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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 • 11 месяцев назад •via X (Twitter)

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🇺🇸 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

93,985 просмотров • 2 месяцев назад

BREAKING: FDA APPROVES NEW PFIZER AND MODERNA XFG COVID-19 mRNA SHOTS BASED ON DATA FROM JUST 8–10 MICE NO HUMAN CLINICAL TRIAL DATA on the new XFG formulations were available before approval—the required human safety and immunogenicity studies begin AFTER FDA approval. The reach of these dangerous approvals is sweeping: Pfizer can now be given to every American age 65 and older and to “high-risk” individuals as young as 5, while Moderna extends all the way down to “high-risk” infants just 6 months old. The previous COVID shots have already been linked to MILLIONS of deaths, injuries, and disabilities, along with demonstrated cardiotoxicity, neurotoxicity, carcinogenicity, body-wide biodistribution, and years-long persistence. This is extremely worrisome and needs to be reversed IMMEDIATELY.

BREAKING: FDA APPROVES NEW PFIZER AND MODERNA XFG COVID-19 mRNA SHOTS BASED ON DATA FROM JUST 8–10 MICE NO HUMAN CLINICAL TRIAL DATA on the new XFG formulations were available before approval—the required human safety and immunogenicity studies begin AFTER FDA approval. The reach of these dangerous approvals is sweeping: Pfizer can now be given to every American age 65 and older and to “high-risk” individuals as young as 5, while Moderna extends all the way down to “high-risk” infants just 6 months old. The previous COVID shots have already been linked to MILLIONS of deaths, injuries, and disabilities, along with demonstrated cardiotoxicity, neurotoxicity, carcinogenicity, body-wide biodistribution, and years-long persistence. This is extremely worrisome and needs to be reversed IMMEDIATELY.

Nicolas Hulscher, MPH

60,353 просмотров • 1 месяц 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 Ohio

14,844 просмотров • 7 месяцев назад

Pi Network constantly upgrades its protocol, but what has changed? Throughout the year 2026, Pi Network (Pi Network) has been introducing protocol updates to the Mainnet on a compulsory basis. The cycle is quite real. But $PI's price doesn't seem to reflect that. For context, the coin remains 97% below its all-time high mark in February 2025 at $2.99. Over the last twelve months, its price fell 66%, while over the last month, it fell 8%. While the upgrades did not affect the price, what do they bring: (1) Upgrades are node deadline first, followed by chain switch. The operator must upgrade before the deadline; otherwise, they risk disconnection from the network. The on-chain upgrade happens later, after sufficient nodes are prepared. This time difference is usually three days. However, in Protocol 25, it became 34 days, while in Protocol 27, it was seven days. Pioneers do nothing here. (2) Early 2026 was an alignment period. Versions 19.6, 19.9, and 20.2 happened between February 15 and March 13. Protocol 20 served as the base for the smart contracts but not the smart contract itself. The switch to Protocol 21 happened on April 29. Protocol 22, which happened on April 30, made the PiRC1 framework functional. 3) Protocol 23 activated Soroban. This was the functional break: Pi’s Stellar-based chain could support smart contracts. This protocol did not form the application economy. 4) Protocol 24 and 25 separated maintenance from cryptography. Protocol 24, on June 5, focused on migration and node stabilization. Protocol 25, on July 22, implemented BN254 cryptography and Poseidon hashing, components of zero-knowledge solutions, but not yet a functional privacy solution. (5) Protocols 26 and 27 strengthened the contract layer. Protocol 26, roughly around August 14, improved contract security, state management, and interoperability. Protocol 27 hit Mainnet roughly on September 22 and finalized on September 25. It improved smart contract authentication and laid the foundation for pool-like systems for exchanges and RPC access. (6) Protocol 28 is less broad than suggested by its version number. The Testnet was updated on September 25. The Mainnet will be upgraded on October 16. The updates include delaying transaction data, smart contract upgrades, and safer edits to stored application data. Despite these upgrades, token unlock has remained the same, and there has been no sign of substantial on-chain demand. This begs the question: are these upgrades worthwhile?

Pi Network constantly upgrades its protocol, but what has changed? Throughout the year 2026, Pi Network (Pi Network) has been introducing protocol updates to the Mainnet on a compulsory basis. The cycle is quite real. But $PI's price doesn't seem to reflect that. For context, the coin remains 97% below its all-time high mark in February 2025 at $2.99. Over the last twelve months, its price fell 66%, while over the last month, it fell 8%. While the upgrades did not affect the price, what do they bring: (1) Upgrades are node deadline first, followed by chain switch. The operator must upgrade before the deadline; otherwise, they risk disconnection from the network. The on-chain upgrade happens later, after sufficient nodes are prepared. This time difference is usually three days. However, in Protocol 25, it became 34 days, while in Protocol 27, it was seven days. Pioneers do nothing here. (2) Early 2026 was an alignment period. Versions 19.6, 19.9, and 20.2 happened between February 15 and March 13. Protocol 20 served as the base for the smart contracts but not the smart contract itself. The switch to Protocol 21 happened on April 29. Protocol 22, which happened on April 30, made the PiRC1 framework functional. 3) Protocol 23 activated Soroban. This was the functional break: Pi’s Stellar-based chain could support smart contracts. This protocol did not form the application economy. 4) Protocol 24 and 25 separated maintenance from cryptography. Protocol 24, on June 5, focused on migration and node stabilization. Protocol 25, on July 22, implemented BN254 cryptography and Poseidon hashing, components of zero-knowledge solutions, but not yet a functional privacy solution. (5) Protocols 26 and 27 strengthened the contract layer. Protocol 26, roughly around August 14, improved contract security, state management, and interoperability. Protocol 27 hit Mainnet roughly on September 22 and finalized on September 25. It improved smart contract authentication and laid the foundation for pool-like systems for exchanges and RPC access. (6) Protocol 28 is less broad than suggested by its version number. The Testnet was updated on September 25. The Mainnet will be upgraded on October 16. The updates include delaying transaction data, smart contract upgrades, and safer edits to stored application data. Despite these upgrades, token unlock has remained the same, and there has been no sign of substantial on-chain demand. This begs the question: are these upgrades worthwhile?

BSCN

46,046 просмотров • 6 дней 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 • 4 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

77,805 просмотров • 1 год 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 • 9 месяцев 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 • 11 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

231,628 просмотров • 5 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

34,682 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)

128,664 просмотров • 3 месяцев 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

121,787 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 • 9 месяцев назад

How Cloudflare cut query times by 35x without leaving Postgres: Their Postgres tables hit billions of rows, and every time-range query started getting slower. Plain Postgres scans the entire table on every query, regardless of the time window. They tried the manual route by building precomputed aggregates with cron jobs and evaluating ClickHouse, which needed a full ingestion pipeline just to handle their write pattern. Here's where most teams get stuck at exactly this point. They utilize manual partitioning, splitting data into child tables by day or month. Then they wire up cron jobs to refresh aggregate tables. Every schema change after that requires updating the cron logic and coordinating across teams. The infrastructure becomes the project. Tiger Cloud is managed TimescaleDB by Tiger Data - Creators of TimescaleDB on the Postgres you already know, with automatic time-based partitioning, continuous aggregates, and compression built in from the start. Cloudflare moved to TimescaleDB after exhausting the manual route and saw 5-35x query performance improvement on the same data. Here is how it works: → Hypertables partition data by timestamp automatically. Every time-range query hits only the relevant chunk, not the full table. → Continuous aggregates refresh incrementally in the background, with no cron jobs to maintain. To show what this looks like in practice, I built a real-time earthquake intelligence dashboard on a 3D globe using Claude Code and Tiger Cloud in a single session. The USGS earthquake catalog has 400,000+ events since 1900, each a timestamped row. The dashboard plots them as ripple animations sized by magnitude and colored by depth, with a time slider that scrubs through 120 years of seismic history. Every slider position fires a live query against the hypertable, and the side panel pulls from continuous aggregates. Claude Code connected to Tiger Cloud through the Tiger CLI MCP server, provisioned the database, pulled the USGS catalog, and assembled the full Next.js and Three.js frontend without leaving the session. The video below shows the final build in action, and I worked with the Tiger Data team to put this together. Tiger CLI is open-source (Apache 2.0) and works with Claude Code, Cursor, Codex, Gemini CLI, and VS Code. To try this yourself: → Sign up for Tiger Cloud here: New accounts get $1,000 in free credits, no credit card required. → Install Tiger CLI: curl -fsSL https(:)//cli(.)tigerdata(.)com | sh → Run tiger mcp install claude-code → Give Claude Code a prompt and let it build. I also wrote a full walkthrough on how you can turn any coding agent into a production-grade data engineer that can manage over a billion-row Postgres workloads. It covers everything from the database setup to the final build. Read it below.

How Cloudflare cut query times by 35x without leaving Postgres: Their Postgres tables hit billions of rows, and every time-range query started getting slower. Plain Postgres scans the entire table on every query, regardless of the time window. They tried the manual route by building precomputed aggregates with cron jobs and evaluating ClickHouse, which needed a full ingestion pipeline just to handle their write pattern. Here's where most teams get stuck at exactly this point. They utilize manual partitioning, splitting data into child tables by day or month. Then they wire up cron jobs to refresh aggregate tables. Every schema change after that requires updating the cron logic and coordinating across teams. The infrastructure becomes the project. Tiger Cloud is managed TimescaleDB by Tiger Data - Creators of TimescaleDB on the Postgres you already know, with automatic time-based partitioning, continuous aggregates, and compression built in from the start. Cloudflare moved to TimescaleDB after exhausting the manual route and saw 5-35x query performance improvement on the same data. Here is how it works: → Hypertables partition data by timestamp automatically. Every time-range query hits only the relevant chunk, not the full table. → Continuous aggregates refresh incrementally in the background, with no cron jobs to maintain. To show what this looks like in practice, I built a real-time earthquake intelligence dashboard on a 3D globe using Claude Code and Tiger Cloud in a single session. The USGS earthquake catalog has 400,000+ events since 1900, each a timestamped row. The dashboard plots them as ripple animations sized by magnitude and colored by depth, with a time slider that scrubs through 120 years of seismic history. Every slider position fires a live query against the hypertable, and the side panel pulls from continuous aggregates. Claude Code connected to Tiger Cloud through the Tiger CLI MCP server, provisioned the database, pulled the USGS catalog, and assembled the full Next.js and Three.js frontend without leaving the session. The video below shows the final build in action, and I worked with the Tiger Data team to put this together. Tiger CLI is open-source (Apache 2.0) and works with Claude Code, Cursor, Codex, Gemini CLI, and VS Code. To try this yourself: → Sign up for Tiger Cloud here: New accounts get $1,000 in free credits, no credit card required. → Install Tiger CLI: curl -fsSL https(:)//cli(.)tigerdata(.)com | sh → Run tiger mcp install claude-code → Give Claude Code a prompt and let it build. I also wrote a full walkthrough on how you can turn any coding agent into a production-grade data engineer that can manage over a billion-row Postgres workloads. It covers everything from the database setup to the final build. Read it below.

Avi Chawla

22,094 просмотров • 1 месяц назад

Claude Code is now scary good at full-stack! I asked it to build a real-time weather intelligence dashboard with an interactive 3D globe, a forecasting layer that predicts weather 3 days ahead, and an anomaly detector that flags cities whose weather is behaving abnormally. It came back with a spinning globe that has a day/night cycle using NASA satellite imagery, city lights on the dark side, weather icons that switch between sun and moon based on local time, and a time travel slider that scrubs through 10 days of data. And when a city's weather breaks from its own normal, it pulses red (abnormally hot) or blue (abnormally cold) right on the globe, updating live and reflecting the anomaly state at any point you drag the slider to. Claude Code built the whole thing in a single session, including the backend, database, data pipeline, and frontend. For the database, I needed something fast for time-series workloads since the app ingests hourly weather readings across many cities and serves time-range queries on every slider interaction. I used Tiger Cloud by Tiger Data - Creators of TimescaleDB, which gives you managed TimescaleDB on the Postgres you already know. Claude Code connected to it through the Tiger CLI MCP server and set up the entire backend directly: - Provisioned the database service - Created hypertables for time-partitioned weather storage - Set up continuous aggregates for pre-computed rollups - Built the data ingestion pipeline and the full NextJS + ThreeJS frontend The time travel slider queries thousands of rows on every position change. On a regular Postgres table, this would require manual partitioning and index tuning to stay fast as data grows. TimescaleDB partitions the data by timestamp automatically, so each query only hits the relevant time chunk. Continuous aggregates serve the trend charts, the forecast layer, and the anomaly baselines from pre-computed rollups instead of rescanning raw data on every request. The video below shows the final build in action, and I worked with the Tiger Data team to put this together. Tiger CLI is open-source (Apache 2.0) and works with Claude Code, Cursor, Codex, Gemini CLI, and VS Code. To try this yourself: → Sign up for Tiger Cloud (I have shared the link in the replies). It gives you $1,000 free credits (no card needed) → Install Tiger CLI: curl -fsSL https(:)//cli(.)tigerdata(.)com | sh → Run tiger mcp install claude-code → Give Claude Code a prompt and let it build sign-up here: My co-founder also wrote a detailed article on this. The article is quoted below.

Claude Code is now scary good at full-stack! I asked it to build a real-time weather intelligence dashboard with an interactive 3D globe, a forecasting layer that predicts weather 3 days ahead, and an anomaly detector that flags cities whose weather is behaving abnormally. It came back with a spinning globe that has a day/night cycle using NASA satellite imagery, city lights on the dark side, weather icons that switch between sun and moon based on local time, and a time travel slider that scrubs through 10 days of data. And when a city's weather breaks from its own normal, it pulses red (abnormally hot) or blue (abnormally cold) right on the globe, updating live and reflecting the anomaly state at any point you drag the slider to. Claude Code built the whole thing in a single session, including the backend, database, data pipeline, and frontend. For the database, I needed something fast for time-series workloads since the app ingests hourly weather readings across many cities and serves time-range queries on every slider interaction. I used Tiger Cloud by Tiger Data - Creators of TimescaleDB, which gives you managed TimescaleDB on the Postgres you already know. Claude Code connected to it through the Tiger CLI MCP server and set up the entire backend directly: - Provisioned the database service - Created hypertables for time-partitioned weather storage - Set up continuous aggregates for pre-computed rollups - Built the data ingestion pipeline and the full NextJS + ThreeJS frontend The time travel slider queries thousands of rows on every position change. On a regular Postgres table, this would require manual partitioning and index tuning to stay fast as data grows. TimescaleDB partitions the data by timestamp automatically, so each query only hits the relevant time chunk. Continuous aggregates serve the trend charts, the forecast layer, and the anomaly baselines from pre-computed rollups instead of rescanning raw data on every request. The video below shows the final build in action, and I worked with the Tiger Data team to put this together. Tiger CLI is open-source (Apache 2.0) and works with Claude Code, Cursor, Codex, Gemini CLI, and VS Code. To try this yourself: → Sign up for Tiger Cloud (I have shared the link in the replies). It gives you $1,000 free credits (no card needed) → Install Tiger CLI: curl -fsSL https(:)//cli(.)tigerdata(.)com | sh → Run tiger mcp install claude-code → Give Claude Code a prompt and let it build sign-up here: My co-founder also wrote a detailed article on this. The article is quoted below.

Akshay 🚀

37,269 просмотров • 1 месяц 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 • 9 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

263,308 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

1,529,094 просмотров • 4 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 • 5 месяцев назад