Systems | Development | Analytics | API | Testing

We’ve gotten used to understanding our applications through signals, summaries, and traces. Tiny little bits of information about how the app really works. Not because that’s the best way to do it, but because it’s been too hard to get the real thing. The real information exists. It’s on the network. How people called your app and what your code did. What other systems it called, the database queries it made, and the result sets that came back.

As enterprise development teams scale, coordinating deployments across multiple teams, applications, and environments becomes one of the most time-consuming parts of the delivery lifecycle. Today, we're excited to introduce Releases—a new capability in Appian that brings native release management to the platform, helping teams deploy faster and with fewer surprises.

Your team just shipped a microservices refactor. Services are smaller, deployments are faster, and boundaries are clearer. Then, during a design review, someone inevitably suggests: “We should use Kafka.”That suggestion might be the exact architectural breakthrough you need—or it could quietly introduce months of unnecessary operational complexity.This article serves as a practical decision framework.

If your team is evaluating browser test automation, there’s a good chance the conversation starts with Cypress vs Playwright. Both tools have earned their popularity. Playwright is widely used by engineering teams that need reliable end-to-end testing, cross-browser support, and strong CI/CD integration. Cypress remains a favorite among frontend developers who want an interactive testing experience, fast local feedback, and approachable debugging tools.

Soccer is a game of stories, but the most fascinating stories are often buried deep inside the numbers. And this year on the world's biggest stage, the tournament has expanded by nearly 60% – traditional scouting reports and pundit hot-takes simply can't keep up with the sheer volume of new data. That’s why we’re looking at the tournament through a much wider lens.

When Temporal launched, a lot of people had the same reaction: "We have queues and retries. We don't need this." (Temporal's own blog addressed this directly.) That reaction made sense. Queues solve queue problems and they do it well. What Temporal gave you was something different: a named execution context that survives a server restart and picks up from its last checkpoint. Not a better queue. A different abstraction entirely. If you built with it, you couldn't imagine going back.

I had the distinct pleasure of hosting a Snowflake Summit ‘26 session with Agustin “Augie” Del Rio, CEO and Founder of Gallus Insights, an analytics platform tailored specifically for mortgage lenders. As we sat down to discuss the future of analytics, one core truth echoed throughout the room: the most ambitious AI goals live or die by the quality of the underlying data.

The biggest operational bottleneck in property management isn’t a lack of technology. It’s the manual coordination required between systems, teams, and processes. Leasing coordinators paste data from the PMS into email threads. Maintenance supervisors scan spreadsheets to find overdue work orders. Accounting teams wait for someone to confirm a deposit before posting. Owner reports get assembled the night before a call because nothing triggers them automatically.

How open standards give security teams more control, not less.

NERSC, the mission high-performance computing center for the U.S. Department of Energy Office of Science, is using ClearML as part of the AI infrastructure stack for Perlmutter, the upcoming Doudna supercomputer, and the broader American Science Cloud. Here is a look at what they are exploring and why it matters for AI for science at scale.