workloads, and external products such as Windsurf. However, commentary also converged on a familiar OpenAI pattern: better performance but higher price. Posts from @scaling01 note $0.75/M input and $4.5/M output for mini, with nano likewise priced above prior nano tiers. Third-party evals were mixed: Mercor’s APEX-Agents result reported 24.5% Pass@1 for mini with xhigh reasoning, ahead of some lightweight and midweight competitors on that benchmark, while BullshitBench placed the new small models relatively low on resistance to false-premise/jargon traps. OpenAI also quietly acknowledged behavior tuning issues, with @michpokrass saying a recent 5.3 instant update reduced “annoyingly clickbait-y” behavior.

Agent Infrastructure: Sandboxes, Subagents, Open SWE, and the Harness Wars

• Code-executing agents are becoming the center of product architecture: Several launches point to a stack maturing around secure execution, orchestration, and deployment ergonomics rather than just better base models. LangChain introduced LangSmith Sandboxes for secure ephemeral code execution, with @hwchase17 explicitly arguing that “more and more agents will write and execute code.” In parallel, LangChain open-sourced Open SWE, a background coding agent patterned after internal systems reportedly used at Stripe, Ramp, and Coinbase. The system integrates with Slack, Linear, and GitHub, uses subagents plus middleware, and separates harness, sandbox, invocation layer, and validation. This is a notable step from “chat copilots” toward deployable internal engineering agents.

• Subagents and secure execution are now first-class product features across the ecosystem: OpenAI’s Codex now supports subagents, and GPT-5.4 mini was framed by OpenAI as especially good for that use case. Hermes Agent’s v0.3.0 release is another strong signal: 248 PRs in 5 days, first-class plugin architecture, live Chrome control via CDP, IDE integrations, local Whisper-based voice mode, PII redaction, and provider integrations like Browser Use. The resulting direction is consistent across vendors: agent value increasingly depends on safe execution environments, composable skills/plugins, and workflow-native surfaces rather than raw benchmark gains alone.

Architecture Research: Attention Residuals, Vertical Attention, and Mamba-3

• Attention over depth is having a moment: Moonshot’s Attention Residuals paper on arXiv triggered substantial technical discussion around “vertical attention” or attention across layers. A detailed explainer from @ZhihuFrontier frames the idea as each layer querying prior-layer states, effectively extending attention from horizontal sequence interactions to inter-layer memory. Community reactions emphasized that this is not entirely isolated: @rosinality noted ByteDance also implemented attention over depth, and @arjunkocher published an implementation walkthrough. The interesting systems claim here is that because number of layers << sequence length, some forms of vertical attention may be hidden under existing compute and impose little or no extra latency.

• Mamba-3 strengthens the case for inference-first hybrid architectures: The other major architecture release was Mamba-3, presented by @_albertgu and @tri_dao as the latest step in making linear/state-space models more competitive in the hybrid era. The emphasis is explicitly on inference efficiency, not replacing transformers outright. Together summarized it as a MIMO variant that improves model strength at similar decode speed, with claims of strongest performance among linear models and fastest prefill+decode at 1.5B. Tri Dao also pointed to inference-heavy RL and long-rollout workloads as especially fertile ground for such architectures. The broader takeaway from both Attention Residuals and Mamba-3 is that labs are still searching for ways to relax the full-transformer bottleneck without sacrificing too much ecosystem compatibility.

GTC: NVIDIA’s Agent Push, Open Models, and the Infrastructure Thesis

• GTC messaging centered on inference, agents, and the “token factory” worldview: Multiple posts reflected Jensen Huang’s framing of future computers as systems for “manufacturing tokens”, with inference now driving the next capacity wave. This showed up in product and ecosystem announcements: LangChain said its frameworks crossed 1B downloads and joined the NVIDIA Nemotron Coalition; @ggerganov highlighted Nemotron 3 Nano 4B support in llama.cpp; and Hugging Face’s @jeffboudier recapped a range of open NVIDIA drops spanning reasoning models, robotics datasets, and world models.

• Open and enterprise agent tooling dominated side announcements: H Company released Holotron-12B, an open multimodal model built with NVIDIA for computer-use agents. Perplexity announced Comet Enterprise, bringing its AI browser to enterprise teams with rollout controls and CrowdStrike Falcon integration. NVIDIA’s broader business thesis also got amplified: @TheTuringPost highlighted Jensen’s remark that the often-cited $1T AI infra opportunity only covers a subset of the stack through 2027, reinforcing that the industry is still very early in inference infrastructure buildout.

Open-Source Tooling, Local Agents, and Developer Stack Upgrades

• Local/private agent workflows keep improving: Hugging Face shipped an hf CLI extension that auto-detects the best local model/quant for available hardware and spins up a local coding agent. Unsloth launched Unsloth Studio, an open-source web UI to train and run 500+ models locally across Mac/Windows/Linux, with claims of 2x faster training using 70% less VRAM, GGUF support, synthetic data tooling, tool calling, and code execution. Ollama added web search/fetch plugins and headless launch support for OpenClaw workflows, while also showing up as a provider in CodexBar.

• The “open coding agent” ecosystem is becoming legible: There’s increasing convergence on patterns: model-agnostic harnesses, structured skills, filesystem/state abstractions, and ephemeral cloud or local execution. LangChain’s Deep Agents was described as an MIT-licensed, inspectable replica of the Claude Code style of agentic harness. Hermes Agent’s plugin system and local-model friendliness pushed it into the same conversation. This is one of the clearer trends in the dataset: the frontier is no longer just open-weight models, but open harnesses and runtime layers for actually deploying agents.

Top tweets (by engagement)

• OpenAI small-model launch: @OpenAIDevs on GPT-5.4 mini/nano was among the day’s most consequential technical announcements, especially for coding-agent workloads.

• Cursor’s RL-based context compaction: @cursor_ai said it trained Composer to self-summarize through RL instead of prompting, cutting compaction error by 50% and enabling harder long-horizon coding tasks.

• Mamba-3 release: @_albertgu and @tri_dao marked one of the most important architecture updates in sequence modeling this cycle.

• Unsloth Studio: @UnslothAI had one of the strongest open-source product launches, aimed squarely at local training/inference practitioners.

• Kimi Attention Residuals: @Kimi_Moonshot drove much of the architecture discussion, with follow-on analysis around vertical attention and inter-layer memory.

• Unsloth announces Unsloth Studio - a competitor to LMStudio? (Activity: 998): Unsloth Studio has been announced as a new open-source, no-code web interface for training and running AI models locally, potentially challenging the dominance of LMStudio in the GGUF ecosystem. It is compatible with Llama.cpp and offers features such as auto-healing tool calling, Python and bash code execution, and support for audio, vision, and LLM finetuning. The platform supports GGUFs and runs on Mac, Windows, and Linux, with capabilities for SVG rendering, synthetic data generation, and fast parallel data preparation. Installation is straightforward via pip install unsloth. More details can be found in the Unsloth Documentation. Some users question the characterization of LMStudio as the ‘go-to’ for advanced users, suggesting alternatives like vLLM or llama.cpp. Others express excitement over the UI’s capabilities, particularly for training and data preparation.

  • danielhanchen highlights the extensive feature set of Unsloth Studio, noting its capabilities such as auto-healing tool calling, Python and bash code execution, and support for multiple operating systems including Mac, Windows, and Linux. The tool also offers advanced functionalities like SVG rendering, synthetic data generation, and fast parallel data preparation, making it a comprehensive solution for various AI tasks. More details and installation instructions are available on GitHub.

  • sean_hash points out the convenience of having both fine-tuning and inference capabilities integrated into a single tool like Unsloth Studio. This contrasts with the current need to use multiple projects to achieve the same functionality, highlighting Unsloth Studio’s potential to streamline AI development workflows.

  • Specter_Origin expresses appreciation for Unsloth Studio’s open-source nature, contrasting it with the closed-source LM Studio. This openness could be a significant advantage for developers who prefer transparency and the ability to modify the tool according to their needs.

• Introducing Unsloth Studio: A new open-source web UI to train and run LLMs (Activity: 579): Unsloth Studio is a new open-source web UI designed to train and run large language models (LLMs) locally on Mac, Windows, and Linux. It claims to train over 500+ models at twice the speed while using 70% less VRAM. The platform supports GGUF, vision, audio, and embedding models, and includes features like model comparison, self-healing tool calling, and web search. It also offers auto-creation of datasets from formats like PDF, CSV, and DOCX, and allows for code execution to enhance LLM output accuracy. Models can be exported to formats such as GGUF and Safetensors, with auto-tuning of inference parameters. Installation is facilitated via pip install unsloth. GitHub and documentation are available for further details. Commenters are enthusiastic about Unsloth Studio as a fully open-source alternative to existing platforms, highlighting its accessibility for fine-tuning models, especially for users with less expertise. There is anticipation for upcoming support for AMD, which is expected to broaden its usability.

  • A user highlights the importance of making fine-tuning accessible, noting that Unsloth Studio provides an easy way to fine-tune models, which has been a challenge since the release of LLaMA 2. This accessibility could potentially revive the ‘golden age of fine-tunes’, making it easier for those with less expertise to engage in model customization.

  • Another user points out a technical issue encountered during installation, where an OSError due to insufficient disk space occurred while downloading a large torch package. This highlights a common challenge in AI/ML projects related to managing dependencies and system resources, suggesting that atomic installation of components might be necessary to lower the entry barrier.

  • An AMD representative expresses readiness to support the upcoming official AMD support for Unsloth Studio, indicating potential improvements in compatibility and performance for AMD hardware users. This collaboration could enhance the usability of Unsloth Studio across different hardware platforms.

• Qwen3.5-9B on document benchmarks: where it beats frontier models and where it doesn’t. (Activity: 295): The image compares the performance of Alibaba’s Qwen3.5-9B and OpenAI’s GPT-5.4 on document AI benchmarks. Qwen3.5-9B ranks #9 with a score of 77.0, excelling in “Key Information Extraction” and “Table Understanding,” while GPT-5.4 ranks #4 with a score of 81.0, leading in other areas. The benchmark results highlight Qwen3.5-9B’s superior performance in “OmniOCR” but its lag in “OmniDoc” and “IDP Core.” This aligns with the detailed breakdown in the post, where Qwen models outperform in OCR and VQA tasks but fall behind in table extraction and handwriting OCR. One commenter suggests that AI technology is reaching a functional ceiling, indicating that current models are sufficient for many tasks and can run efficiently on less powerful hardware. Another comment anticipates interesting comparisons with GLM-OCR, while a third notes the potential energy efficiency of using smaller Qwen models for tasks that can tolerate longer processing times.

  • Qwen3.5-9B’s performance: The model demonstrates competitive performance against larger frontier models, particularly in document processing tasks. Its ability to run efficiently on lower-end hardware, such as ultrabooks, highlights its energy efficiency and accessibility for broader applications. This suggests a shift towards optimizing smaller models for specific tasks rather than relying solely on larger, more resource-intensive models.

  • Energy efficiency and reasoning: The Qwen3.5-9B model is noted for its energy efficiency, especially in tasks requiring extended reasoning. Compared to larger models like Gemini or GPT, Qwen3.5-9B offers a more sustainable option if processing time is not a critical factor. This positions it as a viable alternative for applications where energy consumption is a priority.

  • Model variants and benchmarks: There is curiosity about the absence of larger Qwen model variants, such as the 27B dense and 35B MoE, in the benchmarks. This absence raises questions about the comparative performance and potential advantages of these larger models in specific tasks, suggesting a need for further exploration and benchmarking of these variants.

• Mistral Small 4:119B-2603 (Activity: 1057): Mistral Small 4 is a hybrid AI model with 119 billion parameters and a 256k context length, integrating Instruct, Reasoning, and Devstral capabilities. It supports multimodal input and features an efficient architecture that reduces latency by 40%. The model includes advanced features like speculative decoding and 4-bit float quantization, optimized for tasks such as general chat, coding, and document analysis. It is available under an Apache 2.0 license for both commercial and non-commercial use. More details can be found on the Hugging Face page. Commenters humorously note the shift in scale, with 120 billion parameters now considered ‘small’, reflecting the rapid evolution in AI model sizes and capabilities.

  • The Mistral Small 119B model is being compared to the Qwen3.5-122B-A10B model, with a focus on parameter activation. Mistral activates 6.5 billion parameters, whereas Qwen3.5 utilizes 10 billion, which may explain why Mistral does not outperform Qwen3.5 overall. This highlights the importance of parameter activation in model performance.

• DGX Station is available (via OEM distributors) (Activity: 418): The image depicts a high-performance workstation, likely the NVIDIA DGX Station, which is now available through OEM distributors. This machine is designed for AI and deep learning applications, featuring advanced cooling and performance capabilities. The DGX Station is equipped with NVIDIA’s latest technology, making it a ‘dream machine’ for many in the AI community. The discussion highlights its availability through distributors like Dell and Exxact, with prices reportedly in the 85-90k USD range. The concept of ‘coherent memory’ is mentioned, which refers to a memory architecture that allows for efficient data sharing between CPUs and GPUs, potentially enhancing performance in AI workloads. There is a discussion about the pricing and availability of the DGX Station, with some users noting discrepancies in Dell’s product listings. The concept of ‘coherent memory’ is also questioned, indicating a curiosity about its implications for GPU performance.

  • The DGX Station is priced between 85-90k USD, as noted by users observing current market listings. This pricing positions it as a high-end machine, likely targeting enterprise or research institutions rather than individual consumers.

  • The DGX Station, despite its high cost and advanced capabilities, lacks a video output unless an additional card is installed. This design choice highlights its focus on computational tasks rather than traditional graphical output, aligning with its role as a data center or AI research tool rather than a consumer-grade product.

  • The concept of “coherent memory” in the DGX Station is questioned, with users speculating whether it allows full memory access to the GPU, similar to the DGX Spark. This feature would be significant for tasks requiring large datasets and high-speed processing, emphasizing the machine’s suitability for AI and machine learning applications.

• Mistral Small 4 | Mistral AI (Activity: 323): Mistral Small 4 is a multimodal AI model with 119 billion parameters and a 256k context window, utilizing a Mixture of Experts (MoE) architecture with 128 experts. It is designed to optimize performance across reasoning, multimodal processing, and coding tasks, allowing configurable reasoning effort. Released under the Apache 2.0 license, it supports both text and image inputs, aiming for efficient enterprise deployment with reduced latency and improved throughput over its predecessor, Mistral Small 3. More details can be found in the original announcement. Commenters are intrigued by the model’s 6.5B active parameters, comparing its inference cost to Qwen 3.5 35B-A3B, but with a larger expert pool. Concerns were raised about Mistral’s tool calling issues in previous versions, particularly with hallucinating function signatures and dropping parameters. The model’s performance on agentic tasks and context quality beyond 32k are key areas of interest.

  • RestaurantHefty322 highlights the competitive positioning of Mistral Small 4, noting that its 119B parameters with 6.5B active parameters align its inference cost with models like Qwen 3.5 35B-A3B, but with a larger expert pool. This could challenge Qwen’s dominance in the ~7B active parameter tier, especially if Mistral has improved its tool calling capabilities, which were problematic in Devstral 2 due to issues like hallucinating function signatures and dropping parameters in multi-step chains.

  • The discussion touches on the importance of text and code quality at the 6-7B active parameter range for local deployments, with a particular interest in how Mistral Small 4 handles context quality beyond 32k. This is a critical area where smaller MoE models often struggle, despite having longer advertised context lengths.

  • RepulsiveRaisin7 expresses skepticism about Mistral Small 4’s improvements over Devstral 2, which was perceived as lagging behind competitors. The comment reflects a broader concern about whether Mistral Small 4 can offer tangible advantages over existing models like Qwen, especially given its size and the competitive landscape.

• Mistral 4 Family Spotted (Activity: 687): The Mistral 4 family introduces a hybrid model that integrates capabilities from three distinct model families: Instruct, Reasoning (formerly Magistral), and Devstral. The Mistral-Small-4 model features a Mixture of Experts (MoE) architecture with 128 experts and 4 active, totaling 119 billion parameters with 6.5 billion activated per token. It supports a 256k context length and accepts multimodal input (text and image) with text output. Key functionalities include configurable reasoning effort, multilingual support, and agentic capabilities with native function calling. The model is open-sourced under the Apache 2.0 License. Mistral-Small-4 is designed for both speed and performance, offering a large context window and vision capabilities. Commenters are enthusiastic about the model’s capabilities, particularly its position in the 120 billion parameter range, comparable to models like gpt-oss-120B and Qwen-122B. There is anticipation for its performance and potential applications.

  • The Mistral 4 model is a hybrid architecture that integrates capabilities from three distinct model families: Instruct, Reasoning (formerly Magistral), and Devstral. It features a mixture of experts (MoE) with 128 experts and 4 active, allowing for 119 billion parameters with 6.5 billion activated per token. The model supports a 256k context length and accepts multimodal input, including both text and images, with text output. It also offers configurable reasoning effort, enabling a switch between fast instant replies and more computationally intensive reasoning modes.

  • Mistral 4 is designed to be highly versatile, supporting multilingual capabilities across dozens of languages and offering advanced agentic functionalities with native function calling and JSON output. It is optimized for speed and performance, maintaining strong adherence to system prompts. The model is released under the Apache 2.0 license, which allows for both commercial and non-commercial use and modification, making it accessible for a wide range of applications.

  • The model’s integration with llama.cpp is underway, as indicated by a pull request on GitHub. This suggests that Mistral 4 will soon be supported by llama.cpp, a popular framework for running large language models efficiently. This integration is likely to enhance the model’s accessibility and usability for developers looking to leverage its capabilities in various applications.

/r/Singularity, /r/Oobabooga, /r/MachineLearning, /r/OpenAI, /r/ClaudeAI, /r/StableDiffusion, /r/ChatGPT, /r/ChatGPTCoding, /r/aivideo, /r/aivideo

• INCREDIBLE STUFF INCOMING (Activity: 483): The image presents a slide from a presentation on the NVIDIA Nemotron 3 Ultra Base model, which is approximately 500B in size. It claims to be the “Best Open Base Model” with 5X efficiency and high reasoning accuracy. The slide includes bar graphs that compare the performance of Nemotron 3 Ultra against other models like GLM and Kimi K2 across various benchmarks, including Peak Throughput, Understanding MMLU Pro, Code HumanEval, Math GSM8K, and Multilingual Global MMLU. The Nemotron 3 Ultra is highlighted for its superior performance in these categories. Commenters express skepticism about the benchmarks, noting that NVIDIA does not specify which GLM model is used for comparison and that the Kimi K2 model is relatively old, being eight months old. There is also a critique of the presentation technique, suggesting that starting the graph at 60% exaggerates the performance gap.

  • elemental-mind points out the ambiguity in NVIDIA’s announcement, noting that they don’t specify which GLM model is being referred to. They highlight that the Kimi K2 model, if it’s the base version, is comparable to MiniMax M2.1 and GLM-5-no-reasoning in terms of intelligence, suggesting that the comparison might not be as impressive as it seems.

  • FullOf_Bad_Ideas clarifies the distinction between base models and their finetuned counterparts. They suggest that the models being compared are likely Kimi K2 Base 1T and GLM 4.5 355B Base, rather than the more advanced K2.5 or GLM 5, which are instruct/reasoning finetunes. This distinction is crucial for understanding the performance and capabilities being discussed.

  • ThunderBeanage expresses skepticism about the relevance of Kimi K2, describing it as outdated. They doubt that the GLM model mentioned is the latest GLM 5, implying that the comparison might not reflect the current state-of-the-art models. This skepticism highlights the importance of specifying model versions in performance discussions.

• Showing real capability of LTX loras! Dispatch LTX 2.3 LORA with multiple characters + style (Activity: 932): The post discusses the creation of a LORA model using LTX 2.3, trained on approximately 440 clips from the game Dispatch, each with 121 frames on average. The model includes over 6 characters with distinct voices and styles, achieved by assigning each character a unique trigger word and detailed captions. The training was conducted using the musubi fork by akanetendo25 and involved splitting clips with pyscene, converting them to 24 fps, and using a custom captioning tool. The dataset was divided into HD and SD groups based on clip length, and training involved 31GB VRAM usage with 4 blockswap. The model was trained to 64 rank to accommodate the complexity of the data, and checkpoints were made every 500 steps. The author notes that LTX, while not as visually strong as WAN, offers significant potential for pre-visualization in game development. One commenter expressed skepticism about WAN 2.5 being open source, while another praised the dedication involved in training with 440 clips, noting the clean results.

  • Lars-Krimi-8730 inquires about the technical details of training the LTX 2.3 LORA model, specifically asking about the trainer used, settings, captioning methods, and resolution. This indicates a keen interest in the reproducibility and technical setup of the model training process.

  • Anxious_Sample_6163 highlights the use of 440 clips in the training process, which suggests a significant level of dedication and effort in data preparation. This number of clips implies a robust dataset that likely contributes to the model’s performance and cleanliness.

  • SvenVargHimmel asks about the training duration on a 5090 GPU, which points to interest in the computational resources and time efficiency of the model training process. This question is relevant for understanding the scalability and feasibility of training similar models.

• oldNokia Ultrareal. Flux2.Klein 9b LoRA (Activity: 541): The post announces a retrained version of the Nokia 2MP Camera LoRA, named OldNokia UltraReal, designed to replicate the aesthetic of mid-2000s phone cameras. Key features include a soft-focus plastic lens effect, a washed-out color palette, and digital artifacts like JPEG compression and chroma noise, all trained on the author’s Nokia E61i photo archive. The model is available for download on Civitai and Hugging Face. One commenter humorously notes that Nokia cameras historically lacked the dynamic range depicted in the model. Another suggests training the model on qwen-image for further enhancement, while a third expresses enthusiasm for the LoRA and shares a personal project involving frame injection.

  • jigendaisuke81 suggests training the model on qwen-image, indicating interest in exploring how the model performs with different datasets or architectures. This could imply a focus on enhancing image generation capabilities or testing the model’s adaptability to various image styles.

  • Striking-Long-2960 mentions an interest in ‘frame injection in Wan2GP’, which suggests a technical exploration of integrating frames into generative models. This could involve manipulating or enhancing image sequences, potentially for video or animation purposes, using the LoRA model.

  • berlinbaer highlights the technical achievement of the LoRA model in replicating specific visual effects, such as ‘blown out highlights with their blue-red color shift’. This suggests a focus on the model’s ability to accurately mimic complex photographic effects, which might be challenging to achieve through simple prompting alone.

• Antrophic CEO says 50% entry-level white-collar jobs will be eradicated within 3 years (Activity: 2162): Anthropic CEO predicts that 50% of entry-level white-collar jobs will be eliminated within the next three years due to advancements in AI technologies. This statement highlights the rapid integration of AI in the workplace, potentially replacing tasks traditionally performed by humans, even when AI solutions like copilot may not yet match human expertise in quality and accuracy. The prediction underscores a significant shift in the job market, emphasizing the need for adaptation and skill evolution among the workforce. A notable comment highlights a personal experience where AI is being used to perform tasks inadequately, leading to errors and incorrect conclusions. This reflects a broader concern about the premature reliance on AI in professional settings, potentially undermining human expertise and job security.

  • Due_Answer_4230 highlights a practical issue with AI integration in workplaces, where AI tools like Copilot are being used to replace human work, even when they perform poorly. This results in errors and incorrect conclusions, yet management may prefer AI for its speed, undermining skilled workers who have invested years in developing their expertise.

  • Stahlboden references a prediction from a year ago that AI would write 100% of code, noting that while this hasn’t fully materialized, AI’s role in coding has significantly increased. This reflects a broader trend of AI’s growing capabilities in technical fields, suggesting a potential future where AI could dominate certain tasks.

  • Environmental_Dog331 points out the lack of solutions from AI leaders regarding job displacement due to AI advancements. The comment underscores the challenge of creating new jobs at a pace that matches AI-driven job losses, highlighting a critical gap in strategic planning for workforce transitions.

• NBC News survey finds Americans hate AI even more than ICE (Activity: 1146): An NBC News survey reveals that only 26% of voters have a positive view of AI, while 46% hold negative views, making AI less favorable than most topics except the Democratic Party and Iran. This reflects a broader skepticism towards AI, despite its widespread use and potential as a productivity tool. The survey highlights a disconnect between AI’s perceived capabilities and its actual utility, particularly in replacing jobs that require significant industry knowledge. Commenters note a paradox where frequent AI users still harbor resentment due to overhyped claims about AI’s capabilities, particularly its potential to replace white-collar jobs. There’s a consensus that while AI is a powerful tool, it is not yet capable of replacing jobs requiring deep industry knowledge.

  • TimeTravelingChris highlights the gap between AI’s potential and its current practical applications, noting that while AI can be a powerful productivity tool, it is not yet capable of replacing jobs that require significant industry and company knowledge. The commenter emphasizes the importance of validating AI outputs, as the technology still has notable gaps when scrutinized closely.

  • AlexWorkGuru discusses the disparity between AI’s potential as demonstrated by labs and the everyday experiences of users, which often involve frustrating interactions with basic AI implementations like chatbots and automated phone systems. This gap contributes to a credibility issue for AI, as the companies promoting it are often those that users already distrust, exacerbating negative perceptions.

  • bjxxjj points out that public perception of AI is heavily influenced by negative associations such as job layoffs and surveillance, rather than practical applications like educational chatbots. This suggests that survey results on AI sentiment may be skewed by the specific aspects of AI that respondents are considering.

Unfortunately, Discord shut down our access today. We will not bring it back in this form but we will be shipping the new AINews soon. Thanks for reading to here, it was a good run.

Claude Cowork came out of an accident.

Felix and the Anthropic team noticed something interesting with Claude Code: many users were using it primarily for all kinds of messy knowledge work instead of coding. Even technical builders would use it for lots of non-technical work.

Even more shocking, Claude cowork wrote itself. With a team of humans simply orchestrating multiple claude code instances, the tool was ready after a brief week and a half.

This isn’t Felix’s first rodeo with impactful and playful desktop apps. He’s helped ship the Slack desktop app and is a core maintainer of Electron the open-source software framework used for building cross-platform desktop applications, even putting Windows 95 into an Electron app that runs on macOS, Windows, and Linux.

In this episode, Felix joins us to unpack why execution has suddenly become cheap enough that teams can “just build all the candidates” and why the real frontier in AI products is no longer better chat, but trusted task execution.

He also shares why Anthropic is betting on local-first agent workflows, why skills may matter more than most people realize, and how the hardest questions ahead are about autonomy, safety, portability, and the changing shape of knowledge work itself.

• Felix’s path: Slack desktop app, Electron, Windows 95 in JavaScript, and now building Claude Cowork at Anthropic

• What Claude Cowork actually is: a more user-friendly, VM-based version of Claude Code designed to bring agentic workflows to non-terminal-native users

• Why “user-friendly” does not mean “less powerful”: Cowork as a superset product, much like how VS Code initially looked simpler than Visual Studio but became more hackable and extensible

• Anthropic’s prototype-first culture: why Cowork was built in 10 days using many pre-existing internal pieces, and how internal prototypes shaped the final product

• Why execution is getting cheap: the shift from long memos, specs, and debate toward rapidly building multiple candidates and choosing based on reality instead of theory

• The local debate: why Felix thinks Silicon Valley is undervaluing the local computer, and why putting Claude “where you work” is often more powerful

• Why Claude gets its own computer: the VM as both a safety boundary and a capability unlock, letting Claude install tools, run scripts, and work more independently without constant approval

• Safety through sandboxing: why “approve every command” is not a real long-term UX, and how virtual machines create a middle ground between uselessly safe and dangerously autonomous

• How Cowork differs from Claude Code: coding evals vs. knowledge-work evals, different system-prompt tradeoffs, longer planning horizons, and heavier use of planning and clarification tools

• Why skills matter: simple markdown-based instructions as a lightweight abstraction layer for reusable workflows, personalized automation, and portable agent behavior

• Skills vs. MCPs: why Felix is increasingly interested in file-based, text-native interfaces that tell the model what to do, rather than forcing everything through rigid tool schemas

• The portability problem: why personal skills should move across agent products, and the unresolved tension between public reusable workflows and private user-specific context

• Real use cases already happening today: uploading videos, organizing files, handling taxes, managing calendars, debugging internal crashes, analyzing finances, and automating repetitive browser workflows

• Why AI products should work with your existing stack: Anthropic’s bias toward integrating with Chrome, Office, and existing workflows instead of rebuilding every app from scratch

• Computer use one year later: how much better it has gotten, why vision plus browser context is such a superpower, and why letting Claude see the thing it is working on changes everything

• Why many “AI verticals” may get compressed: specialized wrappers may matter in the short term, but better general models and stronger primitives could absorb a lot of narrow use cases

• The future of junior work: Felix’s concerns about entry-level roles, labor-market disruption, and whether AI can compress early-career learning into denser simulated experience

• Why Waterloo grads stand out: internships, shipping experience, and learning how real teams build products versus purely theoretical academic preparation

• The agentic future of the desktop: what it means for Claude to have its own computer, whether AI should act on your machine or a remote one, and how intimacy with personal data changes the product design space

• Why Electron still mattered: shipping Chromium as a controlled rendering stack, the limits of OS-native webviews, and why browser engines remain one of the great software abstractions

  @electronjs into just 55 pages.\n\nThanks to @OReillyMedia's great @allymacdonald, it's now a pretty solid read!\n\nsafaribooksonline.com/library/view/i… ","username":"felixrieseberg","name":"Felix Rieseberg","profile_image_url":"https://pbs.substack.com/profile_images/1544558915819487233/qMrauBqx_normal.jpg","date":"2017-12-11T18:34:15.000Z","photos":[{"img_url":"https://pbs.substack.com/media/DQyRXazU8AAEmIG.jpg","link_url":"https://t.co/xKYYtH82NC"}],"quoted_tweet":{},"reply_count":3,"retweet_count":28,"like_count":107,"impression_count":0,"expanded_url":null,"video_url":null,"belowTheFold":true}" data-component-name="Twitter2ToDOM">

• Anthropic’s Labs mentality: wild internal experiments, half-broken future-looking prototypes, and the broader effort to move users from asking questions to delegating increasingly long and valuable tasks

• Why the endgame is not just more capability, but more independence: teaching users to trust AI with bigger scopes of work, for longer durations, with fewer interventions

• X: https://x.com/felixrieseberg

• LinkedIn: https://www.linkedin.com/in/felixrieseberg

• Website: https://felixrieseberg.com/

• Website: http://anthropic.com

00:00 — Cheap execution and building all the candidates
00:44 — Intro in the new Kernel studio
02:47 — What Claude Cowork is
04:18 — Why user-friendly can be more powerful
05:33 — How Anthropic built Cowork
07:09 — Prototype-first product development
08:00 — Why local computers still matter
09:20 — Skills, primitives, and platform leverage
12:13 — Cowork’s architecture: VM + Chrome + system prompt
15:38 — Felix’s own bug-fixing Cowork workflows
17:38 — Local-first agents
20:16 — Evals, planning, and knowledge-work optimization
23:14 — What Anthropic means by evals
24:21 — Scaffolding, tools, and why skills matter
27:44 — Demo: YouTube uploads and self-generated skills
31:03 — Calendar automation and cleaning your desktop
34:47 — Browser context and why DOM access matters
37:47 — Skills portability and plugins
44:36 — Which AI categories survive?
46:19 — Junior jobs, simulated work, and labor disruption
52:00 — Gradual takeoff vs big-bang takeoff
53:42 — Finance, taxes, and enterprise verticals
56:24 — Vision and the improvement in computer use
57:31 — Why Claude writes its own scripts
58:06 — Should Claude have its own computer?
1:01:26 — Windows 95 in JavaScript
1:03:19 — VM tradeoffs and sandbox design
1:07:23 — Approval fatigue and safe delegation
1:11:18 — The future of Cowork
1:12:27 — What comes next for agentic knowledge work
1:15:13 — Electron, Chromium, and desktop software lessons
1:22:16 — Multiplayer agents and coworker-to-coworker workflows
1:26:05 — Anthropic Labs and closing thoughts

Alessio: Hey everyone. Welcome to the Latent Space Podcast, our first one in the new studio. This is Alessio, founder of Kernel Labs, and I’m joined by swyx, editor of Latent Space.

swyx: Yeah, so nice to be here. Thanks to, uh, TJ, Alessio, Allen helping to set everything up. It looks beautiful. We even have the logo outside.

Yeah, kind.

Felix: It’s like really nice, right? When you walk in here as a guest, you’re like, ah, this is a serious production. You’re like, feel it immediately.

swyx: Yeah. Felix, you’ve been, you’re, you’re currently a product manager of Cowork or,

Felix: uh, really Technic

swyx: Eng. Yeah. The, the identities are kind of vague member technical staff.

Felix: I know member staff is like, the official title will carry around forever.

swyx: Yeah. I basically kind of wanted, like we’ve been. Kinda obsessed. I, I’ve been using it a lot, even for managing latent space. Like, uh, cowork helps me upload videos and like title things and like edit and everything. It’s, it’s like really amazing.

Alessio: Cool. He said multiple times Cowork has said gi in the group track.

swyx: Yeah, yeah, yeah. So, so we have a second, uh, we have a second channel, uh, for latent space tv. Uh, and I, uh, and uh, we basically, this is our Discord meetup. Um, and I I, we have like Claude Coworks, it might be a GI, I don’t know if we, we have, uh, uploaded it yet, but one of the sessions was like a, like a Claude cowork thing.

Felix: I, you have to see, I would love to see it. Like, I’m so curious, like one of the most fun parts of my job is like constantly see the weird things people use Cowork for because it’s obviously like very hard for us to actually design for specific use cases we do. But like every single person who’s like most amazed is usually amazed about a thing that I didn’t even expect cowork would be good at.

Um, we have a new designer and it’s one of the first small tasks. I was like, Hey, we need like a new emoji for cowork for our internal stock. It’s like a pretty small thing. I like, can you please do it? And he drew an SVG and just gave it to coworker was like, can you animate this emoji? And now it has like this beautiful loopy animation.

Um, and I mean, I think obviously this goes down to like, it turns out you can do more things with code than you expected, but it, it’s like that kind of stuff that is really fun to me. So, long story short, I would love to see like, the kind of things you’re doing.

swyx: I’ll pull it up. I’ll pull it up.

Felix: Yeah. Yeah.

swyx: Uh, but before we get into it, I, I think always wanna start with like a top level. What is Claude Cowork for people who haven’t heard of it? Haven’t tried it out.

Felix: Okay. Uh, real quick, Claude Cowork is a user friendly version of Claude Code. So the way it basically works is we have Claude Code and for us, fairly impressive agent harness that over December we noticed more and more people are using either, even though they’re not technical, they, they’re not at home in the terminal or they are at home in the terminal, but they started using Claude Code for non-coding workloads, right?

Like managing expenses or like filling out receipts or organizing a knowledge base. Like there was a big obsidian moment that a lot of people liked and we wanted to capitalize on that, but also bring, bring this capability to people who are not terminal native and who might not know how to like brew and store something.

So cowork is Claude Code running in original machine with a little bit of padding, a little bit more guardrails, making it a little safer and a little bit more convenient for people who don’t wanna first open up the terminal when they go to work.

swyx: It’s interesting, uh, that is kind of. Pitch that way as a more user friendly thing because I always feel like it, it, to me, I I treat it as like why I’m familiar with Claude Code.

Like we, we did a Claude Code episode Yeah. A year ago. But this one is like even more power user tools ‘cause it, uh, it kind of integrates much better with like clotting Chrome and, uh, in all the, all the other tooling. But like, maybe, maybe that’s like a perception thing, right? Like

Felix: No, honestly, I don’t think you’re wrong.

This is like a, a thing I’ve been thinking a lot about for like the last two weeks. So,

swyx: but when they say user friendly, it’s like, oh, it’s the dumb down version. But no, actually this is the superset.

Felix: Yeah. Like, I think a similar thing happened, A similar thing happened to me about 10 years ago, like maybe 12 years ago when I was at Microsoft and we started working on, on Electron and like browser-based technologies and cross-platform stuff.

And one of the first use cases was Visual Studio Code, which used to be a website. And the initial narrative was, or Visual Studio Code is, is like a more user-friendly version of Visual Studio. But in a similar vein, I think there was some voices saying, oh, this is. For serious developers, like, we’re not gonna use this.

Right? For like anything. And I think in the end what happened is people have different stories about why Visual Studio Code became such a big thing. But my personal, my personal belief is that the Hackability and the extendability has like played a pretty big role, right? You can hook in Visual Studio Code that like almost any workload, it’s so easy to hack on, so easy to put extensions for it.

And I think cowork might be hitting a similar thing where it’s very easy to extend and it’s very easy to bring into your workflows. Uh, so the convenience I think is a bit of a, it’s obviously the thing we strive for as developers, but I think the way people find value in it then is by probably mapping it onto whatever they actually have to do in their job.

Alessio: So end of last year, you see the spike of like non-technical usage and clock code. What’s the design process to say we should make clock code work? Because I mean, you built it in only 10 days. Um, I’m sure there was some discussion before on whether it’s easier to use mean. You know, like making, making like a desktop GUI is obviously one way to do it, but like there’s a lot of nuance in the product.

Like maybe talk people through what was like the trigger of like, we should build a separate thing. We should not build like a different plot code thing. And then maybe some of the more interesting design decisions that maybe you didn’t take.

Felix: Yeah, I think philanthropic, we’ve been thinking about ways to move people who are comfortable with using Claude to answer questions and bring more of the power of like this thing to now like, execute tasks for you.

I can like solve problems for you can like build things for you. How do we bring that capability to people who are currently mostly comfortable with like a like question answer paradigm within the chat. And we’ve had a lot of prototypes around that. Just going back as far as like easily a year and a half.

Like we had a lot of people working on that. Um, and internally philanthropic is a very prototype demo, first culture. We have a lot of like internal prototypes that don’t reach the public. What Cowork actually became is like we sort of picked the right pieces out of the many prototypes that we had.

Right. And that’s, that’s maybe also like, I think an important qualifier whenever people mention this like 10 day number. I do think it’s important to me to mention that within Double Scratch there was like a lot of stuff already happening, right? Like, and I think it’s important for people to remember that when you build a website, you use React, you use like a bunch of other things.

And this is like a similar scenario with like a lot of pieces we already had. Um, and in terms of decision path, I think we live in like an interesting new world where execution is actually quite cheap.

swyx: Mm-hmm.

Felix: So maybe, maybe what you would do That’s so crazy. The year. I know it’s wild.

swyx: You should be, ideas are cheap.

Execution is the hard part. I

Felix: know. And like the, we, we used to live in this world maybe where you would take a product manager and the product manager would go to a number of potential customers and in this like very low bandwidth way, would try to. Try to like tease out what are the problems they’re having, what are they willing to buy?

Um, and then maybe what can you build to like drive out that need and then you go back and you like draft a spec and you think about it and then like you make a design and you execute it. We internally philanthropic app, not pretty much closer to the point where we’re like, don’t even write a memo, just like build, like let’s build all the candidates very quickly.

Let’s just build all of them and then pick the best ones. I think the, the decision that is most impactful both for the product as well for the users right now is like the way we put value on your local computer. I think that’s a big decision point a lot of people have thought about. Should this thing, whatever it is, should it ultimately run into computer or should it run in the cloud?

‘cause they’re big trade offs, right?

Alessio: I guess like if we solve auth, it would be easy to do in the cloud. But I think like the fact that I can just download any file from anywhere and then put it and cowork there, it’s like a big unlock. Um, I mean it’s interesting you mentioned reusing certain pieces. I think this is something I’ve been thinking about even with Claude Code, right?

The price of like writing code is going to zero, blah, blah, blah. But it actually seems like the value of having some sort of platform substrate is like increasing because as you build these new things, you can kind of plug them together.

Felix: Yeah.

Alessio: So I almost feel like when people are saying, oh, the value of a lot of software is gonna zero because you can recreate it, to me it’s almost like the opposite.

It’s like having an existing platform to build on top of. It’s like even more valuable because you can kind of bolt things on.

Felix: Yeah.

Alessio: You have obviously mcps, you have skills, you have like obviously the models, which is a big part. All these things kind of come together. Do you feel like that’s a valid way to think about it, where people should invest even more in kind of like primitives.

To rebuild on or are you like recreating a lot of it each time because like things change and it’s easier to rewrite than reuse?

Felix: You know, I think, I think you’re right. I think you’re right that the holistic platform is really useful. And this is maybe a whole like a somewhat contrarian view to a lot of people in ai.

I actually don’t think that the future is going to be hyper personalized software down to the point where everyone is running their own version. Like, I actually think it’s going to be quite hard for all of us to have our own internal chat tool and like, if I wanna talk to you, like

swyx: how

Felix: is that gonna work, right?

In the, in the context of cowork and how we build it, I think it’s a bit of a combination. Like what the, the execution that gets cheap is not necessarily rebuilding all the primitives. I think our priori, there’s also not a lot of value in it. So for instance, my team did not think about rebuilding clock code.

We’re like very much started with the. The core thesis of this should be Claude Code.

Mm-hmm.

Felix: And then we’ll like build things on top of it. The part of the execution that gets a little cheaper is like, how do you take all of these Lego pieces and put them together in a way that makes sense for users?

It’s like actually valuable. You have so many different approaches now in terms of what kind of, what kind of things do you actually elevate to a primitive, do you strongly believe that all your products should be built by just combining primitive that the public also has available? Do you keep some things internal?

Um, and I think that’s still evolving, but I think what’s probably gonna go away is like, I’m not sure if it’s gonna fully go away, but I’m gonna say, I think for me personally, I will probably no longer try to come up with a really good product without testing up with people. This is not a new concept, but wherever you used to have to make costly decisions around, do we pick technology A or technology B, or do we like, um, build it this way, build it the other way.

I really strongly believe now you just build all of them and try them out with a small focus group and then whatever, whatever is better is what you go with. Right. And that, that is probably quite different even from how we maybe worked a year ago. Right. Like, I think, I think this happened very recently.

Alessio: Yeah. I started building something in on Electron since you’re here. Coincidence. Uh, but then Electron and like SQL Light are like, there’s like some issues that like between development and like, uh, building anyway. And I was like, let’s just rebuild the whole thing in Swift and just recreated the whole thing in Swift.

And it’s like, I. It’s done.

swyx: You know, I didn’t take any effort. I, I, I don’t even know Swift.

Alessio: Yeah, exactly. I was like, I’m the, I’m not reviewing it anyway, whatever. You can write in whatever language you pick, but the important stuff that I did was not write the electron bindings. Yeah. It was like the logic of what happens in the app, you know, and then the model is like, yeah, I can just recreate the same thing as with

swyx: Yeah.

I, I think you still want, especially for people who are doing like high performance software or like very complex software, uh, you still want like, some view of the architecture. Uh, but you can use markdown for that,

Felix: right? Yeah.

swyx: Uh, you don’t actually have to read the code again. I, I’m still like on a sort of like a definitional thing.

Um, can we build a good mental model of Claude Cowork? Um, this is what I have, right? Like you you said it’s like fundamentally cloud co. We don’t wanna touch it. There’s the cloud app, there’s clouding Chrome. I think you guys do something different in planning, but, uh, I’ve been talking with Tariq who is on the cloud co team, and you guys are, he’s like, no, we just exposed planning.

Maybe we can clarify like, what are the major pieces. That people should be aware. It goes into cowork, like,

Felix: okay, I think you basically have them. So really, um, you can, you can take planning more or less out. I think there’s a few things that are really valuable in cowork. Um, the virtual machine is probably the most powerful thing.

So we currently run like a, we currently run like a lightweight VM and we put clocked out into the vm and we do that for, for, um, a number of reasons. Safety and security is a big one, but even if you, even if you ignore for a second safety and security and you’re just like, okay, Yolo, I want this thing to do whatever.

It is quite powerful to give Claus on computer that is like generally a good idea. And in terms of architecture and UX and everything else that we’ve been working on, philanthropic, it often is quite useful for you to like anthropomorphize, um, clot aggressively and just be like, this is a person. What will you do if you give a, if you had a person, right?

Yeah. And the analogy I’ve given my dad this morning who is still like quite insistent on using chat even for like coding things, is if you were a developer and your employer told you that you don’t need a computer, they’re just gonna like, send you emails with a code and you send emails with code back like that, maybe work for Patrick Miles in the back, but that it’s not very effective.

Um, so what we can do with the VM is because it’s a, it’s a Linux system, Claude Code has more or less free reign to install whatever needs to install. It can install Python, it can install no js. We do have strict network ingress and egress controls. So you can still, as, as a user in like plain human language, make it clear to, to the entire system what you’re okay with and what you’re not okay with.

But at no point do we have to ask a real person, like a, like a person who might be in marketing or a lawyer. I’d have to go to a lawyer and be like, are you okay with me installing Homebrew?

Alessio: Yeah, yeah.

Felix: Right. Because the implications of the question and the answer are complex and nuanced and like, not, not easy to reason about.

This gives us a lot of distraction that makes Cloud very powerful. Now then around it, we, we do probably have a number of things that also keeps growing almost every single week that you’re probably noticing that make cowork maybe better for certain tasks than just cloud. Cloud on its own. Yeah. But most of those actually live in the system prompt.

They’re about like, what can we infer about the work that you do? What can we, what can we intru in the system prompt to make that more effective? It’s of course the like very tight integration with Cloud and Chrome. You’re noticing that a lot of people, especially as the models get better, a lot of people throw up their hands when it comes to MCP connectors in this area.

I’m not gonna, I’m not gonna go through like 25 M CCP connectors, click off everywhere and then like half of them don’t let me do the things anyway. So Cloud and Chrome is quite powerful because we can just talk to the cloud and Chrome sub agent and that will just do things for you.

swyx: Yeah, so, so one example right in MCPI, honestly, I think that the state of MCP is kind of, kind of.

Really hard to integrate. Um, I need to, I needed to add, uh, Figma MCP to the coding agent that I use.

Felix: Yeah.

swyx: Uh, and, but I didn’t wanna read the docs, so I just had caught to it. And it’s, it’s great at reading docs and the same, same way I had to set up like a Google Cloud, um, account for some project I was working on and get some API keys somewhere.

And Google Cloud is famously super hard to navigate, so I just didn’t wanna deal with any of it. I just used Claude Cowork

Felix: within the first week of developing on Core. This happened very, very quickly. Um, I caught myself by starting to use cowork for coding tasks, which is not ostensibly what we built it for, right?

We don’t need to. But I found myself, um, I found myself like on our internal, internal tool that we have for, to collect crashes and just like debugging information and I found myself sort like picking out the ones that I think we can easily fix versus the ones that might be like kernel corruption or something else on the operating system.

And I found myself sort of picking these out and then just telling Clark, go fix this bug. I was like, what am I doing here? Go one level up, tell a cowork, I want you to go to all these crash tools. I want you to find all the bugs that you think are fixable and not like an operating system crash. And then I want you to tell another cloud to like fix all of that.

Um, and that’s, that’s, that’s sort of another cloud,

swyx: just so it can spin up another instance or,

Felix: uh, it, currently what I do is, um, and this is a bit of a hack, but I tell it to use clockwork remote to which website itself? Yeah, that’s interesting. So you basically take, if you, if you imagine like a dashboard with like 20 bucks, you, this is remote control or clock or remote, or, sorry, I just wanted to confirm what, the way I’m using it is.

I have cowork running and I’m telling cowork, here’s where I normally go every morning to find the latest bugs. Go read the entire bug list, separate out which ones are fixable, which ones are, are fixable, and then for the fixable ones, four is this almost loop. For each bug, write a markdown file with a prompt.

And then for each markdown v, that is a prompt. Start of a cloud set. So natively Claude Code has

swyx: this concept of subagents. Mm-hmm. And this is basically a subagent, but you’re not using the subagent functionality.

Felix: I’m not using the subagent functionality. And the reason I’m not is because I’m firing that off as a Claude Code remote

swyx: task.

Felix: Yes. That’s kind of nice. ‘cause then I can just fire it off. I can go to my next meeting and in Claude Code remote. Now the work is happening.

swyx: Mm-hmm. Yeah. You, you see like you’re already starting to use the cloud over your local machine. And I think this is one of those things where like. Shouldn’t just everything just be cloud first, right?

Felix: Ah, this is such a good group. I’m like solely bad about this. I have so many thoughts about that. Okay. So I generally believe that Silicon Valley overall is undervaluing the local computer. And my default argument for that is always how come we’re all using MacBooks and not like an iPad or a Chromebook?

Um, that there is like still value in, in having a local machine. And now when I think about Clot, it’s this entity that is supposed to be very useful to you, like it tremendously useful to you. I think that entity needs to have access to all the same tools you have access to. Otherwise it’s gonna be hamstrung in like all these complex ways.

And there’s, there’s sort of two approaches we could take. We could say, okay, we’re gonna like one by one chip away at everything that is at your computer and move it into the cloud. That’s, that’s one way to do it. Um, and I think other products have taken that path. I personally, this is a very personal opinion, but I personally, for the amount of tools that I use.

Just don’t have the patience to give another tool like permissions to every single thing and keep those permissions up to date. The second thing that I’m still grappling with, and I don’t have a good answer for anyone just yet, but the second thing I’m still grappling with is what does it look like for someone to slurp up your entire work and put that in the cloud?

Like if I, just as an example, like if you could click a button and it just clone your entire computer into the cloud, is that something that you would want? I’m not totally convinced yet that all everyone will. Mm-hmm. And that is sort of like upstream of all the technical issues we’re gonna have. ‘cause like in general, I think the world is not ready for this kind of stuff.

Like, I’ll give you one quick example that would probably be very easy for us. So as a desktop app, we in theory with your permission, can do a lot of things on your computer, including reading your Chrome cookies. If we really want to do right, we could take your Chrome cookies, you would have to decrypt them for us.

We could put those on the cloud if we really felt like it. Pretty easy solution. That would be super cool. We could just be like, oh, we can do all your tasks in the cloud now. Um, a lot of websites, thanks, include it. If, if they see the same authentication from like two different locations, we’ll just lock down your account and now you have to go to the branch and be like, okay, I, I’m here with my passport.

You actually know that. Wow. Yeah. As tired as well are of the term agent for the age agent future, I think there’s a lot of stuff that sort of slowly needs to catch up and until that’s the case, the way I, as someone’s working on clock and make Cloud most effective is to like put it where you are working.

swyx: Anything else? I thought with our mental model, so like, basically like, uh, part of me also just want, like the more I understand how it works, the more I can use it to its full potential. Right?

Felix: Yeah.

swyx: And so what I’m get hearing from you is you told me to delete the planning thing. You’re not doing anything special on, on the, that’s only exclusive to Qua cowork.

Felix: We have some tricks for this sort of like change week over week. We eval cowork maybe against different use cases than he would evil clock code, right? If you think about it this way. Okay, so like clock code is our eval clock cowork. Yeah. So clock code is like quite optimized for coding tasks and we mostly value it whether or not we’re getting better or worse depending on how good it is at like a typical suite job.

And Clark Cowork on the other hand, we evaluate more against typical knowledge work, the kind of stuff he would find in finance or in like maybe a, like in like a legal office. Um, my personal use case is always like managing my things, like managing my personal mortgage or something like that, right? Or like wealth planning for me and my family.

Those are the kinds of use cases we eval, clock cowork on. And what you might be picking up on is like the subtle changes we make to the system. Prompt what we put in the system, prompt how we steer, clot with the tools we give it. Um, like either it’d be better in one or the other direction and whether there’s a trade off, try us exist a lot.

CLO code will be better of a code and Claude Cowork will be better. For non-coding tasks, will those gaps still exist in the next three generations of models? It’s like a little unclear to me though.

swyx: Yeah,

Felix: because right now these like hyper optimizations we make, I’m not sure for how long they’re still be relevant.

swyx: I think what I was referring to was also, it, it just, uh, it qualitatively felt different when I probably, it’s just all prompting and I’m reading too much into it, but like the, the fact that it comes out with like a nine step plan, I can edit the plan and give feedback and, and, and see it execute the plan.

Yeah. It felt more long range than in Claude Code, but maybe that already existed in Claude Code and you just build a nicer UI for it.

Felix: It’s kind of both. Um, like if the Clark Code people who build the planning functionalities would city, they probably say yes, we have all of those things in Clark code and they do.

Um, I think people tend to give cowork. Tasks that are maybe of longer time horizon, I thought is

swyx: so long. Yeah.

Felix: That’s like one thing, right? It’s just like that the, the chunk of work tends to be maybe a little bigger. And then the second thing is that because the work, when it gets longer, it gets a little bit more ambiguous.

We do tell co-work to make heavy use of the planning tool or to make heavy use of the ask user question tool, right? We do want it to come up with like. Different scenarios of, okay, tease out what the user actually wants. Don’t go off to work for like four hours and then come back with the wrong thing.

And you’re probably picking up on that.

swyx: Yeah.

Felix: Um, I wish I could tell you I like built this magical thing and it’s like, there’s some secret sauce,

swyx: but No, no, no. I mean, it’s, it’s just clarity is good that, you know, engineers just want to know. Yeah. They can, they can plan around it. And then I think also for me, um, I am realizing I have to switch to my, my other machine because this is a new machine that doesn’t have my session.

But, uh, yeah, the, the, the planning is really important for, for me to like approve or like to see whether it’s like, it’s right. The ask is, the question is so beautifully presented. I mean, it also, it also available in like cursor and, and in Claude Code. But like, I, I think like it’s so nice to see that it, like it’s kind of for me like to understand that it gets me, it gets what I want to do.

Felix: Yeah.

swyx: Yeah.

Felix: It probably very hard

swyx: just on the topical evals. Mm-hmm. When you say eval, I think people are very vague about what it means. Is it just like vibe testing or do you have like automated programmatic evals of Claude Cowork?

Felix: When we say eval, uh, what we really mean is that we essentially take the entire transcript, including all the tools that clot has available ultimately to it, and we then measure what are the outputs, depending on what we tweak, right?

So we do run that a lot. We use that in training. Um, we use that in, in like, if you sort of separate out post training from like the scaffolding around it. Cowork sort of exists in the scaffolding space, but obviously we also train on it a little bit. Um, so when we say eval, we mean given the certain transcript, what do the outputs look like?

Including the file outputs as well as like the actual token outputs, like the ones that you see in the chat window.

Alessio: I’m curious, um, how much of the failure modes are the model intelligence versus like the usage of the end tool to put the intelligence in? Like the well planning is like a good example, right?

It’s like one thing is to come up with a plan. The other thing is like make a nice spreadsheet. Yeah. That kind of runs you through the plan. Like how have you seen that? Well,

Felix: the thing that I grapple with a lot is that whatever scaffolding you come up with, I think we still have a bit of sort of like model overhang where the model is dramatically more capable than right.

Users end up using it for. And I think part of that is that we’re just not getting the model all the tools to do all the things that’s theory capable of, right? There’s like one thing, um, however, whenever you do build the scaffolding, I’m sort of wondering at what point, at what point will that scaffolding go away and like how much you invest in figuring out what the right scaffolding is.

It’s kind of up to, it’s a little bit of a bet. And one thing that I as an NJ quite enjoy is that like working in philanthropic and working at a frontier lab, I maybe have a little bit more insight into what’s coming, coming down the chute in terms of like, what’s the next model, what is the model capable of?

What is good at, what is it bad at? And I’m, I’m increasingly wondering, is the right thing for us to like really invest too much in sort of these like scaffolding corrections where the model might otherwise not misbehave, but just not do the thing that you want?

Alessio: Yeah.

Felix: Or is it to just like give it as many capabilities as possible, try to make those safe so there’s the worst case scenarios, likeno status might be otherwise.

And then just simply wait a second for the next model drop. I’m personally, currently more leaning into the ladder. I think we’re gonna see a lot of like applications and companies that do very impressive things with ai that in the short term might seem very effective ‘cause they’re very specialized to individual use cases.

But I think once models get better generalization and get better at like those specific use cases without being super guided on those, I’m not sure how long that’s gonna stick around. And you can kind of, kind of already see this in like skills and NCP servers, right? Mm-hmm. We’ve, we’ve already seen sort of this like slow shift from MCP service to skills.

And like, maybe a good example is Barry who made skills. He was initially hacking on something that honestly looked a lot, looked, looked a lot like what Cowork does today. It was sort of thinking about what if cowork, but for like people who don’t wanna build code. Mm-hmm. And, um, he too did that as a prototype inside the desktop app.

One of the first use cases we thought of were, okay, what, what are like coding like use cases that could really benefit from graphical interfaces and like from being a little separated from the actual underlying code. And everyone comes with the same answers. Data analysis,

Alessio: right?

Felix: Yeah. Or saying how many users do we have today?

How many, like, it’s always data analysis. And I think the thing that ultimately led to skills is that we wanted to connect this little prototype to our data warehouse and. The team very quickly discovered that like instead of building a custom tool for the thing to talk our data warehouse, they just like meet and embarked on follow like mm-hmm.

Dear Claude, if you want to get data, here’s the end point. Here’s what the API looks like. You’ll figure it out.

swyx: Ah.

Felix: And then it be hand over control. Yeah, yeah. Also just like maybe go one step up in the layer of abstractions, right. Just, yeah. Instead of, instead of telling the thing, here’s ACL I, please call the CLI, or here’s an MCP.

Please call this ECT shape. Just like this is the end point. If you wanna know something, if you post here, maybe you can do post sql. It’s gonna be okay. And that ended up being so effective that they started trying the same pattern of like just giving the model a markdown file that describes whatever it needs to do.

That the whole thing eventually became skills and we’re like. We should package this up. This is a good idea.

swyx: Yeah. Um, we’ve had Barry Mahesh, uh, on, on our conference and uh, he’s uh, definitely got a good idea there.

Felix: Yeah.

swyx: I wanted to show you the, how I’ve been using Claude Cowork.

Felix: Uh, this is was my favorite part.

swyx: This is this. So this is like me, uh, this is how we run the Discord. Uh, we literally, uh, at first I didn’t trust Cloud Core. This was my very first usage.

Felix: Okay.

swyx: Right. So then I was like, okay, I will just try to manually download from Zoom all my recordings and upload it to YouTube. Yeah. Because this is a very laborious process.

I got a click, click, click YouTube, um, isn’t super user friendly. Uh, and it just did it. And then I was like, actually, you know, even the download from Zoom part, I should also. Put into Claude Cowork, and then I did it right. Here’s a bunch of, and it starts compacting here, and it, and it, it starts to even be able to do things like look through the individual frames of the video to name the video so I can upload it auto automatically.

Oh, that is, and this replaces my job as a YouTuber. We will forever appreciate your creative Yes. You know, and so that’s great. Uh, but then by the way, it compacts and makes, makes like a new thing, right? So I, I don’t, I don’t have the initial, initial thing, but then I asked it to make its own skills so that it, so that something that’s repetitive and one-off and human guided becomes more automated and I can use the skills independently and reuse them.

Uh, and it obviously you can write skills and that goes into context and skills at the bottom here, which is, which is so nice. Um, so I have all these skills that, that I now sort of do on a weekly basis. Uh, I know you’ve released scheduled Coworks, which I haven’t done yet, but

Felix: course I should try them. I, I think this is like so wonderful and fun for me to see because.

One thing that is very fun for me about skills in particular is that they’re so easy to make. Like anyone can make a skill, like a text message, could be a skill, and they can be so hyper personalized to you. And this is like sort of the subtraction layer, right? Like, um, I, I’m just guessing, but I assume, heck, you are very good at your job.

You’re probably given this thing some guidance about how to do it, right? I,

swyx: I just said, wrap everything up into, into a skill, right?

Felix: Yeah.

swyx: And then, uh, and then I was like, actually, sometimes I might need to break, uh, things apart because some parts fail or some parts might be needed in individually. So I told it to split one skill into three skills.

So it’s like a skill splitting thing, and then there’s like a parent skill that just orchestrates all of them if I want to use that. You know, like, um, I think that’s, that’s like really good. Uh, and, and, uh, there’s, there’s one more part, which is the, uh, Google Chrome thing that I told you about.

Felix: Yeah.

swyx: Where I’m like, okay, you know, what’s better than uploading, using Claude Coworks to YouTube?

Like actually. Looking at the docs to like programmatically upload to YouTube and then putting that in a skill. And I’ve never done that before. I don’t want to deal with Google Cloud. Yeah. So Claude Cowork does it for me.

Felix: That is really cool.

swyx: So, so I, I just, I don’t care. I just, like, I do a thing. I don’t, it doesn’t really matter.

Felix: That is really cool. And then you’ve, I assume paired the skill just with the script that it’s built.

swyx: Yeah, no, I just update, update the skills.

Felix: Oh, that is beautiful. Yeah. That’s wonderful.

swyx: It’s kind of like a skill, like, uh, uh, basically I think like the way that people ease into Claude Cowork is like take a knowledge work task that you would normally be clicking around for and then, uh, try to turn, turn that, and then you do the, okay, well what if you went further?

Okay. And then when, if you went further, when, if you, and it sort of expand the scope of cowork as you gain trust with it and, and also teach it how to replace you.

Felix: Yeah. It’s like a little bit like playing factorial, but for your own life. Uh, like you say, you start really small.

swyx: Yeah.

Felix: You start automating something really tiny and like.

Once it clicks, you keep adding onto this like automation empire. Just like make your life easier and easier. My favorite skill has been, um, every single morning Kohlberg starts looking at my calendar and make sure that there’s conflicts because people tend to schedule a lot of meetings, sometimes last minute, sometimes miss it soft and painful.

And a lot of products have existed like that A lot. I’ve written in the custom prompt there. I haven’t made it a skill, um, honestly should.

swyx: Yeah.

Felix: But I’ve given it like pretty clear instructions about okay, here are some people, if they book over other meetings, I’m probably gonna go to their meeting. Like if Dario schedules a meeting.

swyx: Right.

Felix: Not try to reschedule down. Right. Um, and I think there’s some other rules in there about like what kind of meetings I care more about what kind of meetings I care less about. What is okay to like, maybe pun like when I want to be, when I want to be working, when I don’t want to be working. And it’s those really small things that I can think kind of click with people.

Right. When we launch co-work, I think one of the US races that went most viral on Twitter. X was clean up your desktop, which is stuff, because silly, that’s such a smart thing, right? Like you don’t need to model to clean up your desktop. Not really. Um,

swyx: like this, like clean up my desktop.

Felix: Yeah, exactly. Yeah.

swyx: I need to, I need to choose my desktop, right? I guess give it access to my desktop.

Felix: Yeah.

swyx: Okay. Uh, okay. This is very scary. Oh, we’ll do it.

Alessio: I did, I did it with my downloads folder. It was like, you have so many term sheets and there’s like eight copies of your rental lease for your office. I was like, all right.

Like, don’t yell at me.

Felix: It’s like, it’s not such a small task. And then like, I, I would never go out there and normally otherwise and tell people I’ve pulled a product. It can organize your folder. Right. Um, because it feels small. But I think to your point like,

swyx: oh, here’s, here’s the, here’s the ask user questions.

Felix: Yeah.

swyx: Uh,

Felix: beautiful. Right. Elite obvious junk. You probably shouldn’t click that.

Alessio: No.

Felix: If he’s not done right.

swyx: As long as it’s reversible, I don’t

Alessio: make up blend to,

swyx: yeah. Uh, yeah. No, I, I have a, I have a typical, everything is super messy folder. So, yes. I think this, this is super helpful. So this is a pretty simple task.

Mm-hmm. But I’ve, okay, here it is. Right. Here’s the progress. I don’t see this in, that’s why I’m like, this gotta be something different than, uh, than Claude Code, because I’m like, we

Felix: do. Yeah. That’s, we do system prompt that. We’re like, all right. We want you to think about like, this task Yeah. Methodology.

Yeah.

swyx: And then I can, I can, I can do like little suggestions for, for, for these things. It’s beautiful. Look at this. I, I can, I can like say like, oh, don’t do that. Don’t do this. It’s amazing.

Felix: I’m so happy. You like it. Um, I mean, the other way around, like we’re part of the Clark core team, if you would like this in Clark COVID.

swyx: Yeah. Yeah. Yeah. Uh, so, so yeah, I mean, uh, this is really good. Obviously I, I’m like kind of raving about it. Uh, you know, I have other things like sign up for pg e so if you can do phone calls for me, that’d be great. Um, I, I do, people

Felix: have done that. Obviously you can’t do that natively, but people have done that with like, various other providers.

swyx: Yeah. Uh, and then this is like signing up for the Figma MCP. Um, I, I really am trying to do like everything, um, data analysis as well. I do think, um, oh, design to code, uh, very, very good. Right? So like, here’s a Figma file, take it. And then this is where like a lot of other tasks is like knowledge work, like replace my manual clicking, but this is no, I would normally use Claude Code or uh, Claude Code for this, but because I perceive that you have better Chrome integration

Felix: mm-hmm.

swyx: I, I think you can actually do a better job of this. And I, this, this is one shot at my, uh, conference website.

Felix: That’s pretty cool. Like at some point I would love to like, hear how you feel about code. In the desktop apps, which is like I never use, which is the, the same team. Same team.

swyx: So I use the call code in terminal, which I, I perceive to be the default way of cloud coding.

Felix: So one thing this has,

swyx: sorry, I’m just like, I’m not

Felix: here, I’m not here. All products. Can I talk about other stuff? Like I, I’m not sure if people out there wanna like hear me advertise my stuff for like an hour. Please do that. Um, this thing is like a builtin browser, which is a thing a lot of products have said.

Yeah, it’s a builtin browser. And I think giving cloud eyes into like what you’re actually working on makes it so much more effective. And that’s probably what you’ve seen in cohort because it can see Chrome, it can like debug the dom, it can like see things. Um, that does make it more powerful.

swyx: Yeah. So, so I think, uh, my mental model was kind broken.

‘cause I only use this cowork because I thought it had a, a browser thing in it. But I understand that the Claude Code app. The app version of Claude Code does have a built-in browser. I’ve seen, I’ve seen this preview thing.

Felix: Yeah.

swyx: I just, I’ve never used it.

Felix: But in the end, in the end, you sort of have it by hard.

Yeah. You basically get the same thing. Right? Like the, the, the additional skill that you’re describing is chart is better if we can see what it’s working on. Right. That’s, that’s sort of like the summary here and like whether it’s using your Chrome

swyx: Yeah.

Felix: Or it’s just like making up its own little like browser.

It doesn’t really make a big difference because either way it’s gonna see what it’s working on and that just makes it much better. And then you don’t have to run QA for your cloud.

swyx: Why doesn’t it pick up my existing Claude Code sessions? ‘cause I, I mean, obviously I’ve used Claude Code, but Excellent question.

Um, don’t have a good answer other than like, we’re honest. Just haven’t Yeah. This is what the Open AI team does. Okay. Uh, cool. I I I don’t have other, like, I, I just, I, I do wanna expand people’s minds and also maybe show people if they haven’t really done it, but like, I, I think it’s very interesting how I sometimes use this more than I use, I mean, I use dia, right?

Yeah. Um, I, and I use, uh, I’ve used like all the other agentic browsers and philanthropic didn’t have to build an agentic browser because you just had Claude Cowork and that’s enough.

Felix: Yeah. I also think like maybe integrating with number of excellent browsers out there, it’s like currently on my personal priority list, a little higher than like trying to rebuild a browser from scratch.

Yeah. You know, never say never, but I think going back to this idea of like, we wanna plug this into an entire existing workflow, I think our goal is actually to not replace any of the applications we have in your computer. But instead of like, work really well within a new workflow,

Alessio: make the new one. Yeah.

Are, it seems that nowadays, especially on the browser, most of the innovation is like user ergonomics. It’s not really like the underlying browser engine. So I feel like to call it, it doesn’t really matter if it’s like the, uh, or Chrome or Alice, whatever.

Felix: Yeah. We wanna, we wanna meet you wherever you are.

Which is like, like obviously I would say that, but it’s also just generally true because I don’t wanna shrink my potential user base artificially by saying, okay, like, I’m gonna start building for the people who are willing to switch browsers.

Alessio: Right.

Felix: That’s such a, like, you know, like many lawsuits have been filed over who gets to review the browser and like a lot of money has switched hands over the question of like, which browser is default and which search engine is default within the browser.

Um, I just wanna build for, yeah, I wanna build for swyx essentially. Like, I wanna, I wanna, I wanna build for people who have a number of annoying tasks that they feel like. Maybe clock could do it. Could do it for them.

Alessio: Yeah. What do you think about skills portability? I think there’s been one thing, I use another thing called zo, which is kinda like a cloud computer plus agent.

And I have a skill to add visitors to the office. Yeah. So whenever somebody has to come in after hours, they need to check in downstairs. Um, but I wanna like text the thing, so it doesn’t really work in, in cowork, but now that skill is in the zone harness and it’s not in my cowork thing. And then if I make a change, it’s gotta, I gotta sync them.

How do you see that going? Like I see memory as like. Cloud personal, kinda like, I don’t necessarily want my memories to be cross thing.

Felix: Yeah.

Alessio: But I do want my skills to be cross agent that I use. I think with MTPs, people do the same thing. It’s like, oh, Mt. P Gateway. Mt P registry. I don’t really know if that’s like a business.

So I’m curious like if you’ve had any thoughts in the area.

Felix: I think for me, this is sort of where I go back to the really basic primitives for our skills are file-based instead of like this complicated thing that exists inside a place somewhere that is like super proprietary. I’m really leaning into the idea of like, it’s all just files and vultures, and that makes it very portable on its own.

Right. We do have skills as part of this container format, which was just called plugins.

Alessio: Mm-hmm.

Felix: And plugins are available both for Claude Code and Claude Code work the same format, and you can install plugins. This works in cowork today. You can basically say, I’m gonna add a whole, like just a GitHub repo as a.

Skills marketplace or like a plugin marketplace. And that’s how we’re doing portability. I think we have a lot of room left to grow in. How do we make it easy for people to know that they can write skills? How do we make it easy for them to just like, share a skill with you? Because obviously all the words I just said, right?

Like I’m losing most of the knowledge worker base out there, right. And start by saying, oh, you can connect to GitHub repo. It’s not exactly how most people will end up working in like a general knowledge worker space. Um, but I think there’s something there. And another thing that’s there that I think has not really been properly explored is the, the, the combination of which part of the skill is very portable and then which part of the skill is like very personal to you.

Right. And I think that’s something we haven’t really solved as an industry. Hmm.

swyx: It’s like, which, how you wanna introduce more structure to the skill or have always have like. Public skill, private skill, you know, pair. Yeah, yeah. Kind of. I think there’s

Felix: like a, like the easiest way to do this, which is we do like use string interpolation or something.

Right, right. Yeah, yeah. Insert username here, insert like phone number, insert, like known folder, locations, that kind of stuff. Um, that’s probably clunky. That’s why we haven’t built it. Um, but I do think someone is going to come up with like an interesting way to keep everything we like about skills. The portability is just a file, it’s just marked down.

It’s just text, honestly. Right. Like a text file words. The complete lack of structure, which means you don’t need any kind of tutorial to write a skill. Just like explain it to Claude the way he would explain it to me and Claude will probably get it before I work. Mm-hmm. Right? You’re just like, for booking a flight, tell Claude how to book a flight the same way we tell him somewhere.

I just started working here today. But combine that with a very like, personal thing. Um, maybe we’ll stick with a booking a flight example. I don’t actually think. AI should be booking flights. I think the tools we have is yes.

swyx: Yeah. Finally, somebody says it. It’s the default demo that everyone’s making.

Felix: I’m

swyx: like, I even against like booking demos, it is not a good showcase.

Felix: Yeah. I’m like, I just wanna book my flight myself. But, um, I think there’s a lot of things that have a personal and a non-personal component and that’s maybe why people reach for flight booking because some things are very universal. Yeah. Super flight is usually better, right? Like few people try to book the most expensive flight.

And then some things are quite personal about like what times you prefer, which seat you prefer, which airports you prefer. Combining that and like a skill format that is actually portable, compatible, easy to understand for people. I think that would be very exciting. We just haven’t figured it out yet.

Alessio: Yeah, I think the text part every, I think everybody by now has some sort of like cloud file thing. Either Dropbox, Google Drive, whatever. So it feels like in a way it should basically like sim link. My skills into all my agent harnesses. Yeah. Just keep those ing like we have internally this like valuable tokens repo, which is like all the commands sub agents.

It’s good. Uh, and then I build like a TUI where you can start it and be like, you know, install this command and this three sub agents into this agent in this folder and just copy paste this. It doesn’t do anything. It literally cp the file into that. But I feel like there should be something similar where like whenever I go into a new thing, it’s like, hey, here’s like the link to exactly the cloud folder and just bring down these skills into this.

Yeah. Like today it doesn’t quite work like that. Like if I install a new agent, I cannot, I have to like copy paste all the skills and I don’t even know where they are.

Felix: Yeah.

Alessio: That’s like the big problem. It’s like where do I find them?

Felix: Yeah.

Alessio: Um, so I’m curious like in the future like that, that almost feels like my personal productivity thing will be my skills.

Felix: Yeah.

Alessio: Is not really the product that I use. Everybody has access to the same product. But today there’s, that just looks like copy pasting ME files, I

Felix: think so many things I, I really like thinking about agents and LLMs just as like another coworker. So many attempts have made to build documentation companies that are like, oh, we’re gonna solve oil documentation problems.

Um, I myself, like spend a little bit of time working in notion, right? I’m like deeply familiar with the concept of let’s get everyone on the same page. Mm-hmm. Right? And what you’re basically saying here is you want all your agents to be on the same page about your preferences, about the skills, about the way they ought to work and like how they ought to execute.

And I’m not sure what the right thing is going to be if it’s going to be some, some company that can say, all right, we’re as an independent body, we’re not trying to like, push into any particular product. It’s our job to be like the skill authority, and we provide, I don’t know, we’re gonna be the Dropbox of skills and we can just sim link us into all the products we want to use.

I’m not sure that’s gonna be viable business, but as, as an idea, it would be cool.

Alessio: Yeah. Yeah. I think so many things are just going away as businesses. It’s like, how am I supposed to do it? I’m not even asking somebody to make a product about it. Like yeah. I wanna personally know. And there’s things like you said, it’s like you almost wanna skill and then interpolate it between personal and work.

So if I’m booking a fly for work, it’s different than I’m booking a flight personally.

Felix: Yeah.

Alessio: In some ways, yeah. But like a lot of the scaffolding is the same, you know? Cool.

Felix: I mean, as an engineer I will tell you like, you know, technic a person to technic a person. I will just be like siblings.

Alessio: Well that’s what, that’s what I do.

We call that MD and agents that MD’s just the same how sim length. And so it is like, that works, but it feels like, yeah, I don’t know. Maybe

Felix: you can always go one, you can always tell cowork problem and then cowork will solve it for you. Just make the siblings. That’s like one way to do it.

Alessio: That’s true.

That’s true. All right. Everything is called cowork.

Felix: Uh, potentially spicy. Question for both of you.

swyx: Uh, which of these industries will go away?

Alessio: Okay, so what Felix was saying before is interesting. There’s busy like. The short term pressure of like, we need to turn these tokens into valuable things, which is I should build the last mile product that harness the model.

And then there’s the question of like, long term, which ones are gonna still be valuable? And I think you’re kind of seeing this today with like, uh, you know, the coding space in a way is kind of like everybody’s moving up and up in stack because you need more than just turning tokens into code. I think search, like enterprise search is kind of saying the same thing.

Like with G Clean and like all these different companies is like, at the end of the day, if Cowork is the one doing all the work, the search itself is like such a small part that like, I don’t know if I’m really gonna pay that much money just to do search. It’s almost like everything is like a cowork vertical.

So like how much can cowork first party support?

swyx: Mm-hmm.

Alessio: And how much can it not? I think for a lot of these things, the planning thing that you were showing do Which one? The planning. The planning.

swyx: Okay. Yeah. Yeah.

Alessio: That’s one thing where like most of the value that these agents provide is like they’re better at planning for specific tasks.

Yeah. And have better tools for it.

swyx: Yeah.

Alessio: But I think the models are now moving in that direction and they have the right harnesses and they’re on your computer. So for me it’s almost like if for the end customer trusts your startup to be the provider of that task result, then I think that works. This is, uh, something that, this is a short

swyx: spike that we’re, we’re working on.

Uh, yeah.

Felix: I think, look, I’ll, I’ll, I’ll tell you this, like I don’t think I’m the best person to like actually estimate which industry is going to be hit the hardest. But I do think that at philanthropic as a group of people, we’re deeply worried about the impact. That the tools are going to have on the labor market, especially for like junior employees that, because I think, I think it’s only honest to say that when we talk about automating a lot away, a lot of the work that we personally find annoying that we maybe think’s not the best use of our time.

In a lot of industries, that kind of work would’ve been given to a junior entry level employee. Yeah. Right. And I think it’s, it’s only, it’s only right to be really worried about that and like worry what that’s going to do in particular to people like enter the shop market.

Alessio: Mm-hmm. I have a solution for that.

Which you make them, you create simulative jobs for them.

Felix: Okay.

Alessio: So this is, this is like half joke, half true. So if you think about software engineering, when you’re like a junior engineer, you work like 1, 2, 3 years. And in those three years there’s like maybe like a handful of moments where like you really learn something.

And then a bunch of other days where like you’re not really progressing.

Felix: Yeah.

Alessio: I think now we can use AI and these models to actually like shortcut these careers and almost like simulate the early years of your work and like just make them like super dense and like these learnings, it’s like, hey, we’re working on this feature, which is like a distributed system and you need to learn this thing that might take three months at a company.

And so you take three months here, it’s like we’re just simulating the whole thing. It’s actually not a real thing. And in one week we kind of speed run through the whole thing and you kind of learn your lesson from there. And we kind of repeat that in like one year. You basically get like three years worth of like projects and experience.

Yeah. I think it’s harder for like things like sales or for things like, you know, marketing because you don’t really have a way to get the feedback loop. But I think a lot of it, it sounds kind of silly, it’s like you’re making the new effect job, but it’s almost like you go to college, right? People pay to learn how to do it, and this might feel similar where it’s like, hey, we have the.

Jane Street Simulator is like, you wanna come work at Jane Street? We’ll just put you in the simulator for like three months.

Felix: Wow.

Alessio: And you’ll come out of it. It’s like, you know, I’m ready.

Felix: So there, there is an aspect here. I’m not an expert enough to like actually know what, what is going to happen to marketing or legal or finance, right?

Like, I don’t work in those jobs and I, I don’t think I should talk about them, but I am an engineer and I think I have a pretty good idea of what engineering is like. And I think one thing we’re sort of seeing is that as a company and also as, as the public, we’re like deeply worried about entry level, but we’re also seeing more senior engineers accelerate it.

If like they’re more productive. They, they actually increase the value they provide. And the thing that I’m thinking about a lot is the fact that even before all of this happened, um, I’ve always had a lot of respect for the University of Waterloo and the, the new grads that have joined my teams as from coming from the University of Waterloo always felt like.

More ready than new grads will like literally spend their entire time at the university regardless of how good, but never actually had to work inside an environment where you have to ship things that eventually will be used by users. And I’m, I’m, I’m German. I like initially went to German University and I think the, the, the like information systems programs, there tend to be very theoretical, right?

Like I often give people the example of like trying to become a doctor, but you first have to do four years of biology and as a result when you get a new grad, you sort of have to teach them what it’s like to actually build products and to work in a company and like work with other people. And like some people will have different opinion and like, how do you do all of those things?

And the University of Ulu, it seems like they just. Spend half of their time. I dunno if it’s true, but I think it’s, it’s a year, right? They spend so much time,

swyx: part of your job, uh, a cu a curriculum to do spend a year in internships.

Felix: Yeah. They just like go from company to company. They show up on your team as like a junior engineer who spend like 20 companies.

Not really, but like, it seems like a lot of my new grads have also briefly worked at Apple, Google, Tesla. Yes. And uh, there’s a common meme where they like collect all these logos, like infinity stones, but, and they always put it on LinkedIn and it is very unclear that they’re an intern. Like Yeah, yeah, exactly.

But it does actually make them so much better compared to other new grads. And I wonder if that’s a useful model maybe for the future when we also have to like, crunch down the amount of time you have as a junior employee. ‘cause the value you have as a junior employee is going to like, be impacted.

swyx: My sort of pro young people take is that they’re, you’re more, uh, you have higher neuroplasticity, you can learn more, you have less preexisting biases.

And, uh, what I is assuming is true for you, what OpenAI often says is that. Actually it’s the, the younger, like fresh grad engineers that use Codex or their coding stuff, uh, more innovatively than the, uh, experienced engineers who have a set and preferred way of doing things.

Felix: Yeah. As I talk to people, I, I someone experience.

swyx: Yeah. So maybe you’re more AI native. Yeah. And therefore you’re, you, you get cut. But like, I think the problem is you don’t need that many of them.

Felix: I mean, philanthropic is on the record as saying we do believe that the impact on the market is going to be sizable and we do not think that people overall are ready.

Right. And we do actually think we should probably talk about it as a society much more. Yeah. I’m not sure that I’m like the individual that can add like anything useful there. But I think as societies with economists and, and governments that need to wrestle those questions in a way that is probably more meaningful than me wrestling with them, we’re probably not doing good enough.

swyx: Well, we, we’ll try to educate and then I think also just releasing frequently as, as, as you guys do, or probably maybe too frequently

Felix: Yeah.

swyx: Uh, is helping people to adjust over time. Right. Rather than one big bang thing. There’s like sort of this gradual takeoff that people are living through that we

Felix: Yeah.

swyx: Waking people up. Right.

Felix: Yeah. And I, but I think a lot of us like wondering at what point do we actually have full takeoff, right? Like at what point is there, we’re all sort of expecting this like big bang moment where things will accelerate so quickly that it becomes a self-reinforcing loop.

swyx: Mm-hmm.

Felix: And at that point, it’s sort of like off to the races and there will be no more like slowly catching up.

You notice just have cloud being so good at everything.

swyx: Yeah. It’s when cowork is training models, it’s when it’s looking at tensor board and Exactly. Weight and biases and training things.

Felix: I like we can all debate like how many years it’s away, right? Like some people make a better route, like maybe it’s 10 years away, maybe it’s a year away.

Um, I’m not entirely sure where, where I come on this time, but I’m not totally sure that ultimately it matters all that much, whether or not it happens in four or five years. If we have a decent one, certainly that’s going to happen. It’s probably something we should wrestle with.

swyx: I wanted to talk, so by the way, the, the scheduled task complete, uh, the, the, there’s the clean my desktop task complete and it did it organized by file type, which, okay.

But, you know, I was trying to get it to do more sort of thematic, like read the file, understand what it’s about, group by, uh, the, the topic rather than the file type. But

Felix: I mean, you can just follow up and have it do that. Oh yeah. Here, like it did, it is proposing That’s right.

swyx: Yeah. So it’s, it’s got some like topical things, but uh, yeah, I could probably do better.

Like, yeah, so like I probably need to give it a skill to read video files so that it understands here’s how I like to,

Felix: honestly though, like, um, I see that you’re using Opus 4.6, right? Like my recommendation for people is increasingly don’t worry about it anymore. Just like tell it what you want it to do.

swyx: Yeah.

Felix: And it’s probably gonna figure out a way to do it. It might not be the way that you like necessarily or the way that you’ve gone about it.

swyx: Videos, deeper,

Alessio: lower outsourcing, organizing all of this. So let’s fight. Yeah. Yeah.

Felix: I’m honestly like, so curious what cloud is gonna come up with.

swyx: I’ll kick that off.

I wanted to also just talk about the, the overall, uh, you know, you talk about data analysis, you talk about like, uh, your, your personal finances. You also said, uh, which by the way for us is very timely tax season, right? Like Yeah. Use cloud core for tax season. It is not responsible for any mistakes, but might as well, right?

Like it’s, it’s free knowledge work for you. Yeah. Uh, so I just like, I think cloud for finance is a big deal. Um, and this is definitely like in that mix. I wonder, is it like, do you, is it a separate team? Do you talk to them? How important is it? Right. Like, because you can also natively output Excel files now.

Felix: Yeah.

swyx: Just

Felix: talk about the

swyx: finance effort

Felix: grow. Yeah. We care about the verticals quite a bit. So we do have a dedicated verticals team. We have a dedicated enterprise team,

swyx: and those is business engineering, not sales.

Felix: It’s engineering. Yeah, yeah, yeah. It’s engineering. So we do have people who sort of come to work every single day and they, they ask themselves, how do we make co-work extremely effective for people in those specific industries?

How do we make it easier for them to understand, how do we make it easier for them to plug into this and like sort of get the same value out of it that software engineers get? I think it’s no real surprise that software engineers ended up being sort of at the forefront of the entire AI moment because so much of it is this like Rub Goldberg machine nest where like we’re already used to automating things, right?

Like it’s part of our job. Yeah. So we care about it quite a bit. I think it also like really matches what we see. Cloud being very good and as a model, I think it provides tremendous amount of value to those customers in particular because. We can do so much with the amount of data they have. Those are like data heavy industries.

Their industries for correctness matters quite a bit.

swyx: So for us of, I’ve used it to analyze my business, I just can’t show it. So

Felix: it’s two sense. I had a similar question about, about taxes. Like, I did tweet, I did tweet about the fact, I did tweet about, oh, COVID is doing my taxes. This is honestly incredible.

And, um, it’s like annoying. He is like, this is so cool, but I’m not gonna, Twitter is maybe not the audience that needs to like see my tax return.

swyx: Yeah. That way. Here, here it is. It’s it’s reading on the videos, so it’s like Yeah, it’s getting more, yeah.

Felix: How did it actually do it? I’m actually curious.

swyx: Oh, usually it just like, takes a screenshot and then it reads the screenshot vi by vision.

So this is what I do for my, my Zoom upload thing, right? Because I, I have paper club sessions that I need to upload to Zoom and I want it to automatically. Uh, title them and do show notes and everything. So it just take screenshots and try to try its best. Yeah. It wouldn’t probably benefit from transcribing, which it’s doing by, it’s operating by Pure Vision now, but it’s good enough.

Felix: Yeah.

swyx: And then I, uh, I do have to call, uh, out to Nano Banana to do images. So unless you guys do images for me, uh, I have to call other people your images.

Felix: We’re aware. We’re aware. It’s, it’s just like so fun for me because like, this is the thing that I’m increasingly doing, like increasingly curious about cloud’s, creativity and like figuring out what is great Claude’s approach is like some problem.

swyx: Yeah. Vision for everything is, is like the, the superpower, right? Like, you know, and computer use, you guys were the first to do computer use, right. And when it was launched, I was very unimpressed. I was like, it’s slow, it’s unreliable, it’s wild. How much better? ‘cause it is one year ago.

Felix: Yeah, I know. Like it was barely usable.

Yeah. I, I remember it was very usable, but is it wild how much better things have gotten? Yeah.

swyx: Yeah.

Felix: Over that one year

swyx: we went to the anthropic office because you, uh, for the launch event for computer use. Like there was like this hackathon. Yeah. And like nobody hack on computer use.

Felix: But I did see, I, I I don’t know if you’re okay with me saying that, but I did see briefly that you do have like a, like an automate Mac, SMCB server installed.

Right. Uhhuh, you use that ever.

swyx: What? Sorry? Which one? Where?

Felix: Um, if you go to your settings.

swyx: Oh, settings. Okay. Uh, where, sorry, this one?

Felix: Yeah.

swyx: Yeah.

Felix: Um, I noticed that in your connectors,

swyx: Uhhuh. Uh, I probably said it at one time, but I don’t use it actively.

Felix: Oh, okay. The

swyx: a max automated. Yeah. Yeah. So, so I, yeah, this one I really wanted to like, just automate everything in my thing.

I didn’t find, I didn’t find it super reliable.

Felix: Okay.

swyx: Why?

Felix: No, no, no question at all.

swyx: Cloud is much better writing Apple Script and executing its own Apple Script than relying on these, uh, third party tools.

Felix: Yeah.

swyx: Uh, so I’ve increased, I, I initially installed Im CP and like all these other fcps that people built, and, but now I don’t use any of them anymore.

Like just, just let cloud write its own thing.

Felix: Yeah. It’s

swyx: gonna be more custom made. We keep going up the stack,

Felix: but if using computer uses like a fairly interesting area to me, and it’s like also interesting in the sense that I don’t think we’re far away from, I don’t think we’re far away from clapping, very effective, but like using your computer and not just it’s theoretical computer.

Alessio: Mm-hmm. What’s the relationship between the user and the computer? Like, uh, there, there were some tweets about how huge some of the VMs, the Claude Cowork creates ours, like 12, 15 gigabytes and people complain. Yeah. But at some point it’s like, if you’re using the computer, you’re taking action on, it’s, it’s just your computer.

And I’m just looking at it, you know, it’s like, I, I think that’s why people like the idea of like the Mac mini and the open claw or whatever on it because it’s like, it got its own home. You know? It is doing its thing, I’m doing my thing. I think there’s some kind of like, not like risk condition, but it’s like, okay, if I kickstart this task now I can’t really use the computer.

Felix: Yeah.

Alessio: You know, because car coworkers doing things on it and it’s kind of awkward, like, yeah. I’m not sure.

Felix: I, I do think it’s a super interesting area because I, I can maybe tell you like some of the things I thought about that I think are actually a bad idea. So when, when we initially started working on cowork, I, I did have some dreams about, well, would it look like for cloud of its own cursor?

Could be cool, right? Like it’s a computer, we can write code, we can touch everything. Like who says that computers need to have one cursor? We could do a second cursor, but that actually breaks down quite a bit. Even if you go and like present cool dreams to both Apple and Microsoft, you’re like, wouldn’t it be cool if, um, it breaks down quite a bit?

‘cause so many of our models on a computer are built around this idea of like, there’s only one thing working on it. Yeah, there’s like a foreground app, a background app, cloud and Chrome can work in the background, but that’s like within one application. But the operating system layer, that is a lot harder to implement.

So I’m, I’m still grappling with what, what does it mean for cloud to actually act on your computer. It’s the right format for cloud to have its own computer that you set up. And maybe every now and then you like zoom in and you play with it. Or is the right format for Claude to just like, wait until you are.

Stepping away for a little bit and take over while you’re gone. Or it’s the right move for cloud. Just like if it’s on computer in the cloud, and like whatever you want cloud to do, you have to set up yourself. Right. There’s like a, there’s like a number of different options. Um, this is the thing I think about a lot, like what is the relationship between you and your computer and you and your data on their computer?

Because how intimate that relationship is kind of depends on the tool and Right. The thing that you’re current looking at, right? Like we’re quite comfortable sharing some things, very uncomfortable, sharing other things. And I think whatever product is gonna be successful, we’ll have to deal with those, like, with those different things.

But you probably, even if Claude was capable of making a determination, would you want Claude to make that determination in the first place? It’s tricky, Barry, because it’s like, it’s more than just privacy. It’s like almost intimacy and it’s like tricky to reason about in a way that will make everyone comfortable.

Alessio: Yeah, I could see. You know, a virtual box, like actual virtual box app where like you run the VM and then you have like a screen within the screen, you know, you can put it in the background, but then you can like jump in the screen and like you,

Felix: that’s not a bad idea. Yeah.

Alessio: You know, like, I mean I used it, you know, people used to do it virtualizing like C Linux in a Windows machine.

Felix: Yeah.

Alessio: And like you would just jump in and then you would jump out. But it’s like, it’s not like a dual boot. It’s like within the thing. The problem is that you need twice the amount of ram, twice the amount of, you know, it’s like, it’s kind of taxing on the machine. But I think that would be cool. Kinda like see, you know, the little quad window.

I can see desktop look cute. It is clicking around things

swyx: I was gonna bring up. He’s the original machine and the machine guy, because he has the uh, windows. Windows 95 project. Where’s, where’s the Windows 85 project at?

Felix: It’s probably somewhere in my GI guitar,

swyx: right? No, no, no, no, no. It is like the first thing you see is this one.

Nice. Yeah,

Felix: yeah,

swyx: exactly.

Felix: That was honestly a very fun project though. Like, obviously I didn’t, I, I should say this, just so that No, it’s the wrong impression. I did not write the actual, the actual, obviously I didn’t build Windows only five because I was a child, but also I did not build the actual engine that is capable of like simulating an X 86 processor and JavaScript and m um, that’s a tool called V 86, which is very cool and everyone should try.

But this came out of a, this came out of like a debate we had at work where people were like, they often are in the into debating the merits of electron and whether or not we should be building software in JavaScript, yes or no. And I still am very upset that I can run all of Windows 95 in JavaScript.

And launch Microsoft Excel inside the virtualized JavaScript Windows only five machine, and do things that pro, I can do that entire chain faster than I can do a lot of other things in like traditional SaaS applications. Mm-hmm. Uh, this is sort of like a, like a performance rampage that I went on. So I’m mostly built this as a joke for some of my colleagues at Slack.

This took, took like one night. Um, what, but then that I, it was, it was not hard to do. It was all the hard work is in V 86. Yeah. Like if, go to the repo, it’s gonna say like, 99% of his work is done by, by um, a guy who goes after the, by the name. Copy. His name is Fabian.

swyx: Yeah.

Felix: Um,

swyx: cool. I think you’re, you’re kind of back on the Windows grind ‘cause you’re building out the Windows support.

Uh, I thought there was some really cool technical stories to tell. Uh, and it gives people an appreciation of like, well here’s how hard it is and here’s how important here, how, how you invested the sandbox. So maybe this is like a good opportunity to talk about something in the details.

Felix: Oh yeah, the, the VM honestly is like so cool.

There’s a lot of things we dislike about the vm, right? Like there, there’s a lot of things that are real trade offs and you want to know why you making those trade offs. Um, and you’re right, like a lot of people write me like, Hey, how, how come cloud is taking up 10 gigabytes? I could say on the point, it’s not actually taking up 10 gigabytes.

It’s just like a way that macros displays bites is like wrong, but the way we actually ride it to disc is by we collapse the empty space and the image, so it’s not actually taking up 10 gigs. But that’s a technical differentiation. That’s probably not gonna matter to, like,

swyx: to me, the the, the outcome is it takes too long to start.

Yeah. It’s like 30 seconds sometimes. So I don’t know. Oh, it should be faster than that. Whatever it be te about this feels like 30.

Felix: Yeah. Like even either way, like whatever it is, it’s going to be, it’s going to be slower than just running Log Ultra on your computer. Right. So the trade offs are real, but what we’re doing on Windows, we’re using the Windows, windows, uh, host compute system.

It’s the same thing that WSL two runs on, like the Windows subsystem for Linux that I think a lot of developers appreciate quite a bit. Yeah. Um, and it’s, it’s pretty cool because we sort of like have to separate out which system space the virtual machine runs in, in who gets to talk the virtual machine because obviously you give this virtual machine a decent amount of power.

How do we optimize not just the connection between the two systems, but also how do we make sure that random other application doesn’t get to talk to Clot inside the vm?

swyx: Hmm.

Felix: We do some pretty interesting things. Um, last week we started writing a new networking service. A networking driver. That optimizes how Claw talks to the internet.

If your company’s doing like weird internet things like pack inspection and like, like, you know, taking your part as a cell and inside your company, I think there was probably like a very small, easy version to build of cowork that is much simpler but also breaks on most com most users, computers. And this one is quite nice because it works on most users computers.

Um, and the default example I always go for is I, I really want this to be highly effective on like a, on like a machine that most people pick up. And that machine will probably not have Python, it will not have no j And even if I just take away those two things, cloud is going to be so much less effective from

swyx: your computer.

So what do you do? You don’t even, I mean, may maybe require people to install Node in Python.

Felix: Oh, like, you mean for like a, what does the feature look like without a vm?

swyx: No, no, no. So, so like, like you said, right? Let’s say a target machine is whatever’s a default spec, windows laptop.

Felix: We do this, which is quite cool.

So on, on, uh, mes, we use the, um, apple virtualization framework, which is pretty solid, optimized, like it’s good stuff, and instead simple a p call, right?

swyx: It’s

Felix: like super simple.

swyx: I, I saw the code recently and I’m like, that’s it. What the fuck

Felix: would you, once you start like shipping production code on it, you start adding like all of these edge cases, your new

swyx: Oh

Felix: yeah, it ends up being a little longer, but, um, I think Apple really cooked with a virtualization framework and it’s very, very good.

It is very fast, it’s very reliable. And same on Windows. The, the host compute system. I think WSL two as well is maybe one of the diamonds within Windows. It’s like one of the few things that developers universally rave about is very, very cool. And like hooking into the same subsystem makes a lot easier for us to say We don’t really care how locked down your computer is.

Maybe it’s like your employer’s computer and your employer has decided that you get to install nothing.

Alessio: Mm-hmm.

Felix: Not trusted, but it’s true in a lot of environments, right? Like even at Anthropic, um, our IT department controls what kinda stuff you install, just like a pretty common experience for many companies.

Um, and this gives it departments a decent amount of, like, it makes their job so much easier because we can say you can separate out cloud’s computer from the user’s computer. And then for cloud’s computer, where you probably care about its data loss, you care about like a potentially hostile actor, you care about maybe data being exfiltrated.

And once you control the network and the file system layer, you don’t really care necessarily anymore. That cloud might be writing super useful Python scripts. What worries you about the fact is that like once you install Python, now anyone can do anything on a computer. Once you put that in the vm, that risk really goes down.

swyx: Yeah.

Felix: So that’s why we jumped through all of these hoops.

swyx: Yeah. I think you, you had a different, uh, tweet about this. Um, but it, it’s, it’s almost like people have also approved exhaustion. Like, it’s like you can’t approve every single commands. Like sometimes by, by default, some of the theis, I think even early called code, uh, we have to approve every single command.

Yeah. And, and like it’s so, so there’s this sort of dichotomy between either approve every step or dangerously get permissions.

Felix: Yeah.

swyx: And actually sandboxing is like, kind of like the middle ground.

Felix: Yeah. I do think, I do think it, it’s maybe on us as like the AI industry to come up something better than, oh, this is super safe as long as it doesn’t do anything right.

Right. But if you want this to be useful, then you have to like approve every single step of the way. And like, computer use is a good example. The only way to make computer use on your host, like super safe, like really super safe is probably if you approve every single action, right. Like models, like, I would like to type the word.

You’re like, okay, that seems fine. I know, I know. Which, like cursor is focused. Yeah. It’s not

swyx: automation if you don’t delegate.

Felix: Yeah, exactly. You need to like properly delegate. You need to be able to like delegate and walk away and trust that this thing is not gonna like mess dramatically. And I don’t even think we need to build perfect systems.

I don’t think we need to wait for like a hundred percent model alignment. We can rely on the same Swiss cheese model we’ve used in the industry for a long time. But I do think we need to like universally maybe eventually invest more. And that’s what we’re doing. We need to invest more in systems where we can say, you do not need to approve everything.

swyx: Speaking of Swiss cheese model, he just wrote a thing about this.

Felix: Oh cool.

swyx: Yeah. Uh, yeah. Um, yeah. Super cool. I mean, yeah, it’s, it’s weird how like, I guess usually I think safety and security is kind of like a boring word to, to engineers. They’re like, just gimme be unsafe, gimme unsecure. But, um, I think.

Achieving the right thing. Like you are going after a consumer slash prosumer.

Felix: Yeah. Yeah. Talking both kind of like both. I think I, I also want to capture people who would’ve no trouble using clock code like yourself, right?

swyx: Yeah. Yeah.

Felix: But still find it maybe just convenient, easier. You’re like, oh cool.

That’s like the list on the right. I can edit it. Those things are just easier to do if you have

swyx: to. But this is like clearly the knowledge work side. Yeah. Claude Code will clearly capture the development workflow. But like I, I, I do think like you have to sweat this like safety and security details in order for people to trust it.

And like the even Claude and Chrome, like having the whatever API uses to do the background thing.

Felix: Yeah.

swyx: Um, that’s the only reason I use it is because otherwise I would have to just get a separate machine.

Felix: Yeah.

swyx: And just run it, run to the, and that sounds like

Felix: super annoying.

swyx: Yeah. I mean, like currently doing it, but,

Felix: and I think, I think also as developers, um, maybe we’re, we are more risk tolerant, but we’re also just like accepting we are more risk tolerant, but I think we also just have.

I don’t wanna say arrogance, but like sort of the trust that if like the really bad thing happens, we can probably fix it.

swyx: I just tell Claude to like, check with me before doing any irreversible action. Like sending an email or doing permanently. Yeah, it’s good enough.

Felix: But like, not even Claude, I mean like simple things such as NPM install, right?

Like we’re all running NPM install with full user permissions and if it wants to like read SSH, it well crazy that that is the default kind of why. Yeah, I know. I agree. I agree. Fine. Like I’m obviously doing it every single day. No, right. Like, uh, and I think obviously NPM and GitHub too have like done a pretty good job maybe over the last couple months to like clean house and come up with like more specific tokens.

But generally speaking, I think as engineers we’ve always been a little bit more risk tolerant. And if you do a little bit of introspection and you ask yourself, is that how we should be doing things, you might not always come up with the right answer. And I think for models too, like my approach, like I’m not gonna, the the safest thing is to do nothing.

We do want products that are quite capable, but to the extent possible, I don’t wanna ask you, are you okay with the script? Because I kind of believe that once it starts becoming a part of your workflow, you’re probably not either, either you don’t have the skill to understand whether or not the python, the script is safe or you’re not gonna read it anyway.

swyx: Cool. I guess a, a couple partying questions. Uh, what’s the future of clockwork?

Felix: I think we’re still, we’re still such early days. We’re gonna keep shipping things that we’re gonna keep shipping, things that, um, we’re gonna keep iterating on this thing like pretty quickly, but, which I mean, you can sort of continue to expect that every single week there’s gonna be like a small new feature, if not a big new feature.

Um, I’m going to continue probably to double down on your computer and like making you effective in your computer and making cloud effective in your computer. Um, we’re starting to grapple, as we talked about today, grapple more with a question of like, what does it mean? What does your computer mean? Does it have to be the one in front of you or like a VM on your computer or like a computer somewhere else?

And then the third thing that I’m quite excited about is. We’re continuing to go off this hill climbing on slowly taking users who are used to asking questions and getting an answer to slowly teaching them to like step more and more away. And that claw take over like bigger and bigger tasks and work both in time as well as in like scope.

And I think you can probably see most of the, our investments on our feature releases to like work on both of those things, like the ability to do more on your computer and then the ability to do more independently for longer.

swyx: Does remote control work for Claude Cowork yet? No. Right.

Felix: Excellent question.

swyx: Coming soon. I mean, that’s an obvious thing if you want to keep betting on the, on your computer, but I, to me like. You know, we, we talk about like, people are not ready this year. Like the, there’s, there’s no wall. It’s, it’s accelerating to me like what will be we be doing differently at the end of this year that, you know, we are maybe not even thinking about this, uh, at the start of this year.

Right. Like, I’m just trying to look ahead as to like, what, what’s like a good use case that you’re, that we sort of aim towards? So for, for example, for the machine learning scientists, it’s always, okay, well I want AI scientists, I can automate, automate machine learning, but like for, for knowledge work, I mean, I can already, you know, get it to sign up for Google Cloud to mean as a GI.

Felix: Yeah. ‘

swyx: cause Google cuts are, but like, what, what is, what’s beyond that? I don’t know.

Felix: I think it’s basically the idea that like you still had to tell her to build your script, right? He was still kind of involved.

swyx: Yes.

Felix: In maybe a way that felt kind of magical to you, but like, maybe to me on the other side is the person building this product still feels kind of heavy handed.

I see so much process that I’m like, oh, lemme take that away from you. Okay. But like, how do I just go, I will continues to go or continue to go like further and further up the stack. Make your life easier and easier.

swyx: Oh, here’s one. Right?

Felix: Yeah.

swyx: Watch, uh, I, you know, I don’t care about my own privacy or whatever, or I trust cloud, I trust philanthropic.

So just watch everything I do on a normal day-to-day basis. At the end of the day, tell me what you is called co workable.

Felix: Yeah. I

swyx: dunno.

Felix: I think the funny thing about a lot of these products is that like, for good reason, I don’t enjoy, I, I don’t, throughout my entire career, I’ve never like teased too much what I’m working on because I think you should just like, yeah.

Release it. Yeah. Build the base and release it, and then talk about it. Like I’m, I’m not a big fan of the like vague posting my own work ahead of time.

swyx: Yeah.

Felix: But the thing that is like always so fascinating to me is like, both of you all multiple times a day, you’ve like mentioned things and I’m like, yeah, that is obviously like very obvious

swyx: Okay.

Felix: That someone should be working on those things. Um, and I think we’re still in the space where if you look at cowork. The things that we will be releasing will probably not be a big surprise to either of you. You’re gonna be like, yeah, obviously that’s valuable obviously that we’re working on those things.

swyx: Yeah.

Yeah.

Felix: And obviously that’s good and useful. And the more I hit those points, the more our features fit into that category, I think the better it is for us because then we don’t end up building things that are too hyper specialized to difficult harness style.

swyx: Yeah. I think the hyper specialized thing is very important.

It keeps you like general purpose. It, it means you’re not thinking too small. Maybe I don’t, I don’t know what the, the word is.

Felix: Yeah, yeah, exactly. It’s like the whole concept that like at no point if we release, you know, there’s no Claude Code for no jazz applications that use React and 10 Stack. I know any of those two things.

And like if it’s anything else, I know several startups like that. I think that’s pretty, like, I’m not a vc, I’m not an investor. It’s like hard for me to predict where the markets go. But in terms of the building box that I’m interested in, the electron is probably by far the most popular thing I ever built.

And, um, electron itself is like. Very abstractable and generalizable. Right? Like so many apps run in it. And I think it would’ve been hard for me to predict how many apps actually end up using Electron.

swyx: Yeah.

Felix: Um, and what would’ve been even less useful for me to predict this in what those apps do. I distinctly remember a bloom coming out of being like, that is cool.

Like you are a camera in a little circle in the corner. That is pretty smart.

swyx: That’s an app. Yeah. Yeah.

Felix: Or at least was, I’m not sure if it still is. It was for a while. Or like one password has so many interesting things. Right. It, it’s, it’s, it’s a level of the stack that I’m quite comfortable with. And whenever I give other engineers, advisors actually that layer that I think is most valuable to invest in because the tools of that layer are not that good.

But that’s where you get the most leverage

swyx: for like,

Felix: the future in general.

swyx: Just quick tangent on Electron. ‘cause I always wonder this, uh, have you looked at Tori?

Felix: I have, yeah.

swyx: What’s your take? Uh, you know, look, my, my my, my view is like most things should be Tori by default, unless you really need the full power of electron, but.

Felix: Yeah, I can give like my take on, I can give my big take. Why do we ship an entire version of chromium inside the thing, right? Like why do we do that? And, um, people ask me this question a lot because it’s like very counterintuitive. Wouldn’t it be much easier to use the web use that are on the operating system?

Wouldn’t it be much easier not to have to do that? And the answer is yes. And like obviously I did that once upon a time. I did that there was a version of the Slack app that used just the operating system that use Wait, did you, did you start the Slack app? I would, well, team effort and

swyx: Yeah, but I was, I was there.

We built the Slack app.

Felix: Yeah. It’s crazy. Um, I mean obviously you get the electron guy to do it, but, well, but this is an interesting point. Like, by the time, by the time I joined Slack, they already had an app that was built with something at the time called Met Gap. It was a little bit like the same app gap thing for mobile.

It just used the operating systems. Web views. Um, and that didn’t work for like so many reasons. Um, and they were like, all right, maybe we need like bigger guns. We need to like take more control of the rendering stack. And there’s, there’s a few things I always mention here. Um, I think if you’re building a small app, just going with the operating systems web view is perfectly fine.

If you’re building an app, maybe that doesn’t have too many users who will like cry bloody murder. If it doesn’t work, that is fine. The reason to go with your own embedded rendering engine is because, and this is still true in 2026, the operating system render engines are not that good. They’re just not that good.

Both Microsoft and Apple are trying to move away from that. They so far really haven’t, the only way to upgrade those is to upgrade your operating system. So if you are, say Slack and you have critical rendering bug in WK WebU and some of the other WebU options, your only recourse is to tell your customer, oh, sorry, you’re too poor.

You didn’t bother the, its MacBook. Unacceptable.

swyx: Mm-hmm.

Felix: Unacceptable to user, unacceptable to user developer. So you sort of need to like go down the stack and like find the best rendering engine, then put it in your app. Why chromium, even though it’s very big chromium is by far the best thing. Like I, I often like to remind people the unreal engine, you wanna render some text.

They use chromium. Like chromium is part of the unreal engine for same purposes. Chromium is very, very good. I think it’s like one of the marvels of engineering. It’s very hard for, we’re in San Francisco right now where we’re recording. Most of the people in the city are web developers. It’s hard for me to like overstate how magical it is.

They run seat like rendering a YouTube video dynamically. Negotiating a bit rate, figuring out what to do about your extremely broken hardware driver. Actually, this is a fun thing. Um, okay, you can enter Chrome call on Wack Wack GPU. Okay? And if you scroll down a little bit, these are all the enabled workarounds because something is going wrong on your computer.

If you’re doing this on a Windows computer with like A GPU, that is not the most popular GPO, it will be much longer. And all of these are usually just there to make sure that if I say as a developer, I want a red pixel to appear here, that that actually happens. Chrome is such a marvel because of works on all the machines that user might throw you and it’s gonna work fairly reliably.

And if it doesn’t, they will probably fix it within 24 hours.

swyx: I see. So this is the super operating system, right? That that works everywhere.

Felix: Yeah.

swyx: Right. Okay. Yeah.

Felix: So a lot of the magic of Electron is honestly just that it makes it very easy for you to ch chromium in a way that serves you exactly in your use cases.

Elect, uh, exactly.

swyx: Our next interview is with Morgan Dreesen.

Felix: Yeah.

swyx: Who had the phrase like, desktop OSS are just poorly deep, uh, poor implications of the, the actual os, which is Chrome, which like actually works everywhere. And this is this, this is the platform where you ship apps.

Felix: I, I think the wild thing is that like as engineers, we so often sort of assume that the platform, like the layer below us is like super stable.

Mm-hmm. And then you talk to those people and they’re like, ah, we are also just like guessing. Um, uh, and I had like a distinct moment at Slack where one of our customers at Slack was Nvidia, and for a while I really put GPU developers on this pedestal in my head. And I do think they’re still probably much smarter than I am.

But I was like hardware engineers who built the chips, who then like built the drivers. Their work must be so much harder than mine. They must be very good. And we had like one bug in Slack where like if you had a YouTube video in Slack, it wouldn’t quite render why. Like it would have these weird artifacts.

And, um, that ended up being a chromium bug. And I ended up on this like giant thread. So I got to see a lot of the source code. And they also are just like common to do. We don’t know why this is weird, but if you flip this bit, things work. You know, this is just like happening with every layer of the stack.

Maybe the, uh, you know, the,

swyx: the end of year a GI prediction is that clock can build chromium. You see, you see you, you laugh now. But yeah, like, you know, someday

Felix: it’s, it’s sounding, it could get pretty good. Like it used to be completely useless. Um, mostly just like overwhelmed, both with how hyper specialized tools are inside the chromium repo.

Like for, for a long time. Chrome has like sort of reinvent all the tools because none of them are capable of ending Chrome. I think the EGI moment I am kind of waiting for is at what point are we gonna say Electron is probably no longer necessary because you can just build fully native apps. The Swifty?

Yeah. Like not just in Swift because this is one thing, like it’s pretty easy if you, I think our current models are quite capable of taking an electron app and replicating it Swift, are they gonna be capable of like building an app that is actually more performant, which is less memory? All of that stuff, um, is gonna go into the same hyper optimization that developers have done for like a long time.

We’re not quite there yet. Work and like point even our best models at a thing and say, just replicate this, a native code. Make no mistakes. Ultra think. Right? We’re not quite there yet. Um, ultra

swyx: think is bad

Felix: today. Think is back. Yes. Okay.

swyx: Or we’ll get an ultra think for like days,

Felix: just a pretty long time before,

swyx: but he worked on Ultra think for days.

Yeah. Why he just, it’s just. Front,

Alessio: I’ll let it, the

Felix: more goes into

swyx: it. Yeah. Okay.

Alessio: Another question I had is like coworks. So if I have my Claude Cowork, like what’s kinda like the multiplayer mode? I think sub agents is like single player Split up the context.

Felix: Yeah.

Alessio: And the multiplayer cowork is like, my colleague is some file on their machine that I wanna know about or I wanna know how their task is going to then update my thing.

Like is that interesting? Is that something that makes sense for you to build or for like

Felix: It’s like super interesting to me it, it almost goes back to like some of the scaffolding room. Like okay, are we gonna be end up, are we, will we end up building scaffolding that will just go away? And like a question I have here is at what point do we just assign these things, like their own Gmail account?

We just give them their like Slack handle and then they will just like use the same tools we humans use to interact with each other. You mentioned our finance people, they’ve been working pretty hard on very good office integrations. And I think for a while we’ve been like, we built so much tech around cloud, leaving useful comments inside a Google Doc, and now it just does, it just like leaves a comment in your Google Doc and that’s how you interact with it.

Maybe like the similar thing where I still have open questions around what is the best interaction mode? Is it for us to build something super custom for cowork agents to talk to each other? Or is it okay, let’s just jump straight to the finish line and say, well, we’re just gonna give this thing, if you use Slack at work, we’re just gonna give this thing a Slack handle.

And that’s going to be the way, it’s like multiplayer capable.

Alessio: They communicate with each other. Yeah. Yeah. Like, you know, as a, as a fun project, I build this thing called piq, which basically takes any repo and the PI agent, uh, coding agent, it puts it in a VPS, and then there’s a public web hook where anybody can submit a coding task.

Oh. And then there’s a dashboard in which you review the task and then piq pi, pi, uh, queue.

Yeah. You basically get all these like tasks, anybody can submit a task.

Felix: Mm-hmm.

Alessio: And to me it’s almost like in the organization of the future, it’s like the sales people are talking to the engineering team that is talking to the marketing team, to the product team, and all these coworker are going to like queue up decisions for other people to approve in a way.

Felix: Yeah.

Alessio: You know, and I’m kind of curious what that looks like and like how do you, how do I give my cowork the ability to build a proof task without asking me

Felix: Yeah.

Alessio: And how to decide which one I need to review. Yeah. You know, because for some of these things it’s like, you know, you wanna change the color of something that’s kinda like a branding decision.

Or another one is like, hey, your thing is just broken. It’s like, this is like how you fix it. Yeah. And Claude can actually review whether or not that prompt matches what he’s trying to do today. Everything is still very, it’s like multiplayer within the single player, you know? Yeah. I guess spin up many of them, but like, how do I get multiple people to hand off to each other things using their particular context?

Felix: Yeah. And for both of your coworkers to like talk to each other. Right,

Alessio: right. Yeah. Hey, we got an episode today. Can you like, have you, you know, or

Felix: Yeah. This is like a, uh, I know we’re like running out of time here, but like we, we previously talked about sharing skills and I did have this question of like, what if your cowork would just like ask the other coworks if they have a skill for this task?

Doesn’t matter. These could do.

swyx: Right. Like, okay, so skill transfer.

Felix: Yeah, like,

swyx: um, and again, that’s, maybe

Felix: this maybe goes back into the territory of like building something very powerful and building something creepy often goes hand in hand. Um, because I could tell from the reaction that my fellow engineers said that this is probably not what we’re gonna do, but like.

We have Bluetooth le right? Like I, this computer can figure out that it’s sitting right next to this computer. So you’re probably working on the same thing. Um, well, you see that in cowork, probably not. But, um, there’s like, I think really creative solutions to problems that we really haven’t tried yet.

Yeah,

Alessio: yeah, yeah. Yeah.

swyx: Excellent. I guess the, the last thing is, uh, philanthropic labs. Uh, I always have this mental model of a model lab versus, uh, agent lab. And this is basically Anthropics internal agent lab, which co Claude Code, uh, is now under, right? It’s part of the whole org.

Felix: I mean, people are so fungible, right?

Like,

swyx: okay, this is just, I, I don’t know how, I don’t know real. This is, I don’t know.

Felix: No, it’s a real team. It’s a very, um, the, the last team is primarily working though on things that you don’t see in public yet. Um, they’re trying like really wild out there, ideas that seem quite improbable. Um, the mad science

swyx: thing.

But you, you’re, are you officially under this thing or

Felix: No? We’re, where is the Claude Code is, but now Claude Code is like a fairly big group where. I actually know many people we are like, like I remember yesterday coming into our weekly COVID meeting. I was like, woo,

Alessio: this is hot.

Felix: There’s a lot of people here.

Um, but we still have a labs team and we actually made the labs team a lot bigger. Mike just joined the labs team as a, as an ic, which I think is very cool and very fun. But they’re, they’re working on things that you have not seen yet that are extremely out there and probably half broken. Right? Like the sort of the idea of a lab team is that it should only work on things that make really no sense for anyone else to work on.

swyx: Okay. Well, looking for exciting things from there, but thank you so much. I know we’re out of time, but uh, appreciate your joining us. I appreciate co cowork, everyone go use it. Uh, it is the closest I’ve felt to a I this year. That’s so nice you to say. Thank you very much. Yeah. Thank you for your time. Yeah.

It is NVIDIA GTC day again, and over his signature 2hr unrehearsed keynote, Jensen gave updates on the entire NVIDIA universe and ecosystem and celebrated his InferenceMAX champions belt. As one might expect, Blackwell and Rubin are selling very very well (some accounting is necesary), and now Vera:

The final section of the keynote was focused on OpenClaw, where Jensen went extremely hard in complimenting it and then pointed out the security issues, then pitched his solution, NemoClaw:

NVIDIA moves at impressive speed for a $4T company, and we had some of their next generation leaders on the pod to give more insight on how NVIDIA works this fast:

AI News for 3/14/2026-3/16/2026. We checked 12 subreddits, 544 Twitters and no further Discords. AINews’ website lets you search all past issues. As a reminder, AINews is now a section of Latent Space. You can opt in/out of email frequencies!

Architecture Research: Moonshot’s Attention Residuals and the Debate Around Prior Art

• Moonshot’s Attention Residuals paper was the clearest technical story in the feed: @Kimi_Moonshot introduced a replacement for fixed residual accumulation with input-dependent attention over prior layers, plus Block AttnRes to keep cross-layer attention practical. Claimed results: 1.25x compute advantage, <2% inference latency overhead, validated on Kimi Linear 48B total / 3B active; follow-up posts highlighted improved hidden-state magnitude control and more uniform gradients across depth (paper thread, paper link). The release triggered strong positive reactions from practitioners and researchers including @Yuchenj_UW, @elonmusk, @nathancgy4, and multiple visual explainers such as @eliebakouch and @tokenbender.

• The interesting second-order discussion was whether this is new, or “new at scale”: @behrouz_ali argued the idea substantially overlaps with prior work like DeepCrossAttention, criticizing missing citations and broader ML novelty inflation; @cloneofsimo made a similar point that Google had explored related ideas earlier, while others countered that the systems work and scaling evidence matter as much as the core intuition (context, more context). Net: the paper mattered both as an architectural proposal and as a live example of the field’s ongoing tension between idea novelty, citation quality, and frontier-scale validation.

Coding Agents, Harnesses, and Skills Infrastructure

• OpenAI’s Codex momentum showed up repeatedly: OpenAI Devs promoted a Codex x Notion event, while company posts and leadership commentary emphasized fast adoption. @fidjissimo said Codex is at 2M+ weekly active users, up nearly 4x YTD, with OpenAI also building a deployment arm for enterprise rollout. @sama added that “hardcore builders” are switching to Codex, and @gdb said GPT-5.4 reached 5T tokens/day within a week and a $1B annualized run-rate in net-new revenue. Product-wise, Codex also added subagents, reinforcing the shift toward multi-agent coding workflows.

• The infrastructure layer around coding agents is maturing fast: @AndrewYNg expanded Context Hub / chub, an open CLI for current API docs that now supports agent feedback loops on documentation. @AssemblyAI shipped a maintained skill for Claude Code, Codex, Cursor, and compatible agents so they can use current API patterns rather than stale training priors. @dair_ai highlighted a paper on automated extraction of agent skills from GitHub repos into standardized SKILL.md, with claimed 40% knowledge-transfer gains. Together these point toward a new agent tooling stack: skills files, up-to-date docs, feedback channels, and repo-mined procedural knowledge.

• LangChain pushed further into “agent harness engineering”: @LangChain launched LangGraph CLI for terminal-based deploy/dev flows, and the ecosystem open-sourced Deep Agents, framed by @itsafiz and @simplifyinAI as an MIT-licensed recreation of the workflow behind top coding agents: planning/todos, filesystem ops, shell access, sub-agents, and context management. Internally, @Vtrivedy10 said this is also the base for production agent work and evals. The notable pattern is that teams are no longer just shipping models; they’re shipping reference harnesses.

Open-Source Agents: Hermes’ Breakout, OpenClaw Integrations, and Agent UX

• Hermes Agent had a strong community cycle: hackathon projects spanned home media automation (@rodmarkun’s anime server tool), cyber tooling (@aylacroft), geopolitics/OSINT forecasting (@WeXBT), and research visualization (@t105add4_13). User sentiment was consistently that Hermes is easier to set up and more robust than OpenClaw: see @Zeneca, @fuckyourputs, @austin_hurwitz, and @0xMasonH. @Teknium also posted setup guides like enabling Honcho memory.

• OpenClaw still expanded its ecosystem despite the Hermes comparisons: @ollama announced Ollama as an official provider for OpenClaw; Comet launched an observability plugin for tracing calls/tools/costs; and there were third-party mods like NemoClaw. The broader takeaway is less “winner takes all” and more that open agents are starting to resemble classic software ecosystems: providers, memory backends, tracing, onboarding guides, and hackathon-driven extensions.

Model and Product Releases: Perplexity Computer, Gemini Embeddings, Mistral/Minimax Signals

• Perplexity’s Computer rollout was the most concrete end-user agent launch: @AravSrinivas and @perplexity_ai announced Computer on Android, then extended it so Computer can control Comet and use the local browser as a tool without connectors/MCPs, with local cookies preserved and user visibility into actions (details, implementation note). This is notable because it broadens agentic execution from cloud integrations to permissioned local browser control.

• Google added a foundational multimodal primitive: @Google launched Gemini Embedding 2 in public preview via Gemini API and Vertex AI, positioned as a single embedding space across text, image, video, and audio, supporting 100+ languages. This is the kind of release that may end up more consequential for production search/retrieval systems than another frontier-chat model benchmark.

• Other model and release signals worth noting: @matvelloso praised gemini-3.1-flash-lite-preview on price × latency × intelligence; @QuixiAI reverse-engineered Qwen 3.5 FP8 and also got Qwen3.5-397B-FP8 running on 8× MI210 at 6 tok/s (run note); @AiBattle_ and @kimmonismus pointed to MiniMax 2.7 appearing imminent; @scaling01 surfaced Leanstral as part of Mistral Small 4; and @SeedFold launched SeedProteo for diffusion-based de novo all-atom protein design.

Systems, Inference, and Graphics: GTC, Speculative Decoding, and DLSS 5

• NVIDIA GTC’s message was unequivocal: the center of gravity is inference. Jensen’s framing of the “inference inflection point” was widely repeated (@basetenco quote), alongside ecosystem positioning posts from @nvidia, @kimmonismus, and others. Several infra-adjacent updates landed around the conference: vLLM’s OCI production-stack guide, and a strong systems contribution in P-EAGLE, which removes the sequential bottleneck in speculative decoding by generating K draft tokens in one pass, with reported up to 1.69x speedup over EAGLE-3 on B200 and integration in vLLM v0.16.0.

• On the graphics side, DLSS 5 dominated reactions: NVIDIA positioned it as the biggest graphics leap since real-time ray tracing, with strong reactions from @ctnzr, @GeForce_JacobF, and Digital Foundry-linked discussion. The key technical claim is fully generative neural rendering / relighting with original geometry/assets preserved, pushing visual fidelity materially forward in real time. Not directly an LLM story, but very much part of the broader trend toward neuralized runtime systems.

AI in Science, Healthcare, and Security

• The most substantive science/health post was Microsoft’s GigaTIME thread: @AnishA_Moonka summarized work from Microsoft, Providence, and UW where a model predicts multiplex immunofluorescence-style spatial proteomics from a $5 pathology slide, trained on 40M cells, applied to 14,256 patients across 51 hospitals, producing ~300k virtual protein maps and surfacing 1,234 validated associations. The thread claims the model is open-source and argues this could democratize cancer immune profiling at scale.

• Other technically meaningful science/safety items: @GoogleResearch described a study evaluating LLMs on high-temperature superconductivity reasoning, claiming curated closed-system models outperform web-heavy setups for scientific work; @AISecurityInst evaluated seven frontier models on cyber ranges for autonomous attack capability; and @askalphaxiv highlighted LeCun’s Temporal Straightening for Latent Planning, where straightening latent trajectories improves planning stability by making Euclidean distance better track reachable progress.

Top tweets (by engagement)

• Healthcare foundation-model impact: GigaTIME pathology → spatial proteomics thread was the highest-signal high-engagement technical post.

• Architecture innovation: Moonshot’s Attention Residuals release drew exceptional engagement and broad expert discussion.

• Coding agent product momentum: @sama on Codex growth and @gdb on GPT-5.4 API ramp were the clearest demand-side signals.

• Open agent ecosystem: Ollama becoming an OpenClaw provider was one of the largest open-agent infra announcements by engagement.

• Agent knowledge infrastructure: @AndrewYNg on Context Hub stood out as a concrete proposal for agent-to-agent documentation sharing.

Anthropic is rightfully being celebrated today for releasing their 1M context models in GA, with SOTA MRCR results that fight Context Rot for as long as possible:

Very useful and any default model that pushes back the compaction dumb zone for longer is welcome, but we are still remembering that the 1M context window was GA in March 2024, after Gemini did it in Feb 2024, and GAing after OpenAI GA’ed theirs last week.

It’s been 2 whole years since 1M context windows were theoretically possible, which means just under 1 order of magnitude growth in 2 years in context windows, which is much slower growth than all other dimensions (cost/speed/quality) of LLMs. We’re thinking back to Sam Altman’s Town Hall where he said context windows would get 100x longer… and we would take the under on that.

The issue lies in the global memory shortage - there’s just no HBM, or even DRAM, to take in all of this context at the inference site. Our podcast with Doug O’Laughlin covers this in greater detail:

swyx: One thing I, I do tell people about is everyone, including Sam by the way, is like predicting longer context windows. We’ve been effectively stuck at a million for two years now. I’ve actually been thinking about that a lot. And like, this is not gonna go to a hundred million context windows. It’s not gonna go to trillion. Like, we’re this is it? Yeah. This is it for like five years, 10 years pretty much.

[01:45:48] Doug: Yeah, probably actually. Will capitalism work? Will there be a way for supply to show up? Probably. But on top of that, you have to make a curve of the context windows. Like does free context windows go to like 1000? Hey, you can use chatGPT free now, but your context window is like a thousand tokens or something like that. And then you can just like somehow do a tiny parcel for that just so that you can then charge 100x more for 1 million. The 1 million context window is like a mansion, you know?

[01:46:27] swyx: Oh my God. The word just context rationing just came to me. I’m like, fuck. Like we’re gonna have like vouchers for like, okay, you can have this amount of context today.

I think for me, what matters is you represent the physical constraints that us, the software side can never surmount.

[01:47:43] Because it’s a physical constraint and Well, I mean, it just, it just like, physically we cannot double, we can’t even, we can’t even double and much unless says 10x.

[01:47:53] Doug: Context rationing. It’s pretty good. Context frugality or budget or something. I feel like everyone’s gonna be like, whoa, you’re running outta context window today. Maybe that’s what happens next year.

Mark our words, people. Willing to bet that context windows do not meaningfully go higher than 1M in the next 2 years, which is a big bet to make in AI time.

AI News for 3/12/2026-3/13/2026. We checked 12 subreddits, 544 Twitters and no further Discords. AINews’ website lets you search all past issues. As a reminder, AINews is now a section of Latent Space. You can opt in/out of email frequencies!

Agent Infrastructure, MCP Friction, and Persistent Memory

• The MCP backlash is mostly about ergonomics, not demand: A good slice of the feed was engineers arguing over whether MCP is “dead” or simply over-exposed. @pamelafox joked that “MCP was pronounced dead on Twitter, after mass exposure to curl,” while @tadasayy countered that usage is still booming. The more substantive take came from @llama_index: MCP tools are strong when you need deterministic, centrally maintained APIs and rapidly changing ground truth; skills are lighter-weight local natural-language procedures but more failure-prone. Relatedly, @bromann pointed to new web MCP support in Chrome v146, showing a LangChain Deep Agent that continuously browses X and compiles a daily summary.

• Memory is becoming the differentiator for agents: The most technically interesting agent thread was around persistent memory and self-improvement. @dair_ai highlighted IBM work on extracting reusable strategy/recovery/optimization tips from agent trajectories, improving AppWorld task completion from 69.6% to 73.2% and scenario goals from 50.0% to 64.3%, with the biggest gains on hard tasks. In parallel, @omarsar0 summarized a paper reframing multi-agent memory as a computer architecture problem, with cache/memory hierarchy, coherence, and access-control issues rather than “just more context.” This maps directly onto product work like Hermes Agent, which multiple tweets described as a self-hostable agent that retains skills and user-specific memory over time (overview via @abxxai, demo via @Teknium).

• Agent UX is moving to always-on, cross-device operation: Several launches pushed agents closer to “personal computer as orchestrator.” Perplexity Computer rolled out to iOS with cross-device synchronization, letting users start or manage a browser-computer task from phone or desktop (announcement, Arav follow-up). @bcherny showed the analogous flow for Claude Code, starting sessions on a laptop from a phone. Genspark’s Claw was framed similarly as an “AI employee” with a persistent cloud computer (summary by @kimmonismus). The common pattern: persistent session state, remote execution, and orchestration across many models/tools.

Inference, Long Context, and Systems Performance

• Anthropic quietly shipped one of the bigger infra-relevant updates of the week: Opus 4.6 1M context became the default for Max/Team/Enterprise users (via @_catwu), and Anthropic removed the API’s extra charge for long context while also dropping the beta header requirement and expanding media limits to 600 images/PDF pages per request (details from @alexalbert__). The most notable metric attached was 78.3% on MRCR v2 at 1M tokens, called out by multiple observers as a new frontier long-context high watermark (e.g. @kimmonismus).

• Sparse attention optimization is still yielding meaningful wins: A standout systems thread from @realYushiBai introduced IndexCache, which reuses sparse-attention index information across layers in DeepSeek Sparse Attention. Reported gains: roughly 1.2× end-to-end speedup on GLM-5 (744B) with matching quality, and on a 30B-scale experimental model at 200K context, 1.82× prefill and 1.48× decode after removing 75% of indexers. This was notable because it targets a production-scale sparse-attention stack with “minimal code change,” which is exactly the kind of practical optimization labs care about now.

• KV/cache and serving optimizations are broadening beyond autoregressive LLMs: @RisingSayak highlighted Black Forest Labs’ Klein KV, which injects cached reference-image KVs into later DiT denoising steps for multi-reference editing, claiming up to 2.5× speedups. On the infra side, @satyanadella said Microsoft is the first cloud validating an NVIDIA Vera Rubin NVL72 system, while @LambdaAPI pushed the “bare metal over hypervisor” angle for Rubin-era clusters. @tinygrad added a more radical endpoint: an “exabox” exposed as a single giant Python-driven GPU in 2027.

Post-Training, RL Alternatives, and Evaluation Research

• A provocative post-training result: random Gaussian search can rival RL fine-tuning: The most-discussed research claim was RandOpt / Neural Thickets from MIT-adjacent authors, shared by @yule_gan and @phillip_isola. The claim: by adding Gaussian noise to pretrained model weights and ensembling, one can reach performance comparable to or better than GRPO/PPO on reasoning, coding, writing, chemistry, and VLM tasks. Their explanation is that large pretrained models live in local neighborhoods dense with useful task specialists—“neural thickets”—making post-training much easier than standard optimization intuitions suggest.

• Generic-data replay and pre-pre-training are getting renewed attention: @TheTuringPost summarized Stanford work on generic data replay, reporting 1.87× improvement during fine-tuning and 2.06× during mid-training, with concrete downstream gains like +4.5% on agentic web navigation and +2% on Basque QA. Separate chatter around “pre-pre-training” suggested the community is revisiting staging/mixture design earlier in the training pipeline, not just post-training tricks (commentary from @teortaxesTex).

• Evaluation remains a bottleneck, especially for truthfulness and search strategy: @i shared BrokenArXiv, where even GPT-5.4 rejected only 40% of perturbed false mathematical statements from recent papers. @paul_cal argued this gives GPT-5.4 an edge over Claude on proof-verification-style “bullshit detection,” even if other truthfulness benchmarks disagree. For retrieval/search, MADQA found agents near human answer accuracy by using brute-force search rather than strategic navigation over documents, leaving about a 20% gap to oracle performance (via @HuggingPapers).

Open Source Releases, Datasets, and Reproducibility

• OpenFold3’s new preview is unusually complete by frontier biology standards: @MoAlQuraishi announced OpenFold3 preview 2, saying it closes much of the gap to AlphaFold3 across modalities while releasing not just weights but also training sets and configs, making it “the only current AF3-based model that is functionally trainable & reproducible from scratch.” That reproducibility claim is the key point: many “open” biology releases still stop well short of end-to-end re-trainability.

• Speech data for underrepresented languages got a meaningful boost: @osanseviero announced WAXAL, an open multilingual speech dataset covering 17 African languages for TTS and 19 for ASR, later described by @GoogleResearch as 2,400+ hours spanning 27 Sub-Saharan languages and 100M+ speakers. The exact language/task counts differed between posts, but both positioned WAXAL as a rare, community-rooted resource for African voice AI.

• Open-source sentiment around training data is hardening in favor of permissive reuse: The strongest statement came from @ID_AA_Carmack, who argued that open-source code is a gift whose value is magnified by AI training, not undermined by it. @giffmana and @perrymetzger echoed that view. The most nuanced counterpoint was @wightmanr, who argued that coding agents may bypass attribution and licensing expectations in ways that could demotivate maintainers, suggesting a protocol for agent compliance could become important.

Developer Tooling, Coding Agents, and Research Automation

• Coding-agent workflows are getting more autonomous and more opinionated: There were many examples of engineers moving from “copilot” to multi-agent software factories. @matvelloso described a setup with 5 agents doing code review/test/security/perf work and 2 more merging PRs and running regression checks. @swyx compressed the trend to “Your Code is your Infra,” while @gokulr and @matanSF pointed to FactoryAI as an increasingly common “software factory” layer.

• Autonomous research is becoming a product category, but not a new idea: Karpathy’s autoresearch and related hackathons drew significant attention, but several tweets noted the conceptual overlap with older systems like DSPy, GEPA, and Bayesian optimization pipelines. The most practical pointer was @dbreunig recommending optimize_anything for people interested in this style of iterative self-improvement. Together AI also shipped Open Deep Research v2, open-sourcing its app, eval dataset, code, and blog (launch).

Top tweets (by engagement)

• xAI recruiting reset: @elonmusk said xAI is reviewing historical interview pipelines and re-contacting promising candidates previously rejected, after acknowledging many strong people were missed.

• Claude’s chart UI: @crystalsssup posted a highly engaged reaction to Claude’s new interactive chart UX.

• Perplexity Computer on mobile: @perplexity_ai launched cross-device Computer access on iOS, one of the clearest productizations of remote agent execution this week.

• Microsoft validates Rubin NVL72: @satyanadella announced Azure as the first cloud validating NVIDIA Vera Rubin NVL72.

• Nous / Hermes momentum: Hermes Agent and its memory-centric framing generated wide discussion via @Teknium and others, reflecting strong interest in self-hosted, improving agent harnesses.

We thoroughly enjoyed the charismatic Simon Eskildsen of Turbopuffer on today’s pod, and highly encourage listening in even if you’re not a database nerd:

As for today’s op-ed, it’s a quiet day. This comment from Aidan is currently living rent-free in my head:

Tyler Cowen has noted The high-return activity of raising others’ aspirations 8 years ago, and it is commonly the biggest regret we see in others and the biggest regret I have had in my last 3 years covering and tinkering in AI. The people just on the right side of insane have pushed LLMs to their limits and benefited, whereas the pragmatic people who judge and manage LLMs as they were at their time, mostly didn’t go anywhere.

So a fun question to ask an LLM, which we walk through in our upcoming Claude Cowork pod, is: how can I be more ambitious than what I’m currently doing?

AI News for 3/11/2026-3/12/2026. We checked 12 subreddits, 544 Twitters and no further Discords. AINews’ website lets you search all past issues. As a reminder, AINews is now a section of Latent Space. You can opt in/out of email frequencies!

Agent Infrastructure, Harnesses, and the MCP Debate

• Harnesses are becoming the real product surface: Multiple posts converged on the view that model quality alone is no longer the bottleneck; the surrounding harness, tools, memory, and runtime matter more. @mattturck’s interview with Harrison Chase frames this explicitly around harnesses, sandboxes, filesystem access, skills, memory, and observability, while @hwchase17 emphasized that agent UI/UX is still hard and underbuilt. That same stack perspective shows up in LangChain JS’s new cross-framework useStream hook, Redis’s context-engineering lab, and Artificial Analysis’s Stirrup Slack integration, which adds Slack-native agents with documents, subagents, MCP, browser use, and code execution.

• MCP is not dead; it’s being normalized into production plumbing: Despite a wave of “MCP is dead” jokes (example), the more technical takes cut the other way. @omarsar0 argued MCP’s issue is mostly a harness problem, not a protocol problem, and later noted that Anthropic’s new chart feature appears to be MCP-backed (tweet). Most concretely, @GergelyOrosz pointed to Uber using MCP internally as evidence that MCP is “the life blood” of agent-service integration inside larger companies. In practice, the market signal is clear: agent platforms now treat MCP as baseline interoperability rather than novelty.

Coding Agents, Evaluation, and Dev Workflow Shifts

• The coding-agent stack is maturing from demos into measurable systems: Cursor’s new CursorBench methodology is one of the stronger eval announcements in the set, combining offline benchmarks with online request-derived metrics to score models on both intelligence and efficiency; the team argues public coding benchmarks are increasingly saturated. OpenAI quickly highlighted that GPT-5.4 leads CursorBench on correctness with efficient token usage. Separately, Code Arena reported GPT-5.4-high in the top 6 for real-world web development tasks, while WeirdML results from @htihle showed strong but inconsistent performance and unusually long generated solutions. The common pattern: coding-model comparison is shifting toward multi-axis measurement—correctness, token efficiency, interaction behavior, and real task fit.

• Agent-assisted development is bifurcating into automation-heavy flows and “stay-in-the-loop” tooling: Several practitioners pushed back on the rush to fully autonomous coding. @ThePrimeagen argued that fast inline autocomplete still often outperforms agentic workflows in preserving understanding and reducing cognitive debt. In contrast, posts from @sydneyrunkle and @corbtt showed where agents excel today: reproducing bugs from screenshots, cross-tool organizational retrieval, and automating tedious coordination. OpenAI also shipped more operational features around this mode: Codex Automations are now GA with worktree vs. branch choice, model/reasoning controls, and reusable templates, plus UI customization in the app (themes update).

• Hermes Agent is emerging as a serious open agent platform: Nous’s Hermes Agent v0.2.0 shipped an unusually dense release for a two-week sprint, including full MCP client support, an ACP server for editors, provider expansion (including GLM, Kimi, MiniMax, OpenAI OAuth), filesystem checkpoints with rollback, git worktree isolation, local browser support, and subagent transparency as summarized by @witcheer. Follow-up updates added official Claude provider support and lighter installs. Community reaction suggests real adoption, including migration away from OpenClaw (example).

Multimodal Retrieval, Embeddings, and New Interaction Surfaces

• A big week for multimodal retrieval: Google’s Gemini Embedding 2 is its first natively multimodal embedding model, mapping text, images, audio, video, and PDFs into one vector space. Posts from Weaviate and @victorialslocum highlighted practical use cases like multimodal PDF RAG, flexible output dimensions via Matryoshka Representation Learning, and native support in retrieval pipelines. The strongest competitive response came from Mixedbread’s Wholembed v3, which claims SOTA retrieval across modalities and 100+ languages, with the team and outside observers stressing late-interaction / multi-vector design as the differentiator (@bclavie, @lateinteraction).

• The retrieval debate is crystallizing around single-vector vs. multi-vector: The most technically opinionated commentary came from @lateinteraction, arguing that new multimodal single-vector baselines like Gemini Embedding 2 were almost immediately outperformed by scaled ColBERT/ColPali-style approaches, and later stating it is “borderline irrational” to keep betting on single-vector embeddings (tweet). Even allowing for hype, the broader takeaway is important: retrieval teams are increasingly prioritizing interaction-rich indexing/scoring over one-vector simplicity, provided infra can make it practical at scale (TopK infrastructure note).

• Interfaces are getting richer, not just smarter: Anthropic’s Claude can now generate interactive charts and diagrams directly in chat, a notable product step toward generative UI rather than plain text outputs. This resonated with builders already assembling similar systems via MCP (@omarsar0). In parallel, Perplexity Computer rolled out to Pro users with 20+ models, skills, and connectors, and @alexalbert__ summarized the broader product trend as “Generative UI is here.”

Model Releases, Benchmarks, and Efficiency Trends

• NVIDIA’s Nemotron 3 Super stands out as the most technically discussed model release: The release was highlighted by @rasbt as an open-weight 120B model with strong throughput and benchmarks roughly in the Qwen3.5/GPT-OSS class. The architecture drew extra attention because of its LatentMoE design; @cwolferesearch provided a useful breakdown showing how routing in a lower-dimensional latent space reduces both all-to-all communication costs and expert weight loading costs, then reinvests those savings into more experts and more active experts per token. It is one of the clearer examples in the set of architecture changes aimed at better inference economics, not just benchmark chasing.

• Grok 4.20 Beta looks more like a cost/speed/behavior update than a frontier leap: Artificial Analysis’s evaluation puts Grok 4.20 (reasoning) at 48 on its Intelligence Index, below current top models but with a larger 2M context window, lower pricing ($2/$6 per 1M in/out tokens), strong speed, and the best measured score so far on its non-hallucination metric. Follow-up commentary from @scaling01 and Vals broadly reinforced that story: not frontier-topping, but cheaper, faster, and potentially more usable in some production settings.

• Efficiency and architecture remain central themes: The day also included FLUX.2 klein 9B-KV, which is reported as 2x–2.5x faster for image editing with no quality drop, and Reka Edge, a 7B VLM pitched around 98ms time-to-first-token and low-latency agentic/on-device use. On the research side, tweets surfaced work on looped transformers with gated memory banks, LM head gradient bottlenecks, reasoning probes for early CoT exit, and Flash-KMeans, all consistent with a field still searching for gains in training signal quality, inference efficiency, and adaptive compute.

Applied AI: Maps, Health, Video, and Forecasting

• Google Maps is being rebuilt around Gemini as an interaction layer, not just a map layer: Google’s Maps upgrade thread describes the biggest product update in over a decade, with two notable pieces: a conversational “Ask Maps” mode over Google’s places/community graph, and Immersive Navigation with richer 3D route guidance (details). The more interesting engineering implication came from observers like @dbreunig: the future UX may not “look like a map” at all, with LLMs acting as the primary interface to geospatial knowledge.

• Healthcare copilots are moving toward longitudinal personal context: Microsoft introduced Copilot Health, a US launch that can aggregate EHR records, wearables, personal history, and lab data into a dedicated health profile. The company stressed that user data is not used to train models, and that outputs are grounded in trusted health sources with citations. Separately, Glass Health added self-serve EHR integrations for athenaOne and eClinicalWorks, showing similar movement toward AI systems that are useful only once they are deeply connected to real clinical data systems.

• Video generation APIs are getting more product-ready: OpenAI’s Sora 2-powered Video API update added custom characters/objects, 16:9 and 9:16 export, 20-second clips, continuation, and batch jobs, which is a pragmatic set of features for campaign, storyboarding, and UGC workflows rather than pure research demos.

• Groundsource is one of the stronger “AI for public-good data” announcements: Google Research’s Groundsource uses Gemini to turn 5M+ public reports into a dataset of 2.6M+ flood events across 150+ countries, enabling urban flash-flood forecasting up to 24 hours in advance. The methodological point is broader than floods: using multimodal/LLM pipelines to synthesize structured, open benchmarks from noisy public corpora could become an important pattern for under-instrumented domains.

Top tweets (by engagement)

• Claude interactive charts and diagrams: Anthropic launched interactive charts/diagrams in chat, one of the clearest examples this week of LLMs moving toward richer frontends rather than better text alone.

• Google Maps + Gemini: Google’s major Maps upgrade was by far the biggest mainstream product launch in the set, with conversational place search and immersive navigation.

• CursorBench / coding evals: Cursor’s new eval methodology for coding agents drew outsized attention because it addresses a real gap: how to evaluate coding systems on both capability and efficiency using a mix of online and offline signals.

• Perplexity Computer rollout: Perplexity Computer for Pro users signals continued appetite for “computer-use” products with broader connectors/skills rather than one-model chat.

• OpenJarvis on-device personal AI: Stanford’s OpenJarvis launch stood out as a serious local-first framework for personal AI on device, with modular infra, local retrieval, MCP tools, and efficiency-aware evaluation.

• Qwen3.5-9B is actually quite good for agentic coding (Activity: 428): The post discusses the performance of Qwen 3.5-9B on an Nvidia Geforce RTX 3060 with 12 GB VRAM, highlighting its effectiveness for agentic coding tasks. The user compared various models, including Qwen 2.5 Coder and Unsloth quantizations on Qwen 3 Coder, noting that 1-bit quantizations were fast but unreliable for tool calls, while 2-bit quantizations were slower and unstable. The Qwen3.5-9B model, however, performed well, maintaining functionality for over an hour without issues, suggesting it is well-suited for consumer hardware with limited VRAM. The user also mentions Unsloth-Qwen3 Coder 30B UD-TQ1_0 as effective for code completion. One commenter noted that Qwen3.5-9B performs comparably to larger models like GPT-120B, while another shared a negative experience where the model disrupted their build system, indicating variability in performance. A link to another model, OmniCoder-9B-GGUF, was also shared as a potential alternative.

  • The Qwen3.5-9B model is noted for its impressive performance, benchmarking around the level of GPT-3’s 120B model, which is surprising given its smaller size. This suggests that Qwen3.5-9B is highly efficient in terms of performance relative to its size, making it a strong contender in the field of agentic coding.

  • Despite its strengths, Qwen3.5-9B has been reported to cause significant issues in some cases, such as completely disrupting a build system and deleting projects. This indicates that while the model can perform well, it may also have stability issues or bugs that need addressing, especially when used in complex environments like LM Studio and Claude Code on an RTX 4060.

  • There is a debate around the utility of lower quantized models like Qwen3.5-9B, with some users arguing that these models can indeed perform useful tasks despite their smaller size. This challenges the notion that only larger models are capable of high-quality outputs, highlighting the potential of optimized smaller models in practical applications.

• I spent 8+ hours benchmarking every MoE backend for Qwen3.5-397B NVFP4 on 4x RTX PRO 6000 (SM120). Here’s what I found. (Activity: 349): The post details an extensive benchmarking effort on the nvidia/Qwen3.5-397B-A17B-NVFP4 model using 4x RTX PRO 6000 GPUs, achieving a maximum of 50.5 tok/s despite claims of 130+ tok/s. The bottleneck is attributed to NVIDIA’s CUTLASS kernels, which fail on SM120 hardware due to a bug in the initialization of TMA Warp Specialized grouped GEMM tactics, as documented in CUTLASS issue #3096. The author suggests that the issue is due to a Shared Memory (SMEM) overflow caused by datacenter assumptions, with SM120 having a strict limit of 101 KiB compared to datacenter variants. The post also highlights that Multi-Token Prediction (MTP) results in a -22% performance regression due to dequantization discrepancies. The author has submitted patches to FlashInfer and vLLM to address some of these issues, but the core problem remains unresolved by NVIDIA. A commenter suggests that the author’s bug report on GitHub is too verbose and lacks clear reproduction steps, which may be why NVIDIA hasn’t addressed it. Another commenter identifies the root cause of the SM120 issue as a physical SMEM overflow and suggests that smaller tile shapes could resolve the problem. A third commenter notes that running the setup on bare metal Linux could improve performance by ~10% compared to WSL2.

  • lawdawgattorney provides a detailed analysis of a bug affecting the Qwen3.5-397B NVFP4 model on RTX PRO 6000 GPUs. The issue is traced to a Shared Memory (SMEM) overflow due to datacenter assumptions, where the SM120 dies have a strict 101 KiB limit compared to datacenter dies with ~227 KiB. The auto-computation formula for pipeline stages fails to account for alignas(1024) padding, causing a kErrorInternal crash. A temporary fix involves hardcoding StageCount<2>, but this reduces performance to 4.8 tok/s due to memory latency issues. The proposed solution is to support smaller tile shapes to fit more pipeline stages within the SM120’s SMEM constraints.

  • JockY criticizes the original bug report for being overly verbose and lacking essential details such as reproduction instructions and error logs. The report’s excessive length and inclusion of irrelevant benchmarks make it difficult for developers to address the issue. JockY suggests a more concise approach, focusing on clear reproduction steps and error logs to facilitate debugging and resolution by developers.

  • AndreVallestero suggests running the benchmarks on bare metal Linux instead of WSL2, noting a potential performance improvement of approximately 10%. This implies that the virtualization layer in WSL2 might introduce overhead that affects the performance of GPU-intensive tasks like those involved in running the Qwen3.5-397B NVFP4 model.

• Qwen3.5-9B Quantization Comparison (Activity: 398): The post presents a detailed quantization comparison of the Qwen3.5-9B model using various GGUF quantization methods, focusing on Kullback-Leibler Divergence (KLD) and Perplexity (PPL) as key metrics. The analysis highlights that IQ4_XS from bartowski (4.93 GiB, KLD 0.0127) is optimal for VRAM-limited scenarios without dropping below Q4, while Q4_K_S from bartowski (5.18 GiB, KLD 0.0108) performs well across multiple domains. Notably, bartowski’s Q4_K_M outperforms unsloth’s Q4_K_M (0.0087 vs 0.0222 KLD), and lmstudio’s Q4_K_M scores worse (0.0353 KLD). The post also provides a token-level divergence visualization on HuggingFace, showing divergence across four domains. The evaluation was conducted using llama.cpp on a system with an i3-12100F CPU, 64GB RAM, and an RTX 3060 GPU. Commenters agree with the findings, noting that Bartowski’s quantizations are perceived as more stable compared to others, such as unsloth’s. There is appreciation for the detailed analysis, which aids in making informed decisions about quantization choices.

  • General_Arrival_9176 highlights a significant performance difference between Bartowski’s and Unsloth’s quantizations at the same level, with Bartowski’s Q4_K_M achieving a KL Divergence of 0.0087 compared to Unsloth’s 0.0222. This suggests a substantial impact of the quantization process and possibly the training methodology used by Unsloth, as well as the importance of choosing the right quantization approach to avoid downloading multiple versions unnecessarily.

  • Shingikai emphasizes the value of using KL Divergence (KLD) over Perplexity (PPL) for evaluating quantization performance. While PPL provides an average performance metric, KLD can reveal ‘catastrophic failure’ cases where a model remains fluent but makes incorrect decisions. The discussion also notes that Bartowski’s Q4_K_M outperforms Unsloth’s, suggesting that the choice of calibration data is more critical than the quantization engine itself at the 4-bit level.

  • dark-light92 and General_Arrival_9176 both note the stability and performance of Bartowski’s quantizations over others like Unsloth’s. This indicates a trend towards more reliable quantization methods, with Bartowski’s approach being favored for its stability and lower KL Divergence, which is crucial for maintaining model accuracy and performance in practical applications.

• M5 Max just arrived - benchmarks incoming (Activity: 2679): The post discusses the arrival and benchmarking of the M5 Max 128GB 14” laptop, focusing on testing various AI models using the mlx_lm tool. The author initially faced issues with BatchGenerator, leading to a delay as they switched to a fresh Python environment and used stream_generate for accurate results. The benchmarks cover models like Qwen3.5-122B-A10B-4bit and gpt-oss-120b-MXFP4-Q8, with detailed performance metrics such as tokens-per-second and peak memory usage. The results highlight the M5 Max’s capability to handle large models efficiently, with peak memory usage reaching up to 92.605 GB for some tests. Commenters are eager for the benchmark results, with some expressing interest in specific models like Qwen 3.5 27b MLX 4bit and 6bit. The discussion reflects anticipation and curiosity about the M5 Max’s performance with these AI models.

  • The benchmark results for the M5 Max 128GB 14” using mlx_lm.generate show significant performance across different models and configurations. For instance, the Qwen3.5-122B-A10B-4bit model achieves a prompt speed of 1,239.7 t/s and a generation speed of 60.6 t/s at a 16K context, with a peak memory usage of 73.8 GB. In contrast, the Qwen3.5-27B-Claude-4.6-Opus-Distilled-MLX-6bit model shows lower generation speeds, with 14.9 t/s at a 32K context, but also lower peak memory usage at 30.0 GB.

  • The Qwen3-Coder-Next-8bit model demonstrates impressive performance, particularly at higher contexts, achieving a prompt speed of 1,887.2 t/s and a generation speed of 68.6 t/s at a 32K context, with peak memory usage reaching 89.7 GB. This suggests that the model is optimized for handling larger contexts efficiently, although it requires substantial memory resources.

  • The gpt-oss-120b-MXFP4-Q8 model stands out with its high prompt and generation speeds, especially at a 16K context where it reaches 2,710.5 t/s for prompts and 76.0 t/s for generation, with a relatively low peak memory usage of 64.9 GB. This indicates a strong balance between speed and memory efficiency, making it a competitive option for high-performance tasks.

• Llama.cpp now with a true reasoning budget! (Activity: 444): Llama.cpp has introduced a true reasoning budget feature, enhancing the previous stub implementation. The new feature uses a sampler mechanism to count tokens during reasoning, terminating when the budget is reached. Initial tests on Qwen3 9B showed a performance drop from 94% to 78% on HumanEval when enforcing a reasoning budget, but adding a --reasoning-budget-message improved scores to 89%. This update allows experimentation with different models and settings, potentially improving reasoning efficiency. For more technical details, see the commit. Commenters noted potential confusion between thinking_budget_tokens in HTTP and --reasoning-budget in CLI, suggesting dynamic logit_bias adjustments for better performance. Another suggestion was to enforce a minimum-thinking-budget by setting logit bias to negative infinity, potentially boosting scores. A user reported successful testing with Qwen3.5 35B, where a reasoning budget improved decision-making efficiency without overthinking.

  • coder543 highlights a potential confusion in the API design of Llama.cpp, where the HTTP field is named thinking_budget_tokens but the CLI argument is --reasoning-budget. This discrepancy could lead to errors if users mistakenly send reasoning_budget or reasoning_budget_tokens to the API. Additionally, coder543 suggests dynamically boosting the logit bias for the end-of-think token towards the end of the reasoning budget to help the model conclude more naturally, though they note this might reduce intelligence scores.

  • chris_0611 shares practical testing results with the qwen3.5 35B model in Q5, using a ‘car-wash test’ to evaluate reasoning budgets. With a reasoning budget of 0, the model fails to choose walking for a 100m distance. With an unlimited budget, it overthinks for 83 seconds but passes. A reasoning budget of 1000 tokens results in a successful test with only 18 seconds of thinking, demonstrating a balance between speed and accuracy.

  • audioen proposes a method to gradually increase the likelihood of generating the token, suggesting a linear bias increase of 0.1% per token. This approach aims to naturally conclude the model’s reasoning process by the end of a set token limit, potentially improving efficiency without abrupt interruptions.

• llama.cpp on $500 MacBook Neo: Prompt: 7.8 t/s / Generation: 3.9 t/s on Qwen3.5 9B Q3_K_M (Activity: 636): A user successfully compiled llama.cpp on a MacBook Neo with an Apple A18 Pro chip, achieving 7.8 tokens/second for prompt processing and 3.9 tokens/second for generation using the Qwen3.5 9B Q3_K_M model. The setup utilized 8 GB of unified memory and a 5-core GPU with Metal support. The model was sourced from the Hugging Face repository and occupied 4.4 GB on disk. The user provided a configuration for potentially faster performance, achieving 5 tokens/second for the 9B model and 10 tokens/second for a 4B model, linked in the post. Commenters noted that the performance is likely hindered by the 8GB RAM and potential disk swapping, suggesting that using a smaller model might improve speed. The configuration parameters -b, -ub, -ctk, and -ctv were also highlighted as unusual.

  • coder543 suggests that the performance metrics indicate the system is likely swapping to disk or using compressed memory, which significantly impacts speed. They recommend testing with a smaller 4B model to observe potential non-linear speed improvements, implying that the current setup is not optimal for the 9B model.

  • Technical-Earth-3254 points out that the limited 8GB RAM on the MacBook, especially when running a full operating system, is a major bottleneck affecting performance. This suggests that memory constraints are a critical factor in the observed throughput rates.

  • thisguynextdoor inquires about the potential use of Apple’s Metal API for GPU acceleration, which could significantly enhance performance. They compare this to their own experience with a Gemma 3 27B model on an M1 Pro, achieving 15 t/s, indicating that hardware acceleration could be a key factor in improving throughput.

• OmniCoder-9B | 9B coding agent fine-tuned on 425K agentic trajectories (Activity: 330): OmniCoder-9B is a 9-billion parameter coding agent developed by Tesslate, fine-tuned on the Qwen3.5-9B model using a hybrid architecture of Gated Delta Networks and standard attention. It was trained on 425,000+ curated agentic coding trajectories, including data from models like Claude Opus 4.6 and GPT-5.4, focusing on real-world software engineering tasks and multi-step reasoning. The model features a 262,144 token context window, extensible to 1M+, and demonstrates strong error recovery and reasoning capabilities, such as responding to LSP diagnostics and using minimal edit diffs. It is released under the Apache 2.0 license, with fully open weights. Commenters highlight the impressive capabilities of the Qwen3.5-9B model, suggesting it competes with much larger models despite its smaller size. There is enthusiasm for testing OmniCoder-9B, with some users noting its ability to handle complex tasks efficiently.

  • Uncle___Marty highlights the impressive performance of the Qwen 3.5 9B model, suggesting it rivals much larger models, such as those with 100B+ parameters, in terms of coding capabilities. The commenter emphasizes the potential of small, powerful models like Qwen 3.5 9B, advocating for their future in local applications due to their efficiency and capability despite their size.

  • pilibitti shares a practical example of the model’s capabilities, noting that it successfully completed an agentic task requiring over 20 tool calls with a blank system prompt, a task that Qwen 3.5 9B failed to accomplish even with detailed prompts. This underscores the model’s efficiency and effectiveness in handling complex tasks with minimal guidance.

  • PaceZealousideal6091 inquires about comparative benchmarks between OmniCoder-9B and Qwen 3.5 35B, expressing interest in understanding the performance differences. They also ask about the possibility of a larger version, such as a 35B model, indicating a demand for scaling up the model’s capabilities while maintaining its efficiency.

• I was backend lead at Manus. After building agents for 2 years, I stopped using function calling entirely. Here’s what I use instead. (Activity: 2145): The post discusses a shift from using multiple typed function calls to a single run(command="...") tool with Unix-style commands for AI agents, arguing that this approach aligns with the text-based nature of both Unix and LLMs. The author, a former backend lead at Manus, developed this approach while working on open-source projects like Pinix and agent-clip. The Unix philosophy of text streams and CLI commands is seen as a natural fit for LLMs, which are trained on vast amounts of CLI patterns. The post outlines a two-layer architecture to handle LLM constraints, using techniques like progressive --help discovery, error messages for navigation, and consistent output formats to guide the agent’s use of CLI tools. The approach is contrasted with traditional function-calling methods, highlighting the efficiency and familiarity of CLI for LLMs. One commenter noted a similar experiment using Python code eval as the sole tool for LLMs, which reportedly worked well. Another comment humorously suggested the post might be a ploy to give LLM agents full terminal access, while a third highlighted the power of natural language to command-line tool translation.

  • spaceman_ mentions an experiment where an LLM was restricted to using only Python code evaluation as a tool, which reportedly performed well. This suggests that LLMs can effectively leverage programming languages as a tool for executing complex tasks, potentially simplifying the integration of LLMs in environments where Python is a primary language.

  • raucousbasilisk highlights the potential of using JIT (Just-In-Time) compilation for natural language processing tasks, specifically converting natural language to command-line utilities like sed, awk, and regex. This approach could harness the power of existing command-line tools for text processing, offering a flexible and efficient method for handling diverse data manipulation tasks.

  • johnbbab speculates that the ultimate agent framework might resemble a shell environment, implying that the simplicity and power of shell scripting could serve as a model for developing robust agent frameworks. This perspective underscores the potential for leveraging existing, well-understood technologies in new AI-driven applications.

• Nemotron 3 Super Released (Activity: 755): NVIDIA has released the Nemotron 3 Super, a 120 billion parameter Mixture of Experts (MoE) model with 12 billion active parameters, designed for agentic reasoning. The model is fully open-source, including weights, datasets, and recipes, allowing developers to customize and deploy it on their infrastructure. It features a comprehensive data pipeline with 10 trillion curated tokens for pretraining and 40 million samples for post-training, supporting tasks like reasoning, coding, and multi-step agent tasks. The model is available on Hugging Face and supports Quantization Aware Training (QAT) with NVFP4 precision. Some users have created GGUFs for the model, requiring at least 64GB of memory, and suggest using a specific branch of llama.cpp for compatibility until an official update is available.

  • Nemotron 3 Super is highlighted for its open-source nature, providing full access to weights, datasets, and recipes, allowing developers to customize and deploy the model on their infrastructure. The model’s data pipeline includes 10 trillion curated tokens for pretraining and 40 million samples for post-training, emphasizing reproducibility and agentic AI development. The RL tasks span 21 environments, generating 1.2 million rollouts, showcasing its dynamic capabilities beyond static text.

  • The Nemotron 3 Super model is available in various formats, including BF16 and NVFP4, with Quantization Aware Training (QAT) options. The model’s GGUFs require at least 64GB of memory, and compatibility issues with llama.cpp are noted, suggesting the use of a specific branch from Unsloth for proper functionality. This highlights the model’s demanding hardware requirements and ongoing integration efforts.

  • Initial performance assessments of Nemotron 3 Super indicate it scores below lighter models like Qwen3.5 in the LM Arena Text benchmark, particularly when filtered for open-source and style-control off. This suggests that despite its comprehensive dataset and open-source advantages, its performance may not yet match expectations in certain benchmarks.

• New benchmark just dropped. (Activity: 1359): The post humorously requests a complete Three.js code to create a scene with Michael Jackson, Pepe the Frog, Donald Trump, and Elon Musk performing the “Thriller” dance, emphasizing high-quality rendering and cinematic effects. The comments reference various AI models like Sonnet 4.6 and Gemini, noting their strengths in lighting and animation, respectively. The discussion highlights the playful nature of the request, with models like Deepseek 3.2 and Minimax & GLM mentioned in jest for their attempts at the task. The comments humorously debate the capabilities of different AI models, with Sonnet 4.6 praised for lighting and Gemini for choreography, while others like Deepseek 3.2 are acknowledged for effort despite not excelling.

  • Recoil42 highlights the performance of various models in a new benchmark, noting that Sonnet excelled in lightning and models, while Gemini was impressive in choreography. They also mention Deepseek 3.2’s consistent performance and humorously note that Minimax and GLM lost interest, and Qwen seemed to be attempting a different task altogether.

  • cmdr-William-Riker questions the performance of OpenAI, expressing surprise at its perceived decline. They specifically inquire about the variant of Qwen 3.5 used in the benchmark, suggesting interest in understanding the specific configurations or versions that might have impacted the results.

• Nvidia Will Spend $26 Billion to Build Open-Weight AI Models, Filings Show (Activity: 1146): Nvidia is set to invest $26 billion over the next five years to develop open-weight AI models, as per recent financial filings. This initiative is designed to strengthen Nvidia’s position in the AI infrastructure market, competing with major entities like OpenAI and Anthropic. The investment will likely focus on leveraging Nvidia’s hardware capabilities, such as the H100 clusters, to maintain CUDA as the default inference target, and optimize for NVFP4 precision. For more details, see the original article. Commenters highlight Nvidia’s strategic move as a way to maintain dominance in the AI hardware market by commoditizing AI models, thus reinforcing the ‘Nvidia tax’. This approach is seen as a way to ensure CUDA remains the default inference target, leveraging their hardware’s capabilities.

  • Nvidia’s $26 billion investment in open-weight AI models is seen as a strategic move to maintain its dominance in the AI hardware market. By developing these models, Nvidia aims to ensure that CUDA remains the default inference target, effectively locking in their ecosystem and making it harder for competitors to justify designing alternative chips. This approach aligns with the strategy of ‘commoditizing your product’s complement,’ where Nvidia not only sells the hardware but also provides the software and models that run on it, thus reinforcing their market position.

  • The investment in open-weight AI models is also a way for Nvidia to optimize the use of their own hardware, such as the H100 clusters. By utilizing these resources internally, Nvidia can potentially accelerate the development of advanced AI models, which in turn could drive further demand for their hardware. This move is seen as a way to justify the high costs associated with maintaining CUDA as the preferred platform for AI development, ensuring that Nvidia’s ecosystem remains the go-to choice for AI researchers and developers.

  • Nvidia’s focus on optimizing for NVFP4 (Nvidia’s floating-point format) highlights their commitment to maximizing the performance of their hardware. By tailoring their AI models to leverage NVFP4, Nvidia can achieve higher efficiency and performance, which is crucial for large-scale AI training and inference tasks. This technical optimization not only enhances the capabilities of their models but also reinforces the value proposition of their hardware solutions, making them more attractive to customers who require high-performance AI solutions.

/r/Singularity, /r/Oobabooga, /r/MachineLearning, /r/OpenAI, /r/ClaudeAI, /r/StableDiffusion, /r/ChatGPT, /r/ChatGPTCoding, /r/aivideo, /r/aivideo

• Anthropic: Recursive Self Improvement Is Here. The Most Disruptive Company In The World. (Activity: 1803): Anthropic is accelerating AI development with its model, Claude, which now writes 70% to 90% of the code for future models, indicating a significant step towards recursive self-improvement. Evan Hubinger from Anthropic suggests that fully automated AI research could be imminent, potentially within a year. The release of Claude 3.7 Sonnet was delayed by 10 days due to safety concerns, highlighting the ongoing tension between rapid AI advancement and safety protocols. Dario Amodei warns of AI’s potential to displace half of entry-level white-collar jobs within five years, urging transparency about these impacts. Anthropic’s stance on AI deployment in military contexts and its critique of political influences on AI policy are also notable. Some commenters question the criticism of safety delays, arguing that with 90% of code generated by AI, thorough testing is essential to ensure safety. Others reflect on the historical context of AI safety concerns, referencing Sutskever’s caution with GPT-2.

  • Substantial-Elk4531 raises a critical point about the safety concerns in AI development, questioning the delay in model releases due to these fears. They highlight the importance of testing AI-generated code, especially when a significant portion (up to 90%) is not human-written, to ensure safety and reliability. This reflects ongoing debates in the AI community about balancing innovation with ethical considerations.

  • BiasHyperion784 discusses the timeline for Anthropic’s infrastructure upgrades, noting that by Q3 2027, new hardware (’rubin ultra’) will be deployed, enhancing compute capabilities. They emphasize that improvements in training time are crucial, as bespoke hardware will exponentially increase processing power, suggesting a significant leap in AI capabilities once these upgrades are complete.

  • Pitiful-Impression70 expresses concern over the rapid advancement of AI self-improvement, where models like Claude are increasingly responsible for writing their own code. They argue that this shift from human authorship to AI-driven development poses challenges, as humans become reviewers rather than creators, complicating the oversight process due to the lack of intuitive understanding of AI-generated decisions.

• Claude now creates interactive charts, diagrams and visualizations (Activity: 1174): Claude has introduced a new feature allowing users to create interactive charts, diagrams, and visualizations directly within conversations. These visuals are dynamically generated and can be modified through follow-up interactions, enhancing the conversational experience. This feature is currently in beta and available across all plans, including the free tier. More details can be found on their official blog. Commenters are enthusiastic about the potential of this feature for educational purposes and logic flow setups, suggesting it could replace other tools like ChatGPT for some users. There is curiosity about its integration with Claude’s coding capabilities and whether it can generate links to these visuals.

  • Unlikely_Ad_8060 highlights a significant shift in problem-solving with Claude’s interactive visualizations. If these visuals are generated within the reasoning loop, it allows users to iteratively explore problems by adjusting parameters, testing assumptions, and exploring edge cases. This approach aligns more closely with how engineers and analysts think, moving beyond static outputs to a dynamic, exploratory process.

  • trashcanhat raises a technical question about the integration of these interactive visualizations within Claude’s code environment. The potential for Claude to post links to these visualizations could enhance usability, suggesting a seamless integration between code execution and visual feedback.

  • Much-Inevitable5083 provides a link to a resource on building interactive diagram tools with Claude, indicating that there are existing use cases and possibly tutorials or documentation available for users interested in leveraging this feature. This could be valuable for developers looking to implement or understand the technical capabilities of Claude’s visualization tools.

• I delayed my product launch for months because I couldn’t afford demo videos. Spent a weekend with Claude Code and Remotion. Now my reels are getting thousands of views. (Activity: 1152): The post describes how the author used Remotion, a React-based video generation tool, and Claude Code to create demo videos for their product, bypassing the need for expensive motion designers. By leveraging Claude Skills like remotion-transitions and frontend-design, the author was able to generate SVG-based visuals and animated UI sections quickly, reducing the time to create each reel from 3 hours to under an hour. This approach resulted in thousands of views and increased interest in the product, all with $0 in production costs aside from the Claude Code subscription. Comments highlight that while the generated videos are effective for product demos, they lack the polish of professional editors. Remotion is praised for its utility in creating explainer reels, though it may not suffice for more complex motion design or character animation.

  • Remotion is highlighted as a powerful tool for creating product demos and explainer reels, though it may not be suitable for more complex motion design or character animation. The combination of Remotion and Claude Code allows for efficient production of marketing videos without the need for extensive design skills or budgets, making it ideal for startups and small businesses.

  • A key insight from the discussion is that the real bottleneck in launching products is often the assumption that high production quality requires large budgets. The use of tools like Remotion and Claude Code demonstrates that with minimal resources, founders can produce effective marketing assets. This approach allows for rapid iteration and deployment, as videos can be version controlled like code, eliminating the need for extensive back-and-forth with designers.

  • The Remotion and Claude Code stack is praised for enabling videos to be treated as React components, allowing for version control and easy updates. This workflow is revolutionary as it allows for quick changes and re-rendering of marketing videos, similar to software development processes, which is a novel concept for many in the industry.

• 4 months of Claude Code and honestly the hardest part isn’t coding (Activity: 1448): The post discusses the challenges of using Claude Code to build a full iOS app, highlighting that while the AI can handle coding tasks effectively, design decisions and debugging with real users are more challenging. The app, consisting of 220k lines of code, faced issues like missing transactions when tested by external users, despite extensive internal testing. The author emphasizes the importance of security, using Plaid for bank connectivity and conducting a Snyk security audit to address vulnerabilities, ensuring that sensitive data is securely managed with Firestore rules and Cloud Functions. Commenters highlight the importance of security when handling sensitive information like bank details, suggesting that relying on AI for coding can lead to unexpected user behavior and security risks. There is also a noted difficulty in using Claude Code for CSS tasks, where the AI struggles with precise design requirements.

  • ReddLemon highlights a critical security concern when using AI-generated code for handling sensitive information like bank details. They caution that a ‘vibe coded product’ could lead to security breaches and potential legal issues, emphasizing the unpredictable nature of user interactions with software.

  • CyberMage256 points out a specific issue with Claude Code’s handling of CSS, where the AI fails to accurately interpret and execute instructions for making buttons identical in height. This suggests limitations in Claude’s ability to understand and implement precise design requirements.

  • KILLJEFFREY mentions a ‘220k monolith,’ likely referring to a large codebase or project size, which can be challenging to manage and maintain, especially when using AI-generated code. This highlights the complexity and potential difficulties in scaling AI-assisted development.

• Two Claude Code features I slept on that completely changed how I use it: Stop Hooks + Memory files (Activity: 690): The Reddit post discusses two underutilized features of Claude Code: Stop Hooks and Memory Files. Stop Hooks allow users to automate follow-up actions after Claude completes a task, such as running a linter after code generation or auditing plans for edge cases. This feature streamlines workflows by reducing manual intervention. Memory Files address the issue of context loss in long or complex sessions by providing Claude with a persistent reference file at the start of each session, containing project structure, conventions, and decisions. This transforms Claude from a simple autocomplete tool into a more reliable collaborator. The post suggests these features are particularly beneficial for complex, multi-step tasks. Commenters highlight the utility of these features, with one suggesting the use of a /btw command for side questions and another recommending a structured approach to memory files, using daily logs and a curated summary to prevent bloat. There’s also interest in chaining stop hooks for complex workflows, such as automating a sequence of tasks after editing a file.

  • The use of memory files in Claude Code is highlighted as a significant enhancement, with users like ‘asklee-klawde’ implementing a dual-file system: daily logs for raw notes and a curated MEMORY.md for long-term context. This approach prevents memory bloat while maintaining accessibility to important information, optimizing the AI’s ability to recall and apply project-specific patterns and decisions.

  • Stop hooks are praised for their utility in automating repetitive tasks, such as auto-formatting and running sanity checks post-edit. Users are exploring more complex workflows by chaining stop hooks, like automating a sequence of actions after editing a file, which could include running tests and committing changes if successful. This reduces manual intervention and streamlines the development process.

  • The concept of ‘Fork + Rewind’ is discussed as a method to manage multiple changes efficiently. This approach allows users to maintain the main context while handling unrelated changes separately, thus optimizing the use of context windows and improving workflow management. It is particularly useful for implementing multiple changes post-plan execution without cluttering the main context.

• Anthropic just released free official courses on MCP, Claude Code, and their API (Anthropic Academy). (Activity: 296): Anthropic has launched the “Anthropic Academy,” offering free, comprehensive courses for developers working with Claude. The academy includes a 13-hour course on the Claude API, 10 hours on the Model Context Protocol (MCP), 3 hours on integrating Claude Code, and 4 hours on agent skills. These courses aim to transition users from basic prompting to advanced AI integration, providing official completion certificates. The academy is accessible here, and a detailed course breakdown is available on Mindwired AI. Commenters highlight the value of the courses in moving from basic prompting to engineering, emphasizing the importance of MCP and agent skills for real-world AI integration. They suggest pairing course knowledge with platforms like Runable or Replit Agent for practical application, and recommend using infrastructure tools like Hasura and Kong for effective backend integration.

  • The release of structured courses like Claude 101 and Developer Deep-Dives on Anthropic Academy is significant for those transitioning from casual prompting to engineering. These courses emphasize the Model Context Protocol (MCP) and agent skills, indicating a shift towards integrating AI into real development workflows. This knowledge can be paired with execution platforms like Runable, Replit Agent, or V0 to build functional prototypes, enhancing the practical application of the course material.

  • The advanced MCP content is valuable for designing tool layers that are maintainable over time. The courses encourage modeling tools as small, composable verbs, keeping state and policy external to the agent. A practical approach is to integrate a real backend, such as Postgres, as an MCP server during the lessons, incorporating logging, rate limits, and human approval flows to understand potential challenges early. This approach aligns well with infrastructure patterns using Hasura for typed GraphQL, Kong or Tyk for gateway policies, and DreamFactory for exposing legacy SQL as REST endpoints.

• Maybe is deepseek 4? (Activity: 325): The image depicts a mobile interface of the OpenRouter app, showcasing a list of AI models, including “Hunter Alpha” and “Healer Alpha.” “Hunter Alpha” is described as a frontier intelligence model for agentic use, while “Healer Alpha” is noted for its omni-modal capabilities, including vision and reasoning. This suggests a focus on advanced AI functionalities, potentially indicating a new development or release in AI model capabilities. The presence of a volume control overlay suggests the app may have audio features or integrations. One commenter mentions testing the models in opencode and praises their performance, indicating they are significantly better than previous models. Another commenter speculates on the origin of the models, suggesting they might not be Chinese due to their ability to handle uncensored queries, and speculates they could be Grok, Mistral, or other models, but not Gemini or GPT.

  • Shoddy-Department630 speculates that the model in question is unlikely to be DeepSeek due to its ability to handle queries that would typically be censored by Chinese AI models. They suggest it might be a Grok Model or Mistral, noting that it doesn’t resemble Gemini or GPT models either, indicating a distinct operational behavior or architecture.

  • Traveler3141 raises the possibility of the model being Qwen 4, suggesting that it could be a new iteration or version of an existing model. This implies a consideration of recent advancements or releases in AI models that might align with the observed performance and characteristics.

  • peva3 shares a positive experience with the model in an open code environment, describing it as significantly superior to other models they have used. This suggests notable improvements in performance or capabilities, although specific metrics or comparisons are not provided.

• Deepseek V4 Confirmed? (Activity: 316): The image appears to be a screenshot from a messaging app where a user queries about Taiwan’s status as a country, and the response aligns with China’s One-China Principle. The title “Deepseek V4 Confirmed?” suggests speculation about a new version of a software or AI model, possibly related to the response style or content. However, comments indicate skepticism about whether this is indeed related to DeepSeek V4, with one user suggesting it might be a Chinese model but not necessarily DeepSeek. One comment suggests that the response might be from a Chinese model rather than DeepSeek, indicating a debate about the origin or nature of the AI providing the response.

• Finally it’s near (Activity: 249): The image appears to be a non-technical meme or teaser related to a machine learning model named “DeepSeek-V4-INT8.” The file paths listed in the image suggest a focus on a specific version or iteration of this model, possibly indicating an upcoming release or significant development milestone. The title “Finally it’s near” implies anticipation or culmination of a project, but without further context or technical details, it remains speculative. The comments reflect skepticism and anticipation, with one user humorously suggesting it’s ‘betting market bait’ and another asking for a source, indicating a lack of concrete information or confirmation about the release or development status of the model.

• People are getting OpenClaw installed for free in China. Thousands are queuing for OpenClaw setup. (Activity: 134): Tencent is facilitating free installations of OpenClaw in Shenzhen, China, leveraging its Tencent Cloud’s Lighthouse platform, which still generates revenue from cloud usage. The event targets white-collar professionals eager to adopt AI technologies amidst intense workplace competition and fear of being replaced by AI. The installation event, framed as a charity, highlights the cultural eagerness in China to embrace new technologies, driven by anxiety and status pressure rather than a specific application. Commenters express concerns about the security risks of OpenClaw, noting that users are entrusting significant access to the software without understanding it. There is also a mention of the cost associated with removing OpenClaw, which is approximately 500 RMB.

  • The discussion highlights a significant cultural and economic perspective on why OpenClaw has gained popularity in China compared to Manus. OpenClaw is likened to ‘buying a house’ because it is open-source and allows users to own the infrastructure by installing it on their hardware, only incurring costs for API calls. In contrast, Manus is described as a ‘luxury rental’ due to its closed-box SaaS model, where users pay monthly fees and are charged in credits, making it less appealing to those who prefer ownership over renting.

  • Concerns are raised about the security implications of OpenClaw, with users expressing skepticism about the trustworthiness of installing such software, especially when done by third parties. The open-source nature of OpenClaw is acknowledged, but there is apprehension about users not verifying the installation themselves, potentially leading to security vulnerabilities.

  • The cost of removing OpenClaw from a machine is mentioned, with a specific figure of around 500 RMB. This suggests that while the initial installation might be free, there are hidden costs associated with its removal, indicating a potential barrier for users who might later decide against using the software.

• People are getting OpenClaw installed for free in China. As Chinese tech giants like Alibaba push AI adoption, thousands are queuing. (Activity: 125): Tencent is facilitating free installations of OpenClaw in Shenzhen, China, as part of a charity event, although the installations still utilize Tencent Cloud’s Lighthouse, ensuring Tencent benefits from cloud usage. This initiative is part of a broader push by Chinese tech giants like Alibaba to promote AI adoption. The event targets white-collar professionals who are eager to adopt AI technologies due to workplace pressures and the fear of being replaced by AI. OpenClaw is an open-source AI agent that users can install on their own hardware, contrasting with closed-box SaaS models like Manus, which require ongoing payments and offer less ownership. This reflects a cultural preference in China for owning digital assets, akin to owning real estate, rather than renting them. Commenters highlight the cultural preference in China for owning digital assets, likening OpenClaw to owning real estate, while Manus is seen as a rental model. This preference is driven by the desire for control and equity in digital infrastructure, contrasting with the SaaS model’s ongoing costs and lack of ownership.

  • The comparison between Manus and OpenClaw highlights a significant cultural and technical shift in AI adoption in China. Manus operates as a closed-box SaaS model, requiring users to pay monthly fees and use credits, which can be restrictive. In contrast, OpenClaw is open-source, allowing users to install it on their own hardware, effectively giving them ownership of the AI infrastructure. This aligns with the Chinese preference for owning digital assets, akin to owning real estate, rather than renting, which is seen as a more sustainable and cost-effective approach, especially with domestic models like DeepSeek and Qwen reducing operational costs.

  • The analogy of OpenClaw as ‘raising a lobster’ (养小龙虾) in the Chinese community underscores the cultural value placed on owning and customizing technology. This metaphor suggests a nurturing relationship with technology, where users invest in and grow their AI capabilities over time, contrasting with the transient nature of SaaS models like Manus. This ownership model is further supported by the competitive pricing of domestic AI models, making local operation of OpenClaw economically attractive.

  • The discussion touches on the broader implications of AI adoption, where OpenClaw’s model allows users to become ‘digital landlords’ by building custom workflows on local infrastructure. This approach not only reduces dependency on external SaaS providers but also empowers users to create lasting digital equity. The ongoing price wars among domestic AI models further incentivize this shift, as running OpenClaw locally becomes increasingly cost-effective, challenging the traditional SaaS model’s dominance.

Unfortunately, Discord shut down our access today. We will not bring it back in this form but we will be shipping the new AINews soon. Thanks for reading to here, it was a good run.

Turbopuffer came out of a reading app.

In 2022, Simon was helping his friends at Readwise scale their infra for a highly requested feature: article recommendations and semantic search. Readwise was paying ~$5k/month for their relational database and vector search would cost ~$20k/month making the feature too expensive to ship. In 2023 after mulling over the problem from Readwise, Simon decided he wanted to “build a search engine” which became Turbopuffer.

We discuss:
• Simon’s path: Denmark → Shopify infra for nearly a decade → “angel engineering” across startups like Readwise, Replicate, and Causal → turbopuffer almost accidentally becoming a company
• The Readwise origin story: building an early recommendation engine right after the ChatGPT moment, seeing it work, then realizing it would cost ~$30k/month for a company spending ~$5k/month total on infra and getting obsessed with fixing that cost structure
• Why turbopuffer is “a search engine for unstructured data”: Simon’s belief that models can learn to reason, but can’t compress the world’s knowledge into a few terabytes of weights, so they need to connect to systems that hold truth in full fidelity
• The three ingredients for building a great database company: a new workload, a new storage architecture, and the ability to eventually support every query plan customers will want on their data
• The architecture bet behind turbopuffer: going all in on object storage and NVMe, avoiding a traditional consensus layer, and building around the cloud primitives that only became possible in the last few years

• Why Simon hated operating Elasticsearch at Shopify: years of painful on-call experience shaped his obsession with simplicity, performance, and eliminating state spread across multiple systems
• The Cursor story: launching turbopuffer as a scrappy side project, getting an email from Cursor the next day, flying out after a 4am call, and helping cut Cursor’s costs by 95% while fixing their per-user economics

• The Notion story: buying dark fiber, tuning TCP windows, and eating cross-cloud costs because Simon refused to compromise on architecture just to close a deal faster

• Why AI changes the build-vs-buy equation: it’s less about whether a company can build search infra internally, and more about whether they have time especially if an external team can feel like an extension of their own
• Why RAG isn’t dead: coding companies still rely heavily on search, and Simon sees hybrid retrieval semantic, text, regex, SQL-style patterns becoming more important, not less
• How agentic workloads are changing search: the old pattern was one retrieval call up front; the new pattern is one agent firing many parallel queries at once, turning search into a highly concurrent tool call
• Why turbopuffer is reducing query pricing: agentic systems are dramatically increasing query volume, and Simon expects retrieval infra to adapt to huge bursts of concurrent search rather than a small number of carefully chosen calls
• The philosophy of “playing with open cards”: Simon’s habit of being radically honest with investors, including telling Lachy Groom he’d return the money if turbopuffer didn’t hit PMF by year-end
• The “P99 engineer”: Simon’s framework for building a talent-dense company, rejecting by default unless someone on the team feels strongly enough to fight for the candidate

—

Simon Hørup Eskildsen
• LinkedIn: https://www.linkedin.com/in/sirupsen
• X: https://x.com/Sirupsen
• https://sirupsen.com/about

turbopuffer
• https://turbopuffer.com/

00:00:00 The PMF promise to Lachy Groom
00:00:25 Intro and Simon's background
00:02:19 What turbopuffer actually is
00:06:26 Shopify, Elasticsearch, and the pain behind the company
00:10:07 The Readwise experiment that sparked turbopuffer
00:12:00 The insight Simon couldn’t stop thinking about
00:17:00 S3 consistency, NVMe, and the architecture bet
00:20:12 The Notion story: latency, dark fiber, and conviction
00:25:03 Build vs. buy in the age of AI
00:26:00 The Cursor story: early launch to breakout customer
00:29:00 Why code search still matters
00:32:00 Search in the age of agents
00:34:22 Pricing turbopuffer in the AI era
00:38:17 Why Simon chose Lachy Groom
00:41:28 Becoming a founder on purpose
00:44:00 The “P99 engineer” philosophy
00:49:30 Bending software to your will
00:51:13 The future of turbopuffer
00:57:05 Simon’s tea obsession
00:59:03 Tea kits, X Live, and P99 Live

Simon Hørup Eskildsen: I don’t think I’ve said this publicly before, but I just called Lockey and was like, local Lockie. Like if this doesn’t have PMF by the end of the year, like we’ll just like return all the money to you. But it’s just like, I don’t really, we, Justine and I don’t wanna work on this unless it’s really working.

So we want to give it the best shot this year and like we’re really gonna go for it. We’re gonna hire a bunch of people. We’re just gonna be honest with everyone. Like when I don’t know how to play a game, I just play with open cards. Lockey was the only person that didn’t, that didn’t freak out. He was like, I’ve never heard anyone say that before.

Alessio: Hey everyone, welcome to the Leading Space podcast. This is Celesio Pando, Colonel Laz, and I’m joined by Swix, editor of Leading Space.

swyx: Hello. Hello, uh, we’re still, uh, recording in the Ker studio for the first time. Very excited. And today we are joined by Simon Eski. Of Turbo Farer welcome.

Simon Hørup Eskildsen: Thank you so much for having me.

swyx: Turbo Farer has like really gone on a huge tear, and I, I do have to mention that like you’re one of, you’re not my newest member of the Danish AHU Mafia, where like there’s a lot of legendary programmers that have come out of it, like, uh, beyond Trotro, Rasmus, lado Berg and the V eight team and, and Google Maps team.

Uh, you’re mostly a Canadian now, but isn’t that interesting? There’s so many, so much like strong Danish presence.

Simon Hørup Eskildsen: Yeah, I was writing a post, um, not that long ago about sort of the influences. So I grew up in Denmark, right? I left, I left when, when I was 18 to go to Canada to, to work at Shopify. Um, and so I, like, I’ve, I would still say that I feel more Danish than, than Canadian.

This is also the weird accent. I can’t say th because it, this is like, I don’t, you know, my wife is also Canadian, um, and I think. I think like one of the things in, in Denmark is just like, there’s just such a ruthless pragmatism and there’s also a big focus on just aesthetics. Like, they’re like very, people really care about like where, what things look like.

Um, and like Canada has a lot of attributes, US has, has a lot of attributes, but I think there’s been lots of the great things to carry. I don’t know what’s in the water in Ahu though. Um, and I don’t know that I could be considered part of the Mafi mafia quite yet, uh, compared to the phenomenal individuals we just mentioned.

Barra OV is also, uh, Danish Canadian. Okay. Yeah. I don’t know where he lives now, but, and he’s the PHP.

swyx: Yeah. And obviously Toby German, but moved to Canada as well. Yes. Like this is like import that, uh, that, that is an interesting, um, talent move.

Alessio: I think. I would love to get from you. Definition of Turbo puffer, because I think you could be a Vector db, which is maybe a bad word now in some circles, you could be a search engine.

It’s like, let, let’s just start there and then we’ll maybe run through the history of how you got to this point.

Simon Hørup Eskildsen: For sure. Yeah. So Turbo Puffer is at this point in time, a search engine, right? We do full text search and we do vector search, and that’s really what we’re specialized in. If you’re trying to do much more than that, like then this might not be the right place yet, but Turbo Buffer is all about search.

The other way that I think about it is that we can take all of the world’s knowledge, all of the exabytes and exabytes of data that there is, and we can use those tokens to train a model, but we can’t compress all of that into a few terabytes of weights, right? Compress into a few terabytes of weights, how to reason with the world, how to make sense of the knowledge.

But we have to somehow connect it to something externally that actually holds that like in full fidelity and truth. Um, and that’s the thing that we intend to become. Right? That’s like a very holier than now kind of phrasing, right? But being the search engine for unstructured, unstructured data is the focus of turbo puffer at this point in time.

Alessio: And let’s break down. So people might say, well, didn’t Elasticsearch already do this? And then some other people might say, is this search on my data, is this like closer to rag than to like a xr, like a public search thing? Like how, how do you segment like the different types of search?

Simon Hørup Eskildsen: The way that I generally think about this is like, there’s a lot of database companies and I think if you wanna build a really big database company, sort of, you need a couple of ingredients to be in the air.

We don’t, which only happens roughly every 15 years. You need a new workload. You basically need the ambition that every single company on earth is gonna have data in your database. Multiple times you look at a company like Oracle, right? You will, like, I don’t think you can find a company on earth with a digital presence that it not, doesn’t somehow have some data in an Oracle database.

Right? And I think at this point, that’s also true for Snowflake and Databricks, right? 15 years later it’s, or even more than that, there’s not a company on earth that doesn’t, in. Or directly is consuming Snowflake or, or Databricks or any of the big analytics databases. Um, and I think we’re in that kind of moment now, right?

I don’t think you’re gonna find a company over the next few years that doesn’t directly or indirectly, um, have all their data available for, for search and connect it to ai. So you need that new workload, like you need something to be happening where there’s a new workload that causes that to happen, and that new workload is connecting very large amounts of data to ai.

The second thing you need. The second condition to build a big database company is that you need some new underlying change in the storage architecture that is not possible from the databases that have come before you. If you look at Snowflake and Databricks, right, commoditized, like massive fleet of HDDs, like that was not possible in it.

It just wasn’t in the air in the nineties, right? So you just didn’t, we just didn’t build these systems. S3 and and and so on was not around. And I think the architecture that is now possible that wasn’t possible 15 years ago is to go all in on NVME SSDs. It requires a particular type of architecture for the database that.

It’s difficult to retrofit onto the databases that are already there, including the ones you just mentioned. The second thing is to go all in on OIC storage, more so than we could have done 15 years ago. Like we don’t have a consensus layer, we don’t really have anything. In fact, you could turn off all the servers that Turbo Buffer has, and we would not lose any data because we have all completely all in on OIC storage.

And this means that our architecture is just so simple. So that’s the second condition, right? First being a new workload. That means that every company on earth, either indirectly or directly, is using your database. Second being, there’s some new storage architecture. That means that the, the companies that have come before you can do what you’re doing.

I think the third thing you need to do to build a big database company is that over time you have to implement more or less every Cory plan on the data. What that means is that you. You can’t just get stuck in, like, this is the one thing that a database does. It has to be ever evolving because when someone has data in the database, they over time expect to be able to ask it more or less every question.

So you have to do that to get the storage architecture to the limit of what, what it’s capable of. Those are the three conditions.

swyx: I just wanted to get a little bit of like the motivation, right? Like, so you left Shopify, you’re like principal, engineer, infra guy. Um, you also head of kernel labs, uh, inside of Shopify, right?

And then you consulted for read wise and that it kind of gave you that, that idea. I just wanted you to tell that story. Um, maybe I, you’ve told it before, but, uh, just introduce the, the. People to like the, the new workload, the sort of aha moment for turbo Puffer

Simon Hørup Eskildsen: For sure. So yeah, I spent almost a decade at Shopify.

I was on the infrastructure team, um, from the fairly, fairly early days around 2013. Um, at the time it felt like it was growing so quickly and everything, all the metrics were, you know, doubling year on year compared to the, what companies are contending with today. It’s very cute in growth. I feel like lot some companies are seeing that month over month.

Um, of course. Shopify compound has been compounding for a very long time now, but I spent a decade doing that and the majority of that was just make sure the site is up today and make sure it’s up a year from now. And a lot of that was really just the, um, you know, uh, the Kardashians would drive very, very large amounts of, of data to, to uh, to Shopify as they were rotating through all the merch and building out their businesses.

And we just needed to make sure we could handle that. Right. And sometimes these were events, a million requests per second. And so, you know, we, we had our own data centers back in the day and we were moving to the cloud and there was so much sharding work and all of that that we were doing. So I spent a decade just scaling databases ‘cause that’s fundamentally what’s the most difficult thing to scale about these sites.

The database that was the most difficult for me to scale during that time, and that was the most aggravating to be on call for, was elastic search. It was very, very difficult to deal with. And I saw a lot of projects that were just being held back in their ambition by using it.

swyx: And I mean, self-hosted.

Self-hosted. ‘cause

Simon Hørup Eskildsen: it’s, yeah, and it commercial, this is like 2015, right? So it’s like a very particular vintage. Right. It’s probably better at a lot of these things now. Um, it was difficult to contend with and I’m just like, I just think about it. It’s an inverted index. It should be good at these kinds of queries and do all of this.

And it was, we, we often couldn’t get it to do exactly what we needed to do or basically get lucine to do, like expose lucine raw to, to, to what we needed to do. Um, so that was like. Just something that we did on the side and just panic scaled when we needed to, but not a particular focus of mine. So I left, and when I left, I, um, wasn’t sure exactly what I wanted to do.

I mean, it spent like a decade inside of the same company. I’d like grown up there. I started working there when I was 18.

swyx: You only do Rails?

Simon Hørup Eskildsen: Yeah. I mean, yeah. Rails. And he’s a Rails guy. Uh, love Rails. So good. Um,

Alessio: we all wish we could still work in Rails.

swyx: I know know. I know, but some, I tried learning Ruby.

It’s just too much, like too many options to do the same thing. It’s, that’s my, I I know there’s a, there’s a way to do it.

Simon Hørup Eskildsen: I love it. I don’t know that I would use it now, like given cloud code and, and, and cursor and everything, but, um, um, but still it, like if I’m just sitting down and writing a teal code, that’s how I think.

But anyway, I left and I wasn’t, I talked to a couple companies and I was like, I don’t. I need to see a little bit more of the world here to know what I’m gonna like focus on next. Um, and so what I decided is like I was gonna, I called it like angel engineering, where I just hopped around in my friend’s companies in three months increments and just helped them out with something.

Right. And, and just vested a bit of equity and solved some interesting infrastructure problem. So I worked with a bunch of companies at the time, um, read Wise was one of them. Replicate was one of them. Um, causal, I dunno if you’ve tried this, it’s like a, it’s a spreadsheet engine Yeah. Where you can do distribution.

They sold recently. Yeah. Um, we’ve been, we used that in fp and a at, um, at Turbo Puffer. Um, so a bunch of companies like this and it was super fun. And so we’re the Chachi bt moment happened, I was with. With read Wise for a stint, we were preparing for the reader launch, right? Which is where you, you cue articles and read them later.

And I was just getting their Postgres up to snuff, like, which basically boils down to tuning, auto vacuum. So I was doing that and then this happened and we were like, oh, maybe we should build a little recommendation engine and some features to try to hook in the lms. They were not that good yet, but it was clear there was something there.

And so I built a small recommendation engine just, okay, let’s take the articles that you’ve recently read, right? Like embed all the articles and then do recommendations. It was good enough that when I ran it on one of the co-founders of Rey’s, like I found out that I got articles about, about having a child.

I’m like, oh my God, I didn’t, I, I didn’t know that, that they were having a child. I wasn’t sure what to do with that information, but the recommendation engine was good enough that it was suggesting articles, um, about that. And so there was, there was recommendations and uh, it actually worked really well.

But this was a company that was spending maybe five grand a month in total on all their infrastructure and. When I did the napkin math on running the embeddings of all the articles, putting them into a vector index, putting it in prod, it’s gonna be like 30 grand a month. That just wasn’t tenable. Right?

Like Read Wise is a proudly bootstrapped company and it’s paying 30 grand for infrastructure for one feature versus five. It just wasn’t tenable. So sort of in the bucket of this is useful, it’s pretty good, but let us, let’s return to it when the costs come down.

swyx: Did you say it grows by feature? So for five to 30 is by the number of, like, what’s the, what’s the Scaling factor scale?

It scales by the number of articles that you embed.

Simon Hørup Eskildsen: It does, but what I meant by that is like five grand for like all of the other, like the Heroku, dinos, Postgres, like all the other, and this then storage is 30. Yeah. And then like 30 grand for one feature. Right. Which is like, what other articles are related to this one.

Um, so it was just too much right to, to power everything. Their budget would’ve been maybe a few thousand dollars, which still would’ve been a lot. And so we put it in a bucket of, okay, we’re gonna do that later. We’ll wait, we will wait for the cost to come down. And that haunted me. I couldn’t stop thinking about it.

I was like, okay, there’s clearly some latent demand here. If the cost had been a 10th, we would’ve shipped it and. This was really the only data point that I had. Right. I didn’t, I, I didn’t, I didn’t go out and talk to anyone else. It was just so I started reading Right. I couldn’t, I couldn’t help myself.

Like I didn’t know what like a vector index is. I, I generally barely do about how to generate the vectors. There was a lot of hype about, this is a early 2023. There was a lot of hype about vector databases. There were raising a lot of money and it’s like, I really didn’t know anything about it. It’s like, you know, trying these little models, fine tuning them.

Like I was just trying to get sort of a lay of the land. So I just sat down. I have this. A GitHub repository called Napkin Math. And on napkin math, there’s just, um, rows of like, oh, this is how much bandwidth. Like this is how many, you know, you can do 25 gigabytes per second on average to dram. You can do, you know, five gigabytes per second of rights to an SSD, blah blah.

All of these numbers, right? And S3, how many you could do per, how much bandwidth can you drive per connection? I was just sitting down, I was like, why hasn’t anyone build a database where you just put everything on O storage and then you puff it into NVME when you use the data and you puff it into dram if you’re, if you’re querying it alive, it’s just like, this seems fairly obvious and you, the only real downside to that is that if you go all in on o storage, every right will take a couple hundred milliseconds of latency, but from there it’s really all upside, right?

You do the first go, it takes half a second. And it sort of occurred to me as like, well. The architecture is really good for that. It’s really good for AB storage, it’s really good for nvm ESSD. It’s, well, you just couldn’t have done that 10 years ago. Back to what we were talking about before. You really have to build a database where you have as few round trips as possible, right?

This is how CPUs work today. It’s how NVM E SSDs work. It’s how as, um, as three works that you want to have a very large amount of outstanding requests, right? Like basically go to S3, do like that thousand requests to ask for data in one round trip. Wait for that. Get that, like, make a new decision. Do it again, and try to do that maybe a maximum of three times.

But no databases were designed that way within NVME as is ds. You can drive like within, you know, within a very low multiple of DRAM bandwidth if you use it that way. And same with S3, right? You can fully max out the network card, which generally is not maxed out. You get very, like, very, very good bandwidth.

And, but no one had built a database like that. So I was like, okay, well can’t you just, you know, take all the vectors right? And plot them in the proverbial coordinate system. Get the clusters, put a file on S3 called clusters, do json, and then put another file for every cluster, you know, cluster one, do js O cluster two, do js ON you know that like it’s two round trips, right?

So you get the clusters, you find the closest clusters, and then you download the cluster files like the, the closest end. And you could do this in two round trips.

swyx: You were nearest neighbors locally.

Simon Hørup Eskildsen: Yes. Yes. And then, and you would build this, this file, right? It’s just like ultra simplistic, but it’s not a far shot from what the first version of Turbo Buffer was.

Why hasn’t anyone done that

Alessio: in that moment? From a workload perspective, you’re thinking this is gonna be like a read heavy thing because they’re doing recommend. Like is the fact that like writes are so expensive now? Oh, with ai you’re actually not writing that much.

Simon Hørup Eskildsen: At that point I hadn’t really thought too much about, well no actually it was always clear to me that there was gonna be a lot of rights because at Shopify, the search clusters were doing, you know, I don’t know, tens or hundreds of crew QPS, right?

‘cause you just have to have a human sit and type in. But we did, you know, I don’t know how many updates there were per second. I’m sure it was in the millions, right into the cluster. So I always knew there was like a 10 to 100 ratio on the read write. In the read wise use case. It’s, um, even, even in the read wise use case, there’d probably be a lot fewer reads than writes, right?

There’s just a lot of churn on the amount of stuff that was going through versus the amount of queries. Um, I wasn’t thinking too much about that. I was mostly just thinking about what’s the fundamentally cheapest way to build a database in the cloud today using the primitives that you have available.

And this is it, right? You just, now you have one machine and you know, let’s say you have a terabyte of data in S3, you paid the $200 a month for that, and then maybe five to 10% of that data and needs to be an NV ME SSDs and less than that in dram. Well. You’re paying very, very little to inflate the data.

swyx: By the way, when you say no one else has done that, uh, would you consider Neon, uh, to be on a similar path in terms of being sort of S3 first and, uh, separating the compute and storage?

Simon Hørup Eskildsen: Yeah, I think what I meant with that is, uh, just build a completely new database. I don’t know if we were the first, like it was very much, it was, I mean, I, I hadn’t, I just looked at the napkin math and was like, this seems really obvious.

So I’m sure like a hundred people came up with it at the same time. Like the light bulb and every invention ever. Right. It was just in the air. I think Neon Neon was, was first to it. And they’re trying, they’re retrofitted onto Postgres, right? And then they built this whole architecture where you have, you have it in memory and then you sort of.

You know, m map back to S3. And I think that was very novel at the time to do it for, for all LTP, but I hadn’t seen a database that was truly all in, right. Not retrofitting it. The database felt built purely for this no consensus layer. Even using compare and swap on optic storage to do consensus. I hadn’t seen anyone go that all in.

And I, I mean, there, there, I’m sure there was someone that did that before us. I don’t know. I was just looking at the napkin math

swyx: and, and when you say consensus layer, uh, are you strongly relying on S3 Strong consistency? You are. Okay.

So

Simon Hørup Eskildsen: that is your consensus layer. It, it is the consistency layer. And I think also, like, this is something that most people don’t realize, but S3 only became consistent in December of 2020.

swyx: I remember this coming out during COVID and like people were like, oh, like, it was like, uh, it was just like a free upgrade.

Simon Hørup Eskildsen: Yeah.

swyx: They were just, they just announced it. We saw consistency guys and like, okay, cool.

Simon Hørup Eskildsen: And I’m sure that they just, they probably had it in prod for a while and they’re just like, it’s done right.

And people were like, okay, cool. But. That’s a big moment, right? Like nv, ME SSDs, were also not in the cloud until around 2017, right? So you just sort of had like 2017 nv, ME SSDs, and people were like, okay, cool. There’s like one skew that does this, whatever, right? Takes a few years. And then the second thing is like S3 becomes consistent in 2020.

So now it means you don’t have to have this like big foundation DB or like zookeeper or whatever sitting there contending with the keys, which is how. You know, that’s what Snowflake and others have do so much

swyx: for gone

Simon Hørup Eskildsen: Exactly. Just gone. Right? And so just push to the, you know, whatever, how many hundreds of people they have working on S3 solved and then compare and swap was not in S3 at this point in time,

swyx: by the way.

Uh, I don’t know what that is, so maybe you wanna explain. Yes. Yeah.

Simon Hørup Eskildsen: Yes. So, um, what Compare and swap is, is basically, you can imagine that if you have a database, it might be really nice to have a file called metadata json. And metadata JSON could say things like, Hey, these keys are here and this file means that, and there’s lots of metadata that you have to operate in the database, right?

But that’s the simplest way to do it. So now you have might, you might have a lot of servers that wanna change the metadata. They might have written a file and want the metadata to contain that file. But you have a hundred nodes that are trying to contend with this metadata that JSON well, what compare and Swap allows you to do is basically just you download the file, you make the modifications, and then you write it only if it hasn’t changed.

While you did the modification and if not you retry. Right? Should just have this retry loops. Now you can imagine if you have a hundred nodes doing that, it’s gonna be really slow, but it will converge over time. That primitive was not available in S3. It wasn’t available in S3 until late 2024, but it was available in GCP.

The real story of this is certainly not that I sat down and like bake brained it. I was like, okay, we’re gonna start on GCS S3 is gonna get it later. Like it was really not that we started, we got really lucky, like we started on GCP and we started on GCP because tur um, Shopify ran on GCP. And so that was the platform I was most available with.

Right. Um, and I knew the Canadian team there ‘cause I’d worked with them at Shopify and so it was natural for us to start there. And so when we started building the database, we’re like, oh yeah, we have to build a, we really thought we had to build a consensus layer, like have a zookeeper or something to do this.

But then we discovered the compare and swap. It’s like, oh, we can kick the can. Like we’ll just do metadata r json and just, it’s fine. It’s probably fine. Um, and we just kept kicking the can until we had very, very strong conviction in the idea. Um, and then we kind of just hinged the company on the fact that S3 probably was gonna get this, it started getting really painful in like mid 2024.

‘cause we were closing deals with, um, um, notion actually that was running in AWS and we’re like, trust us. You, you really want us to run this in GCP? And they’re like, no, I don’t know about that. Like, we’re running everything in AWS and the latency across the cloud were so big and we had so much conviction that we bought like, you know, dark fiber between the AWS regions in, in Oregon, like in the InterExchange and GCP is like, we’ve never seen a startup like do like, what’s going on here?

And we’re just like, no, we don’t wanna do this. We were tuning like TCP windows, like everything to get the latency down ‘cause we had so high conviction in not doing like a, a metadata layer on S3. So those were the three conditions, right? Compare and swap. To do metadata, which wasn’t in S3 until late 2024 S3 being consistent, which didn’t happen until December, 2020.

Uh, 2020. And then NVMe ssd, which didn’t end in the cloud until 2017.

swyx: I mean, in some ways, like a very big like cloud success story that like you were able to like, uh, put this all together, but also doing things like doing, uh, bind our favor. That that actually is something I’ve never heard.

Simon Hørup Eskildsen: I mean, it’s very common when you’re a big company, right?

You’re like connecting your own like data center or whatever. But it’s like, it was uniquely just a pain with notion because the, um, the org, like most of the, like if you’re buying in Ashburn, Virginia, right? Like US East, the Google, like the GCP and, and AWS data centers are like within a millisecond on, on each other, on the public exchanges.

But in Oregon uniquely, the GCP data center sits like a couple hundred kilometers, like east of Portland and the AWS region sits in Portland, but the network exchange they go through is through Seattle. So it’s like a full, like 14 milliseconds or something like that. And so anyway, yeah. It’s, it’s, so we were like, okay, we can’t, we have to go through an exchange in Portland.

Yeah. And

swyx: you’d rather do this than like run your zookeeper and like

Simon Hørup Eskildsen: Yes. Way rather. It doesn’t have state, I don’t want state and two systems. Um, and I think all that is just informed by Justine, my co-founder and I had just been on call for so long. And the worst outages are the ones where you have state in multiple places that’s not syncing up.

So it really came from, from a a, like just a, a very pure source of pain, of just imagining what we would be Okay. Being woken up at 3:00 AM about and having something in zookeeper was not one of them.

swyx: You, you’re talking to like a notion or something. Do they care or do they just, they

Simon Hørup Eskildsen: just, they care about latency.

swyx: They latency cost. That’s it.

Simon Hørup Eskildsen: They just cared about latency. Right. And we just absorbed the cost. We’re just like, we have high conviction in this. At some point we can move them to AWS. Right. And so we just, we, we’ll buy the fiber, it doesn’t matter. Right. Um, and it’s like $5,000. Usually when you buy fiber, you buy like multiple lines.

And we’re like, we can only afford one, but we will just test it that when it goes over the public internet, it’s like super smooth. And so we did a lot of, anyway, it’s, yeah, it was, that’s cool.

Alessio: You can imagine talking to the GCP rep and it’s like, no, we’re gonna buy, because we know we’re gonna turn, we’re gonna turn from you guys and go to AWS in like six months.

But in the meantime we’ll do this. It’s

Simon Hørup Eskildsen: a, I mean, like they, you know, this workload still runs on GCP for what it’s worth. Right? ‘cause it’s so, it was just, it was so reliable. So it was never about moving off GCP, it was just about honesty. It was just about giving notion the latency that they deserved.

Right. Um, and we didn’t want ‘em to have to care about any of this. We also, they were like, oh, egress is gonna be bad. It was like, okay, screw it. Like we’re just gonna like vvc, VPC peer with you and AWS we’ll eat the cost. Yeah. Whatever needs to be done.

Alessio: And what were the actual workloads? Because I think when you think about ai, it’s like 14 milliseconds.

It’s like really doesn’t really matter in the scheme of like a model generation.

Simon Hørup Eskildsen: Yeah. We were told the latency, right. That we had to beat. Oh, right. So, so we’re just looking at the traces. Right. And then sort of like hand draw, like, you know, kind of like looking at the trace and then thinking what are the other extensions of the trace?

Right. And there’s a lot more to it because it’s also when you have, if you have 14 versus seven milliseconds, right. You can fit in another round trip. So we had to tune TCP to try to send as much data in every round trip, prewarm all the connections. And there was, there’s a lot of things that compound from having these kinds of round trips, but in the grand scheme it was just like, well, we have to beat the latency of whatever we’re up against.

swyx: Which is like they, I mean, notion is a database company. They could have done this themselves. They, they do lots of database engineering themselves. How do you even get in the door? Like Yeah, just like talk through that kind of.

Simon Hørup Eskildsen: Last time I was in San Francisco, I was talking to one of the engineers actually, who, who was one of our champions, um, at, AT Notion.

And they were, they were just trying to make sure that the, you know, per user cost matched the economics that they needed. You know, Uhhuh like, it’s like the way I think about, it’s like I have to earn a return on whatever the clouds charge me and then my customers have to earn a return on that. And it’s like very simple, right?

And so there has to be gross margin all the way up and that’s how you build the product. And so then our customers have to make the right set of trade off the turbo Puffer makes, and if they’re happy with that, that’s great.

swyx: Do you feel like you’re competing with build internally versus buy or buy versus buy?

Simon Hørup Eskildsen: Yeah, so, sorry, this was all to build up to your question. So one of the notion engineers told me that they’d sat and probably on a napkin, like drawn out like, why hasn’t anyone built this? And then they saw terrible. It was like, well, it literally that. So, and I think AI has also changed the buy versus build equation in terms of, it’s not really about can we build it, it’s about do we have time to build it?

I think they like, I think they felt like, okay, if this is a team that can do that and they, they feel enough like an extension of our team, well then we can go a lot faster, which would be very, very good for them. And I mean, they put us through the, through the test, right? Like we had some very, very long nights to to, to do that POC.

And they were really our biggest, our second big customer off the cursor, which also was a lot of late nights. Right.

swyx: Yeah. That, I mean, should we go into that story? The, the, the sort of Chris’s story, like a lot, um, they credit you a lot for. Working very closely with them. So I just wanna hear, I’ve heard this, uh, story from Sole’s point of view, but like, I’m curious what, what it looks like from your side.

Simon Hørup Eskildsen: I actually haven’t heard it from Sole’s point of view, so maybe you can now cross reference it. The way that I remember it was that, um, the day after we launched, which was just, you know, I’d worked the whole summer on, on the first version. Justine wasn’t part of it yet. ‘cause I just, I didn’t tell anyone that summer that I was working on this.

I was just locked in on building it because it’s very easy otherwise to confuse talking about something to actually doing it. And so I was just like, I’m not gonna do that. I’m just gonna do the thing. I launched it and at this point turbo puffer is like a rust binary running on a single eight core machine in a T Marks instance.

And me deploying it was like looking at the request log and then like command seeing it or like control seeing it to just like, okay, there’s no request. Let’s upgrade the binary. Like it was like literally the, the, the, the scrappiest thing. You could imagine it was on purpose because just like at Shopify, we did that all the time.

Like, we like move, like we ran things in tux all the time to begin with. Before something had like, at least the inkling of PMF, it was like, okay, is anyone gonna hear about this? Um, and one of the cursor co-founders Arvid reached out and he just, you know, the, the cursor team are like all I-O-I-I-M-O like, um, contenders, right?

So they just speak in bullet points and, and facts. It was like this amazing email exchange just of, this is how many QPS we have, this is what we’re paying, this is where we’re going, blah, blah, blah. And so we’re just conversing in bullet points. And I tried to get a call with them a few times, but they were, so, they were like really writing the PMF bowl here, just like late 2023.

And one time Swally emails me at like five. What was it like 4:00 AM Pacific time saying like, Hey, are you open for a call now? And I’m on the East coast and I, it was like 7:00 AM I was like, yeah, great, sure, whatever. Um, and we just started talking and something. Then I didn’t know anything about sales.

It was something that just comp compelled me. I have to go see this team. Like, there’s something here. So I, I went to San Francisco and I went to their office and the way that I remember it is that Postgres was down when I showed up at the office. Did SW tell you this? No. Okay. So Postgres was down and so it’s like they were distracting with that.

And I was trying my best to see if I could, if I could help in any way. Like I knew a little bit about databases back to tuning, auto vacuum. It was like, I think you have to tune out a vacuum. Um, and so we, we talked about that and then, um, that evening just talked about like what would it look like, what would it look like to work with us?

And I just said. Look like we’re all in, like we will just do what we’ll do whatever, whatever you tell us, right? They migrated everything over the next like week or two, and we reduced their cost by 95%, which I think like kind of fixed their per user economics. Um, and it solved a lot of other things. And we were just, Justine, this is also when I asked Justine to come on as my co-founder, she was the best engineer, um, that I ever worked with at Shopify.

She lived two blocks away and we were just, okay, we’re just gonna get this done. Um, and we did, and so we helped them migrate and we just worked like hell over the next like month or two to make sure that we were never an issue. And that was, that was the cursor story. Yeah.

swyx: And, and is code a different workload than normal text?

I, I don’t know. Is is it just text? Is it the same thing?

Simon Hørup Eskildsen: Yeah, so cursor’s workload is basically, they, um, they will embed the entire code base, right? So they, they will like chunk it up in whatever they would, they do. They have their own embedding model, um, which they’ve been public about. Um, and they find that on, on, on their evals.

It. There’s one of their evals where it’s like a 25% improvement on a very particular workload. They have a bunch of blog posts about it. Um, I think it works best on larger code basis, but they’ve trained their own embedding model to do this. Um, and so you’ll see it if you use the cursor agent, it will do searches.

And they’ve also been public around, um, how they’ve, I think they post trained their model to be very good at semantic search as well. Um, and that’s, that’s how they use it. And so it’s very good at, like, can you find me on the code that’s similar to this, or code that does this? And just in, in this queries, they also use GR to supplement it.

swyx: Yeah.

Simon Hørup Eskildsen: Um, of course

swyx: it’s been a big topic of discussion like, is rag dead because gr you know,

Simon Hørup Eskildsen: and I mean like, I just, we, we see lots of demand from the coding company to ethics

swyx: search in every part. Yes.

Simon Hørup Eskildsen: Uh, we, we, we see demand. And so, I mean, I’m. I like case studies. I don’t like, like just doing like thought pieces on this is where it’s going.

And like trying to be all macroeconomic about ai, that’s has turned out to be a giant waste of time because no one can really predict any of this. So I just collect case studies and I mean, cursor has done a great job talking about what they’re doing and I hope some of the other coding labs that use Turbo Puffer will do the same.

Um, but it does seem to make a difference for particular queries. Um, I mean we can also do text, we can also do RegX, but I should also say that cursors like security posture into Tur Puffer is exceptional, right? They have their own embedding model, which makes it very difficult to reverse engineer. They obfuscate the file paths.

They like you. It’s very difficult to learn anything about a code base by looking at it. And the other thing they do too is that for their customers, they encrypt it with their encryption keys in turbo puffer’s bucket. Um, so it’s, it’s, it’s really, really well designed.

swyx: And so this is like extra stuff they did to work with you because you are not part of Cursor.

Exactly like, and this is just best practice when working in any database, not just you guys. Okay. Yeah, that makes sense. Yeah. I think for me, like the, the, the learning is kind of like you, like all workloads are hybrid. Like, you know, uh, like you, you want the semantic, you want the text, you want the RegX, you want sql.

I dunno. Um, but like, it’s silly to like be all in on like one particularly query pattern.

Simon Hørup Eskildsen: I think, like I really like the way that, um, um, that swally at cursor talks about it, which is, um, I’m gonna butcher it here. Um, and you know, I’m a, I’m a database scalability person. I’m not a, I, I dunno anything about training models other than, um, what the internet tells me and what.

The way he describes is that this is just like cash compute, right? It’s like you have a point in time where you’re looking at some particular context and focused on some chunk and you say, this is the layer of the neural net at this point in time. That seems fundamentally really useful to do cash compute like that.

And, um, how the value of that will change over time. I’m, I’m not sure, but there seems to be a lot of value in that.

Alessio: Maybe talk a bit about the evolution of the workload, because even like search, like maybe two years ago it was like one search at the start of like an LLM query to build the context. Now you have a gentech search, however you wanna call it, where like the model is both writing and changing the code and it’s searching it again later.

Yeah. What are maybe some of the new types of workloads or like changes you’ve had to make to your architecture for it?

Simon Hørup Eskildsen: I think you’re right. When I think of rag, I think of, Hey, there’s an 8,000 token, uh, context window and you better make it count. Um, and search was a way to do that now. Everything is moving towards the, just let the agent do its thing.

Right? And so back to the thing before, right? The LLM is very good at reasoning with the data, and so we’re just the tool call, right? And that’s increasingly what we see our customers doing. Um, what we’re seeing more demand from, from our customers now is to do a lot of concurrency, right? Like Notion does a ridiculous amount of queries in every round trip just because they can’t.

And I’m also now, when I use the cursor agent, I also see them doing more concurrency than I’ve ever seen before. So a bit similar to how we designed a database to drive as much concurrency in every round trip as possible. That’s also what the agents are doing. So that’s new. It means just an enormous amount of queries all at once to the dataset while it’s warm in as few turns as possible.

swyx: Can I clarify one thing on that?

Simon Hørup Eskildsen: Yes.

swyx: Is it, are they batching multiple users or one user is driving multiple,

Simon Hørup Eskildsen: one user driving multiple, one agent driving.

swyx: It’s parallel searching a bunch of things.

Simon Hørup Eskildsen: Exactly.

swyx: Yeah. Yeah, exactly. So yeah, the clinician also did, did this for the fast context thing, like eight parallel at once.

Simon Hørup Eskildsen: Yes.

swyx: And, and like an interesting problem is, well, how do you make sure you have enough diversity so you’re not making the the same request eight times?

Simon Hørup Eskildsen: And I think like that’s probably also where the hybrid comes in, where. That’s another way to diversify. It’s a completely different way to, to do the search.

That’s a big change, right? So before it was really just like one call and then, you know, the LLM took however many seconds to return, but now we just see an enormous amount of queries. So the, um, we just see more queries. So we’ve like tried to reduce query, we’ve reduced query pricing. Um, this is probably the first time actually I’m saying that, but the query pricing is being reduced, like five x.

Um, and we’ll probably try to reduce it even more to accommodate some of these workloads of just doing very large amounts of queries. Um, that’s one thing that’s changed. I think the right, the right ratio is still very high, right? Like there’s still a, an enormous amount of rights per read, but we’re starting probably to see that change if people really lean into this pattern.

Alessio: Can we talk a little bit about the pricing? I’m curious, uh, because traditionally a database would charge on storage, but now you have the token generation that is so expensive, where like the actual. Value of like a good search query is like much higher because they’re like saving inference time down the line.

How do you structure that as like, what are people receptive to on the other side too?

Simon Hørup Eskildsen: Yeah. I, the, the turbo puffer pricing in the beginning was just very simple. The pricing on these on for search engines before Turbo Puffer was very server full, right? It was like, here’s the vm, here’s the per hour cost, right?

Great. And I just sat down with like a piece of paper and said like, if Turbo Puffer was like really good, this is probably what it would cost with a little bit of margin. And that was the first pricing of Turbo Puffer. And I just like sat down and I was like, okay, like this is like probably the storage amp, but whenever on a piece of paper I, it was vibe pricing.

It was very vibe price, and I got it wrong. Oh. Um, well I didn’t get it wrong, but like Turbo Puffer wasn’t at the first principle pricing, right? So when Cursor came on Turbo Puffer, it was like. Like, I didn’t know any VCs. I didn’t know, like I was just like, I don’t know, I didn’t know anything about raising money or anything like that.

I just saw that my GCP bill was, was high, was a lot higher than the cursor bill. So Justine and I was just like, well, we have to optimize it. Um, and I mean, to the chagrin now of, of it, of, of the VCs, it now means that we’re profitable because we’ve had so much pricing pressure in the beginning. Because it was running on my credit card and Justine and I had spent like, like tens of thousands of dollars on like compute bills and like spinning off the company and like very like, like bad Canadian lawyers and like things like to like get all of this done because we just like, we didn’t know.

Right. If you’re like steeped in San Francisco, you’re just like, you just know. Okay. Like you go out, raise a pre-seed round. I, I never heard a word pre-seed at this point in time.

swyx: When you had Cursor, you had Notion you, you had no funding.

Simon Hørup Eskildsen: Um, with Cursor we had no funding. Yeah. Um, by the time we had Notion Locke was, Locke was here.

Yeah. So it was really just, we vibe priced it 100% from first Principles, but it wasn’t, it, it was not performing at first principles, so we just did everything we could to optimize it in the beginning for that, so that at least we could have like a 5% margin or something. So I wasn’t freaking out because Cursor’s bill was also going like this as they were growing.

And so my liability and my credit limit was like actively like calling my bank. It was like, I need a bigger credit. Like it was, yeah. Anyway, that was the beginning. Yeah. But the pricing was, yeah, like storage rights and query. Right. And the, the pricing we have today is basically just that pricing with duct tape and spit to try to approach like, you know, like a, as a margin on the physical underlying hardware.

And we’re doing this year, you’re gonna see more and more pricing changes from us. Yeah.

swyx: And like is how much does stuff like VVC peering matter because you’re working in AWS land where egress is charged and all that, you know.

Simon Hørup Eskildsen: We probably don’t like, we have like an enterprise plan that just has like a base fee because we haven’t had time to figure out SKU pricing for all of this.

Um, but I mean, yeah, you can run turbo puffer either in SaaS, right? That’s what Cursor does. You can run it in a single tenant cluster. So it’s just you. That’s what Notion does. And then you can run it in, in, in BYOC where everything is inside the customer’s VPC, that’s what an for example, philanthropic does.

swyx: What I’m hearing is that this is probably the best CRO job for somebody who can come in and,

Simon Hørup Eskildsen: I mean,

swyx: help you with this.

Simon Hørup Eskildsen: Um, like Turbo Puffer hired, like, I don’t know what, what number this was, but we had a full-time CFO as like the 12th hire or something at Turbo Puffer, um, I think I hear are a lot of comp.

I don’t know how they do it. Like they have a hundred employees and not a CFO. It’s like having a CFO is like a running

swyx: business man. Like, you know,

Simon Hørup Eskildsen: it’s so good. Yeah, like money Mike, like he just, you know, just handles the money and a lot of the business stuff and so he came in and just hopped with a lot of the operational side of the business.

So like C-O-O-C-F-O, like somewhere in between.

swyx: Just as quick mention of Lucky, just ‘cause I’m curious, I’ve met Lock and like, he’s obviously a very good investor and now on physical intelligence, um, I call it generalist super angel, right? He invests in everything. Um, and I always wonder like, you know, is there something appealing about focusing on developer tooling, focusing on databases, going like, I’ve invested for 10 years in databases versus being like a lock where he can maybe like connect you to all the customers that you need.

Simon Hørup Eskildsen: This is an excellent question. No, no one’s asked me this. Um, why lockey? Because. There was a couple of people that we were talking to at the time and when we were raising, we were almost a little, we were like a bit distressed because one of our, one of our peers had just launched something that was very similar to Turbo Puffer.

And someone just gave me the advice at the time of just choose the person where you just feel like you can just pick up the phone and not prepare anything. And just be completely honest, and I don’t think I’ve said this publicly before, but I just called Lockey and was like local Lockie. Like if this doesn’t have PMF by the end of the year, like we’ll just like return all the money to you.

But it’s just like, I don’t really, we, Justine and I don’t wanna work on this unless it’s really working. So we want to give it the best shot this year and like we’re really gonna go for it. We’re gonna hire a bunch of people and we’re just gonna be honest with everyone. Like when I don’t know how to play a game, I just play with open cards and.

Lockey was the only person that didn’t, that didn’t freak out. He was like, I’ve never heard anyone say that before. As I said, I didn’t even know what a seed or pre-seed round was like before, probably even at this time. So I was just like very honest with him. And I asked him like, Lockie, have you ever have, have you ever invested in database company?

He was just like, no. And at the time I was like, am I dumb? Like, but I think there was something that just like really drew me to Lockie. He is so authentic, so honest, like, and there was something just like, I just felt like I could just play like, just say everything openly. And that was, that was, I think that that was like a perfect match at the time, and, and, and honestly still is.

He was just like, okay, that’s great. This is like the most honest, ridiculous thing I’ve ever heard anyone say to me. But like that, like that, why

swyx: is this ridiculous? Say competitor launch, this may not work out. It was

Simon Hørup Eskildsen: more just like. If this doesn’t work out, I’m gonna close up shop by the end of the mo the year, right?

Like it was, I don’t know, maybe it’s common. I, I don’t know. He told me it was uncommon. I don’t know. Um, that’s why we chose him and he’d been phenomenal. The other people were talking at the, at the time were database experts. Like they, you know, knew a lot about databases and Locke didn’t, this turned out to be a phenomenal asset.

Right. I like Justine and I know a lot about databases. The people that we hire know a lot about databases. What we needed was just someone who didn’t know a lot about databases, didn’t pretend to know a lot about databases, and just wanted to help us with candidates and customers. And he did. Yeah. And I have a list, right, of the investors that I have a relationship with, and Lockey has just performed excellent in the number of sub bullets of what we can attribute back to him.

Just absolutely incredible. And when people talk about like no ego and just the best thing for the founder, I like, I don’t think that anyone, like even my lawyer is like, yeah, Lockey is like the most friendly person you will find.

swyx: Okay. This is my most glow recommendation I’ve ever heard.

Alessio: He deserves it.

He’s very special.

swyx: Yeah. Yeah. Yeah. Okay. Amazing.

Alessio: Since you mentioned candidates, maybe we can talk about team building, you know, like, especially in sf, it feels like it’s just easier to start a company than to join a company. Uh, I’m curious your experience, especially not being n SF full-time and doing something that is maybe, you know, a very low level of detail and technical detail.

Simon Hørup Eskildsen: Yeah. So joining versus starting, I never thought that I would be a founder. I would start with it, like Turbo Puffer started as a blog post, and then it became a project and then sort of almost accidentally became a company. And now it feels like it’s, it’s like becoming a bigger company. That was never the intention.

The intentions were very pure. It’s just like, why hasn’t anyone done this? And it’s like, I wanna be the, like, I wanna be the first person to do it. I think some founders have this, like, I could never work for anyone else. I, I really don’t feel that way. Like, it’s just like, I wanna see this happen. And I wanna see it happen with some people that I really enjoy working with and I wanna have fun doing it and this, this, this has all felt very natural on that, on that sense.

So it was never a like join versus versus versus found. It was just dis found me at the right moment.

Alessio: Well I think there’s an argument for, you should have joined Cursor, right? So I’m curious like how you evaluate it. Okay, I should actually go raise money and make this a company versus like, this is like a company that is like growing like crazy.

It’s like an interesting technical problem. I should just build it within Cursor and then they don’t have to encrypt all this stuff. They don’t have to obfuscate things. Like was that on your mind at all or

Simon Hørup Eskildsen: before taking the, the small check from Lockie, I did have like a hard like look at myself in the mirror of like, okay, do I really want to do this?

And because if I take the money, I really have to do it right. And so the way I almost think about it’s like you kind of need to ha like you kind of need to be like fucked up enough to want to go all the way. And that was the conversation where I was like, okay, this is gonna be part of my life’s journey to build this company and do it in the best way that I possibly can’t.

Because if I ask people to join me, ask people to get on the cap table, then I have an ultimate responsibility to give it everything. And I don’t, I think some people, it doesn’t occur to me that everyone takes it that seriously. And maybe I take it too seriously, I don’t know. But that was like a very intentional moment.

And so then it was very clear like, okay, I’m gonna do this and I’m gonna give it everything.

Alessio: A lot of people don’t take it this seriously. But,

swyx: uh, let’s talk about, you have this concept of the P 99 engineer. Uh, people are 10 x saying, everyone’s saying, you know, uh, maybe engineers are out of a job. I don’t know.

But you definitely see a P 99 engineer, and I just want you to talk about it.

Simon Hørup Eskildsen: Yeah, so the P 99 engineer was just a term that we started using internally to talk about candidates and talk about how we wanted to build the company. And you know, like everyone else is, like we want a talent dense company.

And I think that’s almost become trite at this point. What I credit the cursor founders a lot with is that they just arrived there from first principles of like, we just need a talent dense, um, talent dense team. And I think I’ve seen some teams that weren’t talent dense and like seemed a counterfactual run, which if you’ve run in been in a large company, you will just see that like it’s just logically will happen at a large company.

Um, and so that was super important to me and Justine and it’s very difficult to maintain. And so we just needed, we needed wording for it. And so I have a document called Traits of the P 99 Engineer, and it’s a bullet point list. And I look at that list after every single interview that I do, and in every single recap that we do and every recap we end with.

End with, um, some version of I’m gonna reject this candidate completely regardless of what the discourse was, because I wanna see people fight for this person because the default should not be, we’re gonna hire this person. The default should be, we’re definitely not hiring this person. And you know, if everyone was like, ah, maybe throw a punch, then this is not the right.

swyx: Do, do you operate, like if there’s one cha there must have at least one champion who’s like, yes, I will put my career on, on, on the line for this. You know,

Simon Hørup Eskildsen: I think career on the line,

swyx: maybe a chair, but

Simon Hørup Eskildsen: yeah. You know, like, um, I would say so someone needs to like, have both fists up and be like, I’d fight.

Right? Yeah. Yeah. And if one person said, then, okay, let’s do it. Right?

swyx: Yeah.

Simon Hørup Eskildsen: Um. It doesn’t have to be absolutely everyone. Right? And like the interviews are always the sign that you’re checking for different attributes. And if someone is like knocking it outta the park in every single attribute, that’s, that’s fairly rare.

Um, but that’s really important. And so the traits of the P 99 engineer, there’s lots of them. There’s also the traits of the p like triple nine engineer and the quadruple nine engineer. This is like, it’s a long list.

swyx: Okay.

Simon Hørup Eskildsen: Um, I’ll give you some samples, right. Of what we, what we look for. I think that the P 99 engineer has some history of having bent, like their trajectory or something to their will.

Right? Some moment where it was just, they just, you know, made the computer do what it needed to do. There’s something like that, and it will, it will occur to have them at some point in their career. And, uh. Hopefully multiple times. Right.

swyx: Gimme an example of one of your engineers that like,

Simon Hørup Eskildsen: I’ll give an eng.

Uh, so we, we, we launched this thing called A and NV three. Um, we could, we’re also, we’re working on V four and V five right now, but a and NV three can search a hundred billion vectors with a P 50 of around 40 milliseconds and a p 99 of 200 milliseconds. Um, maybe other people have done this, I’m sure Google and others have done this, but, uh, we haven’t seen anyone, um, at least not in like a public consumable SaaS that can do this.

And that was an engineer, the chief architect of Turbo Puffer, Nathan, um, who more or less just bent this, the software was not capable of this and he just made it capable for a very particular workload in like a, you know, six to eight week period with the help of a lot of the team. Right. It’s been, been, there’s numerous of examples of that, like at, at turbo puff, but that’s like really bending the software and X 86 to your will.

It was incredible to watch. Um. You wanna see some moments like that?

swyx: Isn’t that triple nine?

Simon Hørup Eskildsen: Um, I think Nathan, what’s called

Alessio: group nine, that was only nine. I feel like this is too high for

Simon Hørup Eskildsen: Nathan. Nathan is, uh, Nathan is like, yeah, there’s a lot of nines. Okay. After that p So I think that’s one trait. I think another trait is that, uh, the P 99 spends a lot of time looking at maps.

Generally it’s their preferred ux. They just love looking at maps. You ever seen someone who just like, sits on their phone and just like, scrolls around on a map? Or did you not look at maps A lot? You guys don’t look at

swyx: maps? I guess I’m not feeling there. I don’t know, but

Simon Hørup Eskildsen: you just dis What about trains?

Do you like trains?

swyx: Uh, I mean they, not enough. Okay. This is just like weapon nice. Autism is what I call it. Like, like,

Simon Hørup Eskildsen: um, I love looking at maps, like, it’s like my preferred UX and just like I, you know, I like

swyx: lots

Alessio: of, of like random places, so

swyx: like,

you

swyx: know.

Alessio: Yes. Okay. There you go. So instead of like random places, like how do you explore the maps?

Simon Hørup Eskildsen: No, it’s, it’s just a joke.

swyx: It’s autism laugh. It’s like you are just obsessed by something and you like studying a thing.

Simon Hørup Eskildsen: The origin of this was that at some point I read an interview with some IOI gold medalist

swyx: Uhhuh,

Simon Hørup Eskildsen: and it’s like, what do you do in your spare time? I was just like, I like looking at maps.

I was like, I feel so seen. Like, I just like love, like swirling out. I was like, oh, Canada is so big. Where’s Baffin Island? I don’t know. I love it. Yeah. Um, anyway, so the traits of P 99, P 99 is obsessive, right? Like, there’s just like, you’ll, you’ll find traits of that we do an interview at, at, at, at turbo puffer or like multiple interviews that just try to screen for some of these things.

Um, so. There’s lots of others, but these are the kinds of traits that we look for.

swyx: I’ll tell you, uh, some people listen for like some of my dere stuff. Uh, I do think about derel as maps. Um, you draw a map for people, uh, maps show you the, uh, what is commonly agreed to be the geographical features of what a boundary is.

And it shows also shows you what is not doing. And I, I think a lot of like developer tools, companies try to tell you they can do everything, but like, let’s, let’s be real. Like you, your, your three landmarks are here, everyone comes here, then here, then here, and you draw a map and, and then you draw a journey through the map.

And like that. To me, that’s what developer relations looks like. So I do think about things that way.

Simon Hørup Eskildsen: I think the P 99 thinks in offs, right? The P 99 is very clear about, you know, hey, turbo puffer, you can’t run a high transaction workload on turbo puffer, right? It’s like the right latency is a hundred milliseconds.

That’s a clear trade off. I think the P 99 is very good at articulating the trade offs in every decision. Um. Which is exactly what the map is in your case, right?

swyx: Uh, yeah, yeah. My, my, my world. My world.

Alessio: How, how do you reconcile some of these things when you’re saying you bend the will the computer versus like the tradeoffs?

You know, I think sometimes it’s like, well, these are the tradeoffs, but the three nines, it’s like, actually it’s not a real trade off because we can make something that nobody has ever made before and actually make it work.

Simon Hørup Eskildsen: The way I think about the bending trajectory to your will is, um, if you sit down and do the napkin math, right, where you’re just like, okay, like if I have a hundred machines, they have this many terabytes of disc, they have this bandwidth, whatever, right?

And you sit down and you just do the like high school napkin math on this is how many qps we should be able to drive to it. Similar to how I did the vibe pricing, right? If you can sit down and do that, and then you observe the real system and you see, oh, we’re off by like 10 x bendings trajectory to your will is like just making the software get closer and closer to that first principle line.

The P 99 might even be able to cross the line Right. By finding even more optimizations than, than, than from first principle. So bending the software to your rail is about that, right? Like a hundred millisecond P 99 to um, to S3. I mean now you’re talking like someone really high agency that like goes to Seattle finds CS three team and it’s like, how are we gonna make this 10?

You know, like it’s that, that’s not quite what we talk about. Right. But yeah.

swyx: What’s the future? Turbo Puffer.

Simon Hørup Eskildsen: Turbo Puffer started out act one of Turbo Puffer was vector search. That was all we did to begin with Act two of Turbo Puffer is. Is and was full text Search Turbo Puffer today has a fairly start of the state-of-the-art full text search engine.

Um, we beat Lucine on some queries, in particular very long queries that we’ve optimized for because those are the text search queries we see today. They’re generated by LLMs or augmented by LLMs. Um, and we see them on Webscale datasets, right? Like someone searching for a very long texturing on all of Common Crawl.

We beat Lucine on some of those benchmarks and we expect to continue to beat Lucine on more and more queries. Um, that’s the performance and scale. Turbo Puffer does phenomenally now at full text search performance at scale. What we work on now is more and more features for full tech search. People expect a lot of features with full tech search.

And full tech search is still very valuable, right? If you go in and you press Command K and you search for si. Embedding based search might be like, oh, this is something agreeable. ‘cause that seat that’s yes, in Spanish, right,

Alessio: an Italian too,

Simon Hørup Eskildsen: but in full night search. That’s the prefix of maybe a document of like, you know, these are all the reasons I hate Simon, right?

Like this, this is like, that’s a completely different. So that augmentation to like how the human brain works and mapping like data to user is very important, but it’s a lot of features. That feature grind is what we’re firmly on and you will see us just adding to the change log every month, just more and more full tech search features.

Um, so we’re like fully compatible and we see we’re seeing people move from some of the traditional search engine onto Turbo Puffer, um, for that. That’s a big focus of Turbo Puffer this year. The other, the other focus of, of Turbo Puffer this year is just on scale. We’re seeing more and more companies that wanna search basically common crawl level types of data sets.

Um, both internally a companies and externally at, at a time like Cory, like a hundred billion vectors or a hundred billion documents at once. This is tricky and we wanna make it cheaper and we wanna make it faster. Um, that’s a big focus for Turbo Puffer this year. That’s, you know, we just released a NNV three, which we talked about before.

We are working on A-N-N-V-V four and we’re also have planned when we’re gonna do with a and NV five. Right. And then on full Tech search, we’re working on a lot of these features. We’ll be like FTSV three, but it will all roll out incrementally. Um, those are some of the really big features. And then the other thing is, um, our dashboard.

Have any of you ever locked into the Turbo Hover dashboard? It’s not very much there. It almost looks like if, um, a founder two years ago just sat down and wrote enough dashboard that there was at least something there, and then other people just sort of added stuff on for the next two, like the, the following two years, and then at some point SSO and other things to just catch up.

And it may or may not be have what happened, but adding like, I want PHP my admin back. Like, do you, do you guys remember? Like, it was, it was so good. Right? And I think that that like software hardware integration between the, the, the dashboard of the console of the database and the database itself. Um, I’m, I’m really excited for that.

There’s lots of other things, um, that are gonna come out in the next two. Like we talked a bit about some, some pricing and, and things like that, but those would be some of the big hitters. Right Now

swyx: you talk about eras of like turbo profile. I, I just, I have to ask like, yes, there’s the stuff that you’re working on this year, but like I’m sure in your mind you already have the next phase that you’re already thinking about

Simon Hørup Eskildsen: Act three.

swyx: Yes. Act four. Yeah.

Simon Hørup Eskildsen: Act five.

swyx: What I say about that, the candidates, you don’t have to decide. Yeah, but you know,

Simon Hørup Eskildsen: I, I’ll just say that if you wanna build a big database company, the database over time has to implement more or less every quarry plan. Because when you have your data in a database, you expect it to over time, not just search, but also, Hey, I want to aggregate this column, I want to join this data, all of that.

But when you’re a startup, your only moat is really just focus. You have to lay out the vaccine and you have to not get overeager. And I think we’ve seen some of our peers get very overeager and overextend themselves. And what I keep telling the team, I was just having breakfast this morning with our CTO and and chief architect, we were talking about like what we’re most likely to regret at the end of the year is having tried to do too much.

Um, and so Act three candidates could be, you know, a bunch of simpler ola queries, right? It could be, um, lending ourselves a little bit more into, we see some people who wanna do traces and logging and things like that. Some very simple use cases. Could be that, right? It could be maybe some time series.

Some people are trying to do that, right? Like, there’s lots of different things that you can do with turbo puffer, but for now, the, like, if you’re trying, trying to do not search on turbo puffers, the primary use case, you probably shouldn’t, but we see some customers that are like, oh, um, like at some point Cursor moved like 20 terabytes of Postgres data into Turbo Puffer because it’s like, it’s di it’s there, it works.

And these particular query plants we know work well. And so they just moved it all to defer sharding. Um, so we look for patterns like that in what future acts of turban puffer are going to be before firmly doubling down on them. But we wouldn’t, if. Today, if you’re using Turbo Puffer, it should be because search is very important to you.

And then we might do a lot of accelerated queries to that, but that should not be the main reason to go to Turbo Puffer at this point in time.

swyx: Yeah. Uh, you didn’t mention, uh, one thing I was looking for was graph type queries, like graph, database, graph, uh, queries. Can you basically trivially replicate this with what you already have?

Simon Hørup Eskildsen: We see some people doing

swyx: that, right? Because you have parallel queries and it’s It’s the same thing.

Simon Hørup Eskildsen: Exactly. So we see some people doing that, right? Like at the under, like is just a kv, right? And then we expose things on top of it. So we are seeing people do that. And I think, you know, our roadmap is very much just the database that connects AI to a very large amount of data is what the path is to do that in the right order, which is what a good startup is around what is the order to do things in.

Our customers are P 99, and they will tell us what they care most about next. And so some of them are doing graphs now, and if they need more graph database features, they’ll be banking our door and we’ll prioritize accordingly.

swyx: Tea. Okay. Give us the tea. Uh, this, you, you, uh, you kindly gifted us your favorite tea.

This is Yabu Keita Kacha, uh, from the Green Tea Shop. That’s right. Talk about your love of tea.

Simon Hørup Eskildsen: Yeah, we, we were just talking beforehand about, um, um, um. Caffeine, I think, um, and, uh, especially when I’m on a trip like this to San Francisco, I consume a lot of caffeine. Um, but this is my preferred, uh, preferred caffeine.

It’s this green tea. I have an Airtable with 200 teas that I’ve tried over time, over the past, like 15 years, and this one is my favorite. Now, when you drink a tea, um, there’s different, there’s like six different types of tea. I like green tea in particular. I generally prefer Chinese green tea. And I don’t really like Japanese green tea, but this little prefecture somewhere in Japan has specialized in like, they’re like Japanese, but doing it the Chinese way and it’s just phenomenal.

But then the interesting thing about the tea world is that all of the different, um, like you can find this particular tea, there’s probably, you know, hundreds of. Places that sell it, but they all go to a different family right on whatever mountain that they have. These like chameleon, ensis, bush bushes on and this woman, Japanese woman in Toronto from the Green tea shop, um, I don’t know, she’s just like, I has found a really good family.

‘cause that’s the best one. The best time of years to get this is in a few months when they do the spring harvest. Uh, now it’s like kind of old. Um, it’s just like, I love the spring for the fresh tea, so I hope you enjoy it. But it’s not the right time of year.

swyx: It’s out of season. Yeah. I, I, I actually didn’t even know Tea has seasons.

This is unsophisticated, but I, I think it, like, it ties in with like, you know, loving maps and being obsessed and being keen on united in everything that you do. Um, yeah. But. That’s great.

Alessio: Awesome. Well, as we were saying, we have instant hot water at Kernel. So, uh, MET lover can come by any,

Simon Hørup Eskildsen: I I have a little ticket where I bring a, uh, where I bring like a little thermometer to like a little thermoworks thermometer.

Um, last Friday when we do demos, I have this thing where if there’s not enough demos, then I fill the remaining time talking about something completely ridiculous as an incentive for people to actually demo. And last night in time, I spent 20 minutes do, walking through my air table and going through my entire tea travel kit, including the, the, the temperature monitor.

‘cause like Yeah, you’ll show up. There’s only a boiler. You can’t get it to the right. Yeah. You know, you need this at 80 degrees, but anyway. Yeah, sorry.

Alessio: Yeah, we have a, we have electric kettle with the temperature thing at home.

swyx: I would watch this. You should start a company, YouTube, but it doesn’t have anything about search.

It just has and like other brands,

Simon Hørup Eskildsen: I don’t think I could talk, but something that I started doing. Do you, um, do you two know Sam Lambert of, of course, planet Scale? Of

swyx: course.

Simon Hørup Eskildsen: Um,

swyx: very outspoken guy.

Simon Hørup Eskildsen: I love the guy and we just, um, we just last, like last week we just went on X live and just sat and like shut the shit for like an hour and I think we’ll probably do that again.

Yes. We’ll probably come up there. Well, I don’t know what we’ll call it. Maybe P 99 live or the P 99 pod or something like that.

swyx: Um, P pod.

Simon Hørup Eskildsen: P pod.

swyx: Uh, cool. Well thank you so much for your time. I know you have to go, uh, but this is a, a blast and you’re clearly very passionate and charismatic, so, uh, I I bet you’ll get some, uh, P nine nine engineers outta this podcast.

Yeah.

Simon Hørup Eskildsen: Thank you so much for having me. It was a pleasure.