Resources
Playbooks, guides, and best practices for AI-native E2E testing.
What Is API Testing? And What It Cannot Tell You
API tests are fast, stable and cheap to maintain, which is why teams over-invest in them. A fully green API suite is compatible with a completely broken product.
What Is MCP? The Model Context Protocol Explained
MCP is an open standard that lets AI agents call external tools through one interface instead of a bespoke integration per tool. Here is what it actually is, what an MCP server does, where it helps, and what it does not solve.
Windsurf vs Cursor: Which AI Editor Fits Your Workflow?
Both are AI-native editors built on VS Code, and the feature lists have largely converged. The choice comes down to how much you want the tool to act on its own, and how much of the resulting diff you intend to read.
Best UI Regression Testing Tools in 2026: Functional and Visual Compared
UI regression testing has two layers: functional tools that replay user flows and visual tools that diff rendered pixels. Here are the 6 best UI regression testing tools in 2026, how the layers combine, and a workflow for catching frontend regressions automatically on every PR.
AI Code Review vs Verification: Why Reading Code Is Not Enough
Code review reasons about the code as written. Verification proves the running software behaves correctly. AI-generated code is engineered to look plausible, which is exactly the failure mode review cannot catch. This page draws the line and shows why teams shipping with coding agents need both.
The AI-Native Development Lifecycle: A Stage-by-Stage Guide
The AI-native development lifecycle is the end-to-end process teams use when agents write most of the code: plan, generate, verify, ship, and maintain. Generation got roughly 10x faster while verification did not, so verification is now the bottleneck stage. This guide maps each stage against the traditional SDLC and shows where the constraint actually moved.