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"lessons learned" (lemmy.ml)
submitted 9 months ago by to c/fuck_ai@lemmy.world
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screenshot of followup to the first tweet, also by @BenjaminDEKR: " Made some adjustments based on lessons learned. Combined: roughly 200-400x cheaper heartbeat operation."

https://xcancel.com/BenjaminDEKR/status/2017660150463582282

via this bluesky post:

screenshot of bluesky post by @rusty.todayintabs.com with text "I’m starting to think the people who are excited about “AI agents” have literally never used a computer in their lives" and screenshots of the above tweets

screenshot of tweet by @BenjaminDEKR with text: OpenClaw is interesting, but will also drain your wallet if you aren't careful. Last night around midnight I loaded my Anthropic API account with $20, then went to bed. When I woke up, my Anthropic balance was $0. Opus was checking "is it daytime yet?" every 30 minutes, paying $0.75 each time to conclude "no, it's still night." Doing literally nothing, OpenClaw spent the entire balance. How? The "Heartbeat" cron job, even though literally the only thing I had going was one silly reminder, ("remind me tomorrow to get milk") 1. Sent ~120,000 tokens of context to Opus 4.5 2. Opus read HEARTBEAT .md, thought about reminders 3. Replied "HEARTBEAT_OK" 4. Cost: ~$0.75 per heartbeat (cache writes) The damage: - Overnight = ~25+ heartbeats - 25 × $0.75 = ~$18.75 just from heartbeats alone - Plus regular conversation = ~$20 total The absurdity: Opus was essentially checking "is it daytime yet?" every 30 minutes, paying $0.75 each time to conclude "no, it's still night." The problem is: 1. Heartbeat uses Opus (most expensive model) for a trivial check 2. Sends the entire conversation context (~120k tokens) each time 3. Runs every 30 minutes regardless of whether anything needs checking That's $750 a month if this runs, to occasionally remind me stuff? Yeah, no. Not great.
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[–] 34 points 9 months ago (1 child)

Why even use an LLM for that? That seems like the completely wrong use-case for an LLM.

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