Systems | Development | Analytics | API | Testing

AI coding tool spend is projected to top $13 billion in 2026, compounding at more than 60% a year. These agents don't just autocomplete a line of code anymore. They plan. They act. They call tools, run commands, and touch other systems on your behalf. This is pretty different from the assistant that used to live in your IDE sidebar. And that's exactly where the risk lives. An AI coding agent is software that writes, tests, and ships code independently. This level of autonomy earlier tools never had.

Finding a vulnerability is only the beginning. Modern applications depend on hundreds or even thousands of software components, and vulnerability scanners are essential for identifying known security issues across those dependencies. But there is a problem: the presence of a vulnerable dependency does not always mean your application is actually exploitable.

Investment leaders may see the same portfolio figures across board decks, monthly reports, and multiple reporting tools. Occupancy, cash flow, returns, and budget variances are all there. What is harder to get from that reporting is a quick understanding of what changed, whether it matters, and which asset or market deserves attention next. A reporting screen shows the numbers.

Bots make up more than a third of internet traffic, and API endpoints attract more of it than web pages do. Scanners hunting for.env files, credential-stuffing scripts, LLM training crawlers, and the long tail of low-effort probes all hit your gateway before they ever reach a service. Every one of those requests costs you — in upstream load, in noise across your analytics, and in the bill.

As part of my work, I regularly consult with teams comparing Unreal Engine version control performance and weighing whether Perforce P4 is the right fit for their projects. Based on these conversations, I decided to develop a repeatable framework that others can use to objectively benchmark P4 against other version control systems.