How Legible Papers works
AI research, explained for people who write software.
What the site does
New AI and machine-learning papers land on arXiv and Hugging Face every day, far more than anyone has time to read. Legible Papers picks a few and explains them in plain English.
Each summary is written from the paper itself and links back to it. Some also come with a glossary, a diagram or a short code example. How AI is used explains how the summaries get made and checked, and where they can still go wrong.
Who it's for
- Engineers who use AI in what they build but don’t follow the research.
- People building agents and LLM features who want to know what’s now possible, and what it costs.
- Teams deciding whether a new result should change their plans.
Summaries assume you write software. They explain the machine-learning parts and skip what you already know. If a site called Legible Papers ever sends you to a dictionary, that’s a bug. The Flag an issue link on every article is how to tell me.
Who runs it
I’m Felix Stubner, and I build and run Legible Papers on my own. A pipeline writes the summaries, and how AI is used explains how. Since 30 August 2026, I read every summary before it goes out and decide whether it’s worth publishing. When I add a note under one, that’s me explaining why I picked it.