Systems | Development | Analytics | API | Testing

As organizations increasingly recognize the value of generative artificial intelligence, many are moving away from cloud hosted models in favor of on premises Large Language Models. This shift is primarily driven by the need to protect sensitive corporate data, maintain regulatory compliance, and reduce latency. However, an isolated local model offers limited utility. To truly unlock the potential of an on premises LLM, enterprises must connect it to their internal databases and APIs.

If you’ve spent any time debugging slow endpoints in a Phoenix application, you know the pattern: something is slow, you open your APM, you see a big chunk of time attributed to “Ecto” or maybe just “Controller,” and then you go make coffee while you grep through logs trying to figure out what actually happened. With v2.0 of the Scout APM Elixir agent, we’re closing that gap.

API traffic replay testing is a method of capturing real application traffic across protocols — HTTP, gRPC, database queries, message queues, and more — from a production environment and replaying it against a staging, QA, or development environment to validate software behavior under realistic conditions. In modern systems, HTTP is critical, but it is only one part of the picture.

If you run a software engineering tools team, you have almost certainly had this conversation: a developer asks for production data access to debug a real incident, and someone in the room says no. Not because the request is unreasonable (it isn’t), but because nobody wants to be the person who said yes when something goes wrong. That instinct is understandable. Production environments carry real risk. But the reflex to lock everything down has a cost that rarely gets accounted for.

Have you noticed how quickly the retail industry is changing? It's all thanks to digital transformation, which is happening at an incredible pace. We bet you're curious about why this shift is happening and what's driving it. Don't worry, you're not alone in your questions. Many people are wondering about the same thing. With each passing day, we see new technologies and IT solutions being adopted by retailers to stay ahead of the competition and offer better user experiences.

In 2012, Knight Capital Group lost $440 million in just 45 minutes. The cause? A software deployment error that no one caught during testing. There was no rollback plan. By the time engineers found the issue, thousands of wrong trades had already been executed. This is not a small startup mistake. This happened to a billion-dollar company with a full engineering and QA team. This is exactly why FinTech QA Services are so important. In normal software, a bug might only affect user experience.

Every analyst has been there: Deadline looming, data in hand, and a BI tool that either requires a workflow you haven't learned or a colleague you can't reach. So you open Excel. It's familiar, it's flexible, and it works right now. So that's where the work happens—and now where insight stays, ungoverned and invisible to your team, your analytics stack, and your agents.

Test automation is widely recognized as essential to modern delivery; it enables faster feedback, supports CI/CD practices, and increases release confidence. Yet in many organizations, automation growth lags behind development velocity. The reason is rarely a lack of intent. It’s the effort required to convert validated manual tests into automation scripts.

Kubernetes has become the de facto substrate for enterprise AI infrastructure. Its ability to handle complex, long-running workloads, self-healing capabilities, and rich ecosystem of GPU operators, storage drivers, and networking tools make it the natural platform for organizations scaling AI beyond the lab.

This is a Data App that collects structured product submissions from a team, validates them, queues them for approval, and writes approved entries directly to a Keboola table. I built it with Kai in one conversation. No Google Sheets. No broken column headers. No emailing CSVs. If you've ever needed your team to submit structured data - new products, budget inputs, campaign briefs, vendor details - and the spreadsheet approach keeps falling apart, keep reading.