This reads like the opening to a movie. AGI is near.

🚨 Open-source AI community - stop building everything from scratch, let's build on each other's (and your own) work over time - continually, as we should! Don't waste previoius compute and human effort! See arxiv.org/abs/2403.08763 - simple & useful tips on how to just keep

“Now I am become Microsoft, the devourer of frontier model startups”

Why Google Deepmind's Mixture-of-Depths paper, and more generally dynamic compute methods, matter: Most of the compute is WASTED because not all tokens are equally hard to predict

In 2013, at 23, I felt on top of the world. I'd just sold my company for $5M. Well, kinda. I'll tell you the story of how I lost it all. The $5M was in stock in a VC-backed company. But not just any stock. This company wasn't just any company. The company was doing $35M in

Kling AI is wild. Memes and arts are coming to life. 8 wild examples: 1. Distracted boyfriend - Stop him?

Introducing The AI Scientist: The world’s first AI system for automating scientific research and open-ended discovery! sakana.ai/ai-scientist/ From ideation, writing code, running experiments and summarizing results, to writing entire papers and conducting peer-review, The AI

Introducing *Transfusion* - a unified approach for training models that can generate both text and images. arxiv.org/pdf/2408.11039 Transfusion combines language modeling (next token prediction) with diffusion to train a single transformer over mixed-modality sequences. This

Show-o One Single Transformer to Unify Multimodal Understanding and Generation discuss: huggingface.co/papers/2408.12… We present a unified transformer, i.e., Show-o, that unifies multimodal understanding and generation. Unlike fully autoregressive models, Show-o unifies

In a recent technical report, LearnLM, our set of AI models and capabilities fine-tuned for learning, outperformed other leading AI models on the principles of learning science. Now it’s available to try out in AI Studio. Learn more ↓ goo.gle/4gmEdxp

DeepSeek-R1 paper has close to 200 authors

That a bunch of Chinese hobbyists could release an AI that is more competent than American models, more cost efficient, has 3% of the environmental impact, and can pretty much run on a Raspberry Pi and is... open source, should not be shocking to the West. There's room to be

DeepSeek-R1: What's the main takeaway & what should we expect next? I asked AI researchers and Jordan Schneider from ChinaTalk. FYI: Long post. Finbarr Timbers, finbarr (Artfintel, former DeepMind) 1) What's the main takeaway: The biggest update that we should see is

For friends of open source: imo the highest leverage thing you can do is help construct a high diversity of RL environments that help elicit LLM cognitive strategies. To build a gym of sorts. This is a highly parallelizable task, which favors a large community of collaborators.

Tiny teams are the future: • Cursor: 0 to $100M ARR in 21 months w/ 20 people • Bolt: 0 to $20M ARR in 2 months w/ 15 people • Lovable: 0 to $10M ARR in 2 months w/ 15 people • Mercor: 0 to $50M ARR in 2 years w/ 30 people • ElevenLabs: 0 to $100M ARR in 2 years w/ 50 people

Microsoft just dropped OmniParser V2, this changes everything. This AI sees your screen, understands it, and takes action, just like a human. 100% free & open source!

The PhD thesis of my 14th PhD student, Khurram Javed (Khurram Javed), is now available. Title: Real-time Reinforcement Learning for Achieving Goals in Big Worlds Url: incompleteideas.net/papers/javed_k… Abstract: In this dissertation, I motivate the need for real-time learning and

cat OpenAI Google Gemini App Paul Jankura David Hershey Okay clear definitions for Agents, when to use Agents vs workflows, tips on Evals! Not much new here but always a good reminder to always build your evals early and llm as a judge goes fairly far.

i want to see more of: earnest not edgy whole not cracked infinite games "making the world a better place" - seriously though, i miss that energy

My favourite talks at YC AI Startup School: 1. Karpathy on LLMs as software 3.0 and a type of orchestrator OS with tools 2. Chollet on building AI that can reason - use DL to learn approx repres (intuition) to constrain discrete program search