AI engineering · mapped from public work
Map what
you’ve built.
A shareable, evidence-backed profile of your AI work, for recruiters, collaborators and networking, or just to stay competitive in the AI space.
AI-Atlas reads your public GitHub projects and maps the AI capabilities your work actually demonstrates — judged on what you built, not on what you imported. Ranked only when claimed.
- Public repositories only
- No account required
- Code is never executed
Live Atlas coverage
Pop Alexandru
@pop123-ux · 🇷🇴
AI-Atlas score
71.1/100
Specialist
- #2= global
- #1 in Romania
- ML73
- Vision69
- NLP & LLMs67
- Data42
- Agents33
- MLOps14
Projects behind it
- ORBITLibrary / Framework67.4
- ORBIT-allTraining Experiment66.0
- AlexNet-from-scratchArchitecture Reimplementation60.5
Mapped
Open this AtlasHow it works, on a real Atlas
0101Paste a GitHub username
AI-Atlas finds the public repositories, sets aside forks and empty ones, and reads the code of those that do AI work. Nothing is installed or run.
0202An AI judge scores each project
Each project is scored against a weighted rubric, and the claims in its README are checked against the files the judge read. The owner sees every line of it.
- 0302
Plan your next move
Your Atlas, scores and leaderboards are free. Planning credits turn a project’s evidence into a prioritized roadmap, predict what improving one rubric dimension would do to its score, or rescan a single project you pushed to. Your first roadmap is free.
0403Share your Atlas and your work
One link with your score, your strongest field, the projects behind them and, once you claim it, your place on the global and country leaderboards.
Six domains of evidence
Each domain is scored on its own, from evidence in the code you published.
01. Machine Learning
Model architectures, training loops, optimisation, classical and deep learning method work — the mathematics and mechanics of learning systems.
02. Computer Vision
Image and video understanding: classification, detection, segmentation, generative vision, multimodal perception and vision architectures.
03. Data Science
Data acquisition, cleaning, feature engineering, statistics, analysis, experimentation and visualisation that supports decisions or models.
04. NLP & LLMs
Language modelling, transformer architectures, tokenisation, fine-tuning, retrieval augmented generation, evaluation and inference of LLMs.
05. AI Agents
Tool-using and autonomous systems: planning, orchestration, memory, multi-agent coordination, function calling and agent evaluation.
06. MLOps
Getting models into and keeping them in production: serving, pipelines, experiment tracking, reproducibility, monitoring and infrastructure.
Evidence, not buzzwords.
A score should reflect what the work can prove.
AI-Atlas reads implementation, weighs depth and ownership, then keeps each conclusion connected to the source evidence that supports it.
implementation, not labels
depth over repetition
claims back to files
src/attention.pyCore implementationtrain.pyTraining behaviourexperiments/results.mdEvaluation evidenceDesigned for trust.
Your code is read, never run.
AI-Atlas documents its methodology, shows public project summaries, and gives verified owners the detailed evidence behind every score.
read: public reposNo private code requestedmode: read as textNothing installed or runrubric + cited filesOpen to verified ownersleaderboard: opt-inScanning never ranks anyone