Video wird geladen...

Video konnte nicht geladen werden

Beim Laden dieses Videos ist ein Problem aufgetreten. Dies könnte an einem vorübergehenden Netzwerkproblem liegen oder das Video ist möglicherweise nicht verfügbar.

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

315,819 subscribers

40,933 Aufrufe • vor 5 Monaten •via X (Twitter)

Wissenschaft & Technologie

Anya Rossi• Live Now

Private livecam show

0 Kommentare

Keine Kommentare verfügbar

Kommentare vom Original-Post werden hier angezeigt

Ähnliche Videos

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 Aufrufe • vor 5 Monaten

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 Aufrufe • vor 4 Monaten

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 Aufrufe • vor 1 Monat

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

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 Aufrufe • vor 12 Tagen

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

95,265 Aufrufe • vor 5 Monaten

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 Aufrufe • vor 2 Monaten

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 Aufrufe • vor 4 Monaten

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 Aufrufe • vor 2 Jahren

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 Aufrufe • vor 3 Monaten

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

47,494 Aufrufe • vor 9 Monaten

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

10,183 Aufrufe • vor 28 Tagen

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 Aufrufe • vor 6 Monaten

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 Aufrufe • vor 1 Monat

✨ 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

428,893 Aufrufe • vor 11 Monaten

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

17,197 Aufrufe • vor 3 Monaten

🚨ANTHROPIC'S FOUNDER JUST PREDICTED THAT AI WILL DOUBLE HUMAN LIFESPAN TO 150 YEARS.. CURE MOST CANCER.. AND ELIMINATE POVERTY.. ALL WITHIN 10 YEARS.. AND HE'S NOT EVEN THE OPTIMISTIC ONE.. Everyone thinks Dario Amodei is the guy who wants to slow AI down.. The cautious one.. The safety guy.. He just published an essay predicting what happens if AI goes right.. And it reads like science fiction.. Except he's dead serious.. And he has the credentials to back every word.. Here's what he thinks happens in the next 5 to 10 years.. Nearly all infectious disease.. Prevented or cured.. mRNA vaccines already showed us the path.. AI finishes the job.. Most cancer.. Eliminated.. Not just treated.. 95% or greater reduction in both deaths and new cases.. AI designs treatment regimens tailored to the individual genome of each tumor.. Something that's technically possible today but takes enormous human expertise to do.. AI scales it to everyone.. Alzheimer's.. Solved.. He thinks it's exactly the type of problem AI can crack.. Because it requires better measurement tools to isolate what's actually happening in the brain.. Once we understand it.. Prevention will probably be surprisingly simple.. Genetic disease.. Most of it preventable through improved embryo screening.. And curable in living people through safer descendants of CRISPR.. Most mental illness.. Cured.. Depression.. PTSD.. Addiction.. Schizophrenia.. He believes the answer is some combination of biochemistry and neural network-level problems that AI can untangle.. And here's the line that stopped me.. Human lifespan.. Doubled.. To 150 years.. He points out that life expectancy already doubled in the 20th century.. From 40 to 75.. So doubling it again is "on trend".. Drugs already exist that increase maximum lifespan in rats by 25 to 50%.. Some turtles already live 200 years.. We're clearly not at a biological ceiling.. He calls this the "compressed 21st century".. The idea that AI gives us 100 years of biological progress in 5 to 10 years.. But he doesn't stop at health.. He thinks AI could drive 20% annual GDP growth in the developing world.. Bringing sub-Saharan Africa to China's current GDP per capita within a decade.. He thinks AI could eradicate malaria not through treating millions of people individually.. But by releasing modified mosquitoes that block the disease at the source.. One centralized action instead of a million.. He thinks AI could make democracy structurally stronger.. Not through propaganda.. But by giving every citizen an AI that knows every law they're entitled to.. Every benefit they qualify for.. Every right they have.. And helps them actually access it.. He imagines AI that monitors judicial systems for bias.. AI that helps find common ground between opposing political views.. AI that makes government services actually work the way they're supposed to.. And he addresses the question everyone asks.. What happens to meaning when AI can do everything.. His answer.. Most people aren't the best in the world at anything right now.. And it doesn't bother them.. Meaning comes from relationships and connection.. Not economic productivity.. People will still pursue difficult challenges.. Still compete.. Still create.. The fact that an AI could theoretically do it better won't matter any more than it matters that someone somewhere is already better than you at every hobby you have.. But here's what makes this essay different from every other AI optimism piece.. Dario Amodei runs one of the three most powerful AI companies on earth.. He has a PhD in computational neuroscience.. He personally worked on mass spectrometry and neural probes.. He's not a pundit.. He's a scientist who happens to be a CEO.. And the same man who publicly says there's a 25% chance AI causes human extinction.. Is also saying that if we get it right.. We cure nearly every disease.. Double human lifespan.. Eliminate most poverty.. And fundamentally transform what it means to be alive.. Both things are true at the same time.. That's what makes this the most important essay anyone in AI has written this year.. He ends with this.. "I think many will be literally moved to tears by it".. He's talking about watching disease disappear.. Poverty dissolve.. Human potential unlock all at once.. Not in a century.. In a decade.. If we get it right.

🚨ANTHROPIC'S FOUNDER JUST PREDICTED THAT AI WILL DOUBLE HUMAN LIFESPAN TO 150 YEARS.. CURE MOST CANCER.. AND ELIMINATE POVERTY.. ALL WITHIN 10 YEARS.. AND HE'S NOT EVEN THE OPTIMISTIC ONE.. Everyone thinks Dario Amodei is the guy who wants to slow AI down.. The cautious one.. The safety guy.. He just published an essay predicting what happens if AI goes right.. And it reads like science fiction.. Except he's dead serious.. And he has the credentials to back every word.. Here's what he thinks happens in the next 5 to 10 years.. Nearly all infectious disease.. Prevented or cured.. mRNA vaccines already showed us the path.. AI finishes the job.. Most cancer.. Eliminated.. Not just treated.. 95% or greater reduction in both deaths and new cases.. AI designs treatment regimens tailored to the individual genome of each tumor.. Something that's technically possible today but takes enormous human expertise to do.. AI scales it to everyone.. Alzheimer's.. Solved.. He thinks it's exactly the type of problem AI can crack.. Because it requires better measurement tools to isolate what's actually happening in the brain.. Once we understand it.. Prevention will probably be surprisingly simple.. Genetic disease.. Most of it preventable through improved embryo screening.. And curable in living people through safer descendants of CRISPR.. Most mental illness.. Cured.. Depression.. PTSD.. Addiction.. Schizophrenia.. He believes the answer is some combination of biochemistry and neural network-level problems that AI can untangle.. And here's the line that stopped me.. Human lifespan.. Doubled.. To 150 years.. He points out that life expectancy already doubled in the 20th century.. From 40 to 75.. So doubling it again is "on trend".. Drugs already exist that increase maximum lifespan in rats by 25 to 50%.. Some turtles already live 200 years.. We're clearly not at a biological ceiling.. He calls this the "compressed 21st century".. The idea that AI gives us 100 years of biological progress in 5 to 10 years.. But he doesn't stop at health.. He thinks AI could drive 20% annual GDP growth in the developing world.. Bringing sub-Saharan Africa to China's current GDP per capita within a decade.. He thinks AI could eradicate malaria not through treating millions of people individually.. But by releasing modified mosquitoes that block the disease at the source.. One centralized action instead of a million.. He thinks AI could make democracy structurally stronger.. Not through propaganda.. But by giving every citizen an AI that knows every law they're entitled to.. Every benefit they qualify for.. Every right they have.. And helps them actually access it.. He imagines AI that monitors judicial systems for bias.. AI that helps find common ground between opposing political views.. AI that makes government services actually work the way they're supposed to.. And he addresses the question everyone asks.. What happens to meaning when AI can do everything.. His answer.. Most people aren't the best in the world at anything right now.. And it doesn't bother them.. Meaning comes from relationships and connection.. Not economic productivity.. People will still pursue difficult challenges.. Still compete.. Still create.. The fact that an AI could theoretically do it better won't matter any more than it matters that someone somewhere is already better than you at every hobby you have.. But here's what makes this essay different from every other AI optimism piece.. Dario Amodei runs one of the three most powerful AI companies on earth.. He has a PhD in computational neuroscience.. He personally worked on mass spectrometry and neural probes.. He's not a pundit.. He's a scientist who happens to be a CEO.. And the same man who publicly says there's a 25% chance AI causes human extinction.. Is also saying that if we get it right.. We cure nearly every disease.. Double human lifespan.. Eliminate most poverty.. And fundamentally transform what it means to be alive.. Both things are true at the same time.. That's what makes this the most important essay anyone in AI has written this year.. He ends with this.. "I think many will be literally moved to tears by it".. He's talking about watching disease disappear.. Poverty dissolve.. Human potential unlock all at once.. Not in a century.. In a decade.. If we get it right.

Evan Luthra

476,912 Aufrufe • vor 3 Monaten

VoiceBox just did what everyone thought was impossible. It might have killed ElevenLabs overnight. No subscription. No cloud. No credits. Runs locally on your machine. Clones a voice from 3 seconds of audio. Powered by Qwen 3 TTS. Apache 2.0 license. Commercial use allowed. And your voice data never leaves your computer. For years, if you wanted high quality voice cloning, ElevenLabs was the default. Now there’s a free open-source alternative that sounds shockingly close. Plus it has a built-in multi-track editor, recording, transcription with Whisper, and a local API. This isn’t just another TTS tool. It’s a full voice production studio on your desktop. Open source AI is catching up fast. And this changes the game for creators and businesses. Would you trust the cloud with your voice… or keep it local? 👇

VoiceBox just did what everyone thought was impossible. It might have killed ElevenLabs overnight. No subscription. No cloud. No credits. Runs locally on your machine. Clones a voice from 3 seconds of audio. Powered by Qwen 3 TTS. Apache 2.0 license. Commercial use allowed. And your voice data never leaves your computer. For years, if you wanted high quality voice cloning, ElevenLabs was the default. Now there’s a free open-source alternative that sounds shockingly close. Plus it has a built-in multi-track editor, recording, transcription with Whisper, and a local API. This isn’t just another TTS tool. It’s a full voice production studio on your desktop. Open source AI is catching up fast. And this changes the game for creators and businesses. Would you trust the cloud with your voice… or keep it local? 👇

Julian Goldie SEO

59,167 Aufrufe • vor 5 Monaten

Most people treat AI like Google: ask a question, get an answer. But what if AI could think *like/with you?* I reverse-engineer "Theory of Mind" to test if the model can form a "theory of my mind". Using AI as a mirror to understand myself by giving it personal context and seeing how well it can embody/reflect my expertise back to me helps me to evaluate the model's thinking. This is something like a single-user, multi-model human-AI synergy benchmark where the task is "strategic alignment with your own expertise." Here's my hypothesis: Does pre-loading context create emergent synergy and enhance collective intelligence? If the LLM has: - Your projects (what you've built) - Your career details (how you think about growth) - Your self-description (your values/philosophy) 1. Does this response sound like something I'd say? 2. Does it reveal blind spots I hadn't considered? 3. Does it reduce the "explanation tax" I pay in every conversation? Different models excel at different aspects of "me." Before you think I'm building a digital mirror to talk to myself: this isn't about AI companionship or AI psychosis. It's actually bout me understanding the model's reasoning so I can deploy the right model for the right task. I'm my own test subject because I have ground truth: I know what I actually think, what I'd actually decide, what I'd actually prioritize. Most benchmarks lack this. When a model says "Muratcan would choose X," I can immediately verify: "No, I'd choose Y because of Z." I think everyone should do this with their own domain expertise. - If you're a manager, load your case history and test which model best applies your reasoning. - If you're a creative, load your portfolio and test which model understands your aesthetic principles. - If you're a founder, load your strategy docs and test which model identifies blind spots in your go-to-market plan. AI should serve as an extension of your strategic thinking, a collaborative partner.

Most people treat AI like Google: ask a question, get an answer. But what if AI could think like/with you? I reverse-engineer "Theory of Mind" to test if the model can form a "theory of my mind". Using AI as a mirror to understand myself by giving it personal context and seeing how well it can embody/reflect my expertise back to me helps me to evaluate the model's thinking. This is something like a single-user, multi-model human-AI synergy benchmark where the task is "strategic alignment with your own expertise." Here's my hypothesis: Does pre-loading context create emergent synergy and enhance collective intelligence? If the LLM has: - Your projects (what you've built) - Your career details (how you think about growth) - Your self-description (your values/philosophy) 1. Does this response sound like something I'd say? 2. Does it reveal blind spots I hadn't considered? 3. Does it reduce the "explanation tax" I pay in every conversation? Different models excel at different aspects of "me." Before you think I'm building a digital mirror to talk to myself: this isn't about AI companionship or AI psychosis. It's actually bout me understanding the model's reasoning so I can deploy the right model for the right task. I'm my own test subject because I have ground truth: I know what I actually think, what I'd actually decide, what I'd actually prioritize. Most benchmarks lack this. When a model says "Muratcan would choose X," I can immediately verify: "No, I'd choose Y because of Z." I think everyone should do this with their own domain expertise. - If you're a manager, load your case history and test which model best applies your reasoning. - If you're a creative, load your portfolio and test which model understands your aesthetic principles. - If you're a founder, load your strategy docs and test which model identifies blind spots in your go-to-market plan. AI should serve as an extension of your strategic thinking, a collaborative partner.

Muratcan Koylan

24,128 Aufrufe • vor 8 Monaten

YOUR HOMELAB IS USELESS IF YOU CAN'T REACH IT FROM ANYWHERE - AND YOU SHOULD NEVER OPEN A PORT TO DO IT. that clip is a homelab in a tiny rack: cheap used mini-pcs running a proxmox cluster with ollama for local ai. but the hardware isn't the hard part. reaching it safely when you're not home is. the wrong way: forward a port on your router. now your services face the whole internet, and every bot on earth starts knocking. the right way, and what he's running: a mesh vpn like twingate, netbird or tailscale. it builds a private encrypted network between your devices and the lab. no open ports, no public ip, nothing exposed. your phone in a cafe talks to your proxmox box at home like they're on the same wifi, and the internet sees nothing. that's the piece that turns a pile of mini-pcs into infrastructure you actually use. no port forwarding, no exposed dashboard, no public attack surface. the uncomfortable part isn't building the lab. it's that most people either can't reach it, or reach it by leaving the front door wide open. follow RetroChainer for the homelab step everyone skips, and bookmark this before you forward a single port.

YOUR HOMELAB IS USELESS IF YOU CAN'T REACH IT FROM ANYWHERE - AND YOU SHOULD NEVER OPEN A PORT TO DO IT. that clip is a homelab in a tiny rack: cheap used mini-pcs running a proxmox cluster with ollama for local ai. but the hardware isn't the hard part. reaching it safely when you're not home is. the wrong way: forward a port on your router. now your services face the whole internet, and every bot on earth starts knocking. the right way, and what he's running: a mesh vpn like twingate, netbird or tailscale. it builds a private encrypted network between your devices and the lab. no open ports, no public ip, nothing exposed. your phone in a cafe talks to your proxmox box at home like they're on the same wifi, and the internet sees nothing. that's the piece that turns a pile of mini-pcs into infrastructure you actually use. no port forwarding, no exposed dashboard, no public attack surface. the uncomfortable part isn't building the lab. it's that most people either can't reach it, or reach it by leaving the front door wide open. follow RetroChainer for the homelab step everyone skips, and bookmark this before you forward a single port.

RetroChainer

57,909 Aufrufe • vor 8 Tagen