ThursdAI - The top AI news from the past week

Hey ya’ll, Alex here, and this week I was especially giddy to record the show! Mostly because when a thing clicks for me that hasn’t clicked before, I can’t wait to tell you all about it!

This week, that thing is Agent Skills! The currently best way to customize your AI agents with domain expertise, in a simple, repeatable way that doesn’t blow up the context window! We mentioned skills when Anthropic first released them (Oct 16) and when they became an open standard but it didn’t really click until last week! So more on that below.

Also this week, Anthropic released a research preview of Claude Cowork, an agentic tool for non coders, OpenAI finally let loos GPT 5.2 Codex (in the API, it was previously available only via Codex), Apple announced a deal with Gemini to power Siri, OpenAI and Anthropic both doubled down on healthcare and much more! We had an incredible show, with an expert in Agent Skills, Eleanor Berger and the usual gang on co-hosts, strongly recommend watching the show in addition to the newsletter!

Also, I vibe coded skills support for all LLMs to Chorus, and promised folks a link to download it, so look for that in the footer, let’s dive in!

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Big Company LLMs + APIs: Cowork, Codex, and a Browser in a Week

Anthropic launches Claude Cowork: Agentic AI for Non‑Coders (research preview)

Anthropic announced Claude Cowork, which is basically Claude Code wrapped in a friendly UI for people who don’t want to touch a terminal. It’s a research preview available on the Max tier, and it gives Claude read/write access to a folder on your Mac so it can do real work without you caring about diffs, git, or command line.

The wild bit is that Cowork was built in a week and a half, and according to the Anthropic team it was 100% written using Claude Code. This feels like a “we’ve crossed a threshold” moment. If you’re wondering why this matters, it’s because coding agents are general agents. If a model can write code to do tasks, it can do taxes, clean your desktop, or orchestrate workflows, and that means non‑developers can now access the same leverage developers have been enjoying for a year.

It also isn’t just for files—it comes with a Chrome connector, meaning it can navigate the web to gather info, download receipts, or do research and it uses skills (more on those later)

Earlier this week I recorded this first reactions video about Cowork and I’ve been testing it ever since, it’s a very interesting approach of coding agents that “hide the coding” to just... do things. Will this become as big as Claude Code for anthropic (which is reportedly a 1B business for them)? Let’s see!

There are real security concerns here, especially if you’re not in the habit of backing up or using git. Cowork sandboxes a folder, but it can still delete things in that folder, so don’t let it loose on your whole drive unless you like chaos.

GPT‑5.2 Codex: Long‑Running Agents Are Here

OpenAI shipped GPT‑5.2 Codex into the API finally! After being announced as the answer for Opus 4.5 and only being available in Codex. The big headline is SOTA on SWE-Bench and long‑running agentic capability. People describe it as methodical. It takes longer, but it’s reliable on extended tasks, especially when you let it run without micromanaging.

This model is now integrated into Cursor, GitHub Copilot, VS Code, Factory, and Vercel AI Gateway within hours of launch. It’s also state‑of‑the‑art on SWE‑Bench Pro and Terminal‑Bench 2.0, and it has native context compaction. That last part matters because if you’ve ever run an agent for long sessions, the context gets bloated and the model gets dumber. Compaction is an attempt to keep it coherent by summarizing old context into fresh threads, and we debated whether it really works. I think it helps, but I also agree that the best strategy is still to run smaller, atomic tasks with clean context.

Cursor vibe-coded browser with GPT-5.2 and 3M lines of code

The most mind‑blowing thing we discussed is Cursor letting GPT‑5.2 Codex run for a full week to build a browser called FastRenderer. This is not Chromium‑based. It’s a custom HTML parser, CSS cascade, layout engine, text shaping, paint pipeline, and even a JavaScript VM, written in Rust, from scratch. The codebase is open source on GitHub, and the full story is on Cursor’s blog

It took nearly 30,000 commits and millions of lines of code. The system ran hundreds of concurrent agents with a planner‑worker architecture, and GPT‑5.2 was the best model for staying on task in that long‑running regime. That’s the real story, not just “lol a model wrote a browser.” This is a stress test for long‑horizon agentic software development, and it’s a preview of how teams will ship in 2026.

I said on the show, browsers are REALLY hard, it took two decades for the industry to settle and be able to render websites normally, and there’s a reason everyone’s using Chromium. This is VERY impressive 👏

Now as for me, I began using Codex again, but I still find Opus better? Not sure if this is just me expecting something that’s not there? I’ll keep you posted

Gemini Personal Intelligence: The Data Moat king is back!

What kind of car do you drive? Does ChatGPT know that? welp, it turns our Google does (based on your emails, Google photos) and now Gemini can tap into this personal info (if you allow it, they are stressing privacy), and give you much more personalized answers!

Flipping this Beta feature on, lets Gemini reason across Gmail, YouTube, Photos, and Search with explicit opt‑in permissions, and it’s rolling out to Pro and Ultra users in the US first.

I got to try it early, and it’s uncanny. I asked Gemini what car I drive, and it told me I likely drive a Model Y, but it noticed I recently searched for a Honda Odyssey and asked if I was thinking about switching. It was kinda... freaky because I forgot I had early access and this was turned on 😂

Pro Tip: if you’re brave enough to turn this on, ask for a complete profile on you 🙂

Now the last piece is for Gemini to become proactive, suggesting things for me based on my needs!

Apple & Google: The Partnership (and Drama Corner)

We touched on this in the intro, but it’s official: Apple Intelligence will be powered by Google Gemini for “world knowledge” tasks. Apple stated that after “careful evaluation,” Google provided the most capable foundation model for their.. apple foundation models. It’s confusing, I agree.

Honestly? I got excited about Apple Intelligence, but Siri is still... Siri. It’s 2026 and we are still struggling with basic intents. Hopefully, plugging Gemini into the backend changes that?

In other drama: The silicon valley carousel continues. 3 Co-founders (Barret Zoph, Sam Schoenholz and Luke Metz) from Thinking Machines (and former OpenAI folks) have returned to the mothership (OpenAI), amid some vague tweets about “unethical conduct.” It’s never a dull week on the timeline.

This Week’s Buzz: WeaveHacks 3 in SF

I’ve got one thing in the Buzz corner this week, and it’s a big one. WeaveHacks 3 is back in San Francisco, January 31st - February 1st. The theme is self‑improving agents, and if you’ve been itching to build in person, this is it. We’ve got an amazing judge lineup, incredible sponsors, and a ridiculous amount of agent tooling to play with.

You can sign up here: https://luma.com/weavehacks3

If you’re coming, add to the form you heard it on ThursdAI and we’ll make sure you get in!

Deep Dive: Agent Skills With Eleanor Berger

This was the core of the episode, and I’m still buzzing about it. We brought on Eleanor Berger, who has basically become the skill evangelist for the entire community, and she walked us through why skills are the missing layer in agentic AI.

Skills are simple markdown files with a tiny bit of metadata in a directory together optional scripts, references, and assets. The key idea is progressive disclosure. Instead of stuffing your entire knowledge base into the context, the model only sees a small list of skills and let it load only what it needs. That means you can have hundreds of skills without blowing your context window (and making the model dumber and slower in result)

The technical structure is dead simple, but the implications are huge. Skills create a portable, reusable, composable way to give agents domain expertise, and they now work across most major harnesses. That means you can build a skill once and use it in Claude, Cursor, AMP, or any other agent tool that supports the standard.

Eleanor made the point that skills are an admission that we now have general‑purpose agents. The model can do the work, but it doesn’t know your preferences, your domain, your workflows. Skills are how you teach it those things. We also talked about how scripts inside skills reduce variance because you’re not asking the model to invent code every time; you’re just invoking trusted tools.

What really clicked for me this week is how easy it is to create skills using an agent. You don’t need to hand‑craft directories. You can describe your workflow, or even just do the task once in chat, and then ask the agent to turn it into a skill. It really is very very simple! And that’s likely the reason everyone is adopting this simple formart for extension their agents knowledge.

Get started with skills

If you use Claude Chat, the simplest way to get started is ask Claude to review your previous conversations and suggest a skill for you. Or, at the end of a long chat where you went back and forth with Claude on a task, ask it to distill the important parts into a skill. If you want to use other people’s skills, and you are using Claude Code, or any of the supported IDE/Agents, here’s where to download the folders and install them:

If you aren’t a developer and don’t subscribe to Claude, well, I got good news for you! I vibecoded skill support for every LLM 👇

The Skills Demo That Changed My Mind

I was resistant to skills at first, mostly because I wanted them inside my chat interface and not just in CLI tools. And I wasn’t subscribed to Claude for a while. Then I realized I could add skill support directly to Chorus, the open‑source multi‑model chat app, and I used Claude Code plus Ralph loops to vibe code it in a few hours. Now I can run skills with GPT‑5.2 Codex, Claude Opus, and Gemini from the same chat interface. That was my “I know kung fu” moment.

If you want to try Chorus with skills enabled, you can download my release here! Only for mac, and they are unsigned, mac will not like it, but you can run them anyway.

And if you want to explore more awesome skills, check out Vercel’s React Best Practices skills and UI Skills. It’s the beginning of a new kind of distribution: knowledge packaged as skills, shared like open source libraries (or paid for!) and

Open Source AI

Baichuan-M3 is a 235B medical LLM fine-tuned from Qwen3, released under Apache 2.0. The interesting claim here is that it beats GPT-5.2 on OpenAI’s HealthBench, including a remarkably low 3.5% hallucination rate.

What makes it different from typical medical models is that it’s trained to run actual clinical consultations asking follow-up questions and reasoning through differential diagnoses rather than just spitting out answers. Nisten pointed out that if you’re going to fine-tune something for healthcare, Qwen3 MoE is an excellent base because of its multilingual capabilities, which matters a lot in clinical settings. You can run it with vLLM or SGLang if you’ve got the hardware. (HF)

LongCat-Flash-Thinking-2601 from Meituan is a 560B MoE (27B active) released fully MIT-licensed. It’s specifically built for agentic tasks, scoring well on tool-use benchmarks like τ²-Bench and BrowseComp.

There’s a “Heavy Thinking” mode that pushes AIME-25 to 100%. What I like about this one is the training philosophy, they inject noise and broken tools during RL to simulate messy real-world conditions, which is exactly what production agents deal with. You can try it at longcat.chat and Github

We also saw Google release MedGemma this week (blog) a 4B model optimized for medical imaging like X-rays and CT scans and TranslateGemma (X) a family of on device translations (4B, 12B and 27B) which seem kind of cool! Didn’t have tons of time to dive into them unfortunately.

Vision, Voice & Art (Rapid Fire)

* Veo 3.1 adds native vertical video, 4K output, and better consistency in the Gemini API. Huge for creators (blog)

* Viral Kling motion‑transfer vids are breaking people’s brains about what AI video pipelines will look like.

* Pocket TTS from Kyutai Labs: a 100M‑parameter open‑source TTS model that runs on CPU and clones voices from seconds of audio (X)

* GLM‑Image drops as an open‑source hybrid AR + diffusion image model with genuinely excellent text rendering but pretty bad for everything else

* Black Forest Labs drops open source Flux.2 [Klein] 4B and 9B small models that create images super fast! (X, Fal, HF)

Phew, ok. I was super excited about this one and I’m really really happy with the result. I was joking on the pod that to prepare for this podcast, I not only had to collect all the news, I also had to ramp up on Agent Skills, and I wish we had an ability to upload information like the Matrix, but alas we didn’t. I also really enjoyed vibecoding a whole feature into Chorus just to explore skills fully, mind was absolutely blown when it worked after 3 hours of Ralphing!

See you next week, I think I have one more super exciting thing to play with this week before I talk about it!

TL;DR and Show Notes

* Hosts & Guests

* Alex Volkov - AI Evangelist & Weights & Biases (@altryne)

* Co-Hosts: Wolfram Ravenwolf (@WolframRvnwlf), Yam Peleg (@yampeleg), Nisten Tahiraj (@nisten), LDJ (@ldjconfirmed)

* Guest: Eleanor Berger (@intellectronica)

* Open Source LLMs

* Baichuan-M3 - A 235B open-source medical LLM that beats GPT-5.2 on HealthBench with a 3.5% hallucination rate, featuring full clinical consultation capabilities. (HF, Blog, X Announcement)

* LongCat-Flash-Thinking-2601 - Meituan’s 560B MoE (27B active) agentic reasoning model, fully MIT licensed. Features “Heavy Thinking” mode scoring 100% on AIME-25. (GitHub, Demo, X Announcement)

* TranslateGemma - Google’s open translation family (4B, 12B, 27B) supporting 55 languages. The 4B model runs entirely on-device. (Arxiv, Kaggle, X Announcement)

* MedGemma 1.5 & MedASR - Native 3D imaging support (CT/MRI) and a speech model that beats Whisper v3 by 82% on clinical dictation error rates. (MedGemma HF, MedASR HF, Arxiv)

* Big CO LLMs + APIs

* Claude Cowork - Anthropic’s new desktop agent allows non-coders to give Claude file system and browser access to perform complex tasks. (TechCrunch, X Coverage)

* GPT-5.2 Codex - Now in the API ($1.75/1M input). Features native context compaction and state-of-the-art performance for long-running agentic loops. (Blog, Pricing)

* Cursor & FastRenderer - Cursor used GPT-5.2 Codex to build a 3M+ line Rust browser from scratch in one week of autonomous coding. (Blog, GitHub, X Thread)

* Gemini Personal Intelligence - Google leverages its data moat, letting Gemini reason across Gmail, Photos, and Search for hyper-personalized proactive help. (Blog, X Announcement)

* Partnerships & Drama

* Apple + Gemini - Apple officially selects Gemini to power Siri backend capabilities.

* OpenAI + Cerebras - A $10B deal for 750MW of high-speed compute through 2028. (Announcement)

* Thinking Machines - Co-founders and CTO return to OpenAI amidst drama; Soumith Chintala named new CTO.

* This Week’s Buzz

* WeaveHacks 3 - Self-Improving Agents Hackathon in SF (Jan 31-Feb 1). (Sign Up Here)

* Vision, Voice & Audio

* Veo 3.1 - Native 9:16 vertical video, 4K resolution, and reference image support in Gemini API. (Docs)

* Pocket TTS - A 100M parameter CPU-only model from Kyutai Labs that clones voices from 5s of audio. (GitHub, HF)

* GLM-Image - Hybrid AR + Diffusion model with SOTA text rendering. (HF, GitHub)

* FLUX.2 [klein] - Black Forest Labs releases fast 4B (Apache 2.0) and 9B models for sub-second image gen. (HF Collection, X Announcement)

* Kling Motion Transfer - Viral example of AI video pipelines changing Hollywood workflows. (X Thread)

* Deep Dive: Agent Skills

* Vercel React Best Practices - Pre-packaged skills for agents. (Blog)

* UI Skills - Documentation and skill standards. (Docs)

* Chorus with Skills - My fork of Chorus enabling skills for all LLMs. (Release)

Hey folks, Alex here from Weights & Biases, with your weekly AI update (and a first live show of this year!)

For the first time, we had a co-host of the show also be a guest on the show, Ryan Carson (from Amp) went supernova viral this week with an X article (1.5M views) about Ralph Wiggum (yeah, from Simpsons) and he broke down that agentic coding technique at the end of the show.

LDJ and Nisten helped cover NVIDIA’s incredible announcements during CES with their Vera Rubin upcoming platform (4-5X improvements) and we all got excited about AI medicine with ChatGPT going into Health officially!

Plus, a bunch of Open Source news, let’s get into this:

ThursdAI - Recaps of the most high signal AI weekly spaces is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.

Open Source: The “Small” Models Are Winning

We often talk about the massive frontier models, but this week, Open Source came largely from unexpected places and focused on efficiency, agents, and specific domains.

Solar Open 100B: A Data Masterclass

Upstage released Solar Open 100B, and it’s a beast. It’s a 102B parameter Mixture-of-Experts (MoE) model, but thanks to MoE magic, it only uses about 12B active parameters during inference. This means it punches incredibly high but runs fast.

What I really appreciated here wasn’t just the weights, but the transparency. They released a technical report detailing their “Data Factory” approach. They trained on nearly 20 trillion tokens, with a huge chunk being synthetic. They also used a dynamic curriculum that adjusted the difficulty and the ratio of synthetic data as training progressed. This transparency is what pushes the whole open source community forward.

Technically, it hits 88.2 on MMLU and competes with top-tier models, especially in Korean language tasks. You can grab it on Hugging Face.

MiroThinker 1.5: The DeepSeek Moment for Agents?

We also saw MiroThinker 1.5, a 30B parameter model that is challenging the notion that you need massive scale to be smart. It uses something they call “Interactive Scaling.”

Wolfram broke this down for us: this agent forms hypotheses, searches for evidence, and then iteratively revises its answers in a time-sensitive sandbox. It effectively “thinks” before answering. The result? It beats trillion-parameter models on search benchmarks like BrowseComp. It’s significantly cheaper to run, too. This feels like the year where smaller models + clever harnesses (harnesses are the software wrapping the model) will outperform raw scale.

Liquid AI LFM 2.5: Running on Toasters (Almost)

We love Liquid AI and they are great friends of the show. They announced LFM 2.5 at CES with AMD, and these are tiny ~1B parameter models designed to run on-device. We’re talking about running capable AI on your laptop, your phone, or edge devices (or the Reachy Mini bot that I showed off during the show! I gotta try and run LFM on him!)

Probably the coolest part is the audio model. Usually, talking to an AI involves a pipeline: Speech-to-Text (ASR) -> LLM -> Text-to-Speech (TTS). Liquid’s model is end-to-end. It hears audio and speaks audio directly. We watched a demo from Maxime Labonne where the model was doing real-time interaction, interleaving text and audio. It’s incredibly fast and efficient. While it might not write a symphony for you, for on-device tasks like summarization or quick interactions, this is the future.

NousCoder-14B and Zhipu AI IPO

A quick shoutout to our friends at Nous Research who released NousCoder-14B, an open-source competitive programming model that achieved a 7% jump on LiveCodeBench accuracy in just four days of RL training on 48 NVIDIA B200 GPUs. The model was trained on 24,000 verifiable problems, and the lead researcher Joe Li noted it achieved in 4 days what took him 2 years as a teenager competing in programming contests. The full RL stack is open-sourced on GitHub and Nous published a great WandB results page as well!

And in historic news, Zhipu AI (Z.ai)—the folks behind the GLM series—became the world’s first major LLM company to IPO, raising $558 million on the Hong Kong Stock Exchange. Their GLM-4.7 currently ranks #1 among open-source and domestic models on both Artificial Analysis and LM Arena. Congrats to them!

Big Companies & APIs

NVIDIA CES: Vera Rubin Changes Everything

LDJ brought the heat on this one covering Jensen’s CES keynote that unveiled the Vera Rubin platform, and the numbers are almost hard to believe. We’re talking about a complete redesign of six chips: the Rubin GPU delivering 50 petaFLOPS of AI inference (5x Blackwell), the Vera CPU with 88 custom Olympus ARM cores, NVLink 6, ConnectX-9 SuperNIC, BlueField-4 DPU, and Spectrum-6 Ethernet.

Let me put this in perspective using LDJ’s breakdown: if you look at FP8 performance, the jump from Hopper to Blackwell was about 5x. The jump from Blackwell to Vera Rubin is over 3x again—but here’s the kicker—while only adding about 200 watts of power draw. That’s insane efficiency improvement.

The real-world implications Jensen shared: training a 10 trillion parameter mixture-of-experts model now requires 75% fewer GPUs compared to Blackwell. Inference token costs drop roughly 10x—a 1MW cluster goes from 1 million to 10 million tokens per second at the same power. HBM4 memory delivers 22 TB/s bandwidth with 288GB capacity, exceeding NVIDIA’s own 2024 projections by nearly 70%.

As Ryan noted, when people say there’s an AI bubble, this is why it’s hilarious. Jensen keeps saying the need for inference is unbelievable and only going up exponentially. We all see this. I can’t get enough inference—I want to spin up 10 Ralphs running concurrently! The NVL72 rack-scale system achieves 3.6 exaFLOPS inference with 20.7TB total HBM, and it’s already shipping. Runway 4.5 is already running on the new platform, having ported their model from Hopper to Vera Rubin NVL72 in a single day.

NVIDIA also recently acqui-hidred Groq (with a Q) in a ~$20 billion deal, bringing the inference chip expertise from the guy who created Google’s TPUs in-house.

Nemotron Speech ASR & The Speed of Voice (X, HF, Blog)

NVIDIA also dropped Nemotron Speech ASR. This is a 600M parameter model that offers streaming transcription with 24ms latency.

We showed a demo from our friend Kwindla Kramer at Daily. He was talking to an AI, and the response was virtually instant. The pipeline is: Nemotron (hearing) -> Llama/Nemotron Nano (thinking) -> Magpie TTS (speaking). The total latency is under 500ms. It feels like magic. Instant voice agents are going to be everywhere this year.

XAI Raises $20B While Grok Causes Problems (Again)

So here’s the thing about covering anything Elon-related: it’s impossible to separate signal from noise because there’s an army of fans who hype everything and an army of critics who hate everything. But let me try to be objective here.

XAI raised another massive Round E of $20 billion! at a $230 billion valuation, with NVIDIA and Cisco as strategic investors. The speed of their infrastructure buildout is genuinely incredible. Grok’s voice mode is impressive. I use Grok for research and it’s really good, notable for it’s unprecedented access to X !

But. This raise happened in the middle of a controversy where Grok’s image model was being used to “put bikinis” on anyone in reply threads, including—and this is where I draw a hard line—minors. As Nisten pointed out on the show, it’s not even hard to implement guardrails. You just put a 2B VL model in front and ask “is there a minor in this picture?” But people tested it, asked Grok not to use the feature, and it did it anyway. And yeah, putting Bikini on Claude is funny, but basic moderation is lacking!

The response of “we’ll prosecute illegal users” is stupid when there’s no moderation built into the product. There’s an enormous difference between Photoshop technically being able to do something after hours of work, and a feature that generates edited images in one second as the first comment to a celebrity, then gets amplified by the platform’s algorithm to millions of people. One is a tool. The other is a product with amplification mechanics. Products need guardrails. I don’t often link to CNN (in fact this is the first time) but they have a great writeup about the whole incident here which apparently includes the quitting of a few trust and safety folks and Elon’s pushback on guardrails. Crazy

That said, Grok 5 is in training and XAI continues to ship impressive technology. I just wish they’d put the same engineering effort into safety as they do into capabilities!

OpenAI Launches GPT Health

This one’s exciting. OpenAI CEO Fidji Simo announced ChatGPT Health, a privacy-first space for personalized health conversations that can connect to electronic health records, Apple Health, Function Health, Peloton, and MyFitnessPal.

Here’s why this matters: health already represents about 5% of all ChatGPT messages globally and touches 25% of weekly active users—often outside clinic hours or in underserved areas. People are already using these models for health advice constantly.

Nisten, who has worked on AI doctors since the GPT-3 days and even published papers on on-device medical AI, gave us some perspective: the models have been fantastic for health stuff for two years now. The key insight is that medical data seems like a lot, but there are really only about 2,000 prescription drugs and 2,000 diseases (10,000 if you count rare ones). That’s nothing for an LLM. The models excel at pattern recognition across this relatively contained dataset.

The integration with Function Health is particularly interesting to me. Function does 160+ lab tests, but many doctors won’t interpret them because they didn’t order them. ChatGPT could help bridge that gap, telling you “hey, this biomarker looks off, you should discuss this with your doctor.” The bad news is, this is just a waitlist and you can add yourself to the waitlist here, we’ll keep monitoring the situation and let you know when it opens up

Doctronic: AI Prescribing Without Physician Oversight

Speaking of healthcare, Doctronic launched a pilot in Utah where AI can autonomously renew prescriptions for chronic conditions without any physician in the loop. The system covers about 190 routine medications (excluding controlled substances) at just $4 per renewal. Trial data showed 99.2% concordance with physician treatment plans, and they’ve secured pioneering malpractice insurance that treats the AI like a clinician.

Nisten made the case that it’s ethically wrong to delay this kind of automation when ER wait times keep increasing and doctors are overworked. The open source models are already excellent at medical tasks. Governments should be buying GPUs rather than creating administrative roadblocks. Strong strong agree here!

Google Brings Gmail into the Gemini Era (X)

Breaking news from the day of our show: Google announced Gmail’s biggest AI transformation since its 2004 launch, powered by Gemini 3. This brings AI Overviews that summarize email threads, natural language queries (”Who gave me a plumber quote last year?”), Help Me Write, contextual Suggested Replies matching your writing style, and the upcoming AI Inbox that filters noise to surface VIPs and urgent items.

For 3 billion Gmail users, this is huge. I’m very excited to test it—though not live on the show because I don’t want you reading my emails.

This weeks buzz - covering Weights & Biases updates

Not covered on the show, but a great update on stuff from WandB, Chris Van Pelt (@vanpelt), one of the 3 co-founders released a great project I wanted to tell you about! For coders, this is an app that allows you to run multiple Claude Codes on free Github sandboxes, so you can code (or Ralph) and control everything away from home!

GitHub gives personal users 120 free Codespaces hours/month, and Catnip automatically shuts down inactive instances so you can code for quite a while with Catnip!

It’s fully open source on Github and you can download the app here

Interview: Ryan Carson - What the hell is Ralph Wiggum?

Okay, let’s talk about the character everyone is seeing on their timeline: Ralph Wiggum. My co-host Ryan Carson went viral this week with an article about this technique, and I had to have him break it down.

Ralph isn’t a new model; it’s a technique for running agents in a loop to perform autonomous coding. The core idea is deceptively simple: Ralph is a bash script that loops an AI coding agent. In a loop, until it a certain condition is met. But why is it blowing up?

Normally when you use a coding agent like Cursor, Claude Code, or AMP, you need to be in the loop. You approve changes, look at code, fix things when the agent hits walls or runs out of context. Ralph solves this by letting the agent run autonomously while you sleep.

Here’s how it works: First, you write a Product Requirements Doc (PRD) by talking to your agent for a few minutes about what you want to build. Then you convert that PRD into a JSON file containing atomic user stories with clear acceptance criteria. Each user story is small enough for the agent to complete in one focused thread.

The Ralph script then loops: it picks the first incomplete user story, the agent writes code to implement it, tests against the acceptance criteria, commits the changes, marks the story as complete, writes what it learned to a shared “agents.md” file, and loops to the next story. That compound learning step is crucial—without it, the agent would keep making the same mistakes.

What makes this work is the pre-work. As Ryan put it, “no real work is done one-shot.” This is how software engineering has always worked—you break big problems into smaller problems into user stories and solve them incrementally. The innovation is letting AI agents work through that queue autonomously while you sleep! Ryan’s excellent (and viral) X article is here!

Vision & Video

LTX-2 Goes Fully Open Source (HF, Paper)

Lightricks finally open-sourced LTX-2, marking a major milestone as the first fully open audio-video generation model. This isn’t just “we released the weights” open—it’s complete model weights (13B and 2B variants), distilled versions, controllable LoRAs, a full multimodal trainer, benchmarks, and evaluation scripts. For a video model that is aiming to be the open source SORA, supports audio and lipsync

The model generates synchronized audio and video in a single DiT-based architecture—motion, dialogue, ambience, and music flow simultaneously. Native 4K at up to 50 FPS with audio up to 10 seconds. And there’s also a distilled version (Thanks Pruna AI!) hosted on Replicate

ComfyUI provided day-0 native support, and community testing shows an A6000 generating 1280x720 at 120 frames in 50 seconds. This is near Sora-level quality that you can fine-tune on your own data for custom styles and voices in about an hour.

What a way to start 2026. From chips that are 5x faster to AI doctors prescribing meds in Utah, the pace is only accelerating. If anyone tells you we’re in an AI bubble, just show them what we covered today. Even if the models stopped improving tomorrow, the techniques like “Ralph” prove we have years of work ahead of us just figuring out how to use the intelligence we already have.

Thank you for being a ThursdAI subscriber. See you next week!

As always, here’s the show notes and TL;DR links:

* Hosts & Guests

* Alex Volkov - AI Evangelist & Weights & Biases (@altryne)

* Co-Hosts - @WolframRvnwlf, @nisten, @ldjconfirmed

* Special Guest - Ryan Carson (@ryancarson) breaking down the Ralph Wiggum technique.

* Open Source LLMs

* Solar Open 100B - Upstage’s 102B MoE model. Trained on 19.7T tokens with a heavy focus on “data factory” synthetic data and high-performance Korean reasoning (X, HF, Tech Report).

* MiroThinker 1.5 - A 30B parameter search agent that uses “Interactive Scaling” to beat trillion-parameter models on search benchmarks like BrowseComp (X, HF, GitHub).

* Liquid AI LFM 2.5 - A family of 1B models designed for edge devices. Features a revolutionary end-to-end audio model that skips the ASR-LLM-TTS pipeline (X, HF).

* NousCoder-14B - competitive coding model from Nous Research that saw a 7% LiveCodeBench accuracy jump in just 4 days of RL (X, WandB Dashboard).

* Zhipu AI IPO - The makers of GLM became the first major LLM firm to go public on the HKEX, raising $558M (Announcement).

* Big Co LLMs & APIs

* NVIDIA Vera Rubin - Jensen Huang’s CES reveal of the next-gen platform. Delivers 5x Blackwell inference performance and 75% fewer GPUs needed for MoE training (Blog).

* OpenAI ChatGPT Health - A privacy-first vertical for EHR and fitness data integration (Waitlist).

* Google Gmail Era - Gemini 3 integration into Gmail for 3 billion users, featuring AI Overviews and natural language inbox search (Blog).

* XAI $20B Raise - Elon’s XAI raises Series E at a $230B valuation, even as Grok faces heat over bikini-gate and safety guardrails (CNN Report).

* Doctronic - The first US pilot in Utah for autonomous AI prescription renewals without a physician in the loop (Web).

* Alexa+ Web - Amazon brings the “Smart Alexa” experience to browser-based chat (Announcement).

* Autonomous Coding & Tools

* Ralph Wiggum - The agentic loop technique for autonomous coding using small, atomic user stories. Ryan Carson’s breakdown of why this is the death of “vibe coding” (Viral X Article).

* Catnip by W&B - Chris Van Pelt’s open-source iOS app to run Claude Code anywhere via GitHub Codespaces (App Store, GitHub).

* Vision & Video

* LTX-2 - Lightricks open-sources the first truly open audio-video generation model with synchronized output and full training code (GitHub, Replicate Demo).

* Avatar Forcing - KAIST’s framework for real-time interactive talking heads with ~500ms latency (Arxiv).

* Qwen Edit 2512 - Optimized by PrunaAI to generate high-res realistic images in under 7 seconds (Replicate).

* Voice & Audio

* Nemotron Speech ASR - NVIDIA’s 600M parameter streaming model with sub-100ms stable latency for massive-scale voice agents (HF).

Hey all,

Happy new year! This is Alex, writing to you for the very fresh start of this year, it’s 2026 already, can you believe it?

There was no live stream today, I figured the cohosts deserve a break and honestly it was a very slow week. Even the chinese labs who don’t really celebrate X-mas and new years didn’t come out with a banger AFAIK.

ThursdAI - AI moves fast, we’re here to make sure you never miss a thing! Subscribe :)

Tho I thought it was an incredible opportunity to finally post the Will Brow interview I recorded in November during the AI Engineer conference.

Will is a researcher at Prime Intellect (big fans on WandB btw!) and is very known on X as a hot takes ML person, often going viral for tons of memes!

Will is the creator and maintainer of the Verifiers library (Github) and his talk at AI Engineer was all about RL Environments (what they are, you can hear in the interview, I asked him!)

TL;DR last week of 2025 in AI

Besides this, my job here is to keep you up to date, and honestly this was very easy this week, as… almost nothing has happened, but here we go:

Meta buys Manus

The year ended with 2 huge acquisitions / aquihires. First we got the news from Alex Wang that Meta has bought Manus.ai which is an agentic AI startup we covered back in March for an undisclosed amount (folks claim $2-3B)

The most interesting thing here is that Manus is a Chinese company, and this deal requires very specific severance from Chinese operations.

Jensen goes on a new years spending spree, Nvidia buys Groq (not GROK) for $20B

Groq which we covered often here, and are great friends, is going to NVIDIA, in a… very interesting acqui-hire, which is a “non binding license” + most of Groq top employees apparently are going to NVIDIA. Jonathan Ross the CEO of Groq, was the co-creator of the TPU chips at Google before founding Groq, so this seems like a very strategic aquihire for NVIDIA! Congrats to our friends from Groq on this amazing news for the new year!

Tencent open-sources HY-MT1.5 translation models with 1.8B edge-deployable and 7B cloud variants supporting 33 languages (X, HF, HF, GitHub)

It seems that everyone’s is trying to de-throne whisper and this latest attempt from Tencent is a interesting one. a 1.8B and 7B translation models with very interesting stats.

Alibaba’s Qwen-Image-2512 drops on New Year’s Eve as strongest open-source text-to-image model, topping AI Arena with photorealistic humans and sharper textures (X, HF, Arxiv)

Our friends in Tongyi decided to give is a new years present in the form of an updated Qwen-image, with much improved realism

That’s it folks, this was a quick one, hopefully you all had an amazing new year celebration, and are gearing up to an eventful and crazy 2026.

I wish you all happiness, excitement and energy to keep up with everything in the new year, and will make sure that we’re here to keep you up to date as always!

P.S - I got a little news of my own this yesterday, not related to AI. She said yes 🎉

Ho Ho Ho, Alex here! (a real human writing these words, this needs to be said in 2025)

Merry Christmas (to those who celebrate) and welcome to the very special yearly ThursdAI recap! This was an intense year in the world of AI, and after 51 weekly episodes (this is episode 52!) we have the ultimate record of all the major and most important AI releases of this year!

So instead of bringing you a weekly update (it’s been a slow week so far, most AI labs are taking a well deserved break, the Cchinese AI labs haven’t yet surprised anyone), I’m dropping a comprehensive yearly AI review! Quarter by quarter, month by month, both in written form and as a pod/video!

Why do this? Who even needs this? Isn’t most of it obsolete? I have asked myself this exact question while prepping for the show (it was quite a lot of prep, even with Opus’s help). I eventually landed on, hey, if nothing else, this will serve as a record of the insane week of AI progress we all witnessed. Can you imagine that the term Vibe Coding is less than 1 year old? That Claude Code was released at the start of THIS year?

We get hedonicly adapt to new AI goodies so quick, and I figured this will serve as a point in time check, we can get back to and feel the acceleration!

With that, let’s dive in - P.S. the content below is mostly authored by my co-author for this, Opus 4.5 high, which at the end of 2025 I find the best creative writer with the best long context coherence that can imitate my voice and tone (hey, I’m also on a break! 🎅)

“Open source AI has never been as hot as this quarter. We’re accelerating as f*ck, and it’s only just beginning—hold on to your butts.” — Alex Volkov, ThursdAI Q1 2025

🏆 The Big Picture — 2025 - The Year the AI Agents Became Real

Looking back at 51 episodes and 12 months of relentless AI progress, several mega-themes emerged:

1. 🧠 Reasoning Models Changed Everything

From DeepSeek R1 in January to GPT-5.2 in December, reasoning became the defining capability. Models now think for hours, call tools mid-thought, and score perfect on math olympiads.

2. 🤖 2025 Was Actually the Year of Agents

We said it in January, and it came true. Claude Code launched the CLI revolution, MCP became the universal protocol, and by December we had ChatGPT Apps, Atlas browser, and AgentKit.

3. 🇨🇳 Chinese Labs Dominated Open Source

DeepSeek, Qwen, MiniMax, Kimi, ByteDance — despite chip restrictions, Chinese labs released the best open weights models all year. Qwen 3, Kimi K2, DeepSeek V3.2 were defining releases.

4. 🎬 We Crossed the Uncanny Valley

VEO3’s native audio, Suno V5’s indistinguishable music, Sora 2’s social platform — 2025 was the year AI-generated media became indistinguishable from human-created content.

5. 💰 The Investment Scale Became Absurd

$500B Stargate, $1.4T compute obligations, $183B valuations, $100-300M researcher packages, LLMs training in space. The numbers stopped making sense.

6. 🏆 Google Made a Comeback

After years of “catching up,” Google delivered Gemini 3, Antigravity, Nano Banana Pro, VEO3, and took the #1 spot (briefly). Don’t bet against Google.

By the Numbers

Q1 2025 — The Quarter That Changed Everything

DeepSeek R1 crashed NVIDIA’s stock, reasoning models went mainstream, and Chinese labs took over open source. The quarter that proved AI isn’t slowing down—it’s just getting started.

Key Themes:

* 🧠 Reasoning models went mainstream (DeepSeek R1, o1, QwQ)

* 🇨🇳 Chinese labs dominated open source (DeepSeek, Alibaba, MiniMax, ByteDance)

* 🤖 2025 declared “The Year of Agents” (OpenAI Operator, MCP won)

* 🖼️ Image generation revolution (GPT-4o native image gen, Ghibli-mania)

* 💰 Massive infrastructure investment (Project Stargate $500B)

January — DeepSeek Shakes the World

(Jan 02 | Jan 10 | Jan 17 | Jan 24 | Jan 30)

The earthquake that shattered the AI bubble. DeepSeek R1 dropped on January 23rd and became the most impactful open source release ever:

* Crashed NVIDIA stock 17% — $560B loss, largest single-company monetary loss in history

* Hit #1 on the iOS App Store

* Cost allegedly only $5.5M to train (sparking massive debate)

* Matched OpenAI’s o1 on reasoning benchmarks at 50x cheaper pricing

* The 1.5B model beat GPT-4o and Claude 3.5 Sonnet on math benchmarks 🤯

“My mom knows about DeepSeek—your grandma probably knows about it, too” — Alex Volkov

Also this month:

* OpenAI Operator — First agentic ChatGPT (browser control, booking, ordering)

* Project Stargate — $500B AI infrastructure (Manhattan Project for AI)

* NVIDIA Project Digits — $3,000 desktop that runs 200B parameter models

* Kokoro TTS — 82M param model hit #1 on TTS Arena, Apache 2, runs in browser

* MiniMax-01 — 4M context window from Hailuo

* Gemini Flash Thinking — 1M token context with thinking traces

February — Reasoning Mania & The Birth of Vibe Coding

(Feb 07 | Feb 13 | Feb 20 | Feb 28)

The month that redefined how we work with AI.

OpenAI Deep Research (Feb 6) — An agentic research tool that scored 26.6% on Humanity’s Last Exam (vs 10% for o1/R1). Dr. Derya Unutmaz called it “a phenomenal 25-page patent application that would’ve cost $10,000+.”

Claude 3.7 Sonnet & Claude Code (Feb 24-27) — Anthropic’s coding beast hit 70% on SWE-Bench with 8x more output (64K tokens). Claude Code launched as Anthropic’s agentic coding tool — marking the start of the CLI agent revolution.

“Claude Code is just exactly in the right stack, right around the right location... You can do anything you want with a computer through the terminal.” — Yam Peleg

GPT-4.5 (Orion) (Feb 27) — OpenAI’s largest model ever (rumored 10T+ parameters). 62.5% on SimpleQA, foundation for future reasoning models.

Grok 3 (Feb 20) — xAI enters the arena with 1M token context and “free until GPUs melt.”

Andrej Karpathy coins “Vibe Coding” (Feb 2) — The 5.2M view tweet that captured a paradigm shift: developers describe what they want, AI handles implementation.

OpenAI Roadmap Revelation (Feb 13) — Sam Altman announced GPT-4.5 will be the last non-chain-of-thought model. GPT-5 will unify everything.

March — Google’s Revenge & The Ghibli Explosion

(Mar 06 | Mar 13 | Mar 20 | Mar 27)

Gemini 2.5 Pro Takes #1 (Mar 27) — Google reclaimed the LLM crown with AIME jumping nearly 20 points, 1M context, “thinking” integrated into the core model.

GPT-4o Native Image Gen — Ghibli-mania (Mar 27) — The internet lost its collective mind and turned everything into Studio Ghibli. Auto-regressive image gen with perfect text rendering, incredible prompt adherence.

“The internet lost its collective mind and turned everything into Studio Ghibli” — Alex Volkov

MCP Won (Mar 27) — OpenAI officially adopted Anthropic’s Model Context Protocol. No VHS vs Betamax situation. Tools work across Claude AND GPT.

DeepSeek V3 685B — AIME jumped from 39.6% → 59.4%, MIT licensed, best non-reasoning open model.

ThursdAI Turns 2! (Mar 13) — Two years since the first episode about GPT-4.

Open Source Highlights:

* Gemma 3 (1B-27B) — 128K context, multimodal, 140+ languages, single GPU

* QwQ-32B — Qwen’s reasoning model matches R1, runs on Mac

* Mistral Small 3.1 — 24B, beats Gemma 3, Apache 2

* Qwen2.5-Omni-7B — End-to-end multimodal with speech output

Q2 2025 — The Quarter That Shattered Reality

VEO3 crossed the uncanny valley, Claude 4 arrived with 80% SWE-bench, and Qwen 3 proved open source can match frontier models. The quarter we stopped being able to tell what’s real.

Key Themes:

* 🎬 Video AI crossed the uncanny valley (VEO3 with native audio)

* 🧠 Tool-using reasoning models emerged (o3 calling tools mid-thought)

* 🇨🇳 Open source matched frontier (Qwen 3, Claude 4)

* 📺 Google I/O delivered everything

* 💸 AI’s economic impact accelerated ($300B valuations, 80% price drops)

April — Tool-Using Reasoners & Llama Chaos

(Apr 03 | Apr 10 | Apr 17 | Apr 24)

OpenAI o3 & o4-mini (Apr 17) — The most important reasoning upgrade ever. For the first time, o-series models can use tools during reasoning: web search, Python, image gen. Chain 600+ consecutive tool calls. Manipulate images mid-thought.

“This is almost AGI territory — agents that reason while wielding tools” — Alex Volkov

GPT-4.1 Family (Apr 14) — 1 million token context across all models. Near-perfect recall. GPT-4.5 deprecated.

Meta Llama 4 (Apr 5) — Scout (17B active/109B total) & Maverick (17B active/400B total). LMArena drama (tested model ≠ released model). Community criticism. Behemoth teased but never released.

Gemini 2.5 Flash (Apr 17) — Set “thinking budget” per API call. Ultra-cheap at $0.15/$0.60 per 1M tokens.

ThursdAI 100th Episode! 🎉

May — VEO3 Crosses the Uncanny Valley & Claude 4 Arrives

(May 01 | May 09 | May 16 | May 23 | May 29)

VEO3 — The Undisputed Star of Google I/O (May 20) — Native multimodal audio generation (speech, SFX, music synced perfectly). Perfect lip-sync. Characters understand who’s speaking. Spawned viral “Prompt Theory” phenomenon.

“VEO3 isn’t just video generation — it’s a world simulator. We crossed the uncanny valley this quarter.” — Alex Volkov

Claude 4 Opus & Sonnet — Live Drop During ThursdAI! (May 22) — Anthropic crashed the party mid-show. First models to cross 80% on SWE-bench. Handles 6-7 hour human tasks. Hybrid reasoning + instant response modes.

Qwen 3 (May 1) — The most comprehensive open source release ever: 8 models, Apache 2.0. Runtime /think toggle for chain-of-thought. 4B dense beats Qwen 2.5-72B on multiple benchmarks. 36T training tokens, 119 languages.

“The 30B MoE is ‘Sonnet 3.5 at home’ — 100+ tokens/sec on MacBooks” — Nisten

Google I/O Avalanche:

* Gemini 2.5 Pro Deep Think (84% MMMU)

* Jules (free async coding agent)

* Project Mariner (browser control via API)

* Gemini Ultra tier ($250/mo)

June — The New Normal

(Jun 06 | Jun 13 | Jun 20 | Jun 26)

o3 Price Drop 90% (Jun 12) — From $40/$10 → $8/$2 per million tokens. o3-pro launched at 87% cheaper than o1-pro.

Meta’s $15B Scale AI Power Play (Jun 12) — 49% stake in Scale AI. Alex Wang leads new “Superintelligence team” at Meta. Seven-to-nine-figure comp packages for researchers.

MiniMax M1 — Reasoning MoE That Beats R1 (Jun 19) — 456B total / 45B active parameters. Full weights on Hugging Face.

Gemini CLI (Jun 26) — Google’s open source terminal agent brings Gemini 2.5 Pro to your command line.

Flux Kontext — SOTA image editing with character consistency.

Q3 2025 — The Quarter of GPT-5 & Trillion-Parameter Open Source

GPT-5 arrived after 32 months. Open source hit trillion-parameter scale. World models became playable. Chinese labs continued their dominance.

Key Themes:

* 👑 GPT-5 Era began (unified reasoning + chat)

* 🇨🇳 Open source hit trillion-scale (Kimi K2, Qwen3-Coder)

* 🌍 World models became playable (Google Genie-3)

* 🎥 Video reached “can’t tell” quality

* 💰 Unprecedented investment ($100B pledges, $183B valuations)

July — Trillion-Parameter Open Source Arrives

(Jul 03 | Jul 11 | Jul 17 | Jul 24)

Kimi K2 — The Trillion-Parameter King (Jul 17) — Moonshot dropped a 1 trillion parameter MoE model: 65.8% on SWE-bench Verified (beating Claude Sonnet without reasoning), 32B active parameters, 128K context, Modified MIT license.

“This isn’t just another model release. This is ‘Sonnet at home’ if you have the hardware.” — Alex Volkov

Grok-4 & Grok Heavy (Jul 10) — 50% on Humanity’s Last Exam with tools. 100% on AIME25. xAI finally became a serious contender.

ChatGPT Agent (Odyssey) (Jul 17) — Unified agentic AI: browser + terminal + research. 41.6% on HLE (double o3).

Chinese Open Source Explosion:

* Baidu ERNIE 4.5 (10 models, Apache 2.0)

* Tencent Hunyuan-A13B (80B MoE, 256K context)

* Huawei Pangu Pro (trained entirely on Ascend NPUs — no Nvidia!)

* Qwen3-Coder-480B (69.6% SWE-bench)

August — GPT-5 Month

(Aug 01 | Aug 07 | Aug 15 | Aug 21)

GPT-5 Launch (Aug 7) — 32 months after GPT-4:

* 400K context window

* $1.25/$10 per million tokens (Opus is $15/$75)

* Unified thinking + chat model

* Router-based architecture (initially buggy)

* Free tier access for back-to-school

“32 months since GPT-4 release, 32 months of ThursdAI” — Alex Volkov

GPT-OSS (Aug 5) — OpenAI goes Apache 2.0 open source for the first time since GPT-2: 120B and 20B models, configurable reasoning, full chain-of-thought access.

Google Genie-3 (Aug 7) — DeepMind’s world model generates fully interactive 3D environments: real-time at 24fps, memory/consistency breakthrough, walk/fly/control in generated worlds.

DeepSeek V3.1 Hybrid (Aug 21) — Matches/beats R1 with fewer thinking tokens. 66% SWE-bench Verified. Tool calls inside thinking. MIT licensed.

September — Shiptember Delivers

(Sep 05 | Sep 12 | Sep 19 | Sep 26)

GPT-5-Codex (Sep 18) — Works 7+ hours independently. 93% fewer tokens on simple tasks. Reviews majority of OpenAI’s own PRs. Perfect 12/12 on 2025 ICPC.

Meta Connect 25 (Sep 18) — AI glasses with built-in display, neural band wristband, live translation with subtitles, $799 shipping immediately.

Qwen-mas Strikes Again (Sep 26):

* Qwen3-VL-235B (vision reasoner, 1M context for video)

* Qwen3-Omni-30B (end-to-end omni-modal)

* Qwen-Max (over 1T parameters, roadmap to 100M token context)

NVIDIA $100B pledge to OpenAI — “Biggest infrastructure project in history”

Suno V5 — The music generation model where we officially can’t tell anymore.

“I can no longer tell which music is AI and which is human. This is it. We’ve passed the Rubicon.” — Alex Volkov

Q4 2025 — The Quarter of Agents, Gemini’s Crown & The Reasoning Wars

The densest quarter in AI history. Google took the throne with Gemini 3, OpenAI fired back with GPT-5.2, and agents became real products. Someone trained an LLM in space.

Key Themes:

* 🚀 Reasoning wars peaked (Gemini 3 → GPT-5.2 → DeepSeek gold medals)

* 🤖 Agents became products (Atlas, AgentKit, ChatGPT Apps)

* 👑 Google’s comeback (Gemini 3, Antigravity, Nano Banana)

* 🏃 ASI race accelerated ($1.4T compute, 2028 autonomous researchers)

* 🎬 Sora 2 launched AI-native social media

October — Sora Changes Social Media Forever

(Oct 03 | Oct 10 | Oct 17 | Oct 24 | Oct 30)

Sora 2 — AI Social Media is Born (Oct 2):

* Shot to #3 on iOS App Store within days

* Cameos: upload your face, star in any video

* Sam Altman shared his Cameo publicly, becoming the internet’s most meme-able person

* All content is AI-generated — no uploads, only creations

“This is the first social media with UGC where content can ONLY be generated” — Alex Volkov

OpenAI Dev Day (Oct 9):

* ChatGPT Apps for 800M+ weekly active users

* AgentKit: drag-and-drop agent builder

* GPT-5-Pro in API

* Sam revealed $1.4 trillion in compute obligations

AI Makes Novel Cancer Discovery (Oct 16) — A 27B Gemma-based model generated a novel hypothesis about cancer cells validated in a wet lab. First confirmed case of AI creating genuinely new scientific knowledge.

Claude Sonnet 4.5 — 61.4% OS World (computer use)Claude Haiku 4.5 — 73.3% SWE-Bench, lightning fast

November — The Week That Changed Everything

(Nov 07 | Nov 13 | Nov 20 | Nov 27)

THE MOST INSANE WEEK IN AI HISTORY. In a single span of ~10 days:

* Grok 4.1 — #1 LMArena (briefly)

* Gemini 3 Pro — Took the throne with 45.14% on ARC-AGI-2 (Deep Think)

* GPT-5.1-Codex-Max — 24+ hour autonomous coding

* Nano Banana Pro — 4K image generation with perfect text rendering

* Meta SAM 3 & SAM 3D — Open-vocabulary segmentation

* Claude Opus 4.5 — 80.9% SWE-Bench Verified, beats GPT-5.1

“This week almost broke me as a person whose full-time job is to cover and follow AI releases.” — Alex Volkov

Gemini 3 Pro + Deep Think (Nov 20) — Google finally took the LLM throne: 45.14% on ARC-AGI-2, roughly double previous SOTA.

Google Antigravity IDE (Nov 20) — Free agent-first VS Code fork with browser integration, multiple parallel agents.

Nano Banana Pro (Nov 20) — Native 4K resolution with “thinking” traces, perfect text rendering.

Claude Opus 4.5 (Nov 27) — 80.9% SWE-Bench Verified. $5/$25 per MTok (1/3 previous cost). “Effort” parameter for reasoning control.

“Opus 4.5 is unbelievable. You can ship a full feature on a mature code base in one day, always. It’s just mind blowing.” — Ryan Carson

1X NEO (Oct 30) — First consumer humanoid robot, pre-orders at $20,000, delivery early 2026.

December — GPT-5.2 Fires Back

(Dec 02 | Dec 05 | Dec 12 | Dec 19)

GPT-5.2 — OpenAI’s Answer to Gemini 3 (Dec 11) — Dropped live during ThursdAI:

* 90.5% on ARC-AGI-1 (Pro X-High configuration)

* 54%+ on ARC-AGI-2 — reclaiming frontier from Gemini 3

* 100% on AIME 2025 — perfect math olympiad score

* 70% on GDPval (up from 47% in Sept!)

* Reports of models thinking for 1-3 hours on hard problems

DeepSeek V3.2 & V3.2-Speciale — Gold Medal Reasoning (Dec 4):

* 96% on AIME (vs 94% for GPT-5 High)

* Gold medals on IMO (35/42), CMO, ICPC (10/12), IOI (492/600)

* $0.28/million tokens on OpenRouter

MCP Donated to Linux Foundation (Dec 11) — Agentic AI Foundation launched under Linux Foundation. MCP, AGENTS.md, and goose donated to vendor-neutral governance.

Mistral 3 Returns to Apache 2.0 (Dec 4) — Mistral Large 3 (675B MoE), Ministral 3 (vision, edge-optimized).

Starcloud: LLM Training in Space (Dec 11) — An H100 satellite trained nanoGPT on Shakespeare. SSH into an H100… in space… with a US flag in the corner.

“Peak 2025 energy — the era of weird infra ideas has begun.” — Karpathy reacts

Gemini 3 Flash (Dec 18) — Fastest frontier model, pairs with Gemini 3 Pro for speed vs depth tradeoffs.

🙏 Thank You

This has been an incredible year of ThursdAI. 51 episodes, countless releases, and a community that keeps showing up every week to make sense of the madness together.

Huge thanks to our amazing co-hosts and friends of the pod:

* Alex Volkov — AI Evangelist, Weights & Biases (@altryne)

* Wolfram Ravenwolf (@WolframRvnwlf)

* Yam Peleg (@yampeleg)

* Nisten Tahiraj (@nisten)

* LDJ (@ldjconfirmed)

* Ryan Carson (@ryancarson)

* Kwindla Hultman Kramer — CEO of Daily (@kwindla)

And to everyone who tunes in — whether you’re listening on your commute, doing dishes, or just trying to keep up with the insanity — thank you. You make this possible.

📢 Stay Connected

* 🎧 Subscribe: thursdai.news

* 🐦 Follow Alex: @altryne

* 💻 This recap is open source: github.com/altryne/thursdAI_yearly_recap

“We’re living through the early days of a technological revolution, and we get to be part of it. That’s something to be genuinely thankful for.” — Alex Volkov

Happy Holidays, and see you in 2026! 🚀

The best is yet to come. Hold on to your butts.

Hey folks 👋 Alex here, dressed as 🎅 for our pre X-mas episode!

We’re wrapping up 2025, and the AI labs decided they absolutely could NOT let the year end quietly. This week was an absolute banger—we had Gemini 3 Flash dropping with frontier intelligence at flash prices, OpenAI firing off GPT 5.2 Codex as breaking news DURING our show, ChatGPT Images 1.5, Nvidia going all-in on open source with Nemotron 3 Nano, and the voice AI space heating up with Grok Voice and Chatterbox Turbo. Oh, and Google dropped FunctionGemma for all your toaster-to-fridge communication needs (yes, really).

Today’s show was over three and a half hours long because we tried to cover both this week AND the entire year of 2025 (that yearly recap is coming next week—it’s a banger, we went month by month and you’ll really feel the acceleration). For now, let’s dive into just the insanity that was THIS week.

00:00 Introduction and Overview

00:39 Weekly AI News Highlights

01:40 Open Source AI Developments

01:44 Nvidia's Nemotron Series

09:09 Google's Gemini 3 Flash

19:26 OpenAI's GPT Image 1.5

20:33 Infographic and GPT Image 1.5 Discussion

20:53 Nano Banana vs GPT Image 1.5

21:23 Testing and Comparisons of Image Models

23:39 Voice and Audio Innovations

24:22 Grok Voice and Tesla Integration

26:01 Open Source Robotics and Voice Agents

29:44 Meta's SAM Audio Release

32:14 Breaking News: Google Function Gemma

33:23 Weights & Biases Announcement

35:19 Breaking News: OpenAI Codex 5.2 Max

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Big Companies LLM updates

Google’s Gemini 3 Flash: The High-Speed Intelligence King

If we had to title 2025, as Ryan Carson mentioned on the show, it might just be “The Year of Google’s Comeback.” Remember at the start of the year when we were asking “Where is Google?” Well, they are here. Everywhere.

This week they launched Gemini 3 Flash, and it is rightfully turning heads. This is a frontier-class model—meaning it boasts Pro-level intelligence—but it runs at Flash-level speeds and, most importantly, Flash-level pricing. We are talking $0.50 per 1 million input tokens. That is not a typo. The price-to-intelligence ratio here is simply off the charts.

I’ve been using Gemini 2.5 Flash in production for a while because it was good enough, but Gemini 3 Flash is a different beast. It scores 71 on the Artificial Analysis Intelligence Index (a 13-point jump from the previous Flash), and it achieves 78% on SWE-bench Verified. That actually beats the bigger Gemini 3 Pro on some agentic coding tasks!

What impressed me most, and something Kwindla pointed out, is the tool calling. Previous Gemini models sometimes struggled with complex tool use compared to OpenAI, but Gemini 3 Flash can handle up to 100 simultaneous function calls. It’s fast, it’s smart, and it’s integrated immediately across the entire Google stack—Workspace, Android, Chrome. Google isn’t just releasing models anymore; they are deploying them instantly to billions of users.

For anyone building agents, this combination of speed, low latency, and 1 million context window (at this price!) makes it the new default workhorse.

Google’s FunctionGemma Open Source release

We also got a smaller, quirkier release from Google: FunctionGemma. This is a tiny 270M parameter model. Yes, millions, not billions.

It’s purpose-built for function calling on edge devices. It requires only 500MB of RAM, meaning it can run on your phone, in your browser, or even on a Raspberry Pi. As Nisten joked on the show, this is finally the model that lets your toaster talk to your fridge.

Is it going to write a novel? No. But after fine-tuning, it jumped from 58% to 85% accuracy on mobile action tasks. This represents a future where privacy-first agents live entirely on your device, handling your calendar and apps without ever pinging a cloud server.

OpenAI Image 1.5, GPT 5.2 Codex and ChatGPT Appstore

OpenAI had a busy week, starting with the release of GPT Image 1.5. It’s available now in ChatGPT and the API. The headline here is speed and control—it’s 4x faster than the previous model and 20% cheaper. It also tops the LMSYS Image Arena leaderboards.

However, I have to give a balanced take here. We’ve been spoiled recently by Google’s “Nano Banana Pro” image generation (which powers Gemini). When we looked at side-by-side comparisons, especially with typography and infographic generation, Gemini often looked sharper and more coherent. This is what we call “hedonistic adaptation”—GPT Image 1.5 is great, but the bar has moved so fast that it doesn’t feel like the quantum leap DALL-E 3 was back in the day. Still, for production workflows where you need to edit specific parts of an image without ruining the rest, this is a massive upgrade.

🚨 BREAKING: GPT 5.2 Codex

Just as we were nearing the end of the show, OpenAI decided to drop some breaking news: GPT 5.2 Codex.

This is a specialized model optimized specifically for agentic coding, terminal workflows, and cybersecurity. We quickly pulled up the benchmarks live, and they look significant. It hits 56.4% on SWE-Bench Pro and a massive 64% on Terminal-Bench 2.0.

It supports up to 400k token inputs with native context compaction, meaning it’s designed for those long, complex coding sessions where you’re debugging an entire repository. The coolest (and scariest?) stat: a security researcher used this model to find three previously unknown vulnerabilities in React in just one week.

OpenAI is positioning this for “professional software engineering,” and the benchmarks suggest a 30% improvement in token efficiency over the standard GPT 5.2. We are definitely going to be putting this through its paces in our own evaluations soon.

ChatGPT ... the AppStore!

Also today (OpenAI is really throwing everything they have to the end of the year release party), OpenAI has unveiled how their App Store is going to look and opened the submission forms to submit your own apps!

Reminder, ChatGPT apps are powered by MCP and were announced during DevDay, they let companies build a full UI experience right inside ChatGPT, and given OpenAi’s almost 900M weekly active users, this is a big deal! Do you have an app you’d like in there? let me know in the comments!

Open Source AI

🔥 Nvidia Nemotron 3 Nano: The Most Important Open Source Release of the Week (X, HF)

I think the most important release of this week in open source was Nvidia Nemotron 3 Nano, and it was pretty much everywhere. Nemotron is a series of models from Nvidia that’s been pushing efficiency updates, finetune innovations, pruning, and distillations—all the stuff Nvidia does incredibly well.

Nemotron 3 Nano is a 30 billion parameter model with only 3 billion active parameters, using a hybrid Mamba-MoE architecture. This is huge. The model achieves 1.5 to 3.3x faster inference than competing models like Qwen 3 while maintaining competitive accuracy on H200 GPUs.

But the specs aren’t even the most exciting part. NVIDIA didn’t just dump the weights over the wall. They released the datasets—all 25 trillion tokens of pre-training and post-training data. They released the recipes. They released the technical reports. This is what “Open AI” should actually look like.

What’s next? Nemotron 3 Super at 120B parameters (4x Nano) and Nemotron 3 Ultra at 480B parameters (16x Nano) are coming in the next few months, featuring their innovative Latent Mixture of Experts architecture.

Check out the release on HuggingFace

Other Open Source Highlights

LDJ brought up BOLMO from Allen AI—the first byte-level model that actually reaches parity with similar-size models using regular tokenization. This is really exciting because it could open up new possibilities for spelling accuracy, precise code editing, and potentially better omnimodality since ultimately everything is bytes—images, audio, everything.

Wolfram highlighted OLMO 3.1, also from Allen AI, which is multimodal with video input in three sizes (4B, 7B, 8B). The interesting feature here is that you can give it a video, ask something like “how many times does a ball hit the crown?” and it’ll not only give you the answer but mark the precise coordinates on the video frames where it happens. Very cool for tracking objects throughout a video!

Mistral OCR 3 (X)

Mistral also dropped Mistral OCR 3 this week—their next-generation document intelligence model achieving a 74% win rate over OCR 2 across challenging document types. We’re talking forms, low-quality scans, handwritten text, complex tables, and multilingual documents.

The pricing is aggressive at just $2 per 1,000 pages (or $1 with Batch API discount), and it outperforms enterprise solutions like AWS Textract, Azure Doc AI, and Google DocSeek. Available via API and their new Document AI Playground.

🐝 This Week’s Buzz: Wolfram Joins Weights & Biases!

I am so, so hyped to announce this. Our very own co-host and evaluation wizard, Wolfram RavenWlf, is officially joining the Weights & Biases / CoreWeave family as an AI Evangelist and “AIvaluator” starting in January!

Wolfram has been the backbone of the “vibe checks” and deep-dive evals on this show for a long time. Now, he’ll be doing it full-time, building out benchmarks for the community and helping all of us make sense of this flood of models. Expect ThursdAI to get even more data-driven in 2026. Match made in heaven! And if you’re as excited as we are, give Weave a try, it’s free to get started!

Voice & Audio: Faster, Cheaper, Better

If 2025 was the year of the LLM comeback, the end of 2025 is the era of Voice AI commoditization. It is getting so cheap and so fast.

Grok Voice Agent API (X)

xAI launched their Grok Voice Agent API, and the pricing is aggressive: $0.05 per minute flat rate. That significantly undercuts OpenAI and others. But the real killer feature here is the integration.

If you drive a Tesla, this is what powers the voice command when you hold down the button. It has native access to vehicle controls, but for developers, it has native tool calling for Real-time X Search. This means your voice agent can have up-to-the-minute knowledge about the world, something purely pre-trained models struggle with. It ranks #1 on Big Bench Audio, and with that pricing, we’re going to see voice ubiquity very soon.

Kwindla had great insights here: it feels like they optimized for the Tesla use case where it’s a question and an answer. You can see this because Big Bench Audio is a hard audio Q&A benchmark but not multi-turn. So it’s super exciting, but it’s not necessarily what we’ll use for multi-turn conversational voice agents yet.

Here’s what’s really interesting: the entire voice stack was built in-house with custom VAD, tokenizer, and audio models for end-to-end optimization. Tesla was a critical design partner—Grok now powers millions of Tesla vehicles. If you’re building AI voice agents, will you give Grok Voice SDK a try?

Resemble AI’s Chatterbox Turbo (X, HF, GitHub, Blog)

For the open-source heads, Resemble AI dropped a bombshell with Chatterbox Turbo. This is a 350M parameter open-source TTS model that is beating proprietary giants like ElevenLabs in blind tests.

It allows for zero-shot voice cloning from just 5 seconds of audio and supports paralinguistic tags—meaning you can type [laugh] or [sigh]and the model actually acts it out naturally. Plus, it has built-in watermarking for safety. It’s MIT licensed, so you can run this yourself. The fact that an open model is winning on quality against the paid APIs is a huge moment for the community.

Meta SAM Audio

Finally, Meta extended their “Segment Anything” magic to audio with SAM Audio. You know how you can click an object in an image to select it? Now you can do that with sound.

With Sam Audio, you could isolate just the sound of a train from a messy audio track, or pick out a specific instrument from a song. You can prompt it with text (”guitar”), visual clicks on a video, or time stamps. It’s incredible for creators and audio engineers, effectively automating what used to be painful manual editing.

Wrapping Up

What a week to close out 2025. Google proved once again that they’re the gorilla that’s learned to dance—Gemini 3 Flash delivering frontier intelligence at flash prices is going to change how people build AI applications. Nvidia showed that the most valuable company in the world is all-in on open source. OpenAI fired off GPT 5.2 Codex just to make sure we don’t forget about them. And the voice AI space is heating up with options that would have seemed impossible just a year ago.

Look out for the full 2025 yearly recap episode coming next week—it’s a banger. We went month by month through every major AI release and talked about what we thought were the best overall. You’ll really feel the acceleration from that one.

Happy holidays, folks! And as always, thanks for being part of the ThursdAI community.

TL;DR and Show Notes

Hosts and Guests

* Alex Volkov - AI Evangelist & Weights & Biases (@altryne)

* Co-hosts: @WolframRvnwlf, @yampeleg, @nisten, @ldjconfirmed, @ryancarson

* Special Guest: @kwindla - CEO of Daily

Open Source LLMs

* NVIDIA Nemotron 3 Nano - 30B-3A hybrid Mamba-MoE model (X, HF, HF FP8)

* FunctionGemma - 270M parameter function calling model (X, Blog, Docs)

* Mistral OCR 3 - Document intelligence model with 74% win rate over v2 (X, Blog, Console)

* BOLMO from Allen AI - First byte-level model reaching parity with regular tokenization (X)

* OLMO 2 from Allen AI - Multimodal with video input (4B, 7B, 8B sizes) (X)

Big CO LLMs + APIs

* Google Gemini 3 Flash - Frontier intelligence at $0.50/1M input tokens, 78% SWE-bench Verified (X, Announcement)

* OpenAI GPT Image 1.5 - 4x faster, 20% cheaper, #1 on LMSYS Image Arena (X)

* OpenAI GPT 5.2 Codex - 56.4% SWE-Bench Pro, 64% Terminal-Bench 2.0, 400K context (X, Blog)

* ChatGPT App Store - MCP-powered apps submission now open (X)

This Week’s Buzz

* 🐝 Wolfram joins Weights & Biases / CoreWeave as AI Evangelist and AIvaluator!

* Try Weave for AI evaluations

Voice & Audio

* xAI Grok Voice Agent API - #1 Big Bench Audio (92.3%), $0.05/min flat rate, powers Tesla vehicles (X)

* Resemble AI Chatterbox Turbo - MIT-licensed 350M TTS, beats ElevenLabs in blind tests (X, HF, GitHub, Blog)

* Meta SAM Audio - Audio source separation with text/visual/temporal prompts (X, HF, GitHub)

Show Links

* Full 2025 Yearly Recap - Coming next week!

Hey everyone,

December started strong and does NOT want to slow down!? OpenAI showed us their response to the Code Red and it’s GPT 5.2, which doesn’t feel like a .1 upgrade! We got it literally as breaking news at the end of the show, and oh boy! The new kind of LLMs is here.

GPT, then Gemini, then Opus and now GPT again... Who else feels like we’re on a trippy AI rolercoaster? Just me? 🫨

I’m writing this newsletter from a fresh “traveling podcaster” setup in SF (huge shoutout to the Chroma team for the studio hospitality).

P.S - Next week we’re doing a year recap episode (52st episode of the year, what is my life), but today is about the highest-signal stuff that happened this week.

Alright. No more foreplay. Let’s dive in. Please subscribe.

🔥 The main event: OpenAI launches GPT‑5.2 (and it’s… a lot)

We started the episode with “garlic in the air” rumors (OpenAI holiday launches always have that Christmas panic energy), and then… boom: GPT‑5.2 actually drops while we’re live.

What makes this release feel significant isn’t “one benchmark went up.” It’s that OpenAI is clearly optimizing for the things that have become the frontier in 2025: long-horizon reasoning, agentic coding loops, long context reliability, and lower hallucination rates when browsing/tooling is involved.

5.2 Instant, Thinking and Pro in ChatGPT and in the API

OpenAI shipped multiple variants, and even within those there are “levels” (medium/high/extra-high) that effectively change how much compute the model is allowed to burn. At the extreme end, you’re basically running parallel thoughts and selecting winners. That’s powerful, but also… very expensive.

It’s very clearly aimed at the agentic world: coding agents that run in loops, tool-using research agents, and “do the whole task end-to-end” workflows where spending extra tokens is still cheaper than spending an engineer day.

Benchmarks

I’m not going to pretend benchmarks tell the full story (they never do), but the shape of improvements matters. GPT‑5.2 shows huge strength on reasoning + structured work.

It hits 90.5% on ARC‑AGI‑1 in the Pro X‑High configuration, and 54%+ on ARC‑AGI‑2 depending on the setting. For context, ARC‑AGI‑2 is the one where everyone learns humility again.

On math/science, this thing is flexing. We saw 100% on AIME 2025, and strong performance on FrontierMath tiers (with the usual “Tier 4 is where dreams go to die” vibe still intact). GPQA Diamond is up in the 90s too, which is basically “PhD trivia mode.”

But honestly the most practically interesting one for me is GDPval (knowledge-work tasks: slides, spreadsheets, planning, analysis). GPT‑5.2 lands around 70%, which is a massive jump vs earlier generations. This is the category that translates directly into “is this model useful at my job.” - This is a bench that OpenAI launched only in September and back then, Opus 4.1 was a “measly” 47%! Talk about acceleration!

Long context: MRCR is the sleeper highlight

On MRCR (multi-needle long-context retrieval), GPT‑5.2 holds up absurdly well even into 128k and beyond. The graph OpenAI shared shows GPT‑5.1 falling off a cliff as context grows, while GPT‑5.2 stays high much deeper into long contexts.

If you’ve ever built a real system (RAG, agent memory, doc analysis) you know this pain: long context is easy to offer, hard to use well. If GPT‑5.2 actually delivers this in production, it’s a meaningful shift.

Hallucinations: down (especially with browsing)

One thing we called out on the show is that a bunch of user complaints in 2025 have basically collapsed into one phrase: “it hallucinates.” Even people who don’t know what a benchmark is can feel when a model confidently lies.

OpenAI’s system card shows lower rates of major incorrect claims compared to GPT‑5.1, and lower “incorrect claims” overall when browsing is enabled. That’s exactly the direction they needed.

Real-world vibes:

We did the traditional “vibe tests” mid-show: generate a flashy landing page, do a weird engineering prompt, try some coding inside Cursor/Codex.

Early testers broadly agree on the shape of the improvement. GPT‑5.2 is much stronger in reasoning, math, long‑context tasks, visual understanding, and multimodal workflows, with multiple reports of it successfully thinking for one to three hours on hard problems. Enterprise users like Box report faster execution and higher accuracy on real knowledge‑worker tasks, while researchers note that GPT‑5.2 Pro consistently outperforms the standard “Thinking” variant. The tradeoffs are also clear: creative writing still slightly favors Claude Opus, and the highest reasoning tiers can be slow and expensive. But as a general‑purpose reasoning model, GPT‑5.2 is now the strongest publicly available option.

AI in space: Starcloud trains an LLM on an H100 in orbit

This story is peak 2025.

Starcloud put an NVIDIA H100 on a satellite, trained Andrej Karpathy’s nanoGPT on Shakespeare, and ran inference on Gemma. There’s a viral screenshot vibe here that’s impossible to ignore: SSH into an H100… in space… with a US flag in the corner. It’s engineered excitement, and I’m absolutely here for it.

But we actually had a real debate on the show: is “GPUs in space” just sci‑fi marketing, or does it make economic sense?

Nisten made a compelling argument that power is the real bottleneck, not compute, and that big satellites already operate in the ~20kW range. If you can generate that power reliably with solar in orbit, the economics start looking less insane than you’d think. LDJ added the long-term land/power convergence argument: Earth land and grid power get scarcer/more regulated, while launch costs trend down—eventually the curves may cross.

I played “voice of realism” for a minute: what happens when GPUs fail? It’s hard enough to swap a GPU in a datacenter, now imagine doing it in orbit. Cooling and heat dissipation become a different engineering problem too (radiators instead of fans). Networking is nontrivial. But also: we are clearly entering the era where people will try weird infra ideas because AI demand is pulling the whole economy.

Big Company: MCP gets donated, OpenRouter drops a report on AI

Agentic AI Foundation Lands at the Linux Foundation

This one made me genuinely happy.

Block, Anthropic, and OpenAI came together to launch the Agentic AI Foundation under the Linux Foundation, donating key projects like MCP, AGENTS.md, and goose. This is exactly how standards should happen: vendor‑neutral, boring governance, lots of stakeholders.

It’s not flashy work, but it’s the kind of thing that actually lets ecosystems grow without fragmenting.

BTW, I was recording my podcast while Latent.Space were recording theirs in the same office, and they have a banger episode upcoming about this very topic! All I’ll say is Alessio Fanelli introduced me to David Soria Parra from MCP 👀 Watch out for that episode on Latent space dropping soon!

OpenRouter’s “State of AI”: 100 Trillion Tokens of Reality

OpenRouter and a16z dropped a massive report analyzing over 100 trillion tokens of real‑world usage. A few things stood out:

Reasoning tokens now dominate. Above 50%, around 60% of all tokens since early 2025 are reasoning tokens. Remember when we went from “LLMs can’t do math” to reasoning models? That happened in about a year.

Programming exploded. From 11% of usage early 2025 to over 50% recently. Claude holds 60% of the coding market. (at least.. on Open Router)

Open source hit 30% market share, led by Chinese labs: DeepSeek (14T tokens), Qwen (5.59T), Meta LLaMA (3.96T).

Context lengths grew massively. Average prompt length went from 1.5k to 6k+ tokens (4x growth), completions from 133 to 400 tokens (3x).

The “Glass Slipper” effect. When users find a model that fits their use case, they stay loyal. Foundational early-user cohorts retain around 40% at month 5. Claude 4 Sonnet still had 50% retention after three months.

Geography shift. Asia doubled to 31% of usage (China key), while North America is at 47%.

Yam made a good point that we should be careful interpreting these graphs—they’re biased toward people trying new models, not necessarily steady usage. But the trends are clear: agentic, reasoning, and coding are the dominant use cases.

Open Source Is Not Slowing Down (If Anything, It’s Accelerating)

One of the strongest themes this week was just how fast open source is closing the gap — and in some areas, outright leading. We’re not talking about toy demos anymore. We’re talking about serious models, trained from scratch, hitting benchmarks that were frontier‑only not that long ago.

Essential AI’s Rnj‑1: A Real Frontier 8B Model

This one deserves real attention. Essential AI — led by Ashish Vaswani, yes Ashish from the original Transformers paper — released Rnj‑1, a pair of 8B open‑weight models trained fully from scratch. No distillation. No “just a fine‑tune.” This is a proper pretrain.

What stood out to me isn’t just the benchmarks (though those are wild), but the philosophy. Rnj‑1 is intentionally focused on pretraining quality: data curation, code execution simulation, STEM reasoning, and agentic behaviors emerging during pretraining instead of being bolted on later with massive RL pipelines.

In practice, that shows up in places like SWE‑bench Verified, where Rnj‑1 lands in the same ballpark as much larger closed models, and in math and STEM tasks where it punches way above its size. And remember: this is an 8B model you can actually run locally, quantize aggressively, and deploy without legal gymnastics thanks to its Apache 2.0 license.

Mistral Devstral 2 + Vibe: Open Coding Goes Hard

Mistral followed up last week’s momentum with Devstral 2, and Mistral Vibe!

The headline numbers are: the 123B Devstral 2 model lands right at the top of open‑weight coding benchmarks, nearly matching Claude 3.5 Sonnet on SWE‑bench Verified. But what really excited the panel was the 24B Devstral Small 2, which hits high‑60s SWE‑bench scores while being runnable on consumer hardware.

This is the kind of model you can realistically run locally as a coding agent, without shipping your entire codebase off to someone else’s servers. Pair that with Mistral Vibe, their open‑source CLI agent, and you suddenly have a credible, fully open alternative to things like Claude Code, Codex, or Gemini CLI.

We talked a lot about why this matters. Some teams can’t send code to closed APIs. Others just don’t want to pay per‑token forever. And some folks — myself included — just like knowing what’s actually running under the hood. Devstral 2 checks all those boxes.

🐝 This week’s Buzz (W&B): Trace OpenRouter traffic into Weave with zero code

We did a quick “Buzz” segment on a feature that I think a lot of builders will love:

OpenRouter launched Broadcast, which can stream traces to observability vendors. One of those destinations is W&B Weave.

The magic here is: if you’re using a tool that already talks to OpenRouter, you can get tracing into Weave without instrumenting your code. That’s especially useful when instrumentation is hard (certain agent frameworks, black-box tooling, restricted environments, etc.).

If you want to set it up: OpenRouter Broadcast settings.

Vision Models Are Getting Practical (and Weirdly Competitive)

Vision‑language models quietly had a massive week.

Jina‑VLM: Small, Multilingual, and Very Good at Docs

Jina released a 2.4B VLM that’s absolutely dialed in on document understanding, multilingual VQA, and OCR‑heavy tasks. This is exactly the kind of model you’d want for PDFs, charts, scans, and messy real‑world docs — and it’s small enough to deploy without sweating too much.

Z.ai GLM‑4.6V: Long Context, Tool Calling, Serious Agent Potential

Z.ai’s GLM‑4.6V impressed us with its 128K context, native tool calling from vision inputs, and strong performance on benchmarks like MathVista and WebVoyager. It’s one of the clearest examples yet of a VLM that’s actually built for agentic workflows, not just answering questions about images.

That said, I did run my unofficial “bee counting test” on it… and yeah, Gemini still wins there 😅

Perceptron Isaac 0.2: Tiny Models, Serious Perception

Perceptron’s Isaac 0.2 (1B and 2B variants) showed something I really like seeing: structured outputs, focus tools, and reliability in very small models. Watching a 2B model correctly identify, count, and point to objects in an image is still wild to me.

These are the kinds of models that make physical AI, robotics, and edge deployments actually feasible.

🧰 Tools: Cursor goes visual, and Google Stitch keeps getting scarier (in a good way)

Cursor: direct visual editing inside the codebase

Cursor shipped a new feature that lets you visually manipulate UI elements—click/drag/resize—directly in the editor. We lumped this under “tools” because it’s not just a nicety; it’s the next step in “IDE as design surface.”

Cursor is also iterating fast on debugging workflows. The meta trend: IDEs are turning into agent platforms, not text editors.

Stitch by Google: Gemini 3 Pro as default, plus clickable prototypes

I showed Stitch on the show because it’s one of the clearest examples of “distribution beats raw capability.”

Stitch (Google’s product born from the Galileo AI acquisition) is doing Shipmas updates and now defaults to “Thinking with Gemini 3 Pro.” It can generate complex UIs, export them, and even stitch multiple screens into prototypes. The killer workflow is exporting directly into AI Studio / agent tooling so you can go from UI idea → code → repo without playing copy-paste Olympics.

Site:

https://stitch.withgoogle.com

🎬 Disney invests $1B into OpenAI (and Sora gets Disney characters)

This is the corporate story that made me do a double take.

Disney—arguably the most IP-protective company on Earth—is investing $1B into OpenAI and enabling use of Disney characters in Sora. That’s huge. It signals the beginning of a more explicit “licensed synthetic media” era, where major IP holders decide which model vendors get official access.

It also raises the obvious question: does Disney now go harder against other model providers that generate Disney-like content without permission?

We talked about how weird the timing is too, given Disney has also been sending legal pressure in the broader space. The next year of AI video is going to be shaped as much by licensing and distribution as by model quality.

Closing thoughts: the intelligence explosion is loud, messy, and accelerating

This episode had everything: open-source models catching up fast, foundation-level standardization around agents, a usage report that shows what developers actually do with LLMs, voice models getting dramatically better, and OpenAI shipping what looks like a serious “we’re not losing” answer to Gemini 3.

And yes: we’re also apparently putting GPUs in space.

Next week’s episode is our year recap, and—of course—we now have to update it because GPT‑5.2 decided to show up like the final boss.

If you missed any part of the show, check out the chapters in the podcast feed and jump around. See you next week.

TL;DR + Show Notes (links for everything)

Hosts

* Alex Volkov — AI Evangelist @ Weights & Biases: @altryne. I host ThursdAI and spend an unhealthy amount of time trying to keep up with this firehose of releases.

* Co-hosts — @WolframRvnwlf, @yampeleg, @nisten, @ldjconfirmed. Each of them brings a different “lens” (agents, infra, evaluation, open source, tooling), and it’s why the show works.

Open Source LLMs

* Essential AI — RNJ‑1 (8B base + instruct): tweet, blog, HF instruct, HF base. This is a from-scratch open pretrain led by Ashish Vaswani, and it’s one of the most important “Western open model” signals we’ve seen in a while.

* Mistral — Devstral 2 + Devstral Small 2 + Mistral Vibe: tweet, Devstral Small 2 HF, Devstral 2 HF, news, mistral-vibe GitHub. Devstral is open coding SOTA territory, and Vibe is Mistral’s swing at the CLI agent layer.

AI in Space

* Starcloud trains and runs an LLM in orbit on an H100: Philip Johnston, Adi Oltean, CNBC, Karpathy reaction. A satellite H100 trained nanoGPT on Shakespeare and ran Gemma inference, igniting a real debate about power, cooling, repairability, and future orbital compute economics.

Putnam Math Competition

* Nous Research — Nomos 1 (Putnam scoring run): tweet, HF, GitHub harness, Hillclimb. This is a strong open-weight math reasoning model plus an open harness, and it shows how orchestration matters as much as raw weights.

* Axiom — AxiomProver formal Lean proofs on Putnam: tweet, repo. Formal proofs are the “no excuses” version of math reasoning, and this is a serious milestone even if you argue about exact framing.

Big Company LLMs + APIs

* OpenAI — GPT‑5.2 release: Alex tweet, OpenAI announcement, ARC Prize verification, Sam Altman tweet. GPT‑5.2 brings major jumps in reasoning, long context, and agentic workflows, and it’s clearly positioned as an answer to the Gemini 3 era.

* OpenRouter x a16z — State of AI report (100T+ tokens): tweet, landing page, PDF. The report highlights the dominance of programming/agents, the rise of reasoning tokens, and real-world usage patterns that explain why everyone is shipping agent harnesses.

* Agentic AI Foundation under Linux Foundation (AAIF): Goose tweet, Block blog, aaif.io, Linux Foundation tweet. MCP + AGENTS.md + Goose moving into vendor-neutral governance is huge for interoperability and long-term ecosystem stability.

* Disney invests $1B into OpenAI / Sora characters: (covered on the show as a major IP + distribution moment). This is an early signal of licensed synthetic media becoming a first-class business line rather than a legal gray zone.

This week’s Buzz (W&B)

* OpenRouter Broadcast → W&B Weave tracing: Broadcast settings. You can trace OpenRouter-based traffic into Weave with minimal setup, which is especially useful when you can’t (or don’t want to) instrument code directly.

Vision & Video

* Jina — jina‑VLM (2.4B): tweet, arXiv, HF, blog. A compact multilingual VLM optimized for doc understanding and VQA.

* Z.ai — GLM‑4.6V + Flash: tweet, HF collection, GLM‑4.6V, Flash, blog. Strong open VLMs with tool calling and long context, even if my bee counting test still humbled it.

* Perceptron — Isaac 0.2 (1B/2B): tweet, HF 2B, HF 1B, blog, demo. The Focus/zoom tooling and structured outputs point toward “VLMs as reliable perception modules,” not just chatty describers.

Voice & Audio

* Google DeepMind — Gemini 2.5 TTS (Flash + Pro): AI Studio tweet, GoogleAI devs tweet, blog, AI Studio speech playground. The key upgrades are control and consistency (emotion, pacing, multi-speaker) across many languages.

* OpenBMB — VoxCPM 1.5: tweet, HF, GitHub. Open TTS keeps getting better, and this release is especially interesting for fine-tuning and voice cloning workflows.

Tools

* Cursor — direct visual editing (new UI workflow): (covered on the show as a major step toward “IDE as design surface”). Cursor continues to push the agentic IDE category into new territory.

* Stitch by Google — Shipmas updates + Gemini 3 Pro “Thinking” + Prototypes: tweet 1, tweet 2, site, plus background articles: TechCrunch launch, acquisition detail. Stitch is turning prompt-to-UI into a full prototype-to-code pipeline with real export paths.

Hey yall, Alex here 🫡

Welcome to the first ThursdAI of December! Snow is falling in Colorado, and AI releases are falling even harder. This week was genuinely one of those “drink from the firehose” weeks where every time I refreshed my timeline, another massive release had dropped.

We kicked off the show asking our co-hosts for their top AI pick of the week, and the answers were all over the map: Wolfram was excited about Mistral’s return to Apache 2.0, Yam couldn’t stop talking about Claude Opus 4.5 after a full week of using it, and Nisten came out of left field with an AWQ quantization of Prime Intellect’s model that apparently runs incredibly fast on a single GPU. As for me? I’m torn between Opus 4.5 (which literally fixed bugs that Gemini 3 created in my code) and DeepSeek’s gold-medal winning reasoning model.

Speaking of which, let’s dive into what happened this week, starting with the open source stuff that’s been absolutely cooking.

Open Source LLMs

DeepSeek V3.2: The Whale Returns with Gold Medals

The whale is back, folks! DeepSeek released two major updates this week: V3.2 and V3.2-Speciale. And these aren’t incremental improvements—we’re talking about an open reasoning-first model that’s rivaling GPT-5 and Gemini 3 Pro with actual gold medal Olympiad wins.

Here’s what makes this release absolutely wild: DeepSeek V3.2-Speciale is achieving 96% on AIME versus 94% for GPT-5 High. It’s getting gold medals on IMO (35/42), CMO, ICPC (10/12), and IOI (492/600). This is a 685 billion parameter MOE model with MIT license, and it literally broke the benchmark graph on HMMT 2025—the score was so high it went outside the chart boundaries. That’s how you DeepSeek, basically.

But it’s not just about reasoning. The regular V3.2 (not Speciale) is absolutely crushing it on agentic benchmarks: 73.1% on SWE-Bench Verified, first open model over 35% on Tool Decathlon, and 80.3% on τ²-bench. It’s now the second most intelligent open weights model and ranks ahead of Grok 4 and Claude Sonnet 4.5 on Artificial Analysis.

The price is what really makes this insane: 28 cents per million tokens on OpenRouter. That’s absolutely ridiculous for this level of performance. They’ve also introduced DeepSeek Sparse Attention (DSA) which gives you 2-3x cheaper 128K inference without performance loss. LDJ pointed out on the show that he appreciates how transparent they’re being about not quite matching Gemini 3’s efficiency on reasoning tokens, but it’s open source and incredibly cheap.

One thing to note: V3.2-Speciale doesn’t support tool calling. As Wolfram pointed out from the model card, it’s “designed exclusively for deep reasoning tasks.” So if you need agentic capabilities, stick with the regular V3.2.

Check out the full release on Hugging Face or read the announcement.

Mistral 3: Europe’s Favorite AI Lab Returns to Apache 2.0

Mistral is back, and they’re back with fully open Apache 2.0 licenses across the board! This is huge news for the open source community. They released two major things this week: Mistral Large 3 and the Ministral 3 family of small models.

Mistral Large 3 is a 675 billion parameter MOE with 41 billion active parameters and a quarter million (256K) context window, trained on 3,000 H200 GPUs. There’s been some debate about this model’s performance, and I want to address the elephant in the room: some folks saw a screenshot showing Mistral Large 3 very far down on Artificial Analysis and started dunking on it. But here’s the key context that Merve from Hugging Face pointed out—this is the only non-reasoning model on that chart besides GPT 5.1. When you compare it to other instruction-tuned (non-reasoning) models, it’s actually performing quite well, sitting at #6 among open models on LMSys Arena.

Nisten checked LM Arena and confirmed that on coding specifically, Mistral Large 3 is scoring as one of the best open source coding models available. Yam made an important point that we should compare Mistral to other open source players like Qwen and DeepSeek rather than to closed models—and in that context, this is a solid release.

But the real stars of this release are the Ministral 3 small models: 3B, 8B, and 14B, all with vision capabilities. These are edge-optimized, multimodal, and the 3B actually runs completely in the browser with WebGPU using transformers.js. The 14B reasoning variant achieves 85% on AIME 2025, which is state-of-the-art for its size class. Wolfram confirmed that the multilingual performance is excellent, particularly for German.

There’s been some discussion about whether Mistral Large 3 is a DeepSeek finetune given the architectural similarities, but Mistral claims these are fully trained models. As Nisten noted, even if they used similar architecture (which is Apache 2.0 licensed), there’s nothing wrong with that—it’s an excellent architecture that works. Lucas Atkins later confirmed on the show that “Mistral Large looks fantastic... it is DeepSeek through and through architecture wise. But Kimi also does that—DeepSeek is the GOAT. Training MOEs is not as easy as just import deepseak and train.”

Check out Mistral Large 3 and Ministral 3 on Hugging Face.

Arcee Trinity: US-Trained MOEs Are Back

We had Lucas Atkins, CTO of Arcee AI, join us on the show to talk about their new Trinity family of models, and this conversation was packed with insights about what it takes to train MOEs from scratch in the US.

Trinity is a family of open-weight MOEs fully trained end-to-end on American infrastructure with 10 trillion curated tokens from Datology.ai. They released Trinity-Mini (26B total, 3B active) and Trinity-Nano-Preview (6B total, 1B active), with Trinity-Large (420B parameters, 13B active) coming in mid-January 2026.

The benchmarks are impressive: Trinity-Mini hits 84.95% on MMLU (0-shot), 92.1% on Math-500, and 65% on GPQA Diamond. But what really caught my attention was the inference speed—Nano generates at 143 tokens per second on llama.cpp, and Mini hits 157 t/s on consumer GPUs. They’ve even demonstrated it running on an iPhone via MLX Swift.

I asked Lucas why it matters where models come from, and his answer was nuanced: for individual developers, it doesn’t really matter—use the best model for your task. But for Fortune 500 companies, compliance and legal teams are getting increasingly particular about where models were trained and hosted. This is slowing down enterprise AI adoption, and Trinity aims to solve that.

Lucas shared a fascinating insight about why they decided to do full pretraining instead of just post-training on other people’s checkpoints: “We at Arcee were relying on other companies releasing capable open weight models... I didn’t like the idea of the foundation of our business being reliant on another company releasing models.” He also dropped some alpha about Trinity-Large: they’re going with 13B active parameters instead of 32B because going sparser actually gave them much faster throughput on Blackwell GPUs.

The conversation about MOEs being cheaper for RL was particularly interesting. Lucas explained that because MOEs are so inference-efficient, you can do way more rollouts during reinforcement learning, which means more RL benefit per compute dollar. This is likely why we’re seeing labs like MiniMax go from their original 456B/45B-active model to a leaner 220B/10B-active model—they can get more gains in post-training by being able to do more steps.

Check out Trinity-Mini and Trinity-Nano-Preview on Hugging Face, or read The Trinity Manifesto.

OpenAI Code Red: Panic at the Disco (and Garlic?)

It was ChatGPT’s 3rd birthday this week (Nov 30th), but the party vibes seem… stressful. Reports came out that Sam Altman has declared a “Code Red” at OpenAI.

Why? Gemini 3.The user numbers don’t lie. ChatGPT apparently saw a 6% drop in daily active users following the Gemini 3 launch. Google’s integration is just too good, and their free tier is compelling.

In response, OpenAI has supposedly paused “side projects” (ads, shopping bots) to focus purely on model intelligence and speed. Rumors point to a secret model codenamed “Garlic”—a leaner, more efficient model that beats Gemini 3 and Claude Opus 4.5 on coding reasoning, targeting a release in early 2026 (or maybe sooner if they want to save Christmas).

Wolfram and Yam nailed the sentiment here: Integration wins. Wolfram’s family uses Gemini because it’s right there on the Pixel, controlling the lights and calendar. OpenAI needs to catch up not just on IQ, but on being helpful in the moment.

Post the live show, OpenAI also finally added GPT 5.1 Codex Max we covered 2 weeks ago to their API and it’s now available in Cursor, for free, until Dec 11!

Amazon Nova 2: Enterprise Push with Serious Agentic Chops

Amazon came back swinging with Nova 2, and the jump on Artificial Analysis is genuinely impressive—from around 30% to 61% on their index. That’s a massive improvement.

The family includes Nova 2 Lite (7x cheaper, 5x faster than Nova Premier), Nova 2 Pro (93% on τ²-Bench Telecom, 70% on SWE-Bench Verified), Nova 2 Sonic (speech-to-speech with 1.39s time-to-first-audio), and Nova 2 Omni (unified text/image/video/speech with 1M token context window—you can upload 90 minutes of video!).

Gemini 3 Deep Think Mode

Google launched Gemini 3 Deep Think mode exclusively for AI Ultra subscribers, and it’s hitting some wild benchmarks: 45.1% on ARC-AGI-2 (a 2x SOTA leap using code execution), 41% on Humanity’s Last Exam, and 93.8% on GPQA Diamond. This builds on their Gemini 2.5 variants that earned gold medals at IMO and ICPC World Finals. The parallel reasoning approach explores multiple hypotheses simultaneously, but it’s compute-heavy—limited to 10 prompts per day at $77 per ARC-AGI-2 task.

This Week’s Buzz: Mid-Training Evals are Here!

A huge update from us at Weights & Biases this week: We launched LLM Evaluation Jobs. (Docs)

If you are training models or finetuning, you usually wait until the end to run your expensive benchmarks. Now, directly inside W&B, you can trigger evaluations on mid-training checkpoints.

It integrates with Inspect Evals (over 100+ public benchmarks). You just point it to your checkpoint or an API endpoint (even OpenRouter!), select the evals (MMLU-Pro, GPQA, etc.), and we spin up the managed GPUs to run it. You get a real-time leaderboard of your runs vs. the field.

Also, a shoutout to users of Neptune.ai—congrats on the acquisition by OpenAI, but since the service is shutting down, we have built a migration script to help you move your history over to W&B seamlessly. We aren’t going anywhere!

Video & Vision: Physics, Audio, and Speed

The multimodal space was absolutely crowded this week.

Runway Gen 4.5 (”Whisper Thunder”)

Runway revealed that the mysterious “Whisper Thunder” model topping the leaderboards is actually Gen 4.5. The key differentiator? Physics and Multi-step adherence. It doesn’t have that “diffusion wobble” anymore. We watched a promo video where the shot changes every 3-4 seconds, and while it’s beautiful, it shows we still haven’t cracked super long consistent takes yet. But for 8-second clips? It’s apparently the new SOTA.

Kling 2.6: Do you hear that?

Kling hit back with Video 2.6, and the killer feature is Native Audio. I generated a clip of two people arguing, and the lip sync was perfect. Not “dubbed over” perfect, but actively generated with the video. It handles multi-character dialogue, singing, and SFX. It’s huge for creators.

Kling was on a roll this week, releasing not one, but two Video Models (O1 Video is an omni modal one that takes Text, Images and Audio as inputs) and O1 Image and Kling Avatar 2.0 are also great updates! (Find all their releases on X)

P-Image: Sub-Second Generation at Half a Cent

Last week we talked about ByteDance’s Z-Image, which was super cool and super cheap. Well, this week Pruna AI came out with P-Image, which is even faster and cheaper: image generation under one second for $0.005, and editing under one second for $0.01.

I built a Chrome extension this week (completely rewritten by Opus 4.5, by the way—more on that in a second) that lets me play with these new image models inside the Infinite Craft game. When I tested P-Image Turbo against Z-Image, I was genuinely impressed by the quality at that speed. If you want quick iterations before moving to something like Nano Banana Pro for final 4K output, these sub-second models are perfect.

The extension is available on GitHub if you want to try it—you just need to add your Replicate or Fal API keys.

SeeDream 4.5: ByteDance Levels Up

ByteDance also launched SeeDream 4.5 in open beta, with major improvements in detail fidelity, spatial reasoning, and multi-image reference fusion (up to 10 inputs for consistent storyboards). The text rendering is much sharper, and it supports multilingual typography including Japanese. Early tests show it competing well with Nano Banana Pro in prompt adherence and logic.

🎤 Voice & Audio

Microsoft VibeVoice-Realtime-0.5B

In a surprise drop, Microsoft open-sourced VibeVoice-Realtime-0.5B, a compact TTS model optimized for real-time applications. It delivers initial audible output in just 300 milliseconds while generating up to 10 minutes of speech. The community immediately started creating mirrors because, well, Microsoft has a history of releasing things on Hugging Face and then having legal pull them down. Get it while it’s hot!

Use Cases: Code, Cursors, and “Antigravity”

We wrapped up with some killer practical tips:

* Opus 4.5 is a beast: As I mentioned, using Opus inside Cursor’s “Ask” mode is currently the supreme coding experience. It debugs logic flaws that Gemini misses completely. I also used Opus as a prompt engineer for my infographics, and it absolutely demolished GPT at creating the specific layouts I needed

* Google’s Secret: Nisten dropped a bomb at the end of the show—Opus 4.5 is available for free inside Google’s Antigravity (and Colab)! If you want to try the model that’s beating GPT-5 without paying, go check Antigravity now before they patch it or run out of compute.

* Microsoft VibeVoice: A surprise drop of a 0.5B speech model on HuggingFace that does real-time TTS (300ms latency). It was briefly questionable if it would stay up, but mirrors are already everywhere.

That’s a wrap for this week, folks. Next week is probably going to be our final episode of the year, so we’ll be doing recaps and looking at our predictions from last year. Should be fun to see how wrong we were about everything!

Thank you for tuning in. If you missed the live stream, subscribe to our Substack, YouTube, and wherever you get your podcasts. See you next Thursday!

TL;DR and Show Notes

Hosts and Guests

* Alex Volkov - AI Evangelist & Weights & Biases (@altryne)

* Co Hosts - @WolframRvnwlf, @yampeleg, @nisten, @ldjconfirmed

* Guest - Lucas Atkins (@latkins) - CTO Arcee AI

Open Source LLMs

* DeepSeek V3.2 and V3.2-Speciale - Gold medal olympiad wins, MIT license (X, HF V3.2, HF Speciale, Announcement)

* Mistral 3 family - Large 3 and Ministral 3, Apache 2.0 (X, Blog, HF Large, HF Ministral)

* Arcee Trinity - US-trained MOE family (X, HF Mini, HF Nano, Blog)

* Hermes 4.3 - Decentralized training, SOTA RefusalBench (X, HF)

Big CO LLMs + APIs

* OpenAI Code Red - ChatGPT 3rd birthday, Garlic model in development (The Information)

* Amazon Nova 2 - Lite, Pro, Sonic, and Omni models (X, Blog)

* Gemini 3 Deep Think - 45.1% ARC-AGI-2 (X, Blog)

* Cursor + GPT-5.1-Codex-Max - Free until Dec 11 (X, Blog)

This Week’s Buzz

* WandB LLM Evaluation Jobs - Evaluate any OpenAI-compatible API (X, Announcement)

Vision & Video

* Runway Gen-4.5 - #1 on text-to-video leaderboard, 1,247 Elo (X)

* Kling VIDEO 2.6 - First native audio generation (X)

* Kling O1 Image - Image generation (X)

Voice & Audio

* Microsoft VibeVoice-Realtime-0.5B - 300ms latency TTS (X, HF)

AI Art & Diffusion

* Pruna P-Image - Sub-second generation at $0.005 (X, Blog, Demo)

* SeeDream 4.5 - Multi-reference fusion, text rendering (X)

Hey, Alex here,

I recorded these conversations just in front of the AI Engineer auditorium, back to back, after these great folks gave their talks, and at the epitome of the most epic AI week we’ve seen since I started recording ThursdAI.

This is less our traditional live recording, and more a real podcast-y conversation with great folks, inspired by Latent.Space. I hope you enjoy this format as much as I’ve enjoyed recording and editing it.

AntiGravity with Kevin

Kevin Hou and team just launched Antigravity, Google’s brand new Agentic IDE based on VSCode, and Kevin (second timer on ThursdAI) was awesome enough to hop on and talk about some of the product decisions they made, what makes Antigravity special and highlighted Artifacts as a completely new primitive.

Gemini 3 in AI Studio

If you aren’t using Google’s AI Studio (ai.dev) then you’re missing out! We talk about AI Studio all the time on the show, and I’m a daily user! I generate most of my images with Nano Banana Pro in there, most of my Gemini conversations are happening there as well!

Ammaar and Kat were so fun to talk to, as they covered the newly shipped “build mode” which allows you to vibe code full apps and experiences inside AI Studio, and we also covered Gemini 3’s features, multimodality understanding, UI capabilities.

These folks gave a LOT of Gemini 3 demo’s so they know everything there is to know about this model’s capabilities!

Tried new things with this one, multi camera angels, conversation with great folks, if you found this content valuable, please subscribe :)

Topics Covered:

* Inside Google’s new “AntiGravity” IDE

* How the “Agent Manager” changes coding workflows

* Gemini 3’s new multimodal capabilities

* The power of “Artifacts” and dynamic memory

* Deep dive into AI Studio updates & Vibe Coding

* Generating 4K assets with Nano Banana Pro

Timestamps for your viewing convenience.

00:00 - Introduction and Overview

01:13 - Conversation with Kevin Hou: Anti-Gravity IDE

01:58 - Gemini 3 and Nano Banana Pro Launch Insights

03:06 - Innovations in Anti-Gravity IDE

06:56 - Artifacts and Dynamic Memory

09:48 - Agent Manager and Multimodal Capabilities

11:32 - Chrome Integration and Future Prospects

20:11 - Conversation with Ammar and Kat: AI Studio Team

21:21 - Introduction to AI Studio

21:51 - What is AI Studio?

22:52 - Ease of Use and User Feedback

24:06 - Live Demos and Launch Week

26:00 - Design Innovations in AI Studio

30:54 - Generative UIs and Vibe Coding

33:53 - Nano Banana Pro and Image Generation

39:45 - Voice Interaction and Future Roadmap

44:41 - Conclusion and Final Thoughts

Looking forward to seeing you on Thursday 🫡

P.S - I’ve recorded one more conversation during AI Engineer, and will be posting that soon, same format, very interesting person, look out for that soon!

Hey ya’ll, Happy Thanskgiving to everyone who celebrates and thank you for being a subscriber, I truly appreciate each and every one of you!

Just wrapped up the third (1, 2) Thanksgiving special Episode of ThursdAI, can you believe November is almost over?

We had another banger week in AI, with a full feast of AI released, Anthropic dropped the long awaited Opus 4.5, which quickly became the top coding LLM, DeepSeek resurfaced with a math model, BFL and Tongyi both tried to take on Nano Banana, and Microsoft dropped a 7B computer use model in Open Source + Intellect 3 from Prime Intellect!

With so much news to cover, we also had an interview with Ido Sal & Liad Yosef (their second time on the show!) about MCP-Apps, the new standard they are spearheading together with Anthropic, OpenAI & more!

Exciting episode, let’s get into it! (P.S - I started generating infographics, so the show became much more visual, LMK if you like them)

ThursdAI - I put a lot of work on a weekly basis to bring you the live show, podcast and a sourced newsletter! Please subscribe if you find this content valuable!

Anthropic’s Opus 4.5: The “Premier Intelligence” Returns (Blog)

Folks, Anthropic absolutely cooked. After Sonnet and Haiku had their time in the sun, the big brother is finally back. Opus 4.5 launched this week, and it is reclaiming the throne for coding and complex agentic tasks.

First off, the specs are monstrous. It hits 80.9% on SWE-bench Verified, topping GPT-5.1 (77.9%) and Gemini 3 Pro (76.2%). But the real kicker? The price! It is now $5 per million input tokens and $25 per million output—literally one-third the cost of the previous Opus.

Yam, our resident coding wizard, put it best during the show: “Opus knows a lot of tiny details about the stack that you didn’t even know you wanted... It feels like it can go forever.” Unlike Sonnet, which sometimes spirals or loses context on extremely long tasks, Opus 4.5 maintains coherence deep into the conversation.

Anthropic also introduced a new “Effort” parameter, allowing you to control how hard the model thinks (similar to o1 reasoning tokens). Set it to high, and you get massive performance gains; set it to medium, and you get Sonnet-level performance at a fraction of the token cost. Plus, they’ve added Tool Search (cutting enormous token overhead for agents with many tools) and Programmatic Tool Calling, which effectively lets Opus write and execute code loops to manage data.

If you are doing heavy software engineering or complex automations, Opus 4.5 is the new daily driver.

📱 The Agentic Web: MCP Apps & MCP-UI Standard

Speaking of MCP updates, Can you believe it’s been exactly one year since the Model Context Protocol (MCP) launched? We’ve been “MCP-pilled” for a while, but this week, the ecosystem took a massive leap forward.

We brought back our friends Ido and Liad, the creators of MCP-UI, to discuss huge news: MCP-UI has been officially standardized as MCP Apps. This is a joint effort adopted by both Anthropic and OpenAI.

Why does this matter? Until now, when an LLM used a tool (like Spotify or Zillow), the output was just text. It lost the brand identity and the user experience. With MCP Apps, agents can now render full, interactive HTML interfaces directly inside the chat!

Ido and Liad explained that they worked hard to avoid an “iOS vs. Android” fragmentation war. Instead of every lab building their own proprietary app format, we now have a unified standard for the “Agentic Web.” This is how AI stops being a chatbot and starts being an operating system.

Check out the standard at mcpui.dev.

🦃 The Open Source Thanksgiving Feast

While the big labs were busy, the open-source community decided to drop enough papers and weights to feed us for a month.

Prime Intellect unveils INTELLECT-3, a 106B MoE (X, HF, Blog, Try It)

Prime Intellect releases INTELLECT-3, a 106B parameter Mixture-of-Experts model (12B active params) based on GLM-4.5-Air, achieving state-of-the-art performance for its size—including ~90% on AIME 2024/2025 math contests, 69% on LiveCodeBench v6 coding, 74% on GPQA-Diamond reasoning, and 74% on MMLU-Pro—outpacing larger models like DeepSeek-R1.

Trained over two months on 512 H200 GPUs using their fully open-sourced end-to-end stack (PRIME-RL async trainer, Verifiers & Environments Hub, Prime Sandboxes), it’s now hosted on Hugging Face, OpenRouter, Parasail, and Nebius, empowering any team to scale frontier RL without big-lab resources. Especially notable is their very detailed release blog, covering how a lab that previously trained 32B, finetunes a monster 106B MoE model!

Tencent’s HunyuanOCR: Small but Mighty (X, HF, Github, Blog)

Tencent released HunyuanOCR, a 1 billion parameter model that is absolutely crushing benchmarks. It scored 860 on OCRBench, beating massive models like Qwen3-VL-72B. It’s an end-to-end model, meaning no separate detection and recognition steps. Great for parsing PDFs, docs, and even video subtitles. It’s heavily restricted (no EU/UK usage), but technically impressive.

Microsoft’s Fara-7B: On-Device Computer Use

Microsoft quietly dropped Fara-7B, a model fine-tuned from Qwen 2.5, specifically designed for computer use agentic tasks. It hits 73.5% on WebVoyager, beating OpenAI’s preview models, all while running locally on-device. This is the dream of a local agent that can browse the web for you, click buttons, and book flights without sending screenshots to the cloud.

DeepSeek-Math-V2: open-weights IMO-gold math LLM (X, HF)

DeepSeek-Math-V2 is a 685B-parameter, Apache-2.0 licensed, open-weights mathematical reasoning model claiming gold-medal performance on IMO 2025 and CMO 2024, plus a near-perfect 118/120 on Putnam 2024.

Nisten did note some limitations—specifically that the context window can get choked up on extremely long, complex proofs—but having an open-weight model of this caliber is a gift to researchers everywhere.

🐝 This Week’s Buzz: Serverless LoRA Inference

A huge update from us at Weights & Biases! We know fine-tuning is powerful, but serving those fine-tunes can be a pain and expensive. We just launched Serverless LoRA Inference.

This means you can upload your small LoRA adapters (which you can train cheaply) to W&B Artifacts, and we will serve them instantly on CoreWeave GPUs on top of a base model. No cold starts, no dedicated expensive massive GPU instances for just one adapter.

I showed a demo of a “Mocking SpongeBob” model I trained in 25 minutes. It just adds that SaRcAsTiC tExT style to the Qwen 2.5 base model. You pass the adapter ID in the API call, and boom—customized intelligence instantly. You can get more details HERE and get started with your own LORA in this nice notebook the team made!

🎨 Visuals: Image & Video Generation Explosion

Flux.2: The Multi-Reference Image Creator from BFL (X, HF, Blog)

Black Forest Labs released Flux.2, a series of models including a 32B Flux 2[DEV]. The killer feature here is Multi-Reference Editing. You can feed it up to 10 reference images to maintain character consistency, style, or specific objects. It also outputs native 4-megapixel images.

Honestly, the launch timing was rough, coming right after Google’s Nano Banana Pro and alongside Z-Image, but for precise, high-res editing, this is a serious tool.

Tongyi drops Z-Image Turbo: 6B single-stream DiT lands sub‑second, 8‑step text‑to‑image (GitHub, Hugging Face)

Alibaba’s Tongyi Lab released Z-Image Turbo, a 6B parameter model that generates images in sub-second time on H800s (and super fast on consumer cards).

I built a demo to show just how fast this is. You know that “Infinite Craft“ game? I hooked it up to Z-Image Turbo so that every time you combine elements (like Pirate + Ghost), it instantly generates the image for “Ghost Pirate.” It changes the game completely when generation is this cheap and fast.

HunyuanVideo 1.5 – open video gets very real

Tencent also shipped HunyuanVideo 1.5, which they market as “the strongest open‑source video generation model.” For once, the tagline isn’t entirely hype.

Under the hood it’s an 8.3B‑parameter Diffusion Transformer (DiT) model with a 3D causal VAE in front. The VAE compresses videos aggressively in both space and time, and the DiT backbone models that latent sequence.

The important bits for you and me:

* It generates 5–10 second clips at 480p/720p with good motion coherence and physics.

* With FP16 or FP8 configs you can run it on a single consumer GPU with around 14GB VRAM.

* There’s a built‑in path to upsample to 1080p for “real” quality.

LTX Studio Retake: Photoshop for Video (X, Try It)

For the video creators, LTX Studio launched Retake. This isn’t just “regenerate video.” This allows you to select a specific 2-second segment of a video, change the dialogue (keeping the voice!), change the emotion, or edit the action, all for like $0.10. It blends it perfectly back into the original clip. We are effectively getting a “Director Mode” for AI video where you can fix mistakes without rolling the dice on a whole new generation.

A secret new model on the Arena called Whisper Thunder beats even Veo 3?

This was a surprise of the week, while new video models get released often, Veo 3 has been the top one for a while, and now we’re getting a reshuffling of the video giants! But... we don’t yet know who this video model is from! You can sometimes get its generations at the Artificial Analysis video arena here, and the outputs look quite awesome!

Thanksgiving reflections from the ThursdAI team

As we wrapped up the show, Wolfram suggested we take a moment to think about what we’re thankful for in AI, and I think that’s a perfect note to end on.

Wolfram put it well: he’s thankful for everyone contributing to this wonderful community—the people releasing models, creating open source tools, writing tutorials, sharing knowledge. It’s not just about the money; it’s about the love of learning and building together.

Yam highlighted something I think is crucial: we’ve reached a point where there’s no real competition between open source and closed source anymore. Everything is moving forward together. Even if you think nobody’s looking at that random code you posted somewhere, chances are someone found it and used it to accelerate their own work. That collective effort is what’s driving this incredible pace of progress.

For me, I want to thank Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan Gomez, Łukasz Kaiser, and Ilya Polosukhin for the 2017 paper “Attention Is All You Need.” Half Joking! But without the seminal attention is you need paper none of this AI was possible. But mostly I want to thank all of you—the audience, the co-hosts, the guests—for making ThursdAI what it is.

If you go back and watch our 2024 Thanksgiving episode, or the one from 2023, you’ll be shocked at how far we’ve come. Tools that seemed magical a year ago are now just... normal. That’s hedonic adaptation at work, but it’s also a reminder to stay humble and appreciate just how incredible this moment in history really is.

We’re living through the early days of a technological revolution, and we get to document it, experiment with it, and help shape where it goes. That’s something to be genuinely thankful for.

TL;DR and Show Notes

* Hosts and Guests

* Alex Volkov - AI Evangelist & Weights & Biases (@altryne)

* Co-Hosts - @WolframRvnwlf @yampeleg @nisten @ldjconfirmed

* Guests: @idosal1 @liadyosef - MCP-UI/MCP Apps

* Big CO LLMs + APIs

* Anthropic launches Claude Opus 4.5 - world’s top model for coding, agents, and tool use (X, Announcement, Blog)

* OpenAI Integrates ChatGPT Voice Mode Directly into Chats (X)

* Open Source LLMs

* Prime Intellect - INTELLECT-3 106B MoE (X, HF, Blog, Try It)

* Tencent - HunyuanOCR 1B SOTA OCR model (X, HF, Github, Blog)

* Microsoft - Fara-7B on-device computer-use agent (X, Blog, HF, Github)

* DeepSeek - Math-V2 IMO-gold math LLM (HF)

* Interview: MCP Apps

* MCP-UI standardized as MCP Apps by Anthropic and OpenAI (X, Blog, Announcement)

* Vision & Video

* Tencent - HunyuanVideo 1.5 lightweight DiT open video model (X, GitHub, HF)

* LTX Studio - Retake AI video editing tool (X, Try It)

* Whisper Thunder - mystery #1 ranked video model on arena

* AI Art & Diffusion

* Black Forest Labs - FLUX.2 32B multi-reference image model (X, HF, Blog)

* Tongyi - Z-Image Turbo sub-second 6B image gen (GitHub, HF)

* This Week’s Buzz

* W&B launches Serverless LoRA Inference on CoreWeave (X, Blog, Notebook)

Hey everyone, Alex here 👋

I’m writing this one from a noisy hallway at the AI Engineer conference in New York, still riding the high (and the sleep deprivation) from what might be the craziest week we’ve ever had in AI.

In the span of a few days:

Google dropped Gemini 3 Pro, a new Deep Think mode, generative UIs, and a free agent-first IDE called Antigravity.xAI shipped Grok 4.1, then followed it up with Grok 4.1 Fast plus an Agent Tools API.OpenAI answered with GPT‑5.1‑Codex‑Max, a long‑horizon coding monster that can work for more than a day, and quietly upgraded ChatGPT Pro to GPT‑5.1 Pro.Meta looked at all of that and said “cool, we’ll just segment literally everything and turn photos into 3D objects” with SAM 3 and SAM 3D.Robotics folks dropped a home robot trained with almost no robot data.And Google, just to flex, capped Thursday with Nano Banana Pro, a 4K image model and a provenance system while we were already live!

For the first time in a while it doesn’t just feel like “new models came out.” It feels like the future actually clicked forward a notch.

This is why ThursdAI exists. Weeks like this are basically impossible to follow if you have a day job, so my co‑hosts and I do the no‑sleep version so you don’t have to. Plus, being at AI Engineer makes it easy to get super high quality guests so this week we had 3 folks join us, Swyx from Cognition/Latent Space, Thor from DeepMind (on his 3rd day) and Dominik from OpenAI! Alright, deep breath. Let’s untangle the week.

TL;DR

If you only skim one section, make it this one (links in the end):

* Google

* Gemini 3 Pro: 1M‑token multimodal model, huge reasoning gains - new LLM king

* ARC‑AGI‑2: 31.11% (Pro), 45.14% (Deep Think) – enormous jumps

* Antigravity IDE: free, Gemini‑powered VS Code fork with agents, plans, walkthroughs, and browser control

* Nano Banana Pro: 4K image generation with perfect text + SynthID provenance; dynamic “generative UIs” in Gemini

* xAI

* Grok 4.1: big post‑training upgrade – #1 on human‑preference leaderboards, much better EQ & creative writing, fewer hallucinations

* Grok 4.1 Fast + Agent Tools API: 2M context, SOTA tool‑calling & agent benchmarks (Berkeley FC, T²‑Bench, research evals), aggressive pricing and tight X + web integration

* OpenAI

* GPT‑5.1‑Codex‑Max: “frontier agentic coding” model built for 24h+ software tasks with native compaction for million‑token sessions; big gains on SWE‑Bench, SWE‑Lancer, TerminalBench 2

* GPT‑5.1 Pro: new “research‑grade” ChatGPT mode that will happily think for minutes on a single query

* Meta

* SAM 3: open‑vocabulary segmentation + tracking across images and video (with text & exemplar prompts)

* SAM 3D: single‑image → 3D objects & human bodies; surprisingly high‑quality 3D from one photo

* Robotics

* Sunday Robotics – ACT‑1 & Memo: home robot foundation model trained from a $200 skill glove instead of $20K teleop rigs; long‑horizon household tasks with solid zero‑shot generalization

* Developer Tools

* Antigravity and Marimo’s VS Code / Cursor extension both push toward agentic, reactive dev workflows

Live from AI Engineer New York: Coding Agents Take Center Stage

We recorded this week’s show on location at the AI Engineer Summit in New York, inside a beautiful podcast studio the team set up right on the expo floor. Huge shout out to Swyx, Ben, and the whole AI Engineer crew for that — last time I was balancing a mic on a hotel nightstand, this time I had broadcast‑grade audio while a robot dog tried to steal the show behind us.

This year’s summit theme is very on‑the‑nose for this week: coding agents.

Everywhere you look, there’s a company building an “agent lab” on top of foundation models. Amp, Cognition, Cursor, CodeRabbit, Jules, Google Labs, all the open‑source folks, and even the enterprise players like Capital One and Bloomberg are here, trying to figure out what it means to have real software engineers that are partly human and partly model.

Swyx framed it nicely when he said that if you take “vertical AI” seriously enough, you eventually end up building an agent lab. Lawyers, healthcare, finance, developer tools — they all converge on “agents that can reason and code.”

The big labs heard that theme loud and clear. Almost every major release this week is about agents, tools, and long‑horizon workflows, not just chat answers.

Google Goes All In: Gemini 3 Pro, Antigravity, and the Agent Revolution

Let’s start with Google because, after years of everyone asking “where’s Google?” in the AI race, they showed up this week with multiple bombshells that had even the skeptics impressed.

Gemini 3 Pro: Multimodal Intelligence That Actually Delivers

Google finally released Gemini 3 Pro, and the numbers are genuinely impressive. We’re talking about a 1 million token context window, massive benchmark improvements, and a model that’s finally competing at the very top of the intelligence charts. Thor from DeepMind joined us on the show (literally on day 3 of his new job!) and you could feel the excitement.

The headline numbers: Gemini 3 Pro with Deep Think mode achieved 45.14% on ARC-AGI-2—that’s roughly double the previous state-of-the-art on some splits. For context, ARC-AGI has been one of those benchmarks that really tests genuine reasoning and abstraction, not just memorization. The standard Gemini 3 Pro hits 31.11% on the same benchmark, both scores are absolutely out of this world in Arc!

On GPQA Diamond, Gemini 3 Pro jumped about 10 points compared to prior models. We’re seeing roughly 81% on MMLU-Pro, and the coding performance is where things get really interesting—Gemini 3 Pro is scoring around 56% on SciCode, representing significant improvements in actual software engineering tasks.

But here’s what made Ryan from Amp switch their default model to Gemini 3 Pro immediately: the real-world usability. Ryan told us on the show that they’d never switched default models before, not even when GPT-5 came out, but Gemini 3 Pro was so noticeably better that they made it the default on Tuesday. Of course, they hit rate limits almost immediately (Google had to scale up fast!), but those have since been resolved.

Antigravity: Google’s Agent-First IDE

Then Google dropped Antigravity, and honestly, this might be the most interesting part of the whole release. It’s a free IDE (yes, free!) that’s basically a fork of VS Code, but reimagined around agents rather than human-first coding.

The key innovation here is something they call the “Agent Manager”—think of it like an inbox for your coding agents. Instead of thinking in folders and files, you’re managing conversations with agents that can run in parallel, handle long-running tasks, and report back when they need your input.

I got early access and spent time playing with it, and here’s what blew my mind: you can have multiple agents working on different parts of your codebase simultaneously. One agent fixing bugs, another researching documentation, a third refactoring your CSS—all at once, all coordinated through this manager interface.

The browser integration is crazy too. Antigravity can control Chrome directly, take screenshots and videos of your app, and then use those visuals to debug and iterate. It’s using Gemini 3 Pro for the heavy coding, and even Nano Banana for generating images and assets. The whole thing feels like it’s from a couple years in the future.

Wolfram on the show called out how good Gemini 3 is for creative writing too—it’s now his main model, replacing GPT-4.5 for German language tasks. The model just “gets” the intention behind your prompts rather than following them literally, which makes for much more natural interactions.

Nano Banana Pro: 4K Image Generation With Thinking

And because Google apparently wasn’t done announcing things, they also dropped Nano Banana Pro on Thursday morning—literally breaking news during our live show. This is their image generation model that now supports 4K resolution and includes “thinking” traces before generating.

I tested it live by having it generate an infographic about all the week’s AI news (which you can see on the top), and the results were wild. Perfect text across the entire image (no garbled letters!), proper logos for all the major labs, and compositional understanding that felt way more sophisticated than typical image models. The file it generated was 8 megabytes—an actual 4K image with stunning detail.

What’s particularly clever is that Nano Banana Pro is really Gemini 3 Pro doing the thinking and planning, then handing off to Nano Banana for the actual image generation. So you get multimodal reasoning about your request, then production-quality output. You can even upload reference images—up to 14 of them—and it’ll blend elements while maintaining consistency.

Oh, and every image is watermarked with SynthID (Google’s invisible watermarking tech) and includes C2PA metadata, so you can verify provenance. This matters as AI-generated content becomes more prevalent.

Generative UIs: The Future of Interfaces

One more thing Google showed off: generative UIs in the Gemini app. Wolfram demoed this for us, and it’s genuinely impressive. Instead of just text responses, Gemini can generate full interactive mini-apps on the fly—complete dashboards, data visualizations, interactive widgets—all vibe-coded in real time.

He asked for “four panels of the top AI news from last week” and Gemini built an entire news dashboard with tabs, live market data (including accurate pre-market NVIDIA stats!), model comparisons, and clickable sections. It pulled real information, verified facts, and presented everything in a polished UI that you could interact with immediately.

This isn’t just a demo—it’s rolling out in Gemini now. The implication is huge: we’re moving from static responses to dynamic, contextual interfaces generated just-in-time for your specific need.

xAI Strikes Back: Grok 4.1 and the Agent Tools API

Not to be outdone, xAI released Grok 4.1 at the start of the week, briefly claimed the #1 spot on LMArena (at 1483 Elo, not 2nd to Gemini 3), and then followed up with Grok 4.1 Fast and a full Agent Tools API.

Grok 4.1: Emotional Intelligence Meets Raw Performance

Grok 4.1 brought some really interesting improvements. Beyond the benchmark numbers (64% win rate over the previous Grok in blind tests), what stood out was the emotional intelligence. On EQ-Bench3, Grok 4.1 Thinking scored 1586 Elo, beating every other model including Gemini, GPT-5, and Claude.

The creative writing scores jumped by roughly 600 Elo points compared to earlier versions. And perhaps most importantly for practical use, hallucination rates dropped from around 12% to 4%—that’s roughly a 3x improvement in reliability on real user queries.

xAI’s approach here was clever: they used “frontier agentic reasoning models as reward models” during RL training, which let them optimize for subjective qualities like humor, empathy, and conversational style without just scaling up model size.

Grok 4.1 Fast: The Agent Platform Play

Then came Grok 4.1 Fast, released just yesterday, and this is where things get really interesting for developers. It’s got a 2 million token context window (compared to Gemini 3’s 1 million) and was specifically trained for agentic, tool-calling workflows.

The benchmark performance is impressive: 93-100% on τ²-Bench Telecom (customer support simulation), ~72% on Berkeley Function Calling v4 (top of the leaderboard), and strong scores across research and browsing tasks. But here’s the kicker: the pricing is aggressive.

At $0.20 per million input tokens and $0.50 per million output tokens, Grok 4.1 Fast is dramatically cheaper than GPT-5 and Claude while matching or exceeding their agentic performance. For the first two weeks, it’s completely free via the xAI API and OpenRouter, which is smart—get developers hooked on your agent platform.

The Agent Tools API gives Grok native access to X search, web browsing, code execution, and document retrieval. This tight integration with X is a genuine advantage—where else can you get real-time access to breaking news, sentiment, and conversation? Yam tested it on the show and confirmed that Grok will search Reddit too, which other models often refuse to do. I’ve used both these models this week in my N8N research agent and I gotta say, 4.1 fast is a MASSIVE improvement!

OpenAI’s Endurance Play: GPT-5.1-Codex-Max and Pro

OpenAI clearly saw Google and xAI making moves and decided they weren’t going to let this week belong to anyone else. They dropped two significant releases: GPT-5.1-Codex-Max and an update to GPT-5.1 Pro.

GPT-5.1-Codex-Max: Coding That Never Stops

This is the headline: GPT-5.1-Codex-Max can work autonomously for over 24 hours. Not 24 minutes, not 24 queries—24 actual hours on a single software engineering task. I talked to someone from OpenAI at the conference who told me internal checkpoints ran for nearly a week on and off.

How is this even possible? The secret is something OpenAI calls “compaction”—a native mechanism trained into the model that lets it prune and compress its working session history while preserving the important context. Think of it like the model taking notes on itself, discarding tool-calling noise and keeping only the critical design decisions and state.

The performance numbers back this up:

* SOTA 77.9% on SWE-Bench Verified (up from 73.7%)

* SOTA 79.9% on SWE-Lancer IC SWE (up from 66.3%)

* 58.1% on TerminalBench 2.0 (up from 52.8%)

And crucially, in medium reasoning mode, it uses 30% fewer thinking tokens while achieving better results. There’s also an “Extra High” reasoning mode for when you truly don’t care about latency and just want maximum capability.

Yam, one of our co-hosts who’s been testing extensively, said you can feel the difference immediately. The model just “gets it” faster, powers through complex problems, and the earlier version’s quirk of ignoring your questions and just starting to code is fixed—now it actually responds and collaborates.

Dominic from OpenAI joined us on the show and confirmed that compaction was trained natively into the model using RL, similar to how Claude trained natively for MCP. This means the model doesn’t waste reasoning tokens on maintaining context—it just knows how to do it efficiently.

GPT-5.1 Pro: Research-Grade Intelligence & ChatGPT joins your group chat1

Then there’s GPT-5.1 Pro, which is less about coding and more about deep, research-level reasoning. This is the model that can run for 10-17 minutes on a single query, thinking through complex problems with the kind of depth that previously required human experts.

OpenAI also quietly rolled out group chats—basically, you can now have multiple people in a ChatGPT conversation together, all talking to the model simultaneously. Useful for planning trips, brainstorming with teams, or working through problems collaboratively. If agent mode works in group chats (we haven’t confirmed yet), that could get really interesting.

Meta drops SAM3 & SAM3D - image and video segmentation models powered by natural language

Phew ok, big lab releases now done, oh.. wait not yet! Because Meta has decided to also make a dent on this Week with SAM3 and SAM3D, which both are crazy. I’ll just add their video release here instead of going on and on!

This Week’s Buzz from Weights & Biases

It’s been a busy week at Weights & Biases as well! We are proud Gold Sponsors of the AI Engineer conference here in NYC. If you’re at the event, please stop by our booth—we’re even giving away a $4,000 robodog!

This week, I want to highlight a fantastic update from Marimo, the reactive Python notebook company we acquired.

Marimo just shipped a native VS Code and Cursor extension. This brings Marimo’s reactive, Git-friendly notebooks directly into your favorite editors.

Crucially, it integrates deeply with uv for lightning-fast package installs and reproducible environments. If you import a package you don’t have, the extension prompts you to install it and records the dependency in the script metadata. This bridges the gap between experimental notebooks and production-ready code, and it’s a huge boost for AI-native development workflows. (Blog , GitHub )

The Future Arrived Early

Phew... if you read all the way until this point, can you leave a ⚡ emoji in the comemnts? I was writing this and it.. is a lot! I was wondering who would even read all the way till here!

This week we felt the acceleration! 🔥 I can barely breathe, I need a nap!

A huge thank you to our guests—Ryan, Swyx, Thor, and Dominik—for navigating the chaos with us live on stage, and to the AI Engineer team for hosting us.

We’ll be back next week to cover whatever the AI world throws at us next. Stay tuned, because at this rate, AGI might be here by Christmas.

TL;DR - show notes and links

Hosts and Co‑hosts

* Alex Volkov – AI Evangelist at Weights & Biases / CoreWeave, host of ThursdAI (X)

* Co‑hosts - Wolfram Ravenwolf – (X), Yam Peleg (X) LDJ (X)

Guests

* Swyx – Founder of AI Engineer World’s Fair and Summit, now at Cognition ( Latent.Space , X)

* Ryan Carson – Amp (X)

* Thor Schaeff – Google DeepMind, Gemini API and AI Studio (X)

* Dominik Kundel – Developer Experience at OpenAI (X)

Open Source LLMs

* Allen Institute Olmo 3 - 7B/32B fully open reasoning suite with end-to-end training transparency (X, Blog)

Big CO LLMs + APIs

* Google Gemini 3 Pro - 1M-token, multimodal, agentic model with Generative UIs (X, X, X)

* Google Antigravity - Agent-first IDE powered by Gemini 3 Pro (Blog, X)

* xAI Grok 4.1 and Grok 4.1 Thinking - big gains in Coding, EQ, creativity, and honesty (X, Blog)

* xAI Grok 4.1 Fast and Agent Tools API - 2M-token context, state-of-the-art tool-calling (X)

* OpenAI GPT-5.1-Codex-Max - long-horizon agentic coding model for 24-hour+ software tasks (X, X)

* OpenAI GPT-5.1 Pro - research-grade reasoning model in ChatGPT Pro

* Microsoft, NVIDIA, and Anthropic partnership - to scale Claude on Azure with massive GPU investments (Announcement, NVIDIA, Microsoft Blog)

This weeks Buzz

* Marimo ships native VS Code & Cursor extension with reactive notebooks and uv-powered environments (X, Blog, GitHub)

Vision & Video & 3D

* Meta SAM 3 & SAM 3D - promptable segmentation, tracking, and single-image 3D reconstruction (X, Blog, GitHub)

AI Art & Diffusion

* Google Nano Banana Pro and SynthID verification - 4K image generation with provenance (Blog)

Show Notes and other Links

* AI Engineer Summit NYC - Live from the conference

* Full livestream available on YouTube

* ThursdAI - Nov 20, 2025