I don't know if I want any of those things. Can I still just throw it my csvs and make it do my data science work for the day so it doesn't distract me from baldurs gate?

Blessed by compiler

Hey. Never heard about it, but I will take a look. thanks

did they stick with the image-based compaction (when vision is supported i guess)? It boggled my mind: "snapcompact, which archives older history into dense images for a vision-capable model"

great idea. Currently working on updating benches on frontiers models, so far on glm-5.3 it's already showed great results. Can I have a link to oh-my-pi to make sure will sure use right one for it?

Well it's beating Claude while using GLM-5.3 in solving the UNSEEN issues from real github PRs on our benches.

Yes, readme maybe sloppy, I'm using it daily along with claude and can say it works much better, but still personal opinion

You will never know if you not try, sloppy readme cannot say the product is bad IMO, but its your choice.

Thank for feedback. I will definitely will look and work on readme. But advise what exactly sloppy – would be much more valuable

From what I can tell this is a harness optimized to be used as headless background jobs, think about CICD jobs, or bugs auto triage. Instead of interactive HITL approvals you supply a policy file with the tools and commands you allow it to execute. Kind of cool idea, similar to pi but seems more specialized to dev cycle triggers.

I highly recommend just using `mise` with the cargo backend[0]. I use mise to manage all the harnesses I try.

[0] https://mise.jdx.dev/dev-tools/backends/cargo.html

I would vote against package managers. It just doesn't make sense to try to support them across Linux distributions.

If it's really static rust then either make it one binary. If that's not reasonable then make the release relocatable to run from any path. Also it should be simple and easy to run out of the source repo.

Thanks for suggestion! Will do. Can I know which distribution are you using? Feel free to leave feedback after you try. Cheers

Our benchmarks shows that it best solver. Solves more than Claude on Opus while using glm 5.3, but still benching new models with most recent version.

The point here it has flexibilty in config, supervisors, automatic learning and also utilizeing jev-like models to gain maximum of efficiency while keeping solving rate intact

[dead]

I like yours too! Perfect fit

[dead]