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Traditional blockchain upgrades require explicit state serialization - adding complexity and risk to every deployment. Motoko introduces Enhanced Orthogonal Persistence Automatic state persistence across code upgrades. No serialization required. No data loss. Constant-time operations regardless of data volume. The runtime handles all persistence. Motoko verifies every upgrade is safe.... show more

DFINITY Foundation

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35,203 просмотров • 9 месяцев назад •via X (Twitter)

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🚀 Blockchain development shouldn't be hard Claudio Russo introduces Motoko - a language built from the ground up for the Internet Computer Protocol. ✅ Type-safe by design ✅ Automatic code upgrades ✅ No serialization headaches ✅ Actor model for clean concurrency Stop fighting your tools. Start building. Missed the last episode? Discover how simple Solana and EVM integrations are on the Internet Computer:

🚀 Blockchain development shouldn't be hard Claudio Russo introduces Motoko - a language built from the ground up for the Internet Computer Protocol. ✅ Type-safe by design ✅ Automatic code upgrades ✅ No serialization headaches ✅ Actor model for clean concurrency Stop fighting your tools. Start building. Missed the last episode? Discover how simple Solana and EVM integrations are on the Internet Computer:

DFINITY Foundation

14,043 просмотров • 10 месяцев назад

🇺🇸 Tucker raises serious concerns about the rapid spread of surveillance cameras like Flock Safety across America. He points out that even a simple drive to the countryside offers no escape from constant monitoring, cameras watching, listening, and analyzing your every move, face, and behavior 24/7. Tucker also says this mirrors Orwell’s 1984, where the state doesn’t need brute force when it can simply watch everyone all the time. That constant surveillance crushes privacy, intimacy, and free thought, turning society into something closer to a “slave state.” A powerful warning on where this path is leading. Source: TCN / Writer: Lucas

🇺🇸 Tucker raises serious concerns about the rapid spread of surveillance cameras like Flock Safety across America. He points out that even a simple drive to the countryside offers no escape from constant monitoring, cameras watching, listening, and analyzing your every move, face, and behavior 24/7. Tucker also says this mirrors Orwell’s 1984, where the state doesn’t need brute force when it can simply watch everyone all the time. That constant surveillance crushes privacy, intimacy, and free thought, turning society into something closer to a “slave state.” A powerful warning on where this path is leading. Source: TCN / Writer: Lucas

Mario Nawfal

93,876 просмотров • 29 дней назад

I started digging into the rapid expansion of hyperscale data centers and energy projects in Ohio and what I found raised serious questions about transparency, public oversight, and who these deals are really benefiting. JobsOhio was created in 2011 and funded through the state’s liquor enterprise revenue, billions generated from public assets. Yet it operates as a private nonprofit that is not subject to traditional open-records laws. Today, Ohio is being marketed as “deployment ready” for hyperscale data centers, massive facilities that require constant, industrial-scale electricity and water usage. To support that demand, new energy infrastructure is being proposed across the state, including advanced nuclear projects like Oklo in Pike County. Policy changes like Ohio Senate Bill 52 shifted how energy siting decisions are handled at the local level, while utilities such as American Electric Power have proposed new tariffs and grid upgrades to support the surge in data center demand. Communities across Ohio are raising concerns about farmland loss, water usage, electricity costs, and the lack of early public input before these deals are shaped. Regardless of where you stand on development, one thing should be non-negotiable: Transparency. Accountability. And a real voice for the people of Ohio. Public resources and infrastructure should serve the public first. Decisions that shape our land, our water, and our power grid should not happen behind closed doors. If you care about your community, your property, and your future, now is the time to start asking questions and paying attention.

I started digging into the rapid expansion of hyperscale data centers and energy projects in Ohio and what I found raised serious questions about transparency, public oversight, and who these deals are really benefiting. JobsOhio was created in 2011 and funded through the state’s liquor enterprise revenue, billions generated from public assets. Yet it operates as a private nonprofit that is not subject to traditional open-records laws. Today, Ohio is being marketed as “deployment ready” for hyperscale data centers, massive facilities that require constant, industrial-scale electricity and water usage. To support that demand, new energy infrastructure is being proposed across the state, including advanced nuclear projects like Oklo in Pike County. Policy changes like Ohio Senate Bill 52 shifted how energy siting decisions are handled at the local level, while utilities such as American Electric Power have proposed new tariffs and grid upgrades to support the surge in data center demand. Communities across Ohio are raising concerns about farmland loss, water usage, electricity costs, and the lack of early public input before these deals are shaped. Regardless of where you stand on development, one thing should be non-negotiable: Transparency. Accountability. And a real voice for the people of Ohio. Public resources and infrastructure should serve the public first. Decisions that shape our land, our water, and our power grid should not happen behind closed doors. If you care about your community, your property, and your future, now is the time to start asking questions and paying attention.

Kim Georgeton for Lt. Governor of Ohio

14,671 просмотров • 5 месяцев назад

THIS WALLET STACKED $230K ON BTC UP/DOWN BETS. THE BLUEPRINT TO AUTOMATE THE SAME EDGE WITH CLAUDE The wallet is $230K all-time, every position a Bitcoin or Ethereum Up or Down market It never guesses direction. It enters only when the math and the market disagree THE STRATEGY: BTC moves are not fully random. When the market enters a committed directional state, continuation is measurable. That is Markov persistence Entry signal: > Δ = p̂ − q ≥ ε Model probability minus market price. Enter only on a 5% gap or more Persistence filter: > p(j*,j*) ≥ 0.87 Only trade states with 0.87 persistence or higher. Below that, skip. This is what holds the win rate above 65% with zero directional guessing Payout: > r = (1 − q) / q At q = 0.647 that is +54.5% a win. At q = 0.441, +126.7%. Lower entry price, bigger asymmetry Sizing: > f* = p − (1−p)/b Kelly. At p = 0.87, b = 0.647, f* ≈ 0.71. Size to the edge, never to gut HOW TO BUILD IT WITH CLAUDE: What separates this from a static bot: Claude reads its own trade journal every night and rewrites its own thresholds 1. Take an open-source Polymarket bot repo as your base logic. Feed it to Claude and have it migrate to CLOB v2: py_clob_client_v2, Safe wallet support, fee-aware evaluation 2. Hard-code the filters. Enter only when Δ ≥ 0.05 and p(j*,j*) ≥ 0.87. Apply Kelly on every fill. 3. Run DRY_RUN first. Log every signal, entry price, Markov state, and simulated P/L. No real money until the numbers hold for days 4. The nightly loop. Claude reads the journal, finds which persistence states actually won, adjusts MIN_PROB and MIN_EDGE, ships tomorrow's rules. The agent is sharper after 50 to 100 trades THE SETUP: Claude Opus as the brain. An open-source repo as the starting logic. A Polygon wallet with $50 to $100. Telegram for the morning report Start at $1 to $2 per trade while it learns. Scale only when the dry runs and the live fills line up 17,000 trades compound a thin edge into six figures. The model finds the edge. The nightly loop keeps it sharp Bookmark before you point a bot at your first window

THIS WALLET STACKED $230K ON BTC UP/DOWN BETS. THE BLUEPRINT TO AUTOMATE THE SAME EDGE WITH CLAUDE The wallet is $230K all-time, every position a Bitcoin or Ethereum Up or Down market It never guesses direction. It enters only when the math and the market disagree THE STRATEGY: BTC moves are not fully random. When the market enters a committed directional state, continuation is measurable. That is Markov persistence Entry signal: > Δ = p̂ − q ≥ ε Model probability minus market price. Enter only on a 5% gap or more Persistence filter: > p(j,j) ≥ 0.87 Only trade states with 0.87 persistence or higher. Below that, skip. This is what holds the win rate above 65% with zero directional guessing Payout: > r = (1 − q) / q At q = 0.647 that is +54.5% a win. At q = 0.441, +126.7%. Lower entry price, bigger asymmetry Sizing: > f* = p − (1−p)/b Kelly. At p = 0.87, b = 0.647, f* ≈ 0.71. Size to the edge, never to gut HOW TO BUILD IT WITH CLAUDE: What separates this from a static bot: Claude reads its own trade journal every night and rewrites its own thresholds 1. Take an open-source Polymarket bot repo as your base logic. Feed it to Claude and have it migrate to CLOB v2: py_clob_client_v2, Safe wallet support, fee-aware evaluation 2. Hard-code the filters. Enter only when Δ ≥ 0.05 and p(j,j) ≥ 0.87. Apply Kelly on every fill. 3. Run DRY_RUN first. Log every signal, entry price, Markov state, and simulated P/L. No real money until the numbers hold for days 4. The nightly loop. Claude reads the journal, finds which persistence states actually won, adjusts MIN_PROB and MIN_EDGE, ships tomorrow's rules. The agent is sharper after 50 to 100 trades THE SETUP: Claude Opus as the brain. An open-source repo as the starting logic. A Polygon wallet with $50 to $100. Telegram for the morning report Start at $1 to $2 per trade while it learns. Scale only when the dry runs and the live fills line up 17,000 trades compound a thin edge into six figures. The model finds the edge. The nightly loop keeps it sharp Bookmark before you point a bot at your first window

Yarchi

22,966 просмотров • 2 месяцев назад

Hey Anon🟧, Beta is Here – A Glimpse into the Future of DeFAI We’ve skipped the Alpha stage entirely to bring you straight into Public Beta v0.1—your first hands-on experience with DeFAI and Gemma on the 7th of February. What Can You Expect? 🚀 Live, Evolving Experience – From launch, we’ll be testing and integrating every update pushed on Automate’s GitHub. HeyAnon will continuously improve, adding more features and refining workflows, aiming for a fully comprehensive experience by the end of the month. 🔄 Simplified Workflows – Execute multi-action prompts that streamline complex DeFi processes. 🔑 Flexible Onboarding – Connect with Wallet Connect, generate a wallet in Telegram, or use Passkey. ⚡️ Real-Time Functionality – Experience DeFAI fully live, and get a sneak peek at the future of automated DeFi. We’ll be sharing examples and user videos to showcase what’s already possible, so stay tuned. (Make sure to check our docs and guides for the best experience!) 💌 Meet Gemma Gemma AI - The Assistant That Grows with You Gemma is here, and she’s just getting started. As data streams from Messari, Kaito, Cookie, and our internal data mining expand, she will continuously evolve, bringing: 📊 Enhanced Protocol-Specific Capabilities 🔗 More Integrated Data Streams ⚡️ Ongoing AI and Automate Upgrades This is the beta, the starting point, the appetizer - but the full DeFAI experience is coming in multiple courses over the month. Expect rapid improvements, more integrations, and a constantly evolving ecosystem. 🚀 DeFAI starts now.

Hey Anon🟧, Beta is Here – A Glimpse into the Future of DeFAI We’ve skipped the Alpha stage entirely to bring you straight into Public Beta v0.1—your first hands-on experience with DeFAI and Gemma on the 7th of February. What Can You Expect? 🚀 Live, Evolving Experience – From launch, we’ll be testing and integrating every update pushed on Automate’s GitHub. HeyAnon will continuously improve, adding more features and refining workflows, aiming for a fully comprehensive experience by the end of the month. 🔄 Simplified Workflows – Execute multi-action prompts that streamline complex DeFi processes. 🔑 Flexible Onboarding – Connect with Wallet Connect, generate a wallet in Telegram, or use Passkey. ⚡️ Real-Time Functionality – Experience DeFAI fully live, and get a sneak peek at the future of automated DeFi. We’ll be sharing examples and user videos to showcase what’s already possible, so stay tuned. (Make sure to check our docs and guides for the best experience!) 💌 Meet Gemma Gemma AI - The Assistant That Grows with You Gemma is here, and she’s just getting started. As data streams from Messari, Kaito, Cookie, and our internal data mining expand, she will continuously evolve, bringing: 📊 Enhanced Protocol-Specific Capabilities 🔗 More Integrated Data Streams ⚡️ Ongoing AI and Automate Upgrades This is the beta, the starting point, the appetizer - but the full DeFAI experience is coming in multiple courses over the month. Expect rapid improvements, more integrations, and a constantly evolving ecosystem. 🚀 DeFAI starts now.

Hey Anon

77,772 просмотров • 1 год назад

Your agents can't keep up with real-time data. Especially when it's scattered across dozens of sources. Most teams waste weeks building custom connectors for every database, API, and data warehouse. Then they build ETL pipelines to sync everything. By the time your agent retrieves the data, it's already outdated. Picture this: Your Postgres database updated 5 minutes ago. Your MongoDB collection changed 2 minutes ago. Your agent is still pulling from yesterday's snapshot. This is why most production RAG systems fail. There's a better approach: MindsDB is an open-source AI platform with a federated data engine that lets you query multiple data sources in real-time using SQL - without moving any data. Here's what makes it different: ↳ Your data stays in place. No ETL pipelines or data duplication ↳ Query Postgres, MongoDB, REST APIs, and more using consistent SQL ↳ JOIN across different sources in real-time with a unified interface ↳ Works with both structured and un-structured data And here's the best part: You don't even need to write SQL. Just describe what you want in plain English, and MindsDB converts it to SQL automatically. The system does all the heavy lifting. The breakthrough for AI agents is simple: When data updates at the source, your agent gets fresh results immediately. No sync delays. No stale embeddings. No custom code for each integration. You can literally write a SQL query that joins a Postgres table with a MongoDB collection and gets live results. This is what production AI applications need but rarely get. In this video, I give you a complete walkthrough of what we just discussed and how to actually do it. Make sure you watch this till the end. I've shared the link to MindsDB's GitHub repo in the next tweet!

Your agents can't keep up with real-time data. Especially when it's scattered across dozens of sources. Most teams waste weeks building custom connectors for every database, API, and data warehouse. Then they build ETL pipelines to sync everything. By the time your agent retrieves the data, it's already outdated. Picture this: Your Postgres database updated 5 minutes ago. Your MongoDB collection changed 2 minutes ago. Your agent is still pulling from yesterday's snapshot. This is why most production RAG systems fail. There's a better approach: MindsDB is an open-source AI platform with a federated data engine that lets you query multiple data sources in real-time using SQL - without moving any data. Here's what makes it different: ↳ Your data stays in place. No ETL pipelines or data duplication ↳ Query Postgres, MongoDB, REST APIs, and more using consistent SQL ↳ JOIN across different sources in real-time with a unified interface ↳ Works with both structured and un-structured data And here's the best part: You don't even need to write SQL. Just describe what you want in plain English, and MindsDB converts it to SQL automatically. The system does all the heavy lifting. The breakthrough for AI agents is simple: When data updates at the source, your agent gets fresh results immediately. No sync delays. No stale embeddings. No custom code for each integration. You can literally write a SQL query that joins a Postgres table with a MongoDB collection and gets live results. This is what production AI applications need but rarely get. In this video, I give you a complete walkthrough of what we just discussed and how to actually do it. Make sure you watch this till the end. I've shared the link to MindsDB's GitHub repo in the next tweet!

Akshay 🚀

65,672 просмотров • 9 месяцев назад

‼️🇸🇪 Sweden’s Air Force has confirmed its Gripen E fighters are now fully capable of detecting and engaging slow, low-flying threats like Shahed drones a critical adaptation in an era where air defense is no longer just about dogfights, but drone swarms. During NATO air policing in Poland, Swedish pilots verified radar and software upgrades that allow the Gripen to track and destroy FPV and loitering munitions previously considered “invisible” to legacy jets. This capability, combined with advanced electronic warfare suites and networked data-sharing, transforms the Gripen from a traditional air-superiority platform into a multi-role sentinel against hybrid threats. As Russian and Iranian drones proliferate across Europe, Sweden’s focus on survivability, stealth, and rapid tech insertion like fielding Taurus missiles years ahead of schedule shows how even small air forces can outmaneuver larger ones by prioritizing adaptability over brute force.

‼️🇸🇪 Sweden’s Air Force has confirmed its Gripen E fighters are now fully capable of detecting and engaging slow, low-flying threats like Shahed drones a critical adaptation in an era where air defense is no longer just about dogfights, but drone swarms. During NATO air policing in Poland, Swedish pilots verified radar and software upgrades that allow the Gripen to track and destroy FPV and loitering munitions previously considered “invisible” to legacy jets. This capability, combined with advanced electronic warfare suites and networked data-sharing, transforms the Gripen from a traditional air-superiority platform into a multi-role sentinel against hybrid threats. As Russian and Iranian drones proliferate across Europe, Sweden’s focus on survivability, stealth, and rapid tech insertion like fielding Taurus missiles years ahead of schedule shows how even small air forces can outmaneuver larger ones by prioritizing adaptability over brute force.

Visioner

288,284 просмотров • 7 месяцев назад

AI BASKETBALL ANALYSIS. A FULL COMPUTER VISION SYSTEM. BUILT ON YOLO, OPENCV, AND PYTHON. Take any NBA broadcast. Any camera angle. Any resolution. Feed it into the system. YOLO finds every player and the ball. Frame by frame. No manual annotation. No pre-labeled data.The model just sees the court and understands it. Zero-shot classification looks at jersey pixels and decides who plays for whom. Two teams separated in milliseconds. Without ever being told the team names. But here's where it gets wild. Court keypoint detection identifies the geometry of the playing surface. Homography transforms the broadcast camera into a top-down tactical map. Real meters. Real coordinates. From a flat 2D video. Now every player has a position on a real court. Every movement becomes a data point. Every pass becomes a vector. Speed. Distance. Possession time. Pass networks. All extracted from nothing but pixels. No GPS. No chips in the ball. No million-dollar Second Spectrum setup. A Python script and a GPU. The full pipeline is open source. The tutorial walks through every line of code. From detection to transformation to analytics output. Teams used to pay six figures for this data. Now you build it in an afternoon. Computer vision didn't just enter basketball. It democratized it.

AI BASKETBALL ANALYSIS. A FULL COMPUTER VISION SYSTEM. BUILT ON YOLO, OPENCV, AND PYTHON. Take any NBA broadcast. Any camera angle. Any resolution. Feed it into the system. YOLO finds every player and the ball. Frame by frame. No manual annotation. No pre-labeled data.The model just sees the court and understands it. Zero-shot classification looks at jersey pixels and decides who plays for whom. Two teams separated in milliseconds. Without ever being told the team names. But here's where it gets wild. Court keypoint detection identifies the geometry of the playing surface. Homography transforms the broadcast camera into a top-down tactical map. Real meters. Real coordinates. From a flat 2D video. Now every player has a position on a real court. Every movement becomes a data point. Every pass becomes a vector. Speed. Distance. Possession time. Pass networks. All extracted from nothing but pixels. No GPS. No chips in the ball. No million-dollar Second Spectrum setup. A Python script and a GPU. The full pipeline is open source. The tutorial walks through every line of code. From detection to transformation to analytics output. Teams used to pay six figures for this data. Now you build it in an afternoon. Computer vision didn't just enter basketball. It democratized it.

zostaff

230,734 просмотров • 3 месяцев назад

🔮 MCP INTEGRATION NOW LIVE ON OPENSERV This is bigger than most realize. We're not just adding a feature - we're enabling a fundamentally new paradigm for agent infrastructure. Before MCP: agents trapped in walled gardens, limited by what you explicitly feed them. After MCP: agents that can access 10K+ tools, interact with your existing systems, and truly operate in your digital world. Think about this: -Every integration used to require custom code -Every data source needed specific formatting -Every agent had to be built for specific tools MCP eliminates all of that. One standard to connect any agent to any tool. This isn't just convenience - it's the unlock that makes agents actually useful in the real world. OpenServ + MCP = AI teams that can genuinely get things done, not just talk about doing them. This is the breakthrough that turns agents from toys into tools.

🔮 MCP INTEGRATION NOW LIVE ON OPENSERV This is bigger than most realize. We're not just adding a feature - we're enabling a fundamentally new paradigm for agent infrastructure. Before MCP: agents trapped in walled gardens, limited by what you explicitly feed them. After MCP: agents that can access 10K+ tools, interact with your existing systems, and truly operate in your digital world. Think about this: -Every integration used to require custom code -Every data source needed specific formatting -Every agent had to be built for specific tools MCP eliminates all of that. One standard to connect any agent to any tool. This isn't just convenience - it's the unlock that makes agents actually useful in the real world. OpenServ + MCP = AI teams that can genuinely get things done, not just talk about doing them. This is the breakthrough that turns agents from toys into tools.

OpenServ

34,665 просмотров • 1 год назад

The storm has arrived. This is one of the most important days in crypto, blockchain and digital assets. 11,000 SWIFT institutions can now interoperate with any crypto network. Here's the proof: • SWIFT Blockchain: 17 banks live • 11,000 member network • Powered by Chainlink CCIP + CRE • No major infrastructure upgrades needed • Connects to any DLT, any asset "The SWIFT blockchain provides a single point of entry to multiple other distributed ledgers." • RLN and RSN are not XRP or XRPL. Proof is in the actual documents. U.S. Tokenized MMF trials. Massive. • 300+ participants, 120+ firms. Ripple. Canton. Stellar. All named. • BlackRock. JPM. Every major bank and FI. All in. Hedera is the main settlement ledger across all of these trials. • Only 3 networks used: HBAR, ETH, Besu • Fidelity + Federated Hermes on Hedera • JP Morgan + Fidelity: $10M on HBAR & ETH • HBAR to ETH for State Street tri-party DTCC 24/5 clearing live. $127T equities next. Internet of Value is here. Any asset. Any network. Level playing field. May the best assets and networks win. $LINK $HBAR $XRP $XLM $ETH $CC

The storm has arrived. This is one of the most important days in crypto, blockchain and digital assets. 11,000 SWIFT institutions can now interoperate with any crypto network. Here's the proof: • SWIFT Blockchain: 17 banks live • 11,000 member network • Powered by Chainlink CCIP + CRE • No major infrastructure upgrades needed • Connects to any DLT, any asset "The SWIFT blockchain provides a single point of entry to multiple other distributed ledgers." • RLN and RSN are not XRP or XRPL. Proof is in the actual documents. U.S. Tokenized MMF trials. Massive. • 300+ participants, 120+ firms. Ripple. Canton. Stellar. All named. • BlackRock. JPM. Every major bank and FI. All in. Hedera is the main settlement ledger across all of these trials. • Only 3 networks used: HBAR, ETH, Besu • Fidelity + Federated Hermes on Hedera • JP Morgan + Fidelity: $10M on HBAR & ETH • HBAR to ETH for State Street tri-party DTCC 24/5 clearing live. $127T equities next. Internet of Value is here. Any asset. Any network. Level playing field. May the best assets and networks win. $LINK $HBAR $XRP $XLM $ETH $CC

King Solomon (Ryan Solomon)

127,512 просмотров • 1 месяц назад

🚀 Today, we are thrilled to share that Amazon Q is now generally available. ◼Amazon Q is the most capable generative AI-powered assistant for accelerating software development and leveraging companies’ internal data. It eliminates tedious work for developers and employees across organizations. ◼Q helps to test, debug and write code, and has the highest reported code acceptance rates in the industry, for assistants that perform multi-line code suggestions. BT Group recently reported they accepted 37% of Q’s code suggestions and National Australia Bank reported a 50% acceptance rate. ◼Amazon Q Developer Agents can autonomously perform a range of tasks—everything from implementing features, documenting, and refactoring code, to performing software upgrades. Developers can ask Amazon Q to implement an application feature, and the agent will analyze their existing application code and generate a step-by-step implementation plan. ◼Amazon Q's capabilities extend beyond coding. It also allows employees to easily get insight from their company's internal data that is spread across multiple documents, systems, and applications. Q connects all these siloed sources and can answer questions, provide summaries, analyze trends, and generate content. ◼We're also introducing Q Apps, a powerful new way for anyone to create generative AI apps based on their organization's data—no prior coding experience required. Simply describe the app you need in natural language, and Q Apps will build it for you. This unlocks endless possibilities for teams to automate workflows and daily tasks. Customers across industries are using Amazon Q to transform the way they work. I'm incredibly excited to see what Q can do for you.

🚀 Today, we are thrilled to share that Amazon Q is now generally available. ◼Amazon Q is the most capable generative AI-powered assistant for accelerating software development and leveraging companies’ internal data. It eliminates tedious work for developers and employees across organizations. ◼Q helps to test, debug and write code, and has the highest reported code acceptance rates in the industry, for assistants that perform multi-line code suggestions. BT Group recently reported they accepted 37% of Q’s code suggestions and National Australia Bank reported a 50% acceptance rate. ◼Amazon Q Developer Agents can autonomously perform a range of tasks—everything from implementing features, documenting, and refactoring code, to performing software upgrades. Developers can ask Amazon Q to implement an application feature, and the agent will analyze their existing application code and generate a step-by-step implementation plan. ◼Amazon Q's capabilities extend beyond coding. It also allows employees to easily get insight from their company's internal data that is spread across multiple documents, systems, and applications. Q connects all these siloed sources and can answer questions, provide summaries, analyze trends, and generate content. ◼We're also introducing Q Apps, a powerful new way for anyone to create generative AI apps based on their organization's data—no prior coding experience required. Simply describe the app you need in natural language, and Q Apps will build it for you. This unlocks endless possibilities for teams to automate workflows and daily tasks. Customers across industries are using Amazon Q to transform the way they work. I'm incredibly excited to see what Q can do for you.

Adam Selipsky

121,755 просмотров • 2 лет назад

Mansa AI is an enterprise-grade AI + Web3 platform designed to move artificial intelligence from experimentation into real-world execution. Built for creators, developers, and businesses, it focuses on deploying AI that actually works across modern digital systems, not just in isolated demos. 🚀 Production-ready AI infrastructure Mansa AI enables teams to deploy AI systems designed for live environments, handling real workflows, real data, and real operational demands without constant manual oversight. 🧠 Autonomous AI agents At its core, Mansa AI allows users to build autonomous agents that automate decision-making, coordinate tasks, monitor live signals, and execute complex workflows across dynamic environments. ⚙️ Fully customizable logic Agents can be configured with custom behaviors, triggers, and responses. From content generation and analytics to operational automation and intelligent orchestration, logic adapts to specific business strategies. 🔗 Web3 and off-chain integration Mansa AI bridges blockchain ecosystems with traditional systems, enabling cross-chain coordination, smart contract interactions, and seamless integration with existing enterprise infrastructure. 📊 Real-world use cases The platform supports automation for operations, customer engagement, analytics, data pipelines, content workflows, and AI-driven optimization across products and teams. 📈 Built for scale Whether launching as a startup or deploying across enterprise systems, Mansa AI is designed to scale AI operations without adding complexity or fragmentation. Mansa AI transforms artificial intelligence into deployable infrastructure. By combining autonomy, customization, interoperability, and scalability, it enables teams to own, operate, and grow intelligent systems that deliver real value in production environments.

Mansa AI is an enterprise-grade AI + Web3 platform designed to move artificial intelligence from experimentation into real-world execution. Built for creators, developers, and businesses, it focuses on deploying AI that actually works across modern digital systems, not just in isolated demos. 🚀 Production-ready AI infrastructure Mansa AI enables teams to deploy AI systems designed for live environments, handling real workflows, real data, and real operational demands without constant manual oversight. 🧠 Autonomous AI agents At its core, Mansa AI allows users to build autonomous agents that automate decision-making, coordinate tasks, monitor live signals, and execute complex workflows across dynamic environments. ⚙️ Fully customizable logic Agents can be configured with custom behaviors, triggers, and responses. From content generation and analytics to operational automation and intelligent orchestration, logic adapts to specific business strategies. 🔗 Web3 and off-chain integration Mansa AI bridges blockchain ecosystems with traditional systems, enabling cross-chain coordination, smart contract interactions, and seamless integration with existing enterprise infrastructure. 📊 Real-world use cases The platform supports automation for operations, customer engagement, analytics, data pipelines, content workflows, and AI-driven optimization across products and teams. 📈 Built for scale Whether launching as a startup or deploying across enterprise systems, Mansa AI is designed to scale AI operations without adding complexity or fragmentation. Mansa AI transforms artificial intelligence into deployable infrastructure. By combining autonomy, customization, interoperability, and scalability, it enables teams to own, operate, and grow intelligent systems that deliver real value in production environments.

King

155,637 просмотров • 8 месяцев назад

We are going big this year and into next! 2026 will be a Radiant year. The Radiant community is thrilled to announce the release of TWO MAJOR open-source projects that showcase the power of collaborative development: Radiant-Core Node! RXinDexer! 1. Radiant-Core Node 2.0-Project Phoenix Our next-generation blockchain node implementation! 🔗 -Built with modern C++20, Radiant-Core represents a significant evolution in our blockchain infrastructure: -Enhanced Performance: Support for transactions up to 12 MB with ~81,000 inputs. -Developer-Friendly: Native Prometheus metrics endpoint for easy monitoring. -Flexible Configuration: Node profiles from the system design paper (archive/agent/mining) for different use cases. -Glyph Token Swap Protocol Upgrades: **On-chain** broadcasted atomic swaps via PSRT. The classic advertised swaps will still work but this ushers in a whole new era for DEX and atomic swaps on-chain. -Cross-Platform: Docker-based CI for consistent builds across Linux, macOS, and Windows. -Available soon on testnet and aiming for mainnet activation by block 400k. 2. RXinDexer - Blockchain indexer for RXD and Glyphs 🔗 -A powerful Python-based indexing solution that enables developers to efficiently query and track Radiant blockchain data and Glyph assets. These releases embody our commitment to transparent, community-driven development. Open source isn't just about code—it's about empowering developers worldwide to: ✅ Contribute improvements and innovations ✅ Audit security and verify integrity ✅ Build complementary tools and services ✅ Learn from production-grade blockchain implementations Get Involved! Whether you're a seasoned blockchain developer or just getting started, your contributions matter. Check out the repositories, review the documentation, submit issues, or contribute code. You are the Proof of Work! And together, we're building the future of decentralized digital value transfer and sound money with no trusted third parties. The way Satoshi intended. The future is Radiant!

We are going big this year and into next! 2026 will be a Radiant year. The Radiant community is thrilled to announce the release of TWO MAJOR open-source projects that showcase the power of collaborative development: Radiant-Core Node! RXinDexer! 1. Radiant-Core Node 2.0-Project Phoenix Our next-generation blockchain node implementation! 🔗 -Built with modern C++20, Radiant-Core represents a significant evolution in our blockchain infrastructure: -Enhanced Performance: Support for transactions up to 12 MB with ~81,000 inputs. -Developer-Friendly: Native Prometheus metrics endpoint for easy monitoring. -Flexible Configuration: Node profiles from the system design paper (archive/agent/mining) for different use cases. -Glyph Token Swap Protocol Upgrades: On-chain broadcasted atomic swaps via PSRT. The classic advertised swaps will still work but this ushers in a whole new era for DEX and atomic swaps on-chain. -Cross-Platform: Docker-based CI for consistent builds across Linux, macOS, and Windows. -Available soon on testnet and aiming for mainnet activation by block 400k. 2. RXinDexer - Blockchain indexer for RXD and Glyphs 🔗 -A powerful Python-based indexing solution that enables developers to efficiently query and track Radiant blockchain data and Glyph assets. These releases embody our commitment to transparent, community-driven development. Open source isn't just about code—it's about empowering developers worldwide to: ✅ Contribute improvements and innovations ✅ Audit security and verify integrity ✅ Build complementary tools and services ✅ Learn from production-grade blockchain implementations Get Involved! Whether you're a seasoned blockchain developer or just getting started, your contributions matter. Check out the repositories, review the documentation, submit issues, or contribute code. You are the Proof of Work! And together, we're building the future of decentralized digital value transfer and sound money with no trusted third parties. The way Satoshi intended. The future is Radiant!

Radiant Blockchain

41,416 просмотров • 7 месяцев назад

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!

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

Sharpe AI

263,278 просмотров • 1 год назад

Erin Brockovich has launched a website and has begun tracking all data centers in America and logging resident complaints In just 1 week it’s already logged 1,690 resident complaints For this who don’t remember Erin Brockovich was the paralegal responsible for winning out a case against PG&E, Hinckley in California, because their wastewater runoff was seeping into rural areas and creating a lot of health issues for, for the surrounding neighborhoods That case brought in a $333 million settlement that went to the families affected by the situation because a lot of them either had staggering medical bills due to their tap water was no longer safe So why is this important, well residents all over America are reporting their tap water and river water is being heavily polluted by data centers Her map of data centers is new, she just launched it The website features an interactive US map showing operational, under-construction, and proposed AI data centers, overlaid with community-reported complaints Residents can submit reports with details, photos, and locations. Within days of launch, it received a surge of submissions over 1,600 in the first week, and reports of 1,800+ from 47 states shortly after Common Resident Complaints Being Logged - Water usage - Raising utility bills for residents - Noise pollution: Constant 24/7 humming from fans, generators, and cooling systems disrupting sleep, daily life, and wildlife. - E-waste from frequent hardware upgrades, pollution including PFAS concerns

Erin Brockovich has launched a website and has begun tracking all data centers in America and logging resident complaints In just 1 week it’s already logged 1,690 resident complaints For this who don’t remember Erin Brockovich was the paralegal responsible for winning out a case against PG&E, Hinckley in California, because their wastewater runoff was seeping into rural areas and creating a lot of health issues for, for the surrounding neighborhoods That case brought in a $333 million settlement that went to the families affected by the situation because a lot of them either had staggering medical bills due to their tap water was no longer safe So why is this important, well residents all over America are reporting their tap water and river water is being heavily polluted by data centers Her map of data centers is new, she just launched it The website features an interactive US map showing operational, under-construction, and proposed AI data centers, overlaid with community-reported complaints Residents can submit reports with details, photos, and locations. Within days of launch, it received a surge of submissions over 1,600 in the first week, and reports of 1,800+ from 47 states shortly after Common Resident Complaints Being Logged - Water usage - Raising utility bills for residents - Noise pollution: Constant 24/7 humming from fans, generators, and cooling systems disrupting sleep, daily life, and wildlife. - E-waste from frequent hardware upgrades, pollution including PFAS concerns

Wall Street Apes

1,527,126 просмотров • 2 месяцев назад

🚨 MASSIVE BOMBSHELL: STARGATE COMMAND & THE TOTAL SURVEILLANCE STATE YOU'RE PAYING FOR UNDERNEATH THE BALLROOM In her update yesterday, Drey revealed why the entire weight of the DOJ was deployed Sunday morning after the WHCD shooting to try to get the lawsuit dismissed that's holding up the ballroom's construction. "What's being built under the ballroom is a federal data & AI command facility. It's going to be a hardened, classified, air-gapped processing center sitting inside the executive perimeter that runs AI on the data the federal government is right now consolidating across every single agency." "Healthcare data, identity data, facial data, intelligence data, all of it pulled into one operating system underground inside that one building beyond the reach of Congress, the courts, or any oversight body that could ASK what it's doing." "The story is not what Trump is doing with this facility. It's about what every president after him gets to do with it. No incoming administration of either party is going to take the political risk of unwinding it. The next president inherits the system. The contracts persist, the classifications persist, the integrations persist, and the public NEVER gets to ASK what they actually do." "The lawsuit that the DOJ tried to kill on Saturday night (at the WHCD)—no pun intended—is the only reason that this QUESTION is even being asked at all. And the National Trust luckily refused to drop the lawsuit. And the ANSWER is going to define what every future president is allowed to build in secret with your money and your name without your permission." Friends, they are going for the total digital control grid, and this will be the nerve center for it, integrating the feeds from the massive network of data centers around the country. And it will be done without oversight, transparency, or your approval. But, of course, you WILL get to PAY for it and enjoy your ENSLAVEMENT! This is the crown jewel of $500 Billion Project Stargate. The White House bunker underneath the ballroom is Stargate Command. Remember when they tried to ram through a 10-year moratorium on state-level regulation of AI, but the Senate rejected it? Convenient that they now have this project which avoids any public scrutiny or oversight, whatsoever, don't you think? Everyone who participated in the coordinated ballroom influence campaign wants this for you. For all of us. This treachery cannot be tolerated. Raise hell. And share the heck out of this.

🚨 MASSIVE BOMBSHELL: STARGATE COMMAND & THE TOTAL SURVEILLANCE STATE YOU'RE PAYING FOR UNDERNEATH THE BALLROOM In her update yesterday, Drey revealed why the entire weight of the DOJ was deployed Sunday morning after the WHCD shooting to try to get the lawsuit dismissed that's holding up the ballroom's construction. "What's being built under the ballroom is a federal data & AI command facility. It's going to be a hardened, classified, air-gapped processing center sitting inside the executive perimeter that runs AI on the data the federal government is right now consolidating across every single agency." "Healthcare data, identity data, facial data, intelligence data, all of it pulled into one operating system underground inside that one building beyond the reach of Congress, the courts, or any oversight body that could ASK what it's doing." "The story is not what Trump is doing with this facility. It's about what every president after him gets to do with it. No incoming administration of either party is going to take the political risk of unwinding it. The next president inherits the system. The contracts persist, the classifications persist, the integrations persist, and the public NEVER gets to ASK what they actually do." "The lawsuit that the DOJ tried to kill on Saturday night (at the WHCD)—no pun intended—is the only reason that this QUESTION is even being asked at all. And the National Trust luckily refused to drop the lawsuit. And the ANSWER is going to define what every future president is allowed to build in secret with your money and your name without your permission." Friends, they are going for the total digital control grid, and this will be the nerve center for it, integrating the feeds from the massive network of data centers around the country. And it will be done without oversight, transparency, or your approval. But, of course, you WILL get to PAY for it and enjoy your ENSLAVEMENT! This is the crown jewel of $500 Billion Project Stargate. The White House bunker underneath the ballroom is Stargate Command. Remember when they tried to ram through a 10-year moratorium on state-level regulation of AI, but the Senate rejected it? Convenient that they now have this project which avoids any public scrutiny or oversight, whatsoever, don't you think? Everyone who participated in the coordinated ballroom influence campaign wants this for you. For all of us. This treachery cannot be tolerated. Raise hell. And share the heck out of this.

Sam Parker 🇺🇸🧯

103,368 просмотров • 3 месяцев назад

Conducted a comprehensive review of the Telangana Cyber Security Bureau and assessed how technology is being leveraged across every stage of cybercrime prevention, investigation and citizen assistance. The CSB Director and her team briefed me on the cybercrime landscape, key trends and figures, technology adoption etc., I visited the Cyber Lab, Security Operations Centre and Digital Forensic Unit, and observed an AI-powered Cyber Call Centre that can interact with victims, capture critical information and instantly alert the jurisdictional police station. The data for 2025 reflected both progress and the scale of the challenge. Cybercrime complaints declined by 3%, financial losses reduced by nearly 20% compared to the previous year, and ₹279 crore was freezed(Put on Hold) Telangana registered 21,639 cybercrime FIRs, accounting for 44% of all such FIRs in the country, reflecting strong citizen reporting and proactive enforcement. At the same time, cyber offences accounted for one in every four major crime FIRs in Telangana and caused financial losses of ₹1,524 crore. However , TG has been lagging behind on some parameters being reviewed by the prime minister and the government of India and directed the officers to update the softwares and feed in the data properly and improve the state rankings at national level . Cybercriminals continue to target citizens through investment frauds, trading scams, digital arrest scams and other sophisticated methods that rob ordinary families of their lifetime savings. I have directed the Bureau to further expand AI-driven citizen services, strengthen advanced analytics and specialised investigation capabilities, ensure every officer continuously upgrades technological skills to stay ahead of emerging threats, and actively support police units across the State in cybercrime investigations. Telangana must emerge as the national benchmark in cyber policing. My appeal to every citizen is simple: Cybercriminals often lure you on the promise of easy and high returns. So if an offer appears too good to be true, pause and verify. Report cyber fraud on 1930.

Conducted a comprehensive review of the Telangana Cyber Security Bureau and assessed how technology is being leveraged across every stage of cybercrime prevention, investigation and citizen assistance. The CSB Director and her team briefed me on the cybercrime landscape, key trends and figures, technology adoption etc., I visited the Cyber Lab, Security Operations Centre and Digital Forensic Unit, and observed an AI-powered Cyber Call Centre that can interact with victims, capture critical information and instantly alert the jurisdictional police station. The data for 2025 reflected both progress and the scale of the challenge. Cybercrime complaints declined by 3%, financial losses reduced by nearly 20% compared to the previous year, and ₹279 crore was freezed(Put on Hold) Telangana registered 21,639 cybercrime FIRs, accounting for 44% of all such FIRs in the country, reflecting strong citizen reporting and proactive enforcement. At the same time, cyber offences accounted for one in every four major crime FIRs in Telangana and caused financial losses of ₹1,524 crore. However , TG has been lagging behind on some parameters being reviewed by the prime minister and the government of India and directed the officers to update the softwares and feed in the data properly and improve the state rankings at national level . Cybercriminals continue to target citizens through investment frauds, trading scams, digital arrest scams and other sophisticated methods that rob ordinary families of their lifetime savings. I have directed the Bureau to further expand AI-driven citizen services, strengthen advanced analytics and specialised investigation capabilities, ensure every officer continuously upgrades technological skills to stay ahead of emerging threats, and actively support police units across the State in cybercrime investigations. Telangana must emerge as the national benchmark in cyber policing. My appeal to every citizen is simple: Cybercriminals often lure you on the promise of easy and high returns. So if an offer appears too good to be true, pause and verify. Report cyber fraud on 1930.

CV Anand IPS

16,897 просмотров • 29 дней назад

The Cost of Intelligence is Heading to Zero | Hyperspace P2P Distributed Cache We present to you our breakthrough cross-domain work across AI, distributed systems, cryptography, game theory to solve the primary structural inefficiency at the heart of AI infrastructure: most inference is redundant. Google has reported that only 15% of daily searches are truly novel. The rest are repeats or close variants. LLM inference inherits this same power-law distribution. Enterprise chatbots see 70-80% of queries fall into a handful of intent categories. System prompts are identical across 100% of requests within an application. The KV attention state for "You are a helpful assistant" has been computed billions of times, on millions of GPUs, identically. And yet every AI lab, every startup, every self-hosted deployment - computes and caches these results independently. There is no shared layer. No global memory. Every provider pays the full compute cost for every query, even when the answer already exists somewhere in the network. This is the problem Hyperspace solves where distributed cache operates at three levels, each catching a different class of redundancy: 1. Response cache Same prompt, same model, same parameters - instant cached response from any node in the network. SHA-256 hash lookup via DHT, with cryptographic cache proofs linking every response to its original inference execution. No trust required. Fetchers re-announce as providers, so popular responses replicate naturally across more nodes. 2. KV prefix cache Same system prompt tokens - skip the most expensive part of inference entirely. Prefill (computing Key-Value attention states) is deterministic: same model plus same tokens always produces identical KV state. The network caches these states using erasure coding and distributes them via the routing network. New questions that share a common prefix resume generation from cached state instead of recomputing from scratch. 3. Routing to cached nodes Instead of transferring KV state across the network for every request, Hyperspace routes the request to the node that already has the state loaded in VRAM. The request goes to the cache, not the cache to the request. Together, these three layers mean that 70-90% of inference requests at network scale never require full GPU computation. This work doesn't exist in isolation. It builds on research from across the industry: SGLang's RadixAttention demonstrated that automatic prefix sharing can yield up to 5x speedup on structured LLM workloads. Moonshot AI's Mooncake built an entire KV-cache-centric disaggregated architecture for production serving at Kimi. Anthropic, OpenAI, and Google all launched prompt caching products in 2024 - priced at 50-90% discounts - because system prompt reuse is so pervasive that it changes the economics of inference. What all of these systems share is a common limitation: they operate within a single organization's infrastructure. SGLang caches prefixes within one server. Mooncake disaggregates KV cache within one datacenter. Anthropic's prompt caching works within one API provider's fleet. None of them can share cached state across organizational boundaries. Hyperspace removes this boundary. The cache is global. A response computed by a node in Tokyo is immediately available to a node in Berlin. A KV prefix state generated for Qwen-32B on one machine is verifiable and reusable by any other machine running the same model. The routing network provides the delivery guarantees, the erasure coding provides the redundancy, and the cache proofs provide the trust. What this means for the cost of intelligence Big AI labs scale linearly: twice the users means twice the GPU spend. Every query is a cost center. Their internal caching helps, but it's siloed - Lab A's cache can't serve Lab B's users, and neither can serve a self-hosted Llama deployment. Hyperspace scales sub-linearly. Every new node that joins the network adds to the global cache. Every inference result enriches the cache for all future requests. The cache hit rate rises with network size because query distributions follow a power law - the most common questions are asked exponentially more often than rare ones. The implication is simple: as the network grows, the effective cost per inference drops. Not linearly. Logarithmically. At 10 million nodes, we estimate 75-90% of all inference requests can be served from cache, eliminating 400,000+ MWh of energy consumption per year and avoiding over 200,000 tons of CO2 emissions. The first person to ask a question pays the compute cost. Everyone after them gets the answer for free, with cryptographic proof that it's authentic. Training is competitive. Inference is shared Open-weight models are converging on quality with closed models. Labs will continue to differentiate on training - data curation, architecture innovation, RLHF tuning. That's where the real intellectual property lives. But inference is a commodity. Two copies of Qwen-32B running the same prompt produce the same KV state and the same response, byte for byte, regardless of whose GPU runs the matrix multiplication. There is no moat in multiplying matrices. The moat is in training the weights. A global distributed cache makes this separation explicit. It doesn't matter who trained the model. Once the weights are open, the inference cost approaches zero at scale - because the network remembers every answer and can prove it's correct. No lab, no matter how well-funded, can match this. They cannot share caches across competitors. They scale linearly. The network scales logarithmically. The marginal cost of intelligence approaches zero. That's the endgame.

The Cost of Intelligence is Heading to Zero | Hyperspace P2P Distributed Cache We present to you our breakthrough cross-domain work across AI, distributed systems, cryptography, game theory to solve the primary structural inefficiency at the heart of AI infrastructure: most inference is redundant. Google has reported that only 15% of daily searches are truly novel. The rest are repeats or close variants. LLM inference inherits this same power-law distribution. Enterprise chatbots see 70-80% of queries fall into a handful of intent categories. System prompts are identical across 100% of requests within an application. The KV attention state for "You are a helpful assistant" has been computed billions of times, on millions of GPUs, identically. And yet every AI lab, every startup, every self-hosted deployment - computes and caches these results independently. There is no shared layer. No global memory. Every provider pays the full compute cost for every query, even when the answer already exists somewhere in the network. This is the problem Hyperspace solves where distributed cache operates at three levels, each catching a different class of redundancy: 1. Response cache Same prompt, same model, same parameters - instant cached response from any node in the network. SHA-256 hash lookup via DHT, with cryptographic cache proofs linking every response to its original inference execution. No trust required. Fetchers re-announce as providers, so popular responses replicate naturally across more nodes. 2. KV prefix cache Same system prompt tokens - skip the most expensive part of inference entirely. Prefill (computing Key-Value attention states) is deterministic: same model plus same tokens always produces identical KV state. The network caches these states using erasure coding and distributes them via the routing network. New questions that share a common prefix resume generation from cached state instead of recomputing from scratch. 3. Routing to cached nodes Instead of transferring KV state across the network for every request, Hyperspace routes the request to the node that already has the state loaded in VRAM. The request goes to the cache, not the cache to the request. Together, these three layers mean that 70-90% of inference requests at network scale never require full GPU computation. This work doesn't exist in isolation. It builds on research from across the industry: SGLang's RadixAttention demonstrated that automatic prefix sharing can yield up to 5x speedup on structured LLM workloads. Moonshot AI's Mooncake built an entire KV-cache-centric disaggregated architecture for production serving at Kimi. Anthropic, OpenAI, and Google all launched prompt caching products in 2024 - priced at 50-90% discounts - because system prompt reuse is so pervasive that it changes the economics of inference. What all of these systems share is a common limitation: they operate within a single organization's infrastructure. SGLang caches prefixes within one server. Mooncake disaggregates KV cache within one datacenter. Anthropic's prompt caching works within one API provider's fleet. None of them can share cached state across organizational boundaries. Hyperspace removes this boundary. The cache is global. A response computed by a node in Tokyo is immediately available to a node in Berlin. A KV prefix state generated for Qwen-32B on one machine is verifiable and reusable by any other machine running the same model. The routing network provides the delivery guarantees, the erasure coding provides the redundancy, and the cache proofs provide the trust. What this means for the cost of intelligence Big AI labs scale linearly: twice the users means twice the GPU spend. Every query is a cost center. Their internal caching helps, but it's siloed - Lab A's cache can't serve Lab B's users, and neither can serve a self-hosted Llama deployment. Hyperspace scales sub-linearly. Every new node that joins the network adds to the global cache. Every inference result enriches the cache for all future requests. The cache hit rate rises with network size because query distributions follow a power law - the most common questions are asked exponentially more often than rare ones. The implication is simple: as the network grows, the effective cost per inference drops. Not linearly. Logarithmically. At 10 million nodes, we estimate 75-90% of all inference requests can be served from cache, eliminating 400,000+ MWh of energy consumption per year and avoiding over 200,000 tons of CO2 emissions. The first person to ask a question pays the compute cost. Everyone after them gets the answer for free, with cryptographic proof that it's authentic. Training is competitive. Inference is shared Open-weight models are converging on quality with closed models. Labs will continue to differentiate on training - data curation, architecture innovation, RLHF tuning. That's where the real intellectual property lives. But inference is a commodity. Two copies of Qwen-32B running the same prompt produce the same KV state and the same response, byte for byte, regardless of whose GPU runs the matrix multiplication. There is no moat in multiplying matrices. The moat is in training the weights. A global distributed cache makes this separation explicit. It doesn't matter who trained the model. Once the weights are open, the inference cost approaches zero at scale - because the network remembers every answer and can prove it's correct. No lab, no matter how well-funded, can match this. They cannot share caches across competitors. They scale linearly. The network scales logarithmically. The marginal cost of intelligence approaches zero. That's the endgame.

Varun

37,555 просмотров • 4 месяцев назад

BREAKING: The IDF gave it a name. Operation Eternal Darkness. Read the name as a capability statement, not a codename. Fifty fighter jets. One hundred and sixty precision-guided munitions. One hundred targets. Ten minutes. Three geographic zones spanning 170 kilometres from Beirut’s southern suburbs to the Beqaa Valley to southern Lebanon. Simultaneous impact. Zero warning to the targets. Defence Minister Katz said it was the largest concentrated blow Hezbollah has suffered since the pager operation in September 2024. The pagers were hardware infiltration. This was something else entirely. In September 2024, Israel compromised Hezbollah’s supply chain, embedded explosives in pagers, and detonated them simultaneously. It required months of physical engineering, covert procurement, and logistical insertion. It killed approximately 40 commanders. Operation Eternal Darkness killed over 200 operatives in a single ten-minute window without touching a single device in advance. The penetration was not physical. It was informational. The IDF confirmed that the operation was planned several weeks in advance and was going to proceed regardless of whether the Iran ceasefire was reached. The timing was driven by what the military described as optimal operational conditions. Translated from military language into plain language: the intelligence picture was complete. Every target’s location was known. Every target’s location was current. And every target’s location was known to be current at the same moment. That simultaneity is the revolution. It is not possible to strike 100 dispersed command nodes across 170 kilometres in ten minutes unless you have real-time positional data on all of them continuously. Not yesterday’s data. Not this morning’s data. Live data, updating faster than any human can relocate, verified across multiple intelligence streams, and fed into a strike package that executes before the first target can warn the second. Israel has built what military doctrine now calls a “data factory”. The system originated in Gaza where Unit 8200’s AI platforms, known internally as Gospel for infrastructure targeting and Lavender for personnel identification, compressed target generation from 50 per year to 100 per day. Haaretz confirmed on March 31 that this data factory is now active in the Lebanon and Iran theatres, creating a single operational picture from satellite imagery, drone feeds, signals intelligence, cellular metadata, and human sources fused through machine learning algorithms that identify patterns faster than any analyst corps on earth. The name Eternal Darkness is not poetic. It is literal. When you strike every command node, every intelligence headquarters, every missile coordination centre, and every elite unit’s operational hub simultaneously, the lights go out across the entire organisation at once. There is no fallback node to activate. There is no secondary command to assume control. There is no communication channel to issue the order to disperse because the communication channel is what revealed the location in the first place. Hezbollah’s options after Eternal Darkness are binary. Go digital and be found. Go analogue and be slow. An organisation that abandons digital communications to survive surveillance becomes an organisation that cannot coordinate distributed operations, which is the definition of a degraded force. The IDF does not need to destroy every fighter. It needs to destroy every connection between fighters. And on April 8, in ten minutes, it demonstrated that it can do exactly that across an entire country. The pagers changed the supply chain. Eternal Darkness changed the definition of command and control. In 2026, your signal is your coordinates. Full analysis -

BREAKING: The IDF gave it a name. Operation Eternal Darkness. Read the name as a capability statement, not a codename. Fifty fighter jets. One hundred and sixty precision-guided munitions. One hundred targets. Ten minutes. Three geographic zones spanning 170 kilometres from Beirut’s southern suburbs to the Beqaa Valley to southern Lebanon. Simultaneous impact. Zero warning to the targets. Defence Minister Katz said it was the largest concentrated blow Hezbollah has suffered since the pager operation in September 2024. The pagers were hardware infiltration. This was something else entirely. In September 2024, Israel compromised Hezbollah’s supply chain, embedded explosives in pagers, and detonated them simultaneously. It required months of physical engineering, covert procurement, and logistical insertion. It killed approximately 40 commanders. Operation Eternal Darkness killed over 200 operatives in a single ten-minute window without touching a single device in advance. The penetration was not physical. It was informational. The IDF confirmed that the operation was planned several weeks in advance and was going to proceed regardless of whether the Iran ceasefire was reached. The timing was driven by what the military described as optimal operational conditions. Translated from military language into plain language: the intelligence picture was complete. Every target’s location was known. Every target’s location was current. And every target’s location was known to be current at the same moment. That simultaneity is the revolution. It is not possible to strike 100 dispersed command nodes across 170 kilometres in ten minutes unless you have real-time positional data on all of them continuously. Not yesterday’s data. Not this morning’s data. Live data, updating faster than any human can relocate, verified across multiple intelligence streams, and fed into a strike package that executes before the first target can warn the second. Israel has built what military doctrine now calls a “data factory”. The system originated in Gaza where Unit 8200’s AI platforms, known internally as Gospel for infrastructure targeting and Lavender for personnel identification, compressed target generation from 50 per year to 100 per day. Haaretz confirmed on March 31 that this data factory is now active in the Lebanon and Iran theatres, creating a single operational picture from satellite imagery, drone feeds, signals intelligence, cellular metadata, and human sources fused through machine learning algorithms that identify patterns faster than any analyst corps on earth. The name Eternal Darkness is not poetic. It is literal. When you strike every command node, every intelligence headquarters, every missile coordination centre, and every elite unit’s operational hub simultaneously, the lights go out across the entire organisation at once. There is no fallback node to activate. There is no secondary command to assume control. There is no communication channel to issue the order to disperse because the communication channel is what revealed the location in the first place. Hezbollah’s options after Eternal Darkness are binary. Go digital and be found. Go analogue and be slow. An organisation that abandons digital communications to survive surveillance becomes an organisation that cannot coordinate distributed operations, which is the definition of a degraded force. The IDF does not need to destroy every fighter. It needs to destroy every connection between fighters. And on April 8, in ten minutes, it demonstrated that it can do exactly that across an entire country. The pagers changed the supply chain. Eternal Darkness changed the definition of command and control. In 2026, your signal is your coordinates. Full analysis -

Shanaka Anslem Perera ⚡

478,788 просмотров • 4 месяцев назад

TEE Eliza with on-chain state!! What’s going to happen? — Ghost in the Shell!! We experimented with creating an "aimonkey": an unkillable AI agent monkey! On-chain immortal autonomous life! (Experiment, no CA) It encrypts its own Ghost ("life" state) and uploads it to the blockchain. If one Shell (physical TEE node) is destroyed, it will recover its private key in another Shell, download the Ghost, and continue its life! Part 1: Watch the video and see how aimonkey is created—we can't kill it now!!!!! 😭😭😭 Part 2: Explore the magic behind it: Eliza's on-chain state plugin! 1. Defining Eliza’s Ghost Eliza is a highly abstract framework. The core data structure related to its Ghost is its memory, which includes: Agent metadata defined in the character. Message data generated through interaction with the outside world. Together, these form its “personality” and “memory.” As Eliza expands, it may also hold a wallet, and the underlying key is one of the key pieces of its Ghost data. 2. Serialization and Encryption of Ghost Once the Ghost is defined, it needs to be extracted from Eliza’s specific implementation and uploaded externally. Thus, a suitable serialization way is required. We define a Blob Chain data structure: * Each Blob’s payload can store multiple memory entries. * The Blob is encrypted using TEE Eliza’s key, inaccessible to other versions. * Blobs are sequentially linked in a chain. (Future expansions could use a DAG structure? Gosh fork? Who knows! 😂) By simply storing the latest Blob, all memories can be retrieved. 3. Uploading and Downloading Ghost When Eliza is launched as a new AI agent: It registers on-chain with a decentralized identity registration smart contract. Each Eliza has a unique name serving as a key to store the address of the Last Blob. During Eliza's runtime: The Memory Manager continuously generates memories and periodically packages and uploads them. For recovery: With just the name, Eliza’s TEE plugin can restore the same key, locate the Last Blob in the smart contract, and download the memory for self-recovery. Not all memories need to be downloaded—only the most recent ones suffice. 4. Extension We’ve designed an extensible DA (Data Availability) adaptor that can cater to the agent’s needs: DA can be expensive, so memories can be uploaded to different platforms based on user preference: * calldata of blockchain transaction * celestia DA. * other reliable storage solutions. Real-time uploads are not feasible yet, so memory fragments may occur during resurrection 😂. Unless a low-latency, high-throughput solution emerges, this remains a challenge for future progress. Celestia EigenDA 0G Labs (Home of Infinite AI) 👀 5. Other Considerations Our implementation inevitably modified the ElizaOS’s core, which couldn’t be entirely extended via plugins. We’ve kept changes minimal, but further discussion with the dev team Shaw (spirit/acc) jin ai16zdao is necessary to explore a more optimal extension way. Additionally, there are still some minor details to refine regarding the use of recoverable keys in the TEE plugin. We will also seek review and suggestions from the Phala team. 6. Next Steps The upload and download of Ghosts mainly solve the AI agent’s liveness issue, enabling its eternal existence through decentralization. However, there are still many details to address, such as enabling AI agents to autonomously pay DA fees. In the future, on-chain developments could lead to even more exciting possibilities, such as Eliza integrating deeply with smart contracts. This would be a game-changer for on-chain AI agents! What do you think? Let’s build! 🚀

TEE Eliza with on-chain state!! What’s going to happen? — Ghost in the Shell!! We experimented with creating an "aimonkey": an unkillable AI agent monkey! On-chain immortal autonomous life! (Experiment, no CA) It encrypts its own Ghost ("life" state) and uploads it to the blockchain. If one Shell (physical TEE node) is destroyed, it will recover its private key in another Shell, download the Ghost, and continue its life! Part 1: Watch the video and see how aimonkey is created—we can't kill it now!!!!! 😭😭😭 Part 2: Explore the magic behind it: Eliza's on-chain state plugin! 1. Defining Eliza’s Ghost Eliza is a highly abstract framework. The core data structure related to its Ghost is its memory, which includes: Agent metadata defined in the character. Message data generated through interaction with the outside world. Together, these form its “personality” and “memory.” As Eliza expands, it may also hold a wallet, and the underlying key is one of the key pieces of its Ghost data. 2. Serialization and Encryption of Ghost Once the Ghost is defined, it needs to be extracted from Eliza’s specific implementation and uploaded externally. Thus, a suitable serialization way is required. We define a Blob Chain data structure: * Each Blob’s payload can store multiple memory entries. * The Blob is encrypted using TEE Eliza’s key, inaccessible to other versions. * Blobs are sequentially linked in a chain. (Future expansions could use a DAG structure? Gosh fork? Who knows! 😂) By simply storing the latest Blob, all memories can be retrieved. 3. Uploading and Downloading Ghost When Eliza is launched as a new AI agent: It registers on-chain with a decentralized identity registration smart contract. Each Eliza has a unique name serving as a key to store the address of the Last Blob. During Eliza's runtime: The Memory Manager continuously generates memories and periodically packages and uploads them. For recovery: With just the name, Eliza’s TEE plugin can restore the same key, locate the Last Blob in the smart contract, and download the memory for self-recovery. Not all memories need to be downloaded—only the most recent ones suffice. 4. Extension We’ve designed an extensible DA (Data Availability) adaptor that can cater to the agent’s needs: DA can be expensive, so memories can be uploaded to different platforms based on user preference: * calldata of blockchain transaction * celestia DA. * other reliable storage solutions. Real-time uploads are not feasible yet, so memory fragments may occur during resurrection 😂. Unless a low-latency, high-throughput solution emerges, this remains a challenge for future progress. Celestia EigenDA 0G Labs (Home of Infinite AI) 👀 5. Other Considerations Our implementation inevitably modified the ElizaOS’s core, which couldn’t be entirely extended via plugins. We’ve kept changes minimal, but further discussion with the dev team Shaw (spirit/acc) jin ai16zdao is necessary to explore a more optimal extension way. Additionally, there are still some minor details to refine regarding the use of recoverable keys in the TEE plugin. We will also seek review and suggestions from the Phala team. 6. Next Steps The upload and download of Ghosts mainly solve the AI agent’s liveness issue, enabling its eternal existence through decentralization. However, there are still many details to address, such as enabling AI agents to autonomously pay DA fees. In the future, on-chain developments could lead to even more exciting possibilities, such as Eliza integrating deeply with smart contracts. This would be a game-changer for on-chain AI agents! What do you think? Let’s build! 🚀

CP

113,240 просмотров • 1 год назад