Deep technical series and architecture overviews. From first principles to production-level detail, with interactive visualizations throughout.
Demystifying how large language models actually work — from the mechanics of tokenization to the algorithms behind alignment and the systems that serve them at scale.
Demystifying diffusion models and flow matching — from probability foundations and DDPMs to score-based models, SDEs, modern architectures, and state-of-the-art generation.
How images and text merge into a single intelligence — from vision transformers and CLIP to multimodal fusion, visual instruction tuning, and spatial grounding.
Where perception meets physical action — from imitation learning and behavioral cloning to RT-2, OpenVLA, and the foundation models teaching robots to act.
A teardown of the architectures, losses, and training recipes that move modern manipulators — from behavior cloning’s first sin to flow-matched VLAs and the pixel-RL renaissance.
VLMs, VLAs, and World Models — from architecture internals to quantization, inference acceleration, and production serving. Includes autonomous driving perception/planning and five portfolio projects.
The four-lane map: managed harnesses, frameworks, specialized agents, and durable execution. MCP + A2A protocols, 43 platforms compared, 12 concepts shown working.
Real-time human-AI collaboration via 200ms micro-turns, encoder-free fusion, and streaming sessions.
Combining on-policy sampling with dense teacher supervision for 9–30x cheaper training than RL.
When LoRA matches full fine-tuning, why all layers matter, and the information theory of RL capacity.
Constraining weight matrices to Stiefel manifolds with spectral-norm budgeting across layers.
Why LLM inference is nondeterministic (batch variance, not atomics) and how to make it bitwise reproducible.
A bird's-eye map of every major AI architecture — interactive diagrams, intuitive explanations, and the research context to navigate the field.