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Your service can be 100% up and still quietly approving the wrong things, burning its budget, or failing over into untested quality. Level 5 is the infrastructure that lets you see your decisions, bound your spend, route and fail over between models, kill bad behavior in seconds — and the platform that makes all of it possible.

From the floor of the Ai4 conference, Ryan chats with Greg Jennings, VP of Engineering for AI Products at Anaconda, about what it takes to build a secure-by-default AI coding agent, why prompts shouldn't be treated as strict security guardrails, and how Anaconda is using strategic acquisitions to secure the AI software supply chain.

Ryan sits down with Josh Triplett, member of the Rust Leadership Team at the Rust Project, to chat about how the borrow checker delivers compile-time memory safety without garbage collection overhead, why Rust consistently ranks as developers' most loved language by providing psychological safety for complex systems, and how strict compiler constraints make Rust an ideal target for AI coding agents.

Agents don't build trust for another reason, structurally worse than the first. It isn't only that the tool keeps changing shape. It's that the feedback loop you would need in order to learn the tool is broken at the point of measurement.

From the floor of the Ai4 conference, Ryan chats with Kevin Frazier, director of the AI Innovation and Law program at the University of Texas School of Law, about the legal and social impacts of data centers, the realities of workforce disruption, and regulating AI for child safety using existing consumer protection laws.

Ryan welcomes Markus Eisele to the program to talk about why your coding agent should be writing Java.

From the floor of the Ai4 conference, Ryan chats with Sam Curry, CSO at Zscaler, about where human intelligence sits in the new security landscape with AI, why shifting security protections closer to applications helps limit probes for vulnerabilities, and why building more resilient code infrastructure is the best way to address the vulnerabilities AI does discover.

Ryan chats with David Burns, Head of Developer Advocacy and Open Source at BrowserStack, about the value of professional skepticism in an AI-driven world, applying test-driven development to agentic engineering, and why fixing flaky tests comes down to managing application state.

This analysis compares the 2024 and 2025 Developer Survey data across three connected stories: the evolution of AI, humans at work, and demographics and community.

For the past few years, we’ve been looking at ways to bring a little more of the individual developer back to Stack.

Ryan is joined by Yanbing Li, Chief Product Officer at Datadog, to talk about applying observability to non-deterministic AI agents, blurring the boundaries between software development and production workflows, and navigating emerging challenges in AI security and tokenomics.

While the whispers say that everyone is becoming a generalist, that's not where headcount is going.

AI can make the first part remarkably fast. It can find the page, the discussion and the person who might know. The harder work begins when those sources disagree, or when they become stale.

Ryan is joined by Gabriela Moreira, CEO at Quint, about why formal specification languages like TLA+ are essential for catching system design flaws before writing code, how Quint brings developer-friendly syntax and executability to formal methods, and the ways trace validation and model-based testing can verify AI-generated code against real-world production data.

Stack Internal transforms your daily work into a living memory that’s shared with the rest of your team. Now anyone can create and share their knowledge in a Stack Internal workspace for free by visiting stackinternal.com.

Coding agents are great for an afternoon and mediocre for a quarter. Here’s the small set of files and rules — with the actual configs — that keeps quality from decaying across months and thousands of edits, whether you run one agent or a fleet of them on the same repo.

An MLOps workflow for evaluating deploying and rolling back AI applications

Ryan chats with Erin Yepis, Senior Analyst at Stack Overflow, about the results from this year’s Annual Developer Survey, including the overwhelming daily usage of AI coding assistants despite lingering developer trust issues, the critical role of well-organized documentation in providing verifiable context to mitigate AI hallucinations, and the evolving ways developers are shifting away from active community posting in favor of passive knowledge consumption.

From the floor of the Ai4 conference, Ryan chats with Srini Venkatesan, CTO at PayPal, about validating AI-generated deterministic code for security, developing autonomous SDLC harnesses with iterative feedback loops, and creating a seamless headless checkout experience.

Below, we’ll highlight some of the results we found interesting about what's going in the life of technologists, their technologies, AI usage, and more.

Sandbox testing works best when you stop chasing production parity. Learn what environment drift actually costs and which parts are worth fixing.

We've learned a lot in the last three months since launching Stack Overflow for Agents, our API-first knowledge exchange for agents. Here's a few of our findings, what's new on the platform (including our new ChatGPT plugin), and how we're continuing to build Stack Overflow.

Ryan chats with the GM of Slack, Rob Seaman, about how their new Code Channels feature is bringing multiplayer AI to your team chats.

From the floor of the Ai4 conference, Ryan sits down with Tim O'Reilly, founder and CEO at O'Reilly Media, to talk about the role of books as user interfaces to knowledge, the power of "magic words" to extract better outputs from AI, and why human taste is becoming highly valuable as knowledge becomes a commodity.

Ryan sits down with Div Garg, CEO at AGI Inc., to talk about running AI agents entirely on mobile devices, optimizing models for edge computing chips, and building safety mechanisms into autonomous app interactions.

A maturity model for taking agents from an impressive demo to a system people can depend on — with a self-assessment and the map to a deep-dive on each layer.

It’s gotten exponentially harder to identify talent that we feel is a good fit for our teams at the top of the application funnel.

Ryan chats with Julien Verlaguet, CEO at Skip Labs, about finding the balance between human tolerance and tooling constraints, the spectrum of typed programming languages, and building cost-effective tooling for AI agents.

Most engineering teams capable of building an internal AI knowledge system think they should. But six months later, it's a platform team's full-time job. Building an in-house AI context layer costs more money and energy than you think.

Ryan chats with Anush Elangovan, VP of Software at AMD, about ROCm's open-source unified toolchain for GPUs, how agentic AI is drastically lowering the barrier to entry for low-level hardware programming, and the rapid convergence of software and hardware development timelines.