I use Deepseek and just as it to generate a state.md file with a summary of the project every time I've reached a goal or milestone. I then take a few minutes to edit this and add in or take out details. Between token pricing and generous cache discounts This has proved very efficient so far, I reset every few hours of work and bypass the muddle of having too many priorities or over-extrapolation from un-nuanced instructions I gave at an earlier stage.

I do think that this project is interesting in several ways - prioritizing privacy, minimizing spend, and using objective semantic markers to sift and consolidate the key takeaways from long sessions. I'd like to try it on my cline project history. But while it would make a great recording of project history, I wonder if a lot of it doesn't end up detailing blind alleys the project went down and had to back out of.

Generally when this happens I feel that it's due to vague specification on my part, or avoiding architectural decisions I didn't want to deal with and implicitly inviting the model to implement a lowest-common-denominator solution.