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,
Autoregressive decoding is the reason large language models feel slow: every token is generated by a full forward pass, and
If you run a large language model behind a product, you have probably internalized an uncomfortable trade-off. The big frontier
Continue readingLLM Routing and Cascading: Send Every Query to the Cheapest Model That Can Handle It
Serving a 7B model in fp16 takes roughly 14 GB of VRAM. A 70B model takes around 140 GB —
Continue readingLLM Quantization Explained: GPTQ, AWQ, GGUF, and When Each One Wins
Watch a GPU while a large language model generates text and you’ll see something strange: for most of every forward
Continue readingSpeculative Decoding: Getting 2-3x More Out of Every GPU Pass