Systems | Development | Analytics | API | Testing

Every QA team carries a list of things it hasn’t gotten to yet, and that list tends to grow at about the same rate as the application portfolio. There are more tests to automate than there is time to write them, coverage that stops short of the applications nobody wants to touch, and workflows that have stayed manual long enough to become part of how the team works. Automation chips away at that list without ever quite emptying it, because the constraint is rarely how fast the existing tests run.

How does your Magento 2 store’s PHP backend performance compare to other operators of Magento in general? To answer this question, we have aggregated and anonymized performance data from over 250 Magento 2 stores over the first half of 2026 and computed benchmark numbers to compare to for the most important page types: Product details, Category Page, Search, and Homepage. We previously made this benchmark for Q2 2025.

How does your Shopware 6 store’s PHP backend performance compare to other operators of Shopware in general? To answer this question, we have aggregated and anonymized performance data from over 300 Shopware 6 stores over the first half of 2026 and computed benchmark numbers to compare to for the most important page types: Product details, Category, Search, and Homepage. We previously made these benchmarks for 2025 Q1, 2025 Q2, and the second half of 2025.

Node.js 20 reached End-of-Life (EOL) on April 30, 2026. Now, the consequences of that lifecycle milestone are becoming increasingly visible across the JavaScript ecosystem. GitHub Actions removed Node.js 20 from its runners on September 23, 2026, moving JavaScript actions to Node.js 24. Vercel will follow on October 1, 2026, when Node.js 20 will no longer be available for new Builds and Functions. These are not isolated platform decisions.

Enterprise adoption of autonomous agentic workflows has shifted software quality engineering. Deterministic automated testing remains foundational for API behavior, schema validation, authorization, and data integrity. Dynamic, non-deterministic model outputs, however, require additional layers of verification. To manage scale, modern AI application testing increasingly relies on AI test AI workflows using LLM-as-a-judge setups.

Trust is foundational to how Confluent designs, operates, and governs its products. Confluent's Trust Principles shape everything from how Confluent Cloud is engineered to how it is monitored and audited. Each principle is backed by specific, independently verified controls.

One message coming out of Dreamforce caught my attention: context is moving to the center of the enterprise AI conversation. Good. We’ve been arguing for a while that context is what turns AI from an impressive interface into something genuinely useful for the enterprise. Salesforce’s AIforce approach is about bringing its data, workflows, business logic, semantics, permissions, security, and governance into the places people increasingly want to work, including Claude and Slack.

Imagine hiring the sharpest analyst you have ever met. They know the industry cold. They reason like nobody else on the team. They write like a Nobel laureate. Then you realize you never gave them a badge, a laptop, or a login to the CRM. And a week later, nothing has shipped. Every task still routes back through you, because the analyst can think but can’t touch anything. You’ve just hired a very expensive conversation partner. That is most people's experience with AI agents today.

Most mobile automation programs don't stall because a tool can't tap a button. They stall on everything around the tap: the setup before the first test, a device lab nobody can keep current, maintenance that grows faster than coverage, and the moment someone asks "are we ready to ship?" and nobody has a clean answer.