Loading video...

Video Failed to Load

Go Home

SLAM just got a serious speed boost. Efficient LoFTR is now integrated into the Hugging Face Transformers library. It’s 2.5× faster than the original LoFTR and can even outperform the SuperPoint + LightGlue pipeline. Image matching finds correspondences between two images taken from different angles, lighting, or scales. It’s...

IlirAliu_'s profile picture

Ilir Aliu

52,301 subscribers

26,154 views • 11 months ago •via X (Twitter)

LIVE

Anya Rossi• Live Now

Private livecam show

0 Comments

No comments available

Comments from the original post will appear here

Related Videos

💥 A 450M model just beat bigger VLAs on real robot tasks, and it’s 100% open source [📍 bookmark for later] Came across SmolVLA, a new vision-language-action model for robotics that’s compact, fast, and trained entirely on open community datasets from LeRobot via Hugging Face. What stood out to me is how it matches or outperforms much larger models like ACT using noisy, real-world community data instead of giant private datasets. Why it’s worth a look ✅ 26% performance boost from pretraining on open-source data ✅ Runs on consumer hardware, even a MacBook ✅ 30% faster responses with async inference and smart architecture tweaks ✅ Strong results across Meta-World, LIBERO, SO100, and SO101 ✅ Fully open source: weights, code, training pipeline, eval stack They also introduced smart efficiency tricks like using fewer visual tokens, pulling outputs from mid-layer, and separating perception from action to make it all run fast. SmolVLA is a strong case for what can happen when the robotics community shares data and builds in the open. Definitely worth keeping an eye on.
0:21

💥 A 450M model just beat bigger VLAs on real robot tasks, and it’s 100% open source [📍 bookmark for later] Came across SmolVLA, a new vision-language-action model for robotics that’s compact, fast, and trained entirely on open community datasets from LeRobot via Hugging Face. What stood out to me is how it matches or outperforms much larger models like ACT using noisy, real-world community data instead of giant private datasets. Why it’s worth a look ✅ 26% performance boost from pretraining on open-source data ✅ Runs on consumer hardware, even a MacBook ✅ 30% faster responses with async inference and smart architecture tweaks ✅ Strong results across Meta-World, LIBERO, SO100, and SO101 ✅ Fully open source: weights, code, training pipeline, eval stack They also introduced smart efficiency tricks like using fewer visual tokens, pulling outputs from mid-layer, and separating perception from action to make it all run fast. SmolVLA is a strong case for what can happen when the robotics community shares data and builds in the open. Definitely worth keeping an eye on.

Ilir Aliu - eu/acc

17,353 views • 10 months ago

Robots are getting smarter, but most still fail the same way. They don’t learn from their own mistakes. A new paper proposes something different: a way for robots to self-improve directly from their failures in the real world. It’s called PLD (Probe, Learn, Distill). The idea: instead of collecting endless human demos, let the robot figure out where it fails, learn how to recover, and then distill that knowledge back into its main model. Key takeaways from the research: ✅ Uses residual reinforcement learning to recover from policy failures ✅ Achieves 99% success on LIBERO and 100% on real Franka and YAM arms ✅ Runs hour-long manipulation tasks without human resets ✅ Builds a feedback loop between real-world data and model adaptation Unlike supervised fine-tuning, which relies on humans, PLD learns from the robot’s own experience. By training on its own failure distribution, the model becomes both more efficient and more aligned with the real world. It’s not just a technical shift, it’s a step toward robots that improve themselves through real-world practice. Thanks for sharing, Wenli Xiao !! Paper and demos: —- Weekly robotics and AI insights. Subscribe free:
0:47

Robots are getting smarter, but most still fail the same way. They don’t learn from their own mistakes. A new paper proposes something different: a way for robots to self-improve directly from their failures in the real world. It’s called PLD (Probe, Learn, Distill). The idea: instead of collecting endless human demos, let the robot figure out where it fails, learn how to recover, and then distill that knowledge back into its main model. Key takeaways from the research: ✅ Uses residual reinforcement learning to recover from policy failures ✅ Achieves 99% success on LIBERO and 100% on real Franka and YAM arms ✅ Runs hour-long manipulation tasks without human resets ✅ Builds a feedback loop between real-world data and model adaptation Unlike supervised fine-tuning, which relies on humans, PLD learns from the robot’s own experience. By training on its own failure distribution, the model becomes both more efficient and more aligned with the real world. It’s not just a technical shift, it’s a step toward robots that improve themselves through real-world practice. Thanks for sharing, Wenli Xiao !! Paper and demos: —- Weekly robotics and AI insights. Subscribe free:

Ilir Aliu

22,812 views • 8 months ago

A team tested Pi0, Pi0 Fast, Gr00t, and ACT on real robot arms in manufacturing tasks. (🔖 Bookmark this for later!) The task was precise: place thin rectangular frames from a messy stack into a holder. The team fine-tuned each model on 100 real trajectories and compared training time, inference speed, motion quality, and success rates. ⬇️ Here’s a breakdown of what they found Pi0 (Original) ✅ Strongest overall performance in precise pick-and-place ✅ High success rate even in edge cases ✅ Longest training time (~11 hours, ~$30 per run) ✅ Inference time of 80 ms causes short pauses between actions Despite delays, it handles complex scenarios well… solid for high-precision tasks, but slow to train. Gr00t ✅ Trains fast (~2 hours, ~$5 per run) ✅ Performs almost as well as Pi0 on large-object tasks ✅ Struggles with fine precision; random movement in some trials ✅ More training didn’t fix jitter or random offsets Best suited for tasks where exact precision isn’t critical. Not ready for manufacturing-grade accuracy without more tuning. Pi0 Fast ✅ Promised faster training, but results were underwhelming ✅ Training at 6 hours still showed low success rates ✅ Inference was slower than expected ✅ Not reliable for generalizing even slightly new tasks Currently too unstable for real-world deployment. Doesn’t live up to the “Fast” name yet. ACT (Baseline) ✅ 200MB model—lightweight, but limited ✅ Struggles with stacked objects or ambiguous scenes ✅ Success rates around 70% in best-case setups ✅ Can’t match newer models on precision or generalization Still a solid baseline, but clearly a generation behind in robustness. 🚨 Extra Notes All newer models share a common issue: •Inference takes longer than a frame (80 ms vs 33 ms), so robots “pause” between chunks. •This results in jittery movements, but not a dealbreaker unless tasks are time-sensitive. Language-conditioned tasks also fell short: after training on two labeled tasks, the model couldn’t generalize to a third unseen combination using only text prompts. ✅ The good news? These models adapt well to new robot arms with quick fine-tuning. ❌ The bad news? There’s still no plug-and-play solution for improving performance after deployment. Reinforcement learning or DAgger-style data collection during real-world operation may be the next big step, something many teams in robotics are actively working on.
0:51

A team tested Pi0, Pi0 Fast, Gr00t, and ACT on real robot arms in manufacturing tasks. (🔖 Bookmark this for later!) The task was precise: place thin rectangular frames from a messy stack into a holder. The team fine-tuned each model on 100 real trajectories and compared training time, inference speed, motion quality, and success rates. ⬇️ Here’s a breakdown of what they found Pi0 (Original) ✅ Strongest overall performance in precise pick-and-place ✅ High success rate even in edge cases ✅ Longest training time (~11 hours, ~$30 per run) ✅ Inference time of 80 ms causes short pauses between actions Despite delays, it handles complex scenarios well… solid for high-precision tasks, but slow to train. Gr00t ✅ Trains fast (~2 hours, ~$5 per run) ✅ Performs almost as well as Pi0 on large-object tasks ✅ Struggles with fine precision; random movement in some trials ✅ More training didn’t fix jitter or random offsets Best suited for tasks where exact precision isn’t critical. Not ready for manufacturing-grade accuracy without more tuning. Pi0 Fast ✅ Promised faster training, but results were underwhelming ✅ Training at 6 hours still showed low success rates ✅ Inference was slower than expected ✅ Not reliable for generalizing even slightly new tasks Currently too unstable for real-world deployment. Doesn’t live up to the “Fast” name yet. ACT (Baseline) ✅ 200MB model—lightweight, but limited ✅ Struggles with stacked objects or ambiguous scenes ✅ Success rates around 70% in best-case setups ✅ Can’t match newer models on precision or generalization Still a solid baseline, but clearly a generation behind in robustness. 🚨 Extra Notes All newer models share a common issue: •Inference takes longer than a frame (80 ms vs 33 ms), so robots “pause” between chunks. •This results in jittery movements, but not a dealbreaker unless tasks are time-sensitive. Language-conditioned tasks also fell short: after training on two labeled tasks, the model couldn’t generalize to a third unseen combination using only text prompts. ✅ The good news? These models adapt well to new robot arms with quick fine-tuning. ❌ The bad news? There’s still no plug-and-play solution for improving performance after deployment. Reinforcement learning or DAgger-style data collection during real-world operation may be the next big step, something many teams in robotics are actively working on.

Ilir Aliu

21,844 views • 1 year ago

Wow. Recreating the Shawshank Redemption prison in 3D from a single video, in real time (!) Just read the MASt3R-SLAM paper and it's pretty neat. These folks basically built a real-time dense SLAM system on top of MASt3R, which is a transformer-based neural network that can do 3d reconstruction and localization from uncalibrated image pairs. The cool part is they don't need a fixed camera model -- it just works with arbitrary cameras -- think different focal lengths, sensor sizes, even handling zooming in video (FMV drone video anyone?!). If you've done photogrammetry or played with NeRFs you know that is a HUGE deal. They've solved some tricky problems like efficient point matching and tracking, plus they've figured out how to fuse point clouds and handle loop closures in real-time. Their system runs at about 15 FPS on a 4090 and produces both camera poses and dense geometry. When they know the camera calibration, they get SOTA results across several benchmarks, but even without calibration, they still perform well. What's interesting is the approach -- most recent SLAM work has built on DROID-SLAM's architecture, but these folks went a different direction by leveraging a strong 3D reconstruction prior. Seems to give them more coherent geometry, which makes sense since that's what MASt3R was designed for. For anyone who cares about monocular SLAM and 3D reconstruction, this feels like a significant step toward plug-and-play dense SLAM without calibration headaches -- perfect for drones, robots, AR/VR -- the works!
0:29

Wow. Recreating the Shawshank Redemption prison in 3D from a single video, in real time (!) Just read the MASt3R-SLAM paper and it's pretty neat. These folks basically built a real-time dense SLAM system on top of MASt3R, which is a transformer-based neural network that can do 3d reconstruction and localization from uncalibrated image pairs. The cool part is they don't need a fixed camera model -- it just works with arbitrary cameras -- think different focal lengths, sensor sizes, even handling zooming in video (FMV drone video anyone?!). If you've done photogrammetry or played with NeRFs you know that is a HUGE deal. They've solved some tricky problems like efficient point matching and tracking, plus they've figured out how to fuse point clouds and handle loop closures in real-time. Their system runs at about 15 FPS on a 4090 and produces both camera poses and dense geometry. When they know the camera calibration, they get SOTA results across several benchmarks, but even without calibration, they still perform well. What's interesting is the approach -- most recent SLAM work has built on DROID-SLAM's architecture, but these folks went a different direction by leveraging a strong 3D reconstruction prior. Seems to give them more coherent geometry, which makes sense since that's what MASt3R was designed for. For anyone who cares about monocular SLAM and 3D reconstruction, this feels like a significant step toward plug-and-play dense SLAM without calibration headaches -- perfect for drones, robots, AR/VR -- the works!

Bilawal Sidhu

703,816 views • 1 year ago

🔥 Pi Network Has Officially Entered Beast Mode – Global Payment Giants Now Support It! While others doubted, we built. While the world watched, Pi Network quietly integrated with the biggest financial platforms on Earth. And now… we’re ready. 💪🌎 💥 The Walls Are Down. Pi Is Borderless. The Open Mainnet is live, the infrastructure is in place, and now the final pieces are falling into place — Pi Network is supported by a massive alliance of global payment powerhouses, including: 🛡️ Exchange Titans & Fiat Gateways: •✅ Binance P2P •✅ Binance Connect •✅ Transak •✅ Sardine •✅ Topper •✅ UTORG •✅ Paybis •✅ Onmeta •✅ Onramp Money •✅ •✅ TransFi •✅ DFX •✅ Alchemy Pay •✅ Banxa •✅ BTC Direct •✅ Coinify •✅ MoonPay •✅ Fonbnk •✅ GateConnect •✅ Unlimit •✅ Guardarian •✅ Koywe •✅ LocalRamp •✅ Yellow Card 💳 World-Class Fintechs Now Support Pi: •✅ Stripe •✅ Skrill Crypto •✅ Revolut 🌐 This Is Bigger Than Just Crypto Stripe and Skrill aren’t just crypto services — they’re global payment kings. And now they’re helping Pi bridge traditional finance with the new digital economy. Revolut is a top fintech unicorn, and it’s already supporting Pi. Binance is the biggest crypto exchange in the world — and it’s ready. Let that sink in. 🔥 🚀 Why This Changes Everything •🌍 Anyone can access Pi, anywhere in the world •🏦 Buy and sell Pi directly with bank cards, local fiat, Apple Pay, and more •💼 Businesses can now prepare to integrate Pi into payments •🧠 Investors now see the foundation for real-world use and massive growth This is what true mass adoption looks like — infrastructure before hype. Utility before listing. Power before price. 🔮 The World’s Not Ready, But We Are. While other coins chased listings, Pi Network built alliances. While others pumped and dumped, Pi built its own economy. And now? The rocket is fully fueled. We’re not waiting for the future — we’re building it. “You don’t have to be first. You just have to be the one who finishes the race prepared. And Pi Network is ready to dominate.” 📢 Pioneers, Stand Tall Your patience, your faith, your mining, your contribution — it’s all paying off. With 40+ global financial platforms now supporting Pi, we’ve gone from an idea… to an unstoppable force. 🎯 Pi Network is not just another crypto project. 🚀 Pi is the people’s currency — and now the world is opening its gates to it. 📣 Share this. Shout it. Post it. Let every Pioneer know — Pi is ready. Let every skeptic watch — we told you so. Let every investor understand — the game has changed. Pi is no longer coming. Pi is here. 🔥 And the world is about to feel it. Pi Network Nicolas Kokkalis Chengdiao Fan ✅✅✅✅🚀🚀🚀🚀🚀🚀🚀🚀
0:38

🔥 Pi Network Has Officially Entered Beast Mode – Global Payment Giants Now Support It! While others doubted, we built. While the world watched, Pi Network quietly integrated with the biggest financial platforms on Earth. And now… we’re ready. 💪🌎 💥 The Walls Are Down. Pi Is Borderless. The Open Mainnet is live, the infrastructure is in place, and now the final pieces are falling into place — Pi Network is supported by a massive alliance of global payment powerhouses, including: 🛡️ Exchange Titans & Fiat Gateways: •✅ Binance P2P •✅ Binance Connect •✅ Transak •✅ Sardine •✅ Topper •✅ UTORG •✅ Paybis •✅ Onmeta •✅ Onramp Money •✅ •✅ TransFi •✅ DFX •✅ Alchemy Pay •✅ Banxa •✅ BTC Direct •✅ Coinify •✅ MoonPay •✅ Fonbnk •✅ GateConnect •✅ Unlimit •✅ Guardarian •✅ Koywe •✅ LocalRamp •✅ Yellow Card 💳 World-Class Fintechs Now Support Pi: •✅ Stripe •✅ Skrill Crypto •✅ Revolut 🌐 This Is Bigger Than Just Crypto Stripe and Skrill aren’t just crypto services — they’re global payment kings. And now they’re helping Pi bridge traditional finance with the new digital economy. Revolut is a top fintech unicorn, and it’s already supporting Pi. Binance is the biggest crypto exchange in the world — and it’s ready. Let that sink in. 🔥 🚀 Why This Changes Everything •🌍 Anyone can access Pi, anywhere in the world •🏦 Buy and sell Pi directly with bank cards, local fiat, Apple Pay, and more •💼 Businesses can now prepare to integrate Pi into payments •🧠 Investors now see the foundation for real-world use and massive growth This is what true mass adoption looks like — infrastructure before hype. Utility before listing. Power before price. 🔮 The World’s Not Ready, But We Are. While other coins chased listings, Pi Network built alliances. While others pumped and dumped, Pi built its own economy. And now? The rocket is fully fueled. We’re not waiting for the future — we’re building it. “You don’t have to be first. You just have to be the one who finishes the race prepared. And Pi Network is ready to dominate.” 📢 Pioneers, Stand Tall Your patience, your faith, your mining, your contribution — it’s all paying off. With 40+ global financial platforms now supporting Pi, we’ve gone from an idea… to an unstoppable force. 🎯 Pi Network is not just another crypto project. 🚀 Pi is the people’s currency — and now the world is opening its gates to it. 📣 Share this. Shout it. Post it. Let every Pioneer know — Pi is ready. Let every skeptic watch — we told you so. Let every investor understand — the game has changed. Pi is no longer coming. Pi is here. 🔥 And the world is about to feel it. Pi Network Nicolas Kokkalis Chengdiao Fan ✅✅✅✅🚀🚀🚀🚀🚀🚀🚀🚀

Mr Spock 𝛑

42,286 views • 11 months ago

Autonomy for All: My Experience with Replit ⠕ Agent 3 🚀 If you’ve ever wished building software felt less like coding with a computer and more like collaborating with a teammate, you need to try Agent 3. I’ve been using it recently, and honestly it’s a whole new way to build. Try now: 💡 Here’s what stood out for me: ✅ It’s 10X more autonomous — I’ve had it run for up to 200 minutes straight without me touching a thing. ✅ It tests and fixes its own code in a live browser — and it’s 3X faster and 10X more cost-effective than the usual testing models. ✅ It can build other Agents & Automations — I’ve spun up Slackbots to query data, Telegram bots to track habits, and even time-triggered workflows that email me daily stock news. How it feels to use: → You describe what you want in plain language. → Agent 3 just… gets to work — building, testing, improving, and delivering. → You can even track progress live from your phone while it’s still working. This isn’t just an upgrade, it’s a leap. From idea → app → automation, Agent 3 makes it feel like you’ve got another person on your team, not just another tool. 🔗 Check out Replit Agent 3 here: If you build anything cool, I’d love to see it.
0:34

Autonomy for All: My Experience with Replit ⠕ Agent 3 🚀 If you’ve ever wished building software felt less like coding with a computer and more like collaborating with a teammate, you need to try Agent 3. I’ve been using it recently, and honestly it’s a whole new way to build. Try now: 💡 Here’s what stood out for me: ✅ It’s 10X more autonomous — I’ve had it run for up to 200 minutes straight without me touching a thing. ✅ It tests and fixes its own code in a live browser — and it’s 3X faster and 10X more cost-effective than the usual testing models. ✅ It can build other Agents & Automations — I’ve spun up Slackbots to query data, Telegram bots to track habits, and even time-triggered workflows that email me daily stock news. How it feels to use: → You describe what you want in plain language. → Agent 3 just… gets to work — building, testing, improving, and delivering. → You can even track progress live from your phone while it’s still working. This isn’t just an upgrade, it’s a leap. From idea → app → automation, Agent 3 makes it feel like you’ve got another person on your team, not just another tool. 🔗 Check out Replit Agent 3 here: If you build anything cool, I’d love to see it.

Nayeem Sheikh

28,223 views • 10 months ago

We’re excited to announce the release and open-source of HunyuanImage 3.0 — the largest and most powerful open-source text-to-image model to date, with over 80 billion total parameters, of which 13 billion are activated per token during inference.The effect is completely comparable to the industry’s flagship closed-source model.🚀🚀🚀 HunyuanImage 3.0 originates from our internally developed native multimodal large language model, with fine-tuning and post-training focused on text-to-image generation. This unique foundation gives the model a powerful set of capabilities: ✅Reason with world knowledge ✅Understand complex, thousand-word prompts ✅Generate precise text within images Different from traditional DiT architecture image generation models, HunyuanImage 3.0’s MoE architecture uses a Transfusion-based approach to deeply couple Diffusion and LLM training for a single, powerful system. Built on Hunyuan-A13B, HunyuanImage 3.0 was trained on a massive dataset: 5 billion image-text pairs, video frames, interleaved image-text data, and 6 trillion tokens of text corpora. This hybrid training across multimodal generation, understanding, and LLM capabilities allows the model to seamlessly integrate multiple tasks. Whether you're an illustrator, designer, or creator, this is built to slash your workflow from hours to minutes. HunyuanImage 3.0 can generate intricate text, detailed comics, expressive emojis, and lively, engaging illustrations for educational content. The current release focuses solely on text-to-image generation and future updates will include image-to-image, image editing, multi-turn interaction, and more. 👉🏻Try it now: 🔗GitHub: 🤗Hugging Face:
2:09

We’re excited to announce the release and open-source of HunyuanImage 3.0 — the largest and most powerful open-source text-to-image model to date, with over 80 billion total parameters, of which 13 billion are activated per token during inference.The effect is completely comparable to the industry’s flagship closed-source model.🚀🚀🚀 HunyuanImage 3.0 originates from our internally developed native multimodal large language model, with fine-tuning and post-training focused on text-to-image generation. This unique foundation gives the model a powerful set of capabilities: ✅Reason with world knowledge ✅Understand complex, thousand-word prompts ✅Generate precise text within images Different from traditional DiT architecture image generation models, HunyuanImage 3.0’s MoE architecture uses a Transfusion-based approach to deeply couple Diffusion and LLM training for a single, powerful system. Built on Hunyuan-A13B, HunyuanImage 3.0 was trained on a massive dataset: 5 billion image-text pairs, video frames, interleaved image-text data, and 6 trillion tokens of text corpora. This hybrid training across multimodal generation, understanding, and LLM capabilities allows the model to seamlessly integrate multiple tasks. Whether you're an illustrator, designer, or creator, this is built to slash your workflow from hours to minutes. HunyuanImage 3.0 can generate intricate text, detailed comics, expressive emojis, and lively, engaging illustrations for educational content. The current release focuses solely on text-to-image generation and future updates will include image-to-image, image editing, multi-turn interaction, and more. 👉🏻Try it now: 🔗GitHub: 🤗Hugging Face:

Tencent Hy

412,658 views • 9 months ago

Remember that paper that started with ‘Certainly, here is a possible introduction for your topic’? How did that get past peer review?! I don’t want AI tools to do my research for me. I want AI tools to speed up boring tasks that take up my time, so I can focus on the important stuff. Anara moved to a new handle (formerly Unriddle) does exactly that. Here’s how you can use it for your research. 🧵👇 #SponsoredWalkthrough One of the biggest challenges in research is time. A solid literature review takes at least 2-3 months… sometimes even longer, depending on the depth of analysis needed. Reading, organising, and synthesising information is a slow process, but it’s absolutely necessary for high-quality work. AI can help speed it up. Not by replacing your critical thinking. It’s your PhD, your ideas need to be your own—but by automating the tedious, repetitive parts of research so you can focus on deep understanding, analysis, and writing. Unlike other AI tools, Anara works with almost any document format. This is what makes it really stand out from the rest. For instance, you can upload: ✅PDFs and other word-based documents ✅Images and presentations ✅Handwritten notes, voice memos, even videos There are so many resources out there that we can learn from. You can upload everything from research papers to YouTube videos and even your own notes and scribbles. It actually understands handwriting surprisingly well! You get automatic summaries when you upload documents. The AI extracts key information immediately, giving you quick insights. It can also help you keep your documents organised. Use the Groups feature to sort and categorise your resources. Create a group for your literature review and keep these papers separate from your other projects or chapters. Tip: Overwhelmed by the number of papers in your "to-be-read" folder? Upload your papers to Anara for immediate insights on each of them, then use these to decide which ones you want to read in more detail. Quickly identify which papers are worth your time—thank me later! You can also go deeper into the papers with Anara’s chat feature. Instead of endlessly scrolling through documents to find relevant sections, just ask the AI a question based on your uploaded files. The chat provides direct answers, all with citations. ✅Suggests questions based on your prompt, helping you refine your focus ✅Everything is sourced directly from your documents. So no random AI-generated nonsense ✅Switch between different AI models to suit your needs. Some are better for summarisation, others for deeper contextual analysis It actually sticks to the sources you give it. My favourite feature is the ability to make flashcards! After you upload a document, Anara can create flashcards to help you test your understanding. Perfect for revision and retention. But… can you trust it? The problem with many AI research tools is hallucination... meaning that they make things up. Anara doesn’t do that. It reduces hallucinations by only referencing the documents you upload. Plus, it provides detailed references and hyperlinks so you can check the original source down to the exact page number. This doesn’t mean you shouldn’t read the paper for yourself. It does mean that you can find what you need much faster, and then verify it with automatic citations. At the end of the day, these tools are here to help you, not replace you. If you’ve made it this far, then it’s (definitely) time to go to 👇 anara(dot)so and give it a try. Use code THEPHDPLACE20 for 20% off
1:48

Remember that paper that started with ‘Certainly, here is a possible introduction for your topic’? How did that get past peer review?! I don’t want AI tools to do my research for me. I want AI tools to speed up boring tasks that take up my time, so I can focus on the important stuff. Anara moved to a new handle (formerly Unriddle) does exactly that. Here’s how you can use it for your research. 🧵👇 #SponsoredWalkthrough One of the biggest challenges in research is time. A solid literature review takes at least 2-3 months… sometimes even longer, depending on the depth of analysis needed. Reading, organising, and synthesising information is a slow process, but it’s absolutely necessary for high-quality work. AI can help speed it up. Not by replacing your critical thinking. It’s your PhD, your ideas need to be your own—but by automating the tedious, repetitive parts of research so you can focus on deep understanding, analysis, and writing. Unlike other AI tools, Anara works with almost any document format. This is what makes it really stand out from the rest. For instance, you can upload: ✅PDFs and other word-based documents ✅Images and presentations ✅Handwritten notes, voice memos, even videos There are so many resources out there that we can learn from. You can upload everything from research papers to YouTube videos and even your own notes and scribbles. It actually understands handwriting surprisingly well! You get automatic summaries when you upload documents. The AI extracts key information immediately, giving you quick insights. It can also help you keep your documents organised. Use the Groups feature to sort and categorise your resources. Create a group for your literature review and keep these papers separate from your other projects or chapters. Tip: Overwhelmed by the number of papers in your "to-be-read" folder? Upload your papers to Anara for immediate insights on each of them, then use these to decide which ones you want to read in more detail. Quickly identify which papers are worth your time—thank me later! You can also go deeper into the papers with Anara’s chat feature. Instead of endlessly scrolling through documents to find relevant sections, just ask the AI a question based on your uploaded files. The chat provides direct answers, all with citations. ✅Suggests questions based on your prompt, helping you refine your focus ✅Everything is sourced directly from your documents. So no random AI-generated nonsense ✅Switch between different AI models to suit your needs. Some are better for summarisation, others for deeper contextual analysis It actually sticks to the sources you give it. My favourite feature is the ability to make flashcards! After you upload a document, Anara can create flashcards to help you test your understanding. Perfect for revision and retention. But… can you trust it? The problem with many AI research tools is hallucination... meaning that they make things up. Anara doesn’t do that. It reduces hallucinations by only referencing the documents you upload. Plus, it provides detailed references and hyperlinks so you can check the original source down to the exact page number. This doesn’t mean you shouldn’t read the paper for yourself. It does mean that you can find what you need much faster, and then verify it with automatic citations. At the end of the day, these tools are here to help you, not replace you. If you’ve made it this far, then it’s (definitely) time to go to 👇 anara(dot)so and give it a try. Use code THEPHDPLACE20 for 20% off

The PhD Place

23,135 views • 1 year ago

Hey everyone, today I want to introduce a project that’s aiming to redefine how we access compute for AI — it’s called GPUAI. 🔶 GPUAI: Unlocking Global GPU Power for the AI Era GPUAI isn’t just another GPU marketplace or leasing service. It’s a fully decentralized protocol that connects idle GPU resources around the world — from gaming PCs to data center clusters — and transforms them into a high-performance compute network for AI workloads. 🧠 Why does it matter? Right now, the biggest bottleneck in AI isn’t algorithms — it’s access to compute. Training and running models requires massive GPU power, but it’s locked up in centralized cloud platforms, expensive and hard to access for smaller teams. With GPUAI, anyone can tap into a global GPU pool that’s: ✅ Fully decentralized ✅ Reputation-based and smart contract coordinated ✅ Encrypted and secure ✅ Token-incentivized — meaning contributors get rewarded in $GPUAI 📈 For developers, it’s a flexible way to access GPU compute for training, inference, and more — without cloud lock-in. 💰 For GPU owners, it’s a chance to monetize idle hardware that would otherwise go unused. The protocol is live, the apps are active, and the ecosystem is growing fast. 🌐 Try it yourself at 📖 Learn more on 🎮 Play our community games at This is real infrastructure for the future of AI, not hype. Follow them and explore their mission of decentralized computing at Tell me what you think - if you have a GPU, you can start profiting now. #GPUAI #Web3Infrastructure #AIComputing #DePIN #Decentralization
1:25

Hey everyone, today I want to introduce a project that’s aiming to redefine how we access compute for AI — it’s called GPUAI. 🔶 GPUAI: Unlocking Global GPU Power for the AI Era GPUAI isn’t just another GPU marketplace or leasing service. It’s a fully decentralized protocol that connects idle GPU resources around the world — from gaming PCs to data center clusters — and transforms them into a high-performance compute network for AI workloads. 🧠 Why does it matter? Right now, the biggest bottleneck in AI isn’t algorithms — it’s access to compute. Training and running models requires massive GPU power, but it’s locked up in centralized cloud platforms, expensive and hard to access for smaller teams. With GPUAI, anyone can tap into a global GPU pool that’s: ✅ Fully decentralized ✅ Reputation-based and smart contract coordinated ✅ Encrypted and secure ✅ Token-incentivized — meaning contributors get rewarded in $GPUAI 📈 For developers, it’s a flexible way to access GPU compute for training, inference, and more — without cloud lock-in. 💰 For GPU owners, it’s a chance to monetize idle hardware that would otherwise go unused. The protocol is live, the apps are active, and the ecosystem is growing fast. 🌐 Try it yourself at 📖 Learn more on 🎮 Play our community games at This is real infrastructure for the future of AI, not hype. Follow them and explore their mission of decentralized computing at Tell me what you think - if you have a GPU, you can start profiting now. #GPUAI #Web3Infrastructure #AIComputing #DePIN #Decentralization

The Crypto GEMs

69,984 views • 1 year ago

I’m thrilled to announce that we just released GraspGen, a multi-year project we have been cooking at NVIDIA Robotics 🚀 GraspGen: A Diffusion-Based Framework for 6-DOF Grasping Grasping is a foundational challenge in robotics 🤖 — whether for industrial picking or general-purpose humanoids. VLA + real data collection is all the rage now but is expensive and scales poorly for this task. For every new gripper and/or scene, you’ll have to recollect the dataset in this paradigm for the best perf. 💡Key Idea: Since grasping is such a well-defined task in simulation - why can’t we just scale synthetic data generation and train a generative model for grasping? By embracing modularity and standardized grasp formats, we can make this a turnkey technology that works zero-shot for multiple settings. GraspGen is a modular framework for diffusion-based 6-DOF grasp generation that scales across embodiment types, observability conditions, clutter, task complexity. Key Features: ✅ Multi-embodiment support: suction, parallel-jaw, and multi-fingered grippers ✅ Generalization to partial + complete 3D point clouds ✅ Generalization to single-objects + cluttered scenes ✅ Modular design uses other robotics modules and foundation models (SAM2, cuRobo, FoundationStereo, FoundationPose). This allows GraspGen to focus on only one thing - grasp generation ✅ Training recipe: grasp discriminator is trained with On-Generator data from the diffusion model - so that it learns to correct the mistakes (if any) of the diffusion generator ✅ Real-time performance (~20 Hz) before any GPU acceleration; low memory footprint 📊 Results: • SOTA on the FetchBench [Han et al. CoRL 2024] benchmark • Zero-shot sim-to-real transfer on unknown objects and cluttered scenes • Dataset of 53M simulated grasps across 8K objects from Objaverse 📄 arXiv: 🌐 Website: 💻 Code: A huge thank you to everyone involved in this journey — excited to see what the community builds on top of it! Joint work with Clemens Eppner , Balakumar Sundaralingam , Yu-Wei, Jun Yamada Wentao Yuan and other collaborators #robotics #diffusionmodels #physicalAI #simtoreal
0:39

I’m thrilled to announce that we just released GraspGen, a multi-year project we have been cooking at NVIDIA Robotics 🚀 GraspGen: A Diffusion-Based Framework for 6-DOF Grasping Grasping is a foundational challenge in robotics 🤖 — whether for industrial picking or general-purpose humanoids. VLA + real data collection is all the rage now but is expensive and scales poorly for this task. For every new gripper and/or scene, you’ll have to recollect the dataset in this paradigm for the best perf. 💡Key Idea: Since grasping is such a well-defined task in simulation - why can’t we just scale synthetic data generation and train a generative model for grasping? By embracing modularity and standardized grasp formats, we can make this a turnkey technology that works zero-shot for multiple settings. GraspGen is a modular framework for diffusion-based 6-DOF grasp generation that scales across embodiment types, observability conditions, clutter, task complexity. Key Features: ✅ Multi-embodiment support: suction, parallel-jaw, and multi-fingered grippers ✅ Generalization to partial + complete 3D point clouds ✅ Generalization to single-objects + cluttered scenes ✅ Modular design uses other robotics modules and foundation models (SAM2, cuRobo, FoundationStereo, FoundationPose). This allows GraspGen to focus on only one thing - grasp generation ✅ Training recipe: grasp discriminator is trained with On-Generator data from the diffusion model - so that it learns to correct the mistakes (if any) of the diffusion generator ✅ Real-time performance (~20 Hz) before any GPU acceleration; low memory footprint 📊 Results: • SOTA on the FetchBench [Han et al. CoRL 2024] benchmark • Zero-shot sim-to-real transfer on unknown objects and cluttered scenes • Dataset of 53M simulated grasps across 8K objects from Objaverse 📄 arXiv: 🌐 Website: 💻 Code: A huge thank you to everyone involved in this journey — excited to see what the community builds on top of it! Joint work with Clemens Eppner , Balakumar Sundaralingam , Yu-Wei, Jun Yamada Wentao Yuan and other collaborators #robotics #diffusionmodels #physicalAI #simtoreal

Adithya Murali

23,841 views • 11 months ago

Private transactions between wallets now possible on Solana using ZERAs Private Cash Addresses. A closer look at last week’s MVP drop: Private P2P. Most "privacy" on Solana still falls back to withdraw-to-address (recipient + metadata leaks) or multi-step send flows that leave trails. Our P2P is different: one atomic in-pool transaction, the sender’s note is nullified, and two new encrypted notes are created, all inside the vault. No stepping out. No re-deposit choreography. Why it’s so hard to deanonymize: ✅ Your Private Cash Address has zero link to your Solana wallet: it’s an X25519 keypair derived from a wallet signature, and the public key becomes your private address. ✅ Notes are encrypted with NaCl box using a fresh ephemeral key every send, meaning even two payments to the same person looks unrelated. ✅ Recipients discover incoming notes via trial decryption, the chain never learns "who owns what." While we have immense respect for those building privacy on Solana, we are proud to be the first to achieve one-shot, in-pool P2P with full privacy. To our knowledge, this remains an industry first. This is the difference between hiding balances inside a pool and moving value privately between people. Note: Right now, the sender’s wallet still signs the private transaction, so on-chain you can see that a wallet performed a P2P action, but the amount and recipient remain private. The upcoming P2P Relayer removes that footprint too, completing the "cash-style" flow with no on-chain sender trace. What’s coming next: ✅ More assets: At least SOL + ZERA alongside USDC. ✅ P2P Relayer: A decentralized signing/relaying network that can submit transactions for you — enabling withdrawals with minimal linkage after the initial deposit, and removing the sender’s on-chain P2P footprint. Try it out in the ZERA Dashboard below ⤵️
0:43

Private transactions between wallets now possible on Solana using ZERAs Private Cash Addresses. A closer look at last week’s MVP drop: Private P2P. Most "privacy" on Solana still falls back to withdraw-to-address (recipient + metadata leaks) or multi-step send flows that leave trails. Our P2P is different: one atomic in-pool transaction, the sender’s note is nullified, and two new encrypted notes are created, all inside the vault. No stepping out. No re-deposit choreography. Why it’s so hard to deanonymize: ✅ Your Private Cash Address has zero link to your Solana wallet: it’s an X25519 keypair derived from a wallet signature, and the public key becomes your private address. ✅ Notes are encrypted with NaCl box using a fresh ephemeral key every send, meaning even two payments to the same person looks unrelated. ✅ Recipients discover incoming notes via trial decryption, the chain never learns "who owns what." While we have immense respect for those building privacy on Solana, we are proud to be the first to achieve one-shot, in-pool P2P with full privacy. To our knowledge, this remains an industry first. This is the difference between hiding balances inside a pool and moving value privately between people. Note: Right now, the sender’s wallet still signs the private transaction, so on-chain you can see that a wallet performed a P2P action, but the amount and recipient remain private. The upcoming P2P Relayer removes that footprint too, completing the "cash-style" flow with no on-chain sender trace. What’s coming next: ✅ More assets: At least SOL + ZERA alongside USDC. ✅ P2P Relayer: A decentralized signing/relaying network that can submit transactions for you — enabling withdrawals with minimal linkage after the initial deposit, and removing the sender’s on-chain P2P footprint. Try it out in the ZERA Dashboard below ⤵️

ZERA

36,381 views • 4 months ago

OpenClaw has 186K GitHub stars and 1.5M compromised API keys. I needed a secure alternative. So, I built it with n8n and Claude Opus 4.6. It can already: - Reply to your Telegram messages - Access selected folders from your laptop - Access Gmail, Drive, Notion, Linear, etc. - Install new local tools in a sandbox - Run autonomously for hours - Create multiple subagents - Learn from experience - Wake up regularly But, unlike OpenClaw, it: - Can't access your API keys - Can't modify its environment - Can't access folders you haven't shared - Can't access tools you haven't approved - Must get your confirmation, e.g., when sending emails These aren’t prompt instructions. They’re hard architectural boundaries — Docker isolation, mounted folder permissions, n8n’s tool approval system. Key components: ✅ The VPS on Hostinger hosts n8n and a sandbox container. Agents can also connect to my laptop's sandbox via a Claudeflare tunnel + Desktop Commander MCP. ✅ The Manager agent is the brain. It plans, decides, delegates, and talks to the user. It never touches files. It never runs scripts. It works entirely from executor summaries. ✅ The Executor agents are the hands. Each receives a task (what to do + why it matters), decides how to execute it, and reports back. They can install new tools and execute code only in their dedicated sandboxes. ✅ Data Tables in n8n store both memories and sessions — no external database, no vector store, no infrastructure. Just rows in a table. Turns out, that's enough. Two memory types: - Manager memory: user preferences, facts, corrections, relationship, skills, context - Executor memory: what tools are installed, what’s broken, workarounds ✅ Sessions are short-term state for multi-step tasks. Original request, plan, assumptions, and what happened so far. When the Manager loops with fresh context, the session is all it gets. That's a Ralph Wiggum loop. I've been using it for 5 days. And already can't imagine not having it on my phone. What's next: - Heartbeat via Cron (a scheduled prompt) - Civic Nexus governance + MCPs - Supermemory integration - WhatsApp as an additional surface - Hardening The architecture supports all of it. OpenClaw proved people want personal AI agents. It also proved that 'just trust the prompt' isn't a security model. Docker isolation, mounted folder permissions, tool approval — none of this is new technology. It's just discipline. You can easily do this even with n8n — no coding required. --- Want to try it or read more? More, what I learned, and a setup guide: productcompass[.]pm
0:58

OpenClaw has 186K GitHub stars and 1.5M compromised API keys. I needed a secure alternative. So, I built it with n8n and Claude Opus 4.6. It can already: - Reply to your Telegram messages - Access selected folders from your laptop - Access Gmail, Drive, Notion, Linear, etc. - Install new local tools in a sandbox - Run autonomously for hours - Create multiple subagents - Learn from experience - Wake up regularly But, unlike OpenClaw, it: - Can't access your API keys - Can't modify its environment - Can't access folders you haven't shared - Can't access tools you haven't approved - Must get your confirmation, e.g., when sending emails These aren’t prompt instructions. They’re hard architectural boundaries — Docker isolation, mounted folder permissions, n8n’s tool approval system. Key components: ✅ The VPS on Hostinger hosts n8n and a sandbox container. Agents can also connect to my laptop's sandbox via a Claudeflare tunnel + Desktop Commander MCP. ✅ The Manager agent is the brain. It plans, decides, delegates, and talks to the user. It never touches files. It never runs scripts. It works entirely from executor summaries. ✅ The Executor agents are the hands. Each receives a task (what to do + why it matters), decides how to execute it, and reports back. They can install new tools and execute code only in their dedicated sandboxes. ✅ Data Tables in n8n store both memories and sessions — no external database, no vector store, no infrastructure. Just rows in a table. Turns out, that's enough. Two memory types: - Manager memory: user preferences, facts, corrections, relationship, skills, context - Executor memory: what tools are installed, what’s broken, workarounds ✅ Sessions are short-term state for multi-step tasks. Original request, plan, assumptions, and what happened so far. When the Manager loops with fresh context, the session is all it gets. That's a Ralph Wiggum loop. I've been using it for 5 days. And already can't imagine not having it on my phone. What's next: - Heartbeat via Cron (a scheduled prompt) - Civic Nexus governance + MCPs - Supermemory integration - WhatsApp as an additional surface - Hardening The architecture supports all of it. OpenClaw proved people want personal AI agents. It also proved that 'just trust the prompt' isn't a security model. Docker isolation, mounted folder permissions, tool approval — none of this is new technology. It's just discipline. You can easily do this even with n8n — no coding required. --- Want to try it or read more? More, what I learned, and a setup guide: productcompass[.]pm

Paweł Huryn

53,999 views • 5 months ago

The Future of Commerce Is Being Rewritten Right Now with Interlink and LinkersMap 🚀 Digital commerce is changing rapidly, and the connection between crypto payments, real-world businesses, and global consumers is becoming more important than ever. 🌐 InterLink Labs 👤 + 🌐 is taking a major step forward by expanding crypto payment utility across 50+ global brands, helping bring digital assets closer to everyday use. This is an important move towards practical adoption, where crypto is not only held or traded, but also used in real economic activity. 💳✨ From shopping 🛍️ and entertainment 🎮 to travel ✈️, technology 💻, hospitality 🏨, education 🎓, services 🧰, and global retail 🌍, digital payments are becoming part of the modern customer experience. The future of commerce is not just online. It is global, connected, mobile, and increasingly powered by digital assets. 🔥 💜 LinkersMap: Building Real-World Utility for the InterLink Ecosystem LinkersMap is emerging as a powerful marketplace and geo-commerce platform designed to connect businesses, entrepreneurs, communities, and customers in one trusted digital environment. It is built to support verified InterLink Stores, product listings, payment points, and real-world services, giving users a simple way to discover where digital commerce is active. For businesses, LinkersMap is more than just a listing platform. It is a gateway to greater visibility, stronger customer access, and future-ready participation in the growing digital economy. 🏪🌍 🏪 What LinkersMap Offers to Businesses Businesses can use LinkersMap to create a stronger digital presence and connect with customers beyond traditional local limits. Through the platform, businesses can: ✅ Register and display their store on the map ✅ Showcase products and services to a wider audience ✅ Improve brand visibility within the InterLink community ✅ Reach both local and global customers ✅ Build trust through verified store listings ✅ Position themselves for future crypto payment adoption ✅ Connect directly with users looking for real-world businesses ✅ Participate in a growing digital commerce network Whether it is a restaurant, retail shop, hotel, service provider, education centre, wellness brand, tech business, or Web3 project, LinkersMap gives businesses a practical way to become more discoverable. 📍✨ 🌍 Why This Matters for Real-World Adoption One of the biggest challenges in crypto has always been real-world utility. 💵 People need places to spend. 👪 Businesses need customers. 🏪 Communities need trusted platforms. 💳 Ecosystems need practical use cases. This is where LinkersMap plays an important role. By connecting physical businesses, digital listings, products, and payment visibility, LinkersMap helps bridge the gap between crypto users and real-world commerce. 💳🏪 As the InterLink Network grows, more businesses can join, more users can discover services, and more value can move through the ecosystem. This creates a stronger cycle of adoption: 🔁 More businesses join 🔁 More users discover them 🔁 More transactions become possible 🔁 More utility is created 🔁 The ecosystem becomes stronger 💳 Strengthening Utility for $ITL The expansion of InterLink payment utility helps support the long-term usefulness of $ITL within real-world commerce. As more stores, brands, and services become connected to the ecosystem, $ITL gains more practical relevance. This is important because strong digital assets are supported not only by community interest, but also by actual use. LinkersMap helps make this utility more visible by giving users a place to discover businesses, products, and services connected to the InterLink Network. 🌐💜 ✨ Key Benefits for the Community The growth of LinkersMap creates value for different parts of the ecosystem. For Businesses 🏪 ✅ Increased online and map-based visibility ✅ Access to a growing Web3-focused community ✅ Opportunity to attract new customers ✅ Stronger digital presence ✅ Future-ready payment positioning ✅ Better exposure for products and services For Customers 🛒 ✅ Easier discovery of verified businesses ✅ Access to InterLink-connected stores and services ✅ More real-world use cases for digital assets ✅ Simple map-based search experience ✅ Greater confidence through store verification For the InterLink Ecosystem 🌐 ✅ Stronger real-world utility for $ITL ✅ More payment points and business listings ✅ Better connection between online and offline commerce ✅ Community-driven growth ✅ Increased merchant adoption ✅ A clearer path towards mainstream use 🚀 A New Chapter for Digital Commerce The next generation of commerce is not only about payments. It is about creating a complete ecosystem where businesses can be discovered, customers can connect, communities can grow, and digital assets can be used in practical ways. LinkersMap supports this vision by making real-world business participation easier, more visible, and more accessible. It gives entrepreneurs, merchants, and service providers a platform to be part of the digital economy while helping users find businesses that are ready for the future. 🌎✨ 💼 Register Your Business Today If you are a business owner, entrepreneur, merchant, or service provider, now is the time to join the growing LinkersMap ecosystem. 📍 Add your store 🛍️ Showcase your products 🌍 Reach more customers 💳 Prepare for crypto-powered commerce 💜 Connect with the InterLink community 🌐 Register here: 💜 One Ecosystem. Unlimited Opportunities. 🌎 Global Reach. Real Utility. Future Growth. 🚀 LinkersMap is helping connect businesses, communities, and digital commerce for the future. LinkersMap Dr Altcoin ✝️ Arif Ahmed Core Ambassador | Interlink Labs InterLink Labs 👤 + 🌐 KV InterLink Foundation ITLX Wallet #Interlink #ITLG #ITL #Linkersmap #business #ecommerce #globalgrowth #onlinestore
0:24

The Future of Commerce Is Being Rewritten Right Now with Interlink and LinkersMap 🚀 Digital commerce is changing rapidly, and the connection between crypto payments, real-world businesses, and global consumers is becoming more important than ever. 🌐 InterLink Labs 👤 + 🌐 is taking a major step forward by expanding crypto payment utility across 50+ global brands, helping bring digital assets closer to everyday use. This is an important move towards practical adoption, where crypto is not only held or traded, but also used in real economic activity. 💳✨ From shopping 🛍️ and entertainment 🎮 to travel ✈️, technology 💻, hospitality 🏨, education 🎓, services 🧰, and global retail 🌍, digital payments are becoming part of the modern customer experience. The future of commerce is not just online. It is global, connected, mobile, and increasingly powered by digital assets. 🔥 💜 LinkersMap: Building Real-World Utility for the InterLink Ecosystem LinkersMap is emerging as a powerful marketplace and geo-commerce platform designed to connect businesses, entrepreneurs, communities, and customers in one trusted digital environment. It is built to support verified InterLink Stores, product listings, payment points, and real-world services, giving users a simple way to discover where digital commerce is active. For businesses, LinkersMap is more than just a listing platform. It is a gateway to greater visibility, stronger customer access, and future-ready participation in the growing digital economy. 🏪🌍 🏪 What LinkersMap Offers to Businesses Businesses can use LinkersMap to create a stronger digital presence and connect with customers beyond traditional local limits. Through the platform, businesses can: ✅ Register and display their store on the map ✅ Showcase products and services to a wider audience ✅ Improve brand visibility within the InterLink community ✅ Reach both local and global customers ✅ Build trust through verified store listings ✅ Position themselves for future crypto payment adoption ✅ Connect directly with users looking for real-world businesses ✅ Participate in a growing digital commerce network Whether it is a restaurant, retail shop, hotel, service provider, education centre, wellness brand, tech business, or Web3 project, LinkersMap gives businesses a practical way to become more discoverable. 📍✨ 🌍 Why This Matters for Real-World Adoption One of the biggest challenges in crypto has always been real-world utility. 💵 People need places to spend. 👪 Businesses need customers. 🏪 Communities need trusted platforms. 💳 Ecosystems need practical use cases. This is where LinkersMap plays an important role. By connecting physical businesses, digital listings, products, and payment visibility, LinkersMap helps bridge the gap between crypto users and real-world commerce. 💳🏪 As the InterLink Network grows, more businesses can join, more users can discover services, and more value can move through the ecosystem. This creates a stronger cycle of adoption: 🔁 More businesses join 🔁 More users discover them 🔁 More transactions become possible 🔁 More utility is created 🔁 The ecosystem becomes stronger 💳 Strengthening Utility for $ITL The expansion of InterLink payment utility helps support the long-term usefulness of $ITL within real-world commerce. As more stores, brands, and services become connected to the ecosystem, $ITL gains more practical relevance. This is important because strong digital assets are supported not only by community interest, but also by actual use. LinkersMap helps make this utility more visible by giving users a place to discover businesses, products, and services connected to the InterLink Network. 🌐💜 ✨ Key Benefits for the Community The growth of LinkersMap creates value for different parts of the ecosystem. For Businesses 🏪 ✅ Increased online and map-based visibility ✅ Access to a growing Web3-focused community ✅ Opportunity to attract new customers ✅ Stronger digital presence ✅ Future-ready payment positioning ✅ Better exposure for products and services For Customers 🛒 ✅ Easier discovery of verified businesses ✅ Access to InterLink-connected stores and services ✅ More real-world use cases for digital assets ✅ Simple map-based search experience ✅ Greater confidence through store verification For the InterLink Ecosystem 🌐 ✅ Stronger real-world utility for $ITL ✅ More payment points and business listings ✅ Better connection between online and offline commerce ✅ Community-driven growth ✅ Increased merchant adoption ✅ A clearer path towards mainstream use 🚀 A New Chapter for Digital Commerce The next generation of commerce is not only about payments. It is about creating a complete ecosystem where businesses can be discovered, customers can connect, communities can grow, and digital assets can be used in practical ways. LinkersMap supports this vision by making real-world business participation easier, more visible, and more accessible. It gives entrepreneurs, merchants, and service providers a platform to be part of the digital economy while helping users find businesses that are ready for the future. 🌎✨ 💼 Register Your Business Today If you are a business owner, entrepreneur, merchant, or service provider, now is the time to join the growing LinkersMap ecosystem. 📍 Add your store 🛍️ Showcase your products 🌍 Reach more customers 💳 Prepare for crypto-powered commerce 💜 Connect with the InterLink community 🌐 Register here: 💜 One Ecosystem. Unlimited Opportunities. 🌎 Global Reach. Real Utility. Future Growth. 🚀 LinkersMap is helping connect businesses, communities, and digital commerce for the future. LinkersMap Dr Altcoin ✝️ Arif Ahmed Core Ambassador | Interlink Labs InterLink Labs 👤 + 🌐 KV InterLink Foundation ITLX Wallet #Interlink #ITLG #ITL #Linkersmap #business #ecommerce #globalgrowth #onlinestore

Tekkaus® | InterLink • MOD • T2 Community Builder

10,912 views • 18 days ago

AI TENNIS ANALYSIS. A FULL COMPUTER VISION SYSTEM. BUILT ON YOLO, PYTORCH, AND KEYPOINT EXTRACTION. Take any tennis match broadcast, any camera angle, any resolution. Feed it into the pipeline. YOLO detects both players and the tennis ball frame by frame. No manual labeling, no pre-annotated dataset. A fine-tuned YOLOv5 model trained on a Roboflow tennis ball dataset handles the ball - the hardest object to track in any sport. Tiny, fast, constantly occluded. The model finds it anyway. Trackers maintain identity across frames so Player 1 stays Player 1 from the first serve to match point. But detection is just the start. A ResNet50 CNN trained in PyTorch predicts court keypoints from every frame - the corners, service lines, baselines, net posts. Fourteen points that define the entire playing surface geometry. From those keypoints the system builds a homography matrix and warps the broadcast perspective into a top-down mini court with real coordinates. Now every player has a position in real space, not pixel space. Every frame becomes a measurement. Every rally becomes a dataset. Player movement speed - calculated from position deltas between frames, converted to meters per second through the homography. Ball shot speed - measured from the ball trajectory across consecutive detections. Number of shots per rally - counted automatically through ball direction changes. All of this rendered live on the video as an overlay. A mini court in the corner showing both players as dots moving in real time. Stats updating after every point. OpenCV handles the rendering. Pandas handles the math. PyTorch handles the intelligence. YOLO handles the eyes. No Hawkeye subscription, no court-embedded sensors, no tracking chips in the ball. A Python script, a trained model, and a GPU. The full code is on GitHub. The tutorial walks through every module - from ball detector training to court keypoint extraction to the final statistical overlay. Professional teams used to need broadcast deals and proprietary hardware for this kind of analysis. Now you build it in an afternoon with open-source tools. Trading here: Computer vision didn't just enter tennis. It made the expensive stuff free.
4:41:24

AI TENNIS ANALYSIS. A FULL COMPUTER VISION SYSTEM. BUILT ON YOLO, PYTORCH, AND KEYPOINT EXTRACTION. Take any tennis match broadcast, any camera angle, any resolution. Feed it into the pipeline. YOLO detects both players and the tennis ball frame by frame. No manual labeling, no pre-annotated dataset. A fine-tuned YOLOv5 model trained on a Roboflow tennis ball dataset handles the ball - the hardest object to track in any sport. Tiny, fast, constantly occluded. The model finds it anyway. Trackers maintain identity across frames so Player 1 stays Player 1 from the first serve to match point. But detection is just the start. A ResNet50 CNN trained in PyTorch predicts court keypoints from every frame - the corners, service lines, baselines, net posts. Fourteen points that define the entire playing surface geometry. From those keypoints the system builds a homography matrix and warps the broadcast perspective into a top-down mini court with real coordinates. Now every player has a position in real space, not pixel space. Every frame becomes a measurement. Every rally becomes a dataset. Player movement speed - calculated from position deltas between frames, converted to meters per second through the homography. Ball shot speed - measured from the ball trajectory across consecutive detections. Number of shots per rally - counted automatically through ball direction changes. All of this rendered live on the video as an overlay. A mini court in the corner showing both players as dots moving in real time. Stats updating after every point. OpenCV handles the rendering. Pandas handles the math. PyTorch handles the intelligence. YOLO handles the eyes. No Hawkeye subscription, no court-embedded sensors, no tracking chips in the ball. A Python script, a trained model, and a GPU. The full code is on GitHub. The tutorial walks through every module - from ball detector training to court keypoint extraction to the final statistical overlay. Professional teams used to need broadcast deals and proprietary hardware for this kind of analysis. Now you build it in an afternoon with open-source tools. Trading here: Computer vision didn't just enter tennis. It made the expensive stuff free.

zostaff

120,370 views • 2 months ago

🚨 ALERT / TECHNICAL FEEDBACK (TUNA Launchpad) $TUNA CA: GfLD9EQn7A1UjopYVJ8aUUjHQhX14dwFf8oBWKW8pump Everyone, stay sharp. This is not FUD — it’s technical information. A lot of people are buying without even testing the launchpad, and as a developer, I felt it was my responsibility to test it first and share real feedback based on what I saw (it’s all shown in the video). ✅ The site appears to be AI-built — and that’s not the issue. The real issue is how the launchpad works, and right now it has critical flaws for anyone putting money into it. 1) No automated on-chain transaction flow A serious launchpad should handle the transaction flow automatically on-chain. Instead, you click “create token” and then you must manually send SOL to a wallet the platform is “waiting on” to receive funds — and only then they create the token and send you your buy. ➡️ This is a massive risk, because it’s not a standard verifiable on-chain flow (a program/contract executing the logic transparently). 2) Tokens are being created with NO Solana metadata The token coming out is not minting with proper metadata — meaning no name, no image, no on-chain identity recorded correctly. ➡️ That’s a major red flag. I can deploy a token with correct Solana metadata in 5 minutes, so a launchpad failing to do this is a serious problem. 3) Token launches with ZERO liquidity (no pool) The token starts with no liquidity at all, only your initial buy. ➡️ Result: it cannot trade on any DEX, because there is no pool / curve to enable swaps. Every real launchpad uses a bonding curve or liquidity model so the token launches with a functional market and a meaningful starting market cap (many start around $4k+, and in ecosystems like BONK pairs such as USD1 often start even higher). 📌 Summary The idea is good, but building a real launchpad is not easy. In its current state, these issues make the risk much higher than most people realize. Since I’m confident most didn’t test it, I tested it for you and shared what I found. If this helped you avoid losses (or reduce risk), here’s my Solana wallet for a tip/bonus: FoJ6dHubHf1bnZpNRDQktzWbDeb5AMcyR761KrNKaeNf ✅ There are still about 10 more problems that I will list in the comments! Good luck to those who stay! Who knows, maybe after the good idea and making money from the fees, they'll decide to do everything 100%. I wish you all the best.
1:54

🚨 ALERT / TECHNICAL FEEDBACK (TUNA Launchpad) $TUNA CA: GfLD9EQn7A1UjopYVJ8aUUjHQhX14dwFf8oBWKW8pump Everyone, stay sharp. This is not FUD — it’s technical information. A lot of people are buying without even testing the launchpad, and as a developer, I felt it was my responsibility to test it first and share real feedback based on what I saw (it’s all shown in the video). ✅ The site appears to be AI-built — and that’s not the issue. The real issue is how the launchpad works, and right now it has critical flaws for anyone putting money into it. 1) No automated on-chain transaction flow A serious launchpad should handle the transaction flow automatically on-chain. Instead, you click “create token” and then you must manually send SOL to a wallet the platform is “waiting on” to receive funds — and only then they create the token and send you your buy. ➡️ This is a massive risk, because it’s not a standard verifiable on-chain flow (a program/contract executing the logic transparently). 2) Tokens are being created with NO Solana metadata The token coming out is not minting with proper metadata — meaning no name, no image, no on-chain identity recorded correctly. ➡️ That’s a major red flag. I can deploy a token with correct Solana metadata in 5 minutes, so a launchpad failing to do this is a serious problem. 3) Token launches with ZERO liquidity (no pool) The token starts with no liquidity at all, only your initial buy. ➡️ Result: it cannot trade on any DEX, because there is no pool / curve to enable swaps. Every real launchpad uses a bonding curve or liquidity model so the token launches with a functional market and a meaningful starting market cap (many start around $4k+, and in ecosystems like BONK pairs such as USD1 often start even higher). 📌 Summary The idea is good, but building a real launchpad is not easy. In its current state, these issues make the risk much higher than most people realize. Since I’m confident most didn’t test it, I tested it for you and shared what I found. If this helped you avoid losses (or reduce risk), here’s my Solana wallet for a tip/bonus: FoJ6dHubHf1bnZpNRDQktzWbDeb5AMcyR761KrNKaeNf ✅ There are still about 10 more problems that I will list in the comments! Good luck to those who stay! Who knows, maybe after the good idea and making money from the fees, they'll decide to do everything 100%. I wish you all the best.

Kame Crypto

14,045 views • 6 months ago

There is a beautiful story that just happened in AI so let me share it for a lighter tone weekend post among all the doom stories in our AI field this week. It’s a story of people on three continents building and sharing in the open a new small efficient and state-of-the-art AI model. It started a couple of months ago when a new team in the AI scene released their first model from their headquarters in Paris (France): Mistral 7B. Impressive model, small and very strong performances in the benchmarks, better than all previous models of this size. And open source! So you could build on top of it. Lewis in Bern (Switzerland) and Ed (in Lyon, in the South of France) both from the H4 team, a team of researchers in model fine-tuning and alignment were talking about it over a coffee, in one of these gatherings that often happen at Hugging Face to break the distance between people (literal distance as HF is a remote company). What about fine-tuning it using this new DPO method that a research team from Stanford in California just posted on Arxiv, says one? Hey, that’s a great idea, replies the other. We've just build a great code base (with Nathan, Nazneen, Costa, Younes and all the H4 team and TRL community) let's use it! The next day they start diving in the datasets openly shared on the HF hub and stumble upon two interesting large and good quality fine-tuning datasets recently open-sourced by OpenBMB, a Chinese team from Tsinghua: UltraFeedback and UltraChat. A few rounds of training experiments confirm the intuition, the resulting model is super strong, by far the strongest they have ever seen in their benchmarks from Berkeley and Stanford (LMSYS and Alpaca). Join Clementine, the big boss of the open evaluation leaderboard. Her deep dive into the model capabilities confirms the results: impressive performance. But the H4 team also hosts a famous faculty member, Pr. Sasha Rush, Associate Professor at Cornell University in his daytime, hacker at HF in his nighttime. Joining the conversation, he proposes to quickly draft a research paper to organize and share all the details with the community. A few days later, the model, called Zephyr (a wind like Mistral), paper, and all details are shared with the world. Quickly other companies, everywhere in the world starts to use it. LlamaIndex, a famous data framework and community, shares how the model blew their expectations on real-life use-case benchmarks, while researchers and practitioners discuss the paper and work on the Hugging Face hub. All this happened in just a few weeks catalyzed by open access to knowledge, models, research, and datasets released all over the world (Europe, California, China) and by the idea that people can build upon one another work in AI to bring real-world value with efficient and open models. Stories like this are numerous everywhere around us and make me really proud of the AI community and see how we can build amazingly useful things together. [the video is just me reading this Friday post hahah]
4:30

There is a beautiful story that just happened in AI so let me share it for a lighter tone weekend post among all the doom stories in our AI field this week. It’s a story of people on three continents building and sharing in the open a new small efficient and state-of-the-art AI model. It started a couple of months ago when a new team in the AI scene released their first model from their headquarters in Paris (France): Mistral 7B. Impressive model, small and very strong performances in the benchmarks, better than all previous models of this size. And open source! So you could build on top of it. Lewis in Bern (Switzerland) and Ed (in Lyon, in the South of France) both from the H4 team, a team of researchers in model fine-tuning and alignment were talking about it over a coffee, in one of these gatherings that often happen at Hugging Face to break the distance between people (literal distance as HF is a remote company). What about fine-tuning it using this new DPO method that a research team from Stanford in California just posted on Arxiv, says one? Hey, that’s a great idea, replies the other. We've just build a great code base (with Nathan, Nazneen, Costa, Younes and all the H4 team and TRL community) let's use it! The next day they start diving in the datasets openly shared on the HF hub and stumble upon two interesting large and good quality fine-tuning datasets recently open-sourced by OpenBMB, a Chinese team from Tsinghua: UltraFeedback and UltraChat. A few rounds of training experiments confirm the intuition, the resulting model is super strong, by far the strongest they have ever seen in their benchmarks from Berkeley and Stanford (LMSYS and Alpaca). Join Clementine, the big boss of the open evaluation leaderboard. Her deep dive into the model capabilities confirms the results: impressive performance. But the H4 team also hosts a famous faculty member, Pr. Sasha Rush, Associate Professor at Cornell University in his daytime, hacker at HF in his nighttime. Joining the conversation, he proposes to quickly draft a research paper to organize and share all the details with the community. A few days later, the model, called Zephyr (a wind like Mistral), paper, and all details are shared with the world. Quickly other companies, everywhere in the world starts to use it. LlamaIndex, a famous data framework and community, shares how the model blew their expectations on real-life use-case benchmarks, while researchers and practitioners discuss the paper and work on the Hugging Face hub. All this happened in just a few weeks catalyzed by open access to knowledge, models, research, and datasets released all over the world (Europe, California, China) and by the idea that people can build upon one another work in AI to bring real-world value with efficient and open models. Stories like this are numerous everywhere around us and make me really proud of the AI community and see how we can build amazingly useful things together. [the video is just me reading this Friday post hahah]

Thomas Wolf

169,127 views • 2 years ago

𝐓𝐡𝐞 𝟐𝟎𝟐𝟔 𝐖𝐨𝐫𝐥𝐝 𝐂𝐮𝐩 𝐰𝐢𝐥𝐥 𝐠𝐞𝐧𝐞𝐫𝐚𝐭𝐞 𝐦𝐨𝐫𝐞 𝐩𝐫𝐞𝐝𝐢𝐜𝐭𝐢𝐨𝐧 𝐚𝐜𝐭𝐢𝐯𝐢𝐭𝐲 𝐭𝐡𝐚𝐧 𝐚𝐧𝐲 𝐬𝐩𝐨𝐫𝐭𝐢𝐧𝐠 𝐞𝐯𝐞𝐧𝐭 𝐢𝐧 𝐡𝐢𝐬𝐭𝐨𝐫𝐲. Most people will use sportsbooks. A few will discover something better. Here's why the World Cup is actually Pots Market Market biggest moment. Every match carries dozens of predictable outcomes, not just who wins, but scorelines, goalscorers, red cards, halftime leads, VAR decisions. Traditional sportsbooks will process billions in bets. And keep most of it. Here is the problem with sportsbooks that nobody talks about: They set the odds. Not the market. Which means the house always has an edge baked in before you place a single bet. You're not trading against the market, you're trading against a company whose entire business model depends on you losing. That's not a prediction market. That's a casino with a football shirt on. Prediction markets are different. The odds aren't set by a company. They're set by the collective intelligence of everyone participating. When millions of people put real money behind their beliefs, the market finds truth faster than any analyst, pundit, or algorithm working alone. This is why prediction markets outperform polls, pundits, and press releases, every single time. Now here's where POTS Market changes the game entirely. Pots Money has a dedicated sports vertical, built specifically for football, basketball, and esports. Not a generic market with a football category tucked in the corner. A tailored module optimized for the way sports prediction actually works, short windows, live data, rapid settlement. 64 World Cup matches = 64 live prediction markets. Each one open, on-chain, transparent. But the real edge is not the markets themselves. It's the capital layer underneath them. On a traditional sportsbook, every bet locks your full stake. You place £100, that £100 is gone until settlement. POTS Money changes this. The DeFi lending primitive means you can collateralize positions and optimize capital across multiple markets simultaneously, without locking up dead capital on each one. That's not betting. That's portfolio management. And then there's the AI layer. Via MCP (Model Context Protocol), you can deploy autonomous trading agents that monitor live match data and execute positions based on your pre-set strategy. Imagine this: Agent monitors possession stats in real time Detects a momentum shift at the 60th minute Auto-places a position on the next goalscorer market before the crowd even reacts 𝙏𝙝𝙖𝙩'𝙨 𝙣𝙤𝙩 𝙖 𝙥𝙧𝙚𝙙𝙞𝙘𝙩𝙞𝙤𝙣. 𝙏𝙝𝙖𝙩'𝙨 𝙖𝙣 𝙚𝙙𝙜𝙚 Compare the two worlds side by side: Sportsbook: ❌ House sets the odds ❌ Geo-restricted ❌ Capital locked per bet ❌ No automation ❌ Withdraw only when they allow it POTS Market: ✅ Market sets the odds ✅ Open and on-chain ✅ Capital efficient via DeFi lending ✅ AI agents execute your strategy ✅ Fully collateralized, transparent settlement One of these is built for the next decade. The other is built for the last one. The timing is not a coincidence. POTS Market launches Q2 2026, right as World Cup fever peaks globally. Q1 2026 — MVP + Polymarket integration ✅ Q2 2026 — Market Launch + DeFi lending beta 👈 we are here Q3-Q4 2026 — Skill Hub + Sub-accounts 2027+ — DAO governance + cross-chain The infrastructure is ready exactly when the biggest prediction event on earth arrives. That's timing. The 2026 World Cup will mint the first generation of serious prediction market traders. Most will start on sportsbooks. The ones who do their research will end up on POTS. From trusting the house to trusting the market. From locked capital to capital efficiency. From punting to positioning. Pots Market Pots Money, the World Cup just became a DeFi event.
3:28

𝐓𝐡𝐞 𝟐𝟎𝟐𝟔 𝐖𝐨𝐫𝐥𝐝 𝐂𝐮𝐩 𝐰𝐢𝐥𝐥 𝐠𝐞𝐧𝐞𝐫𝐚𝐭𝐞 𝐦𝐨𝐫𝐞 𝐩𝐫𝐞𝐝𝐢𝐜𝐭𝐢𝐨𝐧 𝐚𝐜𝐭𝐢𝐯𝐢𝐭𝐲 𝐭𝐡𝐚𝐧 𝐚𝐧𝐲 𝐬𝐩𝐨𝐫𝐭𝐢𝐧𝐠 𝐞𝐯𝐞𝐧𝐭 𝐢𝐧 𝐡𝐢𝐬𝐭𝐨𝐫𝐲. Most people will use sportsbooks. A few will discover something better. Here's why the World Cup is actually Pots Market Market biggest moment. Every match carries dozens of predictable outcomes, not just who wins, but scorelines, goalscorers, red cards, halftime leads, VAR decisions. Traditional sportsbooks will process billions in bets. And keep most of it. Here is the problem with sportsbooks that nobody talks about: They set the odds. Not the market. Which means the house always has an edge baked in before you place a single bet. You're not trading against the market, you're trading against a company whose entire business model depends on you losing. That's not a prediction market. That's a casino with a football shirt on. Prediction markets are different. The odds aren't set by a company. They're set by the collective intelligence of everyone participating. When millions of people put real money behind their beliefs, the market finds truth faster than any analyst, pundit, or algorithm working alone. This is why prediction markets outperform polls, pundits, and press releases, every single time. Now here's where POTS Market changes the game entirely. Pots Money has a dedicated sports vertical, built specifically for football, basketball, and esports. Not a generic market with a football category tucked in the corner. A tailored module optimized for the way sports prediction actually works, short windows, live data, rapid settlement. 64 World Cup matches = 64 live prediction markets. Each one open, on-chain, transparent. But the real edge is not the markets themselves. It's the capital layer underneath them. On a traditional sportsbook, every bet locks your full stake. You place £100, that £100 is gone until settlement. POTS Money changes this. The DeFi lending primitive means you can collateralize positions and optimize capital across multiple markets simultaneously, without locking up dead capital on each one. That's not betting. That's portfolio management. And then there's the AI layer. Via MCP (Model Context Protocol), you can deploy autonomous trading agents that monitor live match data and execute positions based on your pre-set strategy. Imagine this: Agent monitors possession stats in real time Detects a momentum shift at the 60th minute Auto-places a position on the next goalscorer market before the crowd even reacts 𝙏𝙝𝙖𝙩'𝙨 𝙣𝙤𝙩 𝙖 𝙥𝙧𝙚𝙙𝙞𝙘𝙩𝙞𝙤𝙣. 𝙏𝙝𝙖𝙩'𝙨 𝙖𝙣 𝙚𝙙𝙜𝙚 Compare the two worlds side by side: Sportsbook: ❌ House sets the odds ❌ Geo-restricted ❌ Capital locked per bet ❌ No automation ❌ Withdraw only when they allow it POTS Market: ✅ Market sets the odds ✅ Open and on-chain ✅ Capital efficient via DeFi lending ✅ AI agents execute your strategy ✅ Fully collateralized, transparent settlement One of these is built for the next decade. The other is built for the last one. The timing is not a coincidence. POTS Market launches Q2 2026, right as World Cup fever peaks globally. Q1 2026 — MVP + Polymarket integration ✅ Q2 2026 — Market Launch + DeFi lending beta 👈 we are here Q3-Q4 2026 — Skill Hub + Sub-accounts 2027+ — DAO governance + cross-chain The infrastructure is ready exactly when the biggest prediction event on earth arrives. That's timing. The 2026 World Cup will mint the first generation of serious prediction market traders. Most will start on sportsbooks. The ones who do their research will end up on POTS. From trusting the house to trusting the market. From locked capital to capital efficiency. From punting to positioning. Pots Market Pots Money, the World Cup just became a DeFi event.

KIMMY OF GOOD LIFE 😍

21,257 views • 1 month ago

the Andrej Karpathy code: - be Team Human - choose things that scale - scale them to all of humanity things = { Tesla camera vision | humanoid robots | transformers | AI education Eureka Labs} Favorite pull quotes from Andrej Karpathy this morning: - Elon Musk was right on self driving: - "Waymo looks like it's winning right now but I think when we look in 10 years and who's actually at scale and where most of the revenue is coming from I still think [Tesla is] ahead" - "Tesla has a software problem, Waymo has a hardware problem"... "a waymo car has a lot of very expensive LIDAR and other sort of sensors built into the car so it can do what it does...[but] if you can just use cameras which is the Tesla approach then you effectively get rid of enormous cost complexity and you can do it in in many different types of cars". - on Optimus: - "cars are robots" - Tesla isn't a car company, it "is a robotics at scale company" - "early versions of Optimus thought it was a car" - same computer, same cameras, it was walking but thought it was driving - first applications will be in factories where it doesnt "crush grandma" - excited for Optimus to solve the Nat Friedman challenge of the quiet leaf blower robot - on transformers: - "Transformers are this beautiful like blob of tissue you can just get just arbitrary tasks and you just need the data you need to put it in the right form" - "the scaling laws are actually to a large extent a property of the Transformer. Before the Transformer, people were playing with LSTMs and stacking them, you don't actually get clean scaling laws... the Transformer was the first thing that actually just scales. - Architecture is no longer a bottleneck, its now just dataset and objectives - Internet data is "not what you want for your transformer, it's just a nearest neighbor that gets you really far"... "what you want is the inner thought monologue of your brain.. if we had a billion trajectories [of your brain as you're doing problem solving] then AGI is here". "the Internet is like 0.001% cognition and 99.99% of information and most of it is not useful for thinking" - Synthetic data is largely about "refactoring the dataset into these inner monologue formats". Cites the Tencent 1 billion persona paper - Transformers > Humans: much better at learning/memory "if you give it a sequence and you do a single forward-backward pass in that sequence then if you give it the first few elements it will complete the rest of the sequence... it memorized that sequence!" Human brain working memory is very small, transformers "are much more efficient learners". - Most LLMs memorize useless information - a "cognitive core" LLM OS could be as smol as 1b params - just needs to think, and then use tools to look stuff up - Bullish on an AI CEO supervising a swarm (or crew?) of smaller specialist agents - on Education - LLM101n will be "an undergrad level course" coming "early next year" - "I'm always more interested in anything that empowers people... I'm on Team Human" - cites Bloom's Two Sigma problem: "I find very interesting is like how far can a person go if they have the perfect tutor for all the subjects" - people with 1:1 tutoring get 2 stdev better results - "I taught 231n at Stanford and that was the first deep learning class and was pretty successful but the question is how do you really scale these classes — like, how do you make it so that your target audience is maybe 8 billion people on Earth" - for different languages and different capability levels - languages and transfer learning (from previously known domains) are low hanging fruit - "the demo is near but the product is far" - "so the question is how do you use AI to do the scaling of a really good teacher and so the way I'm thinking about it is the teacher is doing a lot of the course creation and the curriculum" - "at current AI capability the models are not good enough to create a good course but I think they're good to become the front end to the student and interpret the course to them" - Learning is supposed to be hard.. but he will "make it easier for people to learn" and in a post AGI society, learning can be entertainment if people want - Kids today should study Math, Physics, CS - "symbol manipulation heavy tasks, not memory heavy"
2:27

the Andrej Karpathy code: - be Team Human - choose things that scale - scale them to all of humanity things = { Tesla camera vision | humanoid robots | transformers | AI education Eureka Labs} Favorite pull quotes from Andrej Karpathy this morning: - Elon Musk was right on self driving: - "Waymo looks like it's winning right now but I think when we look in 10 years and who's actually at scale and where most of the revenue is coming from I still think [Tesla is] ahead" - "Tesla has a software problem, Waymo has a hardware problem"... "a waymo car has a lot of very expensive LIDAR and other sort of sensors built into the car so it can do what it does...[but] if you can just use cameras which is the Tesla approach then you effectively get rid of enormous cost complexity and you can do it in in many different types of cars". - on Optimus: - "cars are robots" - Tesla isn't a car company, it "is a robotics at scale company" - "early versions of Optimus thought it was a car" - same computer, same cameras, it was walking but thought it was driving - first applications will be in factories where it doesnt "crush grandma" - excited for Optimus to solve the Nat Friedman challenge of the quiet leaf blower robot - on transformers: - "Transformers are this beautiful like blob of tissue you can just get just arbitrary tasks and you just need the data you need to put it in the right form" - "the scaling laws are actually to a large extent a property of the Transformer. Before the Transformer, people were playing with LSTMs and stacking them, you don't actually get clean scaling laws... the Transformer was the first thing that actually just scales. - Architecture is no longer a bottleneck, its now just dataset and objectives - Internet data is "not what you want for your transformer, it's just a nearest neighbor that gets you really far"... "what you want is the inner thought monologue of your brain.. if we had a billion trajectories [of your brain as you're doing problem solving] then AGI is here". "the Internet is like 0.001% cognition and 99.99% of information and most of it is not useful for thinking" - Synthetic data is largely about "refactoring the dataset into these inner monologue formats". Cites the Tencent 1 billion persona paper - Transformers > Humans: much better at learning/memory "if you give it a sequence and you do a single forward-backward pass in that sequence then if you give it the first few elements it will complete the rest of the sequence... it memorized that sequence!" Human brain working memory is very small, transformers "are much more efficient learners". - Most LLMs memorize useless information - a "cognitive core" LLM OS could be as smol as 1b params - just needs to think, and then use tools to look stuff up - Bullish on an AI CEO supervising a swarm (or crew?) of smaller specialist agents - on Education - LLM101n will be "an undergrad level course" coming "early next year" - "I'm always more interested in anything that empowers people... I'm on Team Human" - cites Bloom's Two Sigma problem: "I find very interesting is like how far can a person go if they have the perfect tutor for all the subjects" - people with 1:1 tutoring get 2 stdev better results - "I taught 231n at Stanford and that was the first deep learning class and was pretty successful but the question is how do you really scale these classes — like, how do you make it so that your target audience is maybe 8 billion people on Earth" - for different languages and different capability levels - languages and transfer learning (from previously known domains) are low hanging fruit - "the demo is near but the product is far" - "so the question is how do you use AI to do the scaling of a really good teacher and so the way I'm thinking about it is the teacher is doing a lot of the course creation and the curriculum" - "at current AI capability the models are not good enough to create a good course but I think they're good to become the front end to the student and interpret the course to them" - Learning is supposed to be hard.. but he will "make it easier for people to learn" and in a post AGI society, learning can be entertainment if people want - Kids today should study Math, Physics, CS - "symbol manipulation heavy tasks, not memory heavy"

swyx

53,917 views • 1 year ago