Introducing TabFM in BigQuery: Predictive analytics reimagined
TabFM in BigQuery uses in-context learning (ICL) to deliver highly accurate predictions on your tabular datasets via a single SQL statement.
Systems | Development | Analytics | API | Testing
Modern Software Development For Today's Systems & Applications
What IT Teams Should Know Before Deploying Unified Communications
What can cause a unified communications rollout to go off track even when the initial project plan looks straightforward? Deploying unified communications may sound simple in a kickoff meeting, but in practice, it can be one of the more deceptively complex infrastructure projects an IT team can take on because it affects nearly every department at once. Network capacity, licensing, integrations, security, user adoption, and compliance requirements can all create problems if they aren't addressed early.
Where agentic AI is most valuable in performance testing
Quick summary: Performance teams can generate tests in minutes, but the analysis still takes hours. Agentic Performance Testing in NeoLoad uses domain-specialized AI agents to deliver a finished analysis from a single request, so engineers can start with the conclusions, rather than the raw data. Performance testing answers a critical question in the quality engineering lifecycle: will this hold up when real people use it, under real conditions, at real volume?
Why browser-based automation can't test your ERP apps
Picture a large-scale grocery and retail chain launching its first online storefront. The setup is complicated: a single online order must travel through an SAP eCommerce platform, multiple payment and loyalty systems, before disappearing into back-end SAP supply chain and ERP apps. The complexity of merging the online and offline business is like a “digital tsunami.” For many SAP enterprises, this sounds familiar.
AI Model Bias Verification: Step-by-Step Framework for Auditing and Mitigating Model Bias
Artificial intelligence plays a vital role in high-stakes decision-making across recruitment, credit scoring, healthcare, and criminal justice. This rapid adoption is reflected in market data (Statista): the global AI technology sector is valued at approximately $617 billion and is projected to surpass $1.4 trillion by 2032, with a steady annual growth rate (CAGR 2026-2032) of 14.82%.However, mathematical execution does not guarantee objectivity.
6 ways Open Data Infrastructure accelerates your workflows
Six ways Open Data Infrastructure removes friction from your workflows.
The Evolution from Test Automation to Autonomous Testing
Testing looks nothing like it did five years ago. What once demanded armies of engineers writing brittle scripts now runs on intelligent systems that create, adapt, and analyze tests on their own. AI has rewritten the rules, and the teams that recognize this shift early are pulling ahead of those still patching broken automation night after night. For QA leaders and DevOps directors under pressure to ship faster without sacrificing quality, understanding this evolution is more than academic.
Your AI investment has a governance gap, and it's called testing
Article Summary: Most teams have adopted AI coding tools, but testing is still manual, so the speed gain rarely survives to release. This post covers why that gap forms, how your team can maintain application integrity, and how QMetry’s AI features, from fast test creation to a Release Readiness Advisor, connect coverage, risk, and release decisions in one system instead of a second disconnected tool.
Kong AI Gateway 2.0 Is Now GA - And It's Already Moving Faster
We said you'd see the evidence quickly.. Here it is. **Kong AI Gateway 2.0 is now generally available**, and it isn't arriving quietly.
11 Tools to Monitor API Performance and Availability in Real Time (2026)
Choosing API monitoring tools can be overwhelming, with feature lists and buzzwords competing for attention. When you’re responsible for business-critical APIs, the best tool is the one that delivers real-time, actionable data and fits your system’s actual needs.