AI + SDLC Experiments · Resource Library
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Real experiments. Real code. Real results. Our thinking, experiments, and innovations on AI + CI/CD — grab what you need and start shipping.
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I Proudly Suck at Git
A 40-year coding veteran confesses his deep, abiding hatred of Git, his love of GitHub Desktop, and how AI agents finally liberated him from terminal shame.
8 min
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We Let an AI Agent Say 'I Passed.' Was It Actually Good?
10 controlled experiments reveal that 80% of AI agent code fails CI pipelines when there's no feedback loop. Same agent, same task, same model—the only variable was whether it could see what the pipeline saw. That moved the CI pass rate from 20% to 100%.
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15 min
Humans Were Writing Code Slop Long Before AI Got the Blame
AI code quality and technical debt double standard
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6 min
We Don't Have a QA Team. That's Why We Ship Faster Than You.
Engineering culture and shipping velocity
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12 min
One Agent vs. a Team: What Benchmark Data Says About Multi-Agent Debugging
Multi-agent AI coding benchmarks and practical implications
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10 min
Claude Code's Task Tool: From Sequential to Parallel Work
How Claude Code's Task Tool enables parallel processing, transforming sequential operations into multi-agent orchestration for faster development workflows.
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12 min
Claude Code and Linear: Your Key Partners in AI-Native Development
How Claude Code and Linear form a natural pair for AI-native development workflows, with CircleCI as the execution layer that validates and deploys AI-generated code safely.
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