Digital Shift

Four ways to generate test cases with AI, what each one actually solves, and where each one breaks down.

Classical QA assumes a fixed input produces a fixed output. LLMs don't work that way. Here's how to build a testing strategy that actually catches AI-specific failures, without doubling your QA headcount.

Test coverage grows at the speed of the QA engineer typing. Here's the exact workflow for generating Playwright test cases with Claude, including where it breaks down.

A practical, step-by-step approach to documenting undocumented legacy systems with AI, including where it breaks down and when it's worth running now.

Before you greenlight a "weekend build," here's what vibe coding can deliver, where it breaks, and how to tell if it fits your pitch.

What a 5-day AI-driven prototype sprint covers, where fast builds break in front of investors, and how to avoid the most common mistakes.

Two AI features can run the exact same model and still get opposite results. Human-AI interaction design usually explains why. Find out which four decisions make the difference.

A practical guide to reducing technical debt with AI agents – from scored assessment and agent-led refactoring to the senior-review gates and governance decisions that determine whether the paydown actually holds in production.