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Most codebases get harder over time. Compound engineering makes them easier.
@kieranklaassen's plugin has over 7,000 GitHub stars, and Every just published the definitive guide—principles, implementation details, and a downloadable plugin.
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Which codebase design techniques from the pre-AI era still help agents?
Deep modules, adapters/seams etc.
Writing the 'codebase design' part of my new course and want to make sure I nail it.
My codebase slop theory is there's some threshold past which slop goes exponential
One codebase, three identical PRs, three different CodeRabbit review profiles
quiet - only the most important feedback
chill - balanced (the default)
assertive - most comprehensive
Dial in the review depth that fits your team.
More signal, less noise, tuned to your team.
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Indexes codebases with dependency graphs and git history for AI agents
Turns complex codebases into clear architecture diagrams using Claude Code.
Our Go codebase has more test LOCs than actual code.
Rerunning all those tests really adds up and we needed a solution.
A while ago I built a Go test runner with a shared S3 cache.
Nothing fancy really, but you can share the cache with your team and CI.
But now that we use @AmpCode orbs a lot, it's really paying off.
Every orb runs the full test suite in a fraction of the time and we can ship those changes with more confidence.
just replace `go test ./...` with `rask ./...`
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people will run their codebase through 100 tools (Jev, LLMs, harnesses, skills, etc.) 100 times, before they run it through their own eyes just once
You finally inherit a codebase with actual documentation 😂😂
Mindwalk turns your codebase into a 3D city and lets you replay exactly how Claude Code or Codex explored, read, and edited it. I tested it on a real project.
Watch the breakdown.
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