What's working, what’s still vaporware, who should be paying attention, and what the real path forward looks like for teams that want to be ready when orchestration goes mainstream.

Stanford researcher shares insights from large-scale studies on developer output, why early AI productivity claims were overstated, and what engineering leaders should (and shouldn’t) measure when rolling out AI across the software development lifecycle.

Four industry veterans — Kent Beck, Bryan Finster, Rahib Amin, and Punit Lad — shared their perspectives on how enterprises can adopt AI coding tools wisely.

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.

Developer experience was about making developers happy. AI experience is about shortening the loop without loss of safety.

We still need experts to recognize that it did a good job, though. It might produce something extraordinary with security problems, and we still need to be experts to recognize that.

Internal developer portals are all the rage. And like most shiny tools, they’re being oversold.

History is the teacher of life. There are those who have been shipping software for decades, and there is much we can learn from them.

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.

I don't understand. The article is about doing code reviews, not what the future of software development will look like in two years. Was the right article linked?

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.