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
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
In August 2026, ByteDance founder Zhang Yiming told the company’s Seed AI research team something almost nobody in the industry
Continue readingThe Distillation Wars: Which Chinese AI Labs Actually Train Their Own Models
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 team that serves language models eventually hits the same wall: the GPU has plenty of memory and the model
Continue readingContinuous Batching: The Scheduling Trick Behind Every Fast LLM Server
Every major AI lab now ships a coding agent that lives in your terminal. What started as a niche experiment
Continue readingThe Terminal Wars: Comparing the Major CLI AI Coding Agents
What this is: a chronology of the major LLM releases of 2026, paired with the loudest things the internet said
Continue readingEvery 2026 LLM Release, Paired With the Hype That Announced It — Scored for Accuracy
Run two unrelated documents through a large language model at the same time, and you assume the model keeps them
Continue readingYour Transformer Can Hold Two Thoughts at Once: The New Linear Superposition Result
September 2026 has been one of the densest months for AI releases in recent memory. Within the first 72 hours,