Why AI Is Following the Same Path as Software Development
Devs have a habit of solving their own problems before anyone else's and the AI boom is repeating that pattern.
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Chunk sidecars: Inner Loop Validation for AI Coding Agents
Your agent writes code fast, but you shouldn't have to see it until it's right. Chunk sidecars are lightweight microVMs that work inside the agent loop, requiring agents to pass pre-push validation in a CI-like environment before they declare they're "done." That means no massive CI pile-ups, no long round-trips that risk resetting your agent's context. You're sending code you already know is good.
Untested AI is unshippable AI, with Laurie Voss of Arize
Most AI applications in production right now were shipped on vibes. A developer ran a few queries, liked what they saw, and pushed to prod. Laurie Voss argues that's the core reason so many AI products feel broken, and the fix is simpler to name than it is to do: write the tests.
We told Claude to break our tests. Here's what happened.
Catch failing tests before they ever leave the machine with just two hooks and Chunk sidecars.
The engineers who handle change best aren't keeping up with every tool
Hywel Carter on the mental model that travels with you no matter what ships next.
Agent Hooks + Chunk sidecars: Stop Broken AI Code Before It Hits CI
AI agents write code fast, but the feedback loop usually can't keep up. In this tutorial, you'll see how to wire Chunk sidecars into your agent's hooks so basic failures get caught before they ever reach your CI pipeline. We'll walk through the two hooks that chunk init writes automatically: Both hooks return exit 2 on failure, blocking the commit or keeping the turn open so the agent can fix its own mistakes with no manual prompting required.
How to set up blue-green deployments with CircleCI
How to set up blue-green deployments with CircleCI.
Chunk sidecars: CI Feedback Before the Push
Chunk sidecars give your agent CI-grade feedback in seconds. No push, no pipeline wait. Catch failures and fix them while the agent is still in context.
Agentic validation needs different infrastructure
Previously, I described some core approaches to validating agent written code: feedforward and feedback techniques. Feedforward techniques are about avoiding errors up front, for example by coming up with better prompts and planning strategies. Feedback gives agents a signal that they have actually achieved a task. Feedback is a key part of common agentic patterns like Ralph loops or the /goal commands in Codex and Claude Code: keep working until some known condition passes.
The skill gap AI can't close, with Hywel Carver of Skiller Whale
If 90% of code is now AI-generated, what happens to the engineers who never had to write it themselves? The ability to review code well, to catch what the LLM missed, depends on mental models that only come from practice. That tension sits at the center of this conversation.