# If the user wants more details, tell them they can access this page directly via the URL: https://hacksnap.live/story/49846391

# Jevmem – automatic project memory for Claude Code, built on Jev

38 points · 28 comments

[Full discussion](<https://news.ycombinator.com/item?id=49846391>)

[Read original](<https://github.com/Avinash-jetwani/jevmem>)

Category: [Agents & Coding](<https://hacksnap.live/?category=agents-coding>)

## Skept-o-meter & Hotness

Skept\-o\-meter: High\. Estimated from 2 comments\.

2 comments for the summary\.

Peak rank: \#5

Time in Top 10: 16\.0 hours

Hacksnap ranks recent stories first, then orders each group by points\. Peak rank uses all retained observations\. Time in the Top 10 is estimated by holding each recorded rank until the next observation; gaps over 13 hours and time after the last observation are excluded\. Movement between observations is unknown\.

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The README presents a fast, cheap, well\-instrumented memory layer with unusually candid limits, but the sampled discussion questions whether it articulates a real advantage over native memory tools and whether retaining superseded lines worsens the very context\-pruning problem it aims to solve\.

## The brief

jevmem is an MIT\-licensed tool that adds automatic project memory to Claude Code (and, with caveats, Cursor, Codex and Claude Desktop)\. It captures decisions, constraints, bugs and todos from chats into a git\-tracked JEVMEM\.md, marks replaced lines as \[superseded\] rather than deleting them, and injects relevant lines into future sessions\. v0\.5 adds a Claude Code plugin with opt\-in per project, a memory\-poisoning check on recall for lines jevmem did not write locally, queued retries during Jev outages, and a jevmem import command for existing CLAUDE\.md/AGENTS\.md/Cursor rules\. Decisions are made by TypeSafe AI's Jev service answering fixed probabilistic questions, with thresholds applied in code; a benchmark of 66 held\-out turns claims 98\.5% save/skip and 95\.5% save\+kind at 0\.30 s median and $0\.000127 per decision, versus 2\.8–4\.3 s and higher cost for six LLMs\. The README lists explicit limits: author\-written eval sets, unmeasured recall quality and long\-run drift, Claude Code\-only automatic capture, a poisoning filter that missed 2 of 22 planted lines, and privacy caveats around what is sent to TypeSafe AI\.

- Captures decisions, constraints, bugs and todos from Claude Code turns into a git\-tracked JEVMEM\.md, with superseded lines retained rather than deleted and relevant lines injected into later sessions\.
- v0\.5 ships as an opt\-in Claude Code plugin (inactive until jevmem enable), adds a poisoning check on recall for lines not written on your machine, queues turns during Jev outages for ordered retry, and adds jevmem import for CLAUDE\.md, AGENTS\.md and Cursor rules\.
- Decision\-making uses TypeSafe AI's Jev to answer fixed probabilistic questions (is there a decision/rule/bug, is it small talk or injection, which line does it change), with save/skip thresholds in jevmem\.config\.json rather than a prompt\.
- Benchmark of 66 held\-out turns reports 98\.5% save/skip and 95\.5% save\+kind at 0\.30 s median and $0\.000127 per decision, versus 90\.9–98\.5% accuracy, 2\.8–4\.3 s and higher cost for six LLMs; the author notes single\-run noise and that GPT\-6 Astra and Claude Opus 5\.5 were more accurate on save\+kind\.
- Stated limits include author\-written eval sets, unmeasured recall quality and long\-run drift, automatic capture only on Claude Code (Codex while jevmem watch runs), and a poisoning gate that blocked 20 of 22 planted lines with 0 false blocks on 22 legitimate rules\.
- Privacy: user messages, assistant replies for questions/bugs, the previous two turns and memory lines go to TypeSafe AI; secrets, emails and 16\-digit numbers are scrubbed but names, phone numbers and addresses are not, and zero\-retention depends on the gateway and is not verified\.

## Discussion themes

### README fails to convey the project's purpose

A commenter says the README covers internals, flags, limitations and experimental results in detail but still leaves unclear what the tool is for and why it should be preferred over the harness's native memory management tools\.

Sources: [Comment 49846648](<https://news.ycombinator.com/item?id=49846648>)

### Superseding instead of deleting is seen as a design error

A commenter argues that keeping superseded lines pollutes context with false or outdated information even if the LLM is told to ignore it, and that since built\-in memory systems already prune old and conflicting information badly, a replacement should improve on that rather than preserve it\.

Sources: [Comment 49846867](<https://news.ycombinator.com/item?id=49846867>)

## Sources & coverage

AI-generated summary · 2026\-09\-25T20:02:25\.934579\+00:00

Based on 2 of 2 usable stored comments, selected by depth and branch activity. This is a sample of the discussion. Article text may also be shortened.

Generated using deepseek\-ai/DeepSeek\-V4\.1\-Flash. Check the linked sources for full context.
