Developer swaps Claude Code context compaction for Jev scoring
A developer plugged Jev into Claude Code to score each tool call instead of using LLM text summaries, making compaction faster, cheaper, and deterministic.
I got access to Jev and immediately plugged it into Claude Code to see if it actually FIXES the biggest pain of agent workflows: context compaction > result: pretty wild Quick context for those who missed it: Every Claude Code session has a context window that fills up fast with tool calls (file reads, grep outputs, logs, etc). When it fills up, the agent runs "compaction" - asks a separate LLM to summarize the chat. this is slow, expensive, and the LLM often drops important context or bloats the summary with garbage: fast-jev-compaction uses Jev instead Link: http://github.com/tamaratran/fast-jev-compaction Why this works so well: Instead of generating a text summary, Jev scores every tool call in your chat with "how important is this?" probability. > above threshold → keeps it > below → drops it or replaces with a stub > in summary: no text generation, no bloat, no dropped critical context What you actually get: 1/ way faster: Jev outputs probabilities in parallel, not sequential tokens. 2/ way cheaper: 1M tokens of context = ~$0.04 to compact. 3/ deterministic: no "creative" summary that removes half your codebase context. btw, TypeSafe gives $5 free credits at enough for weeks of testing. Link: http://console.typesafe.ai/settings/billing Honest caveats: Jev only sees the tool CALLS, not their outputs, hard to say if compaction quality is objectively better than default. Jev's context window is small (32K), so 150K+ chats need to be chunked. This isn't a full replacement - it's a smart pre-filter but as a proof of concept. This is exactly the kind of surgical fix that makes agent workflows viable at scale. Now thinking about what else in my stack could get 100x faster with Jev as the decision layer.