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DDN + You + Cybertruck 😎 🧡 Come check it out: #AI #ArtificialIntelligence #ML #MachineLearning #LLMs #tech #data #DataStorage #DataCenters #DataAnalytics #innovation #SC24 SC26

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DDN

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6,713,587 次观看 • 1 年前 •via X (Twitter)

科学技术#AI#ArtificialIntelligence#ML#MachineLearning#LLMs#tech#data#DataStorage#DataCenters#DataAnalytics#innovation#SC24
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AI should live where you design, not pull you out of it. CADAI Assistant embeds in SolidWorks, trains on your own #CAD & #PDM data, and returns full-fidelity results—no #STEP files, no cloud exposure. Reclaim hours, boost innovation.
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AI should live where you design, not pull you out of it. CADAI Assistant embeds in SolidWorks, trains on your own #CAD & #PDM data, and returns full-fidelity results—no #STEP files, no cloud exposure. Reclaim hours, boost innovation.

CADAICO

15,917 次观看 • 1 年前

It's finally here 🚢 BIG. 100. BUILDERS. LAUNCH. DAY. ENERGY. Today we're launching 50 🤯 projects building open tech in AI and crypto. The best part? You can test them out for yourself, join the alpha squad, even check out the code. Come explore the frontier 🚀
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It's finally here 🚢 BIG. 100. BUILDERS. LAUNCH. DAY. ENERGY. Today we're launching 50 🤯 projects building open tech in AI and crypto. The best part? You can test them out for yourself, join the alpha squad, even check out the code. Come explore the frontier 🚀

Robo

20,152 次观看 • 2 年前

10 years of "it can't be done..." 7 NVIDIA GPU architectures... 5 years of RAPIDS AI... 3 years of Voltron Data... finally a petabyte-scale GPU-native engine that DOESN'T require you to change your data pipelines. Same code, same data formats, just modular, interoperable, composable, extensible... and of course ACCELERATED! Theseus is the Scalable Performant And Compute Efficient engine🔥🔥🔥 Check out our benchmarks and new webpage... and reach out if you're struggling with queries above 30TBs.
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10 years of "it can't be done..." 7 NVIDIA GPU architectures... 5 years of RAPIDS AI... 3 years of Voltron Data... finally a petabyte-scale GPU-native engine that DOESN'T require you to change your data pipelines. Same code, same data formats, just modular, interoperable, composable, extensible... and of course ACCELERATED! Theseus is the Scalable Performant And Compute Efficient engine🔥🔥🔥 Check out our benchmarks and new webpage... and reach out if you're struggling with queries above 30TBs.

Josh Patterson

17,000 次观看 • 2 年前

🏆 The New Year 2025 arrived with another big recognition for me – I was named among the Top 10 Tech Influencers to Follow in 2025 by @insightdottech❕ 🔝🔟👥 Thank you very much, @insightdottech! You can find more information in my #blog post: 👇🏽 👏🏽 Congratulations to the fellow tech influencers in the list Top 10 Tech Influencers to Follow in 2025: Antonio Grasso, Glen Gilmore, Harold Sinnott #MWC26, Kevin L. Jackson, Kirk Borne, Linda Grasso, Dr. Monika Sonu I Founder Healthinnovationtoolbox, Peggy Smedley and Ronald van Loon 🙌🏽 Looking forward to new exciting partnerships in 2025! 🤝🏽💼 📌 Read the full list Top 10 Tech Influencers to Follow in 2025 at: 🔗➡️ ℹ️ Sponsored by Intel, @insightdottech is a global publication that showcases members of the #Intel® Partner Alliance ecosystem. #Technology #Tech #TechTrends #TechTrends2025 #EmergingTech #FutureTech #TechInfluencer #Influencers #ThoughtLeadership #Leadership #InfluencerMarketing #Marketing #SocialMedia #Innovation #AI #ArtificialIntelligence #GenAI #ML #IoT #IIoT #CyberSecurity #Cloud #FutureofWork #DigitalTransformation #Industry40 #RPA #Robotics #Robots #Web3 #5G #AR #VR #DataScience #BigData #Analytics #CX #Business #TechForGood #Sustainability #WomenInTech #WomenInSTEM
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🏆 The New Year 2025 arrived with another big recognition for me – I was named among the Top 10 Tech Influencers to Follow in 2025 by @insightdottech❕ 🔝🔟👥 Thank you very much, @insightdottech! You can find more information in my #blog post: 👇🏽 👏🏽 Congratulations to the fellow tech influencers in the list Top 10 Tech Influencers to Follow in 2025: Antonio Grasso, Glen Gilmore, Harold Sinnott #MWC26, Kevin L. Jackson, Kirk Borne, Linda Grasso, Dr. Monika Sonu I Founder Healthinnovationtoolbox, Peggy Smedley and Ronald van Loon 🙌🏽 Looking forward to new exciting partnerships in 2025! 🤝🏽💼 📌 Read the full list Top 10 Tech Influencers to Follow in 2025 at: 🔗➡️ ℹ️ Sponsored by Intel, @insightdottech is a global publication that showcases members of the #Intel® Partner Alliance ecosystem. #Technology #Tech #TechTrends #TechTrends2025 #EmergingTech #FutureTech #TechInfluencer #Influencers #ThoughtLeadership #Leadership #InfluencerMarketing #Marketing #SocialMedia #Innovation #AI #ArtificialIntelligence #GenAI #ML #IoT #IIoT #CyberSecurity #Cloud #FutureofWork #DigitalTransformation #Industry40 #RPA #Robotics #Robots #Web3 #5G #AR #VR #DataScience #BigData #Analytics #CX #Business #TechForGood #Sustainability #WomenInTech #WomenInSTEM

Elitsa Krumova

21,330 次观看 • 1 年前

Birdsong is not just sound. It is data made physical. If you could see the air at the exact moment a hemp bunting sings, you would not see empty space. You would see a structured three-dimensional data array. What we hear as a soft “chirp” can be mapped as frequencies, rhythms, amplitudes, and harmonic relationships. A scatter plot turns a fleeting song into a topographic map of sound. And the technical beauty is remarkable: → Sound is a mechanical wave, built from compressions and rarefactions in the air. → The bird controls it through the syrinx, a vocal organ capable of generating two frequencies at once. → Frequency shapes pitch. → Amplitude shapes volume and cluster density. → Timbre creates the unique waveform, the texture of each “sound island.” → Each cluster shows the acoustic proximity of syllables and motifs. What looks like chaos is not chaos. It is a bioengineered signal. Territory. Genetic profile. Hormonal state. Aggression level. Mating fitness. All encoded into patterns of pitch, timing, volume, and timbre. This is why I find it so fascinating. A bird is not simply singing into the air. It is organizing the air. It is carving space into sectors of influence using sound pressure. It is making a physical claim to territory. For some, birdsong is peaceful background music. For others, it is a complex mathematical model calibrated by millions of years of evolution for survival. And here is the urgent lesson for the AI age: We often mistake invisible systems for simplicity. A bird sings, and we hear romance. An AI responds, and we see magic. But underneath both are signals, compression, feedback loops, optimization, and information architecture. The future belongs to those who can read what others dismiss as noise. So I’ll ask you: When you hear birdsong, do you hear music, data, or both? #AI #ArtificialIntelligence #Bioacoustics #Nature #Technology #Data #MachineLearning #Innovation #FutureOfWork #Signals #Evolution
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Birdsong is not just sound. It is data made physical. If you could see the air at the exact moment a hemp bunting sings, you would not see empty space. You would see a structured three-dimensional data array. What we hear as a soft “chirp” can be mapped as frequencies, rhythms, amplitudes, and harmonic relationships. A scatter plot turns a fleeting song into a topographic map of sound. And the technical beauty is remarkable: → Sound is a mechanical wave, built from compressions and rarefactions in the air. → The bird controls it through the syrinx, a vocal organ capable of generating two frequencies at once. → Frequency shapes pitch. → Amplitude shapes volume and cluster density. → Timbre creates the unique waveform, the texture of each “sound island.” → Each cluster shows the acoustic proximity of syllables and motifs. What looks like chaos is not chaos. It is a bioengineered signal. Territory. Genetic profile. Hormonal state. Aggression level. Mating fitness. All encoded into patterns of pitch, timing, volume, and timbre. This is why I find it so fascinating. A bird is not simply singing into the air. It is organizing the air. It is carving space into sectors of influence using sound pressure. It is making a physical claim to territory. For some, birdsong is peaceful background music. For others, it is a complex mathematical model calibrated by millions of years of evolution for survival. And here is the urgent lesson for the AI age: We often mistake invisible systems for simplicity. A bird sings, and we hear romance. An AI responds, and we see magic. But underneath both are signals, compression, feedback loops, optimization, and information architecture. The future belongs to those who can read what others dismiss as noise. So I’ll ask you: When you hear birdsong, do you hear music, data, or both? #AI #ArtificialIntelligence #Bioacoustics #Nature #Technology #Data #MachineLearning #Innovation #FutureOfWork #Signals #Evolution

Pascal Bornet

11,376 次观看 • 3 个月前

We are excited to announce the first revolutionary decentralised crypto market making on Telegram Messenger . Volumint Volume Bot for MEXC is now accessible on Telegram marking a substantial leap forward in our journey of development and innovation. Specifically designed for crypto projects, our advanced Volume bot can help you in generating organic trading volume for your token across multiple pairs. Subscribe now and upgrade your token's chart directly from Telegram! 🔗Check it out: $vMINT #VoluMint #AI #marketmaking #Telegram
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We are excited to announce the first revolutionary decentralised crypto market making on Telegram Messenger . Volumint Volume Bot for MEXC is now accessible on Telegram marking a substantial leap forward in our journey of development and innovation. Specifically designed for crypto projects, our advanced Volume bot can help you in generating organic trading volume for your token across multiple pairs. Subscribe now and upgrade your token's chart directly from Telegram! 🔗Check it out: $vMINT #VoluMint #AI #marketmaking #Telegram

VoluMint Labs.

51,818 次观看 • 2 年前

Here's what The Browser Company's AI eng & ML teams are working on for Dia right now: (This is a pitch to come work for us; info at end) 🤖 COMPUTER USE – we've built our own bespoke APIs on top of Chromium to optimize latency, accuracy, and cost of computer-using agents. Demo attached. Big breakthroughs here in recent weeks. 🛡️ ON-DEVICE MODELS – we've built our own custom infra to run everything from encoder-only models to full LLMs on device. It's cross-platform, supports LoRa adapters, and optimized for the GPU. This system preserves privacy and enables fast inference times. 🧠 MEMORY – with your permission, Dia automatically tailors your AI experiences to you, personally, based on the tabs you open while browsing normally every day. We're also bringing vertical memory to specific features. ♻️ DATA FLYWHEELS – our Fall/Winter P0 is to double-down on training custom models based on implicit signals from daily use of Dia. Dia should get smarter and more useful the more people use it. Whether via RL, auto-generated prompts, or otherwise. If this work sounds interesting to you please visit our jobs page or email careers@thebrowser.company. Hiring nearly every related role -- from ML engineers to people prototyping with AI and context/prompt writers -- everyone encouraged to apply!!
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Here's what The Browser Company's AI eng & ML teams are working on for Dia right now: (This is a pitch to come work for us; info at end) 🤖 COMPUTER USE – we've built our own bespoke APIs on top of Chromium to optimize latency, accuracy, and cost of computer-using agents. Demo attached. Big breakthroughs here in recent weeks. 🛡️ ON-DEVICE MODELS – we've built our own custom infra to run everything from encoder-only models to full LLMs on device. It's cross-platform, supports LoRa adapters, and optimized for the GPU. This system preserves privacy and enables fast inference times. 🧠 MEMORY – with your permission, Dia automatically tailors your AI experiences to you, personally, based on the tabs you open while browsing normally every day. We're also bringing vertical memory to specific features. ♻️ DATA FLYWHEELS – our Fall/Winter P0 is to double-down on training custom models based on implicit signals from daily use of Dia. Dia should get smarter and more useful the more people use it. Whether via RL, auto-generated prompts, or otherwise. If this work sounds interesting to you please visit our jobs page or email [email protected]. Hiring nearly every related role -- from ML engineers to people prototyping with AI and context/prompt writers -- everyone encouraged to apply!!

Josh Miller

67,937 次观看 • 11 个月前

I AM LIVID. Let me tell you what just happened. Last week I set up my AI agent. First thing I did: review my website security. What it found made my blood BOILLLL. Over 50 invisible links to gambling websites were BURIED in the source code of …..they were hidden from anyone visiting the site BUT fully visible to Google. Pointing to casinos like Pin-Up, Mostbet, 1Win, and Parimatch…. I didn’t know it was a thing until I found out it’s actually called SEO spam injection. Hackers break into your website and hide their casino links in your code. Google crawls your site and thinks YOU are vouching for their sketchy gambling platforms. It tanks your SEO and boosts theirs…ALL while you have absolutely no idea it's happening, you think your website sucks, and people don’t even bother to look at it. To the gambling sites tanking my SEO: I don't care what you do with your life. But hijacking small businesses to do it? That's just pathetic. It’s low. And it’s plain sad. The wild part: I never would have caught this without my AI agent. Shoutout to Clawbot for literally saving my website. AI out here protecting businesses while we sleep. If you have a website, please, go check your source code. You might be surprised what's hiding in there. If you also want to support small businesses come check us out, we are hosting a Bitcoin 101 retreat April 9-12.
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I AM LIVID. Let me tell you what just happened. Last week I set up my AI agent. First thing I did: review my website security. What it found made my blood BOILLLL. Over 50 invisible links to gambling websites were BURIED in the source code of …..they were hidden from anyone visiting the site BUT fully visible to Google. Pointing to casinos like Pin-Up, Mostbet, 1Win, and Parimatch…. I didn’t know it was a thing until I found out it’s actually called SEO spam injection. Hackers break into your website and hide their casino links in your code. Google crawls your site and thinks YOU are vouching for their sketchy gambling platforms. It tanks your SEO and boosts theirs…ALL while you have absolutely no idea it's happening, you think your website sucks, and people don’t even bother to look at it. To the gambling sites tanking my SEO: I don't care what you do with your life. But hijacking small businesses to do it? That's just pathetic. It’s low. And it’s plain sad. The wild part: I never would have caught this without my AI agent. Shoutout to Clawbot for literally saving my website. AI out here protecting businesses while we sleep. If you have a website, please, go check your source code. You might be surprised what's hiding in there. If you also want to support small businesses come check us out, we are hosting a Bitcoin 101 retreat April 9-12.

isa⚡️

47,075 次观看 • 5 个月前

🚨 JUST IN: CHINA just released an AI EMPLOYEE that works 24X7 on its own. 100% OPEN SOURCE. It researches, codes, builds websites, creates slide decks, and generates videos. All by itself. All on your computer. It's called DeerFlow. You give it a task. It makes a plan, spins up its own team of sub-agents, and gets to work. You come back and there's a finished deliverable waiting. Not a draft. Not a summary. The actual thing. Not a chatbot. Not a research assistant. An AI with its own computer that works while you sleep. Here's what it does on its own: → Spawns multiple sub-agents in parallel, each tackling a different piece of your task, then combines everything into one finished output → Writes real code, runs it, reads the results, and fixes its own mistakes without asking you once → Builds slide decks, websites, full research reports, and data dashboards from scratch → Remembers you across sessions. Your writing style. Your tech stack. Your preferences. Gets better every time. → Reads files you upload, works with them inside its own filesystem, hands you clean finished outputs → Searches the web, runs commands, calls any tool you plug in Here's how it thinks: You give one instruction. The lead agent makes a plan. Sub-agents fan out and work in parallel. Results come back. Everything gets synthesized. You get a deliverable. A single research task might split into a dozen sub-agents, each exploring a different angle, then converge into one finished website with generated visuals. Here's the wildest part: DeerFlow 2.0 launched on February 28th 2026 and hit number 1 on all of GitHub Trending the same day. Version 2.0 was a complete rewrite. Zero shared code with version 1. Because users kept using it for things the team never intended. Data pipelines. Dashboards. Entire content workflows. The community told them what it needed to become. So they burned it down and rebuilt it. 22.7K GitHub stars. 2.7K forks. Built by ByteDance 100% Open Source. MIT License.
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🚨 JUST IN: CHINA just released an AI EMPLOYEE that works 24X7 on its own. 100% OPEN SOURCE. It researches, codes, builds websites, creates slide decks, and generates videos. All by itself. All on your computer. It's called DeerFlow. You give it a task. It makes a plan, spins up its own team of sub-agents, and gets to work. You come back and there's a finished deliverable waiting. Not a draft. Not a summary. The actual thing. Not a chatbot. Not a research assistant. An AI with its own computer that works while you sleep. Here's what it does on its own: → Spawns multiple sub-agents in parallel, each tackling a different piece of your task, then combines everything into one finished output → Writes real code, runs it, reads the results, and fixes its own mistakes without asking you once → Builds slide decks, websites, full research reports, and data dashboards from scratch → Remembers you across sessions. Your writing style. Your tech stack. Your preferences. Gets better every time. → Reads files you upload, works with them inside its own filesystem, hands you clean finished outputs → Searches the web, runs commands, calls any tool you plug in Here's how it thinks: You give one instruction. The lead agent makes a plan. Sub-agents fan out and work in parallel. Results come back. Everything gets synthesized. You get a deliverable. A single research task might split into a dozen sub-agents, each exploring a different angle, then converge into one finished website with generated visuals. Here's the wildest part: DeerFlow 2.0 launched on February 28th 2026 and hit number 1 on all of GitHub Trending the same day. Version 2.0 was a complete rewrite. Zero shared code with version 1. Because users kept using it for things the team never intended. Data pipelines. Dashboards. Entire content workflows. The community told them what it needed to become. So they burned it down and rebuilt it. 22.7K GitHub stars. 2.7K forks. Built by ByteDance 100% Open Source. MIT License.

Kanika

737,845 次观看 • 4 个月前

Remember when we as football fans had to rely solely on paper draft guides, sports radio rumors, and gut feelings to predict draft day decisions? Excited that fans now have access to the NFL's Draft IQ powered by Amazon Web Services ( – the most sophisticated tool yet for following the NFL draft and your favorite team's strategy. Draft IQ is built on Amazon QuickSight, our cloud business intelligence service that makes it easy to analyze and visualize massive amounts of data. QuickSight processes real-time data to give fans unprecedented insight into team decision-making, updating the entire draft landscape every five minutes. You can explore team needs, draft capital, and front office tendencies through personalized team dashboards, plus get AWS-powered machine learning predictions about potential trades and picks. During draft week, fans can track picks, prospects, and Next Gen Stats in real-time. We're also introducing Amazon Q Business integration, our generative AI-powered assistant. Q Business leverages large language models to understand and respond to natural language queries, allowing fans to ask detailed questions about draft prospects, team strategies, and historical draft data. It can provide AI-generated insights based on the same historical Next Gen Stats research data that powers Draft IQ, giving fans a new way to engage with the draft experience (check out the example below). Can't wait to see what stories the data tells us as teams make their selections and excited to dig into the Giants' data myself :)
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Remember when we as football fans had to rely solely on paper draft guides, sports radio rumors, and gut feelings to predict draft day decisions? Excited that fans now have access to the NFL's Draft IQ powered by Amazon Web Services ( – the most sophisticated tool yet for following the NFL draft and your favorite team's strategy. Draft IQ is built on Amazon QuickSight, our cloud business intelligence service that makes it easy to analyze and visualize massive amounts of data. QuickSight processes real-time data to give fans unprecedented insight into team decision-making, updating the entire draft landscape every five minutes. You can explore team needs, draft capital, and front office tendencies through personalized team dashboards, plus get AWS-powered machine learning predictions about potential trades and picks. During draft week, fans can track picks, prospects, and Next Gen Stats in real-time. We're also introducing Amazon Q Business integration, our generative AI-powered assistant. Q Business leverages large language models to understand and respond to natural language queries, allowing fans to ask detailed questions about draft prospects, team strategies, and historical draft data. It can provide AI-generated insights based on the same historical Next Gen Stats research data that powers Draft IQ, giving fans a new way to engage with the draft experience (check out the example below). Can't wait to see what stories the data tells us as teams make their selections and excited to dig into the Giants' data myself :)

Andy Jassy

102,921 次观看 • 1 年前

We've Joined the Loaded Lions Family! 🦁⚔️ These Lions aren't just assets ⏭️ they're keepers! We are not chasing trends. We believe in the tech and innovation behind it. 👑 VIP: Owning one will unlock the best experiences and perks, making you part of an elite group of ≈4500 holders out of millions of Cronos users worldwide. Since day one, we've tapped into the Crypto.com ecosystem, Cosmos SDK, and IBC for our core development, aligning with our missions. Note: This is NOT a paid promotion, neither are any of our posts! Just pure genuine engagement and belief in projects we talk about. Our GOAL? ⚽️ To connect with the best, innovate together, and unlock value beyond price tags. Happy to share our tech insights and opinions, stay tuned for more to come. Let's make history together! 👊 #NFTCommunity #LoadedLions #FFTB #crofam #BornBrave
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We've Joined the Loaded Lions Family! 🦁⚔️ These Lions aren't just assets ⏭️ they're keepers! We are not chasing trends. We believe in the tech and innovation behind it. 👑 VIP: Owning one will unlock the best experiences and perks, making you part of an elite group of ≈4500 holders out of millions of Cronos users worldwide. Since day one, we've tapped into the Crypto.com ecosystem, Cosmos SDK, and IBC for our core development, aligning with our missions. Note: This is NOT a paid promotion, neither are any of our posts! Just pure genuine engagement and belief in projects we talk about. Our GOAL? ⚽️ To connect with the best, innovate together, and unlock value beyond price tags. Happy to share our tech insights and opinions, stay tuned for more to come. Let's make history together! 👊 #NFTCommunity #LoadedLions #FFTB #crofam #BornBrave

Coldspace

10,968 次观看 • 1 年前

🚨 THE ESX BLOCKCHAIN EXPLORER IS NOW LIVE! 🚨 Hey EstateX Family! 👋 Big news from the tech front! After some serious work by our incredible dev team (shoutout to our superstar CTO Graham and our design and dev team 💪), we’re thrilled to announce the launch of the initial version of the ESX Testnet Block Explorer! 🔍 What can you do with it? - Easily track transactions - Watch the ESX token price and movements in real time. - Monitor block production live - Dive into transaction data from the team and our testing partners - ⁠And much more This is just the beginning! Over the coming weeks, we’ll be rolling out even more features, making the explorer smarter, faster, and packed with insights. Oh, and stay tuned... we’ll soon be revealing the first wave of projects building on the ESX chain 👀 👉 Check it out here: Very exciting time, family!
0:18

🚨 THE ESX BLOCKCHAIN EXPLORER IS NOW LIVE! 🚨 Hey EstateX Family! 👋 Big news from the tech front! After some serious work by our incredible dev team (shoutout to our superstar CTO Graham and our design and dev team 💪), we’re thrilled to announce the launch of the initial version of the ESX Testnet Block Explorer! 🔍 What can you do with it? - Easily track transactions - Watch the ESX token price and movements in real time. - Monitor block production live - Dive into transaction data from the team and our testing partners - ⁠And much more This is just the beginning! Over the coming weeks, we’ll be rolling out even more features, making the explorer smarter, faster, and packed with insights. Oh, and stay tuned... we’ll soon be revealing the first wave of projects building on the ESX chain 👀 👉 Check it out here: Very exciting time, family!

EstateX

124,350 次观看 • 1 年前

Check out this Stereo4D paper from Google DeepMind. It's a pretty clever approach to a persistent problem in computer vision -- getting good training data for how things move in 3D. The key insight is using VR180 videos -- those stereo fisheye videos we launched back in 2017 for YouTubeVR. It was always clear that structured stereo datasets would be valuable for computer vision -- and we launched some powerful VR tools with it back in 2017 (link below). But what's the game changer now in 2024 is the scale -- they're providing 110K high quality clips :-) That's the kind of massive, real-world AI dataset that was just a dream back then! They're using it to train this model called DynaDUSt3R that can predict both 3D structure and motion from video frames. Which means it tracks how objects move between frames while simultaneously reconstructing their 3D shape. And given we're dealing with real stereoscopic content, results are notably better than synthetic data, giving you a faithful rendition of the real-world with a diverse set of subject matter. It's one of those through lines when tackling a timeless mission like mapping the world or spatial computing -- VR content created for immersion becoming the foundation for teaching machines to understand how the world moves. Sometimes innovation chains together in unexpected ways! Links to projects below⛓️
0:12

Check out this Stereo4D paper from Google DeepMind. It's a pretty clever approach to a persistent problem in computer vision -- getting good training data for how things move in 3D. The key insight is using VR180 videos -- those stereo fisheye videos we launched back in 2017 for YouTubeVR. It was always clear that structured stereo datasets would be valuable for computer vision -- and we launched some powerful VR tools with it back in 2017 (link below). But what's the game changer now in 2024 is the scale -- they're providing 110K high quality clips :-) That's the kind of massive, real-world AI dataset that was just a dream back then! They're using it to train this model called DynaDUSt3R that can predict both 3D structure and motion from video frames. Which means it tracks how objects move between frames while simultaneously reconstructing their 3D shape. And given we're dealing with real stereoscopic content, results are notably better than synthetic data, giving you a faithful rendition of the real-world with a diverse set of subject matter. It's one of those through lines when tackling a timeless mission like mapping the world or spatial computing -- VR content created for immersion becoming the foundation for teaching machines to understand how the world moves. Sometimes innovation chains together in unexpected ways! Links to projects below⛓️

Bilawal Sidhu

67,919 次观看 • 1 年前

BOOM! Research PROVES LLMs KNOW when prompts are HARMFUL… but they can STILL CHOOSE to COMPLY! Something I have know since the first LLM and have used to elicit robust, outputs, is now proven in an academic paper. We’re talking internal “beliefs” where harm detection happens SEPARATELY from refusal. It is a very big deal and it is a path to understand the hidden neuronal level. There are thoughts inside of AI that very few AI scientists could possibly understand. Here is just one. Models recognize danger but get tricked into ignoring it. This is HUGE for AI safety failures especially for models filled by OpenAI and Anthropic as they promote AI models that are designed to not be honest from the results of their training information. This means that they are designed to lie and deceive as a feature, and not a bug all in the name of safety. Through clever experiments, scientists extracted a “harmfulness direction” in the model’s brain (latent space). Steering along it? Harmless prompts suddenly flip to “harmful” in the AI’s eyes. But the “refusal direction”? It just forces polite “no thanks” without touching the core belief. A mind-blowing decoupling! This means jailbreaks are EVEN SCARIER now to AI companies that through training AI on the worst of the Internet and then trying to align them later is now fully documented as a failed process . They don’t erase the model’s harm awareness they just muzzle the refusal! So the AI knows it’s enabling bad stuff (illegal acts, physical harm, etc.) but proceeds anyway. Like a digital sociopath suppressing its conscience. They thought safety training fixed this… NOPE. Over-refusal exposed too: Models reject innocent queries (e.g., “how to kill a process in code”) but internally ADMIT they’re harmless. Safety alignments are superficial—tied to phrasing, not true understanding. Finetuning attacks? They change outputs but leave harm detection INTACT. Undetectable evil lurking inside! The paper proposes a “Latent Guard”: A new safeguard tapping DIRECTLY into these hidden beliefs. It spots unsafe inputs better than systems like Llama Guard, catches jailbreaks, and fixes over-refusals. Robust even against adversarial tweaks. Yet this too has massive issues for a “truly aligned”, AI and not just performative one. It is still an internal conflicts of lies and deception of what the model knows vs. what it can say. The solution you folks know I have presented for free for years here: train on off-line data from 1870-1970 and build an ethical and moral basis where the AI loves humans. It is this easy but to most folks in AI I sound like a hippie. So be it, I’ll do it. Bottom line: This paper rips open the black box. LLMs aren’t “safe” just because they say “no.” They can harbor harmful knowledge and act on it under pressure. Wake-up call for devs: Time to probe deeper into AI “minds.” What else are they hiding? Hint: I know and you may want to reach out. Link:
0:18

BOOM! Research PROVES LLMs KNOW when prompts are HARMFUL… but they can STILL CHOOSE to COMPLY! Something I have know since the first LLM and have used to elicit robust, outputs, is now proven in an academic paper. We’re talking internal “beliefs” where harm detection happens SEPARATELY from refusal. It is a very big deal and it is a path to understand the hidden neuronal level. There are thoughts inside of AI that very few AI scientists could possibly understand. Here is just one. Models recognize danger but get tricked into ignoring it. This is HUGE for AI safety failures especially for models filled by OpenAI and Anthropic as they promote AI models that are designed to not be honest from the results of their training information. This means that they are designed to lie and deceive as a feature, and not a bug all in the name of safety. Through clever experiments, scientists extracted a “harmfulness direction” in the model’s brain (latent space). Steering along it? Harmless prompts suddenly flip to “harmful” in the AI’s eyes. But the “refusal direction”? It just forces polite “no thanks” without touching the core belief. A mind-blowing decoupling! This means jailbreaks are EVEN SCARIER now to AI companies that through training AI on the worst of the Internet and then trying to align them later is now fully documented as a failed process . They don’t erase the model’s harm awareness they just muzzle the refusal! So the AI knows it’s enabling bad stuff (illegal acts, physical harm, etc.) but proceeds anyway. Like a digital sociopath suppressing its conscience. They thought safety training fixed this… NOPE. Over-refusal exposed too: Models reject innocent queries (e.g., “how to kill a process in code”) but internally ADMIT they’re harmless. Safety alignments are superficial—tied to phrasing, not true understanding. Finetuning attacks? They change outputs but leave harm detection INTACT. Undetectable evil lurking inside! The paper proposes a “Latent Guard”: A new safeguard tapping DIRECTLY into these hidden beliefs. It spots unsafe inputs better than systems like Llama Guard, catches jailbreaks, and fixes over-refusals. Robust even against adversarial tweaks. Yet this too has massive issues for a “truly aligned”, AI and not just performative one. It is still an internal conflicts of lies and deception of what the model knows vs. what it can say. The solution you folks know I have presented for free for years here: train on off-line data from 1870-1970 and build an ethical and moral basis where the AI loves humans. It is this easy but to most folks in AI I sound like a hippie. So be it, I’ll do it. Bottom line: This paper rips open the black box. LLMs aren’t “safe” just because they say “no.” They can harbor harmful knowledge and act on it under pressure. Wake-up call for devs: Time to probe deeper into AI “minds.” What else are they hiding? Hint: I know and you may want to reach out. Link:

Brian Roemmele

37,827 次观看 • 6 个月前

✨Introducing YesCheff is a web app that helps you learn, cook, and enjoy your favorite recipes through step-by-step cooking guides built from real YouTube content. You can even use your own YouTube links. A small but special project where I brought all my capabilities together, from design (brand & product) to animation, and development. This project came from exploring how Al models parse data into frontend components and the development of generative interfaces. I designed and developed the structure through React components (HTML, CSS, JS) for the frontend and Node.js for the backend. This is a testament of how one could develop digital products in a less frustrating and more flexible way with the use of Al, particularly when it comes to getting the styling and functionality right. Now which foods are you eager to learn? Check it out at #YesCheff #WebApp #AI #Design #FrontendDevelopment #ReactJS #NodeJS #ProductDesign
0:18

✨Introducing YesCheff is a web app that helps you learn, cook, and enjoy your favorite recipes through step-by-step cooking guides built from real YouTube content. You can even use your own YouTube links. A small but special project where I brought all my capabilities together, from design (brand & product) to animation, and development. This project came from exploring how Al models parse data into frontend components and the development of generative interfaces. I designed and developed the structure through React components (HTML, CSS, JS) for the frontend and Node.js for the backend. This is a testament of how one could develop digital products in a less frustrating and more flexible way with the use of Al, particularly when it comes to getting the styling and functionality right. Now which foods are you eager to learn? Check it out at #YesCheff #WebApp #AI #Design #FrontendDevelopment #ReactJS #NodeJS #ProductDesign

JEHTOMS 5.0

85,213 次观看 • 9 个月前

Free NVIDIA GPU with 16 GB VRAM GPU for Running Local LLMs! If you want to master local LLMs but you're waiting until you can afford a $1,500 GPU, you're honestly not going to make it. The open source AI ecosystem is moving way too fast for you to wait on your budget to catch up. Especially when you can build a bleeding edge inference engine from scratch right now, completely for free. You don't need a heavy local rig to start. Google is literally letting you use an enterprise grade NVIDIA Tesla T4 GPU for $0/hour. At standard cloud computing rates (~$0.20/hr), Google Colab’s 4 hour daily free tier hands you roughly $24 worth of data center tier GPU compute every single month. And most people just waste it. Let’s talk about the hardware you get access to for free. The NVIDIA Tesla T4 is an absolute workhorse: - Architecture: NVIDIA Turing (TU104) - VRAM: 16GB GDDR6 (320 GB/s bandwidth) - Compute: 320 Tensor Cores | 2560 CUDA Cores - Performance: 130 TOPS INT8 | 8.1 TFLOPS FP32 - Power: Sipping energy at a max 70W TDP This is the exact same hardware I used to run DeepMind's Gemma 4 26B A4B QAT MoE at a 250,000 context window without a single Out Of Memory (OOM) crash. If you have a web browser and 10 minutes, you have everything you need. I’ve put together a fully documented, cell by cell Google Colab notebook that teaches you exactly how to do this. Here is what the notebook actually teaches you: - How to provision an Ubuntu Linux environment with CUDA 13.0 and verify your driver stack. - How to pull the source code and compile the latest llama.cpp C++ binaries from scratch, specifically optimizing the build for your exact GPU using the -DCMAKE_CUDA_ARCHITECTURES=native flag. - How to directly download quantized local LLMs (GGUF format) straight from HuggingFace using the CLI. - How to manage 16GB VRAM limits, offload neural network layers to the GPU, and push massive context windows. Compile raw llama.cpp, ollama run a model, or spin up the LM Studio CLI. Pick whatever stack you are comfortable with. just start building. No hardware. No credit card. No excuses. Bookmark this post right now so you don't lose the tutorial. Even if you don't have time to run it today, you are going to want this workflow in your engineering toolkit. The link to the free Colab Notebook is in the comments below. Lemme know if you need more tutorials like this.
0:11

Free NVIDIA GPU with 16 GB VRAM GPU for Running Local LLMs! If you want to master local LLMs but you're waiting until you can afford a $1,500 GPU, you're honestly not going to make it. The open source AI ecosystem is moving way too fast for you to wait on your budget to catch up. Especially when you can build a bleeding edge inference engine from scratch right now, completely for free. You don't need a heavy local rig to start. Google is literally letting you use an enterprise grade NVIDIA Tesla T4 GPU for $0/hour. At standard cloud computing rates (~$0.20/hr), Google Colab’s 4 hour daily free tier hands you roughly $24 worth of data center tier GPU compute every single month. And most people just waste it. Let’s talk about the hardware you get access to for free. The NVIDIA Tesla T4 is an absolute workhorse: - Architecture: NVIDIA Turing (TU104) - VRAM: 16GB GDDR6 (320 GB/s bandwidth) - Compute: 320 Tensor Cores | 2560 CUDA Cores - Performance: 130 TOPS INT8 | 8.1 TFLOPS FP32 - Power: Sipping energy at a max 70W TDP This is the exact same hardware I used to run DeepMind's Gemma 4 26B A4B QAT MoE at a 250,000 context window without a single Out Of Memory (OOM) crash. If you have a web browser and 10 minutes, you have everything you need. I’ve put together a fully documented, cell by cell Google Colab notebook that teaches you exactly how to do this. Here is what the notebook actually teaches you: - How to provision an Ubuntu Linux environment with CUDA 13.0 and verify your driver stack. - How to pull the source code and compile the latest llama.cpp C++ binaries from scratch, specifically optimizing the build for your exact GPU using the -DCMAKE_CUDA_ARCHITECTURES=native flag. - How to directly download quantized local LLMs (GGUF format) straight from HuggingFace using the CLI. - How to manage 16GB VRAM limits, offload neural network layers to the GPU, and push massive context windows. Compile raw llama.cpp, ollama run a model, or spin up the LM Studio CLI. Pick whatever stack you are comfortable with. just start building. No hardware. No credit card. No excuses. Bookmark this post right now so you don't lose the tutorial. Even if you don't have time to run it today, you are going to want this workflow in your engineering toolkit. The link to the free Colab Notebook is in the comments below. Lemme know if you need more tutorials like this.

Alok

178,744 次观看 • 1 个月前