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I gave Qwen 3.8 27B a reverse-engineering job I assumed needed a frontier model, and it finished in 30 minutes
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this post was submitted on 23 Aug 2026
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Yeah so I've been working with Claude on that. Typically it greps to find what it's looking for and that can be a lot of tokens. So I created something halfway between rag and that (semantic search), and overall it lowers token usage a bit, but even if it optimistically reduces tokens 20%, Claude is hungry for docs.
There is automatic compaction, but I typically want to control that myself when I change focus (if I don't just /clear it). Still I'd say almost all of the stuff I do runs north of 100k tokens.
Surprised the community hasn't found better ways to reduce context, 100k is insane. Is it possible to make the agent work on smaller pieces at a time, so less context is needed per piece?
Oh for sure. Agents often use under 30k tokens, but the orchestrator needs to have enough information to instruct the agents so it typically is a fair bit bigger in context. Agents tend to save you money on frontier models, but I'm skeptical about local LLMs. I suppose if you aren't pressed for time it's probably just fine. I haven't played that much with it because anything big enough to bother with agents I typically feel is too big for local anyway. But I'm sure others have experimented more on that front than I have.