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The Redbelly Decentralisation Roadmap outlines how Redbelly will move toward full decentralisation: ⚙️Horizon 1: Accountability 💰 Horizon 2: Staking 📈 Horizon 3: Scale From detecting MEV attacks to onboarding 400 new node operators, each phase strengthens the network. Watch as Vincent Gramoli walks through the key stages.

Redbelly Network

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Chinese robotics company Astribot released their latest World-Action Model (WAM), Lumo-2. Technical breakdown: - based on a frozen 🥶 Qwen-3.5 4B VLM - trained in 3 progressive stages: 1. Action is aligned with latent world dynamics (an abstract representation of action). Real-world actions are anchored to physical constraints, while the latent space is guided to focus on motion-relevant changes. This bidirectional relationship makes the model physically grounded -> critical for a world model. 2. Action is aligned with vision and language. Reusing the vision backbone and action encoder from the frozen VLM, the authors add a custom vocabulary (for new actions), a semantic module, an action decoder, and an action projector. This aligns the (new) action representations with the (existing) vision-language semantic space. Most importantly: it builds a direct mapping from natural-language instructions to motor execution. 3. End-to-end training on language, video, and robot data. Only the new modules (everything outside the frozen backbone) are trained end-to-end across temporal reasoning, physical understanding, long-horizon, and dexterous manipulation. At the end of the day, Lumo-2 is not the best on benchmarks, but that's not the point. What's genuinely new: - a way to combine latent world modeling and action generation through progressive alignment - a physically-grounded latent dynamics space - it lifts performance on unseen objects using un-annotated human egocentric video + Vision Pro captures, no special transfer algorithm needed Why it matters: - the whole model is thin trainable adapters (semantic module, action decoder/projector) on a frozen 4B backbone (cheap) - that scale is suited for real-time embedded inference (~2.71× decode speedup, no accuracy loss) - its real moat is long-horizon execution, where the added temporal memory pays off far more than on any other task As a result, this robot can now make your latte (5x sped up video):

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[Self-Attention] by Hand ✍️ Self-attention is what enables LLMs to understand context. How does it work? This exercise demonstrates how to calculate a 6-3 attention head by hand. Note that if we have two instances of this, we get 6-6 attention (i.e., multi-head attention, n=2). -- 𝗚𝗼𝗮𝗹 -- Transform [6D Features 🟧] to [3D Attention Weighted Features 🟦] -- 𝗪𝗮𝗹𝗸𝘁𝗵𝗿𝗼𝘂𝗴𝗵 -- [1] Given ↳ A set of 4 feature vectors (6-D): x1,x2,x3,x4 [2] Query, Key, Value ↳ Multiply features x's with linear transformation matrices WQ, WK, and WV, to obtain query vectors (q1,q2,q3,q4), key vectors (k1,k2,k3,k4), and value vectors (v1,v2,v3,v4). ↳ "Self" refers to the fact that both queries and keys are derived from the same set of features. [3] 🟪 Prepare for MatMul ↳ Copy query vectors ↳ Copy the transpose of key vectors [4] 🟪 MatMul ↳ Multiply K^T and Q ↳ This is equivalent to taking dot product between every pair of query and key vectors. ↳ The purpose is to use dot product as an estimate of the "matching score" between every key-value pair. ↳ This estimate makes sense because dot product is the numerator of Cosine Similarity between two vectors. [5] 🟨 Scale ↳ Scale each element by the square root of dk, which is the dimension of key vectors (dk=3). ↳ The purpose is to normalize the impact of the dk on matching scores, even if we scale dk to 32, 64, or 128. ↳ To simplify hand calculation, we approximate [ □/sqrt(3) ] with [ floor(□/2) ]. [6] 🟩 Softmax: e^x ↳ Raise e to the power of the number in each cell ↳ To simplify hand calculation, we approximate e^□ with 3^□. [7] 🟩 Softmax: ∑ ↳ Sum across each column [8] 🟩 Softmax: 1 / sum ↳ For each column, divide each element by the column sum ↳ The purpose is normalize each column so that the numbers sum to 1. In other words, each column is a probability distribution of attention, and we have four of them. ↳ The result is the Attention Weight Matrix (A) (yellow) [9] 🟦 MatMul ↳ Multiply the value vectors (Vs) with the Attention Weight Matrix (A) ↳ The results are the attention weighted features Zs. ↳ They are fed to the position-wise feed forward network in the next layer.

Tom Yeh

101,010 Aufrufe • vor 2 Jahren

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As we progress into Q2, it's crucial to align our strategic goals with the execution of our development pipeline to maintain a cohesive vision. Here’s a refined overview of our forthcoming initiatives: 1. OneSec is nearing the completion of its beta phase, with valuable community feedback being integrated by our development team. We are on track to launch the public release this month, closely followed by an enhanced V2. 2. The integration of mainnet nodes, XAI, Myria, and Streamr into CloudVerse is set to enhance our incentive model. Users can look forward to earning rewards through APY, node airdrops, and more. 3. We will initiate staking once the mainnet nodes are operational. For detailed information on rewards, please visit: 4. Following the launch of staking, we will introduce the option to claim tax rewards. 5. Lastly, we are excited about the proof of concept for CloudSec device and its upcoming go-to market strategy. We will be announcing further hardware details regarding how the device will function and create passive income for all, anywhere, anytime. This short term roadmap underscores our commitment to advancing blockchain technology and delivering substantial value to our users. - End of post, beware of scammers -

OpSec

53,120 Aufrufe • vor 2 Jahren

TradFi and DeFi Converged at SmartCon 🤝🏽 SmartCon 2025 proved that the gap between Traditional Finance and DeFi is closing. We were proud to be an integral part of this convergence, demonstrating how is utilizing the Chainlink standard to build the next generation of institutional-grade liquid staking. Here are three ways we are evolving the protocol: 1.Building the Onchain LST Index: As revealed in our SmartCon talk, we are building the industry's first Liquid Staking Token (LST) Index. This product aggregates the best of DeFi, powered by Chainlink’s secure infrastructure. 2.Scaling with CRE: We are among the first to adopt the Chainlink Runtime Environment. This orchestration layer is critical for automating the complex cross-chain workflows required to manage a robust LST Index at scale. 3.Future-Proofing with ACE: To truly marry TradFi and DeFi, compliance is key. We are preparing to integrate the Automated Compliance Engine (ACE) to ensure our products meet the strict regulatory standards required to welcome institutional capital on-chain. Read the full roadmap:

TradFi and DeFi Converged at SmartCon 🤝🏽 SmartCon 2025 proved that the gap between Traditional Finance and DeFi is closing. We were proud to be an integral part of this convergence, demonstrating how is utilizing the Chainlink standard to build the next generation of institutional-grade liquid staking. Here are three ways we are evolving the protocol: 1.Building the Onchain LST Index: As revealed in our SmartCon talk, we are building the industry's first Liquid Staking Token (LST) Index. This product aggregates the best of DeFi, powered by Chainlink’s secure infrastructure. 2.Scaling with CRE: We are among the first to adopt the Chainlink Runtime Environment. This orchestration layer is critical for automating the complex cross-chain workflows required to manage a robust LST Index at scale. 3.Future-Proofing with ACE: To truly marry TradFi and DeFi, compliance is key. We are preparing to integrate the Automated Compliance Engine (ACE) to ensure our products meet the strict regulatory standards required to welcome institutional capital on-chain. Read the full roadmap:

stake.link

19,595 Aufrufe • vor 8 Monaten

What an exciting week it’s been! 😇 Check out the latest updates from Blum Chats for everything that you’d missed and what’s on the horizon: 🎥 Live streams: enjoy watching live streams while trading meme coins. Final testing stage now — launch expected this or early next week. 📱 Full-screen mode: Telegram mini-apps are going full-screen for better usability. Almost ready to go! 📈 BP and MP: earn points for trading — coming soon after final testing wraps up. And more! And don’t forget the referral program: all your old referrals will automatically carry over into the new bot system. Keep growing your network! 🌟 ➡️ As we said, "We're also working on a trading terminal for memes and some exciting AI features to expand the experience for all users." No shortcuts, just real products!

What an exciting week it’s been! 😇 Check out the latest updates from Blum Chats for everything that you’d missed and what’s on the horizon: 🎥 Live streams: enjoy watching live streams while trading meme coins. Final testing stage now — launch expected this or early next week. 📱 Full-screen mode: Telegram mini-apps are going full-screen for better usability. Almost ready to go! 📈 BP and MP: earn points for trading — coming soon after final testing wraps up. And more! And don’t forget the referral program: all your old referrals will automatically carry over into the new bot system. Keep growing your network! 🌟 ➡️ As we said, "We're also working on a trading terminal for memes and some exciting AI features to expand the experience for all users." No shortcuts, just real products!

Blum

51,578 Aufrufe • vor 1 Jahr

In this episode, OpenAI Chief Research Officer Mark Chen joins allen to flambé shrimp, cook Korean stew, and chat about being at the frontier of AI research: why scaling laws and pre-training still matter, how OpenAI chooses research bets and allocates compute, what it means to develop research taste, why evals are in crisis, how to avoid benchmark-maxing, and what it will take for models to handle long-horizon real-world work, multimodal reasoning, and eventually end-to-end AI research. Timestamps: 0:00 Intro 0:28 The Soup Story 1:52 From Trading to AI Research 3:21 How to Develop Research Taste 5:23 RL, Evals, and Superhuman Benchmarks 8:17 Cooking Begins on the Impulse Stove 8:53 Scaling Laws, Pre-Training, and Reasoning 12:33 OpenAI’s Research Roadmap and Compute Allocation 15:48 What Makes a Great Researcher 19:33 The Evals Crisis and Benchmark-Maxing 24:34 Jagged Intelligence, Context, and Long-Horizon Learning 27:14 Shrimp Flambé and New Research Bets 31:32 Multimodal Models and One Architecture 32:36 Vibe Researching and End-to-End AI Research 34:36 Failed Bets, Postmortems, and OpenAI’s Alpha 37:07 Final Taste Test 37:53 Overrated vs. Underrated AI Research 41:00 Closing

In this episode, OpenAI Chief Research Officer Mark Chen joins allen to flambé shrimp, cook Korean stew, and chat about being at the frontier of AI research: why scaling laws and pre-training still matter, how OpenAI chooses research bets and allocates compute, what it means to develop research taste, why evals are in crisis, how to avoid benchmark-maxing, and what it will take for models to handle long-horizon real-world work, multimodal reasoning, and eventually end-to-end AI research. Timestamps: 0:00 Intro 0:28 The Soup Story 1:52 From Trading to AI Research 3:21 How to Develop Research Taste 5:23 RL, Evals, and Superhuman Benchmarks 8:17 Cooking Begins on the Impulse Stove 8:53 Scaling Laws, Pre-Training, and Reasoning 12:33 OpenAI’s Research Roadmap and Compute Allocation 15:48 What Makes a Great Researcher 19:33 The Evals Crisis and Benchmark-Maxing 24:34 Jagged Intelligence, Context, and Long-Horizon Learning 27:14 Shrimp Flambé and New Research Bets 31:32 Multimodal Models and One Architecture 32:36 Vibe Researching and End-to-End AI Research 34:36 Failed Bets, Postmortems, and OpenAI’s Alpha 37:07 Final Taste Test 37:53 Overrated vs. Underrated AI Research 41:00 Closing

Latent.Space

62,796 Aufrufe • vor 1 Monat

Creating a new EOA wallet on Creso 📈 Creso ensures simplicity throughout the process of creating an EOA Wallet. Watch the tutorial attached to this post which follows the easy steps below and see exactly how it will work when the app is launched! 1. Open the Creso app and log in to your account. 2. Navigate to the "Wallets" section and click on "Create New Wallet." 3. Select "EOA Wallet" from the options provided. 4. Set a name for your new EOA wallet. 5. Your wallet private key portion will be downloaded on your device and your wallet will be saved. 6. And just like that, your EOA wallet is ready to use! With these simple steps, you can easily create and manage your EOA wallet on Creso, ensuring your digital assets are secure and accessible.

Creating a new EOA wallet on Creso 📈 Creso ensures simplicity throughout the process of creating an EOA Wallet. Watch the tutorial attached to this post which follows the easy steps below and see exactly how it will work when the app is launched! 1. Open the Creso app and log in to your account. 2. Navigate to the "Wallets" section and click on "Create New Wallet." 3. Select "EOA Wallet" from the options provided. 4. Set a name for your new EOA wallet. 5. Your wallet private key portion will be downloaded on your device and your wallet will be saved. 6. And just like that, your EOA wallet is ready to use! With these simple steps, you can easily create and manage your EOA wallet on Creso, ensuring your digital assets are secure and accessible.

creso

26,403 Aufrufe • vor 2 Jahren

WATCH: TRUMP’S “PROJECT FREEDOM” COULD TRIGGER FULL-SCALE WAR...TODAY! Donald Trump has launched a U.S.-led effort to push ships through the Strait of Hormuz and is a direct challenge to Iran’s control. What happens next comes down to three scenarios: 1. Iran does nothing → Major humiliation. U.S. breaks the blockade without resistance. 2. Iran escalates → Attacks ships, Gulf states, Israel. Back to full war. 3. Iran responds carefully → Symbolic strike to “save face” without triggering escalation. We’ve seen this before. Iran has carried out pre-coordinated strikes designed to respond… without consequences. So the real question is: How does Trump respond? Because today is not routine. This is a stress test of the ceasefire. If this spirals, the Strait of Hormuz becomes the front line of the next phase of war. Stay connected, follow Mossad Commentary

WATCH: TRUMP’S “PROJECT FREEDOM” COULD TRIGGER FULL-SCALE WAR...TODAY! Donald Trump has launched a U.S.-led effort to push ships through the Strait of Hormuz and is a direct challenge to Iran’s control. What happens next comes down to three scenarios: 1. Iran does nothing → Major humiliation. U.S. breaks the blockade without resistance. 2. Iran escalates → Attacks ships, Gulf states, Israel. Back to full war. 3. Iran responds carefully → Symbolic strike to “save face” without triggering escalation. We’ve seen this before. Iran has carried out pre-coordinated strikes designed to respond… without consequences. So the real question is: How does Trump respond? Because today is not routine. This is a stress test of the ceasefire. If this spirals, the Strait of Hormuz becomes the front line of the next phase of war. Stay connected, follow Mossad Commentary

Mossad Commentary

57,030 Aufrufe • vor 3 Monaten

🔥 JUST IN: Open-source robotics dataset from 100% real-world scenarios! 🤯 Chinese robotics company AGIBOT just released AGIBOT WORLD 2026, an open-source dataset systematically covering key embodied AI research directions. Built entirely from real-world environments: commercial spaces, and homes. Collected using AGIBOT G2 robots in free-form collection mode, providing structured, accurately annotated, high-quality data. Digital twin technology creates 1:1 scale replicas in simulation matching the real environments. Both real-world and simulation data are open-sourced. The AGIBOT G2 platform collects multiple data types simultaneously: RGB(D) cameras, tactile sensors, force sensors, LiDAR, IMU, and full-body joint states. Whole-body control coordinates arms, waist, and hands for complex tasks. First-person teleoperation lets operators control the robot from its perspective. The tasks covered are fine-grained manipulation, ultra-long-horizon tasks, spatial navigation, dual-arm coordination, and multi-agent/human-robot collaboration. The dataset includes error-recovery trajectories with annotations. Most datasets only show successful demonstrations. AGIBOT includes failures and how the robot recovers, teaching models how to handle mistakes. After collection, data is tested through policy training and real-robot deployment to ensure quality. Then processed through industrial quality control with multiple screening and cleaning rounds. Making it open-source accelerates embodied AI research by giving researchers access to high-quality real-world robot data at scale. 🇨🇳 Learn more here: ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

🔥 JUST IN: Open-source robotics dataset from 100% real-world scenarios! 🤯 Chinese robotics company AGIBOT just released AGIBOT WORLD 2026, an open-source dataset systematically covering key embodied AI research directions. Built entirely from real-world environments: commercial spaces, and homes. Collected using AGIBOT G2 robots in free-form collection mode, providing structured, accurately annotated, high-quality data. Digital twin technology creates 1:1 scale replicas in simulation matching the real environments. Both real-world and simulation data are open-sourced. The AGIBOT G2 platform collects multiple data types simultaneously: RGB(D) cameras, tactile sensors, force sensors, LiDAR, IMU, and full-body joint states. Whole-body control coordinates arms, waist, and hands for complex tasks. First-person teleoperation lets operators control the robot from its perspective. The tasks covered are fine-grained manipulation, ultra-long-horizon tasks, spatial navigation, dual-arm coordination, and multi-agent/human-robot collaboration. The dataset includes error-recovery trajectories with annotations. Most datasets only show successful demonstrations. AGIBOT includes failures and how the robot recovers, teaching models how to handle mistakes. After collection, data is tested through policy training and real-robot deployment to ensure quality. Then processed through industrial quality control with multiple screening and cleaning rounds. Making it open-source accelerates embodied AI research by giving researchers access to high-quality real-world robot data at scale. 🇨🇳 Learn more here: ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

40,583 Aufrufe • vor 4 Monaten