#andrejkarpathy
He says current models burn capacity memorizing things nobody needs, like SHA hashes, because the training data isn't curated. Strip that out and what he calls the cognitive core could run on a billion parameters. The mechanism is distillation. A very large model, or a huge amount of compute, supervising a very small one. He's already built at the small end. nanochat, his open-source project from October 2025, trains a working ChatGPT-style model for about $100 in four hours on eight GPUs. 🔎 We fact-checked 10 expert AI predictions so you know whose calls are worth listening to. It's free in bio. Follow @hyperbolic.ai for more content on AI and technology. Media: No Priors #andrejkarpathy #ai #llm #technology #openai
Hyperbolic AI90,200 просмотров • 6 дней назад
Using Obsidian for your AI second brain is a bad idea if you’re like me and want to gather a lot of information from different sources and use advanced “project manager” type of AI (e.g. Claude Code) to write advanced analysis out if it. That’s why my second brain library is build on SQLite. #secondbrain #andrejkarpathy #claudecode #obsidian #artificialintelligence
Katri Manninen50,500 просмотров • 5 месяцев назад
Andrej Karpathy’s three-layer method transforms how we work with Claude AI by creating specs, verifiers, and environments to boost accuracy and efficiency without complex prompting. #claudeai #andrejkarpathy #aimethod #aiproductivity #artificialintelligence
AI Honeycove13,400 просмотров • 1 месяц назад
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