AI Product Discovery
Fetch, deduplicate, and rank AI product launches from multiple sources.
Sources
| Source | URL | Notes |
|---|---|---|
| Product Hunt | https://www.producthunt.com/feed | Filter for AI-related |
| Hacker News | https://hn.algolia.com/api/v1/search?tags=show_hn&numericFilters=created_at_i>TIMESTAMP | Show HN posts, 24h window |
| GitHub Trending | https://mshibanami.github.io/GitHubTrendingRSS/daily/python.xml | Python repos |
| Techmeme | https://techmeme.com/river | Product announcements |
Workflow
- Check cache: Look for
50_资源/产品发布/YYYY-MM/YYYY-MM-DD-摘要.md. If exists with today's date, return cached. - Fetch sources: Use WebFetch on each. Extract product name, URL, description, and engagement metrics (votes/points/stars).
- Filter: Keep only AI-related products (keywords: AI, ML, LLM, GPT, Claude, automation, agent, model).
- Deduplicate: Same product across sources = merge. Keep best description, combine metrics, track all sources.
- Rank by:
- AI relevance - Engagement (normalize: PH votes/500, HN points/100, GH stars/1000) - Content potential (tutorial-friendly, review-worthy, open source bonus) - Recency and novelty
- Generate digest: See TEMPLATE.md. Sections:
- 精选推荐 (3-5) with content angles - LLM与AI模型 - 开发者工具 - 生产力与自动化 - 开源亮点
- Save files:
- 50_资源/产品发布/YYYY-MM/YYYY-MM-DD-摘要.md - 50_资源/产品发布/YYYY-MM/原始数据/YYYY-MM-DD_ProductHunt-Raw.md - 50_资源/产品发布/YYYY-MM/原始数据/YYYY-MM-DD_HackerNews-Raw.md - 50_资源/产品发布/YYYY-MM/原始数据/YYYY-MM-DD_GitHub-Raw.md
Output Format
Manual invocation: Full digest with all sections.
From /start-my-day: Condensed list:
**产品发布机会 (5):**
- [产品名] - [内容角度] - [关键指标]
...
完整摘要: [[YYYY-MM-DD-摘要]]Error Handling
- Source down: Continue with others, note in digest
- <2 sources available: Fall back to yesterday's archive
- Empty results: Create minimal digest noting "今日无新AI产品"
Content Angle Logic
- High engagement + tutorial-friendly: "教程机会"
- Novel + early stage: "抢先报道优势"
- Open source + complex: "深度分析"
- SaaS + practical: "工具评测"
- Similar to existing: "对比 vs [竞品]"