Is there a formula or approach to estimate how much you should spend on your quality engineering (QE) efforts? Our CEO, Steve Semelsberger, is frequently asked that question in discussions…

Is there a formula or approach to estimate how much you should spend on your quality engineering (QE) efforts? Our CEO, Steve Semelsberger, is frequently asked that question in discussions…

Cyberweek 2025 demonstrated something unmistakable: the way customers shop, choose, and pay has changed permanently.

AI systems change faster than traditional QA models can react, which means quality risks now emerge in real time rather than at release.

AI is evolving faster than the guardrails meant to validate it, leaving organizations exposed to compliance risk, model drift, opaque decision paths, and breakdowns in trust.

For a long time, we spoke about “AI agents” like they were a future concept, something that might eventually book flights, run workflows, or make payments on our behalf.

AI testing careers are shifting in ways that most people in QA are not fully prepared for, and the changes are creating opportunities that did not exist even a few years ago.

AI doesn’t just learn from data, it learns from us, and we are far from perfect. When it scrapes the internet for knowledge, it also absorbs our biases, blind spots, and noise, shaping how it interprets the world.

The goal of any mobile product is to create an app experience that’s innovative and new. But you must accomplish specific, necessary steps between crafting a clear vision for your…

Did you know the global digital health market is on track to surpass $660 billion by the end of 2025? That’s no surprise, considering how healthcare apps have become indispensable in our daily lives.

Mobile application testing ensures the functional and non-functional quality of mobile application workflows. As more users rely on smartphones for daily tasks, expectations for performance and reliability continue to rise.

Accessibility and localization often seem like separate disciplines, each with its own set of guidelines and goals.
Over the past few years, model providers have invested heavily in “guardrails”: safety layers around large language models that detect risky content, block some harmful queries, and make systems harder to jailbreak.

Across industries, AI systems are being scrutinized under new laws that demand proof of fairness, transparency, and human oversight.