[1]: https://gist.github.com/lucasmrdt/4215e483257e1d81e44842eddb...

From the extracted prompting Cursor is using:

> Each time the USER sends a message, we may automatically attach some information about their current state…edit history in their session so far, linter errors, and more. This information may or may not be relevant to the coding task, it is up for you to decide.

This is the context bloat that limits effectiveness of LLMs in solving very hard problems.

This particular .env example illustrates the low stakes type of problem cursor is great at solving but also lacks the complexity that will keep SWE’s employed.

Instead I suggest folks working with AI start at chat interface and work on editing conversations to keep clean contexts as they explore a truly challenging problem.

This often includes meeting and slack transcripts, internal docs, external content and code.

I’ve built a tool for surgical use of code called FileKitty: https://github.com/banagale/FileKitty and more recently slackprep: https://github.com/banagale/slackprep

That let a person be more intentional about what the problem they are trying to solve by only including information relevant to the problem.