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> - a code map strategy on a big repo. Per file: summary, when_to_use, public_types, public_functions. This is done per file and saved until the file changes. W

> - a code map strategy on a big repo. Per file: summary, when_to_use, public_types, public_functions. This is done per file and saved until the file changes. With a concurrency of 32, I can usually code-map a huge repo in minutes. (Typically Flash, cheap, fast, and with very good results) Thanks, but why use any AI to generate this? I would say: you document your functions-in-code, types are provided from the compiler service, so it should all be deterministically available in seconds iso minutes, without burning tokens. Am I missing something?

jeremychone 7mo ago
Very good point. I had two options: 1) Deterministic - Using a tree-sitter/AST-like approach, I could extract types, functions, and perhaps comments, and put them into an index map. - Cons: - The tricky part of this approach is that what I extract can be pretty large per file, for example, comments. - Then, I would probably need an agentic synthesis step for those comments anyway. 2) Agentic - Since Flash is dirt cheap, I wanted to experiment and skip #1, and go directly to #2. - Because my tool is built for concurrency, when set to 32, it's super fast. - The price is relatively low, perhaps $1 or $2 for 50k LOC, and 60 to 90 seconds, about 30 to 45 minutes of AI work. - What I get back is relatively consistent by file, size-wise, and it's just one trip per file. So, this is why I started with #2. And then, the results in real coding scenarios have been astonishing. Way above what I expected. The way those indexes get combined with the user prompt gets the right files 95% of the time, and with surprisingly high quality. So, I might add deterministic aspects to it, but since I think I will need the agentic step anyway, I have deprioritized it.