Posts
All the articles I've posted.
LangChain and LangGraph, from the Inside: What the Frameworks Compile Down To
Updated:Agent frameworks are an opinion about state and control flow. LangChain's runnables composed with a pipe turn out to be function composition with a shared interface; LangGraph's state graph is a loop with typed state, reducers and a checkpoint after every step. The same agent written by hand and with LangGraph (LangGraph 1.2, langchain-core 1.6): the same 10 lines with LangGraph's prebuilt pieces, under half a millisecond of overhead per run, and what that buys: pausing for a person's approval, editing a pending tool call, resuming, and replaying from any saved step.
Learned Orchestration: A Case Study of Sakana Fugu
Updated:What if the orchestrator of a multi-agent system were itself a model, trained to pick and instruct frontier models? Sakana's Fugu report (2026) answers with two designs: a router that reads one hidden state and picks one worker, trained by supervised fine-tuning and then evolution strategies, and a Conductor trained with GRPO to write whole multi-agent workflows in natural language. How each is built, the failure called orchestration collapse and its fix, and the reported results read critically: ahead of the best single model on 7 of 11 benchmarks each, against baselines gathered from providers and leaderboards rather than run under the same conditions.
Learning Is Optimization: Linear and Logistic Regression, Trained by Gradient Descent
Updated:What it actually means for a model to learn: pick a loss, then walk downhill. Worked end to end on real data from this blog, with the loss choice, the closed-form fit, gradient descent and its learning-rate limit, and a first logistic-regression router whose gradient reappears in every later post.
How LLM Architecture Evolved, 2017–2026: A Short List of Substitutions
Updated:The Transformer of 2017 and a frontier model of 2026 share a skeleton: embeddings, a stack of attention and MLP blocks on a residual stream, an unembedding. What changed is a short list of substitutions, each made because it bought something measurable: pre-norm, RMSNorm, SwiGLU, RoPE, grouped and latent attention, mixtures of experts, and sparse or hybrid attention. Each one, with what it costs and saves, some measured on the tiny GPT from Build a Tiny GPT, End to End, and Train It on This Blog. Then the scaling laws that decided how big to make them, Kaplan to Chinchilla to the replication that fixed it, and why models are now trained far past compute-optimal.