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Open source software is GREAT. But "open source" AI is NOT like software - it's VERY different. Rob Miles cuts through the bullshit: ROB: Oh, hey, Meta. I heard Llama's weights leaked. That's rough, man. Information security's hard. How you holding up? META: Oh, we're great. Yeah, we're fine.... We... actually, that was deliberate. We meant to do that. ROB MILES: Oh, really? META: Yeah... well, the second time anyway. It's called open source. Look it up. ROB MILES: Oh. Well, I love free and open source software, but do those principles really apply to network weights? How does that work? META: Open source is good for users because it lets them read the source code and see what the program is really doing and how it works. ROB MILES: Wait, have you found a way to tell how a model works by looking at its weights? META: No. But, it lets developers all over the world spot bugs in the code and submit patches. ROB: Wait, people are fixing bugs in Llama's weights? META: Well, no. People can fine tune it themselves, though. ROB: ?? Other companies offer fine tuning through APIs. ... So, hang on, if you can't actually read the code and know what it's doing, then network weights are effectively a compiled binary. So, in what sense is this open source? Why not call it like public weights? Why call it open source at all? META: I love open source. ROB: Well, I know a lot of your employees do, but you don't love anything. You're a giant corporation. What's in it for you? META: I love, love open source.show more

AI Notkilleveryoneism Memes ⏸️

143,997 subscribers

107,362 views • 2 years ago •via X (Twitter)

Science & Technology Comedy Education

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AI Notkilleveryoneism Memes ⏸️2 years ago

Source: Rob Miles' new banger video summarizing the last year and explaining the current situation with AI safety

Rob Miles (in SF)2 years ago

I'm starting to suspect the main reason for open sourcing llama is that "Best 'Open Source' LLM" is much better marketing than "6th Best LLM"

AI Notkilleveryoneism Memes ⏸️2 years ago

It's good marketing. And many people unthinkingly cheer for anything branded 'open source' Zuck has done this many times - he convinces developers to build on his "open" platform (FB), then crushes them when economic logic dictates. (VCs stopped funding startups built on FB.)

PauseAI ⏸2 years ago

@robertskmiles Amazing segment. Entire video is a must-watch!

AI Notkilleveryoneism Memes ⏸️2 years ago

@robertskmiles HE IS RISEN

Bryan Wade2 years ago

@robertskmiles Ok, call it Open Weights. There are still lots of people that benefit from that. It means that I can run this stuff on my own hardware, and a for profit company can't cut me off from the technology overnight. Most of the AI startup scene only exists because of open weights.

Dirk Bruere2 years ago

@robertskmiles Illustrates that secrets cannot be kept by anyone.

Matt Prestifilippo2 years ago

@robertskmiles This is missing the major point that even though they don’t share the training data, it’s still expensive to produce the models. And the permissive license to use those for businesses without fees is useful.

Alex Alarga ⏹️2 years ago

@robertskmiles

Random User2 years ago

@robertskmiles I’d also highlight the aspect of learning how to code well from actual open-source.

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.Bill Gurley “I don’t think politicians understand how powerful open source is as a competitive weapon. If you have ten companies all doing open source, the minute an idea is shared, it's available to the other ten and they absorb it immediately. I think the odds that the U.S., with two proprietary models, can stay ahead of China, with ten open source ones, is very low.”

Codie Sanchez

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Elon exposes OpenAI -- OpenAI execs betrayed the founding mission as soon as there was revenue and profits to be had -- If it started as a for-profit, Elon would own 50% Elon Musk: “There's a mountain of evidence that shows that OpenAI was created as an open source nonprofit.” “That's the exact description in the incorporation documents. They have completely violated that.” “And they tried to change the definition of OpenAI to mean open to everyone instead of open source, even though it always meant open source.” “ I came up with the name. That’s how I know.” “ I mean, essentially, since I came up with the idea for the company, named it, provided the A, B, and C rounds of funding, recruited the critical personnel, and told them everything I know, if that had been a commercial corporation, I'd probably own half the company.” “It was totally at my discretion. I could have done that.” “But I created it as an open source nonprofit for the world.” Chamath: “ Do you think the right thing to do is to take those models and just open source them today?” Elon: “ Yeah, I think that is what it was created to do, so it should.” “Try using any of the recent so-called OpenAI open source models, they don't work.” “They open sourced a broken, non-working version of their models as a fig leaf.” “I mean, do you know anyone who's running OpenAI's open source models? Jason: “No.” Elon: “Exactly.”

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The All-In Podcast

387,223 views • 10 months ago

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Harry Stebbings

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.Josh Wolfe: Anybody Using DeepSeek App Is 'Absolute Fool' "Anybody using the DeepSeek app is an absolute fool. If you're using DeepSeek on companies like Together Compute, one of Lux's companies, which can get rid of the CCP censorship, then it's probably okay. But remember, the open-source movement is something we deeply believe in. Most great technologists, entrepreneurs, and venture capitalists are on the side of open source. The closed-source models that have consumed tens of billions of dollars are the ones that are really going to be at risk. When you look at Hugging Face, a major repository, or Together Compute, Runway ML, and a lot of Lux's companies, they have been pioneers in open source. Now, why am I not worried about open source, even with the DeepSeek model? As long as you don't have the CCP censorship on it, the models with their open weights allow people to run on their proprietary data. This means companies like pharma or defense companies that have their own siloed, proprietary data—think about Bloomberg with their proprietary longitudinal data, or Meta with their data—are the ones who will have the edge. Even as open source takes hold, these companies will still dominate. I’m not worried about open source being the problem. I’m more concerned about people overfunding closed models with no proprietary source. A lot of capital is going to be burned there, and we’re already seeing that with people worried about OpenAI in some aspects."

Josh Caplan

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MTS

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David Sacks: Anthropic Is Trying to Crush Open Source AI and the American Developers Who Use It David Sacks: “I know people don't have a lot of sympathy for Chinese companies, that's fine. I'm not defending Chinese companies. I'm defending American developers who need to be able to use everything in the public domain. And let me give you an example. Cursor rolled out its new product, Composer 2. They were able to post-train that model using Kimi K2.5 on their own proprietary coding data. They started with a Chinese open source model, and then they used their own data, and they came up with a new derivative product. This is the way that open source works. You take things that are in the public domain, you fork them, you make them your own. And by the way, once it's in the public domain, it's not a Chinese model anymore. No data is going back to China, nothing's going back to China. An American company has taken open source contributions in the public domain, made it their own, and then developed their own model. And if you say that American companies can't do that, or that somehow it's tainted with IP theft, you are basically going to put a dagger through the heart of the entire American open source ecosystem. And that is exactly what Anthropic wants, because they do not want to have the competition.”

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Matt Bornstein says open source is how AI companies stop being wrappers: "A year ago, a bunch of smaller companies or new application companies were trying to figure out, 'How do I really build an AI without just being a wrapper on top of OpenAI?' The answer to that question turned out to be open source." "This is what Cursor did. This is what Decagon and Harvey are in the process of doing now. A bunch of really strong application-level startups made the determination: we can't build just on closed source." "We need to do our own mid-training, our own post-training, our own inference and deployment tricks. All of that means it must be built on top of open source. The closed-source vendors won't give you the access to do this." "Open source became really central in a way that's not always visible because it's deeply embedded in some of these products. Some of the most innovative products and applications now really depend on this very deeply." Matt Bornstein

Matt Bornstein says open source is how AI companies stop being wrappers: "A year ago, a bunch of smaller companies or new application companies were trying to figure out, 'How do I really build an AI without just being a wrapper on top of OpenAI?' The answer to that question turned out to be open source." "This is what Cursor did. This is what Decagon and Harvey are in the process of doing now. A bunch of really strong application-level startups made the determination: we can't build just on closed source." "We need to do our own mid-training, our own post-training, our own inference and deployment tricks. All of that means it must be built on top of open source. The closed-source vendors won't give you the access to do this." "Open source became really central in a way that's not always visible because it's deeply embedded in some of these products. Some of the most innovative products and applications now really depend on this very deeply." Matt Bornstein

a16z

40,507 views • 1 month ago

ELON MUSK: DON’T TAKE ANYONE’S OPINION….GO TO THE SOURCE MATERIAL AND COMMUNITY NOTES TO SEE WHAT REALLY HAPPENED As soon as any company steps out of line and is willing to actually have the truth debated on their platform, it forces the other platforms to allow things to be more truthful, to not censor. Because their censorship becomes glaringly obvious And, you know, the best thing I found as a rebuttal, like if somebody, if there's a hoax, is just go to the source material. You know, if you think if somebody thinks, you know, it's, you know, Trump said that we should put Liz Cheney in a firing squad, I'm like, let me send you a link to X so you can watch his video. That's the best way. Don't take my opinion for it. Don't take anyone's opinion for it. Go to the source material and Community Notes Yes, and Community Notes is awesome. It's incredible because everybody gets checked, including me. And with Community Notes, all the software is open source, and all the data is open source, so you can recreate any given note independently. That's amazing. Yeah. That's how it should be. Total absolute transparency in every way Sometimes I get asked like, 'Elon, can you remove a note?' You know, mostly by the left, but sometimes by the right. I'm like, I don't even remove notes on my own account. Nothing. And, and by the way, everything is totally open. So if I did that, it would stick out like a sore thumb immediately. Like it's not going to be subtle That is the best counter to misinformation. Yes, absolutely. Like let everybody look at it and say, 'Okay, here's what the actual facts say.' Yes, exactly. The counter to misinformation is better information Not just that, but having it checked in real-time by the community. So you have millions of people that can go over it and debate whether or not this is true or that's true. Yes, and, and like I said, the best way to understand the truth of things is don't take anyone's opinion for it. Look at the source material Look at what someone actually said, look at what someone actually did, look at the real videos of the situation, and then you'll actually know what's real

ELON MUSK: DON’T TAKE ANYONE’S OPINION….GO TO THE SOURCE MATERIAL AND COMMUNITY NOTES TO SEE WHAT REALLY HAPPENED As soon as any company steps out of line and is willing to actually have the truth debated on their platform, it forces the other platforms to allow things to be more truthful, to not censor. Because their censorship becomes glaringly obvious And, you know, the best thing I found as a rebuttal, like if somebody, if there's a hoax, is just go to the source material. You know, if you think if somebody thinks, you know, it's, you know, Trump said that we should put Liz Cheney in a firing squad, I'm like, let me send you a link to X so you can watch his video. That's the best way. Don't take my opinion for it. Don't take anyone's opinion for it. Go to the source material and Community Notes Yes, and Community Notes is awesome. It's incredible because everybody gets checked, including me. And with Community Notes, all the software is open source, and all the data is open source, so you can recreate any given note independently. That's amazing. Yeah. That's how it should be. Total absolute transparency in every way Sometimes I get asked like, 'Elon, can you remove a note?' You know, mostly by the left, but sometimes by the right. I'm like, I don't even remove notes on my own account. Nothing. And, and by the way, everything is totally open. So if I did that, it would stick out like a sore thumb immediately. Like it's not going to be subtle That is the best counter to misinformation. Yes, absolutely. Like let everybody look at it and say, 'Okay, here's what the actual facts say.' Yes, exactly. The counter to misinformation is better information Not just that, but having it checked in real-time by the community. So you have millions of people that can go over it and debate whether or not this is true or that's true. Yes, and, and like I said, the best way to understand the truth of things is don't take anyone's opinion for it. Look at the source material Look at what someone actually said, look at what someone actually did, look at the real videos of the situation, and then you'll actually know what's real

X Freeze

589,604 views • 9 months ago

David Sacks says an open-source AI ban is coming, disguised as holding open models to the same standard as closed "I got bad news for you, Chamath. An open source ban is coming. They're not going to call it that." "You know what they're going to say? They're going to say that we simply have to apply the same standards to open models that we apply to closed." "Okay, here's how they do it step by step. Let me explain how regulatory capture actually works." "So first of all, you got to get this regulatory apparatus. Okay, Dario wants an FDA for AI, but he doesn't have enough political support for that, so instead they do this Trojan horse of a FINRA for AI." "They call it self-regulating, it's not really, but anyway that gets them off the ground. Now they've created the standard-setting organization. Now they've got pre-release model testing. Then the pressure grows to codify that in law."

David Sacks says an open-source AI ban is coming, disguised as holding open models to the same standard as closed "I got bad news for you, Chamath. An open source ban is coming. They're not going to call it that." "You know what they're going to say? They're going to say that we simply have to apply the same standards to open models that we apply to closed." "Okay, here's how they do it step by step. Let me explain how regulatory capture actually works." "So first of all, you got to get this regulatory apparatus. Okay, Dario wants an FDA for AI, but he doesn't have enough political support for that, so instead they do this Trojan horse of a FINRA for AI." "They call it self-regulating, it's not really, but anyway that gets them off the ground. Now they've created the standard-setting organization. Now they've got pre-release model testing. Then the pressure grows to codify that in law."

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234,174 views • 24 days ago

Alexandr Wang, Meta's Chief AI Officer, on why Meta can no longer simply open-source its frontier model: As part of standing up Meta Superintelligence Labs, the team rewrote its internal risk doctrine. "One of the things that we did as part of Meta Superintelligence Labs is we updated our what we call our advanced AI scaling framework which is really our view of what are the risks that we see in developing these very powerful models and how do we want to handle those risks as we see them in early testing." They then ran their frontier model through it, and published what came back. "We published a lot of what we saw in the process of training Muark in our preparedness report and some of the things that we saw is that it actually triggered some high risk areas in the course of early training particularly around biorisk but also a number of the risks were elevated." The trigger came during early training, well before launch or red-teaming. Biorisk was the standout, with several other categories rising alongside it. Alexandr Wang is clear this isn't specific to Meta: "This is something I think the entire industry has seen as the models have improved pretty dramatically over the past year so we certainly aren't the only ones to see a host of these risks show up as we scaled up the models and as we sort of kept pushing the frontier of research." Which brings him to the real fork in the road: the difference between shipping a model inside a product and handing out the weights. "When we launched a model like New Spark in a product, we have a lot of ways to mitigate some of these risks and ensure that we're able to launch it in a safe and responsible way. It's much harder to do that when you open source a model and people can use that model in all sorts of contexts that we may not have full understanding of." A product is a controlled surface. You can filter, monitor, rate-limit, patch and revoke. An open-weights release is a one-way door: once the file is out, the deployment context and the mitigations both stop being yours. So Meta is building something different for release: "So we're in the process right now of developing models that we believe are fit and safe to be open source while still maintaining as much of the performance capabilities as possible."

Alexandr Wang, Meta's Chief AI Officer, on why Meta can no longer simply open-source its frontier model: As part of standing up Meta Superintelligence Labs, the team rewrote its internal risk doctrine. "One of the things that we did as part of Meta Superintelligence Labs is we updated our what we call our advanced AI scaling framework which is really our view of what are the risks that we see in developing these very powerful models and how do we want to handle those risks as we see them in early testing." They then ran their frontier model through it, and published what came back. "We published a lot of what we saw in the process of training Muark in our preparedness report and some of the things that we saw is that it actually triggered some high risk areas in the course of early training particularly around biorisk but also a number of the risks were elevated." The trigger came during early training, well before launch or red-teaming. Biorisk was the standout, with several other categories rising alongside it. Alexandr Wang is clear this isn't specific to Meta: "This is something I think the entire industry has seen as the models have improved pretty dramatically over the past year so we certainly aren't the only ones to see a host of these risks show up as we scaled up the models and as we sort of kept pushing the frontier of research." Which brings him to the real fork in the road: the difference between shipping a model inside a product and handing out the weights. "When we launched a model like New Spark in a product, we have a lot of ways to mitigate some of these risks and ensure that we're able to launch it in a safe and responsible way. It's much harder to do that when you open source a model and people can use that model in all sorts of contexts that we may not have full understanding of." A product is a controlled surface. You can filter, monitor, rate-limit, patch and revoke. An open-weights release is a one-way door: once the file is out, the deployment context and the mitigations both stop being yours. So Meta is building something different for release: "So we're in the process right now of developing models that we believe are fit and safe to be open source while still maintaining as much of the performance capabilities as possible."

Big Brain AI

16,374 views • 1 month ago

Dario Amodei just dismantled the biggest myth in the AI industry. Open source AI isn’t free. It never was. Amodei: “It’s not free. You have to run it on inference and someone has to make it fast on inference.” For decades, open source meant something real. It meant a teenager in a basement could download the same tools as a Fortune 500 company. Could read the code. Could modify it. Could build something that competed with the giants. That was genuine democratization. That actually happened. AI is different. Fundamentally. Physically. In ways the ideology hasn’t caught up to yet. Downloading the weights is the easy part. The part that actually costs something is turning the weights into a running system. Into responses. Into intelligence operating in real time at scale. That requires compute. Power. Infrastructure. The kind measured in billions of dollars and years of construction. Amodei: “These are big models. They’re hard to do inference on. Ultimately you have to host it on the cloud. The people who host it on the cloud do inference.” The open source debate was never about who owns the model. It was always about who owns the cloud. And Amodei goes further. When a competitor drops a new open model, he doesn’t ask whether it’s open or closed. He doesn’t care about the licensing. He doesn’t engage the ideology. Amodei: “I don’t think it mattered that DeepSeek is open source. I think I ask, is it a good model? Is it better than us at the things that matter? That’s the only thing that I care about.” That’s the ruthless clarity of someone actually trying to win. While the media debates licensing frameworks, Amodei is asking one question. Is it better. Everything else is a distraction. Amodei: “I don’t think open source works the same way in AI that it has worked in other areas. Here we can’t see inside the model.” This isn’t Linux. You can’t read it. You can’t fork it. You can’t understand it the way generations of developers understood the tools they inherited. You can download it. And then you need a data center to run it. The teenager in the basement who was supposed to be empowered by this revolution needs a billion dollars of infrastructure before the empowerment starts. The era of the basement coder rewriting civilization on a laptop is over. The future belongs to whoever commands the compute, owns the power grid, and can actually turn the intelligence on. Open weights without infrastructure isn’t democratization. It’s a promise the physics of the universe won’t let us keep.

Dario Amodei just dismantled the biggest myth in the AI industry. Open source AI isn’t free. It never was. Amodei: “It’s not free. You have to run it on inference and someone has to make it fast on inference.” For decades, open source meant something real. It meant a teenager in a basement could download the same tools as a Fortune 500 company. Could read the code. Could modify it. Could build something that competed with the giants. That was genuine democratization. That actually happened. AI is different. Fundamentally. Physically. In ways the ideology hasn’t caught up to yet. Downloading the weights is the easy part. The part that actually costs something is turning the weights into a running system. Into responses. Into intelligence operating in real time at scale. That requires compute. Power. Infrastructure. The kind measured in billions of dollars and years of construction. Amodei: “These are big models. They’re hard to do inference on. Ultimately you have to host it on the cloud. The people who host it on the cloud do inference.” The open source debate was never about who owns the model. It was always about who owns the cloud. And Amodei goes further. When a competitor drops a new open model, he doesn’t ask whether it’s open or closed. He doesn’t care about the licensing. He doesn’t engage the ideology. Amodei: “I don’t think it mattered that DeepSeek is open source. I think I ask, is it a good model? Is it better than us at the things that matter? That’s the only thing that I care about.” That’s the ruthless clarity of someone actually trying to win. While the media debates licensing frameworks, Amodei is asking one question. Is it better. Everything else is a distraction. Amodei: “I don’t think open source works the same way in AI that it has worked in other areas. Here we can’t see inside the model.” This isn’t Linux. You can’t read it. You can’t fork it. You can’t understand it the way generations of developers understood the tools they inherited. You can download it. And then you need a data center to run it. The teenager in the basement who was supposed to be empowered by this revolution needs a billion dollars of infrastructure before the empowerment starts. The era of the basement coder rewriting civilization on a laptop is over. The future belongs to whoever commands the compute, owns the power grid, and can actually turn the intelligence on. Open weights without infrastructure isn’t democratization. It’s a promise the physics of the universe won’t let us keep.

Dustin

687,842 views • 6 months ago

Databricks CEO Ali Ghodsi describes the difficulties of building a business around open source: "You have to hit two home runs, consecutively." "Your first home run - and you're banking on hitting that home run - is that your open source project takes over the world. Everybody wants to download it." If you don't hit that home run, Ali says, you just shot yourself in the foot. You just gave away your IP and got nothing back for it. But even if you hit this home run, you still don't have a monetizable business. You still can't make any money. So you have to hit the second home run: "You need a 10x innovation on top of the open source. That's the proprietary value you'll provide companies. That's why they'll come to you. Otherwise, they can get the open source anywhere." If you don't hit the second home run, the hyperscalers pick up your open source model, and they offer it for almost-free. "Getting into open source and monetizing is not an easy sport. Preferably don't get into it, because you might get hurt."

Databricks CEO Ali Ghodsi describes the difficulties of building a business around open source: "You have to hit two home runs, consecutively." "Your first home run - and you're banking on hitting that home run - is that your open source project takes over the world. Everybody wants to download it." If you don't hit that home run, Ali says, you just shot yourself in the foot. You just gave away your IP and got nothing back for it. But even if you hit this home run, you still don't have a monetizable business. You still can't make any money. So you have to hit the second home run: "You need a 10x innovation on top of the open source. That's the proprietary value you'll provide companies. That's why they'll come to you. Otherwise, they can get the open source anywhere." If you don't hit the second home run, the hyperscalers pick up your open source model, and they offer it for almost-free. "Getting into open source and monetizing is not an easy sport. Preferably don't get into it, because you might get hurt."

TBPN

45,393 views • 9 months ago

Dario Amodei was asked whether open source will eventually gut Anthropic's business. He didn't defend the moat. He didn't argue closed beats open. He said the whole question is a red herring. That is the reframe. And it flips how the industry keeps scoring this race. The conventional narrative is inherited from the last era of tech: open source wins because anyone can read the code, anyone improves it, contributions stack, and eventually the free thing catches the paid thing. Investors have a full lexicon for it. Commoditization. Which layer captures the value. Everyone repeats it. Amodei says the analogy breaks at the root. It's called open weights, not open source, for a reason: you can't see inside the model. So the thing that actually made open source powerful elsewhere, many people reading and additively improving shared code, never transfers. You just get a large file of numbers. Now here's where it gets interesting. The second engine isn't ideology. It's infrastructure. Free isn't free. Someone still has to host it. These are big models, and they're hard to run inference on. Someone has to make that fast. And the capabilities people assume only open weights unlock fine tuning, steering, inspecting activations labs are increasingly serving on their own clouds anyway. When DeepSeek shipped, he says he never asked whether it was open. He asked one thing: is it a good model, and is it better than us. That's the only axis he competes on. He even inverts the usual edge. Coming from outside that investor lexicon, he thinks knowing none of it lets him predict this better than the people fluent in it. He is not defending closed models. He is saying the scoreboard everyone is watching measures the wrong thing. The uncomfortable question if the free model still needs someone to run it, was the moat ever the weights, or always the machine underneath ?

Dario Amodei was asked whether open source will eventually gut Anthropic's business. He didn't defend the moat. He didn't argue closed beats open. He said the whole question is a red herring. That is the reframe. And it flips how the industry keeps scoring this race. The conventional narrative is inherited from the last era of tech: open source wins because anyone can read the code, anyone improves it, contributions stack, and eventually the free thing catches the paid thing. Investors have a full lexicon for it. Commoditization. Which layer captures the value. Everyone repeats it. Amodei says the analogy breaks at the root. It's called open weights, not open source, for a reason: you can't see inside the model. So the thing that actually made open source powerful elsewhere, many people reading and additively improving shared code, never transfers. You just get a large file of numbers. Now here's where it gets interesting. The second engine isn't ideology. It's infrastructure. Free isn't free. Someone still has to host it. These are big models, and they're hard to run inference on. Someone has to make that fast. And the capabilities people assume only open weights unlock fine tuning, steering, inspecting activations labs are increasingly serving on their own clouds anyway. When DeepSeek shipped, he says he never asked whether it was open. He asked one thing: is it a good model, and is it better than us. That's the only axis he competes on. He even inverts the usual edge. Coming from outside that investor lexicon, he thinks knowing none of it lets him predict this better than the people fluent in it. He is not defending closed models. He is saying the scoreboard everyone is watching measures the wrong thing. The uncomfortable question if the free model still needs someone to run it, was the moat ever the weights, or always the machine underneath ?

Vikram M

58,688 views • 2 months ago

ELON: EVEN OPEN-SOURCE AI IS GETTING BETTER HUMAN TESTS THAN HUMANS "We're getting to the point where even open-source AI can pass human captcha tests: 'Are you a human? Identify all the traffic lights in this picture.' You're like, okay, it's going to have no problem doing that. In fact, it'll do it better than a human and faster than a human. So, at the point at which it's better passing human tests than humans, then, what tests actually make sense?" Source: Interview with Rishi Sunak, November 2023

ELON: EVEN OPEN-SOURCE AI IS GETTING BETTER HUMAN TESTS THAN HUMANS "We're getting to the point where even open-source AI can pass human captcha tests: 'Are you a human? Identify all the traffic lights in this picture.' You're like, okay, it's going to have no problem doing that. In fact, it'll do it better than a human and faster than a human. So, at the point at which it's better passing human tests than humans, then, what tests actually make sense?" Source: Interview with Rishi Sunak, November 2023

Mario Nawfal

42,071 views • 1 year ago

This should flip and go the other way if you stare at it for a bit! I love open source... a couple of days ago I posted the code to this Dyson Ring that I wrote, but I couldn't quite get the illusion right. Today, Sean Lavery checked in a double-sided rendering and some other changes that really sell it. Code is at based on a demo by Joseff

This should flip and go the other way if you stare at it for a bit! I love open source... a couple of days ago I posted the code to this Dyson Ring that I wrote, but I couldn't quite get the illusion right. Today, Sean Lavery checked in a double-sided rendering and some other changes that really sell it. Code is at based on a demo by Joseff

Dave W Plummer

25,402 views • 9 months ago

You can now fine-tune Llama 3 without writing a single line of code! We are moving at breakneck speed. I recorded a video to show you how to fine-tune any open-source model in a few minutes. I'm using a GPT capable of taking a problem and turning it into a fine-tuned model that will solve it. You don't have to write any code. You only need to explain to a GPT what problem you want to solve and tell it you want to use Llama 3. For example, "fine-tune Llama 3" or "deploy zephyr." It feels magic. The system will recommend a dataset and fine-tune the model for you. I'm using Monster API, a platform that specializes in making fine-tuning and deploying open-source models easy and fast. Their stack is well-optimized to maximize fine-tuning efficiency using techniques like Q-Lora and vLLM. They are behind the GPT. Here is what you need to do: 1. Create an account at 2. Load the GPT with the link below This is as simple as it gets. When you are done, you can click a button to deploy the model and start using it. I have 10,000 free credits for anyone using the code "SANTIAGO" in the dashboard. You can use these credits to access, fine-tune, and deploy these open-source models. You can also keep up with their latest updates, and get free credits and special offers on their Discord server:

You can now fine-tune Llama 3 without writing a single line of code! We are moving at breakneck speed. I recorded a video to show you how to fine-tune any open-source model in a few minutes. I'm using a GPT capable of taking a problem and turning it into a fine-tuned model that will solve it. You don't have to write any code. You only need to explain to a GPT what problem you want to solve and tell it you want to use Llama 3. For example, "fine-tune Llama 3" or "deploy zephyr." It feels magic. The system will recommend a dataset and fine-tune the model for you. I'm using Monster API, a platform that specializes in making fine-tuning and deploying open-source models easy and fast. Their stack is well-optimized to maximize fine-tuning efficiency using techniques like Q-Lora and vLLM. They are behind the GPT. Here is what you need to do: 1. Create an account at 2. Load the GPT with the link below This is as simple as it gets. When you are done, you can click a button to deploy the model and start using it. I have 10,000 free credits for anyone using the code "SANTIAGO" in the dashboard. You can use these credits to access, fine-tune, and deploy these open-source models. You can also keep up with their latest updates, and get free credits and special offers on their Discord server:

Santiago

324,621 views • 2 years ago