The Stack Overflow Podcast

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.
TRANSCRIPT

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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.
TRANSCRIPT
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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?.
TRANSCRIPT
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