Resources
Browse insights and updates from our blog.
From Prompt to Proof: How to Verify AI-Written UI Changes and Turn Them into Regression Coverage
AI coding agents are already changing how software gets built. They implement UI updates quickly, refactor aggressively, and ship more surface area per sprint than most teams planned for. The bottleneck has simply moved: if code is produced faster than it can be verified, quality becomes a matter of
From Flaky Tests to Actionable Signal: How to Operationalize E2E Testing Without the Maintenance Tax
End-to-end tests are supposed to answer a simple question: “Can a real user complete the journey that matters?” In practice, many teams treat E2E as a necessary evil. The suite grows, the UI evolves, selectors break, and the signal gets buried under noise. When trust erodes, teams stop gating releas
A Practical Quality Gate for Modern Web Apps: From AI-Built Pull Requests to Reliable E2E Coverage
Software teams are shipping faster than ever, but end-to-end testing has not magically gotten easier. If anything, it has become more fragile: UI changes land continuously, product surfaces expand, and AI coding agents can generate meaningful product updates in hours.
From Tribal Knowledge to Executable Specs: How Modern Teams Build E2E Coverage Everyone Can Trust
End-to-end testing often fails for a simple reason: it is written in a language most of the team cannot read.
AI-Native End-to-End Testing in Practice: A Clear Adoption Path With Shiplight AI
Shipping velocity has changed. AI coding assistants can implement features in hours, sometimes minutes. The bottleneck has moved downstream, into the place that has always been hardest to scale: end-to-end validation in real browsers.
The Hybrid Future of E2E Testing: Deterministic Speed With AI-Level Resilience
End-to-end testing is supposed to be the safety net that lets teams ship confidently. In practice, most E2E suites become a drag on velocity. Teams end up choosing between two outcomes that both feel bad: