I feel the exact same! I built this tool to make it much easier for me to bring LLMs into existing workflows: https://github.com/gr-b/repogather
It helps find relevant content to copy to your clipboard (or just copies all files in the repo, with exclusions like gitignore attended to) so you can paste everything into Claude. With the large context sizes, I’ve found that I get way better answers / code edits by dumping as much context as possible (and just starting a new chat with each question).
It’s funny, Anthropic is surely losing money on me from this, and I use gpt-mini via api to compute the relevancy ratings, so OpenAI is making money off me, despite having (in my opinion) an inferior coding LLM / UI.
I do something similar with a script. Don’t know if anything about my approach could improve yours, but I’m also curious if you have any suggestions?
- Mine prepends the result with the output of running `tree -I node_modules --noreport` before any other content. This informs the LLM of the structure of the project, which leads to other insights like it will know which frameworks and paradigms your project uses without you needing to explain that stuff.
- Mine prepends the contents of each included file with “Contents of relative/path/to/file/from/root/of/project/filename.ts:” to reinforce the context and the file’s position in the tree.
It helps find relevant content to copy to your clipboard (or just copies all files in the repo, with exclusions like gitignore attended to) so you can paste everything into Claude. With the large context sizes, I’ve found that I get way better answers / code edits by dumping as much context as possible (and just starting a new chat with each question).
It’s funny, Anthropic is surely losing money on me from this, and I use gpt-mini via api to compute the relevancy ratings, so OpenAI is making money off me, despite having (in my opinion) an inferior coding LLM / UI.