Blog
Field notes on shipping AI-native systems with human engineering judgement: architecture, modernisation, automation, and the tradeoffs in between.
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.
Learn how AI tools can revolutionize tender reviews by identifying scope gaps early, saving time, and reducing costly errors.
Learn how to train an AI defect-detection model using your own site photos, ensuring accuracy and efficiency in identifying flaws.
Explore the strengths and weaknesses of computer vision and manual inspections in defect detection, guiding you to the best choice for your projects.
Explore how self-hosted AI enhances control, compliance, and cost efficiency for construction firms, ensuring data security and operational flexibility.
Technical interviews often miss the mark. Explore effective methods like project-based tests and pair programming to truly assess developer skills.
After facing challenges with one framework, a switch led to improved developer morale, faster delivery, and better product quality with another.
Explore strategies for startups to thrive without funding by focusing on MVPs, user feedback, cost-effective tools, and smart resource management.
Learn how to transform a $200K budget into a $10M product by focusing on lean development, AI tools, and strategic partnerships.
While AI coding tools can speed up development, they often introduce hidden costs, leading to significant time spent on debugging and refactoring.