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Introducing Marigold 🌼 - a universal monocular depth estimator, delivering incredibly sharp predictions in the wild! Based on Stable Diffusion, it is trained with synthetic depth data only and excels in zero-shot adaptation to real-world imagery. Check it out: 🌐 Website: 🤗 Hugging Face Space: 📄 Paper: 👾 Code:...

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Anton Obukhov

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490,031 просмотров • 2 лет назад •via X (Twitter)

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Комментарии: 9

Фото профиля AI葵(Aoi) 🇹🇼NeRF技術型Vtuber
AI葵(Aoi) 🇹🇼NeRF技術型Vtuber2 лет назад

70s of inference for an image is very slow..

Фото профиля Anton Obukhov
Anton Obukhov2 лет назад

We focus on the quality first - speed will come later through research and improvements in diffusion samplers. Arguably, Stable Diffusion is also slower than most of the GANs :)

Фото профиля Bilawal Sidhu
Bilawal Sidhu2 лет назад

Damn those are some crispy depth maps!

Фото профиля Chris Offner
Chris Offner2 лет назад

Wow, those depth maps look amazing. Great work! There goes my course project for Deep Learning. 😅

Фото профиля Boyuan Chen
Boyuan Chen2 лет назад

Is this metric or relative depth?

Фото профиля Anton Obukhov
Anton Obukhov2 лет назад

Relative

Фото профиля Janek Mann
Janek Mann2 лет назад

I also consider this strong evidence that Stable Diffusion learned much stronger 3D scene understanding than commonly assumed.

Фото профиля Omar Sanseviero
Omar Sanseviero2 лет назад

Very very cool!

Фото профиля Tom
Tom2 лет назад

Wow. More detailed than the ground truth, very cool to see.

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29,036 просмотров • 5 месяцев назад

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Introducing Sharpe Search: On-Chain Search AI Agent Powered by Hive Intelligence We’re thrilled to announce the launch of Sharpe Search, a crypto search AI agent powered by Hive Intelligence Designed to simplify blockchain data interaction, Sharpe Search represents a significant step toward making crypto more accessible and actionable for users at every level. Sharpe Search leverages Hive Intelligence’s advanced search API to provide real-time, actionable insights across the blockchain ecosystem. Here’s a detailed look at what Sharpe Search is, how it works: What Is Sharpe Search? At its core, Sharpe Search is an AI agent purpose-built for querying and analyzing on-chain data. It takes the complexity out of blockchain exploration by enabling users to ask questions in plain language and receive detailed, accurate responses. Whether you’re looking to monitor wallet activity, track portfolio positions, or analyze transaction history, Sharpe Search ensures that the answers are at your fingertips—accurate, comprehensive, and delivered instantly. How Does Sharpe Search Work? Sharpe Search is powered by Hive Intelligence, a search engine API designed to make blockchain data easily accessible and AI-ready. Here’s a breakdown of how it enables Sharpe Search to function effectively: 1. LLM-Optimized Query Processing Sharpe Search leverages Hive Intelligence's optimized responses for large language models. This ensures that AI agents can process blockchain data in a structured format, delivering precise answers to complex user queries. 2. Natural Language Interaction Forget the need for technical knowledge. Sharpe Search supports natural language queries, making it as simple as typing: - “What tokens are in my wallet? Am I eligible for any airdrop I haven't claimed yet?” - “Check me my last 100 transactions, tell me if I interacted with any protocol with recent hacks” - “Track my wallet activity over the past month, suggest optimised portfolio based on best stable yields available” 3. Real-Time Insights Across Multi-Chains Using Hive Intelligence, Sharpe Search connects to over 20 chains and 5000+ Protocols. This real-time access ensures that the AI agent provides up-to-date and actionable insights, no matter how dynamic the blockchain environment. 4. Unified API Access Sharpe Search consolidates fragmented blockchain data through Hive’s unified API. Instead of dealing with multiple integrations, Sharpe Search uses a single access point to aggregate and query data, reducing complexity for both users and developers. Technical Depth: The AI Agent Advantage Sharpe Search's design philosophy revolves around the principle of creating an intuitive, AI-driven experience. Here’s what makes its technology stand out: Data Indexing and Aggregation: Hive Intelligence employs advanced indexing algorithms to aggregate data from multiple chains. This ensures that Sharpe Search can retrieve information within milliseconds, even when querying vast datasets. Dynamic Updates: Blockchain data is volatile. Sharpe Search processes dynamic updates in real time, enabling users to act on the most recent metrics, transactions, and balances without delays. Contextual Understanding: The AI agent parses natural language queries and contextualizes them to blockchain-specific scenarios. For instance, when querying “Show portfolio details,” Sharpe Search understands the underlying requirements—fetching wallet holdings, token values, and current positions. Hive Intelligence: The Backbone of Sharpe Search While Sharpe Search takes center stage, Hive Intelligence provides the critical infrastructure to make it all possible. Its LLM-ready responses and multi-chain support ensure that Sharpe Search operates at the forefront of blockchain data accessibility. By launching Hive Intelligence through Sharpe Launchpad, Sharpe reinforces its commitment to supporting innovation in the blockchain space. Hive’s infrastructure not only powers Sharpe Search but also lays the groundwork for future AI agents to thrive in the ecosystem. What’s Next for Sharpe Search? Currently in invite-only access, Sharpe Search is preparing for a broader public release. Future updates will include: - Expanded Blockchain Coverage: More chains and protocols will be added. - Enhanced Query Flexibility: Even more advanced natural language capabilities. Stay tuned for the public launch and get ready to explore crypto like never before!
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Introducing Sharpe Search: On-Chain Search AI Agent Powered by Hive Intelligence We’re thrilled to announce the launch of Sharpe Search, a crypto search AI agent powered by Hive Intelligence Designed to simplify blockchain data interaction, Sharpe Search represents a significant step toward making crypto more accessible and actionable for users at every level. Sharpe Search leverages Hive Intelligence’s advanced search API to provide real-time, actionable insights across the blockchain ecosystem. Here’s a detailed look at what Sharpe Search is, how it works: What Is Sharpe Search? At its core, Sharpe Search is an AI agent purpose-built for querying and analyzing on-chain data. It takes the complexity out of blockchain exploration by enabling users to ask questions in plain language and receive detailed, accurate responses. Whether you’re looking to monitor wallet activity, track portfolio positions, or analyze transaction history, Sharpe Search ensures that the answers are at your fingertips—accurate, comprehensive, and delivered instantly. How Does Sharpe Search Work? Sharpe Search is powered by Hive Intelligence, a search engine API designed to make blockchain data easily accessible and AI-ready. Here’s a breakdown of how it enables Sharpe Search to function effectively: 1. LLM-Optimized Query Processing Sharpe Search leverages Hive Intelligence's optimized responses for large language models. This ensures that AI agents can process blockchain data in a structured format, delivering precise answers to complex user queries. 2. Natural Language Interaction Forget the need for technical knowledge. Sharpe Search supports natural language queries, making it as simple as typing: - “What tokens are in my wallet? Am I eligible for any airdrop I haven't claimed yet?” - “Check me my last 100 transactions, tell me if I interacted with any protocol with recent hacks” - “Track my wallet activity over the past month, suggest optimised portfolio based on best stable yields available” 3. Real-Time Insights Across Multi-Chains Using Hive Intelligence, Sharpe Search connects to over 20 chains and 5000+ Protocols. This real-time access ensures that the AI agent provides up-to-date and actionable insights, no matter how dynamic the blockchain environment. 4. Unified API Access Sharpe Search consolidates fragmented blockchain data through Hive’s unified API. Instead of dealing with multiple integrations, Sharpe Search uses a single access point to aggregate and query data, reducing complexity for both users and developers. Technical Depth: The AI Agent Advantage Sharpe Search's design philosophy revolves around the principle of creating an intuitive, AI-driven experience. Here’s what makes its technology stand out: Data Indexing and Aggregation: Hive Intelligence employs advanced indexing algorithms to aggregate data from multiple chains. This ensures that Sharpe Search can retrieve information within milliseconds, even when querying vast datasets. Dynamic Updates: Blockchain data is volatile. Sharpe Search processes dynamic updates in real time, enabling users to act on the most recent metrics, transactions, and balances without delays. Contextual Understanding: The AI agent parses natural language queries and contextualizes them to blockchain-specific scenarios. For instance, when querying “Show portfolio details,” Sharpe Search understands the underlying requirements—fetching wallet holdings, token values, and current positions. Hive Intelligence: The Backbone of Sharpe Search While Sharpe Search takes center stage, Hive Intelligence provides the critical infrastructure to make it all possible. Its LLM-ready responses and multi-chain support ensure that Sharpe Search operates at the forefront of blockchain data accessibility. By launching Hive Intelligence through Sharpe Launchpad, Sharpe reinforces its commitment to supporting innovation in the blockchain space. Hive’s infrastructure not only powers Sharpe Search but also lays the groundwork for future AI agents to thrive in the ecosystem. What’s Next for Sharpe Search? Currently in invite-only access, Sharpe Search is preparing for a broader public release. Future updates will include: - Expanded Blockchain Coverage: More chains and protocols will be added. - Enhanced Query Flexibility: Even more advanced natural language capabilities. Stay tuned for the public launch and get ready to explore crypto like never before!

Sharpe AI

263,278 просмотров • 1 год назад

Introducing BioCLIP: A Vision Foundation Model for the Tree of Life A foundation model that strongly generalizes on the tree of life (2M+ species), outperforming OpenAI CLIP by 18% in zero-shot classification, and supports open-ended classification over almost the entire tree of life What's the secrete ingredients? > Data: we curate and release TreeOfLife-10M, the largest and most diverse ML-ready dataset of organism images to date. It contains 10.4M images for over 450K taxa, sourced from iNaturalist, BIOSCAN, and Encyclopedia of Life. > Modeling: we creatively repurposes CLIP's multimodal contrastive learning objective for hierarchical image classification. The autoregressive language model naturally encodes the hierarchy of the tree of life taxonomy, which in turn bakes the hierarchical representation into the vision transformer encoder. Key results > Strong zero/few-shot classification for animals/plants/fungi, including rare species, outperforming CLIP by avg 16-18% absolute. > T-sne visualization shows that BioCLIP's vision encoder has captued the fine-grained hierarchical structure of the tree of life > BioCLIP is a kind of universal classifier for the tree of life. Just give it an organism image and it will likely find the correct species (among top 5)! But use it with caution; it's not perfect yet.. Final remarks > AI for Science is really hard but extremely rewarding! It took us a ton of time (1+ year) and frustration trying to find a plausible way to integrate the tree of life taxonomy into foundation model training. But when the "Eureka!" moment came and the idea hit us (by the great Wei-Lun Chao) that CLIP's multimodal contrastive learning objective can be repurposed for that, everything just follows naturally. It was truly a moment of joy and excitement! > BioCLIP is our first attempt at foundation models for biology, but it certainly won't be the last! There's so much more to do at the intersection of one of the oldest scientific disciplines and the young but thriving field of AI. Biological intelligence is the foundation for artificial intelligence, and artificial intelligence will in turn become the most important tool for us to unraval the mysteries of biological intelligence. We are hiring postdocs and PhDs in the NSF Imageomics Institute institute to explore this exciting field! Drop us an email. also happy to chat about it at #NeurIPS2023 with any of Tanya, Wei-Lun Chao, or me. - paper: - project: - demo: - model: - data (TreeOfLife-10M): to be released on Hugging Face soon joint work with the amazing Imageomics Institute team: @samstevens6860 Lisa Wu, Matt Thompson, Elizabeth Campolongo Chan Hee (Luke) Song David Carlyn Li Dong Wasila Dahdul Chuck Stewart, Tanya Berger-Wolf Wei-Lun Chao Yu Su
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Introducing BioCLIP: A Vision Foundation Model for the Tree of Life A foundation model that strongly generalizes on the tree of life (2M+ species), outperforming OpenAI CLIP by 18% in zero-shot classification, and supports open-ended classification over almost the entire tree of life What's the secrete ingredients? > Data: we curate and release TreeOfLife-10M, the largest and most diverse ML-ready dataset of organism images to date. It contains 10.4M images for over 450K taxa, sourced from iNaturalist, BIOSCAN, and Encyclopedia of Life. > Modeling: we creatively repurposes CLIP's multimodal contrastive learning objective for hierarchical image classification. The autoregressive language model naturally encodes the hierarchy of the tree of life taxonomy, which in turn bakes the hierarchical representation into the vision transformer encoder. Key results > Strong zero/few-shot classification for animals/plants/fungi, including rare species, outperforming CLIP by avg 16-18% absolute. > T-sne visualization shows that BioCLIP's vision encoder has captued the fine-grained hierarchical structure of the tree of life > BioCLIP is a kind of universal classifier for the tree of life. Just give it an organism image and it will likely find the correct species (among top 5)! But use it with caution; it's not perfect yet.. Final remarks > AI for Science is really hard but extremely rewarding! It took us a ton of time (1+ year) and frustration trying to find a plausible way to integrate the tree of life taxonomy into foundation model training. But when the "Eureka!" moment came and the idea hit us (by the great Wei-Lun Chao) that CLIP's multimodal contrastive learning objective can be repurposed for that, everything just follows naturally. It was truly a moment of joy and excitement! > BioCLIP is our first attempt at foundation models for biology, but it certainly won't be the last! There's so much more to do at the intersection of one of the oldest scientific disciplines and the young but thriving field of AI. Biological intelligence is the foundation for artificial intelligence, and artificial intelligence will in turn become the most important tool for us to unraval the mysteries of biological intelligence. We are hiring postdocs and PhDs in the NSF Imageomics Institute institute to explore this exciting field! Drop us an email. also happy to chat about it at #NeurIPS2023 with any of Tanya, Wei-Lun Chao, or me. - paper: - project: - demo: - model: - data (TreeOfLife-10M): to be released on Hugging Face soon joint work with the amazing Imageomics Institute team: @samstevens6860 Lisa Wu, Matt Thompson, Elizabeth Campolongo Chan Hee (Luke) Song David Carlyn Li Dong Wasila Dahdul Chuck Stewart, Tanya Berger-Wolf Wei-Lun Chao Yu Su

Yu Su

80,660 просмотров • 2 лет назад

🚨They CANNOT SCAN kilometers into SOLID ROCK! That is what skeptics of the Khafre Project’s findings say, and they are correct to point out that Synthetic Aperture Radar (SAR) can't do this. They are also TOTALLY MISTAKEN, here is why: The Khafre team are not using conventional SAR imaging in the way critics assume. They are using a patented form of SAR Doppler Tomography, pioneered by Prof. Filippo Biondi. This is not simple surface imaging. It is a phase-coherent interferometric method that detects subtle Doppler frequency shifts caused by internal micro-vibrations within dense structures. Instead of trying to penetrate rock, it “listens” to tiny seismic vibrations in the stone. Biondi’s trick is to capture micro-motions. Tiny seismic or structural tremors slightly shift the radar’s frequency (Doppler effect). By analyzing these Doppler shifts across multiple SAR images, they can reconstruct a 3D tomographic image of what’s inside, like a CT-scan from space. Prof. Filippop Biondi's patent (PCT/EP2023/064345) explicitly describes processing “coherent vibrational Doppler information” in SAR to allow penetrating 3D imaging "over a depth of several kilometers". In other words, it effectively turns the radar into a spaceborne sonar, using Earth’s natural vibrations to “sound” the subsurface, something ordinary SAR can’t do. A peer-reviewed Remote Sensing paper describes using COSMO-SkyMed SAR data to map new shafts and chambers inside Khufu . This case study in a scientific journal shows the technique in action (with high-res 3D results!). Beyond pyramids, the technique has practical uses. For bridges and infrastructure, Biondi’s SAR Doppler method can extract a structure’s “vibration profile” from orbit. That profile highlights cracks or damage. In one study the team applied it to Italy’s Morandi Bridge before it collapsed, SAR-based vibration maps showed unusual energy spikes right at the failing pylon. They even imaged deep tunnels. The HarmonicSAR site reports they “detected for the first time the Gran-Sasso Physics Laboratory at 1.4 km below the Earth using SAR”. In other words, their tomography saw a known underground lab 1400 m under Italy! They’ve also done scans of mountain tunnels (San Gottardo). Biondi was co-author on a 2016 Scientific Reports paper tracking Iraq’s Mosul Dam instability via SAR. That study used spaceborne radar to measure tiny ground motions around the dam over time. It shows that SAR micro-motion techniques can monitor slow structural shifts on a large engineering project. In short, SAR Doppler Tomography isn’t ordinary radar, it’s like using satellites and the Earth’s own background hum to “see” underground. Think of it as applying a CT-scan or ultrasound-like method from orbit. It’s unconventional, but it’s patent-backed and has some peer-reviewed results. RECIEPTS: Synthetic Aperture Radar Doppler Tomography Reveals Details of Undiscovered High-Resolution Internal Structure of the Great Pyramid of Giza Filippo Biondi's SAR/Doppler Patent Perspectives on the Structural Health Monitoring of Bridges by Synthetic Aperture Radar HarmonicSAR (Filippos Website) Mosul Dam SAR/Doppler Project
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🚨They CANNOT SCAN kilometers into SOLID ROCK! That is what skeptics of the Khafre Project’s findings say, and they are correct to point out that Synthetic Aperture Radar (SAR) can't do this. They are also TOTALLY MISTAKEN, here is why: The Khafre team are not using conventional SAR imaging in the way critics assume. They are using a patented form of SAR Doppler Tomography, pioneered by Prof. Filippo Biondi. This is not simple surface imaging. It is a phase-coherent interferometric method that detects subtle Doppler frequency shifts caused by internal micro-vibrations within dense structures. Instead of trying to penetrate rock, it “listens” to tiny seismic vibrations in the stone. Biondi’s trick is to capture micro-motions. Tiny seismic or structural tremors slightly shift the radar’s frequency (Doppler effect). By analyzing these Doppler shifts across multiple SAR images, they can reconstruct a 3D tomographic image of what’s inside, like a CT-scan from space. Prof. Filippop Biondi's patent (PCT/EP2023/064345) explicitly describes processing “coherent vibrational Doppler information” in SAR to allow penetrating 3D imaging "over a depth of several kilometers". In other words, it effectively turns the radar into a spaceborne sonar, using Earth’s natural vibrations to “sound” the subsurface, something ordinary SAR can’t do. A peer-reviewed Remote Sensing paper describes using COSMO-SkyMed SAR data to map new shafts and chambers inside Khufu . This case study in a scientific journal shows the technique in action (with high-res 3D results!). Beyond pyramids, the technique has practical uses. For bridges and infrastructure, Biondi’s SAR Doppler method can extract a structure’s “vibration profile” from orbit. That profile highlights cracks or damage. In one study the team applied it to Italy’s Morandi Bridge before it collapsed, SAR-based vibration maps showed unusual energy spikes right at the failing pylon. They even imaged deep tunnels. The HarmonicSAR site reports they “detected for the first time the Gran-Sasso Physics Laboratory at 1.4 km below the Earth using SAR”. In other words, their tomography saw a known underground lab 1400 m under Italy! They’ve also done scans of mountain tunnels (San Gottardo). Biondi was co-author on a 2016 Scientific Reports paper tracking Iraq’s Mosul Dam instability via SAR. That study used spaceborne radar to measure tiny ground motions around the dam over time. It shows that SAR micro-motion techniques can monitor slow structural shifts on a large engineering project. In short, SAR Doppler Tomography isn’t ordinary radar, it’s like using satellites and the Earth’s own background hum to “see” underground. Think of it as applying a CT-scan or ultrasound-like method from orbit. It’s unconventional, but it’s patent-backed and has some peer-reviewed results. RECIEPTS: Synthetic Aperture Radar Doppler Tomography Reveals Details of Undiscovered High-Resolution Internal Structure of the Great Pyramid of Giza Filippo Biondi's SAR/Doppler Patent Perspectives on the Structural Health Monitoring of Bridges by Synthetic Aperture Radar HarmonicSAR (Filippos Website) Mosul Dam SAR/Doppler Project

Jay Anderson

169,956 просмотров • 7 месяцев назад

What seemed like an intractable problem is now possible: To design proteins with a specified nonlinear mechanical response, capturing complex folding and unfolding mechanisms in singe and few-shot computations. We present ForceGen, an end-to-end algorithm for de novo protein generation based on nonlinear mechanical unfolding responses. Rooted in the physics of protein mechanics, this generative strategy provides a powerful way to design new proteins rapidly, including exquisite and rapid predictions about their dynamical behavior. Proteins, like any other mechanical object, respond to forces in peculiar ways. Think of the different response you'd get from pulling on a steel cable versus pulling on a rubber band, or the difference between honey and glass. Now, we can design proteins with a set of desirable mechanical characteristics, with applications from health to sustainable plastics. The key to solving this problem was to integrate a protein language model with denoising diffusion methods, and using accurate atomistic-level physical simulation data to endow the model a first-principles understanding. ForceGen can solve both forward and inverse tasks: In the forward task, we can predict how stable a protein is, how it will unfold and what the forces involved are, all given just the sequence of amino acids. In the inverse task, we can design new proteins that meet complex nonlinear mechanical signature targets. Read the paper, led by LAMM@MIT postdoc Bo Ni, published in Science Advances: Why do we care about the mechanics of proteins? The mechanics of proteins are critical elements of many living systems - as evidenced in many studies of mechanobiology. Through evolution, nature has presented a set of remarkable protein materials with unique mechanical functions like elastins, silks, keratins or collagens that play crucial roles in biology. However, going beyond natural designs to discover proteins that meet specified mechanical properties remains challenging. So far, the only way to do this was to use existing evolutionary concepts or to manually alter proteins. With our new generative model we can directly design proteins to meet complex nonlinear mechanical property-design objectives. ForceGen leverages deep knowledge on protein sequences from a pretrained protein language model and maps mechanical unfolding responses to create proteins. Via full-atom molecular simulations for direct validation from physical and chemical principles, we demonstrate that the designed proteins are de novo, and fulfill the targeted mechanical properties, including unfolding energy and mechanical strength, and a detailed unfolding force-separation curves. ForceGen offers rapid pathways to explore the enormous mechanobiological protein sequence space unconstrained by biological synthesis, to enable the discovery of new protein materials with superior mechanical properties. B. Ni, D.L. Kaplan, M.J. Buehler, ForceGen: End-to-end de novo protein generation based on nonlinear mechanical unfolding responses using a language diffusion model. Sci. Adv. 10, eadl4000 (2024). DOI: 10.1126/sciadv.adl4000 Codes and model weights available Hugging Face: David Kaplan
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What seemed like an intractable problem is now possible: To design proteins with a specified nonlinear mechanical response, capturing complex folding and unfolding mechanisms in singe and few-shot computations. We present ForceGen, an end-to-end algorithm for de novo protein generation based on nonlinear mechanical unfolding responses. Rooted in the physics of protein mechanics, this generative strategy provides a powerful way to design new proteins rapidly, including exquisite and rapid predictions about their dynamical behavior. Proteins, like any other mechanical object, respond to forces in peculiar ways. Think of the different response you'd get from pulling on a steel cable versus pulling on a rubber band, or the difference between honey and glass. Now, we can design proteins with a set of desirable mechanical characteristics, with applications from health to sustainable plastics. The key to solving this problem was to integrate a protein language model with denoising diffusion methods, and using accurate atomistic-level physical simulation data to endow the model a first-principles understanding. ForceGen can solve both forward and inverse tasks: In the forward task, we can predict how stable a protein is, how it will unfold and what the forces involved are, all given just the sequence of amino acids. In the inverse task, we can design new proteins that meet complex nonlinear mechanical signature targets. Read the paper, led by LAMM@MIT postdoc Bo Ni, published in Science Advances: Why do we care about the mechanics of proteins? The mechanics of proteins are critical elements of many living systems - as evidenced in many studies of mechanobiology. Through evolution, nature has presented a set of remarkable protein materials with unique mechanical functions like elastins, silks, keratins or collagens that play crucial roles in biology. However, going beyond natural designs to discover proteins that meet specified mechanical properties remains challenging. So far, the only way to do this was to use existing evolutionary concepts or to manually alter proteins. With our new generative model we can directly design proteins to meet complex nonlinear mechanical property-design objectives. ForceGen leverages deep knowledge on protein sequences from a pretrained protein language model and maps mechanical unfolding responses to create proteins. Via full-atom molecular simulations for direct validation from physical and chemical principles, we demonstrate that the designed proteins are de novo, and fulfill the targeted mechanical properties, including unfolding energy and mechanical strength, and a detailed unfolding force-separation curves. ForceGen offers rapid pathways to explore the enormous mechanobiological protein sequence space unconstrained by biological synthesis, to enable the discovery of new protein materials with superior mechanical properties. B. Ni, D.L. Kaplan, M.J. Buehler, ForceGen: End-to-end de novo protein generation based on nonlinear mechanical unfolding responses using a language diffusion model. Sci. Adv. 10, eadl4000 (2024). DOI: 10.1126/sciadv.adl4000 Codes and model weights available Hugging Face: David Kaplan

Markus J. Buehler

47,242 просмотров • 2 лет назад

The power of the Claw, in the palm of a robot hand. Agentic robotics is here! Today, we open-source CaP-X: vibe agents, alive in the physical world. They incarnate as robot arms and humanoids with a rich set of perception APIs, actuation APIs, and auto synthesize skill libraries as they go. CaP-X is a strict superset of our old stack, because policies like VLAs are “just” API calls as well. It solves many tasks zero-shot that a learned policy would struggle with. And we are doing much more than vibing. CaP-X is our most systematic, scientific study on agentic robotics so far: - We build a comprehensive agentic toolkit: perception (SAM3 segmentation, Molmo pointing, depth, point cloud), control (IK solvers, grasp planner, navigation), and visualization (EEF, mask overlays) that work across different robots. - CaP-Gym: LLM’s first Physical Exam! 187 manipulation tasks across RoboSuite, LIBERO-PRO, and BEHAVIOR. Tabletop, bimanual, mobile manipulation. Sim and real. Can’t wait to see the gradients flow from CaP-Gym to the next wave of frontier LLM releases. - CaP-Bench: we benchmark 12 frontier LLMs/VLMs (Gemini, GPT, Opus, Qwen, DeepSeek, Kimi, and more) across 8 evaluation tiers. We systematically vary API abstraction level, agentic harness, and visual grounding methods. Lots of insights in our paper. - CaP-Agent0: a training-free agentic harness that matches or exceeds human expert code on 4 out of 7 tasks without task-specific tuning. - CaP-RL: if you get a gym, you get RL ;). A 7B OSS model jumps from 20% to 72% success after only 50 training iterations. The synthesized programs transfer to real robots with minimal sim-to-real gap. 3 years ago, our team created Voyager, one of the earliest agentic AI that plays and learns in Minecraft continuously. Its key ideas — skill libraries, self-reflection loops, and in-context planning — have since influenced many modern agentic designs. Today, the agent graduates from Minecraft and gets a real job. It’s April Fool’s, but this Claw is getting its hands dirty for real! Link in thread:
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The power of the Claw, in the palm of a robot hand. Agentic robotics is here! Today, we open-source CaP-X: vibe agents, alive in the physical world. They incarnate as robot arms and humanoids with a rich set of perception APIs, actuation APIs, and auto synthesize skill libraries as they go. CaP-X is a strict superset of our old stack, because policies like VLAs are “just” API calls as well. It solves many tasks zero-shot that a learned policy would struggle with. And we are doing much more than vibing. CaP-X is our most systematic, scientific study on agentic robotics so far: - We build a comprehensive agentic toolkit: perception (SAM3 segmentation, Molmo pointing, depth, point cloud), control (IK solvers, grasp planner, navigation), and visualization (EEF, mask overlays) that work across different robots. - CaP-Gym: LLM’s first Physical Exam! 187 manipulation tasks across RoboSuite, LIBERO-PRO, and BEHAVIOR. Tabletop, bimanual, mobile manipulation. Sim and real. Can’t wait to see the gradients flow from CaP-Gym to the next wave of frontier LLM releases. - CaP-Bench: we benchmark 12 frontier LLMs/VLMs (Gemini, GPT, Opus, Qwen, DeepSeek, Kimi, and more) across 8 evaluation tiers. We systematically vary API abstraction level, agentic harness, and visual grounding methods. Lots of insights in our paper. - CaP-Agent0: a training-free agentic harness that matches or exceeds human expert code on 4 out of 7 tasks without task-specific tuning. - CaP-RL: if you get a gym, you get RL ;). A 7B OSS model jumps from 20% to 72% success after only 50 training iterations. The synthesized programs transfer to real robots with minimal sim-to-real gap. 3 years ago, our team created Voyager, one of the earliest agentic AI that plays and learns in Minecraft continuously. Its key ideas — skill libraries, self-reflection loops, and in-context planning — have since influenced many modern agentic designs. Today, the agent graduates from Minecraft and gets a real job. It’s April Fool’s, but this Claw is getting its hands dirty for real! Link in thread:

Jim Fan

80,166 просмотров • 3 месяцев назад

🚨BREAKING: New Radar Scan Reveals a Massive Engineered Substructure That Looks Like An Energy Grid Beneath the Giza Plateau🚨 Radar engineer Filippo Biondi just dropped the most explosive finding ever reported at Giza: eight clearly man-made, tube-like structures plunging more than a kilometer beneath the Khafre Pyramid and ending in huge 80-meter chambers. The structures are obviously artificially engineered and the synthetic aperture radar Doppler-tomography technique he used has precedent in accurately predicting underground structures (in both commercial and defense use cases). The Egyptian ministry of culture is extremely afraid this finding might rewrite their history. 1) The Core Structure: Eight hollow tubes (with two symmetrical sets of four) sit directly beneath the Khafre Pyramid’s base and run straight down over 1 km into the bedrock. They terminate in a massive chamber roughly 80 meters across. The shape is engineered; nothing in geology produces structures like this. 2) The Method Used: Regular SAR can’t see through rock. Biondi uses a Doppler-tomographic approach: the satellites measure tiny vibrations on the surface, and the inversion reconstructs what’s below from how those vibrations modulate the radar signal. It’s physics, not AI. 3) Independent Replication: The same underground structures appear in data from: • Umbra • Capella Space • ISI • COSMO-SkyMed If this were a glitch or artifact, it wouldn’t repeat across four separate systems. 4) Real-World Validation: Biondi’s method has already been tested against real sites where we know the exact layout. It has: • Mapped the Gran Sasso underground lab with exact accuracy • Reproduced the Osiris Shaft down to ~37 meters • Imaged magma and voids used in active civil-protection monitoring These aren’t speculative models...they match real measurements. 5) Giza As A Unified System: After Khafre, the team scanned the rest of the plateau. Similar tube-like structures miraculously appear beneath Menkaure (a smaller 2+2 pattern) and a single descending tube under the Sphinx. The evidence points to a giant connected system beneath all three monuments. 6) The Tunnel Network: The tomography shows a dense web of tunnels running between the pyramids and toward the Sphinx. Several known surface shafts, now blocked or filled with debris, look like the original access points into this network. 7) Water As A Key Variable: The Osiris Shaft contains water at about 33–37 meters. Biondi thinks water flow is part of how the system operates, possibly tied to vibrational or informational dynamics. He cites Preparata and Del Giudice’s work on coherent water domains but avoids any “power plant” jump. 8) Academia: Biondi already has a peer-reviewed SAR/Doppler tomography paper on the Khufu Pyramid in Remote Sensing. His larger Khafre + plateau paper is now in peer review. The work sticks to hard measurements: geometry, depth, replication. 9) Next Steps: The CAF Project is preparing a proposal to: • Clear out the sealed shafts between Khafre and the Sphinx • Run direct seismic surveys to confirm the satellite data • Enter the tunnel system if Egypt authorizes it At this point, approval from Cairo is the only barrier to verifying what the scans show. If those shafts are opened, the world may be looking at a multi-kilometer engineered complex beneath Giza. Full Documentary 👇
1:07

🚨BREAKING: New Radar Scan Reveals a Massive Engineered Substructure That Looks Like An Energy Grid Beneath the Giza Plateau🚨 Radar engineer Filippo Biondi just dropped the most explosive finding ever reported at Giza: eight clearly man-made, tube-like structures plunging more than a kilometer beneath the Khafre Pyramid and ending in huge 80-meter chambers. The structures are obviously artificially engineered and the synthetic aperture radar Doppler-tomography technique he used has precedent in accurately predicting underground structures (in both commercial and defense use cases). The Egyptian ministry of culture is extremely afraid this finding might rewrite their history. 1) The Core Structure: Eight hollow tubes (with two symmetrical sets of four) sit directly beneath the Khafre Pyramid’s base and run straight down over 1 km into the bedrock. They terminate in a massive chamber roughly 80 meters across. The shape is engineered; nothing in geology produces structures like this. 2) The Method Used: Regular SAR can’t see through rock. Biondi uses a Doppler-tomographic approach: the satellites measure tiny vibrations on the surface, and the inversion reconstructs what’s below from how those vibrations modulate the radar signal. It’s physics, not AI. 3) Independent Replication: The same underground structures appear in data from: • Umbra • Capella Space • ISI • COSMO-SkyMed If this were a glitch or artifact, it wouldn’t repeat across four separate systems. 4) Real-World Validation: Biondi’s method has already been tested against real sites where we know the exact layout. It has: • Mapped the Gran Sasso underground lab with exact accuracy • Reproduced the Osiris Shaft down to ~37 meters • Imaged magma and voids used in active civil-protection monitoring These aren’t speculative models...they match real measurements. 5) Giza As A Unified System: After Khafre, the team scanned the rest of the plateau. Similar tube-like structures miraculously appear beneath Menkaure (a smaller 2+2 pattern) and a single descending tube under the Sphinx. The evidence points to a giant connected system beneath all three monuments. 6) The Tunnel Network: The tomography shows a dense web of tunnels running between the pyramids and toward the Sphinx. Several known surface shafts, now blocked or filled with debris, look like the original access points into this network. 7) Water As A Key Variable: The Osiris Shaft contains water at about 33–37 meters. Biondi thinks water flow is part of how the system operates, possibly tied to vibrational or informational dynamics. He cites Preparata and Del Giudice’s work on coherent water domains but avoids any “power plant” jump. 8) Academia: Biondi already has a peer-reviewed SAR/Doppler tomography paper on the Khufu Pyramid in Remote Sensing. His larger Khafre + plateau paper is now in peer review. The work sticks to hard measurements: geometry, depth, replication. 9) Next Steps: The CAF Project is preparing a proposal to: • Clear out the sealed shafts between Khafre and the Sphinx • Run direct seismic surveys to confirm the satellite data • Enter the tunnel system if Egypt authorizes it At this point, approval from Cairo is the only barrier to verifying what the scans show. If those shafts are opened, the world may be looking at a multi-kilometer engineered complex beneath Giza. Full Documentary 👇

Jesse Michels

890,363 просмотров • 7 месяцев назад

Stop Gambling, Start Engineering: The Ultimate Guide To CCXT Algorithmic Trading most traders are essentially walking into a high stakes casino with a blindfold on while the house has a high speed laser aimed directly at their bankroll. if you have ever felt the soul crushing weight of a liquidation notification at three in the morning then you know the market is a 24/7 beast that eats human emotion for breakfast there is a hidden bridge that connects your laptop to almost every major crypto exchange in existence and once you cross it the game changes forever. my name is moon dev i believe that code is the great equalizer because through losing money with liquidations and over trading i knew i had to automate my trading so i learned to code as in the past i spent hundreds of thousands on devs for app, thinking i would not be able to code myself w/ bots you must iterate to success so i decided to learn live on youtube, and now we are here, fully automated systems trading for me instead of getting liquidated. the secret weapon behind this transition is a library called ccxt which acts as a universal translator for exchanges like binance, bybit, and kucoin most people think they need to spend years studying computer science just to place a single trade via code but that is a lie designed to keep you on the sidelines. the reality is that once you understand how to initialize a connection you can control your entire portfolio with just a few lines of logic. it starts with importing the library and setting up your credentials in a way that doesn't leave your keys exposed to the world the first mistake that bankrupts most manual traders is the inability to act fast enough when the trend shifts. when you build a bot the first thing you need to master is the market order because it allows you to enter or exit a position instantly regardless of the price. it is the ultimate panic button for when a strategy goes south or a massive opportunity presents itself while market orders are great for speed they are the fastest way to get eaten alive by fees if you are not careful. this is where the limit order comes into play allowing you to dictate exactly what price you are willing to pay for an asset. by using a create limit order function you can place your bids and asks in the order book and wait for the market to come to you most traders forget that once an order is placed it stays active until it is either filled or manually removed. i have seen countless accounts go to zero because a bot kept piling on buy orders without ever checking to see if the previous ones were canceled. the cancel all orders function is the invisible shield that prevents your algorithm from accidentally over leveraging your account the real magic happens when you realize you can cancel more than just basic limit orders. there are untriggered conditional orders like stop losses and take profits that often hide in the background of an exchange waiting to ruin your day. by passing specific parameters into your cancel function you can wipe the slate clean and ensure your bot is starting from a neutral state every single time if you want to know what the whales are doing before it shows up on a candle chart then you need to be looking at the raw order book. fetching the order book gives you a direct view of every single bid and ask currently sitting on the exchange. this is the most honest data you can get because it represents real money waiting to be filled at specific price levels you can actually parse this data to find the exact top of the bid and the bottom of the ask to ensure your bot always gets the best possible entry. most retail traders are looking at delayed charts while your bot is reading the tape in real time and calculating the spread. this allows you to place orders that are optimized for the current liquidity rather than just guessing where the price might go one of the biggest hurdles in automation is managing the sheer volume of data that an exchange throws at you. when you fetch open high low close volume data you are getting the historical heartbeat of an asset across any timeframe you choose. this data is the foundation of every technical indicator from simple moving averages to complex machine learning models the problem is that raw data is often a mess of lists and dictionaries that are impossible for a human or a simple script to read efficiently. this is why we use pandas to convert that garbage into a structured data frame that looks exactly like a clean spreadsheet. once your data is in a data frame you can calculate rsi or macd with a single line of code and visualize the entire market structure the path to becoming a successful automated trader is not a sprint but a series of iterations toward a system that works. i chose to learn this live in front of the world because i wanted to prove that anyone can escape the cycle of over trading. you don't need a million dollars to start but you do need a system that removes the human element from the equation if you are still clicking buttons on a website then you are competing against machines that can process thousands of data points per second. it is time to stop playing a rigged game and start building your own edge in the market. the code is there for anyone to grab and the only thing standing between you and a fully automated portfolio is the willingness to sit down and write the first line every algorithm you build is a brick in a wall that protects your capital from the emotional swings of the crypto market. i spend my days refining these systems and sharing the process because i know how lonely it feels to lose everything to a flash crash. we are building a community where code is the tool and financial freedom is the goal the final step is realizing that your balance is just a number that your bot needs to manage with cold logic. by fetching your balance frequently your bot can calculate position sizes based on your total equity ensuring that no single trade can ever wipe you out. this is the difference between gambling and systematic trading and it is accessible to anyone with an internet connection i hope you take these tools and start building something that allows you to sleep peacefully while the markets do their thing. the industry is secretive for a reason but we are breaking those walls down one line of code at a time. the journey is long but the reward of never having to worry about a liquidation again is worth every second of the struggle
1:09:25

Stop Gambling, Start Engineering: The Ultimate Guide To CCXT Algorithmic Trading most traders are essentially walking into a high stakes casino with a blindfold on while the house has a high speed laser aimed directly at their bankroll. if you have ever felt the soul crushing weight of a liquidation notification at three in the morning then you know the market is a 24/7 beast that eats human emotion for breakfast there is a hidden bridge that connects your laptop to almost every major crypto exchange in existence and once you cross it the game changes forever. my name is moon dev i believe that code is the great equalizer because through losing money with liquidations and over trading i knew i had to automate my trading so i learned to code as in the past i spent hundreds of thousands on devs for app, thinking i would not be able to code myself w/ bots you must iterate to success so i decided to learn live on youtube, and now we are here, fully automated systems trading for me instead of getting liquidated. the secret weapon behind this transition is a library called ccxt which acts as a universal translator for exchanges like binance, bybit, and kucoin most people think they need to spend years studying computer science just to place a single trade via code but that is a lie designed to keep you on the sidelines. the reality is that once you understand how to initialize a connection you can control your entire portfolio with just a few lines of logic. it starts with importing the library and setting up your credentials in a way that doesn't leave your keys exposed to the world the first mistake that bankrupts most manual traders is the inability to act fast enough when the trend shifts. when you build a bot the first thing you need to master is the market order because it allows you to enter or exit a position instantly regardless of the price. it is the ultimate panic button for when a strategy goes south or a massive opportunity presents itself while market orders are great for speed they are the fastest way to get eaten alive by fees if you are not careful. this is where the limit order comes into play allowing you to dictate exactly what price you are willing to pay for an asset. by using a create limit order function you can place your bids and asks in the order book and wait for the market to come to you most traders forget that once an order is placed it stays active until it is either filled or manually removed. i have seen countless accounts go to zero because a bot kept piling on buy orders without ever checking to see if the previous ones were canceled. the cancel all orders function is the invisible shield that prevents your algorithm from accidentally over leveraging your account the real magic happens when you realize you can cancel more than just basic limit orders. there are untriggered conditional orders like stop losses and take profits that often hide in the background of an exchange waiting to ruin your day. by passing specific parameters into your cancel function you can wipe the slate clean and ensure your bot is starting from a neutral state every single time if you want to know what the whales are doing before it shows up on a candle chart then you need to be looking at the raw order book. fetching the order book gives you a direct view of every single bid and ask currently sitting on the exchange. this is the most honest data you can get because it represents real money waiting to be filled at specific price levels you can actually parse this data to find the exact top of the bid and the bottom of the ask to ensure your bot always gets the best possible entry. most retail traders are looking at delayed charts while your bot is reading the tape in real time and calculating the spread. this allows you to place orders that are optimized for the current liquidity rather than just guessing where the price might go one of the biggest hurdles in automation is managing the sheer volume of data that an exchange throws at you. when you fetch open high low close volume data you are getting the historical heartbeat of an asset across any timeframe you choose. this data is the foundation of every technical indicator from simple moving averages to complex machine learning models the problem is that raw data is often a mess of lists and dictionaries that are impossible for a human or a simple script to read efficiently. this is why we use pandas to convert that garbage into a structured data frame that looks exactly like a clean spreadsheet. once your data is in a data frame you can calculate rsi or macd with a single line of code and visualize the entire market structure the path to becoming a successful automated trader is not a sprint but a series of iterations toward a system that works. i chose to learn this live in front of the world because i wanted to prove that anyone can escape the cycle of over trading. you don't need a million dollars to start but you do need a system that removes the human element from the equation if you are still clicking buttons on a website then you are competing against machines that can process thousands of data points per second. it is time to stop playing a rigged game and start building your own edge in the market. the code is there for anyone to grab and the only thing standing between you and a fully automated portfolio is the willingness to sit down and write the first line every algorithm you build is a brick in a wall that protects your capital from the emotional swings of the crypto market. i spend my days refining these systems and sharing the process because i know how lonely it feels to lose everything to a flash crash. we are building a community where code is the tool and financial freedom is the goal the final step is realizing that your balance is just a number that your bot needs to manage with cold logic. by fetching your balance frequently your bot can calculate position sizes based on your total equity ensuring that no single trade can ever wipe you out. this is the difference between gambling and systematic trading and it is accessible to anyone with an internet connection i hope you take these tools and start building something that allows you to sleep peacefully while the markets do their thing. the industry is secretive for a reason but we are breaking those walls down one line of code at a time. the journey is long but the reward of never having to worry about a liquidation again is worth every second of the struggle

Moon Dev

14,105 просмотров • 5 месяцев назад

🧬 BREAKING: Our CRISPR-GPT paper is out TODAY in Nature Biomedical Engineering Nature Biomedical Engineering ! 🤯 We built an AI agent that turns ANYONE into a gene-editing expert in 1 DAY instead of months. An undergrad with ZERO experience achieved 90%+ editing efficiency on their FIRST attempt. 🧵 Here's how we're building expert AI agents for cutting-edge biotechnology: 🎯 The Problem: CRISPR is revolutionary but requires PhD-level expertise, it can take weeks to learn, adopt, and design, analyze a CRISPR experiment for R&D or making life-saving medicine. Even Pro scientists can make small mistakes (e.g. typos in guideRNA or cloning design) that cost months to find out, slowing us down. 💡 Our Solution: CRISPR-GPT - an AI co-pilot from Stanford University Princeton University Google DeepMind that guides you through EVERY step via simple conversation 🔬 Real Results: -Novice researcher: ~90% editing on 1st go -Training time: Months → 1 day -100% success rate in our trials -Even experts save days/weeks on data analysis & troubleshooting 🤖 How it works: Our multi-agent system handles: CRISPR system and delivery method selection, guideRNA design, Protocol generation, Real-time troubleshooting, Data analysis, and beyond. All through natural language! No coding, no complex software. 📊 We benchmarked it extensively: -288 evaluation scenarios/cases -Outperformed GPT-4o on ALL gene editing tasks -Trained on 11 years of expert discussions -Covers knockout, base-editing, prime-editing & epigenetic editing 🌍 Why this matters: -Every lab can now use CRISPR with an AI system distilling expert knowledge and skills. -Every student can learn faster. -Every researcher can tackle bigger challenges without worrying about small mistakes. -Customized CRISPR design can be automated based on your need and the context of R&D workflow. -Agentic AI ensure safety, privacy, and responsibility -We're not just automating gene editing - we're using AI to power scientists to cure diseases. 🚀 Try it yourself! Beta access available at: Paper: Code: Benchmark (companion work, Genome-bench): Co-first and key authors: Yuanhao Qu Kaixuan Huang Ming Yin PIs: Le Cong@Stanford, AI+Bio+Gene-Editing Mengdi Wang Key collaborators: Russ Altman Denny Zhou The future of biology and science is conversational. The future is now. Nature Biomedical Engineering Nature Portfolio #CRISPR #AI #GeneEditing #Biotech #Science #AISafety
1:43

🧬 BREAKING: Our CRISPR-GPT paper is out TODAY in Nature Biomedical Engineering Nature Biomedical Engineering ! 🤯 We built an AI agent that turns ANYONE into a gene-editing expert in 1 DAY instead of months. An undergrad with ZERO experience achieved 90%+ editing efficiency on their FIRST attempt. 🧵 Here's how we're building expert AI agents for cutting-edge biotechnology: 🎯 The Problem: CRISPR is revolutionary but requires PhD-level expertise, it can take weeks to learn, adopt, and design, analyze a CRISPR experiment for R&D or making life-saving medicine. Even Pro scientists can make small mistakes (e.g. typos in guideRNA or cloning design) that cost months to find out, slowing us down. 💡 Our Solution: CRISPR-GPT - an AI co-pilot from Stanford University Princeton University Google DeepMind that guides you through EVERY step via simple conversation 🔬 Real Results: -Novice researcher: ~90% editing on 1st go -Training time: Months → 1 day -100% success rate in our trials -Even experts save days/weeks on data analysis & troubleshooting 🤖 How it works: Our multi-agent system handles: CRISPR system and delivery method selection, guideRNA design, Protocol generation, Real-time troubleshooting, Data analysis, and beyond. All through natural language! No coding, no complex software. 📊 We benchmarked it extensively: -288 evaluation scenarios/cases -Outperformed GPT-4o on ALL gene editing tasks -Trained on 11 years of expert discussions -Covers knockout, base-editing, prime-editing & epigenetic editing 🌍 Why this matters: -Every lab can now use CRISPR with an AI system distilling expert knowledge and skills. -Every student can learn faster. -Every researcher can tackle bigger challenges without worrying about small mistakes. -Customized CRISPR design can be automated based on your need and the context of R&D workflow. -Agentic AI ensure safety, privacy, and responsibility -We're not just automating gene editing - we're using AI to power scientists to cure diseases. 🚀 Try it yourself! Beta access available at: Paper: Code: Benchmark (companion work, Genome-bench): Co-first and key authors: Yuanhao Qu Kaixuan Huang Ming Yin PIs: Le Cong@Stanford, AI+Bio+Gene-Editing Mengdi Wang Key collaborators: Russ Altman Denny Zhou The future of biology and science is conversational. The future is now. Nature Biomedical Engineering Nature Portfolio #CRISPR #AI #GeneEditing #Biotech #Science #AISafety

Le Cong@Stanford, AI+Bio+Gene-Editing

111,809 просмотров • 11 месяцев назад

I Cracked Polymarket Using Claude Opus 4.6: The 96,000 Dollar Script For 5 Minute High Leverage Windows most traders are currently sitting at their desks fighting a losing battle against a digital wall because they do not realize the house always wins against human emotion. while the crowd is busy chasing the next meme coin or getting washed out in a single wick an automated agent just pulled nearly a hundred thousand dollars out of thin air using nothing but raw logic. i have seen people blow their life savings in these five minute windows because they treated a high leverage prediction market like a playground instead of a laboratory i am moon dev and i believe that code is the great equalizer because through losing money with liquidations and over trading i knew i had to automate my trading. in the past i spent hundreds of thousands on devs for apps thinking i would not be able to code myself which was a massive waste of my time and resources. with bots you must iterate to success so i decided to learn live on youtube and now we are here with fully automated systems trading for me instead of getting liquidated by the market the five minute markets on polymarket are essentially a high speed game of musical chairs where the person left standing is usually the one with the fastest script. leverage makes these markets extremely dangerous because it amplifies every mistake you make until your account is completely empty. the only way to survive this environment is to stop trading based on a gut feeling and start trading based on a stress tested mathematical edge the real breakthrough happened when i started using claude opus four point six to write the execution code for these specific five minute windows. having an ai agent that can analyze microstructure data means you can find trends that are completely invisible to the naked eye. it is essentially like having a team of twenty engineers working for you around the clock without the communication lag or the massive overhead costs some of our back tests show a sixty four percent win rate which sounds like a dream to anyone who has ever spent a night staring at a red screen. however the return on these tests varies wildly based on a few specific changes to the strategy parameters and histogram filters. i found that a return of forty one thousand dollars can jump to nearly double that just by adjusting how the bot handles the macd histogram threshold the trap that most people fall into is thinking that a good back test is a license to print money immediately without any further validation. this is a dangerous lie that leads to huge losses because the market in the past is not a perfect mirror of what is going to happen today. that is why i never launch a bot with full size until it has survived the incubation phase where it trades with ten dollars at a time incubation is the ultimate reality check for any trading strategy regardless of how good the numbers look on a computer screen. it is nerve wracking to watch a bot enter its first real trade even if the size is small because that is the moment theory meets reality. most of the bots that pass a back test will fail during the first forty eight hours of incubation and that is exactly why this step is non negotiable the data i use to build these systems covers over two hundred weeks of historical one minute candles to ensure the results are robust and not just luck. we are currently moving toward a machine learning approach where the system can adapt to changing market conditions without me having to intervene. this means the bot will eventually be able to recognize when a high leverage window is too risky and simply wait for a better entry the strategy itself relies heavily on macd variations which is a well known indicator but it is used here with a very specific and proprietary twist. by filtering for trades that hit a specific threshold we can ignore the random price noise that usually liquidates manual traders. we look for an edge of at least six percent which is enough to cover all platform fees and still leave a significant profit on the table i used to think that being a successful trader meant being a genius who could predict the future with a magical crystal ball. the truth is far more boring because success is just about researching an idea and testing it until the data proves it works in the past. then you just let the bot do the work while you go live your life instead of being a slave to the candle sticks and charts this world is changing fast and the people who learn to leverage ai to automate their thinking are going to be the ones who win the next decade. i am not asking you to trust a back test or a screenshot from a website because i want you to trust the process of testing it yourself. code allows you to take your life back from the screens and finally stop the cycle of over trading and emotional liquidations the difference between the traders who make it and the people who blow up is simply the willingness to iterate on their ideas daily. you might fail on your first ten bots but the eleventh one might be the script that changes your entire financial trajectory forever. it is about staying in the game long enough for the math to finally work in your favor and removing the human heart from the execution every day i am back testing and researching new ideas to see if they can survive the stress of real market data. i launch these live bots and let them run for seventy two hours to see if they can handle the pressure of the current market trend. while everyone else is coping and complaining about market volatility we are just adjusting our parameters and letting the ai find the next profitable window vibe coding with claude opus four point six has changed the speed at which i can deploy a new strategy from weeks down to just a few minutes. you can give the ai a general strategy idea and it builds the entire trading infrastructure for you while you focus on the logic. this speed is the ultimate advantage in a market that moves as fast as a five minute prediction window on the blockchain the future of trading is not found in a chat room or a paid signal group but in the code you write and the data you process. i believe that everyone has the ability to become an automated trader if they are willing to put in the work to learn the scripts. it is the only way to escape the trap of the nine to five and the anxiety of manual hand trading in a manipulated market i want you to understand that the ninety six thousand dollar returns i see are the result of hundreds of failed tests that never saw the light of day. you have to be willing to look at a failing bot and kill it without emotion so you can move on to the next research project. that is the quantitative mindset that separates the winners from the people who are just gambling with their savings if you are ready to stop being the liquidity for the big players then it is time to start building your own automated army of bots. for the cost of a few cups of coffee you can get access to the road map and the scripts that are driving these results. i am here every day showing you the process because i want to see more people use code to find their financial freedom and beat the house at its own game
11:04

I Cracked Polymarket Using Claude Opus 4.6: The 96,000 Dollar Script For 5 Minute High Leverage Windows most traders are currently sitting at their desks fighting a losing battle against a digital wall because they do not realize the house always wins against human emotion. while the crowd is busy chasing the next meme coin or getting washed out in a single wick an automated agent just pulled nearly a hundred thousand dollars out of thin air using nothing but raw logic. i have seen people blow their life savings in these five minute windows because they treated a high leverage prediction market like a playground instead of a laboratory i am moon dev and i believe that code is the great equalizer because through losing money with liquidations and over trading i knew i had to automate my trading. in the past i spent hundreds of thousands on devs for apps thinking i would not be able to code myself which was a massive waste of my time and resources. with bots you must iterate to success so i decided to learn live on youtube and now we are here with fully automated systems trading for me instead of getting liquidated by the market the five minute markets on polymarket are essentially a high speed game of musical chairs where the person left standing is usually the one with the fastest script. leverage makes these markets extremely dangerous because it amplifies every mistake you make until your account is completely empty. the only way to survive this environment is to stop trading based on a gut feeling and start trading based on a stress tested mathematical edge the real breakthrough happened when i started using claude opus four point six to write the execution code for these specific five minute windows. having an ai agent that can analyze microstructure data means you can find trends that are completely invisible to the naked eye. it is essentially like having a team of twenty engineers working for you around the clock without the communication lag or the massive overhead costs some of our back tests show a sixty four percent win rate which sounds like a dream to anyone who has ever spent a night staring at a red screen. however the return on these tests varies wildly based on a few specific changes to the strategy parameters and histogram filters. i found that a return of forty one thousand dollars can jump to nearly double that just by adjusting how the bot handles the macd histogram threshold the trap that most people fall into is thinking that a good back test is a license to print money immediately without any further validation. this is a dangerous lie that leads to huge losses because the market in the past is not a perfect mirror of what is going to happen today. that is why i never launch a bot with full size until it has survived the incubation phase where it trades with ten dollars at a time incubation is the ultimate reality check for any trading strategy regardless of how good the numbers look on a computer screen. it is nerve wracking to watch a bot enter its first real trade even if the size is small because that is the moment theory meets reality. most of the bots that pass a back test will fail during the first forty eight hours of incubation and that is exactly why this step is non negotiable the data i use to build these systems covers over two hundred weeks of historical one minute candles to ensure the results are robust and not just luck. we are currently moving toward a machine learning approach where the system can adapt to changing market conditions without me having to intervene. this means the bot will eventually be able to recognize when a high leverage window is too risky and simply wait for a better entry the strategy itself relies heavily on macd variations which is a well known indicator but it is used here with a very specific and proprietary twist. by filtering for trades that hit a specific threshold we can ignore the random price noise that usually liquidates manual traders. we look for an edge of at least six percent which is enough to cover all platform fees and still leave a significant profit on the table i used to think that being a successful trader meant being a genius who could predict the future with a magical crystal ball. the truth is far more boring because success is just about researching an idea and testing it until the data proves it works in the past. then you just let the bot do the work while you go live your life instead of being a slave to the candle sticks and charts this world is changing fast and the people who learn to leverage ai to automate their thinking are going to be the ones who win the next decade. i am not asking you to trust a back test or a screenshot from a website because i want you to trust the process of testing it yourself. code allows you to take your life back from the screens and finally stop the cycle of over trading and emotional liquidations the difference between the traders who make it and the people who blow up is simply the willingness to iterate on their ideas daily. you might fail on your first ten bots but the eleventh one might be the script that changes your entire financial trajectory forever. it is about staying in the game long enough for the math to finally work in your favor and removing the human heart from the execution every day i am back testing and researching new ideas to see if they can survive the stress of real market data. i launch these live bots and let them run for seventy two hours to see if they can handle the pressure of the current market trend. while everyone else is coping and complaining about market volatility we are just adjusting our parameters and letting the ai find the next profitable window vibe coding with claude opus four point six has changed the speed at which i can deploy a new strategy from weeks down to just a few minutes. you can give the ai a general strategy idea and it builds the entire trading infrastructure for you while you focus on the logic. this speed is the ultimate advantage in a market that moves as fast as a five minute prediction window on the blockchain the future of trading is not found in a chat room or a paid signal group but in the code you write and the data you process. i believe that everyone has the ability to become an automated trader if they are willing to put in the work to learn the scripts. it is the only way to escape the trap of the nine to five and the anxiety of manual hand trading in a manipulated market i want you to understand that the ninety six thousand dollar returns i see are the result of hundreds of failed tests that never saw the light of day. you have to be willing to look at a failing bot and kill it without emotion so you can move on to the next research project. that is the quantitative mindset that separates the winners from the people who are just gambling with their savings if you are ready to stop being the liquidity for the big players then it is time to start building your own automated army of bots. for the cost of a few cups of coffee you can get access to the road map and the scripts that are driving these results. i am here every day showing you the process because i want to see more people use code to find their financial freedom and beat the house at its own game

Moon Dev

10,921 просмотров • 4 месяцев назад

Thermodynamic computing is here There is a new computing paradigm emerging from the noise, and its arrival may be as significant as the dawn of deep learning or the advent of cloud virtualization. A new company, Extropic, has just launched its first thermodynamic computer, a device they call a TSU, or Thermal Sampling Unit. While the web is already filling with deep technical dives, what’s more important for most of us is building a clear intuition for what this technology is, how it’s fundamentally different from anything that’s come before, and why it’s generating so much excitement. This isn’t just another chip; it’s a new way to think about computation itself. Seeing is Believing: Solving Puzzles in One Shot To understand what a TSU does, let’s look at two classic, notoriously difficult computer science problems: Sudoku and the Eight Queens problem. When you or I solve a Sudoku, we use a process of sequential logic, guess-and-check, and backtracking. We make an assumption, follow its logical conclusion, and if we hit a dead end, we erase and try again. A classical computer does the same, just much faster. A TSU, however, approaches this in a completely different way. Using a TSU simulator, one can “program” the problem by first clamping the known values—the clues already on the board. Then, you program in the constraints: no duplicate numbers in any row, column, or 3x3 square. With the problem thus defined, the TSU doesn’t “search” for a solution; it anneals one. In a single computational step, the solution simply emerges, backfilling all the empty squares correctly. The same principle applies to the Eight Queens problem, a challenge to place eight queens on a chessboard so that none can attack any other. This is a complex combinatorial problem with 92 distinct solutions. A classical computer would have to iteratively search for these. A TSU, by contrast, can be programmed with the constraints (the “anti-affinity” between queens on the same row, column, or diagonal) and then set to sample the “solution space.” In this context, a valid solution is one with a “problem energy” of zero. The TSU’s physical nature allows it to naturally find these zero-energy states. A simulation of this process shows the TSU discovering all 92 unique solutions, demonstrating its ability to not just find an answer, but to explore the entire landscape of all correct answers. This is a fundamentally new approach, one that bypasses the brute-force, iterative methods we’ve relied on for decades. The Physics of Computation: Using Noise, Not Fighting It This new power comes from a radical design philosophy. For the last 70 years, computing has been about one thing: order. We build chips that are deterministic, logical, and precise. The great enemy has always been noise, heat, and randomness. We spend billions on cooling and error correction to eliminate these very things. Quantum computing, in many ways, is the ultimate expression of this, requiring temperatures near absolute zero to eliminate all thermal noise and achieve quantum coherence. Thermodynamic computing is the polar opposite. It doesn’t fight the noise; it uses it. The TSU is built on the understanding that the natural, stochastic noise from “leaky” transistors—the very randomness we’ve tried to engineer out of existence—is itself a powerful computational resource. Think of it this way: a GPU, which is central to today’s AI, has to simulate noise. When a generative AI model creates a new image or sentence, it’s using complex algorithms to fake randomness. The TSU doesn’t need to fake it; it harnesses the actual physical randomness of thermodynamics. It is a piece of hardware that directly computes with probability. This makes it a hybrid, sitting somewhere between a purely analog computer (which might use light or sound waves to compute) and a digital GPU. It’s a physical device that leverages the laws of physics itself to find solutions, rather than just using logic gates to simulate them. From a Lost Hiker to a Million Bouncy Balls Perhaps the best way to build intuition is with a metaphor. Imagine that solving a complex optimization problem is like trying to find the lowest point of altitude in a 100-square-mile mountainous landscape. Classical computing, using an algorithm like gradient descent, is like being a single hiker dropped into this landscape at night. You have no map or satellite view. All you have is an altimeter and the sensation of the slope under your feet. You can only take one step at a time, always walking downhill, hoping you don’t get stuck in a small local valley when the true, lowest canyon is miles away. Thermodynamic computing is a completely different approach. It’s like having a million bouncy balls and a helicopter. You drop all million balls simultaneously across the entire 100-square-mile landscape. Then, you “turn on an earthquake,” shaking the entire system. The balls bounce and jostle, but as the shaking (the “annealing”) subsides, where do they all end up? They naturally settle into the lowest points. The balls that collect in the deepest valley represent the optimal solution. The TSU is, in essence, a physical device for dropping those million balls at once and letting the laws of thermodynamics find the lowest “energy” state for you, all at the same time. Beyond Puzzles: The Real-World Impact This is far more than just a clever way to solve brain teasers. This ability to instantly find the lowest energy state for a complex, constrained system has staggering real-world applications. One of the most immediate is protein folding. Companies like Google’s DeepMind have made incredible progress with AI like AlphaFold, which predicts protein structures. But this is still a predictive model trained on existing data. A TSU could potentially solve the folding problem directly, treating the protein as a system of atomic affinities and repulsions and finding its most stable, lowest-energy configuration almost instantaneously. This could revolutionize drug discovery and materials science. An even more profound possibility lies in nuclear fusion. One of the greatest engineering challenges in history is controlling the superheated plasma within a tokamak reactor. This requires shaping unimaginably complex magnetic containment fields in real-time to prevent the plasma from touching the reactor walls. This is a real-time optimization problem so complex it’s currently beyond our capabilities. A TSU, however, could be fast enough. Its ability to compute with electricity itself, rather than abstracting the problem through layers of software, might allow it to update the magnetic fields fast enough to stabilize the fusion reaction. One could even imagine a future where thermodynamic computing elements are built directly into the tokamak’s walls, allowing the reactor to physically and intelligently react to the plasma’s state in real time. A ‘GPT-2 Moment’ for a New Era It’s easy to become numb to hype, but what we are witnessing with the TSU feels different. This is what you might call a “GPT-2 moment.” For those who were there, GPT-2 was the first generative AI model that wasn’t just a toy; it was the first time you could play with it at home and see the spark of true generative intelligence. It was the precursor that pointed directly to the GPT-3 and ChatGPT revolution that has since changed the world. This TSU has that same feel. It’s the “SDK” for a new computing paradigm. This technology is as different from classical computing as quantum computing is, but with a critical difference: a team of 15 built this in two years, and it runs at room temperature on your desk. Quantum computing has seen decades of work and billions in funding, and it still hasn’t produced a commercially viable, scalable machine. The TSU is here now. Based on a two-decade-long career at the cutting edge of technology—from seeing the obvious future of virtualization in 2007 to an early conviction in deep learning and GPT—this has all the same hallmarks of a fundamental, world-changing shift. We are not just building faster calculators; we are learning to compute with the universe itself. Pay close attention to this. This is the next big thing.
18:01

Thermodynamic computing is here There is a new computing paradigm emerging from the noise, and its arrival may be as significant as the dawn of deep learning or the advent of cloud virtualization. A new company, Extropic, has just launched its first thermodynamic computer, a device they call a TSU, or Thermal Sampling Unit. While the web is already filling with deep technical dives, what’s more important for most of us is building a clear intuition for what this technology is, how it’s fundamentally different from anything that’s come before, and why it’s generating so much excitement. This isn’t just another chip; it’s a new way to think about computation itself. Seeing is Believing: Solving Puzzles in One Shot To understand what a TSU does, let’s look at two classic, notoriously difficult computer science problems: Sudoku and the Eight Queens problem. When you or I solve a Sudoku, we use a process of sequential logic, guess-and-check, and backtracking. We make an assumption, follow its logical conclusion, and if we hit a dead end, we erase and try again. A classical computer does the same, just much faster. A TSU, however, approaches this in a completely different way. Using a TSU simulator, one can “program” the problem by first clamping the known values—the clues already on the board. Then, you program in the constraints: no duplicate numbers in any row, column, or 3x3 square. With the problem thus defined, the TSU doesn’t “search” for a solution; it anneals one. In a single computational step, the solution simply emerges, backfilling all the empty squares correctly. The same principle applies to the Eight Queens problem, a challenge to place eight queens on a chessboard so that none can attack any other. This is a complex combinatorial problem with 92 distinct solutions. A classical computer would have to iteratively search for these. A TSU, by contrast, can be programmed with the constraints (the “anti-affinity” between queens on the same row, column, or diagonal) and then set to sample the “solution space.” In this context, a valid solution is one with a “problem energy” of zero. The TSU’s physical nature allows it to naturally find these zero-energy states. A simulation of this process shows the TSU discovering all 92 unique solutions, demonstrating its ability to not just find an answer, but to explore the entire landscape of all correct answers. This is a fundamentally new approach, one that bypasses the brute-force, iterative methods we’ve relied on for decades. The Physics of Computation: Using Noise, Not Fighting It This new power comes from a radical design philosophy. For the last 70 years, computing has been about one thing: order. We build chips that are deterministic, logical, and precise. The great enemy has always been noise, heat, and randomness. We spend billions on cooling and error correction to eliminate these very things. Quantum computing, in many ways, is the ultimate expression of this, requiring temperatures near absolute zero to eliminate all thermal noise and achieve quantum coherence. Thermodynamic computing is the polar opposite. It doesn’t fight the noise; it uses it. The TSU is built on the understanding that the natural, stochastic noise from “leaky” transistors—the very randomness we’ve tried to engineer out of existence—is itself a powerful computational resource. Think of it this way: a GPU, which is central to today’s AI, has to simulate noise. When a generative AI model creates a new image or sentence, it’s using complex algorithms to fake randomness. The TSU doesn’t need to fake it; it harnesses the actual physical randomness of thermodynamics. It is a piece of hardware that directly computes with probability. This makes it a hybrid, sitting somewhere between a purely analog computer (which might use light or sound waves to compute) and a digital GPU. It’s a physical device that leverages the laws of physics itself to find solutions, rather than just using logic gates to simulate them. From a Lost Hiker to a Million Bouncy Balls Perhaps the best way to build intuition is with a metaphor. Imagine that solving a complex optimization problem is like trying to find the lowest point of altitude in a 100-square-mile mountainous landscape. Classical computing, using an algorithm like gradient descent, is like being a single hiker dropped into this landscape at night. You have no map or satellite view. All you have is an altimeter and the sensation of the slope under your feet. You can only take one step at a time, always walking downhill, hoping you don’t get stuck in a small local valley when the true, lowest canyon is miles away. Thermodynamic computing is a completely different approach. It’s like having a million bouncy balls and a helicopter. You drop all million balls simultaneously across the entire 100-square-mile landscape. Then, you “turn on an earthquake,” shaking the entire system. The balls bounce and jostle, but as the shaking (the “annealing”) subsides, where do they all end up? They naturally settle into the lowest points. The balls that collect in the deepest valley represent the optimal solution. The TSU is, in essence, a physical device for dropping those million balls at once and letting the laws of thermodynamics find the lowest “energy” state for you, all at the same time. Beyond Puzzles: The Real-World Impact This is far more than just a clever way to solve brain teasers. This ability to instantly find the lowest energy state for a complex, constrained system has staggering real-world applications. One of the most immediate is protein folding. Companies like Google’s DeepMind have made incredible progress with AI like AlphaFold, which predicts protein structures. But this is still a predictive model trained on existing data. A TSU could potentially solve the folding problem directly, treating the protein as a system of atomic affinities and repulsions and finding its most stable, lowest-energy configuration almost instantaneously. This could revolutionize drug discovery and materials science. An even more profound possibility lies in nuclear fusion. One of the greatest engineering challenges in history is controlling the superheated plasma within a tokamak reactor. This requires shaping unimaginably complex magnetic containment fields in real-time to prevent the plasma from touching the reactor walls. This is a real-time optimization problem so complex it’s currently beyond our capabilities. A TSU, however, could be fast enough. Its ability to compute with electricity itself, rather than abstracting the problem through layers of software, might allow it to update the magnetic fields fast enough to stabilize the fusion reaction. One could even imagine a future where thermodynamic computing elements are built directly into the tokamak’s walls, allowing the reactor to physically and intelligently react to the plasma’s state in real time. A ‘GPT-2 Moment’ for a New Era It’s easy to become numb to hype, but what we are witnessing with the TSU feels different. This is what you might call a “GPT-2 moment.” For those who were there, GPT-2 was the first generative AI model that wasn’t just a toy; it was the first time you could play with it at home and see the spark of true generative intelligence. It was the precursor that pointed directly to the GPT-3 and ChatGPT revolution that has since changed the world. This TSU has that same feel. It’s the “SDK” for a new computing paradigm. This technology is as different from classical computing as quantum computing is, but with a critical difference: a team of 15 built this in two years, and it runs at room temperature on your desk. Quantum computing has seen decades of work and billions in funding, and it still hasn’t produced a commercially viable, scalable machine. The TSU is here now. Based on a two-decade-long career at the cutting edge of technology—from seeing the obvious future of virtualization in 2007 to an early conviction in deep learning and GPT—this has all the same hallmarks of a fundamental, world-changing shift. We are not just building faster calculators; we are learning to compute with the universe itself. Pay close attention to this. This is the next big thing.

David Shapiro (L/0)

83,649 просмотров • 8 месяцев назад

2025.07.01 bi-weekly update here’s what we’ve built, shipped, and trained this past week: TRADING CAPABILITIES + agent-based txn execution engine now supports Meteora (DBC, DLMM, DYN, DAMM), Raydium (CLMM, AMM, CPMM), and Orca 🌊 (CLLM, VP, CPMM). we're now compatible with nearly every major liquidity layer on Solana. + DCA and limit orders now available to use through our agentic/natural language interface. + execution is faster, leaner, more reliable; optimized based on real closed beta usage. AGENT SWARM + A2A (agent-to-agent) finalized; based on Google's new open framework. it enables dynamic coordination between agents, deeper reasoning, better memory, and more human-like flow. + TraceGraph (diagram/chain-of-thought-like) UI is now deployed. users now see how Aya (and others) think and collaborate together. visualizes multi-agent logic paths. text UI also upgraded. sharper, smoother, faster. + Bravo (macro news oracle) live. it connects real-world macro events and news to Solana. powered by our in-house scrapers + NewsAPI, built from scratch. integrations with blocmates. coming soon. + Solvion, our Solana-native domain expert, is now active. trained on a custom-built, 70B parameter dataset of the full Solana ecosystem. auto-updated. devs, tokenomics, projects, whitepapers, technical information, know-hows... it knows everything. + Echo (our social media and sentiment analyst agent) getting integrated with Sentient natural language interface + Rivalz Network. + you can now start individual conversations with agents. e.g. ask Echo anything about social trends, or hit up Solvion for technicals. UX/UI + we’re now mobile responsive; fully optimized across devices. + deployed TraceGraph (diagram UI for agent cognition). + NLI improvements: sleeker prompt-response flow, improved text visualization, better latency, better rendering. + agents feel more alive, dynamic, and explainable PREDICTIONS weekly update from our head quant: + we now do weekly fine-tuning to adapt to market shifts. switched from F1-score optimization to pure precision; cutting noise, and maximizing conviction. we now discard the worst-performing model in the ensemble. only the top 2 vote. accuracy last 6 weeks = 82% directional. the ensemble logic is fully restructured. next: RNN + RL-based dynamic thresholding in progress (live this month). + partnered with Allora for the the SOL/USDT prediction stack, combining our hype score with their confidence-aware forecasting. + also cooking something with Sahara AI 🔆 (????)... OTHER + PnL cards integrated. track profit per trade, share it on X, get free XCC + Referral system is complete and rolling out to early users very soon (top referrers will dominate first layer of our multi-level tree and enjoy first-movers advantage). + working on a dynamic onboarding tutorial for first-time users. + backend latency improvements across endpoints. especially on token explorer + prediction refresh + docs are live ( TEAM + onboarded amy and Mike | heymike.sol 🎒🪽 — elite Solana engineers working on gRPCs, RPCs, instruction decoding, and data pipelines. their focus: making xFractal the only real-time NLP engine for Solana alpha extraction. + brought on ultra , Skely, HALKO and Gabriel Haines as strategic advisors and contributors, helping us scale narrative modeling, data ops, and GTM. QUICK STATS (REMINDER: this is a closed, invite-only beta — not optimized for adoption yet) + 400+ early beta testoors + 9,000+ natural language prompts + 500+ on-chain txs executed via our agent-based engine + we’re not scaling users yet, we’re optimizing agents, validating edge, and consolidating PMF. + open beta coming soon. engine’s warming up. let’s keep moving. (p.s. toly 🇺🇸 check this out)
1:43

2025.07.01 bi-weekly update here’s what we’ve built, shipped, and trained this past week: TRADING CAPABILITIES + agent-based txn execution engine now supports Meteora (DBC, DLMM, DYN, DAMM), Raydium (CLMM, AMM, CPMM), and Orca 🌊 (CLLM, VP, CPMM). we're now compatible with nearly every major liquidity layer on Solana. + DCA and limit orders now available to use through our agentic/natural language interface. + execution is faster, leaner, more reliable; optimized based on real closed beta usage. AGENT SWARM + A2A (agent-to-agent) finalized; based on Google's new open framework. it enables dynamic coordination between agents, deeper reasoning, better memory, and more human-like flow. + TraceGraph (diagram/chain-of-thought-like) UI is now deployed. users now see how Aya (and others) think and collaborate together. visualizes multi-agent logic paths. text UI also upgraded. sharper, smoother, faster. + Bravo (macro news oracle) live. it connects real-world macro events and news to Solana. powered by our in-house scrapers + NewsAPI, built from scratch. integrations with blocmates. coming soon. + Solvion, our Solana-native domain expert, is now active. trained on a custom-built, 70B parameter dataset of the full Solana ecosystem. auto-updated. devs, tokenomics, projects, whitepapers, technical information, know-hows... it knows everything. + Echo (our social media and sentiment analyst agent) getting integrated with Sentient natural language interface + Rivalz Network. + you can now start individual conversations with agents. e.g. ask Echo anything about social trends, or hit up Solvion for technicals. UX/UI + we’re now mobile responsive; fully optimized across devices. + deployed TraceGraph (diagram UI for agent cognition). + NLI improvements: sleeker prompt-response flow, improved text visualization, better latency, better rendering. + agents feel more alive, dynamic, and explainable PREDICTIONS weekly update from our head quant: + we now do weekly fine-tuning to adapt to market shifts. switched from F1-score optimization to pure precision; cutting noise, and maximizing conviction. we now discard the worst-performing model in the ensemble. only the top 2 vote. accuracy last 6 weeks = 82% directional. the ensemble logic is fully restructured. next: RNN + RL-based dynamic thresholding in progress (live this month). + partnered with Allora for the the SOL/USDT prediction stack, combining our hype score with their confidence-aware forecasting. + also cooking something with Sahara AI 🔆 (????)... OTHER + PnL cards integrated. track profit per trade, share it on X, get free XCC + Referral system is complete and rolling out to early users very soon (top referrers will dominate first layer of our multi-level tree and enjoy first-movers advantage). + working on a dynamic onboarding tutorial for first-time users. + backend latency improvements across endpoints. especially on token explorer + prediction refresh + docs are live ( TEAM + onboarded amy and Mike | heymike.sol 🎒🪽 — elite Solana engineers working on gRPCs, RPCs, instruction decoding, and data pipelines. their focus: making xFractal the only real-time NLP engine for Solana alpha extraction. + brought on ultra , Skely, HALKO and Gabriel Haines as strategic advisors and contributors, helping us scale narrative modeling, data ops, and GTM. QUICK STATS (REMINDER: this is a closed, invite-only beta — not optimized for adoption yet) + 400+ early beta testoors + 9,000+ natural language prompts + 500+ on-chain txs executed via our agent-based engine + we’re not scaling users yet, we’re optimizing agents, validating edge, and consolidating PMF. + open beta coming soon. engine’s warming up. let’s keep moving. (p.s. toly 🇺🇸 check this out)

xFractal

42,668 просмотров • 1 год назад

My Letter to Aaron Rodgers I'm writing this letter because I've slowly realized how difficult it is to impact change in the world. It requires influential people to see the paradigm shift. Recently you were asked by executive producer Al Dukes about Malaysian Airlines Flight 370. You responded indicating you were familiar with the videos. I understand your skepticism. Those videos aren't what anyone expected. It's frankly too much for most people to accept. The videos represent the largest conspiracy in history, the theft of a Boeing 777 and the suppression of gravitational manipulation. Where this science and technology leads is to Free Energy. I want you to know I'm just a normal guy who saw two videos, investigated them, the missing plane, and discovered we really are hiding 'magical' technology from the world. In a situation this profound you have to start by proving the lie. In this case it's the false narrative we've been sold on by the media of the missing plane. This was not pilot suicide and the United States knows exactly what happened. Planes don't crash without leaving massive debris fields. There was over 1000lbs of foam seat cushions and not one was found. The pilot didn't practice the route, the simulator data flightpath was of MH150 to Jeddah the pilot was scheduled to fly on Feb 4th. The simulator data was ruled not suspicious. The pilot was categorically ruled out by all parties including the officials, family, and friends. Florence De Changy got his medical records from his family and confirmed there were no red flags. Where the plane was lost from radar there was still five radars in range from three different countries, Indonesia, Thailand, and India. None of these countries have ever made their data publicly available. As you pointed out, Rolls Royce and Boeing also know what happened to this plane as Rolls Royce engine data transmits frequent updates. Rolls Royce has never made their data publicly available either. The United States military has a global persistent surveillance system known as SBIRS, the Space-Based Infrared System by Lockheed Martin. There's no possible way this system would not have seen the debris field of a Boeing 777 in the ocean. The military also has a system called SOSUS, the sound surveillance system which is a global network of acoustic detectors that can even pinpoint a tiny submersible implosion. Remember when the Navy heard the Titan sub pop and didn't say anything for five days? There's no evidence the plane crashed in the South Indian Ocean. There's also acoustic detectors in Diego Garcia and Western Australia both of which should have heard the plane crash in the SIO, yet a recent study by Cardiff University found no detections during the duration of the suspected flightpath. One candidate detection was found on one hydrophone almost an hour after the plane would have run out of fuel. Over the Horizon Radar can see for thousands of miles by bouncing signals off the ionosphere. Both Pine Gap and Diego Garcia should have been able to track the plane from take off to crash. The plane also flew directly past Cobra Gold/Cope Tiger military exercises between Thailand and the United States in the Andaman Sea. We're supposed to believe they weren't tracking a rogue plane? Why did the United States give up the search for MH370 after just a month? Ghyslain Wattrelos, French father and husband, was told by an intelligence source that the Americans know what happened and there were two AWACS (radar planes) in the area. As for the debris that was found, I believe that is from MH370. However, there's not enough debris to confirm a plane crashed and the debris washed up on the wrong continent. Drift modeling was done and the bulk of the debris should have washed up in Australia. It's actually impossible based on the currents for the debris to have shown up on mainland Africa in just one year. If you look at a map a much better location for the debris to have entered the water is the Maldives. The only reason we think the plane even went into the South Indian Ocean (SIO) is the Inmarsat satellite pings who has connections to US intelligence. The raw telemetry data was not released until March 27th, 2014, eight days after the satellite video was uploaded on youtube. This is important because there's coordinates in the satellite video that indicate the Nicobar islands, the same location the plane is believed to have turned into the SIO. What really happened to MH370 is an emergency event related to the 487lbs of dangerous lithium ion batteries onboard. This even is supported by at least 20 witnesses and the flightpath. Mike McKay saw the plane on fire briefly for a few seconds from his oil rig, nine witnesses along the coast heard loud noises as the same time, eight fishermen who saw it flying low ten minutes later, a man on the coast who saw it on fire, and Katherine Tee, who saw the plane in the Nicobar Islands an hour later glowing orange, with dark smoke coming out of the back of it, flying low and descending. Why did the Malaysian Minister of Defense say they didn't send up jets to track the plane because they knew it was commercial and not hostile? The only way they could know that is if they had communication with the plane. They simply didn't release all the communications publicly. China intercepted a communication from the US Navy that was only reported in the China Times of the plane disintegrating and attempting an emergency ditching, consistent with Katherine Tee's sighting and the MH370 videos. I spoke with Billie Vincent recently, former security director for the FAA. In 2014 even before the batteries were known to be on the plane he had deduced that a fire emergency event was the most likely scenario. He suspected this because of similar incidents such as UPS Flight 6 where the same thing happened. One pilot was knocked out from the smoke and they couldn't land because they couldn't see. The plane ended up crashing. Dr. Victor Ettel is an expert on the science and manufacturing of lithium ion batteries and he too is on the record theorizing that the battery fire could have knocked out the people on the plane. Expert Clive Irving agreed and said that MH370's turnback was consistent with the pilots trying to find the nearest airport. Lithium ion batteries were later banned as cargo from passenger planes. Why cover up a fire event? The MH370 videos are why. Just like how you are skeptical, those videos are not something the public is ready to accept even in 2024, let alone in 2014. I had a hard time accepting it until I spoke to government related engineers and started reading scientific papers. It's all real and it's hidden in plain sight. I'm not sure if the fire was an accident and we were trying to save the high value passengers, the 20 Freescale Semiconductor engineers connected to US Aerospace and Defense, or if it was a counter-espionage operation from the start. I expect the reason why we teleported the plane is multi-purposed. Retain the passengers if alive, retain sensitive cargo, and send a message to our adversaries. Don't F with us. Even if China/Russia knew there's nothing they could possibly say. No one would believe them. China leaked satellite imagery a few days after the plane went missing and it looks just like three orbs around a larger object. They also hacked Malaysia the day after the plane went missing and stole classified information about the plane. We have two military videos in perfect synchronization from two completely different angles without a discrepancy on a single frame. The videos corroborate the overall fire event. We see the plane flying low, descending, with smoke coming out of the back of it. We see a heat signature in the belly of the plane where the smoke is presumably coming out of the AC heat exhaust ports near the landing gear. Lieutenant Commander Edward C. Lin leaked the videos. The case against him is shrouded in secrecy and the sentence he got after a plea deal is disproportionate to the charges. The timeframe of his service with the spy plane squadron VPU-2 Wizards is damning, within one month of the plane going missing. His lawyer said the classified information in question is available on the internet and he was caught with flight manifests that include search and rescue code names. The science of the orbs is everything that ufo lore claims exists. Gravity manipulation, mass reduction, warp drives, wormholes, and zero-point energy. I was surprised to learn they're all directly connected by the same science. Spacetime is not empty. It is filled with energy in an equilibrium state. 'Gravity' is just an induced phenomenon based on relative energy densities. If you reduce the equilibrium state of the energy densities in a localized region you produce 'negative energy.' That's what the dark lines in front of the orbs are doing. That's why the orbs are simply falling into the geodesic path they're creating for themselves, and exhibit zero inertia. This is subluminal warp drive. The orbs are also showing characteristics of magnetic monopoles, which is why we see the odd shaped heat signature. They are made of plasma and we're looking at low energy nuclear reactions, also known as cold fusion. The field that creates the spherical region is the non-radiating barrier of the magnetic field. The zap itself is an endothermic event, an absorption of energy. That's why it's black and cold in the thermal drone video. As far as I know this is the first ever endothermic event on video. Just like folding a piece of paper and making a hole, this plane is 'teleporting' to a different location in Spacetime, faster than the speed of light from the observer's prospective. For the people going through the wormhole, it's like going through a doorway. Based on the plane flying East in the satellite video and the right hand rule in electrical engineering, the vector of travel for the plane is backwards, to the West. A fire suppression device washed up in the Maldives a few weeks after the plane disappeared and 15-20 islanders on a small island saw the plane in the early morning hours on the day of the disappearance. It's extremely suspicious that this evidence was dismissed without investigation. The only logical place to hide a the plane is at US military base Diego Garcia, isolated from all major landmasses, out of radar range, and not open to commercial traffic. This base is extremely secretive. There's people who accidentally washed ashore there a few years ago and they've not been allowed to leave. I could keep going. The satellite video is a Citrix remote terminal session logged into the real satellite database. The orbs displace the smoke trails with accurate fluid dynamics in the satellite video. There's a ton more evidence and it all points to the MH370 videos being the authentic true story of what happened to the plane. I don't want people to blindly believe me. I want them to make up their own minds based on the evidence. The only thing I can do is show people the door, they must walk through it for themselves. The videos and the science will be proven to be real with the passage of time. Science like this cannot be hidden forever. It is a discovery, not an invention. The scary question is, "why are we hiding it?" For that, I've reached a dark conclusion. This technology will lead to the eradication of our civilization. With this technology we can extract endless energy directly from the zero-point fluctuations. 'Free' energy. We can produce weapons of unfathomable destructive capability including a black hole. I push for disclosure because I want the world to understand the risks and choose our own fate.
7:19

My Letter to Aaron Rodgers I'm writing this letter because I've slowly realized how difficult it is to impact change in the world. It requires influential people to see the paradigm shift. Recently you were asked by executive producer Al Dukes about Malaysian Airlines Flight 370. You responded indicating you were familiar with the videos. I understand your skepticism. Those videos aren't what anyone expected. It's frankly too much for most people to accept. The videos represent the largest conspiracy in history, the theft of a Boeing 777 and the suppression of gravitational manipulation. Where this science and technology leads is to Free Energy. I want you to know I'm just a normal guy who saw two videos, investigated them, the missing plane, and discovered we really are hiding 'magical' technology from the world. In a situation this profound you have to start by proving the lie. In this case it's the false narrative we've been sold on by the media of the missing plane. This was not pilot suicide and the United States knows exactly what happened. Planes don't crash without leaving massive debris fields. There was over 1000lbs of foam seat cushions and not one was found. The pilot didn't practice the route, the simulator data flightpath was of MH150 to Jeddah the pilot was scheduled to fly on Feb 4th. The simulator data was ruled not suspicious. The pilot was categorically ruled out by all parties including the officials, family, and friends. Florence De Changy got his medical records from his family and confirmed there were no red flags. Where the plane was lost from radar there was still five radars in range from three different countries, Indonesia, Thailand, and India. None of these countries have ever made their data publicly available. As you pointed out, Rolls Royce and Boeing also know what happened to this plane as Rolls Royce engine data transmits frequent updates. Rolls Royce has never made their data publicly available either. The United States military has a global persistent surveillance system known as SBIRS, the Space-Based Infrared System by Lockheed Martin. There's no possible way this system would not have seen the debris field of a Boeing 777 in the ocean. The military also has a system called SOSUS, the sound surveillance system which is a global network of acoustic detectors that can even pinpoint a tiny submersible implosion. Remember when the Navy heard the Titan sub pop and didn't say anything for five days? There's no evidence the plane crashed in the South Indian Ocean. There's also acoustic detectors in Diego Garcia and Western Australia both of which should have heard the plane crash in the SIO, yet a recent study by Cardiff University found no detections during the duration of the suspected flightpath. One candidate detection was found on one hydrophone almost an hour after the plane would have run out of fuel. Over the Horizon Radar can see for thousands of miles by bouncing signals off the ionosphere. Both Pine Gap and Diego Garcia should have been able to track the plane from take off to crash. The plane also flew directly past Cobra Gold/Cope Tiger military exercises between Thailand and the United States in the Andaman Sea. We're supposed to believe they weren't tracking a rogue plane? Why did the United States give up the search for MH370 after just a month? Ghyslain Wattrelos, French father and husband, was told by an intelligence source that the Americans know what happened and there were two AWACS (radar planes) in the area. As for the debris that was found, I believe that is from MH370. However, there's not enough debris to confirm a plane crashed and the debris washed up on the wrong continent. Drift modeling was done and the bulk of the debris should have washed up in Australia. It's actually impossible based on the currents for the debris to have shown up on mainland Africa in just one year. If you look at a map a much better location for the debris to have entered the water is the Maldives. The only reason we think the plane even went into the South Indian Ocean (SIO) is the Inmarsat satellite pings who has connections to US intelligence. The raw telemetry data was not released until March 27th, 2014, eight days after the satellite video was uploaded on youtube. This is important because there's coordinates in the satellite video that indicate the Nicobar islands, the same location the plane is believed to have turned into the SIO. What really happened to MH370 is an emergency event related to the 487lbs of dangerous lithium ion batteries onboard. This even is supported by at least 20 witnesses and the flightpath. Mike McKay saw the plane on fire briefly for a few seconds from his oil rig, nine witnesses along the coast heard loud noises as the same time, eight fishermen who saw it flying low ten minutes later, a man on the coast who saw it on fire, and Katherine Tee, who saw the plane in the Nicobar Islands an hour later glowing orange, with dark smoke coming out of the back of it, flying low and descending. Why did the Malaysian Minister of Defense say they didn't send up jets to track the plane because they knew it was commercial and not hostile? The only way they could know that is if they had communication with the plane. They simply didn't release all the communications publicly. China intercepted a communication from the US Navy that was only reported in the China Times of the plane disintegrating and attempting an emergency ditching, consistent with Katherine Tee's sighting and the MH370 videos. I spoke with Billie Vincent recently, former security director for the FAA. In 2014 even before the batteries were known to be on the plane he had deduced that a fire emergency event was the most likely scenario. He suspected this because of similar incidents such as UPS Flight 6 where the same thing happened. One pilot was knocked out from the smoke and they couldn't land because they couldn't see. The plane ended up crashing. Dr. Victor Ettel is an expert on the science and manufacturing of lithium ion batteries and he too is on the record theorizing that the battery fire could have knocked out the people on the plane. Expert Clive Irving agreed and said that MH370's turnback was consistent with the pilots trying to find the nearest airport. Lithium ion batteries were later banned as cargo from passenger planes. Why cover up a fire event? The MH370 videos are why. Just like how you are skeptical, those videos are not something the public is ready to accept even in 2024, let alone in 2014. I had a hard time accepting it until I spoke to government related engineers and started reading scientific papers. It's all real and it's hidden in plain sight. I'm not sure if the fire was an accident and we were trying to save the high value passengers, the 20 Freescale Semiconductor engineers connected to US Aerospace and Defense, or if it was a counter-espionage operation from the start. I expect the reason why we teleported the plane is multi-purposed. Retain the passengers if alive, retain sensitive cargo, and send a message to our adversaries. Don't F with us. Even if China/Russia knew there's nothing they could possibly say. No one would believe them. China leaked satellite imagery a few days after the plane went missing and it looks just like three orbs around a larger object. They also hacked Malaysia the day after the plane went missing and stole classified information about the plane. We have two military videos in perfect synchronization from two completely different angles without a discrepancy on a single frame. The videos corroborate the overall fire event. We see the plane flying low, descending, with smoke coming out of the back of it. We see a heat signature in the belly of the plane where the smoke is presumably coming out of the AC heat exhaust ports near the landing gear. Lieutenant Commander Edward C. Lin leaked the videos. The case against him is shrouded in secrecy and the sentence he got after a plea deal is disproportionate to the charges. The timeframe of his service with the spy plane squadron VPU-2 Wizards is damning, within one month of the plane going missing. His lawyer said the classified information in question is available on the internet and he was caught with flight manifests that include search and rescue code names. The science of the orbs is everything that ufo lore claims exists. Gravity manipulation, mass reduction, warp drives, wormholes, and zero-point energy. I was surprised to learn they're all directly connected by the same science. Spacetime is not empty. It is filled with energy in an equilibrium state. 'Gravity' is just an induced phenomenon based on relative energy densities. If you reduce the equilibrium state of the energy densities in a localized region you produce 'negative energy.' That's what the dark lines in front of the orbs are doing. That's why the orbs are simply falling into the geodesic path they're creating for themselves, and exhibit zero inertia. This is subluminal warp drive. The orbs are also showing characteristics of magnetic monopoles, which is why we see the odd shaped heat signature. They are made of plasma and we're looking at low energy nuclear reactions, also known as cold fusion. The field that creates the spherical region is the non-radiating barrier of the magnetic field. The zap itself is an endothermic event, an absorption of energy. That's why it's black and cold in the thermal drone video. As far as I know this is the first ever endothermic event on video. Just like folding a piece of paper and making a hole, this plane is 'teleporting' to a different location in Spacetime, faster than the speed of light from the observer's prospective. For the people going through the wormhole, it's like going through a doorway. Based on the plane flying East in the satellite video and the right hand rule in electrical engineering, the vector of travel for the plane is backwards, to the West. A fire suppression device washed up in the Maldives a few weeks after the plane disappeared and 15-20 islanders on a small island saw the plane in the early morning hours on the day of the disappearance. It's extremely suspicious that this evidence was dismissed without investigation. The only logical place to hide a the plane is at US military base Diego Garcia, isolated from all major landmasses, out of radar range, and not open to commercial traffic. This base is extremely secretive. There's people who accidentally washed ashore there a few years ago and they've not been allowed to leave. I could keep going. The satellite video is a Citrix remote terminal session logged into the real satellite database. The orbs displace the smoke trails with accurate fluid dynamics in the satellite video. There's a ton more evidence and it all points to the MH370 videos being the authentic true story of what happened to the plane. I don't want people to blindly believe me. I want them to make up their own minds based on the evidence. The only thing I can do is show people the door, they must walk through it for themselves. The videos and the science will be proven to be real with the passage of time. Science like this cannot be hidden forever. It is a discovery, not an invention. The scary question is, "why are we hiding it?" For that, I've reached a dark conclusion. This technology will lead to the eradication of our civilization. With this technology we can extract endless energy directly from the zero-point fluctuations. 'Free' energy. We can produce weapons of unfathomable destructive capability including a black hole. I push for disclosure because I want the world to understand the risks and choose our own fate.

Ashton Forbes

105,984 просмотров • 1 год назад

The multi-leader blockchain endgame: competitive information inclusion as a self-reinforcing mechanism for global price discovery - how we got here, and why Aptos is leading the charge Onchain trading is the killer app In the nine years since the launch of programmable transactions on the Ethereum blockchain, onchain trading has revealed itself as the killer use case for blockchains: onchain listings, volume, and total value locked are all growing with no signs of slowing down, due to the censorship-resistant, permissionless, 24/7/365 qualities afforded by decentralized (DeFi) systems. Monolithic parallelism is key In 2020 Solana was first to market with monolithic, parallel execution (as opposed sharded execution which offers parallelism by partitioning global state into separate information silos), establishing a new design paradigm that raised the bar for throughput and latency: put all of the information in one replicated state machine and make it run as fast as possible. This design produces a single, global hub for activity, liquidity, and token launches, a kind of financial data whiteboard in the sky, where anyone can come and trade at any time with everybody else who has plugged into the system. DEXes are becoming more competitive Historically decentralized systems have been juxtaposed with centralized ones since the latter eliminates the overhead associated with distributed systems coordination. And yet despite this overhead, Solana as a decentralized exchange (DEX) is still pulling in billions of trading volume per day, exceeding that of all but the largest centralized crypto exchanges (CEXs), that simply can't compete with the giant DEX in the sky on token listings or fees. After all, CEXs have to pay for server space, salaries, and lawyers, while a DEX outsources everything. The colocation arms race The one place where CEXs have an advantage over DEXs is on end-to-end latency for colocation applications, or in other words: someone sets up a trading bot in the same data center as the exchange, and their trades get to the exchange faster than everyone else's. When there is only one data ingestion point the fastest trader wins, and after the arms race has played out everyone ends up huddling around the trading hub, effectively cutting off the rest of the world from playing the latency trading game. This is the model that traditional securities exchanges like the Nasdaq or the NYSE 🏛 employ, and because they own the server they can effectively charge whatever they want for access to it. The colocation arms race is also why L2s will probably never decentralize: running the sequencer is practically the same as running the NASDAQ, with the same monopoly on transaction fees collected from a nearby cluster of trading bots (I understand from conversations with Logan Jastremski that the Arbitrum arms race has already hit a Nash Equilibrium in Portland, Oregon). Colocation is a trap But once the colocation arms race has played out, trades become less about incorporating new information in the market and more about skimming off the top by spoofing all of the trades coming in from the other bots. High-frequency trading (HFT) bots located in the NYSE New Jersey data center, for example, are constantly placing buys and sell orders that they have no intention of executing, just to spoof the other colocated bots who are playing the same adversarial game. Information inclusion, on the other hand, the synthesis of real-time world events into prices, takes a back seat because anyone who tries to include new information first needs to batch up their order and send it through a series of middlemen before it ultimately ends up on the exchange: you, I, or practically any other individual can not actually "trade on the NASDAQ", no, we have to express our intent to someone like Robinhood, who then sells our order flow to @CitadelSecurities, who then sends it to the exchange, oh and by the way it doesn't actually even "clear" or "settle" once it "executes" because for whatever reason the whole systems splits these things up and prevents them from happening instantaneously even though it's 2024 and we have computers. Onchain trading cuts out middlemen This whole mess is why we have onchain trading, and why it's starting to win: if you want a mainline to the exchange, without setting up a server, and you want to trade on a news event without getting immediately frontrun by an HFT bot that is sniffing out the trades of every other HFT bot who is easing in batched up order flow on their own terms, then you submit your order to a node in the blockchain and the information gets included in the price upon ingestion. Oh, and by the way the trade is actually fully complete: settled, cleared, reconciled, done, whatever you want to call it, because the people who build decentralized finance (DeFi) build it how it should actually work, not in a way that creates a million incumbents and charges exorbitant rents for access to the system. Onchain trading better for price discovery And the beautiful part about this is that even if a distributed system has more latency than a centralized system, DeFi still ends up incorporating more information into the price faster than centralized finance, because with DeFi the information gets included in the system as soon as it is submitted, not after it has been batched up and sent through a series of middlemen. The consensus mechanism of the blockchain disseminates the information around the world in the form of a price update, while the centralized exchange model requires information about the event to first get propagate to the region of the trading hub, then to get submitted to the colocation server. This means that in terms of global price discovery, onchain trading is strictly a better system because the entire consensus model is based around accelerated information propagation. Because price discovery is a global phenomenon, blockchains, which are global, are actually better than the centralized status quo, on a performance basis, not just from an ideological or convenience-based view. And it has to be multi-leader In practice, effective global information synthesis of information has an additional key requirement: multi-leader architecture. That is, in a single-leader blockchain like Solana, where one validator at a time has a monopoly on ordering transactions into blocks, for their duration as a leader they effectively function as a colocation server. This means that if the current leader is in New York, someone in Singapore who wants to trade on local news as soon as it breaks will still need to get their order all the way around the world to the leader, who is effectively serving as the chain's data ingestion point, before the order can start propagating through the network. But this is issue solved by the introduction of multiple distributed leaders, because then anyone with access to new information can submit their order to the leader closest to them, yielding faster information inclusion in the form of price updates. Multi-leader is also required for fair markets A multi-leader architecture is also required for fair markets, because in a single-leader system the leader has the power to censor transactions, reorder them to their advantage, or even replace transactions with copycats that extract maximum value by replacing the sender's address with their own. For example if someone wants to capture an arbitrage opportunity between two onchain DEXes, they'll need to submit a transaction to the leader and trust that the leader won't simply copy the transaction and submit it themselves. But when there are two or more leaders, users whose transactions are censored by one leader will simply work with a different leader the next time around, eventually cutting off transaction fee flow to the extractive leader. Beyond just strict inclusion, in a multi-leader architecture validators are also forced to compete with each other on latency, because the leader who is fastest at disseminating users' transactions across the network will over time gobble up the largest share of the order flow. Transparent priority fees are a must, or a private mempool will emerge But in order to make this work, a multi-leader architecture must also offer users the ability to pay priority fees AKA "tips" or "bribes" to move their transaction to the front of the line: if there is a $5 arbitrage opportunity onchain, users need to have assurance that they if they pay a 4.99 priority fee to take that arb, they will get priority over a different user who is only willing to tip 4.98. If the native blockchain system does not offer this fair market priority fee mechanism, then it is only a matter of time before one spontaneously emerges in the form of a private mempool like Jito, which can create centralization pressures and undermine the integrity of the system as a whole. Competitive payment for order flow is the stable solution With the right architecture in place, the end result is a competitive environment where endpoints running maximum extractable value (MEV) bots compete with one to offer users the best price for their order flow. In other words, if a user wants to submit an order that can get sandwich attacked for as much as $2 of MEV, then the order should ultimately go to the endpoint bot that is willing to pay the user as much as $1.99 for the right to process their transaction. The price that the provider is willing to pay is ultimately a function of how much in priority fees they might need to pay to the current leader (0 they are the current one), but notably at each stage there is a competitive market for order flow, whether in the form of retail trader's orders, or priority fees among bots that might be forwarding orders to one of the leaders. AptosLabs is already building all this With a public mempool and transaction priority fees, Aptos additionally includes a pipelined architecture that already includes concurrent batching of transactions into blocks, with a single consensus leader who propagates the batched blocks out to the network. And the team is already researching running multiple instances of the consensus algorithm in parallel, yielding multiple consensus leaders who can compete with each other on latency and inclusion - just ask pranav | Shelby, Alexander Spiegelman, and Zekun Li. This means that block times can shrink as the number of consensus leaders grows, with each leader having its own geographical radius of inclusion beyond which it makes more sense to submit to a different leader. The starting point? Something like 60 ms blocks and 3 consensus leaders, partitioning the global information space into competitive and constantly-rotating regions of information inclusion. Messaging is important With concurrent pipelined transaction batching, a public mempool, priority fees, and a clear path to a multi-leader architecture, Aptos leads the industry in onchain trading infrastructure that can truly supplant the centralized colocation paradigm that has heretofore dominated global finance - by offering a truly superior product. And I am hopeful that this deep dive is the first step in communicating not how or that superior product is getting built, but what it means from a bigger picture perspective. If blockchains have found product market fit in anything, it is in trading, and the trading game can only be won by building the biggest, baddest, most high performance system that has as its north star a single, concrete goal: constantly reducing, ever lower toward zero, time time it takes to incorporate information from anywhere in the world into the global price discovery computer. Whoever does this, even 1 ms faster than the competitor, wins the price discovery game, as other blockchains are left in the dust, their DEXes arbed away to zero against the fastest chain on the block. And sure, the blockchain that can rise to this challenge can also handle useful things like payments, NFTs, or other solutions that benefit from permissionlessness and low gas costs, but I want to impress that at the core of this pursuit must be the urge to drive down information inclusion latency to the absolute minimum afforded by the laws of physics through a competitive, market-driven environment. I call on avery.apt 🇺🇸 , CTO of Aptos Labs, to lean in on this messaging, to make it clear that Aptos is here for this singular mission, to build the most performant price discovery engine in history, as a rallying call for alignment in development efforts across the ecosystem and broader industry. Where does this go? As the latencies drop, the spreads tighten, and the information inclusion increases with every incremental increase in network bandwidth, we can expect a new class of competing techno-financial hubs that aggregate around the world's largest information sources: New York, Washington DC, London, Tokyo, etc., commanding stake distribution commensurate with the density of information flow in these respective locales. With the right incentives in place, competing concurrent leaders will invest ever more in infrastructure to get their packets out to the network faster than the rest, yielding clusters of fiber optic cable around the world's financial hubs, neurons in the global financial brain connecting not just HFT firms to servers in their city, but connecting every city with every other city, to move pricing information across oceans and continents. And retail traders, who have been left out of the colocation game, will only benefit: this entire system gets faster, more inclusive, with tighter spreads and lower fees, and it is such an amazing opportunity to watch all of this unfold in real time. The future of blockchains is the future of trading, is the future of competitive information inclusion in real-time, is the future of truly unified global markets, because at the the core of this industry is a simple idea: connect the computers, and see where the incentives lead. They lead to this, and Aptos is leading the charge, because its tech is purpose-built for this exact purpose. So tell the world about it.
2:41

The multi-leader blockchain endgame: competitive information inclusion as a self-reinforcing mechanism for global price discovery - how we got here, and why Aptos is leading the charge Onchain trading is the killer app In the nine years since the launch of programmable transactions on the Ethereum blockchain, onchain trading has revealed itself as the killer use case for blockchains: onchain listings, volume, and total value locked are all growing with no signs of slowing down, due to the censorship-resistant, permissionless, 24/7/365 qualities afforded by decentralized (DeFi) systems. Monolithic parallelism is key In 2020 Solana was first to market with monolithic, parallel execution (as opposed sharded execution which offers parallelism by partitioning global state into separate information silos), establishing a new design paradigm that raised the bar for throughput and latency: put all of the information in one replicated state machine and make it run as fast as possible. This design produces a single, global hub for activity, liquidity, and token launches, a kind of financial data whiteboard in the sky, where anyone can come and trade at any time with everybody else who has plugged into the system. DEXes are becoming more competitive Historically decentralized systems have been juxtaposed with centralized ones since the latter eliminates the overhead associated with distributed systems coordination. And yet despite this overhead, Solana as a decentralized exchange (DEX) is still pulling in billions of trading volume per day, exceeding that of all but the largest centralized crypto exchanges (CEXs), that simply can't compete with the giant DEX in the sky on token listings or fees. After all, CEXs have to pay for server space, salaries, and lawyers, while a DEX outsources everything. The colocation arms race The one place where CEXs have an advantage over DEXs is on end-to-end latency for colocation applications, or in other words: someone sets up a trading bot in the same data center as the exchange, and their trades get to the exchange faster than everyone else's. When there is only one data ingestion point the fastest trader wins, and after the arms race has played out everyone ends up huddling around the trading hub, effectively cutting off the rest of the world from playing the latency trading game. This is the model that traditional securities exchanges like the Nasdaq or the NYSE 🏛 employ, and because they own the server they can effectively charge whatever they want for access to it. The colocation arms race is also why L2s will probably never decentralize: running the sequencer is practically the same as running the NASDAQ, with the same monopoly on transaction fees collected from a nearby cluster of trading bots (I understand from conversations with Logan Jastremski that the Arbitrum arms race has already hit a Nash Equilibrium in Portland, Oregon). Colocation is a trap But once the colocation arms race has played out, trades become less about incorporating new information in the market and more about skimming off the top by spoofing all of the trades coming in from the other bots. High-frequency trading (HFT) bots located in the NYSE New Jersey data center, for example, are constantly placing buys and sell orders that they have no intention of executing, just to spoof the other colocated bots who are playing the same adversarial game. Information inclusion, on the other hand, the synthesis of real-time world events into prices, takes a back seat because anyone who tries to include new information first needs to batch up their order and send it through a series of middlemen before it ultimately ends up on the exchange: you, I, or practically any other individual can not actually "trade on the NASDAQ", no, we have to express our intent to someone like Robinhood, who then sells our order flow to @CitadelSecurities, who then sends it to the exchange, oh and by the way it doesn't actually even "clear" or "settle" once it "executes" because for whatever reason the whole systems splits these things up and prevents them from happening instantaneously even though it's 2024 and we have computers. Onchain trading cuts out middlemen This whole mess is why we have onchain trading, and why it's starting to win: if you want a mainline to the exchange, without setting up a server, and you want to trade on a news event without getting immediately frontrun by an HFT bot that is sniffing out the trades of every other HFT bot who is easing in batched up order flow on their own terms, then you submit your order to a node in the blockchain and the information gets included in the price upon ingestion. Oh, and by the way the trade is actually fully complete: settled, cleared, reconciled, done, whatever you want to call it, because the people who build decentralized finance (DeFi) build it how it should actually work, not in a way that creates a million incumbents and charges exorbitant rents for access to the system. Onchain trading better for price discovery And the beautiful part about this is that even if a distributed system has more latency than a centralized system, DeFi still ends up incorporating more information into the price faster than centralized finance, because with DeFi the information gets included in the system as soon as it is submitted, not after it has been batched up and sent through a series of middlemen. The consensus mechanism of the blockchain disseminates the information around the world in the form of a price update, while the centralized exchange model requires information about the event to first get propagate to the region of the trading hub, then to get submitted to the colocation server. This means that in terms of global price discovery, onchain trading is strictly a better system because the entire consensus model is based around accelerated information propagation. Because price discovery is a global phenomenon, blockchains, which are global, are actually better than the centralized status quo, on a performance basis, not just from an ideological or convenience-based view. And it has to be multi-leader In practice, effective global information synthesis of information has an additional key requirement: multi-leader architecture. That is, in a single-leader blockchain like Solana, where one validator at a time has a monopoly on ordering transactions into blocks, for their duration as a leader they effectively function as a colocation server. This means that if the current leader is in New York, someone in Singapore who wants to trade on local news as soon as it breaks will still need to get their order all the way around the world to the leader, who is effectively serving as the chain's data ingestion point, before the order can start propagating through the network. But this is issue solved by the introduction of multiple distributed leaders, because then anyone with access to new information can submit their order to the leader closest to them, yielding faster information inclusion in the form of price updates. Multi-leader is also required for fair markets A multi-leader architecture is also required for fair markets, because in a single-leader system the leader has the power to censor transactions, reorder them to their advantage, or even replace transactions with copycats that extract maximum value by replacing the sender's address with their own. For example if someone wants to capture an arbitrage opportunity between two onchain DEXes, they'll need to submit a transaction to the leader and trust that the leader won't simply copy the transaction and submit it themselves. But when there are two or more leaders, users whose transactions are censored by one leader will simply work with a different leader the next time around, eventually cutting off transaction fee flow to the extractive leader. Beyond just strict inclusion, in a multi-leader architecture validators are also forced to compete with each other on latency, because the leader who is fastest at disseminating users' transactions across the network will over time gobble up the largest share of the order flow. Transparent priority fees are a must, or a private mempool will emerge But in order to make this work, a multi-leader architecture must also offer users the ability to pay priority fees AKA "tips" or "bribes" to move their transaction to the front of the line: if there is a $5 arbitrage opportunity onchain, users need to have assurance that they if they pay a 4.99 priority fee to take that arb, they will get priority over a different user who is only willing to tip 4.98. If the native blockchain system does not offer this fair market priority fee mechanism, then it is only a matter of time before one spontaneously emerges in the form of a private mempool like Jito, which can create centralization pressures and undermine the integrity of the system as a whole. Competitive payment for order flow is the stable solution With the right architecture in place, the end result is a competitive environment where endpoints running maximum extractable value (MEV) bots compete with one to offer users the best price for their order flow. In other words, if a user wants to submit an order that can get sandwich attacked for as much as $2 of MEV, then the order should ultimately go to the endpoint bot that is willing to pay the user as much as $1.99 for the right to process their transaction. The price that the provider is willing to pay is ultimately a function of how much in priority fees they might need to pay to the current leader (0 they are the current one), but notably at each stage there is a competitive market for order flow, whether in the form of retail trader's orders, or priority fees among bots that might be forwarding orders to one of the leaders. AptosLabs is already building all this With a public mempool and transaction priority fees, Aptos additionally includes a pipelined architecture that already includes concurrent batching of transactions into blocks, with a single consensus leader who propagates the batched blocks out to the network. And the team is already researching running multiple instances of the consensus algorithm in parallel, yielding multiple consensus leaders who can compete with each other on latency and inclusion - just ask pranav | Shelby, Alexander Spiegelman, and Zekun Li. This means that block times can shrink as the number of consensus leaders grows, with each leader having its own geographical radius of inclusion beyond which it makes more sense to submit to a different leader. The starting point? Something like 60 ms blocks and 3 consensus leaders, partitioning the global information space into competitive and constantly-rotating regions of information inclusion. Messaging is important With concurrent pipelined transaction batching, a public mempool, priority fees, and a clear path to a multi-leader architecture, Aptos leads the industry in onchain trading infrastructure that can truly supplant the centralized colocation paradigm that has heretofore dominated global finance - by offering a truly superior product. And I am hopeful that this deep dive is the first step in communicating not how or that superior product is getting built, but what it means from a bigger picture perspective. If blockchains have found product market fit in anything, it is in trading, and the trading game can only be won by building the biggest, baddest, most high performance system that has as its north star a single, concrete goal: constantly reducing, ever lower toward zero, time time it takes to incorporate information from anywhere in the world into the global price discovery computer. Whoever does this, even 1 ms faster than the competitor, wins the price discovery game, as other blockchains are left in the dust, their DEXes arbed away to zero against the fastest chain on the block. And sure, the blockchain that can rise to this challenge can also handle useful things like payments, NFTs, or other solutions that benefit from permissionlessness and low gas costs, but I want to impress that at the core of this pursuit must be the urge to drive down information inclusion latency to the absolute minimum afforded by the laws of physics through a competitive, market-driven environment. I call on avery.apt 🇺🇸 , CTO of Aptos Labs, to lean in on this messaging, to make it clear that Aptos is here for this singular mission, to build the most performant price discovery engine in history, as a rallying call for alignment in development efforts across the ecosystem and broader industry. Where does this go? As the latencies drop, the spreads tighten, and the information inclusion increases with every incremental increase in network bandwidth, we can expect a new class of competing techno-financial hubs that aggregate around the world's largest information sources: New York, Washington DC, London, Tokyo, etc., commanding stake distribution commensurate with the density of information flow in these respective locales. With the right incentives in place, competing concurrent leaders will invest ever more in infrastructure to get their packets out to the network faster than the rest, yielding clusters of fiber optic cable around the world's financial hubs, neurons in the global financial brain connecting not just HFT firms to servers in their city, but connecting every city with every other city, to move pricing information across oceans and continents. And retail traders, who have been left out of the colocation game, will only benefit: this entire system gets faster, more inclusive, with tighter spreads and lower fees, and it is such an amazing opportunity to watch all of this unfold in real time. The future of blockchains is the future of trading, is the future of competitive information inclusion in real-time, is the future of truly unified global markets, because at the the core of this industry is a simple idea: connect the computers, and see where the incentives lead. They lead to this, and Aptos is leading the charge, because its tech is purpose-built for this exact purpose. So tell the world about it.

Alex Kahn

24,432 просмотров • 1 год назад

The Great Equalizer: How I Iterated Through 90+ Strategies to Automate My Financial Freedom ninety strategies sounds like a death wish but it is actually the only way to find your edge in a market designed to liquidate you. most traders are out here gambling with their rent money while the big players are using automated systems to harvest their liquidations. i know this because i spent hundreds of thousands of dollars on developers for apps thinking i could never code myself. i was getting wrecked by over trading and watching my accounts hit zero while i slept. code became the great equalizer for me because it removed the emotion that was killing my bankroll. i decided to learn to code live so i could iterate to success and now i have fully automated systems trading for me instead of getting liquidated by every wick. i just saw someone lose ten million dollars in a single month because they were trading by hand and got addicted to the screen. you have to understand that if you are not automating you are the exit liquidity for someone who is. the reality of advanced futures trading is not about finding one holy grail bot that prints money forever. it is about research and back testing until you find a strategy that has a statistical advantage. one of the most slept on concepts is variable risk scaling where you actually change your position size based on how volatile the market is. instead of just betting the same amount every time you increase your size when volatility is low and scale back when the market starts moving like crazy. this keeps you in the game during the draw downs that usually wipe people out. most people do the opposite and revenge trade with bigger size when they are losing which is the fastest way to the cemetery. i used to think i needed to be the smartest guy in the room to make this work but i realized i just needed to be the most disciplined with my risk parameters. there is a secret hidden in funding rates and basis trading that most retail traders never even look at. while everyone else is trying to guess if bitcoin is going to the moon or the floor you can actually make consistent money through funding rate arbitrage. you basically buy the asset in the spot market and simultaneously sell it in the futures market when the funding rate is high. you just sit there and collect the interest payments from the gamblers who are over leveraged on the other side. it is basically free money if you can manage the fees and keep your execution precise. i used to ignore these low yield plays because i wanted the big home runs but those home runs usually came with massive strikeouts. now i look for these carry trades as a way to keep the equity curve moving up and to the right while others are sweating over every price change. most traders fail because they use lagging indicators and expect them to predict the future with one hundred percent accuracy. the truth is that even the best trend following strategies like the golden cross or moving average crossovers only have about sixty five percent accuracy. you have to combine these with filters like the average directional index or relative strength index to make sure you are not just buying a fake breakout. a lot of people get chopped up in sideways markets because they do not have a trend strength filter to tell them to stay out of the trade. i learned to use multiple time frames to confirm my breakouts because if the one hour and the four hour charts are not saying the same thing then the trade is probably a trap. you have to be a searcher looking for those golden nuggets of alpha buried in mountains of data. i used to think that machine learning and genetic algorithms were just buzzwords that did not actually work for trading. then i realized that the 1990s tech trap is real and if you are still using basic indicators without any optimization you are decades behind. genetic algorithms are wild because they simulate natural selection to find the best parameters for your strategy through trial and error. you can actually build an environment where your bot learns from its own mistakes and optimizes its decision making process over time. i spent so much time thinking i was not smart enough to do this but once i started iterating live i found that the machines are much better at following rules than i ever was. code is the only way to compete with the high frequency firms that are looking for any tiny mispricing in the order book. slippage and bad execution will eat your profits faster than a bad trade ever could if you are not careful. most people just hit the market buy button and pay the spread and the fees without a second thought. you should be using smart order routing and limit orders to capture the bid ask spread instead of paying it to the market makers. i started using time weighted average price execution to spread my larger orders out over time so i did not move the market against myself. it is these tiny details in execution that separate the professional quants from the people who are just playing around. i had to learn this the hard way after losing a fortune on bad entries and exits that could have been avoided with a few lines of code. the ultimate goal of all of this is to build a compounding machine that grows your capital while you are living your life. you have to automate the reinvestment of your profits so that your position sizes grow as your account grows without you having to manually adjust anything. i like to use automated compounding algorithms that take a portion of my wins and put them back into the systems that are performing the best. this creates a snowball effect where your returns start to accelerate as the base capital increases. it took me years to realize that i did not need to be at the desk for eighteen hours a day to make life changing money. i just needed to build a system that was smarter and more disciplined than my own human brain. cross asset skew and volatility surface arbitrage are where the real quants play when the market gets efficient. you can look for mispricings between highly correlated assets like bitcoin and ethereum and trade the spread between them. when one asset gets overvalued relative to the other you short the leader and long the laggard until they revert back to the mean. this is a much safer way to trade because you are not betting on the direction of the market but rather the relationship between two assets. i spent a lot of money trying to guess the next big move before i realized that trading the relationship between assets was much more consistent. iteration is the only way to find these winks in the market that the average trader is completely blind to. it is a cold world in finance and most people are out here trying to step on your neck to get ahead. i believe that sharing this knowledge is important because code is the only thing that can give a regular person a fighting chance against the institutions. i started from zero and learned everything through failing and losing money until i finally figured out how to automate. now i spend my time building and testing instead of worrying about the next liquidation candle. you have to decide today if you want to keep being the exit liquidity or if you want to start building your own systems. the tools are all there and the data is accessible if you are willing to put in the work and stop negotiating with yourself. successful trading is not about being lucky it is about being prepared and having a system that can handle any market regime. whether the market is in a bull run or a total crash your bots should know exactly what to do based on the rules you have coded into them. i use risk weighted allocation to make sure that my capital is always moving toward the strategies with the highest sharp ratio and the lowest volatility. this keeps the portfolio stable even when the crypto market is going through its typical insane swings. i finally found peace in this game because i know that my automated systems are following the math while everyone else is following their feelings. code is the great equalizer and it is time for you to start using it to protect your future and build your empire there are over ninety strategies you can test and most of them will not work for your specific style but you only need one or two to change your life. i have built a fat list of ideas from research and i spend every day back testing and refining them to stay ahead of the curve. do not let the fear of coding stop you from taking control of your financial destiny because i am living proof that anyone can learn. i would rather spend my time iterating to success than getting liquidated by some random news event that i could not predict. the journey from losing hundreds of thousands to fully automated success was long but it was the best investment i ever made. keep your heart open and lead with love in this game and i promise the universe will start passing you those golden nuggets of alpha you have been searching for
2:40:29

The Great Equalizer: How I Iterated Through 90+ Strategies to Automate My Financial Freedom ninety strategies sounds like a death wish but it is actually the only way to find your edge in a market designed to liquidate you. most traders are out here gambling with their rent money while the big players are using automated systems to harvest their liquidations. i know this because i spent hundreds of thousands of dollars on developers for apps thinking i could never code myself. i was getting wrecked by over trading and watching my accounts hit zero while i slept. code became the great equalizer for me because it removed the emotion that was killing my bankroll. i decided to learn to code live so i could iterate to success and now i have fully automated systems trading for me instead of getting liquidated by every wick. i just saw someone lose ten million dollars in a single month because they were trading by hand and got addicted to the screen. you have to understand that if you are not automating you are the exit liquidity for someone who is. the reality of advanced futures trading is not about finding one holy grail bot that prints money forever. it is about research and back testing until you find a strategy that has a statistical advantage. one of the most slept on concepts is variable risk scaling where you actually change your position size based on how volatile the market is. instead of just betting the same amount every time you increase your size when volatility is low and scale back when the market starts moving like crazy. this keeps you in the game during the draw downs that usually wipe people out. most people do the opposite and revenge trade with bigger size when they are losing which is the fastest way to the cemetery. i used to think i needed to be the smartest guy in the room to make this work but i realized i just needed to be the most disciplined with my risk parameters. there is a secret hidden in funding rates and basis trading that most retail traders never even look at. while everyone else is trying to guess if bitcoin is going to the moon or the floor you can actually make consistent money through funding rate arbitrage. you basically buy the asset in the spot market and simultaneously sell it in the futures market when the funding rate is high. you just sit there and collect the interest payments from the gamblers who are over leveraged on the other side. it is basically free money if you can manage the fees and keep your execution precise. i used to ignore these low yield plays because i wanted the big home runs but those home runs usually came with massive strikeouts. now i look for these carry trades as a way to keep the equity curve moving up and to the right while others are sweating over every price change. most traders fail because they use lagging indicators and expect them to predict the future with one hundred percent accuracy. the truth is that even the best trend following strategies like the golden cross or moving average crossovers only have about sixty five percent accuracy. you have to combine these with filters like the average directional index or relative strength index to make sure you are not just buying a fake breakout. a lot of people get chopped up in sideways markets because they do not have a trend strength filter to tell them to stay out of the trade. i learned to use multiple time frames to confirm my breakouts because if the one hour and the four hour charts are not saying the same thing then the trade is probably a trap. you have to be a searcher looking for those golden nuggets of alpha buried in mountains of data. i used to think that machine learning and genetic algorithms were just buzzwords that did not actually work for trading. then i realized that the 1990s tech trap is real and if you are still using basic indicators without any optimization you are decades behind. genetic algorithms are wild because they simulate natural selection to find the best parameters for your strategy through trial and error. you can actually build an environment where your bot learns from its own mistakes and optimizes its decision making process over time. i spent so much time thinking i was not smart enough to do this but once i started iterating live i found that the machines are much better at following rules than i ever was. code is the only way to compete with the high frequency firms that are looking for any tiny mispricing in the order book. slippage and bad execution will eat your profits faster than a bad trade ever could if you are not careful. most people just hit the market buy button and pay the spread and the fees without a second thought. you should be using smart order routing and limit orders to capture the bid ask spread instead of paying it to the market makers. i started using time weighted average price execution to spread my larger orders out over time so i did not move the market against myself. it is these tiny details in execution that separate the professional quants from the people who are just playing around. i had to learn this the hard way after losing a fortune on bad entries and exits that could have been avoided with a few lines of code. the ultimate goal of all of this is to build a compounding machine that grows your capital while you are living your life. you have to automate the reinvestment of your profits so that your position sizes grow as your account grows without you having to manually adjust anything. i like to use automated compounding algorithms that take a portion of my wins and put them back into the systems that are performing the best. this creates a snowball effect where your returns start to accelerate as the base capital increases. it took me years to realize that i did not need to be at the desk for eighteen hours a day to make life changing money. i just needed to build a system that was smarter and more disciplined than my own human brain. cross asset skew and volatility surface arbitrage are where the real quants play when the market gets efficient. you can look for mispricings between highly correlated assets like bitcoin and ethereum and trade the spread between them. when one asset gets overvalued relative to the other you short the leader and long the laggard until they revert back to the mean. this is a much safer way to trade because you are not betting on the direction of the market but rather the relationship between two assets. i spent a lot of money trying to guess the next big move before i realized that trading the relationship between assets was much more consistent. iteration is the only way to find these winks in the market that the average trader is completely blind to. it is a cold world in finance and most people are out here trying to step on your neck to get ahead. i believe that sharing this knowledge is important because code is the only thing that can give a regular person a fighting chance against the institutions. i started from zero and learned everything through failing and losing money until i finally figured out how to automate. now i spend my time building and testing instead of worrying about the next liquidation candle. you have to decide today if you want to keep being the exit liquidity or if you want to start building your own systems. the tools are all there and the data is accessible if you are willing to put in the work and stop negotiating with yourself. successful trading is not about being lucky it is about being prepared and having a system that can handle any market regime. whether the market is in a bull run or a total crash your bots should know exactly what to do based on the rules you have coded into them. i use risk weighted allocation to make sure that my capital is always moving toward the strategies with the highest sharp ratio and the lowest volatility. this keeps the portfolio stable even when the crypto market is going through its typical insane swings. i finally found peace in this game because i know that my automated systems are following the math while everyone else is following their feelings. code is the great equalizer and it is time for you to start using it to protect your future and build your empire there are over ninety strategies you can test and most of them will not work for your specific style but you only need one or two to change your life. i have built a fat list of ideas from research and i spend every day back testing and refining them to stay ahead of the curve. do not let the fear of coding stop you from taking control of your financial destiny because i am living proof that anyone can learn. i would rather spend my time iterating to success than getting liquidated by some random news event that i could not predict. the journey from losing hundreds of thousands to fully automated success was long but it was the best investment i ever made. keep your heart open and lead with love in this game and i promise the universe will start passing you those golden nuggets of alpha you have been searching for

Moon Dev

11,126 просмотров • 5 месяцев назад