Systems | Development | Analytics | API | Testing

Migrating a load testing project is rarely difficult, but it takes time and it's easy to give up halfway. That's why OctoPerf 18 comes with a Gatling migration playbook for AI agents. In our NeoLoad and LoadRunner walkthroughs, the projects were converted into JMeter Virtual Users. A Gatling simulation is code, so here it stays code: the agent rewrites it as a K6 script, and its injection profile becomes an OctoPerf scenario.

On October 20, a power failure in one of Google Cloud’s three London zones disrupted Revolut, Lloyds, HSBC and others for hours. One zone failed, errors spread region-wide, and Google warned customers they might not be able to create new workloads to fail over to. Most teams find out how resilient they are during an outage, not before one. These five questions let you find out first.

In interconnected software environments, a security vulnerability rarely exists in isolation. A weakness in one component can expose credentials, create unintended access paths, or give attackers a foothold in connected services. The consequences depend on what that component can access, the permissions it holds, and the safeguards around it. As organizations adopt cloud platforms, third-party integrations, and AI-powered development tools, the boundaries between systems matter more than ever.

We just released OctoPerf 18 and its main addition is a new load testing engine: K6. You can now import your K6 scripts and run them without changing a single line, next to JMeter and Playwright Virtual Users, in the same scenarios and the same reports. In this blog post I import a K6 script that browses our public demo shop, validate it, run it and go through the results. The scripts can be downloaded if you want to follow along. I will finish with the things that work differently than with JMeter.

At dbt Summit, leaders from Fivetran + dbt Labs, Snowflake, Microsoft, and MotherDuck discussed why openness includes compute, governance, business context, and AI.

Healthcare legacy software modernization transforms aging clinical and operational systems into scalable, interoperable, and maintainable platforms. It addresses technical debt without compromising patient care, data integrity, or critical healthcare workflows. Legacy healthcare systems often remain deeply embedded across EHRs, billing, scheduling, diagnostics, integrations, and administrative processes. Replacing them in one step can introduce significant operational and financial risk.