Earlier this week I left a comment on a LinkedIn post about testing in an AI-assisted workflow. The post itself was fine, better than most. It argued that when the same AI writes both the function and the test, the two share the same blind spots, so you should write the test first and decide […]
Should the tester role even exist? Read More »
Here is what I do when I write code these days. I describe the change I want to one agent and let it write the code. I hand the result to a second agent, configured as a reviewer, using Bryan Finster’s Agentic Dev Team plugin for Claude. The reviewer reads what was written and gives
The Wrong Human-in-the-Loop Read More »
This is part of a series where I write about what I’m learning in an AI evals and analytics course. Earlier posts cover the basics of evals, sniff tests, and quantitative evaluation with LLM-as-a-judge. In traditional testing, the oracle is trustworthy. You know what the right answer is, or at least you know who decides.
The oracle has opinions Read More »
Additional thoughts on session three of the AI Evals and Analytics Playbook1. The first part is here2. Every major shift in how we build and ship software has been followed by a wave of tooling that automates the tractable part and leaves the actual problem to the practitioner. Agile gave us story pointing ceremonies and
The Hard Part of AI Evals Isn’t the Tooling Read More »
Someone posted a thinking piece in a Slack channel I’m in last week, long and earnest and well-structured, arguing that quality engineering needs to evolve for the age of agentic AI, that we need to stop thinking about quality as testing and start treating it as a systemic property. I read it and felt a
The Verification Trap Read More »
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 »
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 tester has had that moment. You look for guidance on a current problem and end up with advice that feels twenty years old. You find articles on writing test cases in Excel, or long arguments about whether automated testing can ever replace manual testing. It is a little surreal. It is easy to assume
The reason so much testing content feels stuck in the past Read More »
The struggle every quality professional knows too well If you’re responsible for quality in your organisation, this scenario probably sounds painfully familiar: You’ve identified critical quality issues that need addressing. You’ve meticulously documented the bugs, you’ve raised concerns about technical debt, and you’ve even suggested process improvements that could prevent these problems. But when you
From frustrated tester to respected quality advisor: The Q.E.D. method Read More »
“What do feelings have to do with quality assurance?” This question emerged from a question on LinkedIn I posted this week, asking QA engineers what feels worse: finding a critical bug right before release, or being known as the person who always blocks releases. While the post generated significant engagement, what truly caught my attention
From Gatekeeper to Guide: Why Emotions Matter in Quality Engineering Read More »
