JEV is INSANE. We gave it 700 high-intent leads and personalised outreach messages. In 40 seconds, it predicted how each message would perform, assigned a confidence score and detected lead-message mismatches. All for just $0.09. JEV can also score leads, analyse buying Show more
Hippo
A local SQLite memory layer for CLI agents with an opt-in Jev reranker that batches Noul judgments over the top 40 recalled memories and falls back to a local cross-encoder on errors.
- Category
- Tools & Integrations
- Published by
- Community
- Author
- kitfunso
- Added
- 2026-09-22
Tagscommunitytypescriptagentsmcpsearchreranking
Highlights
- The opt-in Jev reranker lifted R@1 from 0.41 to 0.62 on a private 300-query store for about $0.0004 per recall.
- The author's study found better ranking but no demonstrated answer-quality gain over the free local cross-encoder, and that negative result is published.
- Default recall uses BM25 with no embeddings and scores R@5 74.0% on LongMemEval; the 1000-event ingestion smoke made 0 outbound HTTP.
- Works with Claude Code, Codex, Cursor, OpenClaw, OpenCode, and any MCP client, storing memories in SQLite with markdown mirrors and zero runtime dependencies.
- Enabling the Jev reranker is the one switch that sends data out: the query and candidate memories go to TypeSafe.
Quickstart
bash
npm install -g hippo-memory && hippo init --scan ~
hippo recall "data pipeline" --why --limit 5
hippo recall "data pipeline" --reranker jevWatch out
MIT-licensed. Needs Node.js 22.5+ and, for the reranker, a TypeSafe key; enabling Jev sends the query and candidate memories to TypeSafe.
More like this
2.6kGitHub stars
A cross-platform memory layer for AI coding agents: conversations from Claude Code, Codex, DeepSeek Harness, OpenClaw, and OpenCode become Markdown memories indexed in Milvus, with optional Jev reranking through the TypeSafe API.
Tools & Integrations#community#python#agents
Communitymemsearch
815GitHub stars
An MCP server and agent skill that verifies coding-agent work by driving the real running app and returning pass, fail, or couldn't-tell verdicts with the file:line to fix; its listing frames the approach as Jev-style machine-native runtime perception.
Tools & Integrations#community#typescript#mcp
Communityreticle
From the community
Posts from builders shipping with Jev right now.
700 leads scored for $0.09
Beating Gemini Flash Lite on an eval
Ran @typesafeai's Jev against an existing classifier eval that previously used Gemini 2.5 Flash Lite. It won both on quality (saturated the eval) and speed (6x)
Browser Use Ultrafast, powered by Jev
Breaking: Browser Use + Jev = Ultrafast ⚡ Findings flights took 7s and cost only $0.0039 🤯 > new action space every step > DOM state space > small LLM fallback to type (this video is at 1x speed btw) Built a tiny open source browser agent. try it below ↓
A really smart switch statement
hype-free explanation of jev: jev does not replace gpt / claude jev is just a *really* smart switch statement like if 2016 ml classifiers got 2026 levels of intelligence it's a new* type of tool that will make a lot of workloads insanely fast, cheap, and accurate * = and by Show more
@CompleteSkeptic
After co-inventing ChatGPT, I kept asking myself: why have superhuman chat models not led to AGI? I’ve spent the last 2 years in stealth building a new way to train models (RLCD), and a new type of frontier AI model that we are releasing today: Jev • 20-200x faster • 40-400x
When a designer gets Jev
when a designer gets access to Jev
Full Jev video tutorial
Full Jev Tutorial What it is, how you can build with it and what new applications it can unlock → 0:00 Intro → 0:34 Jev explained → 4:06 API setup → 5:59 Demo 1: Voice-controlled browser → 11:33 Demo 2: AI memory → 17:27 Demo 3: YouTube predictor