惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

Vercel News
Vercel News
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
Apple Machine Learning Research
Apple Machine Learning Research
T
Tailwind CSS Blog
让小产品的独立变现更简单 - ezindie.com
让小产品的独立变现更简单 - ezindie.com
人人都是产品经理
人人都是产品经理
V
V2EX
量子位
Last Week in AI
Last Week in AI
Jina AI
Jina AI
博客园 - 【当耐特】
爱范儿
爱范儿
宝玉的分享
宝玉的分享
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
Hugging Face - Blog
Hugging Face - Blog
博客园 - 三生石上(FineUI控件)
有赞技术团队
有赞技术团队
小众软件
小众软件
IT之家
IT之家
博客园_首页
博客园 - 聂微东
S
SegmentFault 最新的问题
阮一峰的网络日志
阮一峰的网络日志
博客园 - 叶小钗

DEV Community

Authentication Security Deep Dive: From Brute Force to Salted Hashing (With Java Examples) Why AI Systems Don’t Fail — They Drift Spilling beans for how i learn for exam😁"Reinforcement Learning Cheat Sheet" I Replaced Chrome with Safari for AI Browser Automation. Here's What Broke (and What Finally Worked) How Python Borrows Other People's Work The $40 Architecture: Processing 1 Billion API Requests with 99.99% Uptime Vibe Coding: A Workflow Guide (From Zero to SaaS) Most webhook security guides protect the wrong side. The scary part is delivery. Headless CMS for TanStack Start: Build a Blog with Cosmic EU Age Verification App "Hacked in 2 Minutes" — What Actually Happened Comfy Cloud’s delete function does not actually remove files Running AI Models on GPU Cloud Servers: A Beginner Guide Event-driven media intelligence with AWS Step Functions and Bedrock I scored 500 AI prompts across 8 quality dimensions — here's what broke How to Call Google Gemini API from Next.js (Free Tier, No Backend Needed) The Portal Protocol: Reclaiming Human Connection in the Age of AI How to Fix Your Team's Scattered Knowledge Problem With a Self-Hosted Forum Intro to tc Cloud Functors: A Graph-First Mental Model for the Modern Cloud Designing Multi-Tenant Backends With Both Ownership and Team Access I Built a Neumorphic CSS Library with 77+ Components — Here's What I Learned PostgreSQL Performance Optimization: Why Connection Pooling Is Critical at Scale Cómo construí un SaaS multi-rubro para gestionar expensas en Argentina con FastAPI + Vue 3 🚀 I Built an Ethical Hacking Scanner Tool – Open Source Project I Replaced /usage and /context in Claude Code With a Single Statusline A Pythonic Way to Handle Emails (IMAP/SMTP) with Auto-Discovery and AI-Ready Design I Collected 8.9 Million Polymarket Price Points — Here's What I Found About How Markets Really Move EcoTrack AI — Carbon Footprint Tracker & Dashboard Everyone's Using AI. No One Agrees How. 5 self-hosted ebook managers worth trying in 2026 Building Your First AI Agent with LangChain: From Chatbot to Autonomous Assistant
CTX: I gave Claude Code a memory that actually works
Jaewon Jang · 2026-05-03 · via DEV Community

Jaewon Jang

The problem

Claude Code resets every session. There is no built-in memory. You open a new terminal, start coding, and the model has no idea what you decided yesterday, what architecture you settled on, or which files matter. You explain it again. Every time.

I spent three months building something to fix this.

What CTX does

CTX hooks into Claude Code's UserPromptSubmit event. Before every prompt, three things happen — in under 1ms:

G1 — Decision memory
Parses your git log and surfaces the most relevant past decisions. "Why did we switch to BM25?" "What was the reasoning behind this architecture?" CTX pulls those commit messages and injects them before you even ask.

G2 — Code and doc search
BM25 search across your entire codebase and markdown docs. When you ask about a function, the right files are already in context. No more "I can't find that file" hallucinations.

CM — Chat memory vault
A local SQLite database of past conversations, hybrid-searched (BM25 + optional vector). The things you explained once, you should only have to explain once.

The numbers

I ran rigorous benchmarks — not synthetic toy tests.

Memory recall (MAB, N=50)

System Recall Wilson CI 95%
None (baseline) 0.00 [0.00, 0.07]
CTX 0.40 [0.28, 0.54]
CTX v2 0.58 [0.44, 0.71]
CTX v3 0.88 [0.762, 0.944]

CTX v3 vs baseline: McNemar p < 0.001. Statistically significant.

Real-world telemetry (10,000+ turns)

  • Overall utility rate: 39.6% (items injected that Claude actually cited)
  • CM block: 52.6% utility rate (highest — chat memory is the most cited)
  • G1 block: 39.6%
  • G2 docs: 27.8%

A 42 percentage point gap between KEYWORD (16%) and SEMANTIC (42%) queries confirms retrieval method selection matters — and CTX routes them differently.

How it installs

pip install ctx-retriever && ctx-install

Enter fullscreen mode Exit fullscreen mode

Or natively in Claude Code:

/plugin install ctx@jaytoone

Enter fullscreen mode Exit fullscreen mode

Two steps. The installer copies hooks to ~/.claude/hooks/ and patches settings.json atomically (backup-first, never overwrites existing hooks).

Validated in a clean Docker container (ubuntu:22.04) — all 4 install steps pass.

What it does not do

  • No cloud sync. Everything stays local.
  • No LLM calls. Pure BM25 + SQLite.
  • No mandatory telemetry. Opt-in only.
  • Does not replace Claude's context window — it fills it intelligently before you ask.

Links