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The parent claim was that devs don’t open-source their personal AI tools. FileKitty is mine and it is MIT-licensed on GitHub.

It began as an experiment in AI-assisted app design and a cross-platform “cat these files” utility.

Since then it has picked up:

- Snapshot history (and change flags) for any file selection

- A rendered folder tree that LLMs can digest, with per-prompt ignore filters

- String-based ignore rules for both tree and file output, so prompts stay surgical

My recent focus is making that generated context modular, so additional inputs (logs, design docs, architecture notes) can plug in cleanly. Apple’s new on-device foundation models could pair nicely with that.

The bigger point: most AI tooling hides the exact nature of context. FileKitty puts that step in the open and keeps the programmer in the loop.

I continue to believe LLMs can solve big problems with appropriate context and that intentionality in context prep is important step in evaluating ideas and implementation suggestions found in LLM outputs.

There's a Homebrew build available and I'd be happy to take contributions: https://github.com/banagale/FileKitty



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