Latest Blog Posts

Part 3: The Hidden Cost of Codeless AI Testing — And The Cheatsheet to Control It Our team was about 3 sprints into a “codeless revolution.” Product managers were writing tests. Support was filing “tests” straight from customer tickets. QA analysts were finally unblocked from waiting on automation bandwidth. Coverage charts looked amazing. Then the […]

Part 2 — How to review Cursor-generated test automation code before hits master Then a senior engineer pinged me: “Cursor rewrote the failing tests and added self-healing so issue won’t happen again. Can we merge?” I opened the PR. The diff was… enormous. Fuzzy matching. Locator fallbacks. An auto-retry loop that “stabilized” the flow. Fancy! […]

Part 1 — How We Write Tests Now (and Why Review Became the Bottleneck) The story I keep seeing on real teams : We merged a “beautiful” new end-to-end suite in one afternoon. It was a checkout flow—login → add to cart → apply promo → pay → confirmation. Cursor helped a senior engineer draft […]

Most AI testing still looks like this: we ship a model into a SaaS product, add a few golden test prompts, patch a handful of “bad answers,” and “hope” production traffic doesn’t wander into the dark corners. AI sets testing result from “Assure” to “Hope” because AI system fails like living systems through drift, surprising […]

In the world of finance, a biased AI isn’t just a technical flaw, it’s a business risk with real regulatory and reputational stakes. As a seasoned QA leader, I’ve seen our industry evolve: in 2026, ensuring fairness in AI is as critical as validating accuracy or uptime. Senior engineers and quality professionals can no longer […]

AI-powered test automation isn’t the problem. Choosing the right way to adopt it is. Engineering leaders are flooded with AI testing platforms promising speed, intelligence, and lower cost — yet many teams still struggle to turn those promises into real production impact. The challenge isn’t capability. It’s decision timing and fit. There is no universal […]