Session two closed with a question I couldn’t answer.1 When a product scores 78% on a given metric, what tells you whether that’s good enough to ship? I flagged it as something session three would probably address, and it did, though not in the way I expected. The question can’t be meaningfully answered until you’ve […]
The anatomy of a metric Read More »
Session one left me with two things I hadn’t resolved.1 The first was a line the instructor said almost in passing: “the hard part is scalability, not automation.” I wrote it down because it piqued something, but I couldn’t quite work out what problem it was pointing at. The second was a question I kept
The AI evals field chose a flawed tool and stuck with it Read More »
If you’ve been following me on Linkedin, or indeed have been reading my blog for a while, you’ll know that I’m interested in AI applications. So it’s probably not coming as a total surprise to you that my main learning goals this year evolve around AI evals, “which are systematic tests and measurements to that
What I don’t understand about AI evals (yet) Read More »
You know the feature inside out. You have tested it, broken it, rebuilt the test suite around it twice. So you ask the AI to generate a few test cases. Save yourself twenty minutes. What comes back looks reasonable. The structure is right. The naming conventions are close enough. Then you start reading. The first
You were promised speed. You got a new job instead. Read More »
I’ve been seeing a lot of posts on LinkedIn recently that make the same claim: speed of writing code was never the bottleneck in software engineering. The real bottleneck was always thinking, choosing the right thing to build, understanding the problem. AI has just made that painfully obvious. I partly agree, but the framing is
Why the bottleneck was never just about thinking Read More »
Every time the topic of using AI for code reviews comes up, someone will eventually say that it is like letting the AI mark its own homework. It sounds clever. It also sounds responsible. Underneath it sits a familiar discomfort about oversight, trust, and professional judgement. As an analogy, it points attention away from the
Review as analysis, not authority, when working with AI Read More »
Sometimes I despair about the QA profession. Not because the work is hard, or because quality is complex. Both of those are true, but they always have been. What gets to me is where so much of our collective energy still goes. We have much bigger fish to fry. We have the tools to do
AI did not break testing Read More »
That day I rage quit my project It finally fell apart over a UI reskin. I had an application that mostly worked. It was not polished, but it was coherent, testable, and good enough to keep moving. When I asked the AI to reskin it using my brand colours, what I expected to be a
Stop vibe coding before you delete your repo Read More »
When I look at the current excitement around AI in testing, I cannot help feeling a sense of déjà vu. I felt the same thing when the first wave of no code and low code testing tools arrived. At the time, vendors promised that these tools would democratise testing, eliminate the need for technical skills,
A More Balanced AI Story Read More »
AI has been the background noise of every software conversation lately. Some people treat it as a superpower that will solve everything. Others point to low-quality AI slop online and see it as the end of original thought. I am somewhere in the middle. I use AI a lot, and I trust it for some
AI, curiosity, and a little procrastination Read More »
