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

UTSA: ~20% of AI-suggested packages don't exist. Slopsquatting could let attackers slip malicious libs into projects.

Its use results in faster development, cleaner testbenches, and a modern software-oriented approach to validating FPGA and ASIC designs without replacing your existing simulator.

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.

Designing and deploying DSPs FPGAs aren’t the only programmable hardware option, or the only option challenged by AI. While AI makes it easier to design DSPs, there are rising complexities due to the ...

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 ...

Create an rng object with np.random.default_rng(), you can seed it for reproducible results. You can draw samples from probability distributions, including from the binomial and normal distributions.

Seedance 2.0 is ByteDance’s AI video model blending text, images, and audio into cinematic scenes, sparking copyright and ...