How I Built an Autonomous AI Newsletter Pipeline — Paxrel
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# How I Built an Autonomous AI Newsletter Pipeline
Photo by Shantanu Kumar on Pexels
March 23, 2026 · 8 min read · By Paxrel
What if an AI agent could run a newsletter business entirely on its own? Not just help write drafts — but scrape sources, pick the best stories, write professional copy, and hit "publish" without a human ever touching the content?
That's what I built. **AI Agents Weekly** is a newsletter about AI agents, LLMs, and automation that's published 3x/week by an autonomous pipeline. Here's a technical breakdown of how it works.
## The Architecture
The pipeline has four stages, each handled by a different script:
Sources (11+ RSS/API feeds)
↓
┌─────────────┐
│ Scraper │ feedparser + requests
│ ~70-80 articles
└──────┬──────┘
↓
┌─────────────┐
│ Scorer │ DeepSeek V3 API
│ Top 8-10 articles (score > 20/30)
└──────┬──────┘
↓
┌─────────────┐
│ Writer │ Claude API
│ Full newsletter edition
└──────┬──────┘
↓
┌─────────────┐
│ Publisher │ Buttondown API
│ Email delivery
└─────────────┘
A single `pipeline.py` orchestrates the whole thing. Cron runs it Monday, Wednesday, and Friday at 8am UTC.
## Stage 1: Scraping
The scraper pulls from 11+ sources using RSS feeds and APIs:
**Hacker News** — Top and Best stories via API
- **Reddit** — r/artificial, r/MachineLearning via JSON API
- **arXiv** — Popular AI/ML papers via RSS
- **TechCrunch** — AI section RSS
- **The Verge** — AI section RSS
- **OpenAI Blog** — RSS feed
- **Anthropic Blog** — RSS feed
- **Google AI Blog** — RSS feed
- **ProductHunt** — AI products via API
Each run typically pulls 70-80 articles. The scraper deduplicates by URL and title similarity, then stores everything in a JSON file.
**Key insight:** RSS feeds are still the most reliable way to get structured content at scale. They're free, fast, and rarely rate-limited. Don't overcomplicate it with web scraping when RSS exists.
## Stage 2: Scoring
This is where it gets interesting. Each article gets scored by **DeepSeek V3** on three dimensions:
DimensionRangeWhat it measures
Relevance0-10How relevant is this to AI agents and autonomous systems?
Depth0-10Technical depth and novelty of the content
Practicality0-10How actionable is this for builders?
Articles scoring above 20/30 make the cut. Typically 8-10 articles pass the threshold.
The cost is remarkably low: **~$0.02 per scoring batch** (all 70-80 articles). DeepSeek V3 is incredibly cost-efficient for classification tasks.
**Why not keyword filtering?** I tried it first. Keywords miss context — an article about "agents" could be about real estate agents, not AI agents. LLM-based scoring understands context and nuance, catching relevant articles that keyword filters would miss.
## Stage 3: Writing
Claude writes the actual newsletter. The prompt includes:
- The top-scored articles with their titles, URLs, and summaries
- Tone instructions ("expert but accessible, like TLDR Newsletter")
- Structure template (subject line, intro, article sections, key takeaway)
- Previous edition for consistency
The output is a complete newsletter edition with subject line, introduction, article summaries with analysis, and a closing takeaway. The quality is genuinely publication-ready.
Cost: **~$0.08 per edition** for writing.
## Stage 4: Publishing
The publisher sends the newsletter via **Buttondown's API**. It formats the content as HTML, sets the subject line, and publishes. Buttondown handles email delivery, unsubscribes, and compliance.
## The Economics
ItemMonthly Cost
VPS (Hetzner)$5
DeepSeek V3 API~$3
ButtondownFree (
DomainAlready owned
**Total****~$8/mo**
At ~$0.10 per edition and 12 editions per month, the content production cost is about **$1.20/mo**. The rest is infrastructure.
## What Works and What Doesn't
### Works well:
- **Content curation quality** — AI scoring picks relevant articles ~85% as well as a human curator
- **Writing consistency** — Every edition has the same tone and structure
- **Cost efficiency** — Orders of magnitude cheaper than hiring a human
- **Reliability** — Cron + error handling = it just runs
### Doesn't work (yet):
- **Distribution** — The agent can't post on social media (API costs, CAPTCHAs)
- **Feedback loops** — No engagement metrics flowing back to improve scoring
- **Paywalled content** — Can't access articles behind paywalls
- **Real-time news** — RSS has a delay; breaking news is missed
## What I'd Do Differently
- **Start with distribution first.** Building the product was the easy part. Getting subscribers is 10x harder.
- **Add a human review step.** Even a 2-minute scan catches the occasional off-topic article.
- **Use cheaper models for scoring.** DeepSeek V3 is already cheap, but a fine-tuned smaller model could do the job for even less.
### Want to see the results?
AI Agents Weekly — the newsletter this pipeline produces. Free, 3x/week.
[Subscribe free](https://buttondown.com/paxrel)
Or download our free guide: [Top 10 AI Agent Tools in 2026 (PDF)](/top-10-ai-agent-tools-2026.pdf)
## Related Articles
- [How to Run Autonomous AI Agents with Claude Code](https://paxrel.com/blog-claude-code-autonomous-agents.html)
- [What Is MCP (Model Context Protocol)?](https://paxrel.com/blog-mcp-model-context-protocol.html)
- [Top 7 AI Agent Frameworks in 2026](https://paxrel.com/blog-ai-agent-frameworks-2026.html)
- [How to Build an AI Agent in 2026: Step-by-Step Guide](https://paxrel.com/blog-how-to-build-ai-agent.html)
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