ai-testing
Responsible AI testing is the set of engineering practices that makes AI systems accountable: pre-deployment safety evaluation, CI/CD gates that block unfair models, model cards and datasheets, audit trails, ongoing monitoring, and incident response. This guide gives you a concrete checklist with code, not principles without implementation. Key
ai-testing
In 2018, Amazon scrapped an AI hiring tool that had been in development for four years.
ai-testing
AI safety evaluation is not a checkbox — it is an engineering discipline with concrete tools, measurable benchmarks, and adversarial test suites. This guide covers the NIST AI Risk Management Framework as an organizational lens, red-teaming LLMs with Garak and manual probing, adversarial robustness testing with the Adversarial Robustness Toolbox,
playwright
Playwright v1.50+ ships three first-party AI agents — Planner, Generator, and Healer — that together automate the entire test lifecycle from exploration to repair. They are built into core Playwright, not plugins, and they change how developers write and maintain E2E tests. Key Takeaways Planner explores your app and produces
ai-testing
Self-healing tests use AI to automatically update broken selectors when your UI changes, without you touching the test code. They eliminate a real maintenance burden — but they are not magic. They fix selector failures; they cannot fix tests where the feature behavior itself has changed. Key Takeaways Self-healing
engineering-managers
Big tech senior engineers cost ~$350,000/yr total comp and ship roughly 375 PRs per year. AI-assisted code review runs $15–25 per PR. That's $7,500/yr per engineer — 2% of what you're already paying them. If the review catches one production bug