Most discussions of AI-generated code focus on whether AI can write code. The harder question is whether you can trust it.

Anthropic has actively been tuning these settings across different segments, which could plausibly affect user perceptions ...

The next important milestone for AI research is to automate model development. Every advance in reasoning, language, and perception is, in some sense, a step toward that goal. However, the path to ...

Anthropic’s new model is a godsend for hackers — and a financial bonanza for the company.

AI coding will accelerate innovation across every industry. That acceleration doesn't diminish application security; ...

At the core of these advancements lies the concept of tokenization — a fundamental process that dictates how user inputs are interpreted, processed and ultimately billed. Understanding tokenization is ...

The model, built from scratch by Meta Superintelligence Labs under the leadership of Alexandr Wang, represents something of a ...

AI power users are pulling away from everyone else. This story originally appeared in The Algorithm, our weekly newsletter on ...

Apps and platforms allow novice and veteran coders to generate more code more easily, presenting significant quality and ...

As AI drives “coding abundance,” the former CEO of Cognizant says clients are buying certainty—not effort—as services models ...

While current AI coding assistants are trapped in a loop of individual, disposable sessions, the true bottleneck for engineering teams isn't coding speed but the "staggering" loss of tribal knowledge.

Most people judging AI are using the wrong product. Here's why enterprise coding agents and consumer chatbots are different ...