Today we have Dr. Ewelina Kurtys on the show. Ewelina has a background in Neuroscience and is currently working at FinalSpark. FinalSpark is using live Neurons for computations instead of traditional electric CPUs. The advantage is that live Neurons are significantly more energy efficient than traditional computing, and given all the energy concerns right now with regards to running AI workloads and data centers, this seems quite relevant, even though bioprocessors are still very much in the research phase.
Today we're talking with one of our favorite engineers, Rafal Wilinski. Rafal has been on the cutting edge of AI development in the last few years as he has led AI teams at Zapier and Vendr. Rafal walks us through the hard-won lessons about actually integrating AI tools into the applications you're building. One of the hardest things in integrating these AI tools is how to ensure you're getting better and not regressing as you improve your prompts and upgrade your models. He shows how using evals is one part of the story along with deeply investigating customer signals to see how they are or aren't succeeding with AI. Along the way, we also talk about RAG, his favorite models, his AI development toolset, and why Poland has been killing it lately. Check it out and be sure to follow Rafal if you want to learn more on building with AI.
If you’ve ever felt like engineering teams are stuck in execution mode—heads down, building what they’re told—then today’s episode is for you. We're talking about what it really takes to build high ownership engineering cultures where devs aren't simply just shipping code, but they're helping shape the product. And our guest this week is Matt Watson. He's a long time founder, engineer, and now the CEO of Full Scale, a company that helps startups and scale ups, grow their engineering teams with top talent from the Philippines. Matt's also the author of a book called Product Driven that shows how engineers can build with more clarity, purpose, customer focus and we get into some of the details in that book during this podcast. So in this episode, we get into everything from the downsides of specialization to the importance of empathy, to why code shipped isn't the same as value delivered. We hope you enjoy it.
Today's episode is with Aayush Shah. Aayush is one of the co-founders of Blacksmith, which is a CI compute platform. Basically, Blacksmith will run your GitHub Actions jobs faster and with more visibility with the standard GitHub Actions CI runners. The founding team has a fun background doing systems work at Cockroach and Faire, and they're taking on a big problem in running this massive CI fleet. The explosion in AI agents has really changed the CI world. CI is more useful than ever, as you want to be sure the changes from your agents aren't breaking your existing functionality. At the same time, there's a huge increase in demand and spikiness of CI workloads as developers can fire off multiple agents to work in parallel, each needing to run the CI suite before merging. Aayush talked about how they're handling this load and facilitating visibility into test failures. We also covered cloud economics. Aayush said the traditional cloud-based storage options don't work for them -- EBS and locally attached SSDs are too expensive for their workloads where they don't need the standard durability guarantees. He walks us through building their own fleet outside the hyperscalers and the plans going forward, along with some of the economics of multi-tenancy that Blacksmith has previously written about.
Today, we're talking Valkey, Redis, and all things caching. Our guest is Madelyn Olson, who is a principal engineer at AWS working on Elasticache and is one of the most well-known people in the caching community. She was a core maintainer of Redis prior to the fork and was one of the creators of Valkey, an open-source fork of Redis. In this episode, we talk about Madelyn's road to becoming a Redis maintainer and how she found out about the March 2024 license change. Then, Madelyn shares the story of Valkey being created, philosophical differences between the projects, and her reaction to re-relicensing of Redis in May 2025. Next, we dive into the performance improvements of recent Valkey releases, including the I/O threads improvements and the new hash table layout. Along the way, Madelyn dispels the notion that the single-threaded nature of Redis / Valkey is that big of a hindrance for most workloads. Finally, she compares some of the Valkey improvements to some of the other recent cache competitors in the space.
Today, Sam Lambert from Planetscale is back for a third time. Planetscale just announced Planetscale Postgres, so we had to get Sam back to tell us how and why they decided to add support for Postgres. It's always great to have Sam on -- he brings great stories about real customers and honest insight about the state of the database industry. In this episode, we talk about the road to Postgres and how operational excellence is the only true advantage in database providers. Sam walks us through the current Planetscale Postgres offering, along with details on Nova, a new sharded Postgres project that Planetscale is working on. Along the way, we get updates on Planetscale Metal, how demand has been for Planetscale Postgres, and future plans for Planetscale.
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. We cover a bunch of other topics too, like what it's like putting on a conference (Epic Web Conf) plus how AI has changed the educational space. Check it out! *Timestamps* 01:12 Start 06:52 The pitch for MCP 14:30 Where does MCP architecturally sit? 17:27 Contrasting with REST 23:07 Should I be building these now? 23:47 Are there any frameworks? 26:31 Why Cloudflare 34:10 MCP Spec 35:35 Authentication 38:29 A2A by Google 41:50 What caught Kent's attention? 44:28 What got Kent interested in React? 46:16 Jarvis 47:44 Frontend Development in the long run 51:44 What needs to get better for this to happen? 57:42 How has AI impacted education landscape? 01:04:46 Like the travel? 01:12:35 App Stack 01:13:48 React Server Components Follow Kent: https://twitter.com/kentcdodds Follow Alex: https://twitter.com/alexbdebrie Follow Sean: https://twitter.com/seanfalconer *Software Huddle ⤵︎* X: https://twitter.com/SoftwareHuddle
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 Software Huddle ⤵︎ X: https://twitter.com/SoftwareHuddle Substack: https://softwarehuddle.substack.com
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.
Today, we have David Cramer on the show. David is one of the co-founders of Sentry, an application monitoring tool that's one of the most widely-adopted tools for developers. Sentry does over 300,000 events per second on average, and there's a lot of fancy work to process these application errors, from rate limiting to fingerprinting to counting to source map unminifying. We walk through some of the architectural changes and systems design work here, including some of David's thoughts on shipping. David and Sentry also have a unique approach to developer marketing. They do some cool things -- sponsoring and then buying the amazing SyntaxFM podcast, sending $100k of free gifts to developers, and launching the Open Source Pledge with $500k donated to open source developers.
Today we have Philip Kiely from Baseten on the show. Baseten is a Series B startup focused on providing infrastructure for AI workloads. We go deep on Inference Optimization. We cover choosing a model, discuss the hype around Compound AI, choosing an Inference Engine, Optimization Techniques like Quantization and Speculative Decoding all the way down to your GPU choice.
Today on the show, we have Kevin Dubois. Kevin is a Senior Principal Developer Advocate at Red Hat, Java Champion, and well known open source contributor. In our conversation with Kevin, we talk about his history with Java and the evolution of the language and where it now fits within the world of AI. Kevin's been building AI applications with Java using Quarkus andLangChain4j. Kevin's a java expert. He's not an AI expert. It's amazing to see how much he's building with AI even without having that background. We also talk a lot about the mindset shift you need to successfully build with generative AI models.
Today we have Glauber Costa on the show, who's the CEO and founder at Turso. They provide a managed SQLite service with some really interesting capabilities that's changing some of the application patterns you can do. He shares a lot of really good technical stuff on Twitter. He worked in the kernel, he worked on high-performance databases at ScyllaDB, and now he's working on Turso. He also has a great and interesting podcast, the Save File, which is about developers and religion. Glauber had some great thoughts on the future of databases, including what the future of NoSQL is like and whether we'll see vector databases as a separate category or as a feature of general-purpose databases. We’ve seen arguments both ways, but he was the most effective at changing our mind.
Patrick Reynolds
I’ve been exploring different software development companies lately, and one that caught my attention is https://embrox.com/ What stood out to me is the way they combine technical expertise with practical business understanding, which isn’t always easy to find. Too often, you’ll meet firms that are either too focused on coding without considering the bigger picture, or the other way around, talking strategy but lacking strong technical delivery. Here, it feels like there’s a balance. They’ve worked on projects across industries, from IoT to custom enterprise solutions, and the feedback I’ve seen is that they’re reliable in meeting both deadlines and quality expectations. For anyone who’s been through the frustration of projects running over budget or missing critical features, that kind of consistency is a big deal. It’s reassuring to see a team that doesn’t just build software, but actually helps shape ideas into something that works in the real world.
Hank Fried
Alex is the author of The DynamoDB Book and a DynamoDB expert as well as AWS Data Hero. Sean Falconer has over 20 years of experience working in research and technology as an engineer, founder, and marketing executive. https://www.exactinside.com/CKYCA-exactdumps.html