Systems | Development | Analytics | API | Testing

This is the sixth and final post in our "Features Sitting Idle" series, where we shine a light on OctoPerf features that are already in your account, ready to save you hours every week. It's a quiet but recurring friction. Teams that test regularly all end up with a preferred report layout. But in practice, that structure is often rebuilt manually after each run, or lost entirely when moving between projects.

The OctoPerf web application is a large Angular workspace: 7,786 TypeScript files, around 381,000 lines of code, 1,166 components and 2,738 test files. We have just taken it from Angular 20 to Angular 22, deleted zone.js, and replaced Karma and Jasmine with Vitest. The interesting part of this story is not the version number. It is that we got the strategy wrong first. We spent months migrating the codebase component by component, drowned in manual regression testing, and still let bugs reach production.

Every QA budget has a line item for building test coverage, but 30–50% of that automation budget ends up spent on maintenance instead of new tests. That disparity stays invisible until a release goes out, the application shifts underneath the tests, and the QA team spends the next three days rewriting broken scripts instead of finding new bugs.

Enterprise AI is moving beyond experimentation. Today, the real challenge is not building machine learning models but operationalizing them at scale through reliable training, deployment, monitoring, governance, and continuous improvement. This shift is accelerating rapidly. Gartner reports that organizations with high AI maturity are more than twice as likely to keep AI initiatives operational for three years or more, underscoring the growing importance of robust MLOps practices.

Leadership has visibility into more AI-generated code than ever. But seeing it and trusting it are two separate things.

I spent the day at NVIDIA thinking I’d be learning about all the newest models and RAM throughput achievements. Instead, a version of the future started to crystallize where AI really is good enough, pervasive and cheap(er). Let me share some of my observations and conclusions.

The Internet of Medical Things (IoMT) is growing faster than ever. According to 2026 data from The Business Research Company, the global IoMT market has reached over $124 billion this year and is on track to hit nearly $300 billion by 2030. This growth is happening because healthcare is moving outside hospital walls and into patients' homes through remote monitoring. But for engineering teams, connecting these devices is a massive headache.

As AI rapidly integrates into software development, from code generation to interface review, it promises unmatched efficiency.