Are your AI efforts good enough? image
  • Rosie Sherry's profile
  • Mr Stephen P Platten's profile
  • Nataliia Burmei's profile
  • Jonathan Cole's profile
10 ideas to question your quality AI work.
Untangling the DOM: How automation really breaks (and how to fix it) image
  • Ujjwal Kumar Singh's profile
Browsers keep four different pictures of your page at once, and automation bugs live in the gap between them.
Motivation and strategy: the two forces behind technical learning image
  • Simon Tomes's profile
  • Simon Rigler's profile
What actually sustains technical learning once the novelty fades?
The Exploratory Testing Charter Planner (ETCP)  image
  • Simon Tomes's profile
Lightly plan every exploratory testing session before you dive in
Making sense of the DORA AI Capabilities Model image
  • Lisa Crispin's profile
  • Simon Tomes's profile
AI amplifies the good and the bad, so where should your team start?
Quality tooling silos image
  • Rosie Sherry's profile
The tool that makes your testing easier might be the same one cutting you off from the team that builds the product.
Don't worry about motivation: grow with “MoTivation” image
  • Judy Mosley's profile
What if showing up was enough, because the rest is already built for you?
We still need humans to speak with image
  • Simon Tomes's profile
  • Cristina Lanca Carrageis's profile
Why does mentorship work best human to human, even in an age of AI?
Turn your learning into evidence image
  • Sarah Deery's profile
What if everything you learned became evidence of your growth?
Stability first: less coverage, more impact image
  • Simon Tomes's profile
  • Pranav Pandit's profile
When has chasing automation coverage cost you more in maintenance than it gave back?
A practical introduction to testing LLMs image
  • Demi Van Malcot's profile
Learn how to evaluate LLM quality and limitations using a range of testing techniques, from unit and regression testing to bias, adversarial and explainability testing.
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