Category AI

Spec-driven development is not a choice but a necessity as we move from vibe coding a cool app to building real-world brownfield projects.

Building software with AI agents isn’t a solo sport, especially when projects touch multiple repos, services, and prompt engineering knowledge.

Runbooks capture context from repositories or code reviews, combine it with the team's AI prompting knowledge and get smarter with each use.

AI is a developer productivity force multiplier, but poor documentation, unclear ownership and fragmented tooling don’t magically disappear with AI.

Systemic engineering bottlenecks won’t disappear by just throwing AI at them. You’ve got to fix the roadblocks to realize the full value of AI tools.

AI based Coding assistants are becoming very popular with the developers today. Find out how you can understand and measure its real impact on productivity

LLM agents are changing how developers handle code migration turning tedious, error-prone refactors into intelligent, semi-automated workflows. In this case study, we show how agents migrated a Java codebase to TypeScript by analyzing code, planning steps, and executing changes with architectural awareness and CI-backed validation.

Instead of adding yet another hot take on whether vibe coding is real or if AI is about to replace software engineers, I wanted to take a shot at predicting what software engineering might look like in 2027. 

Code migrations are like cleaning the house. We know we have to do it, but we wish we didn't have to. Yet, we are still only using AI to create new code vs cleaning the existing mess.