Stack Overflow Blog

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.

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…

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.

A Treatise on Model Oriented Programming Languages

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.

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.

Part 3: Knowing when your agent doesn’t know: the confidence layer

Part 2: Evals as a deployment gate — and how to know when they drift

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.

Implementing a Modular Master-Agent Telemetry & Diagnostic Framework in Python: Prime-Sentinel Command (PSC)

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…

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.

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.

A look back before we look forward: A Developer Survey retrospective

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.

Organizations need decision-grade knowledge. AI makes it urgent.

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.

Anyone can start building verified knowledge with Stack Internal

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.

Getting ready for 2026 results: A look back on Developer Survey findings

Why model versioning is not enough for production AI

An MLOps workflow for evaluating deploying and rolling back AI applications

Is Your “Human-in-the-Loop” Actually Slowing You Down? Here’s What We Learned