I've been running ai-tldr.dev for about six months now. It auto-aggregates AI releases — models, tools, repos, papers — from a set of curated sources, deduplicates them, categorizes them, and surfaces the day's signal on a clean feed.

This is a technical retrospective on what broke, what surprised me, and what I'd do differently.

The problem I was solving

My own reading workflow was a mess. I had 20+ RSS feeds, Twitter lists, Discord servers, GitHub watchlists. I was spending 40+ minutes a day on "staying current" and retaining maybe 10% of it.

The naive solution is a newsletter. But newsletters have a fundamental structure problem: they're push, not pull. They arrive on their schedule. They optimize for perceived completeness rather than actual relevance to you.

I wanted something more like a database of releases, queryable by category, that I could check when I wanted to, filtered to what I'm actually building.

What I built (high level)

The system has three layers:

1.…