Systems | Development | Analytics | API | Testing

We’re rolling out a new wave of improvements across Tideways in our first Release of 2026, focusing on deeper visibility, smarter automation, and broader ecosystem support. From automatic tracepoints for selected transactions and improved exception workflows to enhanced FrankenPHP worker-mode instrumentation, these features continue to reduce manual effort while increasing observability.

Choosing a software deployment model for modern organizations is complex. Regulatory compliance, data privacy, security, and operational overheads are just some of the factors that need to be considered. These factors can also change over time for reasons ranging from the introduction of new government regulations, to changing business models, to business expansion to new geographies, and more.

Achievements include appointment of new CEO, the launch of LeoAI Engine suite, expanded partner ecosystem, and the addition of leading brands to global customer roster.

Data virtualization can help modern organizations solve the complex challenges that come with managing data. With information scattered across multiple systems, accessing data can lead to operational bottlenecks in your organization.

As AI becomes more powerful and easier to use, more and more software will be built and consumed on the fly.

Your service doesn’t crash. It just gets slower. Latency creeps up. Requests that used to take 20ms now take 120ms. p99 drifts. Throughput drops slightly. Nothing is obviously broken — but the system feels congested. You open your dashboards. And yet, something is clearly off. In many production systems, this is what Event Loop pressure looks like. Not a failure. Not an outage. But a runtime that is struggling to make forward progress. The JavaScript thread is not dead. It’s busy.

Provisioning data for software testing requires balancing realism against security. Teams need production-like data to validate applications effectively. But they also have to adhere to strict privacy regulations. Two of the leading methods for creating and securing test data are data masking and synthetic data generation. Data masking de-identifies sensitive production data, preserving its scale, realism and referential integrity.

High-Performance Computing (HPC) has traditionally been an exclusive club. If you wanted to run massive simulations or crunch petabytes of data, you had to leverage the predominant languages used on supercomputing hardware—usually C, C++, or Fortran. Although fast and efficient, these languages demand strict memory management and complex syntax that require strong software development skills. Without them, development time can slow down significantly. But the landscape is shifting.

OpenClaw adoption is accelerating fast, and so are the security incidents. Within two weeks of broad adoption, over 42,000 gateway instances were found exposed to the public internet with no authentication. Nearly all of them had authentication bypasses. Eight were completely open with full shell access. Meanwhile, 341 malicious skills were confirmed on ClawHub, and infostealers like RedLine and Lumma are already targeting OpenClaw installations to harvest API keys.