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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Google AI Studio: What is it and how does it work?
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Objective Cardiovascular diseases (CVD) remain the leading cause of mortality globally, necessitating early risk ...
When a Matplotlib volunteer declined its pull request, the bot published a personal attack. Sign of the times: An AI agent autonomously wrote and published a personalized attack article against an ...
Claude Code vs ChatGPT Codex compared for performance, pricing, workflows, and privacy to find the best AI coding assistant ...
Researchers found that AI chatbots like ChatGPT, Claude, and Gemini are not good at producing secure passwords.
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