Blog

Documentation debt silently accumulates while code debt announces itself through failing tests. Learn why AI-assisted development accelerates this problem and how to keep docs in sync by including documentation updates in every prompt.

Why budget LLMs with constrained context windows can outperform premium models with bloated context - and how to use this to ship faster at lower cost.

Learn why killing derailed AI coding tasks and restarting with fixed workflows beats mid-flight correction, saving engineering teams hours weekly.

Discover the five-point readiness checklist that predicts whether your AI coding agent will close the loop or leave you as the manual verification layer.

Why agent configuration errors multiply across every task and how to allocate review effort proportionally to blast radius, not lines changed.

Learn why structured, prescriptive rollouts drive faster AI coding tool adoption than open exploration, and how shared defaults get 90% of your team productive.

Why engineering teams trust new hires to approve PRs but not AI agents - and what organizational scaffolding is actually needed for AI-assisted code review adoption.

Why the same LLM model produces different results depending on OpenRouter provider, and how to debug model evals by testing provider and temperature combinations.

Sonnet 4.5 excels at spec-driven implementation but guesses at debugging tasks instead of reading your codebase first. Learn when to use it and when to switch models.

Learn why temperature settings determine whether your AI-generated frontend code looks hand-crafted or generic, and how to configure model parameters for design vs. functional work.

Why optimizing for model speed over investigation depth costs teams more time on complex debugging tasks, and how to choose the right tradeoff for your work.

Learn why asking AI coding agents about design patterns before implementation produces more maintainable code and reduces refactoring debt.