| Machine Learning | 50 |
|---|---|
| Computer Vision | 10 |
| Data Science | 0 |
| NLP & LLMs | 10 |
| AI Agents | 44 |
| MLOps | 24 |
Each node is one domain, scored only from evidence in these public repositories. Select one to see why.
Substantial implementations with real depth.
11 public repositories → 1 with AI work, evaluated. The other 10 (repositories without AI code, forks and empty repositories) are not scored and have no effect on the result. How repositories are selected
Ordered by how much each project adds to the Atlas score, not by its own score. A project with evidence in several domains can rank above one with a slightly higher project score. Choose a domain to focus on the projects with meaningful evidence there.
100% of Atlas Score
CQC is a reinforcement learning project that trains multi-agent agents using a custom transformer-based architecture with convolutional encoding and auxiliary decoders in a grid-based environment. It implements PPO with self-play and distributed TPU training via torch_xla.
