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?
Also regular pi, which is particularly minimal
How is oh-my-pi compaction different? Is there other harnesses that employ the same tricks?
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"
For obvious reasons it underperforms on models that hide their reasoning chain. Other than that it works fine for a non-customizable method. But properly customized handoff is still better.