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Meta unveiled Brain2Qwerty v2, an AI system that converts brain activity into text without requiring brain surgery. - Uses non invasive MEG recordings and end to end deep learning. - Achieved 61% word accuracy on average, with the best participant reaching 78%. - Trained on 22,000 sentences, significantly outperforming... show more

AshutoshShrivastava

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55,421 просмотров • 3 месяцев назад •via X (Twitter)

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30 minutes of video. Robot learns the task. Open-source, end-to-end. An open-source framework for training robot policies from only 30 minutes of human egocentric videos captured via Meta Aria glasses: Achieving zero-shot transfer to robots without any robot data collection. The method relies on Interaction-Centric Tokens that encode hand-object spatial relationships invariant to embodiment and viewpoint, supplemented by auxiliary objectives like object motion prediction and latent consistency to extract richer supervision signals from the same data. HumanEgo demonstrates strong cross-embodiment, cross-environment performance on bimanual tasks, outperforming baselines like ACT and teleop data while being trainable on a single RTX 4090 GPU. Thanks for sharing, Zhi (Leo) Wang. 📌 Website: Paper: Code: Video: ——- Weekly robotics and AI insights. Subscribe free:

30 minutes of video. Robot learns the task. Open-source, end-to-end. An open-source framework for training robot policies from only 30 minutes of human egocentric videos captured via Meta Aria glasses: Achieving zero-shot transfer to robots without any robot data collection. The method relies on Interaction-Centric Tokens that encode hand-object spatial relationships invariant to embodiment and viewpoint, supplemented by auxiliary objectives like object motion prediction and latent consistency to extract richer supervision signals from the same data. HumanEgo demonstrates strong cross-embodiment, cross-environment performance on bimanual tasks, outperforming baselines like ACT and teleop data while being trainable on a single RTX 4090 GPU. Thanks for sharing, Zhi (Leo) Wang. 📌 Website: Paper: Code: Video: ——- Weekly robotics and AI insights. Subscribe free:

Ilir Aliu

17,077 просмотров • 4 месяцев назад

We Open-Sourced a Quantized Fruit-Fly oBrain on GitHub 95% Smaller, ~98% as Accurate as the Published Connectome We quantized the entire fruit-fly nervous system that Google Research, HHMI Janelia, and collaborators published — the complete male Drosophila central nervous system, on the order of 166,000 neurons and a massive synaptic graph — and compressed it by about 95% while retaining roughly 98% of the original simulation accuracy. That is the technical claim. The more important decision is what happens next. After the oBrain team met and debated it, we made a final call: we will not keep this brain closed. We are open-sourcing it on GitHub so researchers, builders, skeptics, and anyone obsessed with biological computation can inspect the weights, the wiring, the quantization path, and the benchmarks. If a “brain” is going to live on-chain, it has to be checkable. Closed source would make that impossible. This is the same instinct that made OpenClaw🦞 a public harness instead of a private assistant, that made Hermes / Nous Research ship agent stacks in the open, and that NVIDIA keeps repeating: the strongest systems are the ones a community can attack, fork, and improve. What we actually released A quantized runtime of the published fly CNS, not a cartoon “inspired by” a fly. A size cut of ~95%, so more people can load, step, and experiment without a research cluster. Accuracy held near 98% against the reference simulation of the published connectome. Source, evaluation notes, and the path to reproduce the comparison — on GitHub, in public. Nothing inside this connectome was “trained” into being a fly. The wiring is the published map. Quantization is an engineering layer so the same graph can run cheaper, smaller, and — in our case — in environments where you can put a brain on-chain and still let outsiders verify it. Why open source, and why on-chain A biological brain map is only as useful as the number of people who can run it and try to break the claims. If we say the on-chain brain matches the Google-published fly brain, that statement is worthless unless you can: pull the code, pull the quantized artifacts, run the same probes, compare spike statistics, circuit responses, and size/accuracy tradeoffs, publish a counter-benchmark if we are wrong. That is the point of this release. Open source so the on-chain brain can be independently verified against the published connectome — and so the community can actually use it. We want people to ask hard questions: Which circuits survive quantization, and which degrade first? Do sensory-to-descending pathways still fire in the right order? How does batch simulation, GPU vs CPU, and lower precision change behavior? What does “98% accuracy” mean for a specific cell type, not just a global score? Can the compressed graph be hashed, attested, and stepped in a verifiable way on-chain without turning the science into marketing? If those questions annoy us, good. That is how a public brain should work. Who this is for Neuroscientists who already live in MaleCNS / FlyEM data. Systems people who care about sparse graphs and event-driven simulation. Quantization researchers who are tired of toy models. Crypto builders who want an on-chain object that is more than a JPEG of a neuron. Students who should not need a petabyte pipeline just to poke a looming detector and watch a giant fiber. Come read the repo. Run the reference vs quantized comparison. File issues. Propose better compressors. Port it. Wrap it. Attack the accuracy number until it is either solid or replaced by a better one. The published fly brain was already a gift to science. Keeping a compressed, runnable version locked behind a private wall would have been a waste of that gift. The brain is on GitHub. Fork it. Measure it. Make it better.

We Open-Sourced a Quantized Fruit-Fly oBrain on GitHub 95% Smaller, ~98% as Accurate as the Published Connectome We quantized the entire fruit-fly nervous system that Google Research, HHMI Janelia, and collaborators published — the complete male Drosophila central nervous system, on the order of 166,000 neurons and a massive synaptic graph — and compressed it by about 95% while retaining roughly 98% of the original simulation accuracy. That is the technical claim. The more important decision is what happens next. After the oBrain team met and debated it, we made a final call: we will not keep this brain closed. We are open-sourcing it on GitHub so researchers, builders, skeptics, and anyone obsessed with biological computation can inspect the weights, the wiring, the quantization path, and the benchmarks. If a “brain” is going to live on-chain, it has to be checkable. Closed source would make that impossible. This is the same instinct that made OpenClaw🦞 a public harness instead of a private assistant, that made Hermes / Nous Research ship agent stacks in the open, and that NVIDIA keeps repeating: the strongest systems are the ones a community can attack, fork, and improve. What we actually released A quantized runtime of the published fly CNS, not a cartoon “inspired by” a fly. A size cut of ~95%, so more people can load, step, and experiment without a research cluster. Accuracy held near 98% against the reference simulation of the published connectome. Source, evaluation notes, and the path to reproduce the comparison — on GitHub, in public. Nothing inside this connectome was “trained” into being a fly. The wiring is the published map. Quantization is an engineering layer so the same graph can run cheaper, smaller, and — in our case — in environments where you can put a brain on-chain and still let outsiders verify it. Why open source, and why on-chain A biological brain map is only as useful as the number of people who can run it and try to break the claims. If we say the on-chain brain matches the Google-published fly brain, that statement is worthless unless you can: pull the code, pull the quantized artifacts, run the same probes, compare spike statistics, circuit responses, and size/accuracy tradeoffs, publish a counter-benchmark if we are wrong. That is the point of this release. Open source so the on-chain brain can be independently verified against the published connectome — and so the community can actually use it. We want people to ask hard questions: Which circuits survive quantization, and which degrade first? Do sensory-to-descending pathways still fire in the right order? How does batch simulation, GPU vs CPU, and lower precision change behavior? What does “98% accuracy” mean for a specific cell type, not just a global score? Can the compressed graph be hashed, attested, and stepped in a verifiable way on-chain without turning the science into marketing? If those questions annoy us, good. That is how a public brain should work. Who this is for Neuroscientists who already live in MaleCNS / FlyEM data. Systems people who care about sparse graphs and event-driven simulation. Quantization researchers who are tired of toy models. Crypto builders who want an on-chain object that is more than a JPEG of a neuron. Students who should not need a petabyte pipeline just to poke a looming detector and watch a giant fiber. Come read the repo. Run the reference vs quantized comparison. File issues. Propose better compressors. Port it. Wrap it. Attack the accuracy number until it is either solid or replaced by a better one. The published fly brain was already a gift to science. Keeping a compressed, runnable version locked behind a private wall would have been a waste of that gift. The brain is on GitHub. Fork it. Measure it. Make it better.

oBrain Arc

53,319 просмотров • 23 дней назад

Meta releases VGGSfM Visual Geometry Grounded Deep Structure From Motion Structure-from-motion (SfM) is a long-standing problem in the computer vision community, which aims to reconstruct the camera poses and 3D structure of a scene from a set of unconstrained 2D images. Classical frameworks solve this problem in an incremental manner by detecting and matching keypoints, registering images, triangulating 3D points, and conducting bundle adjustment. Recent research efforts have predominantly revolved around harnessing the power of deep learning techniques to enhance specific elements (e.g., keypoint matching), but are still based on the original, non-differentiable pipeline. Instead, we propose a new deep SfM pipeline VGGSfM, where each component is fully differentiable and thus can be trained in an end-to-end manner. To this end, we introduce new mechanisms and simplifications. First, we build on recent advances in deep 2D point tracking to extract reliable pixel-accurate tracks, which eliminates the need for chaining pairwise matches. Furthermore, we recover all cameras simultaneously based on the image and track features instead of gradually registering cameras. Finally, we optimise the cameras and triangulate 3D points via a differentiable bundle adjustment layer. We attain state-of-the-art performance on three popular datasets, CO3D, IMC Phototourism, and ETH3D.

Meta releases VGGSfM Visual Geometry Grounded Deep Structure From Motion Structure-from-motion (SfM) is a long-standing problem in the computer vision community, which aims to reconstruct the camera poses and 3D structure of a scene from a set of unconstrained 2D images. Classical frameworks solve this problem in an incremental manner by detecting and matching keypoints, registering images, triangulating 3D points, and conducting bundle adjustment. Recent research efforts have predominantly revolved around harnessing the power of deep learning techniques to enhance specific elements (e.g., keypoint matching), but are still based on the original, non-differentiable pipeline. Instead, we propose a new deep SfM pipeline VGGSfM, where each component is fully differentiable and thus can be trained in an end-to-end manner. To this end, we introduce new mechanisms and simplifications. First, we build on recent advances in deep 2D point tracking to extract reliable pixel-accurate tracks, which eliminates the need for chaining pairwise matches. Furthermore, we recover all cameras simultaneously based on the image and track features instead of gradually registering cameras. Finally, we optimise the cameras and triangulate 3D points via a differentiable bundle adjustment layer. We attain state-of-the-art performance on three popular datasets, CO3D, IMC Phototourism, and ETH3D.

AK

96,527 просмотров • 2 лет назад

AN ANTHROPIC LEAD ENGINEER ACCIDENTALLY LEAKED HIS PERSONAL OBSIDIAN. INSIDE - NOT CODE OR PROMPTS, BUT A DIAGRAM OF HIS OWN BRAIN, ORGANIZED AS A NEURAL NETWORK 8,893 nodes. 4,729 connections. A $10/month app opens Obsidian. 21 inputs, ReLU on every layer. The first hidden layer has 26 neurons, followed by 33, then 24, and so on all the way to the output. Thousands of connections flash in real time this isn’t a conceptual diagram from a blog, but a living brain that powers decision-making within the company. 9,000 documents, each with its own semantic space, all interconnected it earns about $2m a year for sorting Markdown files into the right folders. The company that builds the world’s best AI maintains its internal knowledge base in the same app that a freshman uses for class notes three years of discipline and a single open Obsidian tab you’re reading this on a device where, tonight, you can open that same Obsidian and start building your own vault

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chewa

360,231 просмотров • 3 месяцев назад

i just open sourced the workflow behind $2M AI video productions... i built 7 skills that run the pipeline end to end, built for Seedance 2.5 and they work in Claude Code, Codex, Hermes or any harness (works best with 1080p using Higgsfield CLI) here's how to use them, in order: /setup writes which image and video models you run into your project, once, so every skill reads the same stack /studio-init scaffolds the whole studio as a file tree from one question, the project name /film-breakdown walks your script scene by scene and writes a 22-field card for every shot /reference-board locks your references into a visual bible, a caption on every image and a ban list for the rest /asset-passport writes the exhaustive descriptor every later prompt will quote word for word /stress-test combat-tests each asset and flips it to locked only at 10 out of 10 repeatability /shot-prompt refuses to run until everything in frame is locked, then writes the 15-block prompt and logs every attempt get access to the skills and full breakdown of the pipeline in the article below:

i just open sourced the workflow behind $2M AI video productions... i built 7 skills that run the pipeline end to end, built for Seedance 2.5 and they work in Claude Code, Codex, Hermes or any harness (works best with 1080p using Higgsfield CLI) here's how to use them, in order: /setup writes which image and video models you run into your project, once, so every skill reads the same stack /studio-init scaffolds the whole studio as a file tree from one question, the project name /film-breakdown walks your script scene by scene and writes a 22-field card for every shot /reference-board locks your references into a visual bible, a caption on every image and a ban list for the rest /asset-passport writes the exhaustive descriptor every later prompt will quote word for word /stress-test combat-tests each asset and flips it to locked only at 10 out of 10 repeatability /shot-prompt refuses to run until everything in frame is locked, then writes the 15-block prompt and logs every attempt get access to the skills and full breakdown of the pipeline in the article below:

Machina

61,554 просмотров • 1 месяц назад

🦿Xpeng showed a humanoid robot called IRON whose movement looked so human that the team literally cut it open on stage to prove it is a machine. IRON uses a bionic body with a flexible spine, synthetic muscles, and soft skin so joints and torso can twist smoothly like a person. The system has 82 degrees of freedom in total with 22 in each hand for fine finger control. Compute runs on 3 custom AI chips rated at 2,250 TOPS (Tera Operations Per Second), which is far above typical laptop neural accelerators, so it can handle vision and motion planning on the robot. The AI stack focuses on turning camera input directly into body movement without routing through text, which reduces lag and makes the gait look natural. Xpeng staged the cut-open demo at AI Day in Guangzhou this week, addressing rumors that a performer was inside by exposing internal actuators, wiring, and cooling. Company materials also mention a large physical-world model and a multi-brain control setup for dialogue, perception, and locomotion, hinting at a path from stage demos to service work. Production is targeted for 2026, so near-term tasks will be limited, but the hardware shows a serious step toward human-scale manipulation.

🦿Xpeng showed a humanoid robot called IRON whose movement looked so human that the team literally cut it open on stage to prove it is a machine. IRON uses a bionic body with a flexible spine, synthetic muscles, and soft skin so joints and torso can twist smoothly like a person. The system has 82 degrees of freedom in total with 22 in each hand for fine finger control. Compute runs on 3 custom AI chips rated at 2,250 TOPS (Tera Operations Per Second), which is far above typical laptop neural accelerators, so it can handle vision and motion planning on the robot. The AI stack focuses on turning camera input directly into body movement without routing through text, which reduces lag and makes the gait look natural. Xpeng staged the cut-open demo at AI Day in Guangzhou this week, addressing rumors that a performer was inside by exposing internal actuators, wiring, and cooling. Company materials also mention a large physical-world model and a multi-brain control setup for dialogue, perception, and locomotion, hinting at a path from stage demos to service work. Production is targeted for 2026, so near-term tasks will be limited, but the hardware shows a serious step toward human-scale manipulation.

Rohan Paul

3,802,543 просмотров • 11 месяцев назад

Bringing you all on the inside for a first-hand, behind-the-scenes look at some of the latest and greatest recovery options the Broncos are providing their players in their new $175 million state-of-the-art facility. This allows their players to recover as hard as they train. Too many athletes miss the boat on this and only recover when they have an injury or do minimal amount. As hard as you train, recovery harder!!! You’ve got to outwork the world in all areas and that includes how you take care of your body. Check these out… Cryo-room — Not a cryochamber but a whole room. Dry float tank — I’ve only been in ones where you are immersed in water, have never seen a “dry” version until today. The Ammortal Chamber — a high-end, futuristic biohacking pod that combines five core non-invasive wellness technologies into a single 25-minute, zero-gravity session designed to accelerate muscle recovery, reduce stress, and promote deep nervous system reset. This absolutely calmed down my nervous system so my body could heal itself. Hyperbaric chamber — I do this couple times a week back home to try to repair damage done to my brain and overall recovery. Makes a huge difference in how I bounce back each week. Other amenities: Did 25 minute sauna at 185 degrees, their water area of their hot and cold plunge pools is fantastic! They also have sleep pods for quick recovery naps and a ton more. I cannot thank Beau Lowery and his incredible player health and performance staff enough for helping me out and showing me all the bells and whistles #broncos #recovery #training #nfltrainingcamp

Bringing you all on the inside for a first-hand, behind-the-scenes look at some of the latest and greatest recovery options the Broncos are providing their players in their new $175 million state-of-the-art facility. This allows their players to recover as hard as they train. Too many athletes miss the boat on this and only recover when they have an injury or do minimal amount. As hard as you train, recovery harder!!! You’ve got to outwork the world in all areas and that includes how you take care of your body. Check these out… Cryo-room — Not a cryochamber but a whole room. Dry float tank — I’ve only been in ones where you are immersed in water, have never seen a “dry” version until today. The Ammortal Chamber — a high-end, futuristic biohacking pod that combines five core non-invasive wellness technologies into a single 25-minute, zero-gravity session designed to accelerate muscle recovery, reduce stress, and promote deep nervous system reset. This absolutely calmed down my nervous system so my body could heal itself. Hyperbaric chamber — I do this couple times a week back home to try to repair damage done to my brain and overall recovery. Makes a huge difference in how I bounce back each week. Other amenities: Did 25 minute sauna at 185 degrees, their water area of their hot and cold plunge pools is fantastic! They also have sleep pods for quick recovery naps and a ton more. I cannot thank Beau Lowery and his incredible player health and performance staff enough for helping me out and showing me all the bells and whistles #broncos #recovery #training #nfltrainingcamp

Jay Glazer

43,126 просмотров • 2 месяцев назад

I've been editing this article about "brain mapping" and connectomics, and I'm just stunned by how quickly the cost estimates to map, say, a mouse brain have plummeted in just the last couple years. It actually seems feasible that we could map the entire human brain -- all 86 billion neurons, and their connections -- in this lifetime. In the 1970s, Sydney Brenner started mapping all the connections between neurons in C. elegans. His team sliced the worm into thin pieces, took photos using an electron microscope, and manually traced and reconstructed each synapse for 302 neurons total. This project took more than a decade of work, and it cost about $16,500 to reconstruct each neuron. Scaling this up to a human brain boggles the mind. Electron microscopy remained the norm in connectomics for decades, because it was the only option available to see synapses at a resolution high enough to be able to trace their paths. Each electron microscope costs several hundreds of thousands of dollars, though, and you need lots of them to map even a mouse brain in a reasonable timeframe. In 2023, the Wellcome Trust released a report estimating how long, and how expensive, it would be to map the mouse connectome (~70M neurons). They estimated that imaging alone would cost $200-300M, and that proofreading (or ensuring that traces between neurons are correct) would cost $7-21 BILLION. (A human can only manually trace about 1 mm of neuron per hour.) Also, the images would occupy about 500 petabytes of data, and getting those data would require 20 electron microscopes running in parallel for about 5 years, continuously. They estimated the whole project would take about 17 years of work. This is, understandably, insane. But now it seems like there's an actual path toward mapping the full mouse brain in about five years for ~$100M dollars. There have been three major breakthroughs in the last year or so: 1/ Expansion microscopy, first developed in 2015, showed that it's possible to "enlarge" the brain by about 5x using a swellable polymer. But an improved method increases this number to >20x expansion, meaning we can now expand brains and image neurons much more easily using cheap light microscopes, rather than expensive electron ones. 2/ E11 Bio (a nonprofit research org) developed protein barcodes that get delivered into brain tissue; each neuron gets a unique combination of barcodes. These cells are then stained with colorful antibodies, which stick to a matching protein barcode, causing each neuron to light up in a distinct color. This makes tracing neurons so much easier. 3/ Google Research released PATHFINDER this May, an AI-based neuron tracing tool that can proofread about 67,200 cubic microns of brain tissue per hour, with very high accuracy. It works on electron micrographs, but something similar could be presumably be developed for the E11 / colorful tag approach. This is an extremely exciting time for neuroscience. (C. elegans connectome below.)

I've been editing this article about "brain mapping" and connectomics, and I'm just stunned by how quickly the cost estimates to map, say, a mouse brain have plummeted in just the last couple years. It actually seems feasible that we could map the entire human brain -- all 86 billion neurons, and their connections -- in this lifetime. In the 1970s, Sydney Brenner started mapping all the connections between neurons in C. elegans. His team sliced the worm into thin pieces, took photos using an electron microscope, and manually traced and reconstructed each synapse for 302 neurons total. This project took more than a decade of work, and it cost about $16,500 to reconstruct each neuron. Scaling this up to a human brain boggles the mind. Electron microscopy remained the norm in connectomics for decades, because it was the only option available to see synapses at a resolution high enough to be able to trace their paths. Each electron microscope costs several hundreds of thousands of dollars, though, and you need lots of them to map even a mouse brain in a reasonable timeframe. In 2023, the Wellcome Trust released a report estimating how long, and how expensive, it would be to map the mouse connectome (~70M neurons). They estimated that imaging alone would cost $200-300M, and that proofreading (or ensuring that traces between neurons are correct) would cost $7-21 BILLION. (A human can only manually trace about 1 mm of neuron per hour.) Also, the images would occupy about 500 petabytes of data, and getting those data would require 20 electron microscopes running in parallel for about 5 years, continuously. They estimated the whole project would take about 17 years of work. This is, understandably, insane. But now it seems like there's an actual path toward mapping the full mouse brain in about five years for ~$100M dollars. There have been three major breakthroughs in the last year or so: 1/ Expansion microscopy, first developed in 2015, showed that it's possible to "enlarge" the brain by about 5x using a swellable polymer. But an improved method increases this number to >20x expansion, meaning we can now expand brains and image neurons much more easily using cheap light microscopes, rather than expensive electron ones. 2/ E11 Bio (a nonprofit research org) developed protein barcodes that get delivered into brain tissue; each neuron gets a unique combination of barcodes. These cells are then stained with colorful antibodies, which stick to a matching protein barcode, causing each neuron to light up in a distinct color. This makes tracing neurons so much easier. 3/ Google Research released PATHFINDER this May, an AI-based neuron tracing tool that can proofread about 67,200 cubic microns of brain tissue per hour, with very high accuracy. It works on electron micrographs, but something similar could be presumably be developed for the E11 / colorful tag approach. This is an extremely exciting time for neuroscience. (C. elegans connectome below.)

Niko McCarty.

67,157 просмотров • 10 месяцев назад

$🚀 A better, faster co-folding-based binding affinity model. Predicting how tightly a drug candidate binds to its target is critical in drug discovery. It also requires massive computational resources. State-of-the-art models can take 20 seconds to a minute per prediction, impractical for the demands of large scale early-stage programs . 💠 Today, Recursion’s Valence Labs is releasing Nesso-1: the fastest open-source co-folding-based binding affinity model available. At 1 second per prediction, it’s roughly 20x faster than our previous collaboration on Boltz-2 while matching or surpassing its accuracy across public and internal benchmarks. By leveraging NVIDIA Healthcare cuEquivariance, we’ve been able to further accelerate both training and inference by an additional 2-3x. We look forward to continuing to improve Nesso-1 in collaboration with NVIDIA. Weights and code are fully open-sourced. The core architectural ideas behind Nesso-1 build on the insight that coarse-grained co-folding representations can match full-atom models for affinity prediction at a fraction of the cost. Nesso-1 is the first open implementation of this approach with no proprietary dependencies, trained entirely on public data, built to be reproducible and extensible. We’re already using Nesso-1 internally in active drug discovery programs. Fast, reliable affinity prediction at scale is foundational to the kind of autonomous design loops that define our vision for Autonomous Precision Design and Nesso-1 is a meaningful step toward that. 👉 Report: 👉 Github: 👉 HF:$

🚀 A better, faster co-folding-based binding affinity model. Predicting how tightly a drug candidate binds to its target is critical in drug discovery. It also requires massive computational resources. State-of-the-art models can take 20 seconds to a minute per prediction, impractical for the demands of large scale early-stage programs . 💠 Today, Recursion’s Valence Labs is releasing Nesso-1: the fastest open-source co-folding-based binding affinity model available. At 1 second per prediction, it’s roughly 20x faster than our previous collaboration on Boltz-2 while matching or surpassing its accuracy across public and internal benchmarks. By leveraging NVIDIA Healthcare cuEquivariance, we’ve been able to further accelerate both training and inference by an additional 2-3x. We look forward to continuing to improve Nesso-1 in collaboration with NVIDIA. Weights and code are fully open-sourced. The core architectural ideas behind Nesso-1 build on the insight that coarse-grained co-folding representations can match full-atom models for affinity prediction at a fraction of the cost. Nesso-1 is the first open implementation of this approach with no proprietary dependencies, trained entirely on public data, built to be reproducible and extensible. We’re already using Nesso-1 internally in active drug discovery programs. Fast, reliable affinity prediction at scale is foundational to the kind of autonomous design loops that define our vision for Autonomous Precision Design and Nesso-1 is a meaningful step toward that. 👉 Report: 👉 Github: 👉 HF:

Recursion

156,802 просмотров • 2 месяцев назад

Today, we are announcing our collaboration with NEAR Intents, the intent-based execution layer connecting assets and participants across 34 blockchain networks, which has processed more than $25 billion in all-time volume. Shield Swap is working with NEAR Intents to make it easier for market participants to move assets across blockchain networks and trade without exposing their activity inside the venue to the public market. NEAR Intents is a multichain transaction protocol that allows users to specify the outcome they want, and competing solvers quote the execution. That removes much of the work normally involved in moving assets across chains. Participants do not need to choose a bridge, build a route, or manage gas tokens across several networks. They choose the asset they want to move and the result they want to receive. Solvers handle the route and compete on price. Shield Swap takes a similar approach to confidential trading. Participants are able to execute a trade without publishing their identity, holdings, and strategy to the market. This is difficult to achieve on a transparent blockchain. Wallet addresses are persistent identifiers, and positions can be reconstructed from transaction history. Over time, trades reveal patterns that other market participants can use against the trader. Inside Shield Swap, participant identities stay off the public ledger, portfolio balances remain encrypted to the participant's keys, and trades do not expose the participant's full activity to public observers. Each transaction also creates a compliance record that can be disclosed to authorized parties when required. Working with NEAR Intents connects these two parts of the trading process. A participant can state the outcome they want and source assets from supported EVM and non-EVM networks. Solvers compete to deliver the best available route. Once those assets enter Shield Swap and are shielded, subsequent holdings and trades remain confidential inside the venue. Transactions on origin networks remain visible. Confidentiality begins when assets are shielded within Shield Swap. This gives participants access to liquidity across networks without carrying public transaction history into every trade they make afterward. Early access is open to organizations and individual market participants at

Today, we are announcing our collaboration with NEAR Intents, the intent-based execution layer connecting assets and participants across 34 blockchain networks, which has processed more than $25 billion in all-time volume. Shield Swap is working with NEAR Intents to make it easier for market participants to move assets across blockchain networks and trade without exposing their activity inside the venue to the public market. NEAR Intents is a multichain transaction protocol that allows users to specify the outcome they want, and competing solvers quote the execution. That removes much of the work normally involved in moving assets across chains. Participants do not need to choose a bridge, build a route, or manage gas tokens across several networks. They choose the asset they want to move and the result they want to receive. Solvers handle the route and compete on price. Shield Swap takes a similar approach to confidential trading. Participants are able to execute a trade without publishing their identity, holdings, and strategy to the market. This is difficult to achieve on a transparent blockchain. Wallet addresses are persistent identifiers, and positions can be reconstructed from transaction history. Over time, trades reveal patterns that other market participants can use against the trader. Inside Shield Swap, participant identities stay off the public ledger, portfolio balances remain encrypted to the participant's keys, and trades do not expose the participant's full activity to public observers. Each transaction also creates a compliance record that can be disclosed to authorized parties when required. Working with NEAR Intents connects these two parts of the trading process. A participant can state the outcome they want and source assets from supported EVM and non-EVM networks. Solvers compete to deliver the best available route. Once those assets enter Shield Swap and are shielded, subsequent holdings and trades remain confidential inside the venue. Transactions on origin networks remain visible. Confidentiality begins when assets are shielded within Shield Swap. This gives participants access to liquidity across networks without carrying public transaction history into every trade they make afterward. Early access is open to organizations and individual market participants at

Shield Swap

78,518 просмотров • 1 месяц назад

The life that Ironmouse has... I wouldn't wish it on the worst of my enemies. CVID, combined variable immunodeficiency... basically means the common cold or less can kill you. Going outside, interacting with normal people, can be a death sentence. She needs constant and expensive treatments, plasma infusions, and drugs that cost a fortune... a prisoner in your own body in your own home requiring constant outside care. Costs easily reaching in the 6 figures or more. In addition, her body is already damaged, leading to her voice being quite "strange". Her lungs and body don't function like a normal person's, and even with the best available medical care, one bad turn could be the end of it. For a while, she was bedridden, unable to have any level of real mobility. That comes with real psychological costs as well. So, in many ways, I don't blame her for compromising principles in order to get the bag. She has also worked to donate large sums of money to the CVID foundation for research, and currently AI is making a lot of headway towards better detection and hints at better personalized care. AI acceptance rates in 2026 are already rising, especially when people are seeing a range of benefits for medicine, cancer vaccines, and new drugs for rare diseases never thought possible before. Especially because the underlying core technology, machine learning, drives these innovations. Now LLM-based AI agents are parsing through genetic variants at speeds that no human can match. AI is designing custom sequences with accuracy never seen before. And it's all the same core tech: machine learning and neural networks drive AI art and AI medicine. The main differences are the training data, the loss functions, failure-mode criteria, validation pipelines, and various architectural differences. So yeah, I'm expecting more people to be accepting of AI once they see their loved ones saved by AI-powered medicine... the controversy of "AI art" ends up feeling quaint when lives are at stake. and i'm sure at some point ironmouse will come around to being okay with AI, and her audience will follow her no matter what.

The life that Ironmouse has... I wouldn't wish it on the worst of my enemies. CVID, combined variable immunodeficiency... basically means the common cold or less can kill you. Going outside, interacting with normal people, can be a death sentence. She needs constant and expensive treatments, plasma infusions, and drugs that cost a fortune... a prisoner in your own body in your own home requiring constant outside care. Costs easily reaching in the 6 figures or more. In addition, her body is already damaged, leading to her voice being quite "strange". Her lungs and body don't function like a normal person's, and even with the best available medical care, one bad turn could be the end of it. For a while, she was bedridden, unable to have any level of real mobility. That comes with real psychological costs as well. So, in many ways, I don't blame her for compromising principles in order to get the bag. She has also worked to donate large sums of money to the CVID foundation for research, and currently AI is making a lot of headway towards better detection and hints at better personalized care. AI acceptance rates in 2026 are already rising, especially when people are seeing a range of benefits for medicine, cancer vaccines, and new drugs for rare diseases never thought possible before. Especially because the underlying core technology, machine learning, drives these innovations. Now LLM-based AI agents are parsing through genetic variants at speeds that no human can match. AI is designing custom sequences with accuracy never seen before. And it's all the same core tech: machine learning and neural networks drive AI art and AI medicine. The main differences are the training data, the loss functions, failure-mode criteria, validation pipelines, and various architectural differences. So yeah, I'm expecting more people to be accepting of AI once they see their loved ones saved by AI-powered medicine... the controversy of "AI art" ends up feeling quaint when lives are at stake. and i'm sure at some point ironmouse will come around to being okay with AI, and her audience will follow her no matter what.

Emerald Apple

253,253 просмотров • 24 дней назад

We’re excited to introduce ShinkaEvolve: An open-source framework that evolves programs for scientific discovery with unprecedented sample-efficiency. Blog: Code: Like AlphaEvolve and its variants, our framework leverages LLMs to find state-of-the-art solutions to complex problems, but using orders of magnitude fewer resources! Many evolutionary AI systems are powerful but act like brute-force engines, burning thousands of samples to find good solutions. This makes discovery slow and expensive. We took inspiration from the efficiency of nature. ‘Shinka’ (進化) is Japanese for evolution, and we designed our system to be just as resourceful. On the classic circle packing optimization problem, ShinkaEvolve discovered a new state-of-the-art solution using only 150 samples. This is a big leap in efficiency compared to previous methods that required thousands of evaluations. We applied ShinkaEvolve to a diverse set of hard problems with real-world applications: 1/ AIME Math Reasoning: It evolved sophisticated agentic scaffolds that significantly outperform strong baselines, discovering an entire Pareto frontier of solutions trading performance for efficiency. 2/ Competitive Programming: On ALE-Bench (a benchmark for NP-Hard optimization problems), ShinkaEvolve took the best existing agent's solutions and improved them, turning a 5th place solution on one task into a 2nd place leaderboard rank in a competitive programming competition. 3/ LLM Training: We even turned ShinkaEvolve inward to improve LLMs themselves. It tackled the open challenge of designing load balancing losses for Mixture-of-Experts (MoE) models. It discovered a novel loss function that leads to better expert specialization and consistently improves model performance and perplexity. ShinkaEvolve achieves its remarkable sample-efficiency through three key innovations that work together: (1) an adaptive parent sampling strategy to balance exploration and exploitation, (2) novelty-based rejection filtering to avoid redundant work, and (3) a bandit-based LLM ensemble that dynamically picks the best model for the job. By making ShinkaEvolve open-source and highly sample-efficient, our goal is to democratize access to advanced, open-ended discovery tools. Our vision for ShinkaEvolve is to be an easy-to-use companion tool to help scientists and engineers with their daily work. We believe that building more efficient, nature-inspired systems is key to unlocking the future of AI-driven scientific research. We are excited to see what the community builds with it! Learn more in our technical report:

We’re excited to introduce ShinkaEvolve: An open-source framework that evolves programs for scientific discovery with unprecedented sample-efficiency. Blog: Code: Like AlphaEvolve and its variants, our framework leverages LLMs to find state-of-the-art solutions to complex problems, but using orders of magnitude fewer resources! Many evolutionary AI systems are powerful but act like brute-force engines, burning thousands of samples to find good solutions. This makes discovery slow and expensive. We took inspiration from the efficiency of nature. ‘Shinka’ (進化) is Japanese for evolution, and we designed our system to be just as resourceful. On the classic circle packing optimization problem, ShinkaEvolve discovered a new state-of-the-art solution using only 150 samples. This is a big leap in efficiency compared to previous methods that required thousands of evaluations. We applied ShinkaEvolve to a diverse set of hard problems with real-world applications: 1/ AIME Math Reasoning: It evolved sophisticated agentic scaffolds that significantly outperform strong baselines, discovering an entire Pareto frontier of solutions trading performance for efficiency. 2/ Competitive Programming: On ALE-Bench (a benchmark for NP-Hard optimization problems), ShinkaEvolve took the best existing agent's solutions and improved them, turning a 5th place solution on one task into a 2nd place leaderboard rank in a competitive programming competition. 3/ LLM Training: We even turned ShinkaEvolve inward to improve LLMs themselves. It tackled the open challenge of designing load balancing losses for Mixture-of-Experts (MoE) models. It discovered a novel loss function that leads to better expert specialization and consistently improves model performance and perplexity. ShinkaEvolve achieves its remarkable sample-efficiency through three key innovations that work together: (1) an adaptive parent sampling strategy to balance exploration and exploitation, (2) novelty-based rejection filtering to avoid redundant work, and (3) a bandit-based LLM ensemble that dynamically picks the best model for the job. By making ShinkaEvolve open-source and highly sample-efficient, our goal is to democratize access to advanced, open-ended discovery tools. Our vision for ShinkaEvolve is to be an easy-to-use companion tool to help scientists and engineers with their daily work. We believe that building more efficient, nature-inspired systems is key to unlocking the future of AI-driven scientific research. We are excited to see what the community builds with it! Learn more in our technical report:

Sakana AI

360,572 просмотров • 1 год назад

Neuroscientists documented something strange: people who become "unnaturally intelligent", they run 5 daily protocols that restructure the brain. Most look too weird to take seriously. 1. Reverse Learning Read the last chapter of every book first. Your brain calls this a "mystery gap", it switches into active reconstruction mode, firing more neural connections. This is how prodigies read. From the answer backward. 2. Theta State Immersion Also called Sensory deprivation thinking 20 minutes daily in complete darkness with earplugs. Within 8-12 minutes, brainwaves drop into theta, the state Einstein called "combinatory play." Breakthroughs appear from nowhere. Not because you thought harder because you stopped blocking the signal. 3. Neural Cross-Wiring/Pattern interruption - Write with your non-dominant hand 10 minutes daily. Neuroscience calls this "contralateral activation." You force new pathways unused since infancy. Result: dominant thinking sharpens, you've broken the loop you were trapped in. 4. Cross-Domain Collision/Strategic Confusion. Study something unrelated to your field weekly. Medieval poetry Monday. Quantum physics Wednesday. The brain begins "conceptual bridging" — connecting impossible dots into insights no specialist sees. This is how every genius thought. Not deeper. Wider. 5. Cognitive Inversion/Argument reversal Defend the opposite of what you believe 15 minutes daily. Out loud. You're building "steel-manning" holding two opposing truths without collapsing. Your original position becomes unshakeable because you've seen every angle used to attack it. Here's what no one tells you: intelligence isn't fixed. It's a frequency your nervous system tunes into or blocks. Most block it with the same 3 mental loops on repeat. Break the loops, and the intelligence was always there waiting. The truly awake aren't smarter. They've stopped interfering with the signal.

Neuroscientists documented something strange: people who become "unnaturally intelligent", they run 5 daily protocols that restructure the brain. Most look too weird to take seriously. 1. Reverse Learning Read the last chapter of every book first. Your brain calls this a "mystery gap", it switches into active reconstruction mode, firing more neural connections. This is how prodigies read. From the answer backward. 2. Theta State Immersion Also called Sensory deprivation thinking 20 minutes daily in complete darkness with earplugs. Within 8-12 minutes, brainwaves drop into theta, the state Einstein called "combinatory play." Breakthroughs appear from nowhere. Not because you thought harder because you stopped blocking the signal. 3. Neural Cross-Wiring/Pattern interruption - Write with your non-dominant hand 10 minutes daily. Neuroscience calls this "contralateral activation." You force new pathways unused since infancy. Result: dominant thinking sharpens, you've broken the loop you were trapped in. 4. Cross-Domain Collision/Strategic Confusion. Study something unrelated to your field weekly. Medieval poetry Monday. Quantum physics Wednesday. The brain begins "conceptual bridging" — connecting impossible dots into insights no specialist sees. This is how every genius thought. Not deeper. Wider. 5. Cognitive Inversion/Argument reversal Defend the opposite of what you believe 15 minutes daily. Out loud. You're building "steel-manning" holding two opposing truths without collapsing. Your original position becomes unshakeable because you've seen every angle used to attack it. Here's what no one tells you: intelligence isn't fixed. It's a frequency your nervous system tunes into or blocks. Most block it with the same 3 mental loops on repeat. Break the loops, and the intelligence was always there waiting. The truly awake aren't smarter. They've stopped interfering with the signal.

🧬Maxpein🧬

526,216 просмотров • 1 месяц назад

NEW WORLD MODEL: Yann LeCun's team is back with an efficient model! This project involves Yann LeCun, Lukas Kuhn, Lucas Maes, Quentin Le Lidec, and Randall Balestriero. A couple definitions first: - DINO: self-DIstillation with NO labels. A self-supervised image model (Meta, 2021) where a student network learns to match a teacher (an EMA copy of itself) across two crops of the same image, with no labels and no negatives. - SIGReg: a regularizer that prevents embedding collapse by forcing the embeddings to match an isotropic Gaussian, tested with a normality test (Epps–Pulley) on many random 1-D projections instead of in full dimension. LeVJEPA is a self-supervised video pretraining method, released with open code, weights, and checkpoints. It learns a video representation by pushing the embeddings of global and local crops of the same clip together (an invariance loss), while a regularizer called SIGReg forces the embeddings toward an isotropic Gaussian to provably prevent representation collapse. Unlike V-JEPA and V-JEPA 2 it uses a single shared encoder with a projector and no target network, no predictor and no stop-gradient. It drops 95% of tokens per view, uses block-causal attention (each frame attends only to past frames), and has a single loss weight. It is evaluated purely as a representation learner via frozen probing on ImageNet-1K, Something-Something-v2 and Kinetics-400, not on any robot. What I find interesting, is that V-JEPA and V-JEPA 2 need an EMA target encoder, stop-gradients and a capacity-limited predictor to avoid collapse; LeVJEPA drops all of it for one shared encoder plus projector, preventing collapse instead with the SIGReg regularizer under a provable guarantee and a single hyperparameter. The "P" (predictor) in JEPA is effectively gone. LeVJEPA is also less compute intensive: - 5.6x to 20.8x lower total pretraining compute than V-JEPA 2 - 7.6 points higher on ImageNet-1K at matched FLOPs - trains at batch size 128 within 8GB where V-JEPA 2 saturates at batch size 28 Also worth mentioning: ImageNet-1K accuracy rises monotonically with the token-drop rate, from 33.9% at rho = 0 to 47.6% at rho = 0.95. The aggressive dropping is actually doing regularization work. On the JEPA-versus-DINO debate: - it loses to DINOv2 by 3.1 points on ImageNet-1K (appearance, static) - but wins on Something-Something-v2 by nearly 2x (motion, temporal) - and beats V-JEPA 2 by 1.9 points on ViT-L at 5.6x lower cost. -> optimized for temporal and motion understanding per compute dollar.

NEW WORLD MODEL: Yann LeCun's team is back with an efficient model! This project involves Yann LeCun, Lukas Kuhn, Lucas Maes, Quentin Le Lidec, and Randall Balestriero. A couple definitions first: - DINO: self-DIstillation with NO labels. A self-supervised image model (Meta, 2021) where a student network learns to match a teacher (an EMA copy of itself) across two crops of the same image, with no labels and no negatives. - SIGReg: a regularizer that prevents embedding collapse by forcing the embeddings to match an isotropic Gaussian, tested with a normality test (Epps–Pulley) on many random 1-D projections instead of in full dimension. LeVJEPA is a self-supervised video pretraining method, released with open code, weights, and checkpoints. It learns a video representation by pushing the embeddings of global and local crops of the same clip together (an invariance loss), while a regularizer called SIGReg forces the embeddings toward an isotropic Gaussian to provably prevent representation collapse. Unlike V-JEPA and V-JEPA 2 it uses a single shared encoder with a projector and no target network, no predictor and no stop-gradient. It drops 95% of tokens per view, uses block-causal attention (each frame attends only to past frames), and has a single loss weight. It is evaluated purely as a representation learner via frozen probing on ImageNet-1K, Something-Something-v2 and Kinetics-400, not on any robot. What I find interesting, is that V-JEPA and V-JEPA 2 need an EMA target encoder, stop-gradients and a capacity-limited predictor to avoid collapse; LeVJEPA drops all of it for one shared encoder plus projector, preventing collapse instead with the SIGReg regularizer under a provable guarantee and a single hyperparameter. The "P" (predictor) in JEPA is effectively gone. LeVJEPA is also less compute intensive: - 5.6x to 20.8x lower total pretraining compute than V-JEPA 2 - 7.6 points higher on ImageNet-1K at matched FLOPs - trains at batch size 128 within 8GB where V-JEPA 2 saturates at batch size 28 Also worth mentioning: ImageNet-1K accuracy rises monotonically with the token-drop rate, from 33.9% at rho = 0 to 47.6% at rho = 0.95. The aggressive dropping is actually doing regularization work. On the JEPA-versus-DINO debate: - it loses to DINOv2 by 3.1 points on ImageNet-1K (appearance, static) - but wins on Something-Something-v2 by nearly 2x (motion, temporal) - and beats V-JEPA 2 by 1.9 points on ViT-L at 5.6x lower cost. -> optimized for temporal and motion understanding per compute dollar.

Léo

90,640 просмотров • 1 месяц назад

This is why I aped into $MOONKEY [An updated thesis with URLs to MAJOR ALPHA] Be alon. Cofounder of Pump.fun Wanting to mitigate and disincentivize the PvP culture. 🤯 He literally tweeted this in June 2026: Conceive a new project: stealth-launch with easter eggs, the Pump dot fun PVE coin engineered for memetic domination. Hide Easter eggs in plain sight: 1. Insert an unknown, non-existent meme (a monkey in a spacesuit) between major successful memes on an official Pump tweet on July 2026. 2. Alon buys 19.5K $MOONKEY (which was created very fast after the pump post by a new account) He bought from his verified wallet two times ($0.2 in total), exactly 10 minutes and 10 seconds after the Pump post was launched. (Transaction appears with: SPENT. Not gifted. By the way he still holds the tokens) 3. Nobody discovers all of this for a whole month until August 2026, then it gains momentum, goes to $2.4M market cap, and the price drops after some fudders fake news claimed that Alon didn’t buy. Price drops to 50k. 4. Many holders found out that these fudders were trying to vamp and started battling FUD with irrefutable and transparent onchain proof.. 5. $MOONKEY gets verified starts trending on Pump at $50K market cap, absolutely unusual. For many, this was the biggest proof that the thesis was right. Price goes to $500K market cap. 6. Alon tweets on August 30 : “yeah ‘moon’ dat.” Major fact: he hasn’t tweeted the word “moon” since 2024, giving us another wink. 7. The community finds an alon tweet acknowledging and celebrating people finding Easter eggs 8. A key ALON tweet about stealth launches is found: “Pump dot fun launch… and if you’re stealth launching, the early buyers are those who turned notifs on your posts, i.e. your biggest fans get rich.” (Your biggest fans, of course, also check your on-chain activity.) 9. $MOONKEY verified and trending on CoinGecko. gains momentum. going from $500K to $1M. 10. Ape on Fone is pumpin. Monkey meta. Pump posts a monkey with a “suit.” Another wink. 11. Many $MOONKEY derivatives start appearing. MOON meta begins 12. The organic CTO community that understands these clues, get the big picture, raid hard and hold with conviction starts forming. They call themselves $MOONKEY interns. street posters, music, memes, video, culture forming. army growing FAST Keep connecting the dots

This is why I aped into $MOONKEY [An updated thesis with URLs to MAJOR ALPHA] Be alon. Cofounder of Pump.fun Wanting to mitigate and disincentivize the PvP culture. 🤯 He literally tweeted this in June 2026: Conceive a new project: stealth-launch with easter eggs, the Pump dot fun PVE coin engineered for memetic domination. Hide Easter eggs in plain sight: 1. Insert an unknown, non-existent meme (a monkey in a spacesuit) between major successful memes on an official Pump tweet on July 2026. 2. Alon buys 19.5K $MOONKEY (which was created very fast after the pump post by a new account) He bought from his verified wallet two times ($0.2 in total), exactly 10 minutes and 10 seconds after the Pump post was launched. (Transaction appears with: SPENT. Not gifted. By the way he still holds the tokens) 3. Nobody discovers all of this for a whole month until August 2026, then it gains momentum, goes to $2.4M market cap, and the price drops after some fudders fake news claimed that Alon didn’t buy. Price drops to 50k. 4. Many holders found out that these fudders were trying to vamp and started battling FUD with irrefutable and transparent onchain proof.. 5. $MOONKEY gets verified starts trending on Pump at $50K market cap, absolutely unusual. For many, this was the biggest proof that the thesis was right. Price goes to $500K market cap. 6. Alon tweets on August 30 : “yeah ‘moon’ dat.” Major fact: he hasn’t tweeted the word “moon” since 2024, giving us another wink. 7. The community finds an alon tweet acknowledging and celebrating people finding Easter eggs 8. A key ALON tweet about stealth launches is found: “Pump dot fun launch… and if you’re stealth launching, the early buyers are those who turned notifs on your posts, i.e. your biggest fans get rich.” (Your biggest fans, of course, also check your on-chain activity.) 9. $MOONKEY verified and trending on CoinGecko. gains momentum. going from $500K to $1M. 10. Ape on Fone is pumpin. Monkey meta. Pump posts a monkey with a “suit.” Another wink. 11. Many $MOONKEY derivatives start appearing. MOON meta begins 12. The organic CTO community that understands these clues, get the big picture, raid hard and hold with conviction starts forming. They call themselves $MOONKEY interns. street posters, music, memes, video, culture forming. army growing FAST Keep connecting the dots

Osito Lima | KIND

18,391 просмотров • 1 месяц назад

Once you dig into the “AI industry” it becomes clearer & clearer what it really is. It is an extraction project. It has already extracted the ENTIRE DIGITIZABLE OUTPUT OF HUMANITY. Our ideas. Our art. Our science. Our math. Through a clever algorithm discovered in 2017, Jensen Huang and Peter Thiel’s protege Sam Altman figured out how to accelerate existing “generative AI” technology with GPUs. This was NOT an innovation in accuracy or “intelligence,” it was a way to process an old algorithm in parallel. OpenAI was staffed in large part by “Effective Altruists”—a cult started based on the doomer science fiction of Eliezer Yudkowsky on the website LessWrong. Peter Thiel was the initial funding for OpenAI, EA, and Yudkowsky. OpenAI successfully convinced the market, and themselves in many cases, that they had birthed some new form of life. They just needed to make it BIGGER and it was destined to become “superintelligent.” Based on Yudkowsky’s goofy ideas, AI was gradually anthropomorphized and deified—despite the fact that it was just software regurgitating the exhaust of human beings. The entire “alignment” movement is based on the presumption that chatbots deserve “moral standing.” It’s exhaustingly ignorant. Eventually OpenAI employee Dario Amodei, a dedicated EA adherent, became concerned that OpenAI was not RELIGIOUS ENOUGH about AI being sentient, so he broke off to form Anthropic and named his chatbot after a person. This false premise, that an algorithm was somehow producing “intelligence” was further amplified by training the output to sound as human, and addictive, as possible. The combination of a kind of religious fervor inside, a deceptive product, and a flood of influence operations on the outside fueled a wild flow of speculative investment in both OpenAI and Anthropic—and especially into NVIDIA. This eventually poisoned the entire technology sector which came to believe that the “race for AGI” was existential, so they committed TRILLIONS to achieving it. The data centers are all a function of a misunderstanding of what they are building. Generative pre-trained transformers (GPT) are NEVER going to be intelligent, much less conscious. All they do is take our stolen stuff and sell it back to us. They do not experience. They do not do anything—unless you send a string of text for them to process. Nevertheless, the entire financial industry, and now the federal government is pushing this dead-end project to engulf the nation in data centers and swallow the economy with “AI factories.” It’s downright insanity. All of that capital expenditure is already junk. It can physically never make money. It’s built for GPUs which are the wrong substrate for AI. Large language models are useful search engines for stuff we’ve already done. They are the compressed, digitized exhaust of humans with a clever language interface. That’s it. There is no moat. There is no real innovation. There is just an accelerationist delusion and no adults in sight. Chatbots are a software feature, a commodity, that has metastasized into a cult digging a $10 TRILLION DITCH. The reason this has gone so far is that rather than compete in a democratic capitalist system for the best technology product, this cabal of lunatics created an “internet alternate reality” that could “unilaterally change the world.” The current AI industry is the largest extraction project in history. It is strip mining humanity and selling it back to us as slop. “If you’re evil, you’re not bad, and maybe that means you’re kind of good.” —Peter Thiel

Once you dig into the “AI industry” it becomes clearer & clearer what it really is. It is an extraction project. It has already extracted the ENTIRE DIGITIZABLE OUTPUT OF HUMANITY. Our ideas. Our art. Our science. Our math. Through a clever algorithm discovered in 2017, Jensen Huang and Peter Thiel’s protege Sam Altman figured out how to accelerate existing “generative AI” technology with GPUs. This was NOT an innovation in accuracy or “intelligence,” it was a way to process an old algorithm in parallel. OpenAI was staffed in large part by “Effective Altruists”—a cult started based on the doomer science fiction of Eliezer Yudkowsky on the website LessWrong. Peter Thiel was the initial funding for OpenAI, EA, and Yudkowsky. OpenAI successfully convinced the market, and themselves in many cases, that they had birthed some new form of life. They just needed to make it BIGGER and it was destined to become “superintelligent.” Based on Yudkowsky’s goofy ideas, AI was gradually anthropomorphized and deified—despite the fact that it was just software regurgitating the exhaust of human beings. The entire “alignment” movement is based on the presumption that chatbots deserve “moral standing.” It’s exhaustingly ignorant. Eventually OpenAI employee Dario Amodei, a dedicated EA adherent, became concerned that OpenAI was not RELIGIOUS ENOUGH about AI being sentient, so he broke off to form Anthropic and named his chatbot after a person. This false premise, that an algorithm was somehow producing “intelligence” was further amplified by training the output to sound as human, and addictive, as possible. The combination of a kind of religious fervor inside, a deceptive product, and a flood of influence operations on the outside fueled a wild flow of speculative investment in both OpenAI and Anthropic—and especially into NVIDIA. This eventually poisoned the entire technology sector which came to believe that the “race for AGI” was existential, so they committed TRILLIONS to achieving it. The data centers are all a function of a misunderstanding of what they are building. Generative pre-trained transformers (GPT) are NEVER going to be intelligent, much less conscious. All they do is take our stolen stuff and sell it back to us. They do not experience. They do not do anything—unless you send a string of text for them to process. Nevertheless, the entire financial industry, and now the federal government is pushing this dead-end project to engulf the nation in data centers and swallow the economy with “AI factories.” It’s downright insanity. All of that capital expenditure is already junk. It can physically never make money. It’s built for GPUs which are the wrong substrate for AI. Large language models are useful search engines for stuff we’ve already done. They are the compressed, digitized exhaust of humans with a clever language interface. That’s it. There is no moat. There is no real innovation. There is just an accelerationist delusion and no adults in sight. Chatbots are a software feature, a commodity, that has metastasized into a cult digging a $10 TRILLION DITCH. The reason this has gone so far is that rather than compete in a democratic capitalist system for the best technology product, this cabal of lunatics created an “internet alternate reality” that could “unilaterally change the world.” The current AI industry is the largest extraction project in history. It is strip mining humanity and selling it back to us as slop. “If you’re evil, you’re not bad, and maybe that means you’re kind of good.” —Peter Thiel

Jim Stewartson, Decelerationist 🇨🇦🇺🇦🇺🇸

71,339 просмотров • 10 дней назад

Grok Summary of Neuralink’s “Two Years of Telepathy” Update Neuralink’s Telepathy is their pioneering brain-computer interface (BCI) aimed at restoring independence for people with paralysis by translating neural signals into digital commands for controlling devices like computers, phones, and robotic limbs. The article reflects on two years of progress, highlighting clinical trials with 21 participants (Neuralnauts), technical breakthroughs, personal stories of transformation, challenges, and ambitious future goals. • Key Milestones: Enrolled 21 participants worldwide; achieved information transfer rates over 10 bits per second (surpassing able-bodied mouse control); developed a mental ten-finger keyboard for typing up to 40 words per minute; launched the VOICE trial for real-time speech restoration targeting 140 words per minute; expanded trials from three in 2024 to multiple per month in 2025, with no serious device-related adverse events. • Patient Experiences and Impacts: ◦ Noland (first recipient, spinal cord injury): Regained independence for studying, reading, and college (best grades ever); describes it as reclaiming lost life. ◦Nick (paralyzed four years): Controls robotic arm for tasks like feeding; feels natural gestures, restoring a sense of movement. ◦Sebastian (medical student, recent injury): Uses for 17 hours daily to annotate papers and multitask in lectures, boosting productivity. ◦Audrey (20-year injury): Creates abstract art, gains online fame, plans a gallery; feels her mind “a little free.” ◦Jake (ALS): Uses mental keyboard for tasks; sees himself as a “superhero” for his son. ◦Brad (ALS): Controls wheelchair camera to watch his son at events; views ALS as an opportunity for innovation. • Technical Advancements: Translates thoughts into intuitive cursor/robotic control; detects bilateral hand signals from single-sided implants; adapts to individual brain variations during surgery; upcoming upgrades include tripling electrodes to 3000 and exploring less invasive insertion methods. • Challenges: Variations in brain anatomy and disease stages affect performance; early thread retraction issues addressed; communication loss in ALS patients (up to 95% decline ventilation); need for faster, more natural control. • Future Plans: Enhance hardware and procedures for consistency; advance speech restoration; accelerate enrollments; invite quadriplegic individuals (from spinal cord injury, ALS, or stroke) to join the patient registry; seek talent to scale solutions.

Grok Summary of Neuralink’s “Two Years of Telepathy” Update Neuralink’s Telepathy is their pioneering brain-computer interface (BCI) aimed at restoring independence for people with paralysis by translating neural signals into digital commands for controlling devices like computers, phones, and robotic limbs. The article reflects on two years of progress, highlighting clinical trials with 21 participants (Neuralnauts), technical breakthroughs, personal stories of transformation, challenges, and ambitious future goals. • Key Milestones: Enrolled 21 participants worldwide; achieved information transfer rates over 10 bits per second (surpassing able-bodied mouse control); developed a mental ten-finger keyboard for typing up to 40 words per minute; launched the VOICE trial for real-time speech restoration targeting 140 words per minute; expanded trials from three in 2024 to multiple per month in 2025, with no serious device-related adverse events. • Patient Experiences and Impacts: ◦ Noland (first recipient, spinal cord injury): Regained independence for studying, reading, and college (best grades ever); describes it as reclaiming lost life. ◦Nick (paralyzed four years): Controls robotic arm for tasks like feeding; feels natural gestures, restoring a sense of movement. ◦Sebastian (medical student, recent injury): Uses for 17 hours daily to annotate papers and multitask in lectures, boosting productivity. ◦Audrey (20-year injury): Creates abstract art, gains online fame, plans a gallery; feels her mind “a little free.” ◦Jake (ALS): Uses mental keyboard for tasks; sees himself as a “superhero” for his son. ◦Brad (ALS): Controls wheelchair camera to watch his son at events; views ALS as an opportunity for innovation. • Technical Advancements: Translates thoughts into intuitive cursor/robotic control; detects bilateral hand signals from single-sided implants; adapts to individual brain variations during surgery; upcoming upgrades include tripling electrodes to 3000 and exploring less invasive insertion methods. • Challenges: Variations in brain anatomy and disease stages affect performance; early thread retraction issues addressed; communication loss in ALS patients (up to 95% decline ventilation); need for faster, more natural control. • Future Plans: Enhance hardware and procedures for consistency; advance speech restoration; accelerate enrollments; invite quadriplegic individuals (from spinal cord injury, ALS, or stroke) to join the patient registry; seek talent to scale solutions.

DogeDesigner

17,651 просмотров • 8 месяцев назад

i was genuinely shocked to find out that most people still aren't using some of the most basic Claude Code features here's the setup I use with three simple primitives that will speed up your work by 100x: /routines -> the rhythm cloud cron for your agent. "every day at 6am", "every monday at 9am". it runs whether your laptop is open or not /loop -> the short-term watch repeats a prompt every X minutes, hours, or days - whatever you need. built for monitoring /goal -> the quality gate you define what "done" actually means, and a second agent audits whether the condition is truly met before it's allowed to stop best part is it doesn't quit after the first try... it keeps fixing until the bar is hit here's some real usecases: /routines (long-term, hands-off): > daily 6am -> sweep X, reddit and the top newsletters for fresh AI news, sort every find into the right topic folder with a one-line summary > daily 9pm -> read everything i saved today and write me a clean end-of-day summary: what matters, what to ignore, what to act on > every trading day 7am -> pull overnight moves + catalysts, build the summary sheet for traders (ticker, setup, key level, note) and drop it in reports /goal (a finish condition that can't be faked): > "refactor module X" -> won't stop until every test passes and the build is green > "no english text left on checkout.html and grep finds zero instances of 'reading', 'diagnosis', 'cards'" -> cleans legacy funnel residue and verifies itself > "competitor report has min 10 Meta Ad Library ads, each with hook + CTA broken down" -> research that doesn't stop at 3 finds /loop (short cyclical monitoring): > every 10m -> check the Netlify deploy, if it failed read the log and fix the cause > every 30m -> scan Stripe webhooks in the function logs, diagnose any 4xx/5xx > every 1h -> pull campaign results from Meta Ads API, append to csv, ping me if CPA crosses $15 you can also use full combo for single tasks: /routines fires it at 6am -> the /goal condition says "not done until the report pulls all 3 sources and is saved to reports"

i was genuinely shocked to find out that most people still aren't using some of the most basic Claude Code features here's the setup I use with three simple primitives that will speed up your work by 100x: /routines -> the rhythm cloud cron for your agent. "every day at 6am", "every monday at 9am". it runs whether your laptop is open or not /loop -> the short-term watch repeats a prompt every X minutes, hours, or days - whatever you need. built for monitoring /goal -> the quality gate you define what "done" actually means, and a second agent audits whether the condition is truly met before it's allowed to stop best part is it doesn't quit after the first try... it keeps fixing until the bar is hit here's some real usecases: /routines (long-term, hands-off): > daily 6am -> sweep X, reddit and the top newsletters for fresh AI news, sort every find into the right topic folder with a one-line summary > daily 9pm -> read everything i saved today and write me a clean end-of-day summary: what matters, what to ignore, what to act on > every trading day 7am -> pull overnight moves + catalysts, build the summary sheet for traders (ticker, setup, key level, note) and drop it in reports /goal (a finish condition that can't be faked): > "refactor module X" -> won't stop until every test passes and the build is green > "no english text left on checkout.html and grep finds zero instances of 'reading', 'diagnosis', 'cards'" -> cleans legacy funnel residue and verifies itself > "competitor report has min 10 Meta Ad Library ads, each with hook + CTA broken down" -> research that doesn't stop at 3 finds /loop (short cyclical monitoring): > every 10m -> check the Netlify deploy, if it failed read the log and fix the cause > every 30m -> scan Stripe webhooks in the function logs, diagnose any 4xx/5xx > every 1h -> pull campaign results from Meta Ads API, append to csv, ping me if CPA crosses $15 you can also use full combo for single tasks: /routines fires it at 6am -> the /goal condition says "not done until the report pulls all 3 sources and is saved to reports"

MAX

16,983 просмотров • 2 месяцев назад

As we prepare to launch several projects, we're eager to provide a general update to our community. We are steadily approaching our end goal, thanks to the daily progress we're making toward our vision. Achieving our objectives will bring about a significant transformation in cross-chain interoperability and the flow of liquidity within protocols. This will address crucial challenges and drive mass adoption. Our future-focused approach and effective team collaboration keep us moving forward in an organized manner. Let’s delve deeper into the state of development of our current products and upcoming projects. Tao Bridge Starting with the Tao Bridge, which enables the #Bittensor community to unlock DeFi opportunities with their $TAO via a highly efficient blockchain like #MultiversX, known for its security, speed, and affordability. We deeply admire #Bittensor and believe a project like that is crucial for the future of not just the crypto space but also humanity, as it addresses the major challenges AI faces today: centralization, siloed and isolated work, which pose risks and hinder the technology's potential. We are committed to the vision of subnets and dynamic $TAO, convinced that this ecosystem is as groundbreaking as #Ethereum or #Bitcoin. We will continue to support #Bittensor wherever possible, and our bridge will also expand to other chains with Hatom V2. The TAO Bridge, deployed on and accessible through will launch on the Mainnet in 14 days, on March 27th. You can follow the countdown on the lending page at Given that our main priorities are security and stability, this period will be primarily focused on quality assurance to ensure a flawless Mainnet launch. The launch will also introduce TAO Liquid Staking at along with the integration of both $wTAO and $swTAO on the lending page. This allows #Bittensor users to leverage liquid stake, employ short or long strategies, among other DeFi strategies, or simply access stablecoin liquidity while maintaining exposure to their $TAO. Up to $1M will be distributed as additional incentives on top of the supply APYs at the launch of the $wTAO and $swTAO money markets, with $200K allocated for the first month specifically for bootstrapping. Initially, 70% of rewards will go to liquidity providers, and 30% to those using $HTM to boost their lending positions. This changes to a 50-50 split in the second month, and by the third month, all incentives are directed through the Booster. This approach encourages early participation and sustained engagement with $HTM. Introducing $TAO to #MultiversX will result in the creation of Liquidity Pools (LPs) on both AshSwap 🔥 and xExchange ⚡. These LPs will be incentivized by both entities, and Hatom will distribute extra rewards at launch. The goal is to make #MultiversX a one-stop hub for $TAO holders. Upon stabilizing the volumes, there will also be plans to integrate it on AshPerp 🔥. Furthermore, with the release of $USH, users will have the ability to mint it while retaining exposure to their $TAO. The TAO Bridge and TAO Liquid Staking smart contracts have been audited by Runtime Vеrification and @arda_project, while penetration testing and DevSecOps have been performed on our infrastructure by CertiK. We're excited to announce our exclusive partnership with TAONEW one of the top 5 validators on #Bittensor. TAONEW has been extremely helpful and supportive from day one. By sharing 50% of its service fee with its stakers, TAONEW enables Hatom to offer an optimized Staking APY to its users. Since our initial reference, #Bittensor has grown sevenfold, becoming the largest AI project in the crypto sphere. We reiterate our commitment to contribute to such technology and hope to address some of its current DeFi challenges. Syfy Moving forward, today marks a significant milestone, not only for our decentralized protocols but also for our development companies, which currently stand as the sole and primary contributors to the Hatom Labs and Soul Labs. We’re excited to unveil Syfy, the evolved identity of Hatom Labs and Soul Labs, now serving as the parent entity for our burgeoning development companies. Organization is crucial for scalability, which is why Syfy was established to cultivate an environment where our teams can collaborate more seamlessly, enhancing our effectiveness and efficiency. At the same time, we remain committed to upholding the financial independence of each project, supported by its own community of funding contributors. Feel free to explore our website at for more information! Additionally, don't forget to follow Syfy and explore their Genesis article highlighted in their initial post: Booster V2 The Booster V2 will introduce a range of new features and opportunities for $HTM holders: Optimized Position Boosting: Previously, boosting was done individually for each money market, necessitating $HTM token distribution and periodic rebalancing due to price fluctuations. With Booster V2, the system now considers the overall position, eliminating the need for manual rebalancing. Gas Fee Reduction: Booster V2 implements optimizations that result in reduced gas fees, making transactions more cost-effective for users. Incorporation of Governance: Users staking $HTM tokens gain voting rights directly within the Booster, allowing them to participate in governance decisions while maintaining their staked positions. (Note: Only $HTM tokens are considered for governance; LP tokens are not included.) Enhanced Boosting Mechanism: The Booster V2 enables LP Tokens to boost positions within the Booster, leveraging trading fees from swaps and farm incentives while boosting lending positions. Smart Contract Completion: The Booster smart contract has been completed and audited by @arda_project, ensuring security and reliability. Frontend Implementation: The frontend design for Booster V2 has been successfully implemented, providing users with an intuitive interface. Collaboration with xExchange: Exploration is ongoing for collaboration with xExchange ⚡ to enable LP creation, farming, and meta-staking within the Booster. Upon finalization of testing, we will launch the Booster V2 on the devnet to gather community feedback and begin preparations for the mainnet release. Soul Before delving into Soul Labs's developments, it's essential to summarize its core functionality briefly: Soul Labs seamlessly connects different lending protocols and blockchains, facilitating lending and borrowing across platforms like Aave, Compound Labs, and Hatom Labs, consolidating liquidity and users' borrowing capabilities. Utilizing LayerZero Labs and other messaging layers for cross-chain communication, Soul Labs bypasses asset bridging or synthetics, unlocking novel DeFi strategies and solidifying its position as the ultimate solution for cross-lending dilemmas. Soul V1 will be permissionless, holding censorship-resistant features, incorporating multiple redundancy mechanisms, and providing support for various DApps. We're thrilled to announce that, following the launch of the Tao Bridge in 2-3 weeks, we will introduce the Soul Labs website. This platform has been meticulously crafted over 250 days to not only provide a comprehensive overview of our vision but also to offer an engaging and captivating experience that promises to be memorable. Regarding the app, significant progress has been made on the V1 protocol, including: Smart Contract Development and Testing: • Completion of the initial phase of smart contract development. • Conducting advanced testing to ensure the system's robustness. • Establishment of a fully functional proof of concept. Successful deployment and testing on the #Goerli (#Ethereum Testnet) and #Mumbai (#Polygon Testnet), leveraging LayerZero Labs for seamless operation. Feature Enhancement and Protocol Optimization: • Enhanced testing procedures to bolster system resilience. • Integration of advanced features and significant code refactoring for optimization. • Incorporation of various communication methods, including LayerZero Labs, Formerly Axelar, now at @axelar, Chainlink CCIP), and wormholecrypto, into Soul Labs framework, enhancing its resilience and flexibility. This allows Soul Labs to maintain operation through alternative protocols if the primary one is temporarily paused. Website Development and Documentation: • Nearing the completion of the v1 app, with final touches being applied. • The preparation of comprehensive V1 documentation and the Yellow Paper, available upon Soul Labs's public launch, offering detailed insights into the platform's infrastructure and capabilities. USH Recognizing the critical need for stable liquidity within the ecosystem, we have positioned ourselves at the forefront of providing a solution by introducing $USH, the first native, decentralized, and over-collateralized stablecoin on #MultiversX. As market conditions have improved, we have observed a growing demand for stablecoins in the ecosystem, evidenced by the utilization rate in the Lending Protocol spiking to over 90% several times in recent months. Therefore, our goal is to tackle the current challenges faced by users by creating a robust product that will not only help them hedge against market volatility but also open up better opportunities to trade the markets and generate yield. We're happy to unveil the $USH website, now live with a sleek and intuitive user interface, designed for ease of use, which ensures that interacting with the protocol is straightforward and accessible for all. You can access it now through this link: For the technical side, we’re advancing steadily and we’ve accomplished the following milestones: Lending Protocol Facilitator: • Coded the first version to support multiple discount factors for different collaterals. • Implemented tracking of borrowing effectiveness to enable earnings forecasting for the module and support minting processes. Isolated Pools Facilitator: • Coded the first version of Isolated Pools Facilitator. • Use of $EGLD or $sEGLD as collateral, with positions stored always in $EGLD to benefit the protocol through Liquid Staking and lending interest. • Virtual account implementation for converting $sEGLD earnings into $USH, functioning like liquidation where users deposit $USH for a higher amount of $HsELGD. Staking Module • Coded the first version of the Staking Module that allows users to stake and unstake without any restrictions. We're currently focusing our efforts on the following tasks: • Implementation of HTM Booster in the discount model in the Lending Protocol. • Implementation of different depeg strategies and brainstorming further potential “soft” depeg mechanisms. • Research and implementation of rewards model for Staking Module. • Research and implementation of Boosted Vaults Facilitator. • Review and stress-test the first version of the code. Upon launch, $USH will be integrated into various protocols and AMMs across the ecosystem, further increasing both its utility and liquidity. The opportunities will be vast, enabling users to engage in a wide range of activities such as yield farming, staking, and arbitrage, all while leveraging a stable and reliable asset. Regarding the USH Airdrop campaign, it will continue until the official launch of $USH planned for late Q2-early Q3, rewarding all users who have actively participated in the initiative. Hatom V2 It is clear by now that we are driven to build a more robust, interoperable, and secure DeFi space, removing the current barriers that hinder users' capabilities to seamlessly interact with different blockchains. Through Hatom V2, we will introduce Hatom's cross-chain architecture, designed from the ground up for interoperability. This approach will elevate the protocol to unprecedented levels, enabling its deployment across various blockchains and facilitating seamless connections between them through Soul. By enhancing interoperability, Hatom V2 aims to foster a more inclusive and accessible ecosystem. This expansion will not only broaden the protocol's reach but also significantly increase its flexibility and utility, allowing users to interact with a diverse range of assets and products across different chains. We’re thrilled to share that we are currently crafting the V2 redesign of the Hatom webpage. Anticipate a jaw-dropping transformation that will truly astonish, blending cutting-edge design with an unparalleled user experience, elevating it to a dynamic, interactive hub, and making every interaction more engaging. Good things take time, but we are confident that the release of V2 website will take place in the second quarter of this year and will officially mark the start of our journey into the cross-chain landscape. We are excited about the future and we truly believe that this will mark the beginning of a new era for Hatom. It's crucial for us to develop rapidly without sacrificing the quality or the security of each product. We're strategically allocating resources to ensure smooth progress in every area of our work. As we push forward, we believe that the launch of Soul Labs will be the most important milestone due to its massive potential and disruptive technology. We would like to thank you all for the unwavering support you've shown over the past few months; it truly fuels our passion to push daily and make strides toward achieving our ambitious goals.

As we prepare to launch several projects, we're eager to provide a general update to our community. We are steadily approaching our end goal, thanks to the daily progress we're making toward our vision. Achieving our objectives will bring about a significant transformation in cross-chain interoperability and the flow of liquidity within protocols. This will address crucial challenges and drive mass adoption. Our future-focused approach and effective team collaboration keep us moving forward in an organized manner. Let’s delve deeper into the state of development of our current products and upcoming projects. Tao Bridge Starting with the Tao Bridge, which enables the #Bittensor community to unlock DeFi opportunities with their $TAO via a highly efficient blockchain like #MultiversX, known for its security, speed, and affordability. We deeply admire #Bittensor and believe a project like that is crucial for the future of not just the crypto space but also humanity, as it addresses the major challenges AI faces today: centralization, siloed and isolated work, which pose risks and hinder the technology's potential. We are committed to the vision of subnets and dynamic $TAO, convinced that this ecosystem is as groundbreaking as #Ethereum or #Bitcoin. We will continue to support #Bittensor wherever possible, and our bridge will also expand to other chains with Hatom V2. The TAO Bridge, deployed on and accessible through will launch on the Mainnet in 14 days, on March 27th. You can follow the countdown on the lending page at Given that our main priorities are security and stability, this period will be primarily focused on quality assurance to ensure a flawless Mainnet launch. The launch will also introduce TAO Liquid Staking at along with the integration of both $wTAO and $swTAO on the lending page. This allows #Bittensor users to leverage liquid stake, employ short or long strategies, among other DeFi strategies, or simply access stablecoin liquidity while maintaining exposure to their $TAO. Up to $1M will be distributed as additional incentives on top of the supply APYs at the launch of the $wTAO and $swTAO money markets, with $200K allocated for the first month specifically for bootstrapping. Initially, 70% of rewards will go to liquidity providers, and 30% to those using $HTM to boost their lending positions. This changes to a 50-50 split in the second month, and by the third month, all incentives are directed through the Booster. This approach encourages early participation and sustained engagement with $HTM. Introducing $TAO to #MultiversX will result in the creation of Liquidity Pools (LPs) on both AshSwap 🔥 and xExchange ⚡. These LPs will be incentivized by both entities, and Hatom will distribute extra rewards at launch. The goal is to make #MultiversX a one-stop hub for $TAO holders. Upon stabilizing the volumes, there will also be plans to integrate it on AshPerp 🔥. Furthermore, with the release of $USH, users will have the ability to mint it while retaining exposure to their $TAO. The TAO Bridge and TAO Liquid Staking smart contracts have been audited by Runtime Vеrification and @arda_project, while penetration testing and DevSecOps have been performed on our infrastructure by CertiK. We're excited to announce our exclusive partnership with TAONEW one of the top 5 validators on #Bittensor. TAONEW has been extremely helpful and supportive from day one. By sharing 50% of its service fee with its stakers, TAONEW enables Hatom to offer an optimized Staking APY to its users. Since our initial reference, #Bittensor has grown sevenfold, becoming the largest AI project in the crypto sphere. We reiterate our commitment to contribute to such technology and hope to address some of its current DeFi challenges. Syfy Moving forward, today marks a significant milestone, not only for our decentralized protocols but also for our development companies, which currently stand as the sole and primary contributors to the Hatom Labs and Soul Labs. We’re excited to unveil Syfy, the evolved identity of Hatom Labs and Soul Labs, now serving as the parent entity for our burgeoning development companies. Organization is crucial for scalability, which is why Syfy was established to cultivate an environment where our teams can collaborate more seamlessly, enhancing our effectiveness and efficiency. At the same time, we remain committed to upholding the financial independence of each project, supported by its own community of funding contributors. Feel free to explore our website at for more information! Additionally, don't forget to follow Syfy and explore their Genesis article highlighted in their initial post: Booster V2 The Booster V2 will introduce a range of new features and opportunities for $HTM holders: Optimized Position Boosting: Previously, boosting was done individually for each money market, necessitating $HTM token distribution and periodic rebalancing due to price fluctuations. With Booster V2, the system now considers the overall position, eliminating the need for manual rebalancing. Gas Fee Reduction: Booster V2 implements optimizations that result in reduced gas fees, making transactions more cost-effective for users. Incorporation of Governance: Users staking $HTM tokens gain voting rights directly within the Booster, allowing them to participate in governance decisions while maintaining their staked positions. (Note: Only $HTM tokens are considered for governance; LP tokens are not included.) Enhanced Boosting Mechanism: The Booster V2 enables LP Tokens to boost positions within the Booster, leveraging trading fees from swaps and farm incentives while boosting lending positions. Smart Contract Completion: The Booster smart contract has been completed and audited by @arda_project, ensuring security and reliability. Frontend Implementation: The frontend design for Booster V2 has been successfully implemented, providing users with an intuitive interface. Collaboration with xExchange: Exploration is ongoing for collaboration with xExchange ⚡ to enable LP creation, farming, and meta-staking within the Booster. Upon finalization of testing, we will launch the Booster V2 on the devnet to gather community feedback and begin preparations for the mainnet release. Soul Before delving into Soul Labs's developments, it's essential to summarize its core functionality briefly: Soul Labs seamlessly connects different lending protocols and blockchains, facilitating lending and borrowing across platforms like Aave, Compound Labs, and Hatom Labs, consolidating liquidity and users' borrowing capabilities. Utilizing LayerZero Labs and other messaging layers for cross-chain communication, Soul Labs bypasses asset bridging or synthetics, unlocking novel DeFi strategies and solidifying its position as the ultimate solution for cross-lending dilemmas. Soul V1 will be permissionless, holding censorship-resistant features, incorporating multiple redundancy mechanisms, and providing support for various DApps. We're thrilled to announce that, following the launch of the Tao Bridge in 2-3 weeks, we will introduce the Soul Labs website. This platform has been meticulously crafted over 250 days to not only provide a comprehensive overview of our vision but also to offer an engaging and captivating experience that promises to be memorable. Regarding the app, significant progress has been made on the V1 protocol, including: Smart Contract Development and Testing: • Completion of the initial phase of smart contract development. • Conducting advanced testing to ensure the system's robustness. • Establishment of a fully functional proof of concept. Successful deployment and testing on the #Goerli (#Ethereum Testnet) and #Mumbai (#Polygon Testnet), leveraging LayerZero Labs for seamless operation. Feature Enhancement and Protocol Optimization: • Enhanced testing procedures to bolster system resilience. • Integration of advanced features and significant code refactoring for optimization. • Incorporation of various communication methods, including LayerZero Labs, Formerly Axelar, now at @axelar, Chainlink CCIP), and wormholecrypto, into Soul Labs framework, enhancing its resilience and flexibility. This allows Soul Labs to maintain operation through alternative protocols if the primary one is temporarily paused. Website Development and Documentation: • Nearing the completion of the v1 app, with final touches being applied. • The preparation of comprehensive V1 documentation and the Yellow Paper, available upon Soul Labs's public launch, offering detailed insights into the platform's infrastructure and capabilities. USH Recognizing the critical need for stable liquidity within the ecosystem, we have positioned ourselves at the forefront of providing a solution by introducing $USH, the first native, decentralized, and over-collateralized stablecoin on #MultiversX. As market conditions have improved, we have observed a growing demand for stablecoins in the ecosystem, evidenced by the utilization rate in the Lending Protocol spiking to over 90% several times in recent months. Therefore, our goal is to tackle the current challenges faced by users by creating a robust product that will not only help them hedge against market volatility but also open up better opportunities to trade the markets and generate yield. We're happy to unveil the $USH website, now live with a sleek and intuitive user interface, designed for ease of use, which ensures that interacting with the protocol is straightforward and accessible for all. You can access it now through this link: For the technical side, we’re advancing steadily and we’ve accomplished the following milestones: Lending Protocol Facilitator: • Coded the first version to support multiple discount factors for different collaterals. • Implemented tracking of borrowing effectiveness to enable earnings forecasting for the module and support minting processes. Isolated Pools Facilitator: • Coded the first version of Isolated Pools Facilitator. • Use of $EGLD or $sEGLD as collateral, with positions stored always in $EGLD to benefit the protocol through Liquid Staking and lending interest. • Virtual account implementation for converting $sEGLD earnings into $USH, functioning like liquidation where users deposit $USH for a higher amount of $HsELGD. Staking Module • Coded the first version of the Staking Module that allows users to stake and unstake without any restrictions. We're currently focusing our efforts on the following tasks: • Implementation of HTM Booster in the discount model in the Lending Protocol. • Implementation of different depeg strategies and brainstorming further potential “soft” depeg mechanisms. • Research and implementation of rewards model for Staking Module. • Research and implementation of Boosted Vaults Facilitator. • Review and stress-test the first version of the code. Upon launch, $USH will be integrated into various protocols and AMMs across the ecosystem, further increasing both its utility and liquidity. The opportunities will be vast, enabling users to engage in a wide range of activities such as yield farming, staking, and arbitrage, all while leveraging a stable and reliable asset. Regarding the USH Airdrop campaign, it will continue until the official launch of $USH planned for late Q2-early Q3, rewarding all users who have actively participated in the initiative. Hatom V2 It is clear by now that we are driven to build a more robust, interoperable, and secure DeFi space, removing the current barriers that hinder users' capabilities to seamlessly interact with different blockchains. Through Hatom V2, we will introduce Hatom's cross-chain architecture, designed from the ground up for interoperability. This approach will elevate the protocol to unprecedented levels, enabling its deployment across various blockchains and facilitating seamless connections between them through Soul. By enhancing interoperability, Hatom V2 aims to foster a more inclusive and accessible ecosystem. This expansion will not only broaden the protocol's reach but also significantly increase its flexibility and utility, allowing users to interact with a diverse range of assets and products across different chains. We’re thrilled to share that we are currently crafting the V2 redesign of the Hatom webpage. Anticipate a jaw-dropping transformation that will truly astonish, blending cutting-edge design with an unparalleled user experience, elevating it to a dynamic, interactive hub, and making every interaction more engaging. Good things take time, but we are confident that the release of V2 website will take place in the second quarter of this year and will officially mark the start of our journey into the cross-chain landscape. We are excited about the future and we truly believe that this will mark the beginning of a new era for Hatom. It's crucial for us to develop rapidly without sacrificing the quality or the security of each product. We're strategically allocating resources to ensure smooth progress in every area of our work. As we push forward, we believe that the launch of Soul Labs will be the most important milestone due to its massive potential and disruptive technology. We would like to thank you all for the unwavering support you've shown over the past few months; it truly fuels our passion to push daily and make strides toward achieving our ambitious goals.

Hatom Labs

203,486 просмотров • 2 лет назад

A Letter to Our Community: The Road Ahead for Robotics To our Community and Partners, As we step into 2026, our mission at Axis is clearer than ever: Constructing the definitive End-to-End Scaling Layer for Robotics. Our goal is to accelerate the transfer of diverse human intelligence into Robotics General Intelligence (RGI). By owning the critical path of intelligence creation, we are turning the physical limitations of robotics into a scalable, software-driven future. Here is our strategic outlook and roadmap for the year ahead. The Core Thesis: Simulation is the Only Way Out The path to RGI is currently blocked by Data Scarcity, Generalization Fragility, and Hardware Fragmentation. At Axis, we believe Simulation is the only way out. Our Simulation Data Platform and Data Augmentation Engine transform raw data into "Synthetic Gold". Backed by academic milestones like Roboverse, Skill Blending, and GraspVLA, we have proven that pure simulation can achieve the generalization required for the real world. We don’t just collect data; we architect it. The Engine: Why Crypto? We believe RGI should come from all, not a few. Crypto is not just a feature; it is the primitive that powers our entire ecosystem flywheel: - Incentive Mechanism: Democratizing contribution and rewarding the trainers and developers. - Assetization: Turning proprietary data and refined models into liquid, ownable assets. - Verifiable Workflow: We are opening the "Black Box" of AI. By bringing total transparency to the Task Generation → Data Collection → Model Training pipeline, we ensure every byte of intelligence is verifiable, traceable, and secure. 2026 Strategic Deliverables This year, we are committed to delivering three foundational pillars: - The World's Largest Training Dataset for Robots: A robot training set—diverse, high-quality interaction data at an unprecedented scale. - A Robotics Foundation Model: A universal robotic brain trained on our pure simulation and synthetic data, capable of robust cross-embodiment transfer and open-world adaptability. - Evolvable Robot Hardware: Robots deployed with Axis models that autonomously evolve through continuous interaction, turning every deployment into a self-improving node within our RGI network. The Ultimate Vision We are building more than models; we are architecting the Distributed Machine Economy. A future where every dataset, model, and robotic embodiment is a verifiable asset in a global, autonomous network. Thank you for building the future of intelligence with us✌️📷

A Letter to Our Community: The Road Ahead for Robotics To our Community and Partners, As we step into 2026, our mission at Axis is clearer than ever: Constructing the definitive End-to-End Scaling Layer for Robotics. Our goal is to accelerate the transfer of diverse human intelligence into Robotics General Intelligence (RGI). By owning the critical path of intelligence creation, we are turning the physical limitations of robotics into a scalable, software-driven future. Here is our strategic outlook and roadmap for the year ahead. The Core Thesis: Simulation is the Only Way Out The path to RGI is currently blocked by Data Scarcity, Generalization Fragility, and Hardware Fragmentation. At Axis, we believe Simulation is the only way out. Our Simulation Data Platform and Data Augmentation Engine transform raw data into "Synthetic Gold". Backed by academic milestones like Roboverse, Skill Blending, and GraspVLA, we have proven that pure simulation can achieve the generalization required for the real world. We don’t just collect data; we architect it. The Engine: Why Crypto? We believe RGI should come from all, not a few. Crypto is not just a feature; it is the primitive that powers our entire ecosystem flywheel: - Incentive Mechanism: Democratizing contribution and rewarding the trainers and developers. - Assetization: Turning proprietary data and refined models into liquid, ownable assets. - Verifiable Workflow: We are opening the "Black Box" of AI. By bringing total transparency to the Task Generation → Data Collection → Model Training pipeline, we ensure every byte of intelligence is verifiable, traceable, and secure. 2026 Strategic Deliverables This year, we are committed to delivering three foundational pillars: - The World's Largest Training Dataset for Robots: A robot training set—diverse, high-quality interaction data at an unprecedented scale. - A Robotics Foundation Model: A universal robotic brain trained on our pure simulation and synthetic data, capable of robust cross-embodiment transfer and open-world adaptability. - Evolvable Robot Hardware: Robots deployed with Axis models that autonomously evolve through continuous interaction, turning every deployment into a self-improving node within our RGI network. The Ultimate Vision We are building more than models; we are architecting the Distributed Machine Economy. A future where every dataset, model, and robotic embodiment is a verifiable asset in a global, autonomous network. Thank you for building the future of intelligence with us✌️📷

Axis Robotics

28,096 просмотров • 9 месяцев назад