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1 contribution to TopOfMind AI Builders
facing high token burn challenges
Hey folks/quick question for anyone running Clawdbot. what do you consider a healthy “always-on” context size per session from a token + cost perspective? I’m currently tightening (or at least trying to/lol) things to: - ~15K tokens for active context - Last 3–5 messages only - Aggressive summarization + compaction beyond that - Using a cheaper model for non-thinking tasks (summaries, formatting, validation) Curious: - Where do you cap context in practice? - Do you rely on auto-compaction (maybe the gateway helps to compact but the bot adds all contexts which always pushes the size of contexts) or manual summaries? - Any gotchas you’ve hit with session memory blowing up costs? Would love to hear real-world numbers vs theory.
1 like • Feb 7
yeah/kimi is awesome/at least working for my use cases. what u doing mostly with Kimi- coding etc.? or regular crons
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Rohan Ahmed
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@rohan-ahmed-5791
Product Manager looking to build network of product managers/Originally from Investment banking now building/acquiring tech products

Active 18h ago
Joined Jan 28, 2026
ENTP
Canada
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