Classic ML and prompt tasks, trained in an in-browser Python notebook — real scikit-learn, nothing to install. Submit to a hidden held-out leaderboard, mentored by TENSOR.
An in-browser Python notebook runs real scikit-learn right here — no setup, no GPU, nothing to download. Fit a model, tune it, and watch your accuracy climb. (Deep-learning tracks are on the roadmap, not here yet.)
Train on the data you can see, then submit predictions. They're scored against a held-out split you never get to peek at — so the leaderboard rewards a model that generalizes, not one that memorized.
Engineer prompts against tasks judged on a clear rubric, not vibes. TENSOR, our research mentor, asks the next sharp question about your approach — never hands you the answer, never spoils the test.
Train classic ML in an in-browser scikit-learn notebook, submit predictions scored on a hidden held-out split, and engineer prompts graded by a rubric. Free to start — no card required.