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Claude Livecaster is now public on CircleCI Research. In this update, Ryan Hamilton walks through the newly open-sourced repo, seven built-in simulation scenarios, and a new two-voice broadcast format featuring an anchor and a field correspondent narrating the action together. The demo scenario: Pipeline Wars, six CI pipelines racing across three providers, with Claude providing live color commentary on every Docker build failure, OOM kill, and production rollout.

What if you didn't have to stare at logs while your AI agent worked? In this Loop Lab experiment, Ryan Hamilton built Claude Livecaster, a tool that gives Claude a live voice to narrate long-running agentic processes like a sports commentator. The demo: six AI models (GPT, Gemini, and Claude variants) race through a CI/CD benchmark, and Claude calls the whole thing play-by-play. Rate limit hits, comeback stories, photo finishes, all of it, out loud.

Even a 10% improvement in delivery speed can reduce queue times.

If a test passes and fails on the same commit, that’s flakiness.

While most teams struggle with the complexity of AI-generated code, Alloy and Chime have built internal cultures and processes that enable them to scale their development while maintaining quality. Join CircleCI’s CTO, Rob Zuber, in conversation with Maciej Makowski, Senior Software Developer at Chime, and Sunny Singh, Senior Software Engineer at Alloy, as they explore the dynamics that set their teams apart. They'll talk through the culture and delivery practices that actually moved the needle.

The better you can describe a bug, the better AI can help fix it.

Most engineering roadmaps are built around certainty, but that model is beginning to break down. When the way people interact with software is changing this quickly, exploration stops being a side project and starts becoming the day-to-day.

The teams succeeding with AI changed how they validate code: machines verify correctness, validation runs in parallel, and senior engineers focus on risk.

Curiosity never really goes away.

Even with perfect data and unlimited time, teams still make the wrong decisions sometimes.

CI/CD systems were designed for predictable workloads and AI is pushing more changes than most pipelines can handle.

Leaders should give engineers the space to experiment with AI tools so they can form real opinions about what works and what doesn’t.

Treating problems as systems issues instead of people problems changes everything.

But someone who knows how to use AI well might.

If a company sells tools that promise faster engineering teams, they need to prove it internally too.

AI can generate code in seconds. But validation, testing, and review still take time.

Even when you stop writing code every day, the way engineers think about systems, feedback, and problems stays with you.

AI agents used to comment on issues and pull requests, now they’re pushing code to repositories.

As AI-assisted coding tools increase the amount of code, commits, and builds, optimizing your CI pipeline becomes more important than ever. In this tutorial, we walk through how to use Chunk, CircleCI’s autonomous agent that validates your code at AI speed, to analyze your pipeline history, identify performance bottlenecks, and suggest optimizations to your CI/CD configuration. Chunk leverages critical CI/CD context like build history, test results, and execution data to keep pipelines healthy and moving at AI speed.

When tooling, tech debt, and friction get in the way, even simple tasks take five times longer.

AI hype is everywhere, but real results depend on your team, your scale, and your workflows.

What does it actually look like to build an AI-native product and lead an engineering team through the AI era when you've been doing it longer than most? Rob Zuber sits down with Loïc Houssier, CTO at Superhuman, to talk about what it meant to be an AI company before AI was everywhere, and how that early foundation shapes the way they build, ship, and think today.