Blog

Million-token context windows work for one-shot queries but degrade to 200K effective context in extended agentic conversations. Learn why attention drift happens and how to calibrate for real-world coding workflows.

Why most agent orchestration systems fail at coordination and what primitives you need for parent-child communication, cost rollup, and persistent context.

Learn why requiring issues before PRs reduces technical debt and improves codebase organization - a proven approach from the Roo Code team.

Learn why fighting a poisoned AI context wastes time and tokens, and how the orchestrator pattern lets you restart fresh for better results.

Why compressing context to save tokens actually costs more tokens, and how task orchestration keeps context clean without fidelity loss.

Google Vertex prompt caching works differently from Anthropic - you must declare TTL upfront. Learn how time-based caching affects multi-provider LLM architectures and cost management.

Model upgrades can break tuned prompts. Learn why capability and predictability diverge, and how to treat model version changes like breaking changes in your AI coding workflow.

Why green tests can deceive you, how LLM-generated tests make this worse, and the simple heuristic to verify your tests actually catch regressions.

Why centralized AI documentation fails and how engineering orgs can drive adoption through trusted internal influencers and recurring demo forums

Learn why routing AI models through OpenAI-compatible gateways can silently degrade code quality, and how to diagnose thinking tokens, prompt caching, and context window issues.

Why LLM evals are the foundation for prompt iteration - how to escape the trap of fixing one edge case while breaking two others, with practical steps to build regression detection into your workflow.

How open source communities ship flagship features faster than internal roadmaps, and why treating external contributions as signal accelerates product development.