Your GitHub’s AI projects, scored from the actual code
AI-Atlas evaluates public GitHub code—not stars or followers—to reveal what developers actually build. Every AI project gets an explainable score using a 7-part rubric, mapped across six fields: ML, Computer Vision, Data Science, NLP and LLMs, AI Agents and MLOps. Map any GitHub profile for free. Claim yours to explore file-level evidence, join opt-in global, country and project leaderboards, add a live README badge and get a free improvement roadmap. Built solo by a 17-year-old student.
AI-Atlas evaluates public GitHub AI projects based on actual code quality rather than popularity metrics. It provides a detailed score using a 7-part rubric across multiple AI fields, allowing users to map GitHub profiles and access various project insights.
A source that found nothing is a measurement. A source that has not run is a gap. Neither means the launch lacks the thing.