I got access to Jev and immediately plugged it into Claude

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Plugged Jev into Claude Code to replace LLM context compaction by scoring each tool call's importance and keeping or dropping it by probability threshold.

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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:

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:

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.

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Numbers are a snapshot taken from X when the case was added to the library (schema v1, collected 2026-09-19); they will not match today.

Where this case fits

Filed under agents & workflows, coding & developer tools. In the pattern Jev is built for, the model answers a bounded question per step — and ordinary code acts on the answer, because the answer is already a value rather than a paragraph. Other posts in the same family are on the agents & workflows page.

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Last updated: 2026-09-22 · sources & corrections · every card links to its author's original post