Systems | Development | Analytics | API | Testing

Siri 2.0 Delay: Testing Gaps That Just Cost Apple 6 Months

The news dropped this week, and it sent shockwaves through the tech industry. Apple has officially pushed back the release of its highly anticipated Sir i 2.0. Reports from Bloomberg indicate that the update, originally slated for iOS 26.4, ran into severe hurdles during internal review. The culprit wasn't a lack of innovation or features. It was a failure in quality assurance.

Build agentic AI in minutes on Snowflake

Agentic AI doesn’t have to mean months of architecture work, custom orchestration layers, or external platforms. In this hands-on workshop, you’ll build Snowflake Intelligence agents using native Snowflake capabilities to reason over structured data, retrieve context from unstructured sources, and execute multi-step analysis directly inside Snowflake within minutes.

Why Your Company Will Be Running OpenClaw Next Year

You’ve probably heard of OpenClaw. Maybe you’ve seen the demos where an AI agent opens a browser, navigates to your CRM, fills in a form, and files a support ticket. No API required. Maybe you thought “that’s cool but I’d never run that at work.” Your employees already are. According to Permiso’s research, 22% of enterprise customers have employees running OpenClaw without IT approval.

How AI Coding Is Breaking Synthetic Data Generation

Traditional synthetic data generation approaches, still called “Test Data Management” (TDM) by legacy vendor, were designed for a world where applications were monolithic, databases were the center of gravity and change happened slowly. The world looks a lot different now. Modern systems are distributed, often times event-driven, and increasingly powered by streaming data and AI agents. In this environment, batch-oriented synthetic data generation fails to capture how systems actually behave.

SmartBear QMetry's AI-based test generation: Execute tests in minutes

In this video, you’ll discover how SmartBear QMetry's AI-powered test generation automatically transforms requirements into complete, executable test cases within minutes. Watch as we demonstrate test generation cases from Jira, Rally, and Azure requirements, demonstrate how to refine existing tests, and save your teams hours of manual work.

Silent Failures: Why AI Code Breaks in Production

You ship a small “safe” change on Friday. The diff is tiny, the tests are green, and the AI assistant was confident. An hour after deploy, your on-call channel lights up. A downstream service is rejecting responses that look fine in code review. Now you’re rolling back and rewriting a fix that should have been obvious if you had real traffic in the loop. This isn’t a hypothetical.
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What AI Has Never Seen: The Context Gap in Code Generation

Your AI coding assistant has read the entire internet. It knows every programming language, every framework, every best practice documented in Stack Overflow answers and GitHub repositories. It can generate a REST API handler in seconds that looks perfect with clean code, proper error handling, following all the patterns. But here's what it's never seen: your production traffic. Data from a real API request. Someone filling out a form with messed up or incomplete data. AI is changing how we write and test code, but there's a fundamental gap between training data and production reality.

How to Prioritize AI Investments Using the Impact-Maturity Matrix?

AI is no longer an experimental line item in the budget. For most U.S. CXOs, the real challenge in 2026 is far more practical: where should we place our bets first? With dozens of AI use cases competing for attention, capital, and executive sponsorship, prioritization has become a boardroom conversation, not a lab discussion. Are you investing in AI initiatives that can move the needle this fiscal year, or are you spreading resources thin across pilots that never scale?

Ep 60 | How AI FOMO and Lost Focus on Culture Harm ROI with Jen Stirrup

As AI and BI projects race to deliver quick wins, many organizations overlook what truly determines long-term success: the people behind the data. In this episode of The AI Forecast, Jen Stirrup, author, speaker, and founder of Data Relish, joins host Paul Muller to dissect the difference between data literacy (reading the numbers) and data fluency (speaking the language of business). Jen challenges the industry’s obsession with AI FOMO, warning that rushing to deploy models on shaky cultural foundations is the fastest way to derail ROI.