Topic archive
4 essays tagged AI. Practical notes on what happens after the demo: prompts, tools, review packets, evals, rollback, and production ownership.
New models matter. They change what is possible. But a serious AI strategy cannot be rebuilt around every launch. The hard work is deciding what should change in your products, teams, controls, and habits.
It was a Tuesday, the kind where the London drizzle seemed to seep into your bones, even if you were miles away in Basingstoke (my home town). I was tasked with a rather daunting project, or not really as you will find out: designing and deploying a real-time Sentiment Analysis product for a major telecommunications company, and, oh, they needed it yesterday. Or so it felt. Now, usually, this would mean weeks of planning, coding, debugging, and endless cups of tea. But this time, something was different. I decided to try something I’d been experimenting with – something I now call “Vibe Coding.” ...
Python is still the easiest place to experiment with AI. Java still earns its keep when AI has to live inside enterprise systems. The real question is where the AI application crosses from prototype into production risk.
Introduction Why isn’t AI revolutionizing business as expected? Despite the widespread use of tools like ChatGPT and customer service bots, the actual productivity gains seem minimal. Could it be that we’re not using AI the right way? AI’s true potential lies beyond basic tasks like writing emails or answering simple queries. To truly harness it, we need to reframe our approach, shifting from AI as a mere tool to AI as a co-worker that can transform operations. ...