Systems | Development | Analytics | API | Testing

After spending hours perfecting your BI dashboard, you export a report only to watch headers shift, page breaks split tables, and logos resize unpredictably. What should take minutes becomes hours of manual reformatting before documents meet compliance standards. This repeats daily across enterprises. Modern BI platforms optimize for responsive dashboards that adapt to screens, but operational documents require absolute precision.

Imagine this: your chatbot responds to an angry customer with sarcasm, or your language model suggests different prompts for your competitor. These aren’t just minor errors; they can break customer trust, damage your brand, and cost you big. That’s why the testing process of Prompt Testing has become a must-have in modern AI development. It’s not just about making prompts sound good; it’s about making sure the responses are accurate, safe, ethical, and on brand.

Even the smallest code changes can carry hidden risks. A minor bug fix may end up breaking a completely different part of the project. That’s where sanity testing comes in. It provides teams a quick, focused approach to ensure that recent changes didn’t introduce new problems. In this blog, we will outline what sanity testing is, when it is used, how it is accomplished, and why it is important when working in fast-moving development cycles.

If you’ve spent any time building cloud-native systems, you’ve probably tripped over the tricky beast that is gRPC streaming. It’s powerful, flexible, and feels like magic when it works. But the minute you need to test it? Suddenly, you’re in “hold my coffee, I need a week” territory. One of the most common places we see gRPC streams in the wild is when clients connect to asynchronous message buses like Google Pub/Sub.

Large Language Models (LLMs) are transforming how organizations interact with their data infrastructure, offering unprecedented capabilities for both technical and business users. However, this transformation brings unique opportunities and challenges that vary significantly based on user personas, security requirements, and implementation approaches. This writeup explores these dimensions through the lens of practical implementation using tools like Keboola MCP and various client interfaces.

From weeks-long projects to self-service data distribution.

Visualize launching a new AI chatbot for your business. It’s supposed to be perfect. But on day one, it recommends out-of-stock products, gives wrong order updates, and even provides wrong pricing information. Confusion spreads, support tickets pile up, and customers start to leave. It’s not always the chatbot’s intelligence, it’s the lack of testing before and after launch.

In today’s digital world, it is essential to be able to deliver high-quality software quickly. Test automation using special tools will automate repetitive test cases, which will give accuracy and speed up delivery. Test automation enables Dev Operationists by assuring continuous testing, which enables earlier bug finding and wider test coverage.

MongoDB is one of the most popular databases for modern applications. Its flexible, document-oriented model makes it easy for developers to store and query everything from user activity logs and product usage events to application telemetry. But while MongoDB is great for storing and organizing data, it’s not always easy for business teams to use it to answer questions or track key metrics. The data often lives in a raw format that requires technical skills to interpret.

The era of simple, scripted AI is swiftly fading. We’re now witnessing the dawn of AI Agents: sophisticated, self-governing digital entities that possess the capacity to comprehend their surroundings, navigate intricate problems, and execute purposeful actions. Multi-agent systems take this even further, multiplying these capabilities by enabling teams of AI agents to collaborate, delegate tasks, and solve challenges collectively in ways a single agent cannot achieve alone.