Not sure if this will help you but it helped me. I was using subagents and workflows for lots of long running tasks, and part of that work included running tests. If tests took longer than 5 minutes, the cache expired, and the whole thing was then being processed as uncached input.

I created a skill to better manage agents, including a way to keep the caches valid, and it has decreased my usage considerably. I now struggle to use my 100% each week and I hit claude hard. I have a reset expiring in 5 days and I'm trying to burn down the tokens (68% with 3d 8h left) quickly so I can use my reset before it expires.

Token efficiency just means understanding the way the system works. Best person to help with that... my old friend claude.

I have some fairly large personal software projects and I still don't understand what useful work people are getting out of burning all these tokens on unattended agents.

What exactly are they doing for you that's giving you $200 of value in such a short timeframe?

As a dev who hasn't used LLMs as an embedded part of their work, and uncertain about it all, i'd be curious to know:

- How long do you reckon it would take a human to do the task?

- What percentage of features/ feature-extensions you add would have been done at all if you didn't have agentic assistance? Is there a tension between llm productivity gains and 'because we can' feature additions?

- Are many of the tasks for throwaway v1 features, exploratory tries?

- How speced out is the work you give it, eg did this task have pages of specs, how long did the spec take to write?

- How deeply will you manually review the work and how much time will it take? Will you read all the code / review at a high level and interrogate with llm / have the llm review and self iterate?

Yes, but what was the task that they were working on? This tells nothing.

That’s the “how” we’re wondering about.

You're doing this wrong.

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