Systems | Development | Analytics | API | Testing

More business teams are doing their thinking inside Claude and ChatGPT than ever before. Research, planning, analysis, content: it's all happening inside LLM platforms now. But the moment someone needs an answer grounded in actual enterprise data, the workflow breaks. They leave the AI, open the BI tool, run the query, copy the result back. Context lost, momentum killed. That's the problem we set out to solve when we launched ThoughtSpot's Agentic MCP Server back in July.

A failed payment is never just a technical failure. It is the moment a customer loses confidence in your platform, abandons their purchase, and often does not return. Unlike most bugs, a payment failure has a direct and immediate cost. This guide gives you the test cases to prevent that. Below you will find over 50 test cases organised by type and filterable by category, guidance on using AI to speed up your test suite, and a pre-launch checklist you can work through before every release.

Native Playwright visual regression is free to start and expensive to scale. The cost shows up in CI, not on day one. Cross-OS rendering breaks pixel diffs: Windows, macOS, and Linux render fonts and spacing differently, so the same code produces different baselines on different machines. Component snapshots beat full-page captures: smaller scope means clearer failure signal, fewer timeouts, and less flake on asset-heavy pages.

Last Updated: May 2026 Your API documentation is just as important as your API itself. It defines how easy it is for users to learn, understand, and use your open-source or paid product. In this post, DreamFactory highlights eight of the best API documentation examples from well-known tools. These examples can serve as inspiration for creating effective, developer-friendly API documentation.

Over the past few years, Easysecrets has established itself as a trusted partner within the Atlassian ecosystem. Beyond consistently delivering efficient solutions through strong development capabilities, the company has also built a reputation for offering a diverse and innovative portfolio tailored to evolving customer needs. When discussing Atlassian, it is impossible not to mention Jira.

A debate is running through the data and analytics community: BI is dead. The framing is wrong. The honest version of the argument points to something most of the industry is still avoiding.

The landscape of software quality assurance is structurally evolving. As we move forward into 2026, the business is shifting from certifying static programs to regulating autonomous entities.

Most real estate and brokerage platforms no longer suffer from a lack of data. They have the opposite problem – too much of it. CRM systems, MLS feeds, listings data, transaction records, marketing performance, and third‑party real estate datasets are all available and constantly growing. But having access to data is no longer the challenge.

Data masking comes in different forms: dynamic vs. static masking. Each has its own characteristics, use cases, and methods for data protection. But when it comes to comprehensive, consistent protection, static data masking rises above. In this blog, we’ll break down where dynamic data masking works, how it fails, and which use cases you need to use static masking for.

As organizations shift from analytics to AI, the modern data stack breaks under tightly coupled, warehouse-centric architectures that limit flexibility and scale.