Blog
Field notes on shipping AI-native systems with human engineering judgement: architecture, modernisation, automation, and the tradeoffs in between.
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.
Real-world review of AI coding tools across 20+ projects: which tools work in production, risks, token costs, and governance practices.
AI-generated code accumulates invisible technical and comprehension debt, causing apps to become fragile around 10,000 users - symptoms, timeline, and fixes.
We tested two AI coding assistants on a production codebase: one was faster but introduced hidden tech debt; the other ran full tests and reduced regressions.
Modularity, feature-based code, clear state layers, performance tuning, and automated testing/CI keep large frontends maintainable.
Why AI needs specialized monitoring: track data and concept drift, bias, real-time outputs, and business KPIs with tools like MLflow and Prometheus.
Design software that respects domain expertise by simplifying technical barriers with Event Storming, adaptive UX, and no-code/AI tools to speed adoption.
How AI speeds ERP/CRM decisions, maps trade-offs with Decision Graphs and ICAs, and pairs autonomy with governance to boost efficiency and ROI.
Render massive blueprints and BIM efficiently, design glove-friendly, high-contrast UIs, and enable offline-first sync and real-time collaboration for construction apps.