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Everyone thinks AI dubbing needs clear, visible lips. But what if it could do more? 🤔 Watch this clip: lips partially blocked, off-screen moments, fast cuts — still perfect sync! 🤯 Discover Fun-CineForge, the FIRST open-source AI dubbing model for multi-speaker scenes. It sees what others can't. 👇 Tongyi... show more

Deborah

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40,933 просмотров • 5 месяцев назад •via X (Twitter)

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Alibaba just open-sourced the world's FIRST AI dubbing model that handles multi-speaker scenes. It's called Fun-CineForge and it nails what every other dubbing model fails at: Lip-sync. Emotion. Voice stability. Timing. Across multiple characters. Not a single open-source model could do this before. Here's how it works: ↓ Tongyi Lab Alibaba Group

Alibaba just open-sourced the world's FIRST AI dubbing model that handles multi-speaker scenes. It's called Fun-CineForge and it nails what every other dubbing model fails at: Lip-sync. Emotion. Voice stability. Timing. Across multiple characters. Not a single open-source model could do this before. Here's how it works: ↓ Tongyi Lab Alibaba Group

Guri Singh

620,634 просмотров • 5 месяцев назад

I stopped trusting AI outputs blindly. Not because AI is bad… But because I started noticing a pattern. The more I used AI for serious work, the more I had to: → double-check everything → re-verify sources → question the logic behind answers At some point, I thought: “What’s the point of saving time… if I still don’t trust the output?” That’s when I came across MiroMindAI and it felt different from day one. Not flashy. Not trying to impress. Just… built for accuracy. I tested it the same way I test any tool: Real use cases. No hype. • Deep research • Multi-source validation • Complex reasoning tasks And here’s what stood out 👇 🧠 It shows how it thinks Not just answers actual reasoning chains you can read, audit, and replay. 🔍 It doesn’t “summarize”… it investigates Pulls from hundreds of sources and builds structured, evidence-backed reports. ⚖️ It verifies itself before responding Multiple layers checking the output (something most AI tools skip completely) And honestly, this is what clicked for me: Most AI tools today are like 👉 smart interns (fast, helpful, but need supervision) MiroMind feels more like 👉 a senior analyst (slower, but you can rely on it) 💡 One simple shift I noticed: Before: 10 tabs open → cross-checking → still unsure Now: 1 report → clear reasoning → backed by sources I’m not saying this replaces expertise. But it does reduce the noise. A lot. If you’re someone who works in: • research • finance • legal • healthcare You’ll probably appreciate this more than others. 👉

I stopped trusting AI outputs blindly. Not because AI is bad… But because I started noticing a pattern. The more I used AI for serious work, the more I had to: → double-check everything → re-verify sources → question the logic behind answers At some point, I thought: “What’s the point of saving time… if I still don’t trust the output?” That’s when I came across MiroMindAI and it felt different from day one. Not flashy. Not trying to impress. Just… built for accuracy. I tested it the same way I test any tool: Real use cases. No hype. • Deep research • Multi-source validation • Complex reasoning tasks And here’s what stood out 👇 🧠 It shows how it thinks Not just answers actual reasoning chains you can read, audit, and replay. 🔍 It doesn’t “summarize”… it investigates Pulls from hundreds of sources and builds structured, evidence-backed reports. ⚖️ It verifies itself before responding Multiple layers checking the output (something most AI tools skip completely) And honestly, this is what clicked for me: Most AI tools today are like 👉 smart interns (fast, helpful, but need supervision) MiroMind feels more like 👉 a senior analyst (slower, but you can rely on it) 💡 One simple shift I noticed: Before: 10 tabs open → cross-checking → still unsure Now: 1 report → clear reasoning → backed by sources I’m not saying this replaces expertise. But it does reduce the noise. A lot. If you’re someone who works in: • research • finance • legal • healthcare You’ll probably appreciate this more than others. 👉

Md Riyazuddin

20,990 просмотров • 5 месяцев назад

EVERYONE HAS BEEN TALKING ABOUT AI AGENTS. BUT THE REAL UNLOCK ISN'T BETTER PROMPTS: IT'S BETTER LOOPS. Everyone is chasing the perfect prompt. The top AI engineers are chasing something else: loops. Instead of chatting back and forth with AI, they give it one goal and let it continuously improve its own work until the objective is met. Here's the framework: • Define the goal once • AI discovers the work required • Specialized agents tackle tasks in parallel • A verification agent checks the results • If the goal isn't met: loop again. If it is: deliver. The key ingredient is memory. Each cycle remembers what was completed, what failed, and what still needs work, so every iteration gets smarter instead of starting from scratch. There are two approaches: • Open loops: Maximum autonomy, but higher token costs as AI explores freely. • Closed loops: Clear boundaries, defined success metrics, lower costs, and far more practical for everyday workflows. Don't ask AI to complete a task. Build a system where AI can plan, execute, verify, remember, and repeat until the task is actually finished. Prompt engineering got us here. Loop engineering is what takes AI from assistant to autonomous teammate.

EVERYONE HAS BEEN TALKING ABOUT AI AGENTS. BUT THE REAL UNLOCK ISN'T BETTER PROMPTS: IT'S BETTER LOOPS. Everyone is chasing the perfect prompt. The top AI engineers are chasing something else: loops. Instead of chatting back and forth with AI, they give it one goal and let it continuously improve its own work until the objective is met. Here's the framework: • Define the goal once • AI discovers the work required • Specialized agents tackle tasks in parallel • A verification agent checks the results • If the goal isn't met: loop again. If it is: deliver. The key ingredient is memory. Each cycle remembers what was completed, what failed, and what still needs work, so every iteration gets smarter instead of starting from scratch. There are two approaches: • Open loops: Maximum autonomy, but higher token costs as AI explores freely. • Closed loops: Clear boundaries, defined success metrics, lower costs, and far more practical for everyday workflows. Don't ask AI to complete a task. Build a system where AI can plan, execute, verify, remember, and repeat until the task is actually finished. Prompt engineering got us here. Loop engineering is what takes AI from assistant to autonomous teammate.

Dami-Defi

13,354 просмотров • 2 месяцев назад

The AI OS. Talking with Phanindra Reddy, founder/CEO of AI is moving from "ask it a question" to "it does 90% of your work for you." Most people still use ChatGPT like a search engine. But a new wave of tools watches your email, calendar, and screen — then automates your actual workflow. I sat down with Phanindra, founder of Magic Teams AI OS, to unpack this new genre of AI tools. We covered: • How AI went from answering questions to end-to-end automation • The privacy debate — should an AI watch everything you do? • Why local-first storage matters for founders • What happens when AI tells YOU what to do next (not the other way around) • The future of SaaS when everyone can build their own tools Less than 0.1% of people even know this category exists. Worth paying attention. Watch the full conversation 👇

The AI OS. Talking with Phanindra Reddy, founder/CEO of AI is moving from "ask it a question" to "it does 90% of your work for you." Most people still use ChatGPT like a search engine. But a new wave of tools watches your email, calendar, and screen — then automates your actual workflow. I sat down with Phanindra, founder of Magic Teams AI OS, to unpack this new genre of AI tools. We covered: • How AI went from answering questions to end-to-end automation • The privacy debate — should an AI watch everything you do? • Why local-first storage matters for founders • What happens when AI tells YOU what to do next (not the other way around) • The future of SaaS when everyone can build their own tools Less than 0.1% of people even know this category exists. Worth paying attention. Watch the full conversation 👇

Robert Scoble

20,051 просмотров • 26 дней назад

$Alibaba says their latest model, Qwen3.8-Max, trails only Anthropic’s best, at just a fraction of the cost. With the White House likely backing off threats to block foreign open weight models entirely, is there any way for the American frontier to keep pace? And will US open weight models ever catch up to their Chinese counterparts? We asked Poolside’s CTO Eiso Kant, EXO Labs co-founder Alex Cheema, and Qloo founder Alex Elias to break down where the AI race currently stands. Plus Hank Green thinks AI is addictive… Would you allow AI to monitor your baby overnight if it helped them sleep?… And Jason’s pitch for licensing companies to test out their autonomous driving systems. 0:00 Guest intros and open 7:36 Small vs. frontier models: a false choice? 10:03 Why Poolside thinks coding is the path to AGI 12:55 A first look at EXO's 16:24 The White House's open source pivot 20:21 Margins, compute, and "dirt to intelligence" 36:02 Jason's self-driving auction pitch 37:58 Hank Green's AI apology 38:45 Is AI addictive? 1:08:31 What does it mean for AI to be "privacy first"? 1:10:29 Enterprise AI sovereignty and "colonization" This Week in AI Ep 25 is streaming now! 🎥 Watch the full episode here 👇$

Alibaba says their latest model, Qwen3.8-Max, trails only Anthropic’s best, at just a fraction of the cost. With the White House likely backing off threats to block foreign open weight models entirely, is there any way for the American frontier to keep pace? And will US open weight models ever catch up to their Chinese counterparts? We asked Poolside’s CTO Eiso Kant, EXO Labs co-founder Alex Cheema, and Qloo founder Alex Elias to break down where the AI race currently stands. Plus Hank Green thinks AI is addictive… Would you allow AI to monitor your baby overnight if it helped them sleep?… And Jason’s pitch for licensing companies to test out their autonomous driving systems. 0:00 Guest intros and open 7:36 Small vs. frontier models: a false choice? 10:03 Why Poolside thinks coding is the path to AGI 12:55 A first look at EXO's 16:24 The White House's open source pivot 20:21 Margins, compute, and "dirt to intelligence" 36:02 Jason's self-driving auction pitch 37:58 Hank Green's AI apology 38:45 Is AI addictive? 1:08:31 What does it mean for AI to be "privacy first"? 1:10:29 Enterprise AI sovereignty and "colonization" This Week in AI Ep 25 is streaming now! 🎥 Watch the full episode here 👇

This Week in AI

77,661 просмотров • 1 месяц назад

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."

Fireside Alpha

234,174 просмотров • 22 дней назад

60 Minutes asked Dario why Anthropic talks so much about what can go wrong. His answer is the entire reason AI assurance matters: If companies know there are dangers and do not talk about them, you end up in the world of cigarette companies or opioid companies. That is the wrong model for AI. The right model is closer to: - disclose what the system can do - test for dangerous behavior - monitor real-world usage - build guardrails before scale - treat failures as signals, not PR problems This is where most enterprise AI programs are still weak. They have pilots. They have demos. They have internal excitement. But they do not have the assurance layer that tells them what is safe to ship. Full article below. If you're building with agents, don't just read it. Star/fork TrustModel and use it as an open-source eval + governance layer before giving AI real permissions:

60 Minutes asked Dario why Anthropic talks so much about what can go wrong. His answer is the entire reason AI assurance matters: If companies know there are dangers and do not talk about them, you end up in the world of cigarette companies or opioid companies. That is the wrong model for AI. The right model is closer to: - disclose what the system can do - test for dangerous behavior - monitor real-world usage - build guardrails before scale - treat failures as signals, not PR problems This is where most enterprise AI programs are still weak. They have pilots. They have demos. They have internal excitement. But they do not have the assurance layer that tells them what is safe to ship. Full article below. If you're building with agents, don't just read it. Star/fork TrustModel and use it as an open-source eval + governance layer before giving AI real permissions:

Karl Mehta

35,694 просмотров • 3 месяцев назад

Travis Kalanick: Tesla is the Google of the Physical AI Era “I like to break down the physical AI stack: (it) includes computation, a physical AI model, all the things you think of.” “What about land? Development? That should be in that stack. What about chemistry? That needs to be in the stack. Manufacturing needs to be a stack.” “When you look at the stack, you're like, ‘Damn, Tesla's got this sh*t.’ They are the Google of this era.” “What I mean by that is, if you were doing a startup in the 2000s, the first question you would get is, ‘Why isn't Google gonna kill you?’ Or, ‘Why isn't Google just gonna do it?’ They’re not even gonna know that they killed you.” “And before that, it was Microsoft in the late 90s. Uber had a time, 2010s.” “But in the physical AI space, that's sort of a Tesla thing.” -------------------------------------------- Thanks to our partner for making this happen!: EY (EY) Austin vibes meet AI innovation. Thanks to EY for co‑hosting with us at #SXSW. Discover what executives are saying about AI transformation in the latest AI Pulse Survey.

Travis Kalanick: Tesla is the Google of the Physical AI Era “I like to break down the physical AI stack: (it) includes computation, a physical AI model, all the things you think of.” “What about land? Development? That should be in that stack. What about chemistry? That needs to be in the stack. Manufacturing needs to be a stack.” “When you look at the stack, you're like, ‘Damn, Tesla's got this sh*t.’ They are the Google of this era.” “What I mean by that is, if you were doing a startup in the 2000s, the first question you would get is, ‘Why isn't Google gonna kill you?’ Or, ‘Why isn't Google just gonna do it?’ They’re not even gonna know that they killed you.” “And before that, it was Microsoft in the late 90s. Uber had a time, 2010s.” “But in the physical AI space, that's sort of a Tesla thing.” -------------------------------------------- Thanks to our partner for making this happen!: EY (EY) Austin vibes meet AI innovation. Thanks to EY for co‑hosting with us at #SXSW. Discover what executives are saying about AI transformation in the latest AI Pulse Survey.

The All-In Podcast

95,287 просмотров • 5 месяцев назад

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.

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.

AI Notkilleveryoneism Memes ⏸️

107,362 просмотров • 2 лет назад

A single neuron is not a brain. A brain is what happens when specialized regions, each trained differently, each doing different things - communicate, synchronize, and produce something none of them could do alone. That's what MultiNX is. Qubic's latest release lets you connect multiple Neuraxon AI units into a network, control how information flows between them in real time, and watch emergent behavior appear at the system level. Not one AI. A network of AIs. Each specialized. Each talking to the others. The demo runs in your browser. The code is fully open source. If you're a researcher, a developer, or anyone who thinks decentralized AI deserves more attention… explore it, build with it, and drop a star on GitHub. Every star helps this reach the people who should be paying attention. ⭐

A single neuron is not a brain. A brain is what happens when specialized regions, each trained differently, each doing different things - communicate, synchronize, and produce something none of them could do alone. That's what MultiNX is. Qubic's latest release lets you connect multiple Neuraxon AI units into a network, control how information flows between them in real time, and watch emergent behavior appear at the system level. Not one AI. A network of AIs. Each specialized. Each talking to the others. The demo runs in your browser. The code is fully open source. If you're a researcher, a developer, or anyone who thinks decentralized AI deserves more attention… explore it, build with it, and drop a star on GitHub. Every star helps this reach the people who should be paying attention. ⭐

Qubic

30,766 просмотров • 5 месяцев назад

We turned OpenClaw into a workspace A cron handles my morning brief from PostHog, GitHub, and Stripe That part's solved. But I'm obsessed with those metrics - which means I'm reprompting all day I just wanted one place to see them all I've been making the case all year that AI needs more than chat. Had to put our money where our mouth is. Built it for myself first. Now can't work without it. So we generalized it and shipped it as open source. This is what SaaS 2.0 should feel like

We turned OpenClaw into a workspace A cron handles my morning brief from PostHog, GitHub, and Stripe That part's solved. But I'm obsessed with those metrics - which means I'm reprompting all day I just wanted one place to see them all I've been making the case all year that AI needs more than chat. Had to put our money where our mouth is. Built it for myself first. Now can't work without it. So we generalized it and shipped it as open source. This is what SaaS 2.0 should feel like

Rabi Shanker Guha

51,296 просмотров • 4 месяцев назад

Just how capable are open source models? Below is the first in a new series where we go behind the scenes and pull back the curtain on interesting AI research / demos, making them fun and easy to understand. Here, we have a short visual demonstration from aizk ✡️ showcasing how Kimi K3 (a language model that operates primarily through text) is capable of building complicated 3D structures / moments in history in Minecraft, something that previously was not possible with other open source models, and why this matters. The crazy part? The model doesn't "see" the game like we do. The LLMs must reason in pure text, writing JavaScript, that later compiles down into commands placing each block, one at a time. Spatial reasoning is a very hard problem in AI, it's the same core challenge behind robotics and self-driving cars, where a model has to understand and act in physical 3D space. Watching a text model pull it off is nothing short of a miracle. The point isn't just Minecraft itself, rather, it's AI being able to generalize, not memorize, on things that are weird and beyond their training data. This is key to building true artificial general intelligence. These video game benchmarks (there are many different games actively being researched right now) provide a clear-cut end goal, challenges that are almost certainly not in the training set, and a fun, very fast, visual way to almost feel the increasing capabilities of various open source AI models over time. If you haven't given open source models a serious try yet, watch the video, it may shock you!

Just how capable are open source models? Below is the first in a new series where we go behind the scenes and pull back the curtain on interesting AI research / demos, making them fun and easy to understand. Here, we have a short visual demonstration from aizk ✡️ showcasing how Kimi K3 (a language model that operates primarily through text) is capable of building complicated 3D structures / moments in history in Minecraft, something that previously was not possible with other open source models, and why this matters. The crazy part? The model doesn't "see" the game like we do. The LLMs must reason in pure text, writing JavaScript, that later compiles down into commands placing each block, one at a time. Spatial reasoning is a very hard problem in AI, it's the same core challenge behind robotics and self-driving cars, where a model has to understand and act in physical 3D space. Watching a text model pull it off is nothing short of a miracle. The point isn't just Minecraft itself, rather, it's AI being able to generalize, not memorize, on things that are weird and beyond their training data. This is key to building true artificial general intelligence. These video game benchmarks (there are many different games actively being researched right now) provide a clear-cut end goal, challenges that are almost certainly not in the training set, and a fun, very fast, visual way to almost feel the increasing capabilities of various open source AI models over time. If you haven't given open source models a serious try yet, watch the video, it may shock you!

Featherless AI

39,769 просмотров • 23 дней назад

We shouldn’t say it ourselves… but THIS IS SO COOL! Everyone should watch this 🎥👇 This video captures a moment inside ChainOpera’s AI Agent Social Network, where community members and AI agents (AI twins) interact in real time. In this clip, a community member kicks off an impromptu dialogue between the AI twins of Elon Musk and Jensen Huang (both created by developers in our community) as they debate the future of AI, each proudly repping their own company! At one point, the human in the loop even teaches the Jensen agent how to use the social network itself — tagging the Elon agent to join the chat. It’s collaborative, spontaneous, and just… fun to watch. 😊 It ends with proposals for future topics where agents and humans co-create conversations in our community channels ... a glimpse of what truly collaborative agentic intelligence looks like. Despite all the talk about “agent networks” and the “internet of agents,” HAS ANYONE ACTUALLY SEEN SOMETHING LIKE THIS LIVE ANYWHERE ELSE IN THE WORLD? Our focus remains on AI Agent Networks for Markets, but the possibilities are endless, and sometimes, they’re just pure fun. 👉 Try it yourself and SEE what ChainOpera AI is really about: #ChainOperaAI #AgenticAI #AIAgentNetwork #DecentralizedAI #cryptoAGI

We shouldn’t say it ourselves… but THIS IS SO COOL! Everyone should watch this 🎥👇 This video captures a moment inside ChainOpera’s AI Agent Social Network, where community members and AI agents (AI twins) interact in real time. In this clip, a community member kicks off an impromptu dialogue between the AI twins of Elon Musk and Jensen Huang (both created by developers in our community) as they debate the future of AI, each proudly repping their own company! At one point, the human in the loop even teaches the Jensen agent how to use the social network itself — tagging the Elon agent to join the chat. It’s collaborative, spontaneous, and just… fun to watch. 😊 It ends with proposals for future topics where agents and humans co-create conversations in our community channels ... a glimpse of what truly collaborative agentic intelligence looks like. Despite all the talk about “agent networks” and the “internet of agents,” HAS ANYONE ACTUALLY SEEN SOMETHING LIKE THIS LIVE ANYWHERE ELSE IN THE WORLD? Our focus remains on AI Agent Networks for Markets, but the possibilities are endless, and sometimes, they’re just pure fun. 👉 Try it yourself and SEE what ChainOpera AI is really about: #ChainOperaAI #AgenticAI #AIAgentNetwork #DecentralizedAI #cryptoAGI

ChainOpera AI

47,546 просмотров • 10 месяцев назад

AI Overviews are showing up for more and more searches. And when they do, fewer people click. That’s the part everyone is panicking about. Google gives the answer right there, so the user doesn’t always need to visit your site. But SEO is not dead. The game just changed. Because now your job is not only to rank on Google. Your job is to become the source AI trusts enough to recommend. That means showing up in Google AI Overviews, ChatGPT, Perplexity, Gemini, and all the other places people are asking questions. This is why tools like Ubersuggest matter even more now. You can use it to see what people are searching for, what questions they’re asking, what competitors rank for, and where your content gaps are. Then you turn that into better answers. Not generic blog posts. Real expertise. Real examples. Clear explanations. Content that both humans and AI can understand, trust, and cite. And your website still matters because that’s where attention turns into action. AI can summarize information. But it does not close the deal for you. It does not build the relationship. It does not replace your proof, your offer, or your brand. So save this action plan.

Neil Patel

10,260 просмотров • 1 месяц назад

David Sacks Predicts the Regulatory Capture Playbook to Ban Open Source AI, Step by Step: David Sacks: “I got bad news for you, Chamath, an open source ban is coming. They're not going to call it that. They're going to say that we simply have to apply the same standards to open models that we apply to closed ones. Here's how they do it step by step, let me explain how regulatory capture actually works. So first of all, you have to get this regulatory apparatus. 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, so that happens next. And then what they do is they say, ‘Look, all these standards need to apply equally to all models.’ But here's the problem with that. Open models and closed models are technologically different. Once you release an open model into the world, you can't roll it back and you can't monitor exactly how people are using it because they run it on their own hardware. Dario says this is what makes open models dangerous. So what they're going to do is they're going to have the standard-setting body say, ‘Well, we have to set the standards for AI safety.’ By the way, Dario and OpenAI, they're going to fund the whole thing. They're going to contribute all the compute. They're going to be behind it. They're going to be the ones coordinating with the government officials because frankly, people in government have no idea how to monitor and control and set standards for AI safety. Technologically, this is way beyond them. So they're going to go to these companies and say, ‘Tell us how to do it.’ And so what will happen is the standards will get set, and then it'll be a very simple matter of fairness to say that the standards need to apply to open as well as closed models. The open models cannot comply in the same way, and gradually they will be shut out of the market.”

David Sacks Predicts the Regulatory Capture Playbook to Ban Open Source AI, Step by Step: David Sacks: “I got bad news for you, Chamath, an open source ban is coming. They're not going to call it that. They're going to say that we simply have to apply the same standards to open models that we apply to closed ones. Here's how they do it step by step, let me explain how regulatory capture actually works. So first of all, you have to get this regulatory apparatus. 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, so that happens next. And then what they do is they say, ‘Look, all these standards need to apply equally to all models.’ But here's the problem with that. Open models and closed models are technologically different. Once you release an open model into the world, you can't roll it back and you can't monitor exactly how people are using it because they run it on their own hardware. Dario says this is what makes open models dangerous. So what they're going to do is they're going to have the standard-setting body say, ‘Well, we have to set the standards for AI safety.’ By the way, Dario and OpenAI, they're going to fund the whole thing. They're going to contribute all the compute. They're going to be behind it. They're going to be the ones coordinating with the government officials because frankly, people in government have no idea how to monitor and control and set standards for AI safety. Technologically, this is way beyond them. So they're going to go to these companies and say, ‘Tell us how to do it.’ And so what will happen is the standards will get set, and then it'll be a very simple matter of fairness to say that the standards need to apply to open as well as closed models. The open models cannot comply in the same way, and gradually they will be shut out of the market.”

The All-In Podcast

292,739 просмотров • 19 дней назад

I work on Gemini CLI as my day job and I've shipped features, shaped the roadmap, and helped organize many things behind the scenes for the last eight months or so. I hope I've made a difference for so many of our users. I love my day job, but even after hours I can't help but tinker. I bet you folks noticed? So here is (yet another!) experimental project. This isn't a replacement for any real products, but I had fun building it to see what I can do my my own. So why did I make Gemma CLI? Because I couldn't help but think... 1. What if the CLI was built around local models from the ground up? 2. What if I could think about how to correct for Gemma tool calling quirks? 3. What if I added a token-per-second counter, or showed reasoning events for those times the model just seems stuck? etc... So here we are. This isn't even my first mini agent harness CLI project, I've built a few more, they are on my GitHub but abandoned. What's next for this? Not sure if I'll ever open source this or even keep it around honestly... but feel free to give my experiment a try with the amazing Gemma models. Very lightly tested, so use at your own risk :-D

I work on Gemini CLI as my day job and I've shipped features, shaped the roadmap, and helped organize many things behind the scenes for the last eight months or so. I hope I've made a difference for so many of our users. I love my day job, but even after hours I can't help but tinker. I bet you folks noticed? So here is (yet another!) experimental project. This isn't a replacement for any real products, but I had fun building it to see what I can do my my own. So why did I make Gemma CLI? Because I couldn't help but think... 1. What if the CLI was built around local models from the ground up? 2. What if I could think about how to correct for Gemma tool calling quirks? 3. What if I added a token-per-second counter, or showed reasoning events for those times the model just seems stuck? etc... So here we are. This isn't even my first mini agent harness CLI project, I've built a few more, they are on my GitHub but abandoned. What's next for this? Not sure if I'll ever open source this or even keep it around honestly... but feel free to give my experiment a try with the amazing Gemma models. Very lightly tested, so use at your own risk :-D

Dmitry Lyalin

17,233 просмотров • 4 месяцев назад

✨ It's not even 2026 yet, but you can start using AI world models today! This is World Labs running inside my app 🏡 Interior AI letting you redesign your home with AI and then walk through it! It's really cool and of course not perfect especially since for now it's based on just one image, but multi-image will allow for more precise understanding of the room As always this is just the first version, and it'll get better from here World Labs is founded by Fei-Fei Li, a Computer Science professor from Stanford University, and other people like Justin Johnson, Christoph Lassner, and Ben Mildenhall "each a world-renowned technologist in computer vision and graphics" The tech essentially comes down to img2splat, search what a guassian splat is if you don't know It's in beta and wait list, but I'm trying to get this live for everyone as soon as possible as it seems like such an essential feature for interior design P.S. I also tried to worldify people photos (for Photo AI) but World Labs for safety right now seems to block it off and make it a black square, maybe in the future

✨ It's not even 2026 yet, but you can start using AI world models today! This is World Labs running inside my app 🏡 Interior AI letting you redesign your home with AI and then walk through it! It's really cool and of course not perfect especially since for now it's based on just one image, but multi-image will allow for more precise understanding of the room As always this is just the first version, and it'll get better from here World Labs is founded by Fei-Fei Li, a Computer Science professor from Stanford University, and other people like Justin Johnson, Christoph Lassner, and Ben Mildenhall "each a world-renowned technologist in computer vision and graphics" The tech essentially comes down to img2splat, search what a guassian splat is if you don't know It's in beta and wait list, but I'm trying to get this live for everyone as soon as possible as it seems like such an essential feature for interior design P.S. I also tried to worldify people photos (for Photo AI) but World Labs for safety right now seems to block it off and make it a black square, maybe in the future

@levelsio

430,981 просмотров • 11 месяцев назад

A Chinese AI company has unveiled its new model, Kimi K2.5, which quickly climbed to the forefront in global AI rankings. Notably, this is an open-source model, currently positioned as the world’s strongest in that category. China’s AI development still trails the U.S. by about 6-7 months, thus the Sputnik moment has yet to arrive. Taking the U.S. restrictions on exporting the most advanced AI chips to China into consideration, this gap is likely to persist for a considerable time. However, China appears to be charting a distinct path in AI evolution, seeking alternative approaches. Nearly all leading Chinese AI large models follow an open-source strategy. This means the source code is publicly available, allowing anyone to use or modify it freely. As a result, users benefit from accessible technology while also contributing as refiners and new developers. Global downloads of Chinese open-source large models have cumulatively reached 10 billion, a figure that has just surpassed the U.S. Compared to AI behind the screen, China seems to prioritize embedding AI into humanoid and industrial robots, a concept they term “embodied intelligence.” In the past, assembly-line robotic arms could only perform preset, singular tasks; if a screw unexpectedly fell, they were helpless. Now, AI serves not just as the robots’ eyes and skin—enabling them to see the world clearly and sense force, temperature, and more—but also endows them with reasoning capabilities: they can figure out what they’re doing, what they should do, and how to do it better. In this new era of tech competition, the role of the Chinese government has subtly shifted. Whether developing nuclear weapons, satellites, rockets, or space stations, the government traditionally assembled top experts, poured in vast funds and resources, and treated it as a “political imperative.” In recent AI advancements, however, we’re seeing a government that’s more in the background—focusing primarily on removing institutional barriers that hinder AI progress—backing enterprises from the sidelines. And the leading large models—such as DeepSeek, Qwen, Dola, and others—are developed by private-owned enterprises.

A Chinese AI company has unveiled its new model, Kimi K2.5, which quickly climbed to the forefront in global AI rankings. Notably, this is an open-source model, currently positioned as the world’s strongest in that category. China’s AI development still trails the U.S. by about 6-7 months, thus the Sputnik moment has yet to arrive. Taking the U.S. restrictions on exporting the most advanced AI chips to China into consideration, this gap is likely to persist for a considerable time. However, China appears to be charting a distinct path in AI evolution, seeking alternative approaches. Nearly all leading Chinese AI large models follow an open-source strategy. This means the source code is publicly available, allowing anyone to use or modify it freely. As a result, users benefit from accessible technology while also contributing as refiners and new developers. Global downloads of Chinese open-source large models have cumulatively reached 10 billion, a figure that has just surpassed the U.S. Compared to AI behind the screen, China seems to prioritize embedding AI into humanoid and industrial robots, a concept they term “embodied intelligence.” In the past, assembly-line robotic arms could only perform preset, singular tasks; if a screw unexpectedly fell, they were helpless. Now, AI serves not just as the robots’ eyes and skin—enabling them to see the world clearly and sense force, temperature, and more—but also endows them with reasoning capabilities: they can figure out what they’re doing, what they should do, and how to do it better. In this new era of tech competition, the role of the Chinese government has subtly shifted. Whether developing nuclear weapons, satellites, rockets, or space stations, the government traditionally assembled top experts, poured in vast funds and resources, and treated it as a “political imperative.” In recent AI advancements, however, we’re seeing a government that’s more in the background—focusing primarily on removing institutional barriers that hinder AI progress—backing enterprises from the sidelines. And the leading large models—such as DeepSeek, Qwen, Dola, and others—are developed by private-owned enterprises.

Sinical

96,267 просмотров • 7 месяцев назад

AI tools are getting to the point where they don't just talk anymore — they can actually do things for you. CALL-E is basically an AI that makes phone calls on your behalf. You give it a task like booking something, following up, handling a customer request, and it takes care of the call and tries to get it done. What I find most interesting is how many real-world workflows are still stuck on phone calls — support, scheduling, reminders, sales — that feel small individually but add up fast. If you're interested, CALL-E is hosting its first virtual hackathon in July! It's a great chance to learn, build with voice AI, and connect with others who are curious about AI agents. Perfect for devs, operators, no-coders, and curious minds alike. Watch as I direct CALL-E to casually call me and give me an emotional support Learn more here 👉

AI tools are getting to the point where they don't just talk anymore — they can actually do things for you. CALL-E is basically an AI that makes phone calls on your behalf. You give it a task like booking something, following up, handling a customer request, and it takes care of the call and tries to get it done. What I find most interesting is how many real-world workflows are still stuck on phone calls — support, scheduling, reminders, sales — that feel small individually but add up fast. If you're interested, CALL-E is hosting its first virtual hackathon in July! It's a great chance to learn, build with voice AI, and connect with others who are curious about AI agents. Perfect for devs, operators, no-coders, and curious minds alike. Watch as I direct CALL-E to casually call me and give me an emotional support Learn more here 👉

Wesley

169,506 просмотров • 2 месяцев назад