Systems | Development | Analytics | API | Testing

Cypress vs Playwright vs No-Code Testing: Which Is Right for Your Team?

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.

Build Your Super Team: What 150 Years of Soccer Data Says

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.

WSO2 Appoints Tanmaya Das as CFO to Support Continued Global Expansion

London, UK. 11th June 2026 - WSO2 today announced the appointment of Tanmaya Das as Chief Financial Officer (CFO), further strengthening its executive leadership team as the company continues to build on its momentum and position itself for its next phase of growth. The appointment comes as WSO2 expands its platform strategy for the agentic enterprise, helping organisations securely integrate AI capabilities into complex technology environments while maintaining control over governance, data sovereignty, and digital infrastructure.

Human in the Loop Testing: Where AI Ends and QA Judgment Begins

The question isn't whether to use AI in QA. It's knowing exactly where to keep a human in control. The core risk: Over 75% of multi-agent failures are silent semantic errors that pass automated checks but violate business logic — detectable only by human inspection (Cemri, Pan et al., NeurIPS 2025). The division of labor: AI owns repetitive generation and execution; humans own risk analysis, requirement interpretation, exploratory investigation, and final sign-off. The operational discipline.

Generative AI for QA: How SDET Workflows and Skills Are Changing

Generative AI for QA is the use of large language models to accelerate the creation and analysis of testing artifacts — drafting test cases, summarizing requirements, and generating synthetic test data. AI agents extend that capability into multi-step autonomous workflows that plan, delegate, and execute testing tasks across an entire delivery pipeline. For SDETs, the shift is not about learning to prompt more cleverly.

Temporal made execution durable. Ably makes sessions durable.

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.

Gallus Insights: From Dashboard Overload to Instant Answers

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.

Real Estate Operations Automation: From Manual Processes to Event-Driven Workflows

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.

Inside NERSC at Berkeley Lab: How a DOE Office of Science User Facility Is Exploring ClearML for Scientific AI Workflows

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.