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.

Your developers are shipping more code than ever. GitHub Copilot, Cursor, and tools like them have fundamentally changed developer throughput - some teams are seeing 40-76% more code per person per sprint. That is the headline everyone celebrates. The part that keeps engineering leaders up at night is the other side of that equation: your testing pipeline has not changed at the same pace. Tests that used to gate two releases a week now need to gate ten.

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.

In the span of five days in March 2026, a single threat actor—TeamPCP—compromised a vulnerability scanner (Trivy), a code analysis platform (Checkmarx), and the most widely used LLM proxy in the Python ecosystem (LiteLLM). The attack chain was surgical: each compromised tool provided credentials to attack the next target.

In March 2026, security researcher isfinne discovered that LiteLLM version 1.82.8—the most popular open-source LLM proxy in the Python ecosystem, with approximately 97 million monthly downloads—contained credential-stealing malware published to PyPI. Within hours, version 1.82.7 was confirmed to carry a similar payload through a different injection method.