TorchCode launches PyTorch LeetCode-style drills

This is a sharp idea because it turns PyTorch fluency into something you can actually rehearse, measure, and repeat instead of just reading about.

• –The judge-first workflow matters: correctness, gradient checks, and timing feedback make it much closer to real engineering than notebook tutorials
• –Self-hosted Jupyter plus Colab and Hugging Face options lowers the friction for teams or solo learners who want to spin up fast
• –The problem set is aimed at interview-core topics like softmax, cross-entropy, dropout, embedding, attention, and Transformer blocks
• –Progress tracking and resettable notebooks make it feel more like a training loop for engineers than a one-off demo
• –This sits in a useful niche between educational content and interview prep, especially for people who need to implement ML primitives from memory