Utilize AI to analyze application runtime data (e.g., rendering time, communication latency), obtain optimization suggestions (such as reducing component re-rendering, reusing hardware connections), ...

In software development, success means going beyond meeting requirements. We must create products that surprise and delight ...

For IT and HR teams, SLMs can reduce the burden of repetitive tasks by automating ticket handling, routing, and approvals, ...

Step aside, LLMs. The next big step for AI is learning, reconstructing and simulating the dynamics of the real world.

Upcoming software purchases should no longer be one-time contracts; they're living partnerships built on shared data and trust.

As AI remakes the travel industry, one acronym is sparking both excitement and confusion: MCP, or Model Context Protocol. But ...

There's a rush to amass as much data as possible to train AI models. Amazon is trying to scrape Microsoft's Github for some of the data it needs.

Anthropic releases its Agent Skills framework as an open standard, with Microsoft, OpenAI, Atlassian, and Figma already ...

C compiler, LustreC, into a generator of both executable code and associated specification. Model-based design tools are ...

AI Impact highlights Rivian’s autonomy strategy, hybrid AI gains in healthcare, AGI skepticism, exec moves and AI Impact ...

For learning here, let’s thank IBM for explaining that, “Diffusion algorithms, particularly Diffusion Models, are advanced AI ...

The potential and reality of AI agents have become a polarizing topic. In 2026, agents will inch toward broader adoption in the enterprise, experts say, with missteps and hurdles ahead.