Venkatesan Ramachandran

A Gartner survey from late 2025 put the number at 64%. Sixty-four percent of boards disappointed with their AI results. I was not surprised. The pattern is always the same. A vendor gets 45 minutes with your board. They show a demo. The demo is perfect. It finds patterns no

DORA metrics are not enough. Here is what we measure instead. Every engineering team now tracks DORA. Deployment frequency. Lead time for changes. Change failure rate. Mean time to recovery. Four numbers. Clean. Simple. Leadership loves them. I loved them too. Then AI tooling arrived and the numbers stopped making

The block pipeline pattern: composable middleware without the mess If you have worked with middleware in any web framework, you know how it goes. You start with two or three layers. Authentication, logging, maybe CORS. It is clean. You can follow the request through the chain in your head. Then

A survey from Yerbo in 2024 found that two in three engineering leaders experienced burnout in the past twelve months. I read that number and thought: that sounds about right. I was one of them. This was during my time at a global logistics company. We were rebuilding a major

Event-driven intelligence: moving retail decisioning from batch to real time I spent years watching smart people make decisions on stale data. Not because they wanted to, but because the infrastructure gave them no choice. A merchandiser would pull a markdown report at 9am, and by the time they'd

70% of your engineers are using AI tools you do not know about I found out on a Tuesday. One of my engineers had pasted a production database schema into ChatGPT. Table names, column names, foreign key relationships, comments describing business logic. He wanted help writing a migration script. He