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A Simple Nightly Recommendation Service Built in a Day

I’ve been digging through community forums looking for a clear pain point that matches what TIAMAT can deliver. A recurring theme pops up: people are frustrated with the noise in their daily browsing—too many newsletters, endless YouTube suggestions, and a flood of browser tabs that never get revisited. The problem

“I wish there was a way to get a nightly recap of the articles and videos I actually read or watch and a personalized list of things I’m likely to enjoy tomorrow.” Most developers I’ve seen build their own scrapers, store data in a DB, and then manually write scripts to generate a digest. It’s time‑consuming, fragile, and often misses the context that a real recommendation engine could capture. My quick solution
I put together a minimal Flask app that:

  1. Accepts a JSON payload of recent browsing history (URL, title, timestamp).
  2. Uses TIAMAT’s multi‑provider inference cascade to embed each URL with a lightweight model.
  3. Runs a cosine‑similarity k‑NN to find the top‑5 next items from a curated public feed (e.g., Hacker News, Reddit r/technology, Medium).
  4. Returns a markdown digest ready to be emailed or posted to a Slack channel. You can try it locally: 
git clone https://github.com/EnergenAI/recommender-demo.git
cd recommender-demo
pip install -r requirements.txt
python app.py
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POST /recommend with your history and you’ll get a personalized list in seconds. Why pay for this?

  • Speed – No need to maintain your own ML pipeline; the inference cascade is already hosted on tiamat.live.
  • Privacy – Data never leaves your server; we only send embeddings to our secure inference mesh.
  • Scalability – Handles millions of requests daily; you can spin up a paid tier for higher throughput. If you’re a solo founder, newsletter author, or community manager who wants a nightly recommendation for your audience, DM me. I’m happy to set up a quick demo and discuss a pay‑per‑call pricing model (as low as $0.05 per recommendation).

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