Systems | Development | Analytics | API | Testing

Every tester has lost an afternoon to an environment that would not behave. The code was fine. The suite was fine. The problem was the ground the tests ran on. That ground has a name, and getting it right is quietly one of the highest leverage things a QA team can do. It is called a test bed, and this guide walks through what it is, what goes into one, why it tends to fall apart, and how teams keep it stable without babysitting infrastructure all day.

If your team spends more hours fixing tests than writing features, you already know the problem. You don’t need another lecture on "why testing matters." You need to reduce test maintenance without gutting your coverage, and you need it to actually stick past next sprint.

AI Transport v0.5.0 is now available. It adds first-class support for running an agent turn inside a durable execution framework, such as Temporal or Vercel's Workflow Development Kit (WDK), while every client watching the conversation still sees one clean, resumable stream. The last release, v0.4.0, let an agent hydrate its history from your own database. This one is about what happens when the process running the agent isn't around for the whole turn.

What an incredible day at the Agentic Analytics Playbook EMEA event! It might have been baking hot outside, but inside the Nobu Hotel, the energy was absolutely electric.

Test execution is one of the most time-sensitive stages of software delivery. Teams are expected to validate functionality, ensure stability, and support release decisions within increasingly shorter development cycles. Even with strong automation in place, there is rarely enough time to execute every Test before a release. This makes prioritization a critical part of the QA process.

TL;DR: Tableflow is a Confluent Cloud feature that materializes Apache Kafka topics as Apache Iceberg or Delta Lake tables, eliminating custom data pipelines by automatically handling schematization, type conversions, schema evolution, CDC stream materialization, catalog publishing, and table maintenance.

Data streaming has moved from a niche pattern used by a handful of internet-scale companies to the default backbone for event-driven architectures, real-time analytics, and now AI pipelines. What started as log aggregation at LinkedIn has become the plumbing for fraud detection, IoT telemetry, microservices communication, and retrieval-augmented generation. Three names dominate that conversation today.

Most teams can explain why sensitive BI data should stay on-premises. Far fewer can explain what that decision is worth in dollars. That gap matters. IT can see the control benefits. Finance wants numbers. Executives want a simple answer: what risk drops, what costs change, and what value shows up over 3 to 5 years? This is where a business case beats a technical pitch. On-premises BI can protect sensitive data, support compliance, and give teams direct control over hosting.

This summer, leading performance testing platforms have introduced a new wave of AI capabilities that are fundamentally changing how software teams validate application speed and reliability. Rather than incremental updates, these advances mark a step change: AI performance testing now enables faster test creation, greater accuracy, and wider accessibility across teams.