Comment by CompoundEyes
14 hours ago
Pretty basic. The codex app with one conversation per project and several running simultaneously all hours. I’m going for max caching that way and it never gets lost even with compaction somehow. Each has a plan with milestones to keep up to date and a thin agents file. I check in on them in the Remote app. Use case is protocol and control reverse engineering of audio hardware. I think they must be identifying the heavy use agent sessions and cranking up their cache lives so it’s not a big deal for them.
A billion tokens a day is 11,000 tokens a second sustained. How many tokens per second are you getting off of GPT 5.6 Sol per project?
Ultra mode spins up many sub-agents. On a particularly challenging task, I’ve had as many as 29 agents working at one time.
Also if you don’t specify, most end up being the same as the parent model which is pretty wasteful.
I engineered a skill that spins up Terra High agents for most sub-agents, resorting to Sol Medium for technical research and Luna High for code/in-project research tasks.
On a slightly different topic, Luna Max is incredibly capable and doesn’t use as much quota (Luna tokens are dirt cheap).
Most importantly, are you seeing a return on investment for time and ultimate outcome?
No one can judge the enjoyment, learning, and hobby aspects. Just wondering if there is an end goal for that much overall expenditure (time, money, energy, etc.)
1 reply →
Often people are counting all tokens, including cached input tokens, for those more impressive "billions of tokens" quotes.
Ah, thanks, I'd missed that nuance in the other reply!
I'd like to see a benchmark on this specific topic: Reverse engineer the hardware protocol from a driver, or just migrate a driver from one OS to another.
I’ve chipped on it with each model since 5.2 but 5.6 sol is something else. When it first came out I’d get some refusals but they’ve since stopped. I wonder what an ideal candidate benchmark task would be for that?