Holy tagline. "The CLI-first AI coding agent runtime. Pipe it, schedule it, embed it. One binary, any model, MCP-native — built for autonomous work, not just chat."
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?
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?
Also, quick question: Could I use octomind upstream of harnesses like omp and get the savings? Or do you primarily get the savings from using the octomind CLI?
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"
We benched it also on glm-5.3 not published. To be honest they act almost the same did not see any significant difference, while other benches show a lot
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.
Yes. Kind of right. No TUI but still old CLI approach that some of us still love.
It can be used interactively and I use it daily, but so far it's optimized for autonomous work at max and so far finalizing task while running in isolated environments (we use docker for that)
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.
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
Holy tagline. "The CLI-first AI coding agent runtime. Pipe it, schedule it, embed it. One binary, any model, MCP-native — built for autonomous work, not just chat."
I feel like I'm taking crazy pills...
Blessed by compiler. Sometimes one of those crazy pills take you out of matrix :)
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?
I'd like to see this benched against oh-my-pi because I've really been digging how it does compaction / snapshotting.
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?
https://omp.sh/
Also, quick question: Could I use octomind upstream of harnesses like omp and get the savings? Or do you primarily get the savings from using the octomind CLI?
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"
it's in the docs! never have used it though https://omp.sh/docs/compaction
I use it. It works flawlessly.
Why would i choose that over maki.sh? A comparison to maki would be great!
Hey. Never heard about it, but I will take a look. thanks
Compare against ante as well: https://ante.run/
The README is AI slop ... is there anything actually special about this?
The feature list doesn't seem to show why it is significant compared to all the other harnesses.
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
glm-5.2*
We benched it also on glm-5.3 not published. To be honest they act almost the same did not see any significant difference, while other benches show a lot
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.
Yes. Kind of right. No TUI but still old CLI approach that some of us still love. It can be used interactively and I use it daily, but so far it's optimized for autonomous work at max and so far finalizing task while running in isolated environments (we use docker for that)
I will try it, but please, make the installation through package managers (linux), these curl scripts aren’t secure.
Thanks for suggestion! Will do. Can I know which distribution are you using? Feel free to leave feedback after you try. Cheers
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.
I think packgae manager still can be as an option. But for sure static binary should stay as is
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
interesting, never heard about it. How its better then just cargo install?
If you're installing just one thing, nothing.
nice name
I like yours too! Perfect fit
Can someone save me a click? Does this improve quality or worsen it?
Tokens are cheap. Quality is what matters.
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