Decompilation of a game and another larger decompile project. I'm working on it solo. I use 50 sub-agent, one per target function or translation unit. Often there is some progress in a unit but it's not done. So it requires a lot of cycles per function. Notably a single ~80kb function took about a week of constant sol-ultra attention before reaching exactness. The game I'm targeting has ~5000 total functions. The other decompile project has ~10k+ functions.
I'm sure I could be more token efficient, but this was/is also a learning process for me since I never did such an extremely large project before that would take multiple man years before AI.
Fascinating! I think that’s the main difference is my usage is probably tool-bound, meaning it writes some code but then there’s a long period of verification where it compiles things and then waits for the compilation and CI to complete before it can continue. That probably doesn’t consume as many tokens as constantly churning on a problem despite the same wall time.
Yes, this is why I mentioned having so many parallel agents and being compute bound. I run on my own laptop and 2 high-end desktop machines all with 64gb RAM. And it still occasionally happens that one OOM kills codex. They also mostly run unattended until I need to switch their accounts because a usage limit has been hit. Each instance usually can keep going when I sleep or do other things.
I only save the last 30% of usage on a single account for most of my other work, and that is almost always enough.
Are you not better off with cheaper models like GLM 5.3 or GLM 5.3 Flash? As a lot of that work is repetitive and only needs a stronger model at later stages of cleanup, no?
Of course I don't run everything on Sol. But weaker models can only do very basic things. I have a round robin style workflow where each unit first gets 10 cycles of each luna medium -> high -> terra medium -> terra high -> Sol Medium -> Sol high -> Sol Ultra. And you can clearly see that luna and terra only get a small amount of real progress.
I'm sure I could be more token efficient, but this was/is also a learning process for me since I never did such an extremely large project before that would take multiple man years before AI.