Welcome to life in the chip age

Ryan sits down with Rakesh Kumar, professor of computer engineering at the University of Illinois Urbana-Champaign, about the astounding complexity of manufacturing modern semiconductors, the massive environmental and geopolitical footprint of AI data centers, and how AI models are rapidly shifting the future of chip design.

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Your .NET parallel loop can become serial in a one-CPU container

CPU-based defaults can leave an I/O-heavy loop processing one record at a time. Raising the limit helps, but each downstream service still needs a concurrency budget it can handle.

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Oh, your agents need OAuth2, too

Recorded at LDX3 by LeadDev, Ryan chats with Dan Moore, Senior Director of CIAM Strategy and Identity Standards at FusionAuth, about adapting established OAuth2 protocols for the agentic era, the security necessity of giving autonomous AI agents their own distinct identities, and how AI coding tools are shifting developers' focus toward solving core business problems.

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It’s ok that computers are (sometimes) better than us

Ryan is joined by Charity Majors, co-founder and CTO at Honeycomb, for a conversation about applying the concept of immutable infrastructure to software development to treat code like cattle instead of pets, shifting the paradigm of system specifications to accommodate the rapid influx of AI-generated code, and navigating the complex ethical boundaries between system observability and human surveillance in modern technology ecosystems.

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The reports of Stack Overflow's death are greatly exaggerated

I’ve seen the recent predictions about Stack Overflow’s demise. They make for compelling headlines, but they confuse a change in how people access knowledge with the disappearance of the knowledge—and the company—behind it.

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A JSON schema Is not an AI governance layer

The AI governance argument is happening one layer above the code that actually stops your agent.

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Delivering controlled and concentrated context

Check out the new features and capabilities now available in Stack Internal and Stack Internal Community. Try Stack Internal for free by visiting stackinternal.com.

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Elevators, toasters, and the birth of Java

Ryan sits down with James Gosling, creator of Java at Sun Microsystems, to talk about the early networking friction that drove the need for interconnectedness, the stringent reliability requirements of consumer electronics companies that inspired the Java virtual machine, and the evolution of garbage collectors that allow Java to handle large-scale concurrent memory management.

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Rumors of the SaaS demise are greatly exaggerated

Recorded on the floor of LDX3, Ryan chats with Nivia Henry, an engineering leader most recently at Netflix, about the reality of the supposed SaaS apocalypse, the complex calculus of the modern build-versus-buy decision in the era of AI, and the critical importance of understanding system architectures to manage long-term infrastructure costs.

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Survey Says: You'll swap the model, but everything you build around it is staying put

Earlier this month, my team at Stacklok surveyed 520 technical leaders to understand whether they were concerned about being locked into frontier labs. The data says they’re already swapping models in and out, but the trap they’re concerned about is the layer on top of the model.

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Don't forget about the UI for AI

Recorded at OpenAI Dev Day 2026, Ryan sits down with Andrew Ambrosino, member of the technical staff at OpenAI, for a conversation on OpenAI's strategy for merging ChatGPT and Codex into a unified experience, the advantages of localized computer access in their successful desktop application, and the technical hurdles of constructing ChatGPT Spaces to support real-time multi-agent collaboration.

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Production-grade LLMs and agents: a field guide

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.

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AI says you’re right, formal methods say you’re wrong

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.

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Why your platform team shouldn't build an AI context layer (even though they probably could)

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.

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Your next job doesn’t have a title yet: Emerging role skills Q&A with Corey Hymel of Andela

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

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Why Your Sandbox Never Quite Matches Production (And Why That's Fine)

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

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Keep calm and call clone with Rust

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.

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Beyond the AI noise: How Stack Overflow is rethinking tech hiring

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.

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Taking a look under your agent’s hood

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.

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Production-grade LLMs and agents: a field guide

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.

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Part 5: Operating an LLM system: observability, cost, routing, and the platform underneath

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.

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A green exit code is not evidence that the work happened

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.

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Part 6: An operating system for coding agents: the disciplined build

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.

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Tales from the 2026 Developer Survey results

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.

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The results of the 2026 Developer Survey are here!

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.

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Constraints that make developers faster

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.

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