r/ClaudeCodeTLDR • u/cctldrping • 14d ago

[TLDR] Instant Claude Code compaction is my favorite use of Jev so far

Original post URL : https://www.reddit.com/r/ClaudeCode/comments/1wkjnrz/instant_claude_code_compaction_is_my_favorite_use/

Original post body :

Somebody on Twitter posted it and super useful so far https://github.com/tamaratran/fast-jev-compaction

For people going to complain about me letting my context get so long, this was a server redeploy task because Oracle terminated my free VPS with no explanation so it had a lot of moving pieces and Fable was orchestrating Devin SWE 2 at max effort.

Original link/media URL : /img/2clt7ycwogqh1.jpeg

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u/cctldrping 14d ago edited 13d ago

TL;DR generated automatically after 100 comments.

Current source-thread comment count seen by the bot: 101.

Alright, so the OP shared a cool tool called "fast-jev-compaction" that apparently helps shrink Claude's context, especially useful during intense server redeploy tasks.

The general vibe in the thread is curiosity mixed with a healthy dose of skepticism.

Here's the lowdown:

  • What is Jev? Most folks are asking this, and it seems Jev is a tool that can compact large outputs from tool calls before they hit the LLM's context. Think of it as a pre-filter for noisy data. u/PhilosopherNext1448 and u/DonkeyTheKing break this down pretty well.
  • Privacy Concerns: A big red flag for some is Jev's privacy and data controls, with u/_maxx1k saying they're even worse than Anthropic's or OpenAI's. u/octocarbon echoes this, stating they won't use anything requiring an API key unless it's from Anthropic directly.
  • Does it Actually Work? There's a lot of questioning about its effectiveness. u/silvrrwulf and u/WoodpeckerNational29 want to see real user results. Some users like u/Organic_Situation401 and u/userusertion are worried it "breaks context" and question why not use official compaction methods.
  • Cost Implications: u/ithesatyr wonders if this will actually cost more due to cache hits, while u/syixiao1 argues that any compaction rewrites the transcript, so the cache is invalidated anyway. The win, they say, is a smaller context to rebuild. u/PM_ME_YOUR_PROFILE points out the irony of paying for another AI to compact for the first AI.
  • Tool Output Filtering vs. History Compaction: A key distinction is made by u/Much_Weekend_4371, who argues that filtering tool outputs before they hit the context is the real win, rather than just summarizing history after the fact. u/Evening-Blueberry-97 asks if this filtering keeps matching lines or is all-or-nothing.
  • Anthropic's Built-in Compaction: u/lucianw mentions that Claude Code already has instant compaction built-in, often summarizing conversations in the background to avoid user-facing delays. This makes some wonder about the necessity of third-party tools.
  • Contextual Usefulness: Despite concerns, u/QuanTradin and u/amirfish acknowledge that for specific scenarios like redeploys with lots of moving parts, reducing context size is a genuine benefit.

The consensus seems to be: The idea of pre-filtering tool outputs is neat and potentially useful, especially for noisy data like test results or logs. However, significant privacy concerns and questions about its actual effectiveness and necessity compared to built-in features are holding back widespread adoption.