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Apprentice Scribe Hedgehog at the Oracle Tower: Sneaking in Answers Made the Prophecies Several Times Faster, and the Oracle Didn't Lose a Single Word. Speculative Decoding 101
Every time the clockwork Oracle in the Oracle Tower spits out one word, the entire tome has to be hauled up from the bottom of the tower. Mogu, the apprentice scribe hedgehog in the mushroom hat, gets bored enough to start writing answers in advance. The prophecies get several times faster, and not one word changes. This light-novel-style tutorial teaches speculative decoding: why LLM generation is stuck on memory bandwidth, how a small model guesses ahead, how the big model checks a whole row at once, and the acceptance rule that leaves the output probability distribution exactly unchanged.
Kubernetes Is Really a Restaurant: Every Term Was Forced Into Existence by the Last Problem
Friday lunch: a breaker trips, three cooks vanish, and nobody panics. This piece builds Kubernetes as a restaurant where each term is forced into existence by the previous problem. Then replay that lunch, name who does what, and see why a default Secret is not actually locked.
The Browser Is Actually a Five-Player Team (Part 1): Why One Crashed Tab Does Not Take Down the Whole Browser
The browser is not one program; it is a coordinated team. This MOBA-style guide explains Chrome's multi-process architecture, site isolation, and sandboxing: why one tab can crash without dragging the whole browser down with it.
The Browser Is Actually a Five-Player Team (Part 2): One Screen Update Is a 16ms Teamfight
After meeting the browser's five-player team, this post follows the rendering combo: DOM, layout, paint, rasterization, and compositing, all inside a 16ms frame budget. It explains why some animations stay smooth while others jank.
How Does an LLM Actually Run? From Tokens to the Next-Token Loop
A large language model looks like it is chatting, but underneath it is a mechanical pipeline: text becomes token IDs, IDs become vectors, position is injected, transformer blocks apply attention and feed-forward processing, residual streams keep the stack stable, and logits become the next token.