🌅 Horizon

Enjoy the News itself. Leave others to Horizon

📡 Your own AI-powered news radar. Generates daily briefings in English & Chinese. | 构建你专属的 AI 新闻雷达

📖 Live Demo · 📋 Configuration Guide · 简体中文 · 日本語

Screenshots

Ranked Daily Briefing

Context, Summary & Discussion

More Screenshots

Terminal Output

Feishu Notification

Email Delivery

Why Horizon?

Good news is scattered; bad news is endless. Horizon gives you a personal first pass over Hacker News, Reddit, Telegram, RSS, and GitHub: it fetches, deduplicates, scores, filters, and enriches stories with background context and community discussion.

But Horizon is not just another summarizer. AI is great at reducing noise, but news still needs human taste: the sources you trust, the comments that change how you read a story, and the hidden gems only people can share. Horizon keeps that human layer in the loop with customizable sources, processing profiles, models, languages, delivery channels, comment summaries, and a community source hub.

Features

• 📡 Watch Your Own Sources — Track Hacker News, RSS, Reddit, Telegram, Twitter/X, GitHub releases or user activity, and OpenBB financial news watchlists in one pipeline
• 🤖 Turn Noise Into a Reading List — Analyze each item with a stable processing profile and apply your own filter threshold
• 🔗 Merge Repeated Stories — Deduplicate the same story across platforms before it reaches your briefing
• 🔍 Understand the Background — Add web-researched context for unfamiliar concepts, companies, projects, and technical terms
• 💬 Read the Conversation — Collect and summarize community comments from Hacker News, Reddit, and other supported sources
• 🌐 Publish in Two Languages — Generate English and Chinese daily briefings from the same source set
• 📝 Ship a Daily Site — Publish generated Markdown as a GitHub Pages daily briefing site
• 📧 Deliver by Email — Run a self-hosted SMTP/IMAP newsletter with automatic subscribe and unsubscribe handling
• 🔔 Push to Chat or Automations — Send templated results to Feishu/Lark, DingTalk, Slack, Discord, or custom webhook endpoints
• 🧙 Start From Your Interests — Use the setup wizard to generate a personalized source configuration
• ⚙️ Tune the Radar — Customize sources, processing profiles, models, languages, and delivery channels

How It Works

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  "themeVariables": {
    "fontFamily": "ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, Segoe UI, sans-serif",
    "fontSize": "18px",
    "primaryTextColor": "#2d2a3e",
    "primaryBorderColor": "#e0dbd3",
    "lineColor": "#7c7891",
    "tertiaryColor": "#faf8f5",
    "clusterBkg": "#f3f0eb",
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}}%%
flowchart LR
    classDef config fill:#fbbf24,stroke:#d4a017,color:#2d2a3e,stroke-width:1.5px;
    classDef source fill:#ede7fb,stroke:#6d4aaa,color:#2d2a3e,stroke-width:1.5px;
    classDef process fill:#ffe8db,stroke:#e0652e,color:#2d2a3e,stroke-width:1.5px;
    classDef output fill:#f9d7e5,stroke:#be185d,color:#2d2a3e,stroke-width:1.5px;

    config["⚙️ Config<br/>sources, profiles, models, outputs"]

    subgraph sources["Configured Sources"]
        rss["📡 RSS"]
        hn["📰 Hacker News"]
        reddit["💬 Reddit"]
        telegram["✈️ Telegram"]
        twitter["🐦 Twitter / X"]
        github["🐙 GitHub"]
        openbb["💹 OpenBB"]
    end

    fetch["📥 Fetch"]
    dedup["🧹 Deduplicate"]
    score["🤖 AI Score & Filter"]
    enrich["🔎 Enrich"]
    summary["📝 Summarize"]

    subgraph outputs["Outputs"]
        direction TB
        site["🌐 Pages"]
        email["📧 Email"]
        webhook["🔔 Webhooks"]
        mcp["🧩 MCP"]
    end

    config --> fetch
    rss --> fetch
    hn --> fetch
    reddit --> fetch
    telegram --> fetch
    twitter --> fetch
    github --> fetch
    openbb --> fetch

    fetch --> dedup --> score --> enrich --> summary
    config --> score
    config --> summary
    config --> outputs

    summary --> site
    summary --> email
    summary --> webhook
    summary --> mcp

    class config config
    class rss,hn,reddit,telegram,twitter,github,openbb source
    class fetch,dedup,score,enrich,summary process
    class site,email,webhook,mcp output

1. Define — Configure sources, processing profiles, models, languages, and delivery.
2. Fetch — Pull latest content from all configured sources concurrently.
3. Deduplicate — Merge items pointing to the same story or URL across platforms.
4. Analyze & Filter — Select a profile, analyze each item with its prompt, and apply the configured user threshold.
5. Enrich — Generate the profile's configured content blocks, using only tools allowed for each block.
6. Summarize — Render localized titles, leads, sections, and cited sources as a Markdown briefing.
7. Deliver — Publish the result to GitHub Pages, email, webhooks such as Feishu, MCP, or local files.

Quick Start

1. Install

Option A: Local Installation

git clone https://github.com/Thysrael/Horizon.git
cd Horizon

# Install with uv (recommended)
uv sync

# Install test/development extras when needed
uv sync --extra dev

# Or with pip
pip install -e .

dev is currently defined as an optional extra in pyproject.toml, so use uv sync --extra dev for pytest and other development dependencies.

If you want the optional OpenBB financial-news source, install its extra too:

uv sync --extra openbb

If openbb pulls packages without wheels on your machine, install the SDK manually with binaries only:

uv pip install --only-binary=:all: openbb openbb-benzinga

Option B: Docker

git clone https://github.com/Thysrael/Horizon.git
cd Horizon

# Optional: build with comma-separated extras before the first run
docker compose build --build-arg EXTRAS=trafilatura horizon

Multiple extras may be supplied as EXTRAS=trafilatura,openbb. The twitter extra also requires a Playwright browser and system packages, which the current Dockerfile does not install.

2. Configure

Option A: Interactive wizard (recommended)

uv run horizon-wizard

The wizard asks about your interests (e.g. "LLM inference", "嵌入式", "web security") and auto-generates data/config.json. See Interactive Wizard for CLI options.

Option B: Manual configuration

cp .env.example .env          # Add your API keys
cp data/config.example.json data/config.json  # Customize your sources

Minimal manual configuration:

{
  "ai": {
    "provider": "openai",
    "model": "gpt-4",
    "api_key_env": "OPENAI_API_KEY"
  },
  "sources": {
    "rss": [
      {
        "name": "Simon Willison",
        "url": "https://simonwillison.net/atom/everything/",
        "profile": "tech-news"
      }
    ]
  },
  "processing": {
    "profiles_dir": "profiles",
    "default_profile": "tech-news",
    "profile_settings": {
      "tech-news": {
        "threshold": 7.0,
        "topic_dedup": true
      }
    }
  }
}

An explicit source profile uses that profile directly. Omit it or set it to "auto" to let AI match the item against all available profiles. Set it to an array such as ["tech-news", "finance-news"] to restrict AI matching to those profiles. See Processing Profiles for profile structure and behavior. Per-profile user preferences such as score thresholds and topic deduplication belong in processing.profile_settings, not in the profile files.

Balanced digest (optional)

Limit the final digest size and prevent one category from dominating the results. Categories come from source configuration such as sources.rss[].category.

{
  "digest": {
    "max_items": 20,
    "category_groups": {
      "ai": {
        "limit": 5,
        "categories": ["ai-news", "ai-tools", "machine-learning"]
      },
      "finance": {
        "limit": 5,
        "categories": ["finance", "business", "equities"]
      }
    },
    "default_group": "other",
    "default_group_limit": 3
  }
}

Group limits are applied after profile filtering and before enrichment. If category_groups and max_items are omitted, no balanced digest limits apply.

api_key_env must be the name of an environment variable, not the API key itself. Put the real secret in .env:

OPENAI_API_KEY=sk-your-key

For Gemini, use GOOGLE_API_KEY:

{
  "ai": {
    "provider": "gemini",
    "model": "gemini-2.0-flash",
    "api_key_env": "GOOGLE_API_KEY"
  }
}

Any string value in data/config.json can reference environment variables with ${VAR_NAME}. This is useful for values such as ai.base_url, private RSS feed URLs, webhook endpoints, or custom header templates.

For the full reference, see the Configuration Guide.

3. Run

A. Local installation

uv run horizon [OPTIONS]

B. Docker

docker compose run --rm horizon [OPTIONS]

Option	Default	Description
--hours N	24	Fetch from last N hours
-d, --data-dir PATH	data	Path to the data directory
-c, --config PATH	<data-dir>/config.json	Path to config file
-l, --log-level LEVEL	WARNING	Logging level (DEBUG/INFO/WARNING/ERROR/CRITICAL)

--data-dir changes the state directory, including summaries, subscribers, and the default config location; --config changes only the config file. The generated report is saved to data/summaries/ (or <data-dir>/summaries/ if --data-dir is set). See Configuration Paths for combining both flags and initializing a custom config location.

4. Automate (Optional)

Horizon works great as a GitHub Actions cron job. See .github/workflows/daily-summary.yml for a ready-to-use workflow that generates and deploys your daily briefing to GitHub Pages automatically.

Supported Sources

Source	What it fetches	Comments
Hacker News	Top stories by score	Yes (top N comments)
RSS / Atom	Any RSS or Atom feed	—
Reddit	Subreddits + user posts	Yes (top N comments)
Telegram	Public channel messages	—
Twitter / X	Tweets from specific users	Yes (top N replies)
GitHub	User events & repo releases	—
OpenBB	Financial company news by watchlist/provider	—

Where Your Briefing Goes

Horizon can publish or deliver the generated briefing in several ways:

Channel	What it does
GitHub Pages Daily Site	Copies generated Markdown into docs/ so GitHub Pages can publish a daily-updated briefing site
Email Subscription	Sends the daily briefing to subscribers and handles subscribe/unsubscribe requests through SMTP/IMAP
Webhook Notification	Pushes success or failure results to Feishu/Lark, DingTalk, Slack, Discord, or any custom webhook endpoint
MCP Server	Exposes Horizon pipeline steps as tools so AI assistants can fetch, score, filter, enrich, summarize, and run the full workflow

For setup details, see the Configuration Guide. For MCP tool references and client setup, see src/mcp/README.md and src/mcp/integration.md.

Supported By

Horizon is an open-source project maintained in spare time. If you'd like to support the project or be listed here, feel free to open an issue or email me.

Supporter	Details
	Compshare currently supports Horizon. Compshare is UCloud's AI cloud platform, offering cost-effective monthly and pay-as-you-go domestic model agent plans starting from RMB 49/month, as well as stable officially relayed overseas models. It supports Claude Code, Codex, and API usage, with enterprise-grade high concurrency, 24/7 technical support, and self-service invoicing. Register through their link to receive a free RMB 5 trial credit.
	Thanks to APIMart for sponsoring this project! APIMart is a low-cost API platform for AI image & video generation — GPT-Image-2 from $0.006/image, 160+ images per dollar. One async API covers both image and video: submit a task, get an ID, fetch results via polling or callback. Batch tens of thousands of images without timeouts, switch models without changing code. Pay-as-you-go with no monthly fee — sign up here to get started.

Documentation

Guide	Description
Configuration	AI providers, sources, profiles, filtering, email, webhook, GitHub Pages, and MCP setup
Processing Profiles	Profile routing, prompts, runtime filtering preferences, enrichment blocks, and tools
Scoring	How Horizon evaluates and ranks news items
Scrapers	Source scraper details and extension notes
Extractors	Full article extraction for RSS sources
MCP Tools	Tool reference for MCP-compatible clients

Project Status

Horizon already supports the full daily briefing loop: multi-source collection, profile-driven analysis and enrichment, deduplication, comment summaries, bilingual generation, GitHub Pages publishing, email delivery, webhook delivery, Docker deployment, MCP integration, and the setup wizard.

Planned improvements:

• More source types, such as Discord
• Publish releases on GitHub
• Publish the package to PyPI for pip install

Contributing

Contributions are welcome. See CONTRIBUTING.md for code, documentation, and source-sharing guidelines.

Share Sources

Want to share valuable source discoveries with the Horizon community? Please submit them through horizon1123.top.

Acknowledgements

• Special thanks to LINUX.DO for providing a promotion platform.
• Special thanks to HelloGitHub for valuable guidance and suggestions.
• Special thanks to AIGC Link for the promotions on XiaoHongShu.

License

MIT