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Frontier labs, model launches, SOTA rankings, and the mechanics of training and scaling. The intelligence layer.
A model is a compressed simulator of the data distribution it was trained on.
Every token a model outputs undergoes a full forward pass through dozens of identical attention and feed-forward layers.
AI capability scales predictably with compute, parameters, and tokens. Understand the power laws and optimal boundaries that dictate frontier training.
A training run is a months-long industrial process that turns data, compute, and engineering discipline into a new capability curve.
The base model learns broad capability. Post-training decides whether that capability behaves like a useful colleague, a tool user, or a liability.
Pretraining learns from examples; reinforcement learning learns from outcomes. It is the loop behind RLHF, reasoning models, and agents.
Scaling compute at inference time. Moving from instant feed-forward responses (System 1) to search, verification, and self-correction (System 2).
Precision is the lever. A weight is a number, and numbers have a size. Count the parameters, multiply by the bytes each takes at FP16, FP8, or FP4, and read the memory bill.
Long context feels like memory. Under the hood it is a live working set that has to be read, routed, cached, and paid for.
A model choice is a workflow choice: capability, latency, cost, context, privacy, tool use, modality, and failure tolerance all matter.