Every conversation with a chatbot hides a growing accounting problem. Each token the model generates must attend to every token
Continue readingThe KV Cache: The Memory Bill Behind Every Token Your Model Generates
Mixture-of-Experts models dominate the frontier right now, and almost every explanation stops at the same level: “a router picks a
Continue readingInside MoE Routing: How the Gate Decides Which Experts See Your Token
Ask a language model for JSON and most of the time it complies. Every so often it wraps the object
Continue readingConstrained Decoding: How LLMs Get Forced Into Valid JSON
Ask a chat model to check the weather and it will happily invent a forecast — it has no way
Continue readingFunction Calling: How LLMs Actually Call Your Tools
Every token an LLM emits costs a full forward pass. The GPU reads hundreds of gigabytes of weights, computes one
Continue readingSpeculative Decoding: The Math, the Drafters, and When the Bet Pays Off
Every LLM serving stack lives or dies by one data structure most developers never see: the KV cache. It is
Continue readingThe KV Cache: Why LLM Serving Is a Memory Problem
Every modern large language model you use is almost certainly a Mixture-of-Experts model. Mixtral, DeepSeek-V3, GPT-OSS, the latest Kimi and
Continue readingInside Mixture-of-Experts: Routing, Load Balancing, and Why 671B Parameters Cost 37B
Most teams treat “thinking mode” as a switch. Turn it on and the model reasons through the problem step by
Continue readingThinking Inertia: Why LLMs Don’t Stop Reasoning When You Tell Them To
Every RAG pipeline starts with the same piece of code: split documents into chunks, embed them, store them. It looks
Continue readingRAG Chunking Is Not Settled: What Token-Level Evaluations Actually Show
Every Mac owner who has run a local LLM knows the feeling: the model downloads fine, the first answer looks