Trace

The fastest way to catch up on tech & AI news. 100+ tech sources, 10 stories, 5 minutes. Free for everyone, open source.

Trace reads tech and AI news from over a hundred sources every morning, groups the stories everyone is covering into one card, ranks them, and summarises each one with every source attached. Read it in the feed, in a daily email you shape yourself, on your phone, in every new Chrome tab, or through your AI assistant.

Live: yourtrace.online · Daily archive: yourtrace.online/brief · Build your brief: yourtrace.online/brief/customize

What you get

The feed	One card per story, every source attached, ranked by how much it matters. Topic lanes (AI, Markets, Tech, Startups, Design, Robotics), Top articles, bookmarks, an archive.
The daily brief	A morning email. Ten stories by default; each reader picks topics, 5/10/15 stories, and summaries, headlines only, or images. The preview in the builder is the real selection.
Public archive	Every edition at /brief/YYYY-MM-DD, with share buttons and its own share image.
Mobile app	Android app on Google Play; the Expo source lives in trace/ in the main repo.
New tab	Chrome extension in trace-extension/: the latest stories on every new tab.
Agents	Paste one URL into Claude, ChatGPT, Cursor, Claude Code, Codex or Gemini CLI, sign in with Google, and your agent reads the brief (/agents).

Connect your AI agent

Trace is a read-only remote MCP server at https://www.yourtrace.online/api/mcp. There is no key to copy: the first time your client connects it opens a Trace page, you sign in with Google and click Allow. Connecting also sends you the daily brief by email (unsubscribe any time; your agent keeps access).

claude mcp add --transport http trace https://www.yourtrace.online/api/mcp   # Claude Code
codex mcp add trace --url https://www.yourtrace.online/api/mcp               # Codex
gemini mcp add --transport http trace https://www.yourtrace.online/api/mcp   # Gemini CLI

In Claude and ChatGPT, add it as a custom connector; in Cursor and most other clients, {"mcpServers": {"trace": {"url": "https://www.yourtrace.online/api/mcp"}}}. Step-by-step for each app, a skill file for personal agents and header-based Agent Keys for bots are on /agents.

Under the hood it follows the MCP authorization spec: OAuth 2.1 with PKCE, protected resource and authorization server metadata under /.well-known/, Client ID Metadata Documents or Dynamic Client Registration, one-hour access tokens bound to /api/mcp and rotating refresh tokens. Only SHA-256 hashes of codes and tokens are stored (frontend/lib/oauth-*.ts). A self-hosted instance needs oauth_clients,oauth_codes,oauth_tokens,oauth_grants in LOCALDB_ALLOWED_COLLECTIONS and TRACE_OAUTH_ORIGIN set to its public URL.

How it works

 sources (RSS/Atom, HN, Reddit, Product Hunt, X, ...)      every ~20 min / ~3 h
        |
        v
 backend/v3  pipeline ── fetch → filter → group same-event coverage → rank
        |                └─ summarise each article once (cached), pick the brief
        v
 LocalDB (SQLite, Firestore-compatible API)  ◄── frontend/ (Next.js) ── readers
        |                                             trace/ (mobile), trace-extension/
        v
 daily email: own Postfix MTA + DKIM, or Brevo

• Grouping. Articles about the same event are merged into one story, so you see "1 story · 5 sources" instead of five near-identical links.
• Ranking. Coverage, source weight, recency and an LLM editor (OpenAI structured outputs) decide what makes the cut and what is worth an email. Model releases get their own slot while their newest coverage is under 36 hours old; Story of the day follows the same rule.
• Cost. Summaries are cached per article, so LLM cost scales with the number of articles, not the number of readers.
• The brief. backend/v3/flows/send_audience_newsletter.py picks each reader's stories. frontend/lib/newsletter-select.ts is a line-for-line TypeScript port used by the live preview; a parity test keeps them identical.

Repository layout

Path	What
frontend/	Next.js web app: landing, feed, /brief, builder, account, API routes
backend/v3/	The pipeline: sources (v3/config), builders, ranking, summaries, scheduler, newsletter
backend/v1/, backend/v2/	Shared modules the pipeline still uses (storage, email sending, templates, push notifications)
backend/localdb/	Self-hosted SQLite document store with a Firestore-compatible client and HTTP server
backend/vps/	systemd units, Caddy example and backup script for a single-server deployment
trace/	Mobile app (Expo / React Native); not yet in the public mirror
trace-extension/	Chrome new-tab extension (Manifest V3)
docker/, docker-compose.yml	Self-hosting stack: LocalDB + pipeline + web app
docs/	Screenshots and deployment notes

Run it yourself

Docker (everything)

Prerequisites: Docker with Compose v2, an OpenAI API key, and a free Firebase project (used for sign-in only).

Hardware: no GPU (the model calls go to the API). Rough guide, not yet measured on a minimal Docker install:

以上为 README 的开头部分。其余内容请阅读原文。 查看完整 README ↗