A personal research blog dedicated to making frontier AI & ML papers approachable — with interactive visualizations, annotated source code, and the kind of depth that connects the math to the implementation.
Most research papers reward careful reading but punish first-time readers with unexplained notation and buried intuitions. This blog exists to close that gap. Each post picks a single paper or tightly related cluster of ideas, digs into the mechanism at the level of code and equations, and pairs the explanation with live, in-browser visualizations so you can build the right mental model rather than just follow the prose.
Posts are written to be standalone and deep — not newsletter summaries. The goal is that after reading, you could reimplement the core idea from scratch.
I'm Tianhao Zhou. I read a lot of ML papers and write up the ones I find genuinely interesting or underexplained. My focus is on the intersection of model architecture, training efficiency, and inference speed — the things that determine what actually ships in production.
If a post has an interactive component, it was written from scratch — no chart libraries, just SVG and a little JavaScript — so the visualization is exactly as precise as the explanation requires.