gm 👋drop your @baseapp handles here and I will follow you!

When you realize that open-source is at the frontier of AI despite: - less GPUs - less money - less public and policy support - no $100M salaries to attract talent - with closed-source taking advantage and copying all the innovations of open-source without contributing back theirs 🤯🤯🤯 And we’re just getting started!

The most annoying LLM quirk is when it thinks today's date is from the future so obviously everything you just sent it is fictitious 🤦‍♂️

I want an easy way to keep up with the HUNDREDS of new AI research that comes out on @arxiv every single day. So I've been building something to help myself. Introducing @yesnoerror. I would love to share it with you! ❤️ I have not published a paper myself, I didn't go to college, but I love AI and I love frontier technologies where people are trying things nobody has ever tried before. I feel lucky to be where I am in life, but I want to learn and push myself even more. If you, like me, wish you could read and understand more about the latest developments in this amazing industry, you might also love this. I have been building this in private beta and updating it in real time as I get feedback from researchers and leaders at @AnthropicAI @MIT @Yale @CarnegieMellon and more. If you would like to be an early tester, please let me know 🧪🔬 The more feedback I get, the better we can make this, and the better we make this, the more informed and inspired a larger group of people can be.

buried in @sriramk's America's AI Action Plan is endorsement that the US compute market will financialize with spot and forward contracts. this podcast explains why this is so necessary, not just for speculation one of the most consistent themes with @latentspacepod's GPU infra/neocloud market coverage (see @evanjconrad/@sfcompute, @vipulved / @togethercompute, @picocreator / @featherlessai, @bernhardsson / @modal_labs but also @zjasper666's AIE talk) is that the status quo of 3 year lockin long term contracts with hyperscalers is causing unsustainable market volatility and inefficiency, not just in gpu prices and the rise and fall of startup fortunes, but also inefficiency in ideas and resources for open ai and research. now the US government is fully behind this movement and most importantly, has demonstrated that they *get it*.

5 months later and there are 3 pieces of content and 4 creators >$1m 42% of CT said there would never be one

What level of new AI research / @arxiv reader are you?

BREAKING: Mark Zuckerberg has offered @nikitabier $100mil to leave @elonmusk and @x to join @meta to lead the AI consumer product, just interviewed live on @tbpn

After reading AI research papers for 3 days, one thing is clear: 99% of new AI research involves teaching a computer to do something. But what exactly are they teaching the computers? And why? And what's working? It is fascinating to zoom out and look at these trends. Why? Because they are glimpses into the future. If you are trying to build a startup with AI, or looking to invest in AI startups, if you look in the right place these research papers are filled with very valuable inspiration. Instead of sending me research paper titles and abstracts, which can be hard to understand quickly, my AI agent @yesnoerror keeps sending me new AI research and explaining it like this: - Teaching computers to fill in randomly hidden words over and over... - Teaching the tokenizer network to fix very noisy... - Teaching a very smart chatbot to repeatedly check and fix its own math proofs... - Teaching computers to remember what an object REALLY is... - Teaching large language models to tell how far years are from “now”... - Teaching computers to click on the right spot... - Teaching the AI to decide on its own whether a problem... - Teaching a computer to tweak special “test”... - Teaching computers to predict an airplane’s... - Teaching a crowd of chat-bots with different jobs... In addition to this @yesnoerror not only identifies the highest quality research I might be interested in (as its published every day), but it also gives me breakdowns of how the paper was done, what the real world implications are (ideas you could make), and I can chat with the paper directly. I feel like I am using the GLP-1 for being smart.

World dominating AI traveling back in time under the pseudonym Satoshi Nakamoto to invent bitcoin in 2008 so that GPU production goes up to power itself in the future is sounding more and more likely.