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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.
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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
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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reworking the codebase from the Claude Code Agent SDK to PI + Vercel AI SDK
Claude reads your codebase and design files to build your team's design system, then applies it automatically, keeping every project on-brand. Give it a try: Read more:
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When you were writing software in your codebase, you felt the pain of friction and would grease the wheels as a result. Now you're firing off dozens of agents and not watching what they're doing. Agents don't complain and they're not going to just tell you the problems that they're running into. They'll just work around them. So you've got to ask your agents about that friction. I've got a process for this that I'm going to make a video about eventually.
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