To understand how software platforms will enhance efficiency, enable innovation, and create leverage, just look at platform revolutions throughout history.

Organizations have a wealth of unstructured data that most AI models can’t yet read. Preparing and contextualizing this data is essential for moving from AI experiments to measurable results.

The world tried to kill Andy off but he had to stay alive to to talk about what happened with databases in 2025.

Google Cloud’s lead engineer for databases discusses the challenges of integrating databases and LLMs, the tools needed to ...

Explore generative AI in financial services: how it works, top use cases, customer experience gains, key risks, and ...

Pocket AVIA/RAD-ALERT warns of solar flare neutrons and radiation and Terrestrial Gamma Bursts, both of which may pose hazard to passengers, crews, and avionics This pocket unit provides real-time ...

As for why SOA faltered, Holt points to heavyweight standards, orchestration and performance issues, lack of reuse, cultural ...

Trump administration considers potential escalation in China trade war Measure could restrict shipments to China of goods containing or made with U.S. software Plan would retaliate against China's ...

A new study by Shanghai Jiao Tong University and SII Generative AI Research Lab (GAIR) shows that training large language models (LLMs) for complex, autonomous tasks does not require massive datasets.

Operating System – What should teams standardize on? Teams should standardize Linux for servers and containers, keep Windows for domain-joined productivity, and use macOS where developer output ...