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

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.

North Korean IT operatives use stolen LinkedIn accounts, fake hiring flows, and malware to secure remote jobs, steal data, ...

Supervised learning algorithms like Random Forests, XGBoost, and LSTMs dominate crypto trading by predicting price directions or values from labeled historical data, enabling precise signals such as ...

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.

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Why write ten lines of code when one will do? From magic variable swaps to high-speed data counting, these Python snippets will transform your code.

Engineers in Silicon Valley have been raving about Anthropic’s AI coding tool, Claude Code, for months. But recently, the buzz feels as if it’s reached a fever pitch. Earlier this week, I sat down ...

ThreatsDay Bulletin tracks active exploits, phishing waves, AI risks, major flaws, and cybercrime crackdowns shaping this week’s threat landscape.