Systems | Development | Analytics | API | Testing

Key Takeaways Risk in health insurance no longer sits still. It changes with every diagnosis, claim, wearable signal, and care interaction. Treating it as a one-time underwriting event no longer works. Automation doesn’t just make things faster — it keeps things going. With automated risk assessment, insurers can track health risk as it changes, using live, up‑to‑date data instead of one‑time snapshots.

It is Monday morning. Your VP of Engineering just forwarded a company-wide memo: every team needs to demonstrate AI adoption by end of quarter. At the same time, you learned last week that your QA budget was trimmed by 15%, because leadership assumes AI will "make testing more efficient." And your developers? Thanks to Copilot, Cursor, and Claude Code, they are now shipping 76% more code per person than they were two years ago.

And more to the point… why do I need to read a whole blog post on it? Two good questions. Well when we’re debugging, stepping removes the guesswork by letting us watch the logic unfold step-by-step. We can pause the code, go through the execution one instruction at a time and isolate the exact point where the bad stuff happens. This is one of the most reliable ways to understand why a bug happens, not just where it shows up. It also shines a microscope on our code flow, showing us.

The automotive industry has been undergoing significant changes as it works to adapt growing market demands and challenges associated with vehicles that are becoming much more software-defined. Here, we take a look at some notable automotive trends 2026, including highlights from our report, the 2026 State of Automotive Software Development, in partnership with Auto IQ and the Eclipse Foundation.

Most teams know they are spending money on AI in production. Far fewer realize how much they are spending outside production. It’s easy to get lost as you evaluate which model has the best responses, is fast enough, and cheap enough to run in production. That is because the AI bill usually shows up as a giant blob. It is easy to see the total.

Enterprise product teams are squarely in the throes of a massive shift. New Gartner research crystalized that companies using AI tools for product management are reaping a significant 35% productivity gain and achieving a two to three-year time-to-market advantage over those who wait.

Bringing APIs and events together has always been a challenge. REST APIs give developers a familiar interface, while event brokers like Solace Broker excel at fan-out, filtering, and scalable, reliable event delivery. The tricky part? Bridging these two worlds without building a lot of custom glue. That’s exactly what the new Kong plugin for Solace upstream mediation does.

As organizations scale their AI initiatives from experimentation into production, CTOs face a pivotal architectural challenge as storage emerges as one of the most common—and most expensive—constraints. While organizations continue to invest aggressively in GPU compute, studies consistently show that infrastructure inefficiencies outside the GPU account for the majority of wasted AI spend.

Most enterprises have made the commitment to AI. They’ve approved the budgets, stood up the pilots, and named it a strategic priority. So why are 95% of them getting zero return on $30–40 billion in GenAI investment? According to MIT research cited in Hitachi Vantara’s 2025 State of Data Infrastructure Global Report — which surveyed more than 1,200 IT leaders across 15 markets — the failure isn’t the model. It’s the infrastructure underneath it.

As organizations embed Quality Assurance into their SDLC with Jira and Xray, the resulting test data becomes a strategic enterprise asset, vital for product quality, test case traceability, and regulatory compliance. Protecting this asset is paramount, and as its scale and importance grow, organizations require specialized data management capabilities that go beyond standard application features to ensure complete resilience and governance.