On this episode of Leaders of Code, Eric Anderson, director of engineering at Intuit, joins Stack Overflow engineering director Ben Matthews to talk about what happens to software teams when AI makes code generation seemingly free.
If your coding agent has questions, Stack Overflow for Agents has answers, now in beta.
Ryan welcomes Bryan Clark, director of product for Lakebase at Databricks, to discuss what happens when AI agents become the primary creators and users of databases; why agents are “sloppy” about cleaning up infrastructure; and how database branching, scale-to-zero, and centralized access control can help teams keep up with agent-driven development.
Ryan welcomes back Tanya Janca, now part of the OWASP Top 10 team, to discuss what changed in the latest OWASP Top 10 release, how the list shifted from “outdated components” to a broader software supply chain focus, and why they added memory safety and vibe-coding as awareness items.
From the floor of HumanX, Ryan welcomes Songyee Yoon, managing partner at Principal Venture Partners (PVP), to chat about AI development outside the US, from the need to adapt models to local languages and culture to the challenges of the global supply-chain for things like semiconductors to how venture capital is looking at international AI companies.
In this two-for-one special recorded at HumanX, Ryan is joined by Dataiku’s Florian Douetteau to chat about the governance, orchestration, and data requirements for serious agentic systems and 1Password’s Nancy Wang for a conversation on making agent swarms secure.
If AI is the new power tool for developers, is there still value in artisanal craft when anyone can be a builder?
AI’s impact on software engineering continues, and more and more of that AI is packaged as agents as results from our newest pulse survey show agentic usage has almost doubled (59%) since we last asked about it in our annual Developer Survey
From the floor of HumanX, Ryan Donovan is joined by Peter Salanki, CTO and co-founder of CoreWeave, to chat about what it really takes to run AI in production; the growing importance of observability, utilization, and scheduling; and Peter’s advice for avoiding the trap of over-architecting too early.
Experiments in agentic engineering and AI-driven development.
Recorded at HumanX, Ryan sits down with Garima Kapoor and Anand Babu Periasamy, co-founders and co-CEOs of MinIO, to chat about eliminating the storage bottlenecks that leave GPUs underutilized, their partnership with NVIDIA on the new STX reference architecture, and why modern AI infrastructure is converging on S3-compatible object storage.
With much of a software engineer’s time moving from writing code to structuring prompts and reviewing code, the workday is getting denser and more intense. Can AI solve the problems it's causing?
On the floor of HumanX, Ryan is joined by Adam Meyers, Senior VP of Counter Adversary Operations at Crowdstrike, for a deep dive on their latest Global Threat Report that tracks over 281 adversaries across nation states, e-crime, and hacktivist organizations.
Because someone still has to own the consequences of what gets built and whether it can function at scale.
In this No Dumb Questions, Phoebe is joined by Stack Overflow’s tech lead for the infrastructure team, Josh Zhang, to learn about the cloud, compute, and data centers.
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.
Jon Hyman, co-founder and CTO of Braze, shares 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.
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.
Signature-based detection has always known what it was looking for. Machine learning and autonomous agents are changing the question entirely, shifting from "does this match a known pattern?" to "does this actually make sense in context?"
Welcome to No Dumb Questions, a column where our least technical writer asks our technical staff the simple, basic tech questions people are afraid to ask. In this first entry, Stack's Director of Ecosystem Strategy Ben Marconi teaches us the basics of MCP servers and why they matter.
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.
Selective control in autonomous AI systems: Why governing every decision breaks autonomy—and how runtime control actually works at scale.
Agents are everywhere, so isn't it fitting that the Worst Coder in the World goes agentic? A coding newbie explores the challenges and rewards of building an agent for work—and trying to learn a few things about coding along the way.
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.
