Blog
Field notes on shipping AI-native systems with human engineering judgement: architecture, modernisation, automation, and the tradeoffs in between.
AI multiplies senior developers’ productivity but raises defect and review burdens for juniors; structured training, oversight, and roadmaps reduce risk.
AI-generated code often causes hidden bugs-missing context, outdated APIs, weak error handling, and duplicated logic. Practical fixes, tests, and staged rollouts.
AI-produced code can look clean but hide edge-case bugs, duplicated logic, and architectural debt that often break during refactoring.
Classify code as Red, Yellow, or Green-use AI scans and a Traffic Light Roadmap to prioritize fixes, lower risk, and align engineering work with business goals.
Hitting $1M ARR exposes scaling failures-spot tech debt, prioritize with a Traffic Light Roadmap, and modernize incrementally to protect growth.
Modernize legacy frontends safely without full rewrites using incremental refactors, UI isolation, and integration layers to cut risk and speed delivery.
Compare autonomous agent IDEs versus developer-led IDEs for modernizing legacy code, weighing automation, control, security, and best use cases.
Design MVPs to be intentionally replaceable-validate demand quickly, reduce technical debt, and avoid costly rebuilds that can exceed $500,000.
Audits of five vibe-coded startups found inconsistent code, exposed secrets, and fragile architecture-fix these early to avoid costly technical debt.
A practical, risk‑aware playbook for modernizing fragile legacy code: AI assessment, phased fixes, feature flags, and measurable performance and cost gains.
Why startups take on technical debt, how to track and prioritize it, and when to pay it down across seed, growth, and scale stages.
AI tools doubled pull requests but also raised bugs, review time, and technical debt; prioritize Net Feature Velocity, DORA metrics, refactoring, and strict reviews.