Observability and human intuition in an AI world

Observability and human intuition in an AI world

In this two for one episode recorded at HumanX, Ryan is first joined by Christine Yen, CEO of Honeycomb, to discuss how AI compresses the software development lifecycle, making observability about capturing the right telemetry. Then, Spiros Xanthos, founder and CEO of Resolve AI, shares with us how AI coding increases code volume but decreases human intuition, making production operations harder than ever.   Episode notes:  Honeycomb [https://www.honeycomb.io/] is an observability platform that enables deep, high-dimensional exploration so you can debug unpredictable behavior with precision. Resolve AI [https://resolve.ai/] allows you to resolve incidents, optimize costs, and code with production context using AI that works across your code, infrastructure, and telemetry. Connect with Christine on LinkedIn [https://www.linkedin.com/in/christineyen/]. Connect with Spiros on LinkedIn [https://www.linkedin.com/in/spiros/].  See Privacy Policy at https://art19.com/privacy [https://art19.com/privacy] and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info [https://art19.com/privacy#do-not-sell-my-info].

How Braze’s CTO is rethinking engineering for the agentic area

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 [https://www.linkedin.com/in/jon-hyman/]. See Privacy Policy at https://art19.com/privacy [https://art19.com/privacy] and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info [https://art19.com/privacy#do-not-sell-my-info].

Connecting the dots for accurate AI

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 [https://neo4j.com/] 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 [https://neo4j.com/product/auradb/] and learn more at their Graph Academy [https://graphacademy.neo4j.com/categories/workshops/].  Connect with Philip on LinkedIn [https://www.linkedin.com/in/prathle/].  See Privacy Policy at https://art19.com/privacy [https://art19.com/privacy] and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info [https://art19.com/privacy#do-not-sell-my-info].

AI giveth and AI taketh CPU

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 [https://open.spotify.com/show/37Pa02uPo0aN25I8PzgdxW], a monthly show hosted by Mark. Connect with Mark on LinkedIn [https://www.linkedin.com/in/mark-papermaster-66914925/].   TRANSCRIPT [https://stackoverflow.blog/2026/05/08/ai-giveth-and-ai-taketh-cpu/] See Privacy Policy at https://art19.com/privacy [https://art19.com/privacy] and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info [https://art19.com/privacy#do-not-sell-my-info].

What (un)exactly do you mean by semantic search?

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 [https://qdrant.tech/] offers high-performance vector search at scale with any deployment model. Connect with Brian on LinkedIn [https://www.linkedin.com/in/brian-ogrady/] or email the Qdrant team at support@qdrant.io [support@qdrant.io].  Congratulations to user Brad Larson [https://stackoverflow.com/users/19679/brad-larson] for winning a Populist badge for their answer to Find the tangent of a point on a cubic bezier curve [https://stackoverflow.com/questions/4089443/find-the-tangent-of-a-point-on-a-cubic-bezier-curve]. TRANSCRIPT [https://stackoverflow.blog/2026/05/05/what-un-exactly-do-you-mean-by-semantic-search/] See Privacy Policy at https://art19.com/privacy [https://art19.com/privacy] and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info [https://art19.com/privacy#do-not-sell-my-info].