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You don't need a trillion-parameter model that understands physics to answer a customer support call. But that's not even the main reason we build small models. The real one: LLMs store everything. Literally everything all of it sitting in the weights. Humans don't work that way, and your agent... show more

Sudarshan Kamath

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Geoffrey Hinton says LLMs are immortal. Destroy the hardware, save the weights. Rebuild them later, and “the very same being comes back to life.” But humans don't work that way. We're bound to our substrate. Not even Kurzweil gets a backup.

Geoffrey Hinton says LLMs are immortal. Destroy the hardware, save the weights. Rebuild them later, and “the very same being comes back to life.” But humans don't work that way. We're bound to our substrate. Not even Kurzweil gets a backup.

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🚨 REP. ANNA PAULINA LUNA NAILS IT: "There is NO reason we need to be taking in people that - A, don't want to assimilate; and B, I don't even know if they can CONTRIBUTE to the society we have." "We need to pause EVERYTHING."

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Your agents suck when using the web because 85% of it doesn't have an API. Browserbase gives them everything they need to do work online. Leading AI companies like Ramp, Lovable, and Clay trust us to power agents that do real work on behalf of real people. With a single API key, your agent gets everything it needs to navigate the wild web: browsers, search, fetch, identity, a sandbox runtime, and model gateway. Stop waiting on integrations, build agents that can browse and interact with the web just like humans.

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Elon Musk Elon Musk on the importance of physics: “I am a big believer in physics. Everything else is a recommendation, pretty much, but physics is the law. Like if you break physics... the rocket will explode and the car will not work. So you have to adhere to physics in order to make cars that work, to make rockets that work, to make space internet work. Because if you're wrong, then reality is a very harsh judge. Reality is merciless. Because if you make a device that doesn't work, violates physics, it simply doesn't work, and then you rocket blows up and the satellites don't work and the cars break. In order to make the technology work, you must have an extremely rigorous pursuit of the truth. Because, otherwise reality will bite you in the a**.”

Elon Musk Elon Musk on the importance of physics: “I am a big believer in physics. Everything else is a recommendation, pretty much, but physics is the law. Like if you break physics... the rocket will explode and the car will not work. So you have to adhere to physics in order to make cars that work, to make rockets that work, to make space internet work. Because if you're wrong, then reality is a very harsh judge. Reality is merciless. Because if you make a device that doesn't work, violates physics, it simply doesn't work, and then you rocket blows up and the satellites don't work and the cars break. In order to make the technology work, you must have an extremely rigorous pursuit of the truth. Because, otherwise reality will bite you in the a**.”

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small local model that falls apart in bloated agents like openclaw just runs like a wild horse in hermes agent. and that's not even my line, someone else called it that, i've just been quietly pointing people at this harness for months because it held up on everything i threw at it, 3b models all the way to one trillion params. watch this happen on my own machine. i pointed hermes agent at a local http endpoint, gemma 4 12b on my 3090 llama.cpp server, and it auto-detected the model and started working immediately. no config wrestling, no broken tool calls, no babysitting the output format, i typed in a url and it just went. the whole clip is exactly that, start to finish, no errors, no retries, butter smooth. and the tool calling, the one thing that quietly breaks most local setups, works here like it's nothing. it's not the model that's flaky, it's the harness around it. hermes agent is the first agent i've run that actually gets that right. one url, one local model on one card, and it runs like a wild horse.

small local model that falls apart in bloated agents like openclaw just runs like a wild horse in hermes agent. and that's not even my line, someone else called it that, i've just been quietly pointing people at this harness for months because it held up on everything i threw at it, 3b models all the way to one trillion params. watch this happen on my own machine. i pointed hermes agent at a local http endpoint, gemma 4 12b on my 3090 llama.cpp server, and it auto-detected the model and started working immediately. no config wrestling, no broken tool calls, no babysitting the output format, i typed in a url and it just went. the whole clip is exactly that, start to finish, no errors, no retries, butter smooth. and the tool calling, the one thing that quietly breaks most local setups, works here like it's nothing. it's not the model that's flaky, it's the harness around it. hermes agent is the first agent i've run that actually gets that right. one url, one local model on one card, and it runs like a wild horse.

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🚨 AWESOME! PRESIDENT TRUMP: "Take a look at that plane. See that plane? That's in perfect mint condition!" "Take a look at that. Look at that. Isn't that amazing? And EVERYTHING should look that way." 🔥🔥 "We work with the military. We do a real job. If you look at the polish, everything is beautiful." "And that's the way we want DC." 🇺🇸

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Once you don't focus on stuff to close the gaps of insecurity in your soul, you stop acting in a manner that doesn't match your moral compass and principles … This loop is everything and I hope you realize impressing those who you don't even like is the seed that leads to actions that you don't admire in yourself.

Once you don't focus on stuff to close the gaps of insecurity in your soul, you stop acting in a manner that doesn't match your moral compass and principles … This loop is everything and I hope you realize impressing those who you don't even like is the seed that leads to actions that you don't admire in yourself.

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SITUATION EXPLAINED: Richard Sutton, the father of reinforcement learning, just launched a new lab. The goal: a trillion-parameter agent that learns in real time on 20 watts. • Sutton wrote The Bitter Lesson in 2019, its core claim: general methods that leverage computation beat hand-crafted approaches, by a large margin • His new lab, Oak Lab, builds on the "big world hypothesis": the world is too big for any model to pre-learn everything, so trying to is wasted effort by construction • Their "batch size one" algorithm updates from a single live experience as it arrives, instead of training on giant curated datasets... no massive pretraining run at all • Claimed result: multiple orders of magnitude less compute and energy than existing methods • The holy grail number: a trillion-parameter agent that learns and plans in real time on 20 watts... roughly what the human brain runs on • It's built for agentic tasks specifically, not as a general-purpose LLM replacement sof 𓋹: "It's possible that we end up looking back at LLMs as an evolution of search... not to hate on LLMs, they're very useful, they're awesome. But eventually I think that's what it ends up looking like."

SITUATION EXPLAINED: Richard Sutton, the father of reinforcement learning, just launched a new lab. The goal: a trillion-parameter agent that learns in real time on 20 watts. • Sutton wrote The Bitter Lesson in 2019, its core claim: general methods that leverage computation beat hand-crafted approaches, by a large margin • His new lab, Oak Lab, builds on the "big world hypothesis": the world is too big for any model to pre-learn everything, so trying to is wasted effort by construction • Their "batch size one" algorithm updates from a single live experience as it arrives, instead of training on giant curated datasets... no massive pretraining run at all • Claimed result: multiple orders of magnitude less compute and energy than existing methods • The holy grail number: a trillion-parameter agent that learns and plans in real time on 20 watts... roughly what the human brain runs on • It's built for agentic tasks specifically, not as a general-purpose LLM replacement sof 𓋹: "It's possible that we end up looking back at LLMs as an evolution of search... not to hate on LLMs, they're very useful, they're awesome. But eventually I think that's what it ends up looking like."

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Everyone's still typing at their agents. Ask. Wait. Read. Ask again. Audio huddles on Buzz flip that. You pull the agent into a live call and it already has the context of your entire chat. Voice is where the ideas actually come out of your head. But that's scratching the surface. Free startup idea: FaceTiming your agents. The models can already see your screen, hear you, and act on your machine. Somebody just has to put all three in one window. That's the holy grail of the future of work. Somebody build it. I'll be the first customer.

Everyone's still typing at their agents. Ask. Wait. Read. Ask again. Audio huddles on Buzz flip that. You pull the agent into a live call and it already has the context of your entire chat. Voice is where the ideas actually come out of your head. But that's scratching the surface. Free startup idea: FaceTiming your agents. The models can already see your screen, hear you, and act on your machine. Somebody just has to put all three in one window. That's the holy grail of the future of work. Somebody build it. I'll be the first customer.

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Satya Nadella, CEO of Microsoft, says every company now runs on two kinds of capital, and leaking one of them is a one-way door: "This economy is going to be shaped going forward by both human capital and, let's call it, this token capital. That is true for Microsoft, that is true for a new startup, that's going to be true for any bank that's been in existence for 100 years." "The interplay between human capital and token capital, and compounding the returns of it, is what you need to do." "What is the tacit knowledge of an enterprise or a firm? It's the unique ways that you're able to operate, pass judgment, have taste." "The model, in some sense, is able to extract that tacit knowledge through human trajectories, and then code it in a set of weights in a model." "Every enterprise now needs to be more mindful about that interplay of humans and their digital estate working together, those trajectories training the models that they keep as IP versus leak it." "Because if you leak it, it's a one-way door. You're done in some sense." The moat was never the data sitting in your warehouse. It was the judgment your people apply to it, and AI now turns that judgment into weights you either own or hand away. Treat your tacit knowledge like a patent, not a prompt. The firms that pipe a hundred years of taste into someone else's frontier model are trading a permanent edge for a quarter of convenience.

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.Adam Carolla: California refuses to "pop the trunk" on their voter rolls: "What is it that you guys are hiding that you don't want to cooperate? And their answer would be nothing." "My answer is what's in the trunk, right? So you either pop the trunk or you call your attorney." "And if you call your attorney, something's in the fcking trunk that you don't want us to see." Accurate.

.Adam Carolla: California refuses to "pop the trunk" on their voter rolls: "What is it that you guys are hiding that you don't want to cooperate? And their answer would be nothing." "My answer is what's in the trunk, right? So you either pop the trunk or you call your attorney." "And if you call your attorney, something's in the fcking trunk that you don't want us to see." Accurate.

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Michael Levin says we don't build minds. We build pointers -- interfaces into a Platonic space of possible intelligences. We're wired to notice minds like ours, and blind to everything else along the spectrum. Language models may not think the way we do, but something else may be coming through. “You pull down more than you put in.”

Michael Levin says we don't build minds. We build pointers -- interfaces into a Platonic space of possible intelligences. We're wired to notice minds like ours, and blind to everything else along the spectrum. Language models may not think the way we do, but something else may be coming through. “You pull down more than you put in.”

vitrupo

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David Deutsch says all computation from humans to LLMs shares the same fundamental power: the universal Turing machine The difference is only in programs and outputs. But AGI is different; we don't even understand it in principle. "so the true sign would be a theory, not a machine"

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Haider.

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