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]"