Insights · research, not marketing
What the data says, not the hype
Analysis backed by cited primary sources — studies, not opinions — on building AI software that actually reaches production.
The Training That Outlives the Tools
You built something with AI that works and want to take it to market. Why tools expire, what lies between your PC and the market, and how to recognize training that is worth it.
Goodbye Title, Hello Archetype: The Five Profiles Melting IT Roles Together
The person who built Claude Code says IT titles are melting together. A map of where Front, Back, QA, Design and Product land across his five archetypes.
Why Does Your ATS Reject Good Candidates? The Keyword-Filter Problem
Keyword filters discard good candidates over a wording or formatting detail. What the Harvard/Accenture study says, and what understanding by meaning does differently.
Fire 40% or Grow 40%? The Three Vectors of AI — Notes from Production
AI just freed 40% of your team’s time. What you decide next — cut from below or grow from above — defines everything else.
Ambient Agents: AI That Assists Without Being Asked
The moments where help is most valuable are exactly the moments where you cannot stop to ask for it. That is the entire case for ambient agents.
The LLM as Validator: What Separates a Demo From a System You Can Trust
Generation proposes, validation disposes. The unglamorous pattern that keeps AI systems trustworthy after the demo applause fades.
The Other Side of the Wall: Why 'It Works' Isn't the Same as 'It's Ready'
Anyone can build a working app in an afternoon now — that part is real. Here's the exact moment that speed stops being the same thing as being ready.
The 70% Wall: Why Most AI-Built Software Never Reaches Production
95% of corporate AI pilots never deliver a return, per MIT. Four independent studies explain exactly where they stall — and it isn't the model.