Karpathy outlines three software eras: human-written code, trained neural weights, and now natural language prompts. He claims LLMs treat English as code, making traditional coding steps optional. The linked talk will explain why this shift matters in 40 minutes.

• Karpathy frames software history as three eras: 1.0 (hand-written code), 2.0 (neural net weights), and now 3.0 (natural language prompts)
• In Software 3.0, English prompts function as code and LLMs act as the CPU running them
• This makes traditional coding infrastructure (compilers, libraries, frameworks) optional rather than mandatory
• A 40-minute talk/demo is promised to explain why this shift matters

Chrome now lets you save reusable Gemini prompts as “Skills” for one-click access instead of retyping or copy-pasting each time. Saved Skills sync across devices when you’re signed in, and you trigger them with “/” or the plus button in Gemini. They still require confirmation for actions like messaging or calendar entries.

• Chrome's Gemini now lets you save reusable prompts as "Skills," triggered via "/" or the "+" button instead of retyping or pasting them each time
• Skills sync across desktop devices when signed into Chrome
• Skills can operate across multiple open tabs at once, useful for tasks like comparing prices or aggregating research
• Actions like sending messages or adding calendar events still require user confirmation, keeping the same security checks as regular Gemini prompts

This resource offers over 30,000 lines of insights into the structure and functionality of AI system prompts. It encourages users to support the project through various means, including cryptocurrency donations and Patreon, while also highlighting the importance of securing AI systems against potential vulnerabilities.

• A GitHub repo has collected over 30,000 lines of leaked/extracted system prompts from major AI tools (Cursor, Claude Code, Devin, Replit, Windsurf, v0, Manus, etc.)
• It's positioned as a reference for understanding how production AI agent tools are actually architected and prompted internally
• The project solicits funding via crypto donations and Patreon
• It frames the leaked prompts as useful for studying AI security vulnerabilities