Ale

Hay personas tan técnicas y tan obsesionadas con las profundidades que no son capaces de conectar sus habilidades con un objetivo tangible, se desvían del problema real y por eso no consiguen trabajo

Karpathy told Dwarkesh that a 1 billion parameter model, trained on clean data, could hit the intelligence of today's 1.8 trillion parameter frontier. That is a 1,800x compression claim. The math behind it is more defensible than it sounds. When researchers at frontier labs look at random samples from their training corpus, they see stock ticker symbols, broken HTML, forum spam, autogenerated gibberish. Not Wikipedia. Not the Wall Street Journal. The actual pretraining dataset is mostly noise, and the model is burning parameters to vaguely remember all of it. One estimate pegs Llama 3's information compression at 0.07 bits per token. Well-structured English carries around 1.5 bits per token of real information. The trillion-parameter model is holding a roughly 5% resolution image of the internet it trained on. So when a lab ships a 1.8 trillion parameter model, the overwhelming majority of those weights are handling rough memorization. They are compression overhead for a noisy training set, taking up capacity that could be doing reasoning instead. Karpathy's proposal is to separate the two. Build a cognitive core: a small model that contains only the algorithms for reasoning and problem-solving, stripped of encyclopedic memorization. Pair it with external memory the model queries when it needs a fact. A 1 billion parameter reasoner plus retrieval beats a 1.8 trillion parameter model trying to do both. The data already supports this direction. GPT-4o runs at roughly 200 billion parameters and outperforms the original 1.8 trillion GPT-4. Inference costs for GPT-3.5 level performance fell 280x between 2022 and 2024, driven almost entirely by smaller, cleaner, better-architected models. The trend line is pointing where Karpathy says it should. The real implication for anyone tracking the AI trade: data quality is the actual constraint. The companies winning the next phase will be the ones who figured out what to train on, and what to throw away.

Este video explica a la perfección porque la equidad en los recursos, no garantiza la igualdad en las condiciones

Delete Notion. Delete your note-taking app. Delete your inbox triage tool. A PhD researcher just replaced all of them with 8 AI agents that manage your Obsidian vault while you sleep. 100% open source. Works in any language. You talk. The crew does the rest: - Architect designs your vault and runs onboarding - Scribe turns messy brain dumps into clean notes - Sorter empties your inbox every evening - Seeker searches your vault and answers with citations - Connector finds hidden links between your notes - Librarian runs weekly health audits and fixes broken links - Transcriber turns meetings into structured notes - Postman scans Gmail and Calendar for deadlines And they talk to each other. When the Transcriber processes a meeting, it alerts the Sorter. When the Postman finds a deadline, it flags the Architect. It's a crew. Not a stack of isolated tools. Runs 100% locally on your machine. MIT license. Built by someone who got tired of forgetting things. Link in reply ↓

It's time to boot the Miami Marlins out of Major League Baseball. To pull just 10,160 fans to their second game of the season on Opening Weekend is an absolute disgrace!