TL;DR: Cosmos builds world models in two paradigms. Diffusion models generate video by iteratively denoising continuous latent tokens — flow matching (velocity prediction) replaces the earlier EDM ...
TL;DR: Physical AI — robots, self-driving cars, humanoids — can’t scale by training in the real world alone. A World Foundation Model (WFM) is a learned video simulator: given past frames and a per...
TL;DR: MCP lets Claude (and any compliant AI host) call your Django REST API as tools. This is a walkthrough of building a production MCP server on top of DRF — OAuth 2.1, discovery endpoints, ngin...
TL;DR: I built a classifieds app for Hot Wheels collectors. Validated the problem, ran pilots, launched — and it died quietly from lack of critical mass. This is about doing everything “right” and ...
Review of all the books I read in 2025.
TL;DR: Deep learning models are black boxes – powerful but opaque. LIME (Local Interpretable Model-agnostic Explanations) explains individual predictions of any model by fitting a simple interpreta...
And What We Actually Know About Knowledge
TL;DR: Not all data lives on grids or in sequences. Social networks, molecules, citation graphs – these are naturally graphs, and CNNs and RNNs cannot handle them. Graph Neural Networks solve this ...
TL;DR: Supervised learning needs labels. Labels are expensive. Self-supervised learning sidesteps this by learning visual representations from unlabeled data. SimCLR does it with contrastive learni...
TL;DR: Standard deep learning needs thousands of examples per class. Humans need one or two. Few-shot learning closes that gap through meta-learning – learning how to learn. MAML finds an initialis...