Comment by nextlevelwizard

3 days ago

How you burn 300 requests in a day? From my Copilot usage Opus consumes surprisingly few requests to do a lot of stuff. It isn’t paying by token but instead by prompt or something.

If you are using subagents for asynchronous work, you can burn through 300 requests in a workday easily.

  • Copilot didn't charge for subagents. You could do an insane amount of work with dozens of subagents with a single request and a deep enough prompt to kick it off.

    I setup entire virtual teams (Dev, QA, product, reviewers etc with the initiating model just acting as the agent manager to keep it's context minimal) to one-shot some stuff and it kept churning and making progress.

    Those days are just about over with the change to token pricing but for a time....

I guess you need automation for that. Run claude with cron to find fulnerabilities, suggest and implement improvements, automatically dig through backlog

  • Hope people doing those kinds of automations are paid to waste prompts and tokens. Any cron based LLM run is just stupid waste

300 prompts in a day isn't that unreasonable to achieve on a heavy day? And Opus has a significant multiplier as well

  • That is a lot of prompts. What kind of prompts do you usually give?

    Mine are usually giving specification and telling Claude to implement some part of it, referring to existing code base, writing unittests and running e2e until it passes. This can easily take 4-5 hours.

    Then again, I have seen colleagues prompt “are you sure?” And other nonsense like that

    • I think we use it differently then, I tend to go heavy on the back and forth.

      For speccing things out, I have a back-and-forth with the grill-me skill, break things down into tickets, as well as kicking off subagents. That said, I significantly overestimated the number of human messages I send.

      My daily 90th percentile wrt number of prompts sent is sitting at 160 queries / day and average at 97 queries / day.

      Ran an analysis of my last 2000 messages, with the following breakdown

      Task delegation / execution: 23% Investigation / diagnosis / “what’s going on?”: 21% Planning / architecture / brainstorming: 15% Testing / verification / release ops: 10% Review / cleanup / quality control: 9% Course-correction / constraints / preferences: 8% Agent / ticket / workflow orchestration: 6% Providing context / evidence / pasted material: 4% Social reactions / acknowledgements / vibes: 2% Other: 2%

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