Latest insights on product thinking, engineering excellence, and building software that ships faster and safer.
Gene Kim and Steve Yegge argue that vibe coding works for production — if you have preventive, detective, and corrective controls. We agree. We built the platform that enforces those controls structurally. Here's what we've learned about why practices alone aren't enough.
Most teams treat engineering quality reactively — ship fast, catch problems in production, fix when it hurts. That cycle keeps you busy without making you better. Prevention, Detection, and Correction form a closed loop where each phase reduces the work the next must do.
Your dashboards show green. Your test coverage is stable. But three threats are compounding underneath — defects, debt, and non-determinism. AI is making each one worse, faster. Here is how to see them before they see you.
The cost arbitrage that made outsourcing attractive has flipped. Three disciplined engineers with AI agents now outship 15-person outsourced teams — at a fraction of the cost, with measurably higher quality.
Most engineering transformations fail not because the team lacks skill, but because they defer improvement to a rewrite that never finishes. Here is how the rewrite trap works, why it compounds, and what to do instead.
A team of two junior engineers and one mid-level engineer shipped 200,000 lines of production-grade code for an Industrial IoT platform — achieving elite DORA metrics and an 8.8 code health score from day one. No seniors. No rewrites. Here's how Prevention made it possible.
AI coding agents don't understand your architecture — they pattern-match from your codebase. One boundary violation becomes a template that AI replicates hundreds of times. Clean Architecture went from best practice to survival requirement.
Most teams treat TDD as a way to verify code works. That misses the point entirely. TDD is a design tool — Red-Green-Refactor shapes architecture before implementation, and the failing test is a specification, not a verification step.
A new framework argues that coding agents need external "harnesses" — feedforward guides and feedback sensors — to produce reliable code. We agree. We also built one. Here's how theory maps to a production system, concept by concept.