Systems | Development | Analytics | API | Testing

Find and fix errors before they become issues. Here’s how four brands use Sauce Error Reporting to optimize error management and prioritize user experience.

You didn’t become a developer to spend hours hunting down missing messages, or debugging consumer issues. Yet here we are. Valuable dev time evaporates as you wrestle with Apache Kafka, or wait for a central team to unblock you, when you should be finding, prepping, and shipping streaming data in minutes. Lenses Community Edition tackles these everyday frustrations.

We recently interviewed 1,400 QA professionals in the State of Quality Report 2025 to arrive at these test automation statistics for 2025. 72% of them have up to 10 years of experience, and 16.2% of them have from 6-9 years of experience. The topics we surveyed include: Here are some notable statistics from the survey: Here are some more insights you may find valuable: Find these insights fascinating?

You set bold goals at the start of the year. Your leaders set a company-wide revenue goal and each team has goals to contribute to it. To make it trackable and achievable, you break them down into quarterly or monthly targets. Your planning is done, and everyone’s excited. But by month three, you don’t know if you’re trending in the right direction or off-track. Your goals are buried in spreadsheets, forgotten in meetings, and difficult to track. Sound familiar?

Keeping your API documentation accurate and up-to-date can be effortless with automation. Automatic API documentation updates use tools like Swagger and Postman to sync documentation with API changes in real time, saving time and reducing errors.

Today’s application development requires today’s test data management best practices. This is especially true for organizations embracing cloud-based application development. So, what can you do to improve your test data management practices? Start here, with our top best practices for test data management.

The performance of your AI applications depends on your underlying infrastructure. Whether leveraging high-performance GPUs, accelerators, or CPUs, AI workloads require high-performance hardware. With a range of different GPUs and accelerators available, choosing the best one for your specific workload is critical. On top of selecting the best GPU for your workload's needs, efficiently running AI workloads in production and at scale is a challenge.

Arpeely uses Google Cloud AI, multimodality and BigQuery to deliver highly relevant ads that optimize for long-term value.

At ThoughtSpot, our mission has always been to empower every decision-maker with instant insights that fuel smarter, faster decisions. Recently, we announced the launch of Spotter, our AI Analyst, which brings AI-powered insights to every user, on any question, and any dataset. This is ThoughtSpot's answer to a growing market of AI agents, and it’s our vision to make AI the new BI.

Let's be honest—nobody wants to deal with the fallout of a security breach. Yet, in our connected world, no business is immune. At Alphabin, we’ve seen firsthand how regular security testing can make the difference between a near miss and a full-blown crisis.