What a week, folks, what a week! Buckle up, because ThursdAI just dropped, and this one's a doozy. We're talking seismic shifts in the open source world, a potential game-changer from DeepSeek AI that's got everyone buzzing, and oh yeah, just a casual $500 BILLION infrastructure project announcement. Plus, OpenAI finally pulled the trigger on "Operator," their agentic browser thingy – though getting it to actually operate proved to be a bit of a live show adventure, as you'll hear. This week felt like one of those pivotal moments in AI, a real before-and-after kind of thing. DeepSeek's R1 hit the open source scene like a supernova, and suddenly, top-tier reasoning power is within reach for anyone with a Mac and a dream. And then there's OpenAI's Operator, promising to finally bridge the gap between chat and action. Did it live up to the hype? Well, let's just say things got interesting.As I’m writing this, White House just published that an Executive Order on AI was just signed and published as well, what a WEEK.Open Source AI Goes Nuclear: DeepSeek R1 is HERE!Hold onto your hats, open source AI just went supernova! This week, the Chinese Whale Bros – DeepSeek AI, that quant trading firm turned AI powerhouse – dropped a bomb on the community in the best way possible: R1, their reasoning model, is now open source under the MIT license! As I said on the show, "Open source AI has never been as hot as this week."This isn't just a model, folks. DeepSeek unleashed a whole arsenal: two full-fat R1 models (DeepSeek R1 and DeepSeek R1-Zero), and a whopping six distilled finetunes based on Qwen (1.5B, 7B, 14B, and 32B) and Llama (8B, 72B). One stat that blew my mind, and Nisten's for that matter, is that DeepSeek-R1-Distill-Qwen-1.5B, the tiny 1.5 billion parameter model, is outperforming GPT-4o and Claude-3.5-Sonnet on math benchmarks! "This 1.5 billion parameter model that now does this. It's absolutely insane," I exclaimed on the show. We're talking 28.9% on AIME and 83.9% on MATH. Let that sink in. A model you can probably run on your phone is schooling the big boys in math.License-wise, it's MIT, which as Nisten put it, "MIT is like a jailbreak to the whole legal system, pretty much. That's what most people don't realize. It's like, this is, it's not my problem. You're a problem now." Basically, do whatever you want with it. Distill it, fine-tune it, build Skynet – it's all fair game.And the vibes? "Vibes are insane," as I mentioned on the show. Early benchmarks are showing R1 models trading blows with o1-preview and o1-mini, and even nipping at the heels of the full-fat o1 in some areas. Check out these numbers:And the price? Forget about it. We're talking 50x cheaper than o1 currently. DeepSeek R1 API is priced at $0.14 / 1M input tokens and $2.19 / 1M output tokens, compared to OpenAI's o1 at $15.00 / 1M input and a whopping $60.00 / 1M output. Suddenly, high-quality reasoning is democratized.LDJ highlighted the "aha moment" in DeepSeek's paper, where they talk about how reinforcement learning enabled the model to re-evaluate its approach and "think more." It seems like simple RL scaling, combined with a focus on reasoning, is the secret sauce. No fancy Monte Carlo Tree Search needed, apparently!But the real magic of open source is what the community does with it. Pietro Schirano joined us to talk about his "Retrieval Augmented Thinking" (RAT) approach, where he extracts the thinking process from R1 and transplants it to other models. "And what I found out is actually by doing so, you may even like smaller, quote unquote, you know, less intelligent model actually become smarter," Pietro explained. Frankenstein models, anyone? (John Lindquist has a tutorial on how to do it here)And then there's the genius hack from Voooogel, who figured out how to emulate a "reasoning_effort" knob by simply replacing the "end" token with "Wait, but". "This tricks the model into keeps thinking," as I described it. Want your AI to really ponder the meaning of life (or just 1+1)? Now you can, thanks to open source tinkering.Georgi Gerganov, the legend behind llama.cpp, even jumped in with a two-line snippet to enable speculative decoding, boosting inference speeds on the 32B model on my Macbook from a sluggish 5 tokens per second to a much more respectable 10-11 tokens per second. Open source collaboration at its finest and it's only going to get better! Thinking like a NeuroticMany people really loved the way R1 thinks, and what I found astonishing is that I just sent "hey" and the thinking went into a whole 5 paragraph debate of how to answer, a user on X answered with "this is Woody Allen-level of Neurotic" which... nerd sniped me so hard! I used Hauio Audio (which is great!) and ByteDance latentSync and gave R1 a voice! It's really something when you hear it's inner monologue being spoken out like this! ByteDance Enters the Ring: UI-TARS Controls Your PCNot to be outdone in the open source frenzy, ByteDance, ...
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