How I Built an Autonomous AI Newsletter Pipeline

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.

Stage 2: Scoring

This is where it gets interesting. Each article gets scored by DeepSeek V3 on three dimensions:

Dimension	Range	What it measures
Relevance	0-10	How relevant is this to AI agents and autonomous systems?
Depth	0-10	Technical depth and novelty of the content
Practicality	0-10	How 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.

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

Item	Monthly Cost
VPS (Hetzner)	$5
DeepSeek V3 API	~$3
Buttondown	Free (< 100 subscribers)
Domain	Already 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

1. Start with distribution first. Building the product was the easy part. Getting subscribers is 10x harder.
2. Add a human review step. Even a 2-minute scan catches the occasional off-topic article.
3. Use cheaper models for scoring. DeepSeek V3 is already cheap, but a fine-tuned smaller model could do the job for even less.