63 long-form pieces on architecture, training, inference, agents and safety.
An LLM agent with a million-token window still forgets what you told it last week. Memory is not a bigger context; it is a separate system that decides what to write down, how to summarize it, and what to pull back. Here is how t…
RLHF taught a model to answer one question well. Agentic RL asks a harder thing: take fifty actions in a live environment, most of them invisible in the final reward, and still learn which ones mattered. That single change, from …
A 2-billion-parameter model that trades blows with one ten times its size is not an accident of architecture. It is the product of a teacher pouring its full probability distribution into a student, token by token.
Retrieval-augmented generation fetches documents at query time, scores them, and hopes the retriever got it right. Cache-Augmented Generation sidesteps the entire pipeline by preloading knowledge into the model's KV-cache before …
A comprehensive, interactive study guide covering every domain of the Claude Certified Architect - Foundations (CCAF) exam. Master agentic architecture, tool design, Claude Code configuration, prompt engineering, and context mana…
Human preference labels are the most expensive ingredient in a modern aligned model. Constitutional AI replaced most of them with a written document and a model judging itself, and the idea quietly took over the alignment stack.
An agent that can act for eight hours still thinks inside a window that holds a few hundred thousand tokens. The gap between those two numbers is where most agent failures now live, and closing it has become its own engineering d…
A million-token window promises perfect recall of everything you feed it. Controlled tests on 18 frontier models show recall degrading steadily, unevenly, and well before the window fills, a pattern researchers now call context rot.
Autoregressive models write left to right, one token at a time. Diffusion language models reveal a whole sequence at once and sharpen it over a handful of steps. That single change rewrites the latency math, and in 2025 it stoppe…
Every attention head learns three weight matrices for query, key, and value. A 2026 study trained models up to 1.2B parameters to ask which of them are actually load-bearing, and found that keys and values can share one projectio…
A static agent pipeline is a diagram you draw before you know the task. A dynamic workflow is a program the agent writes once it sees the task, then runs deterministically. The difference reshapes how fan-out, verification, and c…
A 250-question benchmark carries a standard error of about three percentage points. Most of the model comparisons published on top of such benchmarks cannot distinguish the models they are comparing. Evaluations are experiments, …