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We asked Cory (Founder) why Chinese AI labs hand p0 Systems free inference... Spinning up a new AI lab in China is easy and cheap, per Cory you can whip up your own model in under a day. The hard part isn't building the model. It's getting anyone to... show more

MCG

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Whike everyone's offering free/cheap inference and doing BD, where's the actual moat? Cory (founder of p0 Systems) says you need to own the whole stack. The plan (why he's traveling to China): get P Zero's own GPUs, servers, and infrastructure. Once you own the hardware, three things open up: - Train your own models, it's cheap now, build your own dataset and ship a personalized model - Become an AI lab, not just a router - Rent out spare compute and infrastructure as a whole separate revenue line

Whike everyone's offering free/cheap inference and doing BD, where's the actual moat? Cory (founder of p0 Systems) says you need to own the whole stack. The plan (why he's traveling to China): get P Zero's own GPUs, servers, and infrastructure. Once you own the hardware, three things open up: - Train your own models, it's cheap now, build your own dataset and ship a personalized model - Become an AI lab, not just a router - Rent out spare compute and infrastructure as a whole separate revenue line

MCG

23,056 görüntüleme • 1 ay önce

.p0 Systems has a day-one partnership with xAI for the next Grok model... SerPepe gave the breakdown: - $1M commitment directly with xAI - A dedicated team member on Slack, online whenever P0 needs them - Day-one access to the new Grok coding model when it drops - P0's usage will be subsidized (discounted straight from xAI) The result: "P0 will be the best and cheapest platform to use the new Grok model." His read on Grok itself: 4.7 is going to be heavily coding-focused. "What I'm hearing from my guy at SpaceX is it's a much better model at coding." P0 gets early access to test before launch. How he built the connection: "It's all about talking with people, then negotiating...because we have the user base and the demand." solana:HmTi3CQfKfXWbn1tNoiAxH7GzMV7L3tDAmPWabZEBAGS LP with Meteora using MCG -

.p0 Systems has a day-one partnership with xAI for the next Grok model... SerPepe gave the breakdown: - $1M commitment directly with xAI - A dedicated team member on Slack, online whenever P0 needs them - Day-one access to the new Grok coding model when it drops - P0's usage will be subsidized (discounted straight from xAI) The result: "P0 will be the best and cheapest platform to use the new Grok model." His read on Grok itself: 4.7 is going to be heavily coding-focused. "What I'm hearing from my guy at SpaceX is it's a much better model at coding." P0 gets early access to test before launch. How he built the connection: "It's all about talking with people, then negotiating...because we have the user base and the demand." solana:HmTi3CQfKfXWbn1tNoiAxH7GzMV7L3tDAmPWabZEBAGS LP with Meteora using MCG -

MCG

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Engram cofounder Jack Morris just raised $98M to build a new type of AI. He says models don't need to get smarter over time. Instead, they just need to know you better and better over time. Jack describes what he's building: "Our product is a new type of AI. We have a pretty different vision from a lot of the frontier labs, which are working on one model per lab, and trying to make that model smarter every month." "There's another way to think about it, which is that the model doesn't need to get smarter every month. It needs to know you better." "So we're working on a whole different stack, which is a way to train models that train themselves to know your world better and adjust to the things that you say." "So: new ways of training, new ways of running the models."

Engram cofounder Jack Morris just raised $98M to build a new type of AI. He says models don't need to get smarter over time. Instead, they just need to know you better and better over time. Jack describes what he's building: "Our product is a new type of AI. We have a pretty different vision from a lot of the frontier labs, which are working on one model per lab, and trying to make that model smarter every month." "There's another way to think about it, which is that the model doesn't need to get smarter every month. It needs to know you better." "So we're working on a whole different stack, which is a way to train models that train themselves to know your world better and adjust to the things that you say." "So: new ways of training, new ways of running the models."

TBPN

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What happened one month after launching Action Model? 👾 250,000+ users. 👾 100,000+ browser extension downloads. 👾 Over 2,000 years of training data contributed to our community-owned AI model. And we’re just getting started. We’re building a new kind of AI platform, where users don’t just use AI, they train it, earn from it, and own part of it. Next up: ActionFi and Actionist Two major launches you won’t want to miss. If you’re interested in AI, automation, and the future of community-owned technology, now is the time to get involved.

What happened one month after launching Action Model? 👾 250,000+ users. 👾 100,000+ browser extension downloads. 👾 Over 2,000 years of training data contributed to our community-owned AI model. And we’re just getting started. We’re building a new kind of AI platform, where users don’t just use AI, they train it, earn from it, and own part of it. Next up: ActionFi and Actionist Two major launches you won’t want to miss. If you’re interested in AI, automation, and the future of community-owned technology, now is the time to get involved.

Action Model

31,849 görüntüleme • 4 ay önce

As AI labs race to train and deploy new frontier models, existing models become more affordable with better tokenomics. ✨ "Everybody's trying to get to the next frontier. And every time they get to the next frontier, the last generation AI tokens, the cost starts to decline about a factor of 10x every year," said NVIDIA CEO Jensen Huang in a recent keynote. Model optimization techniques such as speculative decoding and multi-token prediction, combined with inference serving platforms like NVIDIA Dynamo on NVIDIA Blackwell NVL72 systems, enable AI factories to boost throughput by 10x with one-tenth of the cost per token. Learn more about AI factory tokenomics ➡️

As AI labs race to train and deploy new frontier models, existing models become more affordable with better tokenomics. ✨ "Everybody's trying to get to the next frontier. And every time they get to the next frontier, the last generation AI tokens, the cost starts to decline about a factor of 10x every year," said NVIDIA CEO Jensen Huang in a recent keynote. Model optimization techniques such as speculative decoding and multi-token prediction, combined with inference serving platforms like NVIDIA Dynamo on NVIDIA Blackwell NVL72 systems, enable AI factories to boost throughput by 10x with one-tenth of the cost per token. Learn more about AI factory tokenomics ➡️

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Tag someone who shouldn’t just use AI, but own it 👾 Action Model is building the world’s first community-owned Large Action Model. 150,000+ people have already joined to help train it, and earn a stake in the future of automation. This is how we build a fairer AI economy. Owned by the people. Not controlled by a boardroom. Leave your referral codes below, so people can get access to the invite-only platform.

Tag someone who shouldn’t just use AI, but own it 👾 Action Model is building the world’s first community-owned Large Action Model. 150,000+ people have already joined to help train it, and earn a stake in the future of automation. This is how we build a fairer AI economy. Owned by the people. Not controlled by a boardroom. Leave your referral codes below, so people can get access to the invite-only platform.

Georgia | Action Model

11,350 görüntüleme • 6 ay önce

We raised a $40M Series A to build the automated lab for agentic biology. Last week, we shared the work we did with Paul Jankura: Claude designed proteins, sent them to our automated wet lab, and got real experimental data back. We believe this is where biology is going: AI agents designing experiments, running them in automated wet labs, learning from the results and iterating. But AI can only move as fast as the experiments behind it. To use the potential of AI to cure all diseases, we need to build high-throughput wet lab infrastructure: a “biological gigafactory”. That’s exactly what we’re doing at Adaptyv. Over the past year we’ve grown our lab throughput by over 5x and onboarded more than 100 customers, ranging from bio AI labs like Chai Discovery and Boltz to pharmas like Roche and Novo Nordisk to dozens and dozens of new startups that use AI to radically speed up drug discovery.

We raised a $40M Series A to build the automated lab for agentic biology. Last week, we shared the work we did with Paul Jankura: Claude designed proteins, sent them to our automated wet lab, and got real experimental data back. We believe this is where biology is going: AI agents designing experiments, running them in automated wet labs, learning from the results and iterating. But AI can only move as fast as the experiments behind it. To use the potential of AI to cure all diseases, we need to build high-throughput wet lab infrastructure: a “biological gigafactory”. That’s exactly what we’re doing at Adaptyv. Over the past year we’ve grown our lab throughput by over 5x and onboarded more than 100 customers, ranging from bio AI labs like Chai Discovery and Boltz to pharmas like Roche and Novo Nordisk to dozens and dozens of new startups that use AI to radically speed up drug discovery.

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Underrated use case for the new World Labs model: transforming existing spaces. I took a picture of the lobby at a16z NYC, turned it into a "neon nightclub" with Seedream Edit, and then loaded it into World Labs. Fun to step through a familiar space in a new style 😎

Underrated use case for the new World Labs model: transforming existing spaces. I took a picture of the lobby at a16z NYC, turned it into a "neon nightclub" with Seedream Edit, and then loaded it into World Labs. Fun to step through a familiar space in a new style 😎

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The AI race isn't about who has the most agents anymore. It's about who knows which agent to trust, when to use it, and how they work together. Adding another model is easy. Building intelligent orchestration is where the real advantage begins.

The AI race isn't about who has the most agents anymore. It's about who knows which agent to trust, when to use it, and how they work together. Adding another model is easy. Building intelligent orchestration is where the real advantage begins.

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Why is decentralized AI necessary? 🤖 “Anyone contributing to a model can finally get the recognition they deserve,” explains Michael Heinrich (Ø,G) , Co-Founder of 0G Labs (Home of Infinite AI) , in a conversation with Miss Sachi . Decentralization brings fairness, transparency, and genuine collaboration to AI development — leveling the playing field for everyone involved.

Why is decentralized AI necessary? 🤖 “Anyone contributing to a model can finally get the recognition they deserve,” explains Michael Heinrich (Ø,G) , Co-Founder of 0G Labs (Home of Infinite AI) , in a conversation with Miss Sachi . Decentralization brings fairness, transparency, and genuine collaboration to AI development — leveling the playing field for everyone involved.

The Web3 Podcast Team

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This Chinese AI model beat ChatGPT and Gemini Guess what? It's now available on AI Fiesta also. Now you can use up to 7 AI models simultaneously. We are probably the only AI platform in the world where you can do that.

This Chinese AI model beat ChatGPT and Gemini Guess what? It's now available on AI Fiesta also. Now you can use up to 7 AI models simultaneously. We are probably the only AI platform in the world where you can do that.

Dhruv Rathee

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Erik Voorhees explains how Venice's dual token model actually works "You can stake 100 VVV to get pro access to Venice. We take a bunch of our revenue, buy the token, and burn it" "We want to take that to its extreme and try to buy every last token we can and burn it all" "You lock up VVV to mint DIEM. Each DIEM you hold gives you $1/day to use on any AI model you want" "We built it so AI agents could hold an asset and get marginally free inference" "It changes the economic calculation. We thought that'd be an interesting design space to explore"

Erik Voorhees explains how Venice's dual token model actually works "You can stake 100 VVV to get pro access to Venice. We take a bunch of our revenue, buy the token, and burn it" "We want to take that to its extreme and try to buy every last token we can and burn it all" "You lock up VVV to mint DIEM. Each DIEM you hold gives you $1/day to use on any AI model you want" "We built it so AI agents could hold an asset and get marginally free inference" "It changes the economic calculation. We thought that'd be an interesting design space to explore"

Market Bubble

136,094 görüntüleme • 2 ay önce

Chinese AI models are wiping billions off Big Tech right now. Google just lost $200 billion in a single day, and the model it needed to fight back still isn't ready. Gemini 3.5 Pro, Google's most powerful model, is months behind schedule. Alphabet stock dropped 4.4% that same day. The Deepseek moment is happening again, and the new model is FAR bigger. On the same day Google's delay leaked, a Beijing lab called Moonshot released Kimi K3. It is the largest open model ever built, with 2.8 trillion parameters. It took the number one spot on the Frontend Code Arena, a live coding leaderboard, passing Anthropic's best model. And Moonshot is giving it away for free on July 27. The genius part: Anyone with enough computers can download it and run a frontier level AI without paying a cent to a US company. A single task on Kimi K3 costs about 94 cents. The same work on some American models costs nearly double. So why would a company keep paying premium prices for a model it can now get for free? The entire US AI business is built on selling access to models that cost billions to train. If a free Chinese version does most of the same work, that pricing power starts to crack. And Kimi is close to the best. On one closely watched intelligence ranking it scored 57, just behind the top American models GPT-5.6 Sol and Fable 5, and ahead of Claude Opus 4.8. Bank of America told clients that Kimi proves Chinese labs can keep making big leaps even with limited chips. And the founder of Moonshot, Yang Zhilin, learned to build AI as a researcher INSIDE Google. Google literally wrote the 2017 paper that made all of these models possible. Now the people who studied its work are using it to destroy Google, and handing it out for free. What happens next: Kimi K3's weights go public on July 27. Google reports earnings on July 22, and everyone will be asking the same question about Gemini. If free models keep topping the charts, every valuation built on paid AI access has to be rewritten. What do you think?

Chinese AI models are wiping billions off Big Tech right now. Google just lost $200 billion in a single day, and the model it needed to fight back still isn't ready. Gemini 3.5 Pro, Google's most powerful model, is months behind schedule. Alphabet stock dropped 4.4% that same day. The Deepseek moment is happening again, and the new model is FAR bigger. On the same day Google's delay leaked, a Beijing lab called Moonshot released Kimi K3. It is the largest open model ever built, with 2.8 trillion parameters. It took the number one spot on the Frontend Code Arena, a live coding leaderboard, passing Anthropic's best model. And Moonshot is giving it away for free on July 27. The genius part: Anyone with enough computers can download it and run a frontier level AI without paying a cent to a US company. A single task on Kimi K3 costs about 94 cents. The same work on some American models costs nearly double. So why would a company keep paying premium prices for a model it can now get for free? The entire US AI business is built on selling access to models that cost billions to train. If a free Chinese version does most of the same work, that pricing power starts to crack. And Kimi is close to the best. On one closely watched intelligence ranking it scored 57, just behind the top American models GPT-5.6 Sol and Fable 5, and ahead of Claude Opus 4.8. Bank of America told clients that Kimi proves Chinese labs can keep making big leaps even with limited chips. And the founder of Moonshot, Yang Zhilin, learned to build AI as a researcher INSIDE Google. Google literally wrote the 2017 paper that made all of these models possible. Now the people who studied its work are using it to destroy Google, and handing it out for free. What happens next: Kimi K3's weights go public on July 27. Google reports earnings on July 22, and everyone will be asking the same question about Gemini. If free models keep topping the charts, every valuation built on paid AI access has to be rewritten. What do you think?

Ricardo

47,790 görüntüleme • 1 ay önce

China just released an open source AI model that matches the best closed models from OpenAI and Anthropic. Gavin Baker explained exactly how they did it and the answer should concern every American AI lab. The model is called GLM 5.2. It was built by Z. AI. You get 744 billion parameters, 1 million token context window and its MIT license, meaning anyone can download it, fork it, build a company on it, with no restrictions and no Dario. It scored 51 points on the artificial analysis intelligence index. The highest score any open weight model has ever achieved. It beat GPT 5.5 on the frontier software engineering benchmark. It trails Claude Opus 4.8 by less than one percentage point. And it costs 85% less to run than GPT 5.5 for comparable performance. Gavin Baker said on the All-In podcast that this model has challenged some of his beliefs. Then he explained how China built it. The method is called distillation. Just think of tens of thousands of phones and computers running simultaneously, all hitting the frontier model APIs through masked accounts, asking specific questions, and harvesting what happens inside the model when it answers. Every reasoning step, every token. The entire thinking process gets recorded and fed back into the Chinese model during training. It is a cheat sheet. It is the answer key to the exam. And here is the part that should worry everyone. Sacks said it plainly. China was already nine months behind American models. But now that GLM 5.2 is good enough to run its own reinforcement learning, it can improve itself without needing to distill from American models anymore. The cheat sheet let them get close enough to start writing their own answers. Sacks said we are six months behind on the model and 24 months behind on silicon and they are only a few months behind in total. The Z. AI founder told Elon Musk directly that open weight fable-level capability will be here before Q1 2027. Every restriction Anthropic lobbied for, every self-imposed safety guardrail, every month of delay in releasing American frontier models accelerated this. The Chinese labs were not under those restrictions. They were not going to wait. The composable model future Gavin described, where every enterprise runs a frontier model alongside their own fine-tuned open weight model, is coming regardless of what American labs do next. The question is just whether the open weight half of that stack is American or Chinese. Right now it is Chinese. WATCH THE FULL PODCAST ON The All-In Podcast

China just released an open source AI model that matches the best closed models from OpenAI and Anthropic. Gavin Baker explained exactly how they did it and the answer should concern every American AI lab. The model is called GLM 5.2. It was built by Z. AI. You get 744 billion parameters, 1 million token context window and its MIT license, meaning anyone can download it, fork it, build a company on it, with no restrictions and no Dario. It scored 51 points on the artificial analysis intelligence index. The highest score any open weight model has ever achieved. It beat GPT 5.5 on the frontier software engineering benchmark. It trails Claude Opus 4.8 by less than one percentage point. And it costs 85% less to run than GPT 5.5 for comparable performance. Gavin Baker said on the All-In podcast that this model has challenged some of his beliefs. Then he explained how China built it. The method is called distillation. Just think of tens of thousands of phones and computers running simultaneously, all hitting the frontier model APIs through masked accounts, asking specific questions, and harvesting what happens inside the model when it answers. Every reasoning step, every token. The entire thinking process gets recorded and fed back into the Chinese model during training. It is a cheat sheet. It is the answer key to the exam. And here is the part that should worry everyone. Sacks said it plainly. China was already nine months behind American models. But now that GLM 5.2 is good enough to run its own reinforcement learning, it can improve itself without needing to distill from American models anymore. The cheat sheet let them get close enough to start writing their own answers. Sacks said we are six months behind on the model and 24 months behind on silicon and they are only a few months behind in total. The Z. AI founder told Elon Musk directly that open weight fable-level capability will be here before Q1 2027. Every restriction Anthropic lobbied for, every self-imposed safety guardrail, every month of delay in releasing American frontier models accelerated this. The Chinese labs were not under those restrictions. They were not going to wait. The composable model future Gavin described, where every enterprise runs a frontier model alongside their own fine-tuned open weight model, is coming regardless of what American labs do next. The question is just whether the open weight half of that stack is American or Chinese. Right now it is Chinese. WATCH THE FULL PODCAST ON The All-In Podcast

Ihtesham Ali

86,452 görüntüleme • 2 ay önce

In pick the best model > improve your prompts > catch bugs with Braintrust and Cerebras inference. Avoid the AI deleting your entire codebase this halloween and remember, our free tier gets you 1M+ free toks/day per model.

In pick the best model > improve your prompts > catch bugs with Braintrust and Cerebras inference. Avoid the AI deleting your entire codebase this halloween and remember, our free tier gets you 1M+ free toks/day per model.

Cerebras

299,029 görüntüleme • 10 ay önce

Google NotebookLM is absolutely insane, easily the best AI product out there, and it is free to use. The new Mindmap feature makes learning so easy. You can use it to understand your codebase. Just convert your codebase to a single file and try it out.

Google NotebookLM is absolutely insane, easily the best AI product out there, and it is free to use. The new Mindmap feature makes learning so easy. You can use it to understand your codebase. Just convert your codebase to a single file and try it out.

AshutoshShrivastava

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Why is decentralized AI necessary? 🤖 “Anyone contributing to a model can finally get the recognition they deserve,” explains Michael Heinrich (Ø,G), Co-Founder of 0G Labs (Home of Infinite AI), in a conversation with sachi.eth. Decentralization brings fairness, transparency, and genuine collaboration to AI development — leveling the playing field for everyone involved. 🎧 Watch the full episode here:

Why is decentralized AI necessary? 🤖 “Anyone contributing to a model can finally get the recognition they deserve,” explains Michael Heinrich (Ø,G), Co-Founder of 0G Labs (Home of Infinite AI), in a conversation with sachi.eth. Decentralization brings fairness, transparency, and genuine collaboration to AI development — leveling the playing field for everyone involved. 🎧 Watch the full episode here:

Web3Unchained Podcast 🎙️

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Why is decentralized AI necessary? 🤖 “Anyone contributing to a model can finally get the recognition they deserve,” explains Michael Heinrich (Ø,G), Co-Founder of 0G Labs (Home of Infinite AI), in a conversation with sachi.eth. Decentralization brings fairness, transparency, and genuine collaboration to AI development — leveling the playing field for everyone involved. 🎧 Watch the full episode here:

Why is decentralized AI necessary? 🤖 “Anyone contributing to a model can finally get the recognition they deserve,” explains Michael Heinrich (Ø,G), Co-Founder of 0G Labs (Home of Infinite AI), in a conversation with sachi.eth. Decentralization brings fairness, transparency, and genuine collaboration to AI development — leveling the playing field for everyone involved. 🎧 Watch the full episode here:

Web3Unchained Podcast 🎙️

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Why is decentralized AI necessary? 🤖 “Anyone contributing to a model can finally get the recognition they deserve,” explains Michael Heinrich (Ø,G), Co-Founder of 0G Labs (Home of Infinite AI), in a conversation with sachi.eth. Decentralization brings fairness, transparency, and genuine collaboration to AI development — leveling the playing field for everyone involved. 🎧 Watch the full episode here:

Why is decentralized AI necessary? 🤖 “Anyone contributing to a model can finally get the recognition they deserve,” explains Michael Heinrich (Ø,G), Co-Founder of 0G Labs (Home of Infinite AI), in a conversation with sachi.eth. Decentralization brings fairness, transparency, and genuine collaboration to AI development — leveling the playing field for everyone involved. 🎧 Watch the full episode here:

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71,028 görüntüleme • 1 yıl önce

🇳🇱🗽 This is probably the closest you can get to walking around in New Amsterdam in 1670 (current day New York) A 3d world made with World Labs from the AI render below which is based on a real map from then!

🇳🇱🗽 This is probably the closest you can get to walking around in New Amsterdam in 1670 (current day New York) A 3d world made with World Labs from the AI render below which is based on a real map from then!

@levelsio

597,738 görüntüleme • 11 ay önce