Eye of the Storm

Shipping a devblog with Claude Code and Linear

Why I use Linear for a one-person project, and how superpowers skills and git worktrees make Claude Code feel like a real teammate.

How modern LLMs are trained, end to end

Building a frontier large language model today is a three-act play: a massive pretraining phase that absorbs the internet, a post-training phase that turns the resulting "completer" into a helpful assistant, and an increasingly important reinforcement-learning-on-reasoning phase that teaches the model to think before answering.

Designing the design stage: a comprehensive synthesis for an LLM-driven architecture plugin

A practical playbook for the architecture/design stage of an LLM-driven SDLC plugin: what makes design artifacts good, a file-per-artifact schema that mirrors the requirements layer, a methodology-agnostic core (ISO 42010 + arc42 + C4 + MADR + SEI), and a multi-agent pipeline with deterministic validation gates.

The physical layer of the AI revolution

Training a frontier large language model is no longer a software problem — it is a construction problem. The binding constraints have shifted from chips to power, packaging, and grid interconnects, with transformer lead times stretching past three years and over half of planned 2026 US data-center capacity reportedly delayed by electrical-equipment shortages.