Systems | Development | Analytics | API | Testing

As APIs grow across teams, keeping them consistent becomes difficult. Some APIs follow naming conventions and include clear descriptions, while others don’t. Over time, these differences make APIs harder to understand, review, and maintain. That is where API linting helps. That made it possible to apply custom Spectral rules as part of local development, Git workflows, or CI checks.. Teams can now upload and manage custom Spectral rulesets directly from the Insomnia UI.

As enterprises race to adopt AI, weak data foundations are preventing more than half (58%) of organizations in the United States and Canada from realizing value and contributing to an estimated $108 billion in wasted global AI investment each year, according to a report from Hitachi Vantara. The reason is rarely bad models or lack of ambition.

One thing has become increasingly clear to me: the businesses that thrive in the AI era won't be the ones with the most data, they'll be the ones where every employee knows how to use it. The World Economic Forum's 2025 Future of Jobs Report names analytical thinking as the top core skill companies need today, and a 2024 Gartner survey found poor data literacy to be one of the top five obstacles to analytics success.

The fastest way to replace custom Python or PowerShell scripts for client data ingestion is to move each script's logic into a reusable, config-driven pipeline that stores connection details, field mappings, and schedules as metadata instead of code. This guide is for data integration managers and engineers who currently maintain a script per client or per source system. After following it, you'll have a repeatable pattern for onboarding new clients without writing a new script for each one.

Open a brokerage’s CRM instance a few years in, and it rarely looks like a sales tool anymore. Somewhere along the way, it picked up MLS feeds, transaction history, integration logic, reporting dashboards, and lately, the raw data behind a first AI pilot. None of that was the plan. Each piece got bolted on because the CRM was the system already sitting there. A CRM was built for a narrower job than that: logging a call, tracking a pipeline, managing the relationship an agent owns.

Reaching a milestone is always exciting. Some milestones carry a deeper meaning. Today, we're proud to share that the N|Solid Runtime has surpassed 2 million downloads. The milestone reflects growing momentum, with downloads accelerating and putting us on track to nearly double last year's total. To us, this isn't simply a download count.

Xray is transitioning into a Forge App, Atlassian's modern cloud development platform, using Forge Remote to strengthen security, align with Atlassian's long-term roadmap, and support future innovation. If you're wondering what the Xray Forge migration means, the short answer is simple: your testing workflows stay exactly the same. The changes happen behind the scenes, improving the platform that powers Xray while maintaining the features, scalability, and performance your team relies on.

While the benefits of LLM orchestration layers and autonomous agents are clear, they also bring a new set of non-deterministic failure modes that traditional unit testing cannot detect. A study by RAND Corporation found that 80.3% of AI projects fail to achieve the desired business outcomes, and this is because of issues in the data pipeline and model integration, not algorithmic problems.Using traditional software, you will get predictable results from known inputs.

As product delivery cadences shrink, traditional quality assurance approaches are reaching operational constraints. Traditional scripted test scripts, albeit a tried-and-true method in the past, can no longer keep up with the onslaught of dynamic code changes, changing microfrontends, and CI pipelines. In many cases, just changing a label or making a small modification to a layout may break whole integration suites and create huge backlogs.

We're thrilled to announce a strategic technology partnership between **Kong** and **ModelOp**. As enterprises rapidly transition into the agentic era, they face a critical challenge: how to deploy AI fast enough to stay competitive without taking on unacceptable regulatory or security risks. Together, **ModelOp** and **Kong** are solving the "last mile" problem of enterprise AI delivery.