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The biggest launch in Alphractal’s history is now officially live! 🎉 After months of rebuilding the platform from the ground up, the new Alphractal experience is officially here. This release goes far beyond interface changes. It restructures how data is organized, consumed, and acted upon. What’s included in the...

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Alphractal

14,986 subscribers

25,770 views • 6 months ago •via X (Twitter)

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Alphractal's profile picture
Alphractal6 months ago

Explore the new Alphractal platform. 🚀 Experience the upgrade, share your feedback, and be part of the next chapter we’re building together. Access the platform through the link on our website below.

Adrien 🦅🟠🔗's profile picture
Adrien 🦅🟠🔗6 months ago

Congrats to the team! Big update

Alphractal's profile picture
Alphractal6 months ago

Come on, Adrien! 🫡

Gustavo França's profile picture
Gustavo França6 months ago

🔥

Alphractal's profile picture
Alphractal6 months ago

🤝🫡

Pablo Mani's profile picture
Pablo Mani6 months ago

🚀🚀💪

IT Tech's profile picture
IT Tech6 months ago

@joao_wedson Awesome update. Keep building guys!

Burak Kesmeci's profile picture
Burak Kesmeci6 months ago

Great work!!

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🚀 Alphractal V2 is live Faster UX, sharper insights, and a cleaner workflow for serious crypto decisions. What’s new - Altcoin historical data - Macroeconomy coverage - Institutional plan with API & Reports - 5,000+ variables to explore Access now:
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🚀 Alphractal V2 is live Faster UX, sharper insights, and a cleaner workflow for serious crypto decisions. What’s new - Altcoin historical data - Macroeconomy coverage - Institutional plan with API & Reports - 5,000+ variables to explore Access now:

Alphractal

32,019 views • 1 year ago

The time has arrived! The Spectre AI Second AI Platform is ready. AI Screener Beta Test is NOW LIVE! After months of work, our first iteration of our Second AI Platform is here. All our beta testers can now access the platform and experience its functionality in action. There are 2 versions available in Night and Day mode: 1. AI Screener LITE A fast, simplified research setup for investors: • Chart View: Analyze token charts, switch timeframes, and check market cap and price. • Token Info: Instantly view token data, audits, social medias, volume, and price changes. • Dexscan Orders: Track live trades, including maker/taker IDs, position entries and exits. • Screener: Discover trending tokens and new pairs across all major chains. • Watchlist: Create, import, or share custom token lists in seconds. 2. AI Screener PRO Unlock deeper, more dynamic tools for both investors and traders: • Market Widgets: Customize views of on-chain volume, trending, and X Bubbles Mini. • Watchlist Analytics: Visualise all your favourite tokens as bubble charts and compare their performance. • Tweets Preview: Follow live project tweets in a streamlined format. • Chart Compare: Overlay multi-token, multi-chain charts. • Sector Token Bubbles: Analyse market sectors in a visual format. • Bubblemap: Visually track wallet connections and their token movements. Coming soon: Mobile Edition, Swaps, own Trading solutions, KOL Analytics, Holder Analysis, X Bubbles, X Charts, Monarch AI Agent, and much more! You can join the AI Screener Beta Test now by joining our Telegram group at and requesting White List Access. #aidex #SPECT
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The time has arrived! The Spectre AI Second AI Platform is ready. AI Screener Beta Test is NOW LIVE! After months of work, our first iteration of our Second AI Platform is here. All our beta testers can now access the platform and experience its functionality in action. There are 2 versions available in Night and Day mode: 1. AI Screener LITE A fast, simplified research setup for investors: • Chart View: Analyze token charts, switch timeframes, and check market cap and price. • Token Info: Instantly view token data, audits, social medias, volume, and price changes. • Dexscan Orders: Track live trades, including maker/taker IDs, position entries and exits. • Screener: Discover trending tokens and new pairs across all major chains. • Watchlist: Create, import, or share custom token lists in seconds. 2. AI Screener PRO Unlock deeper, more dynamic tools for both investors and traders: • Market Widgets: Customize views of on-chain volume, trending, and X Bubbles Mini. • Watchlist Analytics: Visualise all your favourite tokens as bubble charts and compare their performance. • Tweets Preview: Follow live project tweets in a streamlined format. • Chart Compare: Overlay multi-token, multi-chain charts. • Sector Token Bubbles: Analyse market sectors in a visual format. • Bubblemap: Visually track wallet connections and their token movements. Coming soon: Mobile Edition, Swaps, own Trading solutions, KOL Analytics, Holder Analysis, X Bubbles, X Charts, Monarch AI Agent, and much more! You can join the AI Screener Beta Test now by joining our Telegram group at and requesting White List Access. #aidex #SPECT

SPECTRE AI

234,997 views • 1 year ago

OpenClaw setup made me $23,472 Literally overnight my $100 turned into $2,411 Average bot win rate 71% Copytrade: Here is the full strategy: The system builds automated workflows for trading by turning domain expertise into structured skills that activate automatically when specific market conditions appear Skill architecture Each skill is a modular package that includes instruction scripts and reference data This allows the system to apply specialized workflows without needing manual input for every trade Progressive context loading Skills use a three layer structure Only minimal metadata loads at first Full instructions historical data and supporting resources load only when required This reduces resource usage while keeping advanced trading capability Trigger detection Skills activate automatically when market conditions match predefined triggers such as volatility levels orderflow behavior or news sentiment This ensures the right workflow is used at the right time without manual action Workflow execution Once activated each skill runs a predefined multi step process including Real time price tracking and order book analysis Factor generation and backtesting Signal aggregation from machine learning models news sentiment and orderflow Risk assessment and capital allocation Trade execution with retries and position splitting Consistency and reliability All workflows are embedded directly into the system which ensures consistent execution instead of random decision making Every factor signal and risk rule is applied in a structured way Testing and iteration Skills are continuously improved using historical backtesting simulated trading and live performance tracking to maintain reliability in real market conditions Automation edge Instead of creating new strategies every time the system repeatedly uses optimized workflows This reduces complexity increases consistency and scales performance across thousands of trades Performance snapshot Started one month ago with $500 Current daily profit $2,300 per day Morning profit today $71,452 The system runs fully autonomously constantly scanning markets generating signals auditing trades managing risk and executing orders to maximize compounding returns
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OpenClaw setup made me $23,472 Literally overnight my $100 turned into $2,411 Average bot win rate 71% Copytrade: Here is the full strategy: The system builds automated workflows for trading by turning domain expertise into structured skills that activate automatically when specific market conditions appear Skill architecture Each skill is a modular package that includes instruction scripts and reference data This allows the system to apply specialized workflows without needing manual input for every trade Progressive context loading Skills use a three layer structure Only minimal metadata loads at first Full instructions historical data and supporting resources load only when required This reduces resource usage while keeping advanced trading capability Trigger detection Skills activate automatically when market conditions match predefined triggers such as volatility levels orderflow behavior or news sentiment This ensures the right workflow is used at the right time without manual action Workflow execution Once activated each skill runs a predefined multi step process including Real time price tracking and order book analysis Factor generation and backtesting Signal aggregation from machine learning models news sentiment and orderflow Risk assessment and capital allocation Trade execution with retries and position splitting Consistency and reliability All workflows are embedded directly into the system which ensures consistent execution instead of random decision making Every factor signal and risk rule is applied in a structured way Testing and iteration Skills are continuously improved using historical backtesting simulated trading and live performance tracking to maintain reliability in real market conditions Automation edge Instead of creating new strategies every time the system repeatedly uses optimized workflows This reduces complexity increases consistency and scales performance across thousands of trades Performance snapshot Started one month ago with $500 Current daily profit $2,300 per day Morning profit today $71,452 The system runs fully autonomously constantly scanning markets generating signals auditing trades managing risk and executing orders to maximize compounding returns

winkle.

44,158 views • 6 months ago

NEWS: Figure has unveiling its 3rd generation humanoid robot. • Features a completely redesigned sensory suite and hand system. Wireless charging in feet. • Next-generation vision system engineered for high-frequency visuomotor control. Its new camera architecture delivers twice the frame rate, one-quarter the latency, and a 60% wider field of view per camera within a more compact form factor • Soft goods, wireless charging, improved audio system for voice reasoning, and battery safety advancements • "Engineered from the ground-up for high-volume manufacturing. In order to scale, we established a new supply chain and entirely new process for manufacturing humanoid robots at BotQ." • Lower manufacturing cost • Softer, more adaptive fingertips increase surface contact area, enabling more stable grasps across objects of varied shapes and sizes. Each fingertip sensor can detect forces as small as three grams of pressure - sensitive enough to register the weight of a paperclip resting on your finger. • Multi-density foam to protect against pinch points, and is covered in soft textiles rather than hard machined parts. 9% less mass and less volume than Figure 02 • 10 Gbps mmWave data offload capability, allowing the entire fleet to upload terabytes of data • Upgraded audio hardware system for better real time speech-to-speech. Its speaker is twice the size and nearly four times more powerful, while the microphone has been repositioned for improved performance and clarity. • Charging coils in the robot’s feet allow it to simply step onto a wireless stand and charge at 2 kW Figure: "BotQ is Figure’s dedicated manufacturing facility designed to scale robot production. BotQ’s first-generation manufacturing line will initially be capable of producing up to 12,000 humanoid robots per year, with the goal of producing a total of 100,000 robots over the next four years. Instead of relying on contract manufacturers, Figure brought production of its most critical systems in-house to maintain tight control over quality, iteration, and speed."
6:00

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Sawyer Merritt

140,241 views • 11 months ago

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

263,308 views • 1 year ago

🚀 Three Next-Gen AI & Web3 Projects Are Launching on Mindo AI A new chapter for community-powered intelligence, prediction markets, and open AI infrastructure The AI + Web3 landscape is entering a decisive phase — one where real usage, real revenue, and real ownership matter more than hype. Today, MindoAI is proud to welcome three groundbreaking projects that represent this shift clearly and powerfully: Perceptron Network Space DeepNode AI Each project tackles a different bottleneck in the AI economy — data, forecasting, and infrastructure — but they all share the same vision: decentralization, community ownership, and sustainable value creation. Let’s take a deeper look 👇 🧠 Perceptron Network The world’s first community-powered AI data engine Perceptron Network is redefining how AI data is sourced, validated, and delivered. Instead of relying on expensive, closed, and slow legacy data providers, Perceptron unlocks community-powered data pipelines that are: Faster Cheaper Revenue-generating from day one This isn’t experimental AI infrastructure — Perceptron already serves real clients with real revenue, proving that decentralized data engines can outperform traditional incumbents. Why Perceptron matters: AI models are only as good as their data Centralized data monopolies slow innovation Communities can produce higher-quality data at scale By aligning contributors, validators, and clients through incentives, Perceptron turns unused human and network potential into a living data engine for AI. Launching on Mindo AI gives Perceptron access to a broader AI-native community — accelerating adoption, partnerships, and ecosystem growth. 🌌 intodotspace The first 10× leveraged prediction market on Solana intodotspace is pushing the boundaries of on-chain prediction markets. Built by the $1.5B UFO team, this platform introduces: 10× leveraged predictions Ultra-fast execution on Solana Deep liquidity and composable market design The market’s confidence is already clear — the project completed a record-breaking raise that was oversubscribed by 1,360%. What makes intodotspace different: Leverage amplifies conviction, not noise On-chain transparency replaces opaque odds Markets become real-time intelligence engines Prediction markets are often called “truth machines.” intodotspace upgrades them into high-signal, high-efficiency forecasting layers — useful for traders, protocols, DAOs, and even AI systems that need probabilistic insights. Launching on positions intodotspace at the intersection of AI-driven decision-making and on-chain market intelligence. 🌐 DeepNode AI Infrastructure for open intelligence DeepNode AI is tackling one of the biggest problems in modern AI: centralized ownership. Today, AI is dominated by a handful of corporations. DeepNode flips that model by building open intelligence infrastructure where: Anyone can deploy AI models Builders earn directly from usage Intelligence is co-owned, not extracted Backed by leading validators, miners, and ecosystem builders, DeepNode transforms AI from a closed monopoly into a shared utility. DeepNode’s core philosophy: “Own what you build — or someone else will.” This is more than infrastructure. It’s an economic redesign of AI itself: Builders keep ownership Contributors share upside Networks replace platforms Launching on connects DeepNode to creators, researchers, and communities who believe intelligence should belong to everyone — not just Big Tech. 🤝 Why This Matters for With the launch of Perceptron Network, intodotspace, and DeepNode AI, #MindoAI is rapidly becoming: A hub for AI-native Web3 innovation A launchpad for real, revenue-backed projects A meeting point for data, markets, and intelligence infrastructure These three projects don’t compete — they complement each other: Perceptron supplies data intodotspace produces market intelligence DeepNode powers open AI execution Together, they form the backbone of a decentralized intelligence economy. 🔥 The future of AI is open, composable, and community-owned — and it’s launching now on Which of these projects are you most excited about? And how do you see decentralized intelligence reshaping the next AI cycle? 👇 Share your thoughts and join the conversation.
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🚀 Three Next-Gen AI & Web3 Projects Are Launching on Mindo AI A new chapter for community-powered intelligence, prediction markets, and open AI infrastructure The AI + Web3 landscape is entering a decisive phase — one where real usage, real revenue, and real ownership matter more than hype. Today, MindoAI is proud to welcome three groundbreaking projects that represent this shift clearly and powerfully: Perceptron Network Space DeepNode AI Each project tackles a different bottleneck in the AI economy — data, forecasting, and infrastructure — but they all share the same vision: decentralization, community ownership, and sustainable value creation. Let’s take a deeper look 👇 🧠 Perceptron Network The world’s first community-powered AI data engine Perceptron Network is redefining how AI data is sourced, validated, and delivered. Instead of relying on expensive, closed, and slow legacy data providers, Perceptron unlocks community-powered data pipelines that are: Faster Cheaper Revenue-generating from day one This isn’t experimental AI infrastructure — Perceptron already serves real clients with real revenue, proving that decentralized data engines can outperform traditional incumbents. Why Perceptron matters: AI models are only as good as their data Centralized data monopolies slow innovation Communities can produce higher-quality data at scale By aligning contributors, validators, and clients through incentives, Perceptron turns unused human and network potential into a living data engine for AI. Launching on Mindo AI gives Perceptron access to a broader AI-native community — accelerating adoption, partnerships, and ecosystem growth. 🌌 intodotspace The first 10× leveraged prediction market on Solana intodotspace is pushing the boundaries of on-chain prediction markets. Built by the $1.5B UFO team, this platform introduces: 10× leveraged predictions Ultra-fast execution on Solana Deep liquidity and composable market design The market’s confidence is already clear — the project completed a record-breaking raise that was oversubscribed by 1,360%. What makes intodotspace different: Leverage amplifies conviction, not noise On-chain transparency replaces opaque odds Markets become real-time intelligence engines Prediction markets are often called “truth machines.” intodotspace upgrades them into high-signal, high-efficiency forecasting layers — useful for traders, protocols, DAOs, and even AI systems that need probabilistic insights. Launching on positions intodotspace at the intersection of AI-driven decision-making and on-chain market intelligence. 🌐 DeepNode AI Infrastructure for open intelligence DeepNode AI is tackling one of the biggest problems in modern AI: centralized ownership. Today, AI is dominated by a handful of corporations. DeepNode flips that model by building open intelligence infrastructure where: Anyone can deploy AI models Builders earn directly from usage Intelligence is co-owned, not extracted Backed by leading validators, miners, and ecosystem builders, DeepNode transforms AI from a closed monopoly into a shared utility. DeepNode’s core philosophy: “Own what you build — or someone else will.” This is more than infrastructure. It’s an economic redesign of AI itself: Builders keep ownership Contributors share upside Networks replace platforms Launching on connects DeepNode to creators, researchers, and communities who believe intelligence should belong to everyone — not just Big Tech. 🤝 Why This Matters for With the launch of Perceptron Network, intodotspace, and DeepNode AI, #MindoAI is rapidly becoming: A hub for AI-native Web3 innovation A launchpad for real, revenue-backed projects A meeting point for data, markets, and intelligence infrastructure These three projects don’t compete — they complement each other: Perceptron supplies data intodotspace produces market intelligence DeepNode powers open AI execution Together, they form the backbone of a decentralized intelligence economy. 🔥 The future of AI is open, composable, and community-owned — and it’s launching now on Which of these projects are you most excited about? And how do you see decentralized intelligence reshaping the next AI cycle? 👇 Share your thoughts and join the conversation.

Hồng Ngọc | Ruby💎

12,837 views • 7 months ago

ICYMI: Neko Health Health Opens in NYC FULL TOUR I take you through the full 60min Neko experience from start to finish.. including, of course, where everyone gets the famous yellow robe shots The FOMO is real. Demand in New York was already significant before the doors opened: more than 30,000+ New Yorkers joined the waitlist ahead of launch. Founded by Hjalmar Nilsonne (@HNilsonne) & Spotify's Daniel Ek (Daniel Ek), Neko launched its first clinic in Stockholm in 2023. It has since expanded across Sweden and the UK and completed more than 100,000 scans. The Neko Health Scan costs $499 & takes around 60 minutes (~30min tests/scans, ~30min doctor review). The experience is non-invasive and radiation-free, combining proprietary sensors, imaging, blood analysis and clinical review to capture millions of data points across the body. It measures cardiovascular health, circulation, skin, blood, metabolic health and body composition. Neko’s technology can detect skin changes as small as 0.2 millimeters, while its cardiovascular measurements include ECG, blood pressure, oxygen saturation, arterial stiffness, heart and breathing rate. The company recently added body composition analysis and wearable data integration, giving clinicians additional information on what is happening between annual visits. But one of the biggest differences is speed. Rather than completing tests and waiting days or weeks for results from different providers, Neko designed the experience around getting the data back during the same visit. Results are processed on-site within minutes and then reviewed directly with a clinician. Hjalmar Nilsonne, Neko Health Co-Founder & CEO: “What if a single visit could give you a more complete picture of your health, where it’s heading, and a clear plan from a medical professional to change what happens next?” Neko also builds its hardware, software and clinical protocols in-house. That vertical integration allows the company to control the entire experience — from the sensors collecting the data to how clinicians interpret and present the results. Nilsonne describes that integration as fundamental to the model: “This vertical integration is not incidental; it is the foundation that allows Neko Health to control the experience from start to finish, to move faster and to improve continuously.” There are early signs that members are actually coming back. 75% of members book and prepay for their next annual scan at the end of their first appointment, 80% rebook after their second scan for a third scan. Neko has also started publishing data on what happens to members over time. Among returning members who had previously been identified with severe or life-threatening conditions, the company says 3/4 were either in good health or had the condition under control at follow-up. Across the broader returning population, five of seven key biomarkers showed statistically significant improvement between the first and second scans. That data comes from Neko’s own member population and should be viewed as company-reported observational results, rather than evidence from a randomized clinical trial. The U.S. launch follows a $700 million Series C in July led by Lightspeed and co-led by O.G. Venture Partners. More than 350,000 people globally have now joined the waitlist or registered for a scan. Nilsonne: “The clearest proof is in our members: the vast majority of our members return after their first scan, and when they do, their health markers move in the right direction.” New York is only the first step in the U.S. rollout. Neko plans additional NYC locations, followed by Miami, Washington, D.C. and San Francisco. For Ek and Nilsonne, the larger bet is that healthcare can move from primarily reacting to disease toward measuring more, earlier.. and giving people information they can act on before something feels wrong. Neko is certainly becoming a cultural movement.
0:50

ICYMI: Neko Health Health Opens in NYC FULL TOUR I take you through the full 60min Neko experience from start to finish.. including, of course, where everyone gets the famous yellow robe shots The FOMO is real. Demand in New York was already significant before the doors opened: more than 30,000+ New Yorkers joined the waitlist ahead of launch. Founded by Hjalmar Nilsonne (@HNilsonne) & Spotify's Daniel Ek (Daniel Ek), Neko launched its first clinic in Stockholm in 2023. It has since expanded across Sweden and the UK and completed more than 100,000 scans. The Neko Health Scan costs $499 & takes around 60 minutes (~30min tests/scans, ~30min doctor review). The experience is non-invasive and radiation-free, combining proprietary sensors, imaging, blood analysis and clinical review to capture millions of data points across the body. It measures cardiovascular health, circulation, skin, blood, metabolic health and body composition. Neko’s technology can detect skin changes as small as 0.2 millimeters, while its cardiovascular measurements include ECG, blood pressure, oxygen saturation, arterial stiffness, heart and breathing rate. The company recently added body composition analysis and wearable data integration, giving clinicians additional information on what is happening between annual visits. But one of the biggest differences is speed. Rather than completing tests and waiting days or weeks for results from different providers, Neko designed the experience around getting the data back during the same visit. Results are processed on-site within minutes and then reviewed directly with a clinician. Hjalmar Nilsonne, Neko Health Co-Founder & CEO: “What if a single visit could give you a more complete picture of your health, where it’s heading, and a clear plan from a medical professional to change what happens next?” Neko also builds its hardware, software and clinical protocols in-house. That vertical integration allows the company to control the entire experience — from the sensors collecting the data to how clinicians interpret and present the results. Nilsonne describes that integration as fundamental to the model: “This vertical integration is not incidental; it is the foundation that allows Neko Health to control the experience from start to finish, to move faster and to improve continuously.” There are early signs that members are actually coming back. 75% of members book and prepay for their next annual scan at the end of their first appointment, 80% rebook after their second scan for a third scan. Neko has also started publishing data on what happens to members over time. Among returning members who had previously been identified with severe or life-threatening conditions, the company says 3/4 were either in good health or had the condition under control at follow-up. Across the broader returning population, five of seven key biomarkers showed statistically significant improvement between the first and second scans. That data comes from Neko’s own member population and should be viewed as company-reported observational results, rather than evidence from a randomized clinical trial. The U.S. launch follows a $700 million Series C in July led by Lightspeed and co-led by O.G. Venture Partners. More than 350,000 people globally have now joined the waitlist or registered for a scan. Nilsonne: “The clearest proof is in our members: the vast majority of our members return after their first scan, and when they do, their health markers move in the right direction.” New York is only the first step in the U.S. rollout. Neko plans additional NYC locations, followed by Miami, Washington, D.C. and San Francisco. For Ek and Nilsonne, the larger bet is that healthcare can move from primarily reacting to disease toward measuring more, earlier.. and giving people information they can act on before something feels wrong. Neko is certainly becoming a cultural movement.

Molly O’Shea

14,275 views • 2 days ago

Padawans! We are excited to announce the return of Jediswap with concentrated liquidity, full audits, points, and incentives. Check it out at Since our last update two months ago, we have been working hard on a fresh new version of Jediswap, focused on bringing capital efficiency and the best price execution to our users. After two months of dedicated efforts, a full audit by Nethermind Starknet , and passing rigorous security tests, we are excited to announce the launch of Jediswap v2. Jediswap v2 significantly enhances user experience and performance while introducing new features and surprises. Our commitment to community and user growth remains strong, and we have exciting plans to expand the Jediswap ecosystem. Take a look at key updates coming with the launch. A points system that empowers genuine, loyal users: Jediswap's origins go back to early 2020 when we started our journey not as a product but as a community known as the Mesh community. Our mission was clear: bring Open Finance to billions of people. Recognising the strength of community-driven efforts, we understood that collective belief and collaboration, rather than individual or corporate endeavours, would be the most effective path forward. Early loyal users are the most crucial pillars of any community and product. This point system is Jediswap’s first step in recognising and rewarding the value each user has added to the protocol. We have prepared separate point systems for liquidity providers and traders. In short, as an LP, you can maximise your points by earning more fees on your LP positions and maintaining your liquidity in Jediswap over the long term. You can check out the complete math behind points here. For traders, use Jediswap when you genuinely need to swap tokens. There is no need to do any wash trading. We have published the points system for Jediswap v2 and will soon release points for all the activity that has occurred on Jediswap v1 to date with a boost. Check out the points logic on our docs: Improved performance and user experience: We have significantly enhanced Jediswap's performance, making it faster and more user-friendly. One notable improvement is the integration of pool analytics directly within the Pool page, eliminating the need for users to navigate to a separate analytics page. Additionally, balance fetching has been optimised for smoother operation. Any liquidity added to pools now updates the My Positions page in real-time. Battle-tested security For this launch, we implemented several security measures. We underwent a rigorous 7-week audit process with Nethermind. With the help of the Nethermind team, we also created a test framework for Jediswap to compare security against Uniswap v3, which has been operational for 3+ years and is one of the most battle-tested smart contracts available. We simulated real data from different Uniswap v3 pools on Jediswap. We achieved a 100% match in the contract state after each on-chain action, such as swaps and liquidity adjustments, bolstering our confidence in our code's security. We will announce many cool things over the next few weeks. Keep an out JediSwap ;) Mint a Galxe NFT: To commemorate this launch, we have published a new campaign on Galxe, which rewards users with an NFT for being an early user of Jediswap v2. To earn the Galxe NFT, add at least $25 worth of liquidity to one of the pools listed in the Galxe quest.
0:22

Padawans! We are excited to announce the return of Jediswap with concentrated liquidity, full audits, points, and incentives. Check it out at Since our last update two months ago, we have been working hard on a fresh new version of Jediswap, focused on bringing capital efficiency and the best price execution to our users. After two months of dedicated efforts, a full audit by Nethermind Starknet , and passing rigorous security tests, we are excited to announce the launch of Jediswap v2. Jediswap v2 significantly enhances user experience and performance while introducing new features and surprises. Our commitment to community and user growth remains strong, and we have exciting plans to expand the Jediswap ecosystem. Take a look at key updates coming with the launch. A points system that empowers genuine, loyal users: Jediswap's origins go back to early 2020 when we started our journey not as a product but as a community known as the Mesh community. Our mission was clear: bring Open Finance to billions of people. Recognising the strength of community-driven efforts, we understood that collective belief and collaboration, rather than individual or corporate endeavours, would be the most effective path forward. Early loyal users are the most crucial pillars of any community and product. This point system is Jediswap’s first step in recognising and rewarding the value each user has added to the protocol. We have prepared separate point systems for liquidity providers and traders. In short, as an LP, you can maximise your points by earning more fees on your LP positions and maintaining your liquidity in Jediswap over the long term. You can check out the complete math behind points here. For traders, use Jediswap when you genuinely need to swap tokens. There is no need to do any wash trading. We have published the points system for Jediswap v2 and will soon release points for all the activity that has occurred on Jediswap v1 to date with a boost. Check out the points logic on our docs: Improved performance and user experience: We have significantly enhanced Jediswap's performance, making it faster and more user-friendly. One notable improvement is the integration of pool analytics directly within the Pool page, eliminating the need for users to navigate to a separate analytics page. Additionally, balance fetching has been optimised for smoother operation. Any liquidity added to pools now updates the My Positions page in real-time. Battle-tested security For this launch, we implemented several security measures. We underwent a rigorous 7-week audit process with Nethermind. With the help of the Nethermind team, we also created a test framework for Jediswap to compare security against Uniswap v3, which has been operational for 3+ years and is one of the most battle-tested smart contracts available. We simulated real data from different Uniswap v3 pools on Jediswap. We achieved a 100% match in the contract state after each on-chain action, such as swaps and liquidity adjustments, bolstering our confidence in our code's security. We will announce many cool things over the next few weeks. Keep an out JediSwap ;) Mint a Galxe NFT: To commemorate this launch, we have published a new campaign on Galxe, which rewards users with an NFT for being an early user of Jediswap v2. To earn the Galxe NFT, add at least $25 worth of liquidity to one of the pools listed in the Galxe quest.

JediSwap

107,549 views • 2 years ago

Thrilled to announce Kingnet AI V2 is now officially live ! We have officially deployed on the BNB Chain first ! Whether you're an enthusiast or a professional game developer, come and try it out now： Each generated asset costs approximately $3 and supports export in professional game-editing formats. We will soon support exporting assets in NFT on-chain formats, empowering Web3 users and partners with seamless integration. Jump down more rabbit holes next.👇 📔 Product Introduction： By conversing naturally with agent Joi, users can achieve a complete automated game development cycle - from requirement proposal to finished product delivery. Users simply need to describe their game concepts and design requirements in natural language, and Joi will automatically utilize built-in generator including: • Animation Generator： AI-driven motion generation with auto-rigging technology for instant character animation • Map Generator： Procedural map generation with built-in logic validation for consistent world-building • Numerical Generator： Automated game economy tuning for fair yet challenging gameplay systems • Editable Code Generator： Generates clean, maintainable game logic code with multi-platform/multi-language support • Interface Generator： Intelligent layout engine that optimizes user experience and interaction flow Joi intelligently generates all necessary game components, performs multi-dimensional feasibility checks, and ultimately completes game synthesis, packaging and deployment. Users can directly click to try the game on the chat interface, or download the complete editable code package to achieve rapid iteration and secondary development. 🎯 Core Architecture： 1/ Natural Language Understanding & Multimodal Intent Parsing: Utilizing advanced deep learning NLP models (e.g., Transformer-based language understanding models), Joi precisely interprets user natural language inputs and extracts core game design intents and parameters. Through semantic segmentation and entity recognition, complex requirements are decomposed into specific tasks for animation, map, numerical systems, UI, and code modules. 2/ Modular Editor System & API Integration: Joi employs a unified API framework to enable seamless collaboration between editor modules, ensuring high compatibility in data formats and workflows. 3/ Intelligent Validation & Quality Assurance: The system incorporates multi-dimensional verification mechanisms including animation continuity checks, map pathfinding and physical logic validation, game balance analysis, UI interaction consistency verification, and static/dynamic code security testing. Automated testing and feedback loops ensure outputs meet high-standard game design specifications. 4/ Automatic Synthesis, Packaging & Instant Deployment: Verified resources are automatically integrated to complete game compilation, packaging and deployment. Supports one-click generation of playable online links and downloadable complete code packages for immediate testing or deep customization/iterative development. 5/ Interactive Chat Interface & Seamless UX: The entire workflow is completed within the chat interface, significantly reducing traditional game development's communication and operational barriers. Users accomplish complex game design and development through conversation while receiving real-time feedback and adjustment suggestions, democratizing game creation. 6/ Industry-Disrupting Value: Transforms traditional manual development into AI-driven automated pipelines.
1:24

Thrilled to announce Kingnet AI V2 is now officially live ! We have officially deployed on the BNB Chain first ! Whether you're an enthusiast or a professional game developer, come and try it out now： Each generated asset costs approximately $3 and supports export in professional game-editing formats. We will soon support exporting assets in NFT on-chain formats, empowering Web3 users and partners with seamless integration. Jump down more rabbit holes next.👇 📔 Product Introduction： By conversing naturally with agent Joi, users can achieve a complete automated game development cycle - from requirement proposal to finished product delivery. Users simply need to describe their game concepts and design requirements in natural language, and Joi will automatically utilize built-in generator including: • Animation Generator： AI-driven motion generation with auto-rigging technology for instant character animation • Map Generator： Procedural map generation with built-in logic validation for consistent world-building • Numerical Generator： Automated game economy tuning for fair yet challenging gameplay systems • Editable Code Generator： Generates clean, maintainable game logic code with multi-platform/multi-language support • Interface Generator： Intelligent layout engine that optimizes user experience and interaction flow Joi intelligently generates all necessary game components, performs multi-dimensional feasibility checks, and ultimately completes game synthesis, packaging and deployment. Users can directly click to try the game on the chat interface, or download the complete editable code package to achieve rapid iteration and secondary development. 🎯 Core Architecture： 1/ Natural Language Understanding & Multimodal Intent Parsing: Utilizing advanced deep learning NLP models (e.g., Transformer-based language understanding models), Joi precisely interprets user natural language inputs and extracts core game design intents and parameters. Through semantic segmentation and entity recognition, complex requirements are decomposed into specific tasks for animation, map, numerical systems, UI, and code modules. 2/ Modular Editor System & API Integration: Joi employs a unified API framework to enable seamless collaboration between editor modules, ensuring high compatibility in data formats and workflows. 3/ Intelligent Validation & Quality Assurance: The system incorporates multi-dimensional verification mechanisms including animation continuity checks, map pathfinding and physical logic validation, game balance analysis, UI interaction consistency verification, and static/dynamic code security testing. Automated testing and feedback loops ensure outputs meet high-standard game design specifications. 4/ Automatic Synthesis, Packaging & Instant Deployment: Verified resources are automatically integrated to complete game compilation, packaging and deployment. Supports one-click generation of playable online links and downloadable complete code packages for immediate testing or deep customization/iterative development. 5/ Interactive Chat Interface & Seamless UX: The entire workflow is completed within the chat interface, significantly reducing traditional game development's communication and operational barriers. Users accomplish complex game design and development through conversation while receiving real-time feedback and adjustment suggestions, democratizing game creation. 6/ Industry-Disrupting Value: Transforms traditional manual development into AI-driven automated pipelines.

Kingnet AI

46,437 views • 1 year ago

🚨 WARNING: MONDAY WILL BE THE WORST DAY OF 2026!! → Fed just confirmed rate HIKES. → Iran violated the ceasefire, and the peace deal is CANCELLED. → Japan is DUMPING U.S. Treasuries. → The AI bubble is starting to COLLAPSE. If you hold any assets today, you MUST read this: When markets open next week, this won't be “just another dip.” Stocks will dump. Bonds will dump. Gold and Silver will dump. Bitcoin will collapse. And insiders already know what's coming. They are not buying assets right now. They are reducing exposure and preparing for the biggest sell-off event of the year. At the same time, pressure is intensifying throughout the global financial system. China is continuing to reduce Treasury exposure. Japan's bond market remains under severe pressure, forcing the BOJ into continued support operations. When the world's largest creditors step away from sovereign debt markets simultaneously, liquidity evaporates. → Global bond markets are under extreme stress → Japanese bond yields continue surging higher → Demand for U.S. Treasuries is deteriorating → Liquidity conditions are tightening across markets → Volatility is spreading through every major asset class → Energy markets remain highly unstable → The AI bubble is starting to deflate as equities already weaken → Asset managers are dumping stocks and reducing market exposure This is no longer a localized issue. This is systemic stress building across MULTIPLE sectors simultaneously. And now geopolitical risk has escalated even further. New strikes between the U.S. and Iran have erupted after the ceasefire was violated. That is how energy markets become impossible to control. Oil does not rise slowly. It goes parabolic. Inflation accelerates worldwide. Which means interest rates stay higher for longer. And risk assets? They do not dip. They DUMP HARD. This is exactly how financial chain reactions begin. Because once markets start pricing long-term instability instead of short-term uncertainty, everything changes. Liquidity is already being withdrawn across multiple layers of the financial system. This is no longer about positioning alone - it is about the systemic stress. When one node breaks, it does not stay contained. It collapses EVERYTHING. I have spent decades studying macro cycles, liquidity flows, and systemic market reactions like this. That's how I knew Bitcoin would top out in October 2025 and called the $126K top. When the next move becomes clear, I will share it here first. Follow and turn notifications on. By the time mainstream media starts reporting it, it's already too late.
0:21

🚨 WARNING: MONDAY WILL BE THE WORST DAY OF 2026!! → Fed just confirmed rate HIKES. → Iran violated the ceasefire, and the peace deal is CANCELLED. → Japan is DUMPING U.S. Treasuries. → The AI bubble is starting to COLLAPSE. If you hold any assets today, you MUST read this: When markets open next week, this won't be “just another dip.” Stocks will dump. Bonds will dump. Gold and Silver will dump. Bitcoin will collapse. And insiders already know what's coming. They are not buying assets right now. They are reducing exposure and preparing for the biggest sell-off event of the year. At the same time, pressure is intensifying throughout the global financial system. China is continuing to reduce Treasury exposure. Japan's bond market remains under severe pressure, forcing the BOJ into continued support operations. When the world's largest creditors step away from sovereign debt markets simultaneously, liquidity evaporates. → Global bond markets are under extreme stress → Japanese bond yields continue surging higher → Demand for U.S. Treasuries is deteriorating → Liquidity conditions are tightening across markets → Volatility is spreading through every major asset class → Energy markets remain highly unstable → The AI bubble is starting to deflate as equities already weaken → Asset managers are dumping stocks and reducing market exposure This is no longer a localized issue. This is systemic stress building across MULTIPLE sectors simultaneously. And now geopolitical risk has escalated even further. New strikes between the U.S. and Iran have erupted after the ceasefire was violated. That is how energy markets become impossible to control. Oil does not rise slowly. It goes parabolic. Inflation accelerates worldwide. Which means interest rates stay higher for longer. And risk assets? They do not dip. They DUMP HARD. This is exactly how financial chain reactions begin. Because once markets start pricing long-term instability instead of short-term uncertainty, everything changes. Liquidity is already being withdrawn across multiple layers of the financial system. This is no longer about positioning alone - it is about the systemic stress. When one node breaks, it does not stay contained. It collapses EVERYTHING. I have spent decades studying macro cycles, liquidity flows, and systemic market reactions like this. That's how I knew Bitcoin would top out in October 2025 and called the $126K top. When the next move becomes clear, I will share it here first. Follow and turn notifications on. By the time mainstream media starts reporting it, it's already too late.

0xNobler

186,625 views • 3 months ago

Most $TAO holders are flying blind. They bought the token. They watched the price. They read the threads. But they have never opened the one tool that shows them everything happening inside the Bittensor network in real time. It is called Taostats. It is free. And after reading this, you will never look at $TAO the same way again. Here is exactly how to use it. Step 1: Start at the Subnets page. This is the heartbeat of the entire network. Every subnet running on Bittensor is listed here with: - its current emission rate - the number of active miners and validators - real-time performance data The emission rate is the most important number on this page. It tells you exactly how much TAO is flowing into each subnet every block. High emission means the network is directing significant resources toward that subnet's commodity. Low emission means the market has not yet recognised its value, or the subnet has not yet proven itself. Watch which subnets are gaining emission share over time. That movement tells you where the network believes the most valuable work is being done, before any headline announces it. Step 2: Use the Subnet pages to go deeper. Click any subnet, and you enter a complete dashboard for that individual market. - The TradingView chart shows you the alpha token price history for that subnet. Alpha tokens are the subnet-specific tokens that sit inside TAO's broader economy. Their price relative to TAO tells you how the market is valuing that subnet's specific commodity. - The Metagraph is the full list of every miner and validator currently active in the subnet: their UID, their stake, their trust score, their emission share. This is the raw intelligence layer. The miners consistently earning the most emissions are producing the work the validators collectively agree is the most valuable. - The Sentiment Index gives you a real-time community temperature reading on each subnet. Not price sentiment. Ecosystem sentiment. Whether the participants building inside the subnet believe it is healthy and improving. Step 3: Check Validators before you stake anything. This is the step most people skip and regret. The Validators page on Taostats shows you the performance history of every validator on the network: their VTrust score, their emission consistency, and their weight-setting behaviour across subnets. VTrust is the metric that matters most. It measures how closely a validator's judgments align with the honest stake-weighted majority across the network. High VTrust means the validator is doing genuine work and being rewarded for it. Low VTrust means the validator is either lazy, copying other validators' weights, or attempting to manipulate the system. When you delegate your TAO to a validator, you are trusting them with your emissions. Taostats shows you exactly which validators have earned that trust over time, and which ones have not. Never stake blind again. Step 4: Use the Blockchain explorer to track real movement. The Blockchain section of Taostats logs every transfer, every staking transaction, and every extrinsic called on the Bittensor chain in real time. This is where you track what wallets are actually doing: - Large staking transactions from unknown addresses - Subnet registration events that signal a new market is about to go live - Neuron registration burns that show demand for participation in a specific subnet is accelerating The people who read on-chain data before the narrative catches up to it are the ones who position correctly before the crowd notices the move. Step 5: Track your own portfolio inside the Dashboard. Connect your coldkey address, and Taostats builds you a complete portfolio view: - Your TAO balance - Your staking positions - Your delegation returns - Your yield over time The yield calculator is particularly useful. It shows you the actual return you are generating from your staking position in real TAO terms, not in percentage estimates that assume conditions that may not hold. If your yield is lower than the network average for your validator tier, Taostats shows you that too. Switching validators takes one transaction. The data to make that decision intelligently is right in front of you. The bigger picture. Most people holding $TAO are making decisions based on price charts and social media sentiment. Both of those inputs are downstream of what is actually happening inside the network. Subnet emission shifts. Validator VTrust changes. On-chain registration events. Neuron burn rates. Alpha token price movements relative to TAO. All of it is live on Taostats right now. All of it is free. All of it tells you something the price chart cannot. The investors who understand Bittensor at the data layer will always be positioned ahead of the investors who understand it at the narrative layer. Taostats is the data layer. Bookmark it. Open it daily. The network is telling you exactly what it is doing if you know where to look.
1:52

Most $TAO holders are flying blind. They bought the token. They watched the price. They read the threads. But they have never opened the one tool that shows them everything happening inside the Bittensor network in real time. It is called Taostats. It is free. And after reading this, you will never look at $TAO the same way again. Here is exactly how to use it. Step 1: Start at the Subnets page. This is the heartbeat of the entire network. Every subnet running on Bittensor is listed here with: - its current emission rate - the number of active miners and validators - real-time performance data The emission rate is the most important number on this page. It tells you exactly how much TAO is flowing into each subnet every block. High emission means the network is directing significant resources toward that subnet's commodity. Low emission means the market has not yet recognised its value, or the subnet has not yet proven itself. Watch which subnets are gaining emission share over time. That movement tells you where the network believes the most valuable work is being done, before any headline announces it. Step 2: Use the Subnet pages to go deeper. Click any subnet, and you enter a complete dashboard for that individual market. - The TradingView chart shows you the alpha token price history for that subnet. Alpha tokens are the subnet-specific tokens that sit inside TAO's broader economy. Their price relative to TAO tells you how the market is valuing that subnet's specific commodity. - The Metagraph is the full list of every miner and validator currently active in the subnet: their UID, their stake, their trust score, their emission share. This is the raw intelligence layer. The miners consistently earning the most emissions are producing the work the validators collectively agree is the most valuable. - The Sentiment Index gives you a real-time community temperature reading on each subnet. Not price sentiment. Ecosystem sentiment. Whether the participants building inside the subnet believe it is healthy and improving. Step 3: Check Validators before you stake anything. This is the step most people skip and regret. The Validators page on Taostats shows you the performance history of every validator on the network: their VTrust score, their emission consistency, and their weight-setting behaviour across subnets. VTrust is the metric that matters most. It measures how closely a validator's judgments align with the honest stake-weighted majority across the network. High VTrust means the validator is doing genuine work and being rewarded for it. Low VTrust means the validator is either lazy, copying other validators' weights, or attempting to manipulate the system. When you delegate your TAO to a validator, you are trusting them with your emissions. Taostats shows you exactly which validators have earned that trust over time, and which ones have not. Never stake blind again. Step 4: Use the Blockchain explorer to track real movement. The Blockchain section of Taostats logs every transfer, every staking transaction, and every extrinsic called on the Bittensor chain in real time. This is where you track what wallets are actually doing: - Large staking transactions from unknown addresses - Subnet registration events that signal a new market is about to go live - Neuron registration burns that show demand for participation in a specific subnet is accelerating The people who read on-chain data before the narrative catches up to it are the ones who position correctly before the crowd notices the move. Step 5: Track your own portfolio inside the Dashboard. Connect your coldkey address, and Taostats builds you a complete portfolio view: - Your TAO balance - Your staking positions - Your delegation returns - Your yield over time The yield calculator is particularly useful. It shows you the actual return you are generating from your staking position in real TAO terms, not in percentage estimates that assume conditions that may not hold. If your yield is lower than the network average for your validator tier, Taostats shows you that too. Switching validators takes one transaction. The data to make that decision intelligently is right in front of you. The bigger picture. Most people holding $TAO are making decisions based on price charts and social media sentiment. Both of those inputs are downstream of what is actually happening inside the network. Subnet emission shifts. Validator VTrust changes. On-chain registration events. Neuron burn rates. Alpha token price movements relative to TAO. All of it is live on Taostats right now. All of it is free. All of it tells you something the price chart cannot. The investors who understand Bittensor at the data layer will always be positioned ahead of the investors who understand it at the narrative layer. Taostats is the data layer. Bookmark it. Open it daily. The network is telling you exactly what it is doing if you know where to look.

2xnmore

156,550 views • 4 months ago

Announcing the DVM Terminal Presale! 01/ We are excited to formally announce the next step in our journey: our AI and Signal based trading Terminal. See ALL details on our website, including product, tech, deposit address, and tech documentation: Deposit Address (SOL only): 4pyVRFX56MdqtREcxWnf6XuEGfRNCQaKm1LA4xmHeccv By contributing, you agree to our Terms & Privacy Policy – full docs on site. 02/ We are building ‘DVM Terminal’, a signal and AI powered trading platform for the Solana trenches (initially). The first multi-agent AI trading terminal designed as an institutional-grade dashboard – turning market noise into actionable alpha with agent summaries, live signals, rigid filters, and a full multi-agent system. 03/ The problem. Trench hunting is far too inefficient with real data and insights lacking. - Dashboards are noisy (not even sortable), - No AI agents (in an AI world) - No narratives (a critical component to a thesis), - VERY limited signals (only DB/DS), - No advanced trading (no TP, SL, or VWAP), - No portfolio alert/management system post-trade etc. - The list goes on… Products from major competitors are all just homogeneous, even down to the 3-frame design. We have to piece everything together like broken lego blocks, building a weak matrix from existing platforms, X, FNFs, telegram and discord for little to no alpha. 04/ The solution & moat. We rebuild this from the ground up, leveraging signals and AI. - Clean institutional-like dashboards (we can sort and navigate thru a proper terminal, like Bloomberg or Messari) - AI agents (thank goodness for intelligence, distilling all the important info upfront across 2k+ tokens/day) - A Narrative engine (no need to ask “what is this token about?”; additionally, our engine can identify the newest metas like AI, ICM, Cards, etc.) - 100s of value-add Signals overlaid live on charts (momentum, smart money, sentiment, event data; all of it; tell us what’s happening in real-time) - Advanced trading system (finally, SL, TP, VWAP etc.) - Live portfolio monitoring (AI will give us pertinent live info on our holdings, so we can go live life and not look at screens all day) - All in one place. At a higher-level, our advantage will be managing the massive on/off-chain data pipeline being processed by thousands or millions of context-aware AI agents that recognize patterns, filter noise and deliver only the most actionable insights to a trader with which it can execute a trade effectively. 05/ The opportunity. The Industry leader on Solana makes $600m+ in fees annually, with total industry near $1b on Solana alone, according to Adam. Yet, the entire industry gives us total burnout, fragmented data, either little info or info overload, no real signals, no narratives, no personalized AI-driven strategies, and zero incentives (like buybacks or a flywheel). We’ll flip the script, designing a high-powered scalable signal and AI driven intelligence platform with a flywheel (50-100% fee buy-back & burn). Simply put, we want to be tops. 06/ Development. Our product is MVP. We are building this to scale beyond Solana, into multi-chain. V1 is expected in 4-6 weeks. Our approach to building is an open feedback loop with community members, building to the demands of our users. 07/ Pre-sale terms & Valuation. We are offering 50% public sale, with min $100, no max. Ending valuation is susceptible to change based on amount raised, but will be fixed at 2x raise - i.e. $1m raised=$2m val, $50m raised=$100m val. We are seeking to raise $25m on a $50m valuation, which represents 1% of Solana bot market-share. At TGE event, expect ~65% of our tokens to be floating (or outstanding), with 25% in treasury and 10% of the team allocation locked. Tokens are expected to be distributed just ahead of v1 rollout. Again, find more details on our webpage. 08/ Tailwinds. AI input costs are declining 90%/yr also, so the operational model could become very accretive over time, as we scale our tech to other chains. Solana outputs the most tokens (~35k per day), so we start here, where the challenge is the greatest. 09/ Advisors. Big thanks to our advisors, who’ve been part of this community since inception. Austin Barack, JK 🛡️, cryptic, Tachi, , ZoeyLoo and Chetan Badhe. 10/ The end. Thank you for your consideration; and make sure the SOL address posted here is the same as on our website.
0:52

Announcing the DVM Terminal Presale! 01/ We are excited to formally announce the next step in our journey: our AI and Signal based trading Terminal. See ALL details on our website, including product, tech, deposit address, and tech documentation: Deposit Address (SOL only): 4pyVRFX56MdqtREcxWnf6XuEGfRNCQaKm1LA4xmHeccv By contributing, you agree to our Terms & Privacy Policy – full docs on site. 02/ We are building ‘DVM Terminal’, a signal and AI powered trading platform for the Solana trenches (initially). The first multi-agent AI trading terminal designed as an institutional-grade dashboard – turning market noise into actionable alpha with agent summaries, live signals, rigid filters, and a full multi-agent system. 03/ The problem. Trench hunting is far too inefficient with real data and insights lacking. - Dashboards are noisy (not even sortable), - No AI agents (in an AI world) - No narratives (a critical component to a thesis), - VERY limited signals (only DB/DS), - No advanced trading (no TP, SL, or VWAP), - No portfolio alert/management system post-trade etc. - The list goes on… Products from major competitors are all just homogeneous, even down to the 3-frame design. We have to piece everything together like broken lego blocks, building a weak matrix from existing platforms, X, FNFs, telegram and discord for little to no alpha. 04/ The solution & moat. We rebuild this from the ground up, leveraging signals and AI. - Clean institutional-like dashboards (we can sort and navigate thru a proper terminal, like Bloomberg or Messari) - AI agents (thank goodness for intelligence, distilling all the important info upfront across 2k+ tokens/day) - A Narrative engine (no need to ask “what is this token about?”; additionally, our engine can identify the newest metas like AI, ICM, Cards, etc.) - 100s of value-add Signals overlaid live on charts (momentum, smart money, sentiment, event data; all of it; tell us what’s happening in real-time) - Advanced trading system (finally, SL, TP, VWAP etc.) - Live portfolio monitoring (AI will give us pertinent live info on our holdings, so we can go live life and not look at screens all day) - All in one place. At a higher-level, our advantage will be managing the massive on/off-chain data pipeline being processed by thousands or millions of context-aware AI agents that recognize patterns, filter noise and deliver only the most actionable insights to a trader with which it can execute a trade effectively. 05/ The opportunity. The Industry leader on Solana makes $600m+ in fees annually, with total industry near $1b on Solana alone, according to Adam. Yet, the entire industry gives us total burnout, fragmented data, either little info or info overload, no real signals, no narratives, no personalized AI-driven strategies, and zero incentives (like buybacks or a flywheel). We’ll flip the script, designing a high-powered scalable signal and AI driven intelligence platform with a flywheel (50-100% fee buy-back & burn). Simply put, we want to be tops. 06/ Development. Our product is MVP. We are building this to scale beyond Solana, into multi-chain. V1 is expected in 4-6 weeks. Our approach to building is an open feedback loop with community members, building to the demands of our users. 07/ Pre-sale terms & Valuation. We are offering 50% public sale, with min $100, no max. Ending valuation is susceptible to change based on amount raised, but will be fixed at 2x raise - i.e. $1m raised=$2m val, $50m raised=$100m val. We are seeking to raise $25m on a $50m valuation, which represents 1% of Solana bot market-share. At TGE event, expect ~65% of our tokens to be floating (or outstanding), with 25% in treasury and 10% of the team allocation locked. Tokens are expected to be distributed just ahead of v1 rollout. Again, find more details on our webpage. 08/ Tailwinds. AI input costs are declining 90%/yr also, so the operational model could become very accretive over time, as we scale our tech to other chains. Solana outputs the most tokens (~35k per day), so we start here, where the challenge is the greatest. 09/ Advisors. Big thanks to our advisors, who’ve been part of this community since inception. Austin Barack, JK 🛡️, cryptic, Tachi, , ZoeyLoo and Chetan Badhe. 10/ The end. Thank you for your consideration; and make sure the SOL address posted here is the same as on our website.

Deep Value Memetics

23,075 views • 11 months ago

Cerebras just IPO’d and the stock already ran up over 100% (Save this). For the entire 70 year history of the semiconductor industry, every company on earth has followed the same process. You take a dinner plate sized silicon wafer, put hundreds of tiny chips onto it, and dice it up like a pizza. Nvidia does it this way, AMD does it this way, Intel has done it this way for six decades and everyone who tried to break that convention failed. Until Cerebras asked the most annoyingly obvious question in the industry’s history, what if you just didn’t cut it? The result is the Wafer Scale Engine, a single chip 56 times larger than Nvidia’s H100 and it fundamentally changes the physics of how AI inference works. The reason this matters is not the size, it’s the bandwidth. Every time an AI model generates a single word, it has to reach into memory, pull weights, multiply them together, and produce a prediction and when you’re running millions of concurrent sessions at once, the bottleneck is not raw processing power but how fast data moves between memory and compute. Nvidia’s H100 moves data at roughly 3 terabytes per second, while Cerebras’ WSE-3 moves data at 21 petabytes per second, roughly 7,000 times faster because memory and compute live on the same enormous piece of silicon and data barely has to travel at all. That gap is exactly why OpenAI went from 150 tokens per second on traditional GPUs to 2,000 tokens per second on Cerebras hardware, and why AWS integrated Cerebras into Bedrock to deliver roughly 5x more inference capacity in the same physical footprint. The macro setup is making the trade even more urgent. South Korea DRAM export prices recently jumped 35%, flash memory surged 47%, and SSD pricing spiked nearly 140% and every single one of those increases hits Nvidia-based infrastructure directly, because the H100 requires 80GB of the most expensive, most contested memory in the AI supply chain. Cerebras’ WSE-3 uses zero external HBM memory, baking 44GB of SRAM directly into the wafer itself which means as memory pricing goes parabolic, every CFO evaluating AI infrastructure is suddenly looking much more seriously at the architecture that sidesteps that cost entirely. The demand is already showing up in the backlog. Cerebras ended 2025 with $24.6 billion in remaining performance obligations for a company doing just over $500 million in annual revenue, that is a number that implies years of contracted growth already sitting on the books. The IPO was 20x oversubscribed, the price range was raised twice before listing, and shares opened 89% above their listing price on a $5.55 billion raise that made it the largest semiconductor IPO in history. The risks are real and worth naming. 86% of 2025 revenue came from two entities with UAE ties, U.S. revenue actually fell 34% to $187 million, and the $20 billion OpenAI contract is conditional, if Cerebras misses delivery milestones, OpenAI can terminate and trigger repayment demands on a $1 billion loan facility. And yet the market is valuing Cerebras at roughly 91x trailing revenue, richer than Nvidia, AMD, and Arm combined. What investors are betting on is not that Cerebras beats Nvidia, it is that the inference supercycle is large enough to support an entirely different architecture optimized for a different workload, and that $24.6 billion in contracted backlog converts to diversified revenue before the market starts asking harder questions. CEO Andrew Feldman said this took a decade of late nights to get right, everyone who tried to copy it failed and given that the entire inference economy is now running through exactly the bottleneck Cerebras was built to eliminate, the market is starting to believe him.
2:04

Cerebras just IPO’d and the stock already ran up over 100% (Save this). For the entire 70 year history of the semiconductor industry, every company on earth has followed the same process. You take a dinner plate sized silicon wafer, put hundreds of tiny chips onto it, and dice it up like a pizza. Nvidia does it this way, AMD does it this way, Intel has done it this way for six decades and everyone who tried to break that convention failed. Until Cerebras asked the most annoyingly obvious question in the industry’s history, what if you just didn’t cut it? The result is the Wafer Scale Engine, a single chip 56 times larger than Nvidia’s H100 and it fundamentally changes the physics of how AI inference works. The reason this matters is not the size, it’s the bandwidth. Every time an AI model generates a single word, it has to reach into memory, pull weights, multiply them together, and produce a prediction and when you’re running millions of concurrent sessions at once, the bottleneck is not raw processing power but how fast data moves between memory and compute. Nvidia’s H100 moves data at roughly 3 terabytes per second, while Cerebras’ WSE-3 moves data at 21 petabytes per second, roughly 7,000 times faster because memory and compute live on the same enormous piece of silicon and data barely has to travel at all. That gap is exactly why OpenAI went from 150 tokens per second on traditional GPUs to 2,000 tokens per second on Cerebras hardware, and why AWS integrated Cerebras into Bedrock to deliver roughly 5x more inference capacity in the same physical footprint. The macro setup is making the trade even more urgent. South Korea DRAM export prices recently jumped 35%, flash memory surged 47%, and SSD pricing spiked nearly 140% and every single one of those increases hits Nvidia-based infrastructure directly, because the H100 requires 80GB of the most expensive, most contested memory in the AI supply chain. Cerebras’ WSE-3 uses zero external HBM memory, baking 44GB of SRAM directly into the wafer itself which means as memory pricing goes parabolic, every CFO evaluating AI infrastructure is suddenly looking much more seriously at the architecture that sidesteps that cost entirely. The demand is already showing up in the backlog. Cerebras ended 2025 with $24.6 billion in remaining performance obligations for a company doing just over $500 million in annual revenue, that is a number that implies years of contracted growth already sitting on the books. The IPO was 20x oversubscribed, the price range was raised twice before listing, and shares opened 89% above their listing price on a $5.55 billion raise that made it the largest semiconductor IPO in history. The risks are real and worth naming. 86% of 2025 revenue came from two entities with UAE ties, U.S. revenue actually fell 34% to $187 million, and the $20 billion OpenAI contract is conditional, if Cerebras misses delivery milestones, OpenAI can terminate and trigger repayment demands on a $1 billion loan facility. And yet the market is valuing Cerebras at roughly 91x trailing revenue, richer than Nvidia, AMD, and Arm combined. What investors are betting on is not that Cerebras beats Nvidia, it is that the inference supercycle is large enough to support an entirely different architecture optimized for a different workload, and that $24.6 billion in contracted backlog converts to diversified revenue before the market starts asking harder questions. CEO Andrew Feldman said this took a decade of late nights to get right, everyone who tried to copy it failed and given that the entire inference economy is now running through exactly the bottleneck Cerebras was built to eliminate, the market is starting to believe him.

Milk Road AI

30,441 views • 4 months ago

OpenLedger X Morpheus The partnership of openledger with Morpheus enables Use Morpheus to build "The Autonomous Smart Contract Engineer" on top of OpenLedger. What is Morpheus? Morpheus is a Web3-native AI coding agent that turns natural language into executable smart contracts and full-stack dApps. It is powered by a specialized Solidity model built on top of OpenLedger, tailored for the unique demands of secure and efficient onchain development. It goes beyond code generation. Using fine-tuned models, agent-based architecture, and modular plugin support, Morpheus automates the entire development pipeline-from writing and simulating contracts to deploying and maintaining them. Its mission is to reduce the barrier to dApp creation while enabling autonomous agents and individuals to participate in decentralized economies. Why OpenLedger? The rise of AI agents in Web3 raises urgent questions around transparency, attribution, explainability, and contributor incentives. OpenLedger provides the infrastructure to ensure that contributor data used in model outputs is recorded with verifiable attribution. Through Proof of Attribution, contributors-whether they provide prompts, datasets, or logic refinements-can receive credit and rewards when their work influences model behavior. But attribution alone isn’t enough. In critical domains like smart contract deployment, DeFi automation, and DAO governance, understanding why a model made a decision is just as important as the output itself. OpenLedger supports explainability by linking outputs back to their original data sources-allowing developers and auditors to trace logic, validate decisions, and build trust in AI-powered systems. OpenLedger supports Morpheus by: Recording which data was used in generating model outputs Enabling verifiable attribution of contributed datasets Powering reward mechanisms for contributors Offering scalable and efficient model execution via OpenLoRA Supporting transparency and traceability in model decision-making This creates an open, rewardable foundation for AI-driven coding-without relying on opaque systems. How is the system built? The Morpheus architecture has three layers: Datanet Layer OpenLedger powers Morpheus with a specialized Datanet - a decentralized data layer where developers, auditors, and contributors can share smart contract patterns, audit logs, exploit reports, and logic modules. Each submission is recorded onchain with attribution using OpenLedger’s Proof of Attribution. As the model learns and evolves from this data, contributors receive rewards proportional to their impact on future outputs. The Morpheus architecture has two layers: Intent Layer Users describe what they want to build. Example: "Create a token with tax logic that routes to a DAO." Morpheus parses the instruction, retrieves relevant contract types, and plans a modular execution flow. Agent Layer The agent generates, tests, and assembles the contract. It handles versioning, logic validation, and deployment readiness. Security checks-reentrancy protection, overflow control, gas modeling-are embedded into the generation phase. Generated outputs are mapped to their source data using OpenLedger’s Proof of Attribution, providing traceability across the pipeline. How does the AI model work? Morpheus is being powered by a specialized Solidity model built on top of OpenLedger. This model is purpose-built to handle the nuances of smart contract logic, security, and upgradeability. Unlike generalized coding agents, it is designed specifically for EVM environments and Web3 use cases, drawing from real protocol data and security best practices. Morpheus is fine-tuned on a vertical stack of smart contract data: Audited protocol code (e.g., Uniswap V4, Compound) OpenZeppelin libraries and EIP reference implementations Smart contract vulnerability reports and exploit reconstructions Edge cases from fuzz testing and adversarial examples It uses models like CodeLlama and DeepSeek-Coder, enhanced through RAG pipelines referencing standardized security patterns and emerging protocol designs. This training stack is integrated into a continuous feedback loop, enabling real-time specialization for EVM and beyond. Why a specialized model is needed? Smart contract development is uniquely high-stakes. A generalized AI model is not enough. As 'vibe coding' and natural language programming become more common, we're seeing an influx of AI-generated code in Web3 as well. But smart contracts are not frontends or prototypes-they govern real value, enforce trustless execution, and often become immutable after deployment. Billions have been lost in Web3 due to bugs and inefficiencies: In 2022 alone, over $3.8 billion was stolen due to smart contract exploits, many of which stemmed from avoidable issues like reentrancy, integer overflows, or access control failures. Inefficient contract structures lead to unnecessary gas consumption. Optimizing for gas can reduce costs by up to 40%, saving projects millions over time. Upgradeable contract patterns, like UUPS or Transparent Proxies, require strict adherence to storage layout and initialization rules. Mistakes here often go undetected by generic models and can render a contract unupgradeable or vulnerable. A specialized Solidity model is trained on real-world exploits, EIP standards, and libraries like OpenZeppelin to: Generate secure, gas-efficient code by default Recognize and correctly implement complex proxy patterns Map user intent to modular, auditable contract architectures Incorporate battle-tested logic from audited protocols and fuzz-tested edge cases Morpheus goes beyond syntax-it understands the nuances of decentralized infrastructure and deploys code that meets production-grade standards. What applications will this enable Token creation with built-in logic (tax, liquidity, governance) DeFi automations triggered by market conditions Payment contracts between agents and contributors DAO tooling with dynamic NFT-based voting Cross-chain bridging logic tied to real-world oracles Asset issuance flows through chat-based interfaces Natural language contract templates with reusable logic Each of these flows is backed by OpenLedger’s Proof of Attribution-ensuring traceability, explainability, and fair rewards across the ecosystem. This is the future of AI-native development. Open. Attributed. Explainable. Community-powered. Morpheus and OpenLedger are building the first system for autonomous coding agents where: Contributor work is recorded onchain Reuse is incentivized through attribution Model outputs are traceable and explainable Contracts evolve through human-agent collaboration Anyone can contribute prompts, logic, or flows-and get rewarded The smart contract engineer is no longer a human-only role. It is an agentic, decentralized, and transparent process-powered by OpenLedger.
0:28

OpenLedger X Morpheus The partnership of openledger with Morpheus enables Use Morpheus to build "The Autonomous Smart Contract Engineer" on top of OpenLedger. What is Morpheus? Morpheus is a Web3-native AI coding agent that turns natural language into executable smart contracts and full-stack dApps. It is powered by a specialized Solidity model built on top of OpenLedger, tailored for the unique demands of secure and efficient onchain development. It goes beyond code generation. Using fine-tuned models, agent-based architecture, and modular plugin support, Morpheus automates the entire development pipeline-from writing and simulating contracts to deploying and maintaining them. Its mission is to reduce the barrier to dApp creation while enabling autonomous agents and individuals to participate in decentralized economies. Why OpenLedger? The rise of AI agents in Web3 raises urgent questions around transparency, attribution, explainability, and contributor incentives. OpenLedger provides the infrastructure to ensure that contributor data used in model outputs is recorded with verifiable attribution. Through Proof of Attribution, contributors-whether they provide prompts, datasets, or logic refinements-can receive credit and rewards when their work influences model behavior. But attribution alone isn’t enough. In critical domains like smart contract deployment, DeFi automation, and DAO governance, understanding why a model made a decision is just as important as the output itself. OpenLedger supports explainability by linking outputs back to their original data sources-allowing developers and auditors to trace logic, validate decisions, and build trust in AI-powered systems. OpenLedger supports Morpheus by: Recording which data was used in generating model outputs Enabling verifiable attribution of contributed datasets Powering reward mechanisms for contributors Offering scalable and efficient model execution via OpenLoRA Supporting transparency and traceability in model decision-making This creates an open, rewardable foundation for AI-driven coding-without relying on opaque systems. How is the system built? The Morpheus architecture has three layers: Datanet Layer OpenLedger powers Morpheus with a specialized Datanet - a decentralized data layer where developers, auditors, and contributors can share smart contract patterns, audit logs, exploit reports, and logic modules. Each submission is recorded onchain with attribution using OpenLedger’s Proof of Attribution. As the model learns and evolves from this data, contributors receive rewards proportional to their impact on future outputs. The Morpheus architecture has two layers: Intent Layer Users describe what they want to build. Example: "Create a token with tax logic that routes to a DAO." Morpheus parses the instruction, retrieves relevant contract types, and plans a modular execution flow. Agent Layer The agent generates, tests, and assembles the contract. It handles versioning, logic validation, and deployment readiness. Security checks-reentrancy protection, overflow control, gas modeling-are embedded into the generation phase. Generated outputs are mapped to their source data using OpenLedger’s Proof of Attribution, providing traceability across the pipeline. How does the AI model work? Morpheus is being powered by a specialized Solidity model built on top of OpenLedger. This model is purpose-built to handle the nuances of smart contract logic, security, and upgradeability. Unlike generalized coding agents, it is designed specifically for EVM environments and Web3 use cases, drawing from real protocol data and security best practices. Morpheus is fine-tuned on a vertical stack of smart contract data: Audited protocol code (e.g., Uniswap V4, Compound) OpenZeppelin libraries and EIP reference implementations Smart contract vulnerability reports and exploit reconstructions Edge cases from fuzz testing and adversarial examples It uses models like CodeLlama and DeepSeek-Coder, enhanced through RAG pipelines referencing standardized security patterns and emerging protocol designs. This training stack is integrated into a continuous feedback loop, enabling real-time specialization for EVM and beyond. Why a specialized model is needed? Smart contract development is uniquely high-stakes. A generalized AI model is not enough. As 'vibe coding' and natural language programming become more common, we're seeing an influx of AI-generated code in Web3 as well. But smart contracts are not frontends or prototypes-they govern real value, enforce trustless execution, and often become immutable after deployment. Billions have been lost in Web3 due to bugs and inefficiencies: In 2022 alone, over $3.8 billion was stolen due to smart contract exploits, many of which stemmed from avoidable issues like reentrancy, integer overflows, or access control failures. Inefficient contract structures lead to unnecessary gas consumption. Optimizing for gas can reduce costs by up to 40%, saving projects millions over time. Upgradeable contract patterns, like UUPS or Transparent Proxies, require strict adherence to storage layout and initialization rules. Mistakes here often go undetected by generic models and can render a contract unupgradeable or vulnerable. A specialized Solidity model is trained on real-world exploits, EIP standards, and libraries like OpenZeppelin to: Generate secure, gas-efficient code by default Recognize and correctly implement complex proxy patterns Map user intent to modular, auditable contract architectures Incorporate battle-tested logic from audited protocols and fuzz-tested edge cases Morpheus goes beyond syntax-it understands the nuances of decentralized infrastructure and deploys code that meets production-grade standards. What applications will this enable Token creation with built-in logic (tax, liquidity, governance) DeFi automations triggered by market conditions Payment contracts between agents and contributors DAO tooling with dynamic NFT-based voting Cross-chain bridging logic tied to real-world oracles Asset issuance flows through chat-based interfaces Natural language contract templates with reusable logic Each of these flows is backed by OpenLedger’s Proof of Attribution-ensuring traceability, explainability, and fair rewards across the ecosystem. This is the future of AI-native development. Open. Attributed. Explainable. Community-powered. Morpheus and OpenLedger are building the first system for autonomous coding agents where: Contributor work is recorded onchain Reuse is incentivized through attribution Model outputs are traceable and explainable Contracts evolve through human-agent collaboration Anyone can contribute prompts, logic, or flows-and get rewarded The smart contract engineer is no longer a human-only role. It is an agentic, decentralized, and transparent process-powered by OpenLedger.

OpenLedger

46,944 views • 1 year ago

PHOTON COUNTING CT is NOT a better CT It is a NEW imaging modality Photon Counting CT (PCCT) represents a transformative leap in medical imaging, not only as a molecular imaging modality but also as a technology offering ultra-high resolution and functional imaging capabilities. It is fundamentally more than just an enhanced version of traditional CT—PCCT introduces new ways of seeing and understanding the human body, providing critical insights at the molecular, structural, and functional levels. This positions PCCT as a unique imaging modality that requires a fresh approach to technical implementation, operational workflows, and financial planning. Despite the larger upfront investment, PCCT’s ability to drastically reduce downstream healthcare costs makes it a highly valuable investment in the long run. 1. Technical Innovations • Molecular Imaging and Energy Discrimination: Unlike traditional CT, which simply measures the total absorbed energy, PCCT counts individual X-ray photons and differentiates their energy levels. This allows for precise molecular imaging, revealing the composition of tissues and materials at a biochemical level. By distinguishing between different tissue types and contrast agents, PCCT opens up new diagnostic possibilities, such as identifying molecular biomarkers in tumors or distinguishing between stable and unstable plaque in coronary arteries. This capability shifts the focus of imaging from purely anatomical to both anatomical and molecular, offering more comprehensive diagnostic information. • Ultra-High Spatial Resolution: PCCT features significantly smaller detector elements compared to conventional CT scanners, allowing for ultra-high resolution imaging. This means clinicians can visualize fine structures such as microcalcifications in arteries, small lesions in soft tissues, or the intricate architecture of bones. This level of detail was previously unattainable with traditional CT. When combined with molecular imaging, this ultra-high resolution allows for the precise localization and characterization of disease at very early stages, which is essential for early diagnosis and intervention. • Functional Imaging Capabilities: PCCT also excels as a functional imaging modality. By capturing energy-resolved information, PCCT can provide insights into tissue functionality and dynamic physiological processes. For instance, it can detect changes in blood flow, tissue perfusion, and oxygenation without the need for additional contrast agents or scans. This functionality allows for real-time assessment of physiological processes, making it particularly valuable in cardiology, oncology, and neurology for evaluating organ function and monitoring disease progression. • Reduced Noise and Artifact Reduction: Photon-counting technology dramatically reduces electronic noise and imaging artifacts, such as beam hardening, resulting in clearer and more accurate images. The ability to deliver ultra-high resolution images with minimal artifacts improves diagnostic accuracy, reducing the need for repeat scans and ensuring that even subtle abnormalities are detected. 2. Operational Considerations • New Workflow for Molecular, High-Resolution, and Functional Imaging: The integration of molecular, ultra-high resolution, and functional imaging into routine clinical workflows introduces complexity that requires adaptation. Radiologists and technicians need specialized training to interpret and analyze multi-energy datasets that include molecular and functional information. PCCT produces a vast amount of detailed data, requiring clinicians to adopt new imaging protocols and refine their diagnostic approaches to fully leverage its capabilities. • Post-Processing and Data Management: PCCT generates richer, more complex datasets, which necessitates advanced post-processing tools and data management systems. Existing PACS and imaging software may not be equipped to handle such large volumes of data or to process functional and molecular information effectively. This means healthcare institutions must invest in robust IT infrastructure, including upgraded software and storage solutions, as well as provide additional training for staff on new imaging analysis techniques. • Revised Clinical Protocols: The molecular, functional, and ultra-high resolution imaging capabilities of PCCT will likely prompt changes in clinical protocols. For instance, the need for contrast agents may be reduced, simplifying patient preparation and decreasing the risk of adverse reactions. Additionally, the ability to monitor physiological functions in real-time through functional imaging could lead to more dynamic diagnostic procedures, such as assessing the effectiveness of interventions or treatments in real-time. 3. Financial Impact • Higher Initial Investment: PCCT systems are more expensive than traditional CT scanners due to their advanced technology, which includes photon-counting detectors and the computational power required for high-resolution, molecular, and functional imaging. While this upfront cost is significant, it is crucial to view it in the broader context of the downstream benefits and cost reductions that PCCT offers. • Downstream Cost Reductions: Although the initial capital investment is higher, PCCT’s ability to combine molecular, functional, and ultra-high resolution imaging leads to substantial reductions in downstream healthcare costs. Its superior diagnostic accuracy minimizes the need for follow-up tests, repeat scans, or invasive diagnostic procedures, such as diagnostic coronary angiographies. For example, in cardiology, PCCT can precisely differentiate between types of coronary plaque, reducing the need for invasive procedures to assess risk. • Lower Overall Healthcare Expenditures: By enabling earlier, more accurate diagnoses, PCCT can reduce the overall cost of patient care. Early detection of disease, particularly through its molecular and functional imaging capabilities, allows for more targeted treatments, potentially preventing the need for more aggressive and expensive interventions down the line. For instance, early-stage tumor detection via molecular imaging could lead to less invasive treatments, reducing hospital stays and improving patient outcomes, ultimately driving down healthcare costs. • Increased ROI Through Enhanced Patient Outcomes: Over time, the combination of molecular, functional, and ultra-high resolution imaging enhances diagnostic precision, which translates into better patient outcomes. Improved diagnostic accuracy reduces the incidence of unnecessary procedures, minimizes treatment delays, and results in more personalized and effective care. This leads to increased patient satisfaction, better healthcare outcomes, and greater patient throughput—all factors that improve the institution’s return on investment (ROI). • Competitive Advantage and New Revenue Streams: By adopting PCCT, healthcare institutions position themselves at the forefront of advanced imaging technologies. The ability to offer molecular, functional, and ultra-high resolution imaging creates a competitive advantage, attracting more complex and high-value cases. This can boost the institution’s reputation for excellence in diagnostics, leading to increased referrals, new patient populations, and expanded revenue opportunities. Summary Photon Counting CT (PCCT) is not just an evolution of existing CT technology—it is a molecular, ultra-high resolution, and functional imaging modality that fundamentally transforms the diagnostic landscape. Its ability to capture detailed molecular data, visualize minute anatomical structures with ultra-high resolution, and provide real-time functional imaging opens new possibilities for earlier and more precise diagnoses. While the financial investment in PCCT is larger, the reduction in downstream healthcare costs through improved diagnostic accuracy, fewer unnecessary interventions, and earlier disease detection far outweighs the initial expense. For institutions committed to advancing patient care and improving long-term financial outcomes, PCCT is an essential investment in the future of medical imaging. The video attached shows a patient accessing the Hospital for ACS. PCCT can provide ALL the imaging information of the concurrent imaging modalities (CXR, CAG, Echo, CMR) that you see around it... that's a lot! #PhotonCountingCT #MolecularImaging #UltraHighResolution #FunctionalImaging #FutureOfImaging #AdvancedMedicalImaging #EarlyDiseaseDetection #InnovativeCT #CuttingEdgeHealthcare #PrecisionDiagnostics #HealthcareInnovation #MedicalTechnology #CostEffectiveImaging #NextGenCT #PatientCareRevolution
1:10

PHOTON COUNTING CT is NOT a better CT It is a NEW imaging modality Photon Counting CT (PCCT) represents a transformative leap in medical imaging, not only as a molecular imaging modality but also as a technology offering ultra-high resolution and functional imaging capabilities. It is fundamentally more than just an enhanced version of traditional CT—PCCT introduces new ways of seeing and understanding the human body, providing critical insights at the molecular, structural, and functional levels. This positions PCCT as a unique imaging modality that requires a fresh approach to technical implementation, operational workflows, and financial planning. Despite the larger upfront investment, PCCT’s ability to drastically reduce downstream healthcare costs makes it a highly valuable investment in the long run. 1. Technical Innovations • Molecular Imaging and Energy Discrimination: Unlike traditional CT, which simply measures the total absorbed energy, PCCT counts individual X-ray photons and differentiates their energy levels. This allows for precise molecular imaging, revealing the composition of tissues and materials at a biochemical level. By distinguishing between different tissue types and contrast agents, PCCT opens up new diagnostic possibilities, such as identifying molecular biomarkers in tumors or distinguishing between stable and unstable plaque in coronary arteries. This capability shifts the focus of imaging from purely anatomical to both anatomical and molecular, offering more comprehensive diagnostic information. • Ultra-High Spatial Resolution: PCCT features significantly smaller detector elements compared to conventional CT scanners, allowing for ultra-high resolution imaging. This means clinicians can visualize fine structures such as microcalcifications in arteries, small lesions in soft tissues, or the intricate architecture of bones. This level of detail was previously unattainable with traditional CT. When combined with molecular imaging, this ultra-high resolution allows for the precise localization and characterization of disease at very early stages, which is essential for early diagnosis and intervention. • Functional Imaging Capabilities: PCCT also excels as a functional imaging modality. By capturing energy-resolved information, PCCT can provide insights into tissue functionality and dynamic physiological processes. For instance, it can detect changes in blood flow, tissue perfusion, and oxygenation without the need for additional contrast agents or scans. This functionality allows for real-time assessment of physiological processes, making it particularly valuable in cardiology, oncology, and neurology for evaluating organ function and monitoring disease progression. • Reduced Noise and Artifact Reduction: Photon-counting technology dramatically reduces electronic noise and imaging artifacts, such as beam hardening, resulting in clearer and more accurate images. The ability to deliver ultra-high resolution images with minimal artifacts improves diagnostic accuracy, reducing the need for repeat scans and ensuring that even subtle abnormalities are detected. 2. Operational Considerations • New Workflow for Molecular, High-Resolution, and Functional Imaging: The integration of molecular, ultra-high resolution, and functional imaging into routine clinical workflows introduces complexity that requires adaptation. Radiologists and technicians need specialized training to interpret and analyze multi-energy datasets that include molecular and functional information. PCCT produces a vast amount of detailed data, requiring clinicians to adopt new imaging protocols and refine their diagnostic approaches to fully leverage its capabilities. • Post-Processing and Data Management: PCCT generates richer, more complex datasets, which necessitates advanced post-processing tools and data management systems. Existing PACS and imaging software may not be equipped to handle such large volumes of data or to process functional and molecular information effectively. This means healthcare institutions must invest in robust IT infrastructure, including upgraded software and storage solutions, as well as provide additional training for staff on new imaging analysis techniques. • Revised Clinical Protocols: The molecular, functional, and ultra-high resolution imaging capabilities of PCCT will likely prompt changes in clinical protocols. For instance, the need for contrast agents may be reduced, simplifying patient preparation and decreasing the risk of adverse reactions. Additionally, the ability to monitor physiological functions in real-time through functional imaging could lead to more dynamic diagnostic procedures, such as assessing the effectiveness of interventions or treatments in real-time. 3. Financial Impact • Higher Initial Investment: PCCT systems are more expensive than traditional CT scanners due to their advanced technology, which includes photon-counting detectors and the computational power required for high-resolution, molecular, and functional imaging. While this upfront cost is significant, it is crucial to view it in the broader context of the downstream benefits and cost reductions that PCCT offers. • Downstream Cost Reductions: Although the initial capital investment is higher, PCCT’s ability to combine molecular, functional, and ultra-high resolution imaging leads to substantial reductions in downstream healthcare costs. Its superior diagnostic accuracy minimizes the need for follow-up tests, repeat scans, or invasive diagnostic procedures, such as diagnostic coronary angiographies. For example, in cardiology, PCCT can precisely differentiate between types of coronary plaque, reducing the need for invasive procedures to assess risk. • Lower Overall Healthcare Expenditures: By enabling earlier, more accurate diagnoses, PCCT can reduce the overall cost of patient care. Early detection of disease, particularly through its molecular and functional imaging capabilities, allows for more targeted treatments, potentially preventing the need for more aggressive and expensive interventions down the line. For instance, early-stage tumor detection via molecular imaging could lead to less invasive treatments, reducing hospital stays and improving patient outcomes, ultimately driving down healthcare costs. • Increased ROI Through Enhanced Patient Outcomes: Over time, the combination of molecular, functional, and ultra-high resolution imaging enhances diagnostic precision, which translates into better patient outcomes. Improved diagnostic accuracy reduces the incidence of unnecessary procedures, minimizes treatment delays, and results in more personalized and effective care. This leads to increased patient satisfaction, better healthcare outcomes, and greater patient throughput—all factors that improve the institution’s return on investment (ROI). • Competitive Advantage and New Revenue Streams: By adopting PCCT, healthcare institutions position themselves at the forefront of advanced imaging technologies. The ability to offer molecular, functional, and ultra-high resolution imaging creates a competitive advantage, attracting more complex and high-value cases. This can boost the institution’s reputation for excellence in diagnostics, leading to increased referrals, new patient populations, and expanded revenue opportunities. Summary Photon Counting CT (PCCT) is not just an evolution of existing CT technology—it is a molecular, ultra-high resolution, and functional imaging modality that fundamentally transforms the diagnostic landscape. Its ability to capture detailed molecular data, visualize minute anatomical structures with ultra-high resolution, and provide real-time functional imaging opens new possibilities for earlier and more precise diagnoses. While the financial investment in PCCT is larger, the reduction in downstream healthcare costs through improved diagnostic accuracy, fewer unnecessary interventions, and earlier disease detection far outweighs the initial expense. For institutions committed to advancing patient care and improving long-term financial outcomes, PCCT is an essential investment in the future of medical imaging. The video attached shows a patient accessing the Hospital for ACS. PCCT can provide ALL the imaging information of the concurrent imaging modalities (CXR, CAG, Echo, CMR) that you see around it... that's a lot! #PhotonCountingCT #MolecularImaging #UltraHighResolution #FunctionalImaging #FutureOfImaging #AdvancedMedicalImaging #EarlyDiseaseDetection #InnovativeCT #CuttingEdgeHealthcare #PrecisionDiagnostics #HealthcareInnovation #MedicalTechnology #CostEffectiveImaging #NextGenCT #PatientCareRevolution

Dr. Filippo Cademartiri

11,849 views • 1 year ago

Ahmedabad Crime Branch is making use of technical measures to avoid any stampede kind of situation. Anti stampede visual analytics,using reference area and crowd movement, head count algorithm. Anti-stampede algorithms on CCTV cameras are a crucial advancement in crowd management, leveraging AI and image processing to prevent dangerous situations in densely populated areas. Here's a breakdown of their usage: How they work: Real-time monitoring: AI-powered CCTV cameras continuously analyze video streams in real-time. Crowd density estimation: Algorithms calculate the number of people in a given area. This can involve: Pixel-based analysis: Converting images to black and white and counting "black pixels" (representing people). Object detection: Using machine learning models (like Mask R-CNN) to identify and count individuals, often by detecting heads or torsos. Thresholding: Pre-defined "threshold values" for crowd density are established. When the detected density crosses these thresholds, it triggers an alert. Anomaly detection: Beyond just density, these algorithms can identify unusual crowd behaviors such as: * Sudden surges in movement. * Unusual clustering patterns. * Fallen individuals. * Aggressive movements. Alerting authorities: Upon detecting a potential stampede risk, the system sends immediate alerts to security personnel or control rooms via LCD displays, GSM messages, or other communication channels. Predictive analytics: Some advanced systems use time-series prediction models to forecast crowd behavior and dynamics based on historical and real-time data, helping anticipate potential bottlenecks or overcrowding. Reinforcement learning: Algorithms can learn from past incidents to suggest optimal crowd flow routes and alternative evacuation paths during emergencies. Benefits: Proactive prevention: The primary benefit is the ability to detect and warn of potential stampedes before they occur, allowing authorities to take preventative measures. Real-time insights: Provides immediate and accurate data on crowd density and movement, far surpassing manual observation. Enhanced safety: Significantly improves safety in public spaces by reducing human error and enabling swift responses to risks. Optimized resource allocation: Helps in better deployment of security personnel and resources to areas with high crowd density. Improved efficiency: Automates a labor-intensive task, freeing up human operators for more complex decision-making. Data for future planning: The collected data can be analyzed to improve crowd management strategies for future events. Challenges: Accuracy limitations: While advanced, AI algorithms can still face challenges with: Occlusion: People blocking each other, making accurate counting difficult. Varying conditions: Changes in lighting, weather, and camera angles can affect accuracy. Bias in training data: Can lead to false positives or inaccurate detections. Computational complexity and cost: Developing and deploying such systems can be expensive due to the need for high-resolution cameras, powerful processing units, and sophisticated algorithms. Data privacy and ethical concerns: The extensive use of CCTV and AI raises concerns about individual privacy and potential misuse of data. Integration with existing infrastructure: Integrating new AI-powered systems with older CCTV networks can be complex. Human intervention still crucial: While AI can alert, human responders are still essential for effective intervention and crowd dispersal. As seen in the Kumbh Mela example, even with AI alerts, a lack of ground personnel can limit effectiveness. Defining thresholds: Determining appropriate crowd density thresholds for different environments and cultural contexts can be challenging. Real-world applications: Large public gatherings: Religious festivals (like the Kumbh Mela in India, which has used AI for crowd management), concerts, sports events, and political rallies. Transportation hubs: Railway stations, airports, and bus terminals to manage passenger flow. Shopping malls and commercial centers: To monitor crowd density during peak hours and special events. Stadiums and arenas: For managing ingress, egress, and crowd movement during events. Tourist attractions: To prevent overcrowding at popular sites. Overall, anti-stampede algorithms on CCTV cameras represent a significant leap forward in ensuring public safety, offering a powerful tool for proactive crowd management. However, their successful implementation requires careful consideration of technological limitations, ethical implications, and the continued need for effective human intervention. Ahmedabad Police અમદાવાદ પોલીસ Vijay Patel | Megh Updates 🚨™ | Akash Anand | | #BengaluruStampede | #Stampede
0:37

Ahmedabad Crime Branch is making use of technical measures to avoid any stampede kind of situation. Anti stampede visual analytics,using reference area and crowd movement, head count algorithm. Anti-stampede algorithms on CCTV cameras are a crucial advancement in crowd management, leveraging AI and image processing to prevent dangerous situations in densely populated areas. Here's a breakdown of their usage: How they work: Real-time monitoring: AI-powered CCTV cameras continuously analyze video streams in real-time. Crowd density estimation: Algorithms calculate the number of people in a given area. This can involve: Pixel-based analysis: Converting images to black and white and counting "black pixels" (representing people). Object detection: Using machine learning models (like Mask R-CNN) to identify and count individuals, often by detecting heads or torsos. Thresholding: Pre-defined "threshold values" for crowd density are established. When the detected density crosses these thresholds, it triggers an alert. Anomaly detection: Beyond just density, these algorithms can identify unusual crowd behaviors such as: * Sudden surges in movement. * Unusual clustering patterns. * Fallen individuals. * Aggressive movements. Alerting authorities: Upon detecting a potential stampede risk, the system sends immediate alerts to security personnel or control rooms via LCD displays, GSM messages, or other communication channels. Predictive analytics: Some advanced systems use time-series prediction models to forecast crowd behavior and dynamics based on historical and real-time data, helping anticipate potential bottlenecks or overcrowding. Reinforcement learning: Algorithms can learn from past incidents to suggest optimal crowd flow routes and alternative evacuation paths during emergencies. Benefits: Proactive prevention: The primary benefit is the ability to detect and warn of potential stampedes before they occur, allowing authorities to take preventative measures. Real-time insights: Provides immediate and accurate data on crowd density and movement, far surpassing manual observation. Enhanced safety: Significantly improves safety in public spaces by reducing human error and enabling swift responses to risks. Optimized resource allocation: Helps in better deployment of security personnel and resources to areas with high crowd density. Improved efficiency: Automates a labor-intensive task, freeing up human operators for more complex decision-making. Data for future planning: The collected data can be analyzed to improve crowd management strategies for future events. Challenges: Accuracy limitations: While advanced, AI algorithms can still face challenges with: Occlusion: People blocking each other, making accurate counting difficult. Varying conditions: Changes in lighting, weather, and camera angles can affect accuracy. Bias in training data: Can lead to false positives or inaccurate detections. Computational complexity and cost: Developing and deploying such systems can be expensive due to the need for high-resolution cameras, powerful processing units, and sophisticated algorithms. Data privacy and ethical concerns: The extensive use of CCTV and AI raises concerns about individual privacy and potential misuse of data. Integration with existing infrastructure: Integrating new AI-powered systems with older CCTV networks can be complex. Human intervention still crucial: While AI can alert, human responders are still essential for effective intervention and crowd dispersal. As seen in the Kumbh Mela example, even with AI alerts, a lack of ground personnel can limit effectiveness. Defining thresholds: Determining appropriate crowd density thresholds for different environments and cultural contexts can be challenging. Real-world applications: Large public gatherings: Religious festivals (like the Kumbh Mela in India, which has used AI for crowd management), concerts, sports events, and political rallies. Transportation hubs: Railway stations, airports, and bus terminals to manage passenger flow. Shopping malls and commercial centers: To monitor crowd density during peak hours and special events. Stadiums and arenas: For managing ingress, egress, and crowd movement during events. Tourist attractions: To prevent overcrowding at popular sites. Overall, anti-stampede algorithms on CCTV cameras represent a significant leap forward in ensuring public safety, offering a powerful tool for proactive crowd management. However, their successful implementation requires careful consideration of technological limitations, ethical implications, and the continued need for effective human intervention. Ahmedabad Police અમદાવાદ પોલીસ Vijay Patel | Megh Updates 🚨™ | Akash Anand | | #BengaluruStampede | #Stampede

Janak Dave

339,758 views • 1 year ago

These IPOs Aren’t About Markets They’re About National Security Architecture This looks like a standard tech and markets story. Big IPOs. Trillion dollar valuations. SpaceX, OpenAI, Anthropic. Maybe even a mega merger tying SpaceX to Tesla and xAI. The easy takeaway is familiar..visionary founders, breakthrough products, capital finally catching up to innovation. That framing is comfortable. It’s also incomplete. What’s Actually Being Priced These companies aren’t being valued just for products or future cash flows. They’re being valued because they now sit close to the core of U.S. national security architecture. Launch capacity. Global communications. AI systems feeding into defense, intelligence, and government workflows. Infrastructure the state increasingly depends on, rather than merely contracts with. When markets put trillion dollar numbers on these platforms, they’re not just betting on growth. They’re betting on how deeply embedded these systems have become inside the machinery of American power and how hard they would be to replace. Power Is Being Rerouted This isn’t about the state giving up authority. It’s about how power is exercised when trust in institutions erodes and bureaucracy can’t move at the speed of competition. In the old model, power flowed through public institutions and legacy defense contractors, governed by long timelines and rigid process. In the current model, speed matters more than formality. Adaptability matters more than ownership. So the state reroutes. It embeds sovereign functions..launch, communications, data processing, AI driven analysis inside private platforms that already move fast, scale globally, and can be adjusted without public friction. That shift isn’t ideological. It’s structural. Why Musk Sits At The Center This isn’t just about one entrepreneur. Amazon has cloud. Microsoft runs enterprise software. Palantir handles data. The pattern is widespread. What makes Musk different is concentration. SpaceX isn’t just a launch provider; it’s increasingly how the U.S. gets to orbit at scale. Starlink and Starshield aren’t just connectivity; they form a global communications layer that shows up in real conflicts. And if xAI becomes embedded in defense or intelligence workflows, that adds a decision making layer on top. Stack those together and you’re not looking at products. You’re looking at a privately controlled system touching launch, comms, sensing, and AI..the core inputs of modern state power. That didn’t happen because of hype. It happened because the system needed it. The Dependency Markets Don’t Talk About Once national security operations are built around a platform, exit stops being realistic. Reliance becomes contracts. Contracts become scale. Scale makes alternatives impractical. Markets call that a moat. From a state perspective, it’s dependence. Why DOGE Fits The Picture DOGE is often dismissed as a failed reform experiment. But it revealed something important..trying to impose private sector speed directly onto government, loudly and visibly, triggers immediate resistance. What survived wasn’t the department. It was the method. Efficiency and automation didn’t disappear; they diffused through software, vendors, procurement choices, and external platforms that reshape how the state functions without ever declaring a takeover. That same logic applies here. Power isn’t being handed away. It’s being embedded..quietly and deniably where it can move faster. My View This is a signal that U.S. power in the 21st century is being reorganized around privately owned platforms because the old institutional model can’t keep up. Capital markets aren’t just funding companies. They’re financing infrastructure that now sits inside how American state power actually works. The real question is whether people recognize now that they’re watching the the public listing of the next layer of U.S. national security infrastructure.
1:53

These IPOs Aren’t About Markets They’re About National Security Architecture This looks like a standard tech and markets story. Big IPOs. Trillion dollar valuations. SpaceX, OpenAI, Anthropic. Maybe even a mega merger tying SpaceX to Tesla and xAI. The easy takeaway is familiar..visionary founders, breakthrough products, capital finally catching up to innovation. That framing is comfortable. It’s also incomplete. What’s Actually Being Priced These companies aren’t being valued just for products or future cash flows. They’re being valued because they now sit close to the core of U.S. national security architecture. Launch capacity. Global communications. AI systems feeding into defense, intelligence, and government workflows. Infrastructure the state increasingly depends on, rather than merely contracts with. When markets put trillion dollar numbers on these platforms, they’re not just betting on growth. They’re betting on how deeply embedded these systems have become inside the machinery of American power and how hard they would be to replace. Power Is Being Rerouted This isn’t about the state giving up authority. It’s about how power is exercised when trust in institutions erodes and bureaucracy can’t move at the speed of competition. In the old model, power flowed through public institutions and legacy defense contractors, governed by long timelines and rigid process. In the current model, speed matters more than formality. Adaptability matters more than ownership. So the state reroutes. It embeds sovereign functions..launch, communications, data processing, AI driven analysis inside private platforms that already move fast, scale globally, and can be adjusted without public friction. That shift isn’t ideological. It’s structural. Why Musk Sits At The Center This isn’t just about one entrepreneur. Amazon has cloud. Microsoft runs enterprise software. Palantir handles data. The pattern is widespread. What makes Musk different is concentration. SpaceX isn’t just a launch provider; it’s increasingly how the U.S. gets to orbit at scale. Starlink and Starshield aren’t just connectivity; they form a global communications layer that shows up in real conflicts. And if xAI becomes embedded in defense or intelligence workflows, that adds a decision making layer on top. Stack those together and you’re not looking at products. You’re looking at a privately controlled system touching launch, comms, sensing, and AI..the core inputs of modern state power. That didn’t happen because of hype. It happened because the system needed it. The Dependency Markets Don’t Talk About Once national security operations are built around a platform, exit stops being realistic. Reliance becomes contracts. Contracts become scale. Scale makes alternatives impractical. Markets call that a moat. From a state perspective, it’s dependence. Why DOGE Fits The Picture DOGE is often dismissed as a failed reform experiment. But it revealed something important..trying to impose private sector speed directly onto government, loudly and visibly, triggers immediate resistance. What survived wasn’t the department. It was the method. Efficiency and automation didn’t disappear; they diffused through software, vendors, procurement choices, and external platforms that reshape how the state functions without ever declaring a takeover. That same logic applies here. Power isn’t being handed away. It’s being embedded..quietly and deniably where it can move faster. My View This is a signal that U.S. power in the 21st century is being reorganized around privately owned platforms because the old institutional model can’t keep up. Capital markets aren’t just funding companies. They’re financing infrastructure that now sits inside how American state power actually works. The real question is whether people recognize now that they’re watching the the public listing of the next layer of U.S. national security infrastructure.

EndGame Macro

30,534 views • 8 months ago

The last 20 Moni Wizards NFTs. WL Application is live. The Wizards collection is the highest access to everything Moni. Apply to get whitelisted: 20 seats left. After these, primary closes for good and the only way in is the secondary market. Funds from this sale go straight back into the build: more research, more Smarts coverage, more API surface, more community infrastructure. What’s inside? Full Moni platform access. The off-chain side of our stack and how you find alpha before it's alpha. Discover tracks where smart money is already looking: VCs, founders, alpha hunters, and influencers, in real time. You see the projects they're moving toward one to three weeks before they trend on the main timeline. Behind it: 15,000 hand-curated Smarts and millions of smart mentions collected over five years. Wizards get every filter, every list, every signal we expose internally. Daily and weekly Alpha Reports. Written by the Moni Research team, not aggregated from elsewhere. Our analysts hand-pick the best of what the engine surfaces each day and each week, then write up what the Smarts are watching, why it matters, and where the narrative is forming. Reports usually land in the Wizards Discord before any public version exists. Moni Social API. New for Wizards. The same API we give access to launchpads, exchanges, VCs, and analytical platforms. 30+ data types, 100,000+ tracked events per day, 5,000+ projects under coverage, Smart Mindshare endpoints, real-time and historical social signals. Think of it as the social intelligence layer you'd otherwise have to build yourself. Wizards get a data they can plug into their own tools, alert systems, or agents. Deep Research. One project at a time, long form, the same work we run inside Moni Research. Each one takes the team about a week. Thesis, ecosystem map, mechanism, risks, Smart positioning, what to watch for. Structured the way an analyst would actually use it, not a Twitter thread version of it. Private Wizards-only chats and community. The room where the 180 holders and the private Moni community already share early calls, deal flow, research drafts, and "look at this" moments before anything goes public. Most of the real work happens here, not in the Twitter version of Moni. Alpha bots and curated Smarts lists. The bots that track Smart movements in real time. The Twitter lists we don't share publicly because they are the actual edge. Wizards get both. Early access to every new Moni product. Whatever ships next, Wizards see it first. Sometimes the prototype, sometimes weeks before public release. The feedback loop runs straight back into the team. Partnership raffles and early-stage deal flow. Pre-seed and seed allocations from partner projects. Raffles, intros, primary access to things that come through the table and never leave it. Custom hand-drawn PFP. Designed by our artist around your call. Yours forever. How to buy. Fill the form. The team reads every answer. If your fit makes sense for what this circle is, we'll come back to you with everything you need to complete the purchase. Moni Wizards Opensea Page:
0:45

The last 20 Moni Wizards NFTs. WL Application is live. The Wizards collection is the highest access to everything Moni. Apply to get whitelisted: 20 seats left. After these, primary closes for good and the only way in is the secondary market. Funds from this sale go straight back into the build: more research, more Smarts coverage, more API surface, more community infrastructure. What’s inside? Full Moni platform access. The off-chain side of our stack and how you find alpha before it's alpha. Discover tracks where smart money is already looking: VCs, founders, alpha hunters, and influencers, in real time. You see the projects they're moving toward one to three weeks before they trend on the main timeline. Behind it: 15,000 hand-curated Smarts and millions of smart mentions collected over five years. Wizards get every filter, every list, every signal we expose internally. Daily and weekly Alpha Reports. Written by the Moni Research team, not aggregated from elsewhere. Our analysts hand-pick the best of what the engine surfaces each day and each week, then write up what the Smarts are watching, why it matters, and where the narrative is forming. Reports usually land in the Wizards Discord before any public version exists. Moni Social API. New for Wizards. The same API we give access to launchpads, exchanges, VCs, and analytical platforms. 30+ data types, 100,000+ tracked events per day, 5,000+ projects under coverage, Smart Mindshare endpoints, real-time and historical social signals. Think of it as the social intelligence layer you'd otherwise have to build yourself. Wizards get a data they can plug into their own tools, alert systems, or agents. Deep Research. One project at a time, long form, the same work we run inside Moni Research. Each one takes the team about a week. Thesis, ecosystem map, mechanism, risks, Smart positioning, what to watch for. Structured the way an analyst would actually use it, not a Twitter thread version of it. Private Wizards-only chats and community. The room where the 180 holders and the private Moni community already share early calls, deal flow, research drafts, and "look at this" moments before anything goes public. Most of the real work happens here, not in the Twitter version of Moni. Alpha bots and curated Smarts lists. The bots that track Smart movements in real time. The Twitter lists we don't share publicly because they are the actual edge. Wizards get both. Early access to every new Moni product. Whatever ships next, Wizards see it first. Sometimes the prototype, sometimes weeks before public release. The feedback loop runs straight back into the team. Partnership raffles and early-stage deal flow. Pre-seed and seed allocations from partner projects. Raffles, intros, primary access to things that come through the table and never leave it. Custom hand-drawn PFP. Designed by our artist around your call. Yours forever. How to buy. Fill the form. The team reads every answer. If your fit makes sense for what this circle is, we'll come back to you with everything you need to complete the purchase. Moni Wizards Opensea Page:

Moni 🧙‍♂️

50,196 views • 3 months ago