Nithin Kamath highlights how LLMs evolved from hallucinations to Linus Torvalds-approved code, democratizing tech and transforming software development.

Earlier, Kamath highlighted a massive shift in the tech landscape: Large Language Models (LLMs) have evolved from “hallucinating" random text in 2023 to gaining the approval of Linus Torvalds in 2026.

This study presents a potentially valuable exploration of the role of thalamic nuclei in language processing. The results will be of interest to researchers interested in the neurobiology of language.

Learn how Zero-Knowledge Proofs (ZKP) provide verifiable tool execution for Model Context Protocol (MCP) in a post-quantum world. Secure your AI infrastructure today.

Objective Cardiovascular diseases (CVD) remain the leading cause of mortality globally, necessitating early risk ...

A man calmly restrains a large python during a tense encounter, drawing attention for his steady handling of the snake.

Simplify complex concepts with electric field problems made easy using Python and vectors! ⚡ In this video, we demonstrate step-by-step how to calculate electric fields, visualize vector directions, ...

George Pólya’s random walk theorem absolved him of being a lurker and revealed how the laws of chance interact with physical ...

According to Moderne, this extends OpenRewrite coverage from backend and frontend application code into the data and AI layer ...

It’s a breakthrough in the field of random walks.

Probability underpins AI, cryptography and statistics. However, as the philosopher Bertrand Russell said, “Probability is the ...

See how we created a form of invisible surveillance, who gets left out at the gate, and how we’re inadvertently teaching the ...