Software Huddle

Big time guest today as Arvid Kahl joins us. Arvid is my favorite type of guest -- a deeply technical founder that can talk about both the technical and business challenges of a startup. Lots to enjoy from this episode.

Arvid is known as the Bootstrapped Founder and has documented his path to selling Feedback Panda back in 2019. He's now building Podscan and sharing his journey as he goes.

Podscan is a fascinating project. It's making the content of *every* podcast episode around the world fully searchable. He currently has 3.5 million episodes transcribed and adds another 30,000 - 50,000 episodes every day.

This involves a ton of technical challenges, including how to get the best transcription results from the latest LLMs, whether you should use APIs from public providers or run your own LLMs, and how to efficiently provide full-text search across terabytes of transcription data. Arvid shares the lessons he's learned and the various strategies he's tried over the years.

But there are also unique business challenges. For most technical businesses, your infrastructure costs grow in line with your customers. More customers == more data == more servers. With Podscan, Arvid has to index the entire podcast ecosystem regardless of his customers. This means a lot of upfront investment as he looks to grow his customer base. Arvid tells us how he's optimized his infrastructure to account for this unique challenge.

Today we have the excellent Kent C. Dodds on the program. Kent is an amazing teacher in the web development space, and I've learned a ton from him about React, JavaScript testing, and general web dev.

Lately, Kent has been going all-in on AI, especially with the model context protocol (MCP) space. He's sharing a ton of useful material in this area as he works on a new course. We spent a lot of time going over what MCP is, why it's useful, and why Kent thinks our own personal Jarvis is the next step.

Follow Kent: https://twitter.com/kentcdodds

Follow Alex: https://twitter.com/alexbdebrie

Follow Sean: https://twitter.com/seanfalconer

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Today's guest is AJ Stuyvenberg, a Staff Engineer at Datadog working on their Serverless observability project. He had a great article recently about how they rewrote their AWS Lambda extension in Rust. It's a really interesting look at a big, hard project, from thinking about when it's a good idea to do a rewrite to talking about their focus on performance and reliability above all else and what he thinks about the Rust ecosystem.

Beyond that, AJ is just a learning machine, so I got his thoughts on all kinds of software development topics, from underrated AWS services and our favorite databases to the AWS Free Tier and the annoyances of a new AWS account.

Finally, AJ dishes out some career advice for curious, ambitious developers.

So if you're writing code or keeping systems running, you probably know the drill. Late night pages, chasing down weird bugs, dealing with alert storms. It's tough! It costs money when things break, and honestly, nobody loves that experience.

So the big question is, can we actually use something like AI, AI agents in particular, to make reliability less painful, more systematic? That's what we're talking about today. We have on the show with us Amal Kiran, the CEO and Co-founder of Temperstack.

They're building tools aimed at automating SRE tasks, think, automatically finding monitoring gaps, alerts, helping with root cause analysis, even generating Runbooks using AI. 

So if you wanna hear about applying AI to real world SRE problems and all the tech behind it, we think you're gonna enjoy this.

Today we have Søren from Prisma on the show. Prisma has been the most popular ORM in the TypeScript world for a while, and now they’re moving more into hosted infrastructure.

We spend a lot of time talking about their new offering called Prisma Postgres, which is this unikernel-based Postgres offering. It’s a really unique offering from both a technical and a product perspective.

On the technical side, they’re doing some interesting work compared to other Postgres providers. They’re running on bare metal in a colocation facility rather than the default public clouds like AWS, GCP, and Azure. Further, they’re using unikernels in a Firecracker VM, giving them unique startup and security characteristics.

These technical decisions give them unique economics compared to standard providers, so they’re able to have a generous free tier and a unique billing model that works great for serverless applications with spiky workloads.

Around all of this, it’s very interesting to see a company with such a unique spread of products — a popular, mature open-source library paired with a mission-critical infrastructure service offering. We talked about the difficulties in building a company that accommodates these two very different products.

Timestamps

01:51 Start

06:08 Prisma Postgres

09:10 Accelerate

11:39 Why Postgres

17:32 How Prisma Postgres Works

21:32 Colocation Facility

22:05 Unikernels

27:56 CoLo vs Public Cloud

29:11 Building the team

31:46 Missing Features that are being worked on

32:31 Use Cases

33:37 Colo Locations

34:53 Cloudflare

35:42 Biggest surprises since release

37:34 More Unikernel adoption?

39:08 Supporting Prisma ORM

46:43 Mongo

47:51 Life as A CEO

53:04 MCP

57:23 Søren Questions Alex

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Today we have Hassan back on the show. Hassan was one of our first guests for Huddle when he was working at Vercel, but since then, he's joined Together AI, one of the hottest companies in the world. They just raised a massive series B round.

Hassan joins us to talk about Together AI, inference optimization and building AI applications. We touch on a bunch of topics like customer uses of AI, best practices for building apps, and what's next for Together AI.

Timestamps

01:42 Opportunity at Together AI

04:26 Together raised a big round

06:06 Vision Behind Together AI

08:32 Problems in running Open Source Models

11:40 Speed For Inference

14:24 Fine Tuning

19:23 One or Two Models or a Combination of them

21:32 Serverless

22:21 Cold Start issues?

27:46 How much data do you need?

30:00 Balancing Reliability and Cost

34:07 How customers are using Together

42:36 Agent Recipes

47:03 Typical Mistakes buiilding AI apps

Today on the show, we talked with Yujian Tang. He was on the show previously when he worked at Zilliz, when we talked about vector databases and RAG. He's since branched out on his own, building the tech startup scene in Seattle and organizing AI events all over the place.

We talk about his latest venture, the Seattle Startup Summit, coming up on March 28th. They're still Early Bird Tickets available if you're interested.

We also talk about AI models, the impact AI is having on programming, including our own programming projects and share our takes on some of the recent acquisitions that have happened in tech, including Voyage AI.

Today, we have Sam Lambert back on the show! Sam is the CEO of PlanetScale, and if you follow him on X, you know he’s one of the sharpest voices in the database space—cutting through the hype with deep experience and a no-nonsense approach.

In this episode, we dive into PlanetScale’s new Metal offering, which has been battle-tested with PlanetScale’s high-scale cloud business partners and is now GA.

Sam also shares why staying profitable is crucial—not just for the business but for the stability and reliability it guarantees for customers. While many cloud infrastructure companies chase the next hype cycle, Sam prefers to keep it boring—delivering rock-solid performance with no surprises

Finally, we close with Sam's thoughts on other happenings in the database space -- Aurora DSQL, Aurora Limitless, MySQL benchmarks, and multi-region strong consistency.

Tune in for a deep dive into databases, cloud infrastructure, and what it takes to build a sustainable, high-performance tech company.

Timestamps

01:34 Start

06:42 PlanetScale Metal

11:15 The problem with separation of storage and compute

15:02 EBS Tax

17:32 How does Vitess handle durability

22:58 Metal recommended for all PlanetScale users?

27:20 The hidden expense of IOPS for cloud databases

37:41 Timeline of creating PlanetScale Metal

41:32 Focus on profitability

47:52 Removal of hobby plan

57:45 Deprecation of PlanetScale Boost

01:00:24 DSQL

01:01:51 Aurora Limitless

01:04:15 AWS as a partner

01:07:00 The spectacle of AWS re:Invent

01:12:22 Benchmarks and benchmarketing

01:15:51 AWS Databases + multi-region strong consistency

Redis is consistently one of the most beloved pieces of infrastructure for developers. And in the last few years, we've seen a number of new Redis-compatible projects that aim to improve on the core of Redis in some way.

One of those projects is DragonflyDB, a multi-threaded version of Redis that allows for significantly higher throughput on a single instance. Roman Gershman is the co-founder and CTO at Dragonfly, and he has a fascinating background. Roman initially worked at Google and then was a frustrated user of Redis while working as an engineer at a fast-growing startup. He did a stint on the ElastiCache team at AWS but struck off on his own to make a new, faster version of Redis.

In this episode, we talk through the improvements that Dragonfly makes to Redis and why it matters to high-scale users. We go through the different needs and requirements of high-scale cache applications and what Roman learned at AWS. We also go through the Redis licensing drama and how to attract developer attention in 2025.

Today, we’re joined by Johann Schleier-Smith. Johann co-founded Tagged during the early days of social media, a time when building scalable systems for the web was uncharted territory. Back then, cloud computing didn’t exist—everything ran on on-premises servers or in co-located data centers.

We discuss the challenges of scaling Tagged and draw parallels to the current wave of innovation around Generative AI and large language models. Johann shares how building with these technologies feels like a similar uphill climb.

We also dive into his new venture, CrystalDBA, and how it’s leveraging AI to optimize databases, making advanced database management accessible to everyone.