I Built an AI to Judge AI Projects. Here’s What It Looks For

Описание и применение

AI‑Atlas – это платформа, оценивающая репозитории GitHub по фактическому коду и экспериментам, а не по меткам в README, предоставляя прозрачные оценки качества проектов.

Задача

GitHub repositories often label themselves with generic tags like "Transformer" or "Computer Vision" but this does not reveal the true technical depth, originality, or reproducibility of the work.

Результат

AI‑Atlas provides evidence‑grounded, transparent scores that distinguish between superficial labeling and substantive technical contribution, enabling developers and recruiters to assess AI projects more accurately.

Рабочий процесс

• Discover repositories via public GitHub APIs
• Classify projects into six domains (ML, CV, DS, NLP & LLMs, AI Agents, MLOps)
• Parse code to identify implementation details, training loops, configs, and outputs
• Evaluate each project against the seven rubric dimensions using evidence from files
• Generate a detailed judgment report with reasoning and supporting evidence
• Compute a Project Merit score via versioned scoring rules, separating interpretation from scoring

Доказательства

A GitHub repository can say "Transformer," "RAG," or "Computer Vision" in the README. That does not tell you whether the work is technically deep, original, reproducible, or even really yours.

Источник

AI‑Atlas currently maps projects across Machine Learning, Computer Vision, Data Science, NLP & LLMs, AI Agents, and MLOps, but the domain classification itself is only the beginning.

Источник

Each relevant repository is examined across seven dimensions: Technical Depth, Conceptual Difficulty, Originality/Modification, Completeness, Practical/Research Usefulness, Documentation & Educational Value, and Engineering & Reproducibility.

Источник

AI‑Atlas traces conclusions back toward implementation, training, and evaluation evidence rather than relying only on repository descriptions.

Источник