I feel like this explains about a quarter to half of my token burn. It was never really clear to me whether tool calls in an agent session would keep the context hot or whether I would have to pay the entire context loading penalty after each call; from my perspective it's one request. I have Claude routinely do large numbers of sequential tool calls, or have long running processes with fairly large context windows. Ouch.

> The Anthropic prompt cache has a 5-minute TTL. Sleeping past 300 seconds means the next wake-up reads your full conversation context uncached — slower and more expensive. So the natural breakpoints:

> - *Under 5 minutes (60s–270s)*: cache stays warm. Right for active work — checking a build, polling for state that's about to change, watching a process you just started.

> - *5 minutes to 1 hour (300s–3600s)*: pay the cache miss. Right when there's no point checking sooner — waiting on something that takes minutes to change, or genuinely idle.

> *Don't pick 300s.* It's the worst-of-both: you pay the cache miss without amortizing it. If you're tempted to "wait 5 minutes," either drop to 270s (stay in cache) or commit to 1200s+ (one cache miss buys a much longer wait). Don't think in round-number minutes — think in cache windows.

> For idle ticks with no specific signal to watch, default to *1200s–1800s* (20–30 min). The loop checks back, you don't burn cache 12× per hour for nothing, and the user can always interrupt if they need you sooner.

> Think about what you're actually waiting for, not just "how long should I sleep." If you kicked off an 8-minute build, sleeping 60s burns the cache 8 times before it finishes — sleep ~270s twice instead.

> The runtime clamps to [60, 3600], so you don't need to clamp yourself.

Definitely not clear if you're only used to the subscription plan that every single interaction triggers a full context load. It's all one session session to most people. So long as they keep replying quickly, or queue up a long arc of work, then there's probably a expectation that you wouldn't incur that much context loading cost. But this suggests that's not at all true.

When I task my primary agent with anything, it has to launch the Socratic agent, give it an overview of what are we working on, what our goals are and what it plans to do.

This works better than any thinking tokens for me so far. It usually gets the model to write almost perfectly balanced plan that is neither over, nor under engineered.

And of course all conversations now have to compact 80 tokens earlier, and are marginally worse (since results get worse the more stuff is in the context)

> At some point you just have to accept that llm's, like people, make mistakes, and that's ok!

Except that's not the way many everyday users view LLM's. The carwash prompt went viral because it showed the LLM making a blatant mistake, and many seem to have found this genuinely surprising.

It's not a niche issue at all. 29 million people in the US are struggling with an eating disorder [1].

> This single paragraph is going to legitimately cost anthropic at least 4, maybe 5 digits.

It's 59 out of 3,791 words total in the system prompt. That's 1.48%. Relax.

It should go without saying, but Anthropic has the usage data; they must be seeing a significant increase in the number of times eating disorders come up in conversations with Claude. I'm sure Anthropic takes what goes into the system prompt very seriously.

[1]: from https://www.southdenvertherapy.com/blog/eating-disorder-stat...

The trajectory is troubling. Eating disorder prevalence has more than doubled globally since 2000, with a 124% increase according to World Health Organization data. The United States has seen similar trends, with hospitalization rates climbing steadily year over year.

https://www.anthropic.com/news/claude-new-constitution

  class Claude {}
  
  Claude anthropicInstance = new Claude();
  anthropicInstance.greet();