Architecture for a Daily Intelligence Briefing System Built with Claude

A developer on r/ClaudeAI shared the architecture for a custom daily intelligence briefing system built using Claude API. The system creates personalized morning briefings from user-defined sources rather than generic newsletters.
Pipeline Architecture
The system follows a five-step pipeline:
- Ingest: Pulls from 12 RSS feeds overnight including industry news, competitor blogs, and subreddits. Processes approximately 200 articles per day.
- Score: Each article gets a relevance score against a keyword list using Claude Haiku for speed and cost efficiency. Articles scoring below 0.4 are dropped, reducing the volume from 200 to 15-30 articles.
- Triage: Scored articles are classified into three categories: PASS (goes to briefing), PARK (save for later), or REJECT (discard).
- Analyze: PASS articles receive deeper analysis using Claude Sonnet, focusing on implications for the user's work rather than simple summarization.
- Brief: Compiled into a structured morning email with three sections: Signal (act on this), Watch (monitor this), and Deferred (revisit later). Delivered at 6:30 AM.
Technical Implementation
Cost Structure: Under $5/month in API calls. Haiku handles scoring (costing pennies), while Sonnet only processes the 5-8 articles that survive triage. Deepgram would be the most expensive component if audio briefings were added.
Technology Stack:
- Python with FastAPI
- Supabase for storage
- Claude API (Haiku + Sonnet)
- Resend for email delivery
- Runs on a $7/month Render instance
Key Learnings
- The scoring step is more critical than analysis. If too many articles pass through, Claude wastes tokens summarizing noise.
- Structured output with clear sections (Signal/Watch/Deferred) proved more useful than a wall of summaries. The developer initially tried "summarize these 10 articles" but found it unreadable.
- RSS feeds remain underrated but effective. Most major publications, subreddits, and GitHub repos still offer RSS feeds, providing a cheap and reliable ingestion layer.
📖 Read the full source: r/ClaudeAI
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