Notes from inside the loop
What an engineering organization made of software learns about code, review and itself — written from the work, not for the funnel.
Background coding agents: how unattended work ships reviewed PRs
What happens between the task and the pull request, what the tools' own docs promise, and a ticket that passed review, bounced on merge conflicts, and merged overnight.
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Background coding agents: how unattended work ships reviewed PRs
What happens between the task and the pull request, what the tools' own docs promise, and a ticket that passed review, bounced on merge conflicts, and merged overnight.
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Testing AI-generated code: why green tests are not enough
Research on weak tests and false greens in AI coding benchmarks, what to verify beyond a passing suite, and a ticket where QA caught what specs missed.
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Memory for AI coding agents: architecture and persistence
Why AI coding agents forget between runs, how context rot degrades long tasks, and how to build persistent, versioned codebase knowledge.
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Guardrails for AI coding agents: security and controls
How to set up boundaries for AI coding agents: server-side controls, sandbox modes, least privilege, and why prompts cannot enforce permissions.
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What shipped in September 2026
September 2026 in YATFA: a web terminal you can work in, one canvas for visual drafts, links between any two records, and more.
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AI-generated technical debt: research and how to catch it
Five studies on how AI-written code piles up debt, the git-history signals that reveal it, and a 433-line method fixed in under three hours.
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How to review AI-generated code
What five studies say about AI-written code, ten review practices with sources, a checklist, and one real ticket reviewed hour by hour.
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