Jon Hyman, co-founder and CTO of Braze, joins Stack Overflow CPTO Jody Bailey on Leaders of Code to share how he's led the company's engineering organization over nearly 15 years of growth — and how they transformed into an AI-first team in just a few months.

Jon explains some pivotal moments where his thinking shifted (watching his team ship an MCP server six weeks ahead of schedule will do that!) and talks candidly about the cultural and practical challenges of driving adoption across a 300-person engineering org. He explains how model quality, not mandates, was the key factor in winning over skeptics, and why over 60% of Braze's committed code is now AI-generated.

Jon also addresses the harder questions: how to measure AI's real business value, the surprisingly steep cost of inference at scale, why "vibe-coding your way to scale" is folly, and what comes next as autonomous agents start building features overnight.

Connect with Jon on LinkedIn.

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At HumanX, Ryan is joined by Philip Rathle, CTO at Neo4j to discuss what knowledge context means for AI agents, how limitations like stale training data make the model-only approach to agents a bad fit for enterprise environments, and how Graph RAG raises the bar for accuracy and reduces context rot by combining vectors with a knowledge graph so agents are more targeted and connected.

Episode notes:

Neo4j is a native graph database management system designed to handle complex, highly-connected data by focusing on relationships rather than tables. You can try it out for free on Aura and learn more at their Graph Academy. 

Connect with Philip on LinkedIn. 

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Recorded on the floor of HumanX, Ryan is joined by AMD CTO Mark Papermaster to discuss AMD’s silicon strategy for AI borne of their long history of heterogeneous CPU/GPU computing, how chipmakers are dealing the wide range of AI workloads from training to inference, and the paradox of agents both eating up all the compute and helping AMD accelerate chip innovation. 

Episode notes: 

Want to learn more about the topics Mark and Ryan discussed in this episode? Check out the AMD Advanced Insights podcast, a monthly show hosted by Mark.

Connect with Mark on LinkedIn.  

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Ryan welcomes Bryan O’Grady, Head of Field Research and Solutions Architecture at Qdrant, to discuss the differences between traditional text search engines powered by Lucene and modern vector databases, when vector search’s exact-match needs work for things like logs and security analytics and when semantic search works for user-facing discovery and non-exact results, and how Qdrant is growing into video embeddings and local-agent contexts. 

Episode notes: 

Qdrant offers high-performance vector search at scale with any deployment model.

Connect with Brian on LinkedIn or email the Qdrant team at [email protected]. 

Congratulations to user Brad Larson for winning a Populist badge for their answer to Find the tangent of a point on a cubic bezier curve.

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Ryan welcomes Jason Williams, senior software engineer at Bloomberg and the creator of Rust-based JavaScript engine Boa, to the show to dive into why date and time handling in JavaScript is so difficult and how the Temporal proposal aims to fix it. They explore the current flaws and issues in JavaScript that make the Date object so hard to work with, how libraries like Moment.js helped but eventually became too complex themselves, and why the Temporal proposal took nine years to complete. 

Episode notes: 

Temporal is a new TC39 proposed standard for JavaScript that replaces the Date object. It operates as a top-level namespace and brings a modern date/time API to the ECMAScript language.

Connect with Jason on Bluesky or at his website. 

Congrats to Great Answer badge winner BrenBarn, who won the badge for their answer to rethrowing python exception. Which to catch?.

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Ryan welcomes Harsha Chintalapani, co-founder and CTO at Collate and co-creator of Open Metadata, to the show to discuss why AI and LLMs struggle with real-time, structured production data. They explore how schema changes, inconsistent definitions (like “customer”), and weak governance can break both your analytics and MLs, and what companies can do to get their data AI-ready, from metadata management to observability. 

Episode Notes: 

Collate is a semantic intelligence platform built on a semantic metadata graph for discovery, governance, and AI observability across your data ecosystem.

Connect with Harsha on LinkedIn. 

Congrats to user buttonsrtoys, who won a Famous Question badge for their question Possible to edit PDF without embedded font installed?.

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Ryan is joined on the show by Cult.Repo producers Emma Tracey and Josiah Mcgarvie to discuss making documentaries about open-source software and the people behind the major technologies that uphold the internet. They explore why open-source projects and the people who maintain them are such interesting stories for audiences, how being outsiders has helped them tell these community stories, and what they see as the common stressors that plague all open-source projects, such as sustainability, compensation, and burnout. 

Episode notes: 

Cult.Repo produces documentaries and shorts about the human stories behind open-source technology. Check out their filmography on their YouTube channel.

Have an idea for an open-source community they should cover? Email the Cult.Repo team at [email protected]. 

Shoutout to user kiranvj for winning a Populist badge for their answer to What is a good way to automatically bind JS class methods?.

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SPONSORED BY INTUIT

Chase Roossin, group engineering manager, and Steven Kulesza, staff software engineer, from Intuit join the podcast to chat about what might be the hardest problem in engineering right now: getting multiple AI agents to work together in a complex system. They discuss how automated evals can make agent behaviors more predictable, agent swarms vs. one highly skilled agent, and how customer behavior shaped their technical architecture. 

Episode notes

Want to work on complex engineering problems like these? Explore careers at Intuit.

We’ve worked with Intuit on a few other great blogs and podcasts, including Best practices for building LLMs and How Intuit democratizes AI development across teams through reusability.

Connect with Chase on LinkedIn. 

Connect with Steven on LinkedIn. 

Congrats to Lifejacket badge winner Sean for saving Creating the simplest HTML toggle button? with a great answer.

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Ryan welcomes Mike Swift, co-founder and CEO of Major League Hacking, to the show to chat about the never-ending need for software developer communities and entry points into programming; MLH’s recent acquisition of DEV and how they’re creating a place for shared knowledge, building, and publishing; and why now is the best time to be both an artisan and a builder in a world with AI software development tools.

Episode notes: 

Major League Hacking is a 500k+ global member community that hosts hackathons and open-source fellowships for the next generation of developers. They recently acquired DEV, an online community for 3M+ developers to learn and share together. 

Connect with Mike on LinkedIn or email him at [email protected]o. 

Congrats to Stellar Answer badge winner Antony Hatchkins for getting over a hundred saves on their answer to Git replacing LF with CRLF. 

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Ryan welcomes Stephen Watt, distinguished engineer and VP of Red Hat’s Office of the CTO, to chat about digital sovereignty and sovereign AI. They explore major infrastructure constraints for things like power, cooling, and scarce hardware that cause the regional disparities we see in sovereign AI, plus why we need to extend Kubernetes and integrate PyTorch Stack not just for a sovereign cloud but for sovereign AI.

Episode notes: 

Red Hat’s Office of the CTO is a division of 150 software engineers and researchers working on their Research and Emerging Technologies arms, helping to shape the vision and strategy of Red Hat’s technology. 

Connect with Stephen on LinkedIn. 

Congrats to user Ittiel for winning a Populist badge on their answer to Print timestamps in Docker Compose logs.

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