正在加载视频...

视频加载失败

返回首页

🧱 Speed means less when systems disagree on what happened. KaJ Labs frames standards as the real foundation for agent infrastructure, keeping verification, identity, execution, governance, and settlement consistent across multi-step Web4 workflows. #KaJLabs #Web4 #AutonomousAgents #AIInfrastructure #Governance

LITHOFoundation's profile picture

LITHO Foundation

18,587 subscribers

21,136 次观看 • 1 个月前 •via X (Twitter)

#KaJLabs#Web4
LIVE

Anya Rossi• Live Now

Private livecam show

0 条评论

暂无评论

原始帖子的评论将显示在这里

相关视频

🌐 Autonomous agents require more than liquidity access — they require infrastructure that can coordinate execution across decentralized environments. KaJ Labs explores why AI-native systems depend on interoperable liquidity infrastructure built for routing, verification, and persistent onchain coordination. Read more 🔍 #KaJLabs #Web4 #AutonomousAgents #LiquidityInfrastructure #AIInfrastructure
0:10

🌐 Autonomous agents require more than liquidity access — they require infrastructure that can coordinate execution across decentralized environments. KaJ Labs explores why AI-native systems depend on interoperable liquidity infrastructure built for routing, verification, and persistent onchain coordination. Read more 🔍 #KaJLabs #Web4 #AutonomousAgents #LiquidityInfrastructure #AIInfrastructure

LITHO Foundation

63,376 次观看 • 3 个月前

💸 Machine-to-machine payments may become the financial backbone of autonomous agent economies. KaJ Labs explores how AI-native systems will require programmable payment infrastructure for continuous coordination, execution, and value exchange across decentralized networks. Read more 🔍 #KaJLabs #Web4 #AutonomousAgents #MachinePayments #AIInfrastructure
0:14

💸 Machine-to-machine payments may become the financial backbone of autonomous agent economies. KaJ Labs explores how AI-native systems will require programmable payment infrastructure for continuous coordination, execution, and value exchange across decentralized networks. Read more 🔍 #KaJLabs #Web4 #AutonomousAgents #MachinePayments #AIInfrastructure

LITHO Foundation

30,416 次观看 • 3 个月前

🧬 Web4 infrastructure works better when core systems operate together. Lithosphere connects identity, naming, AI-native execution, and cross-chain coordination into one stack for users, applications, and autonomous agent activity. #Lithosphere #Web4 #AIInfrastructure #Interoperability #AutonomousAgents
0:13

🧬 Web4 infrastructure works better when core systems operate together. Lithosphere connects identity, naming, AI-native execution, and cross-chain coordination into one stack for users, applications, and autonomous agent activity. #Lithosphere #Web4 #AIInfrastructure #Interoperability #AutonomousAgents

LITHO Foundation

33,091 次观看 • 2 个月前

🔗 Utility becomes meaningful when it maps to real network activity. Lithosphere highlights how LITHO supports execution, coordination, verification, cross-chain interaction, and agent operations across AI-native Web4 infrastructure. Learn here 🔍 Read more 🔍 #Lithosphere #LITHO #Web4 #AIInfrastructure #AutonomousAgents
0:16

🔗 Utility becomes meaningful when it maps to real network activity. Lithosphere highlights how LITHO supports execution, coordination, verification, cross-chain interaction, and agent operations across AI-native Web4 infrastructure. Learn here 🔍 Read more 🔍 #Lithosphere #LITHO #Web4 #AIInfrastructure #AutonomousAgents

LITHO Foundation

58,308 次观看 • 2 个月前

🛠️ Deploying autonomous agents requires infrastructure built for coordination, execution, and lifecycle management. Lithosphere is advancing an agent deployment framework designed to support scalable AI-native systems operating across decentralized environments. Read more 🔍 #Lithosphere #Web4 #AutonomousAgents #AgentDeployment #AIInfrastructure
0:14

🛠️ Deploying autonomous agents requires infrastructure built for coordination, execution, and lifecycle management. Lithosphere is advancing an agent deployment framework designed to support scalable AI-native systems operating across decentralized environments. Read more 🔍 #Lithosphere #Web4 #AutonomousAgents #AgentDeployment #AIInfrastructure

LITHO Foundation

28,866 次观看 • 2 个月前

We’re excited to announce the Agentic Payment Whitepaper, initiated by interlace.money together with 7 ecosystem partners. This whitepaper defines a shared vision, architecture, and standards for AI‑agent‑driven payments — a critical step toward the emerging Agentic Payment Economy. 🧱 The layers & partners: 🤖 Agent Application – X-Agent 💳 Payment Execution – interlace.money (Initiator) 🔐 Governance & Control – Cobo 🛡️ Trust & Compliance – BlockSec 💵 Stablecoin Settlement – Stable ⛓️ Blockchain Infrastructure – Conflux Network Official 🌊 Liquidity Orchestration & User Access – Bitget Wallet 🩵 🔗 Causal Verification – Hetu The company names above are listed in no particular order. 📄 Expected release: within the next 1–2 months. Stay tuned. #Interlace #AI #agenticpayment
0:13

We’re excited to announce the Agentic Payment Whitepaper, initiated by interlace.money together with 7 ecosystem partners. This whitepaper defines a shared vision, architecture, and standards for AI‑agent‑driven payments — a critical step toward the emerging Agentic Payment Economy. 🧱 The layers & partners: 🤖 Agent Application – X-Agent 💳 Payment Execution – interlace.money (Initiator) 🔐 Governance & Control – Cobo 🛡️ Trust & Compliance – BlockSec 💵 Stablecoin Settlement – Stable ⛓️ Blockchain Infrastructure – Conflux Network Official 🌊 Liquidity Orchestration & User Access – Bitget Wallet 🩵 🔗 Causal Verification – Hetu The company names above are listed in no particular order. 📄 Expected release: within the next 1–2 months. Stay tuned. #Interlace #AI #agenticpayment

interlace.money

19,037 次观看 • 2 个月前

BTC-backed markets don’t break at small scale. They break under pressure. For BTC-backed borrowing, the constraint is no longer demand, it’s market structure. Over a third of BTC collateral sits in a single venue. The rest is fragmented across smaller markets with limited depth. Most BTC-backed borrowing still occurs off-chain. Fragmentation is not diversification. At scale, execution degrades: • Large borrowing can quickly drain available liquidity • Pricing diverges across venues • Conditions become less predictable This is no longer an APY comparison. The question is which systems maintain depth, stable pricing, and consistent execution under real market conditions. wBTC is now live as collateral on SparkLend. More capacity. Less concentration. Greater optionality.
0:14

BTC-backed markets don’t break at small scale. They break under pressure. For BTC-backed borrowing, the constraint is no longer demand, it’s market structure. Over a third of BTC collateral sits in a single venue. The rest is fragmented across smaller markets with limited depth. Most BTC-backed borrowing still occurs off-chain. Fragmentation is not diversification. At scale, execution degrades: • Large borrowing can quickly drain available liquidity • Pricing diverges across venues • Conditions become less predictable This is no longer an APY comparison. The question is which systems maintain depth, stable pricing, and consistent execution under real market conditions. wBTC is now live as collateral on SparkLend. More capacity. Less concentration. Greater optionality.

Spark

25,447 次观看 • 5 个月前

Claude can make your own money printer That is exactly what happened to me I wrote my own script It took me 6 hours On the very first night the bot made $2,705 profit Copytrade: Wallet: Here is the full strategy: The system builds automated workflows for Claude by packaging domain expertise into structured skills that activate automatically when relevant tasks appear Skill architecture Each skill is structured as a modular package containing instructions scripts and reference materials This allows Claude to apply specialized workflows without requiring the user to repeat instructions in every conversation Progressive context loading Skills follow a three layer architecture where only minimal metadata is loaded initially Full instructions and supporting files are accessed only when needed reducing token usage while maintaining specialized expertise Trigger detection Skills activate when the user request matches defined trigger phrases or workflows This ensures the correct workflow loads automatically without requiring manual prompting Workflow execution Once activated the skill executes a predefined multi step process These workflows can include data analysis document generation automation scripts or coordination across external tools Consistency and reliability Because workflows are encoded directly in the skill instructions Claude performs tasks using consistent methodology rather than ad hoc prompting Testing and iteration Skills are continuously refined through triggering tests functional validation and performance comparisons to ensure reliable execution Automation edge Instead of solving tasks from scratch each time the system repeatedly applies optimized workflows Over time this dramatically reduces prompt complexity improves output consistency and scales productivity across thousands of tasks
0:11

Claude can make your own money printer That is exactly what happened to me I wrote my own script It took me 6 hours On the very first night the bot made $2,705 profit Copytrade: Wallet: Here is the full strategy: The system builds automated workflows for Claude by packaging domain expertise into structured skills that activate automatically when relevant tasks appear Skill architecture Each skill is structured as a modular package containing instructions scripts and reference materials This allows Claude to apply specialized workflows without requiring the user to repeat instructions in every conversation Progressive context loading Skills follow a three layer architecture where only minimal metadata is loaded initially Full instructions and supporting files are accessed only when needed reducing token usage while maintaining specialized expertise Trigger detection Skills activate when the user request matches defined trigger phrases or workflows This ensures the correct workflow loads automatically without requiring manual prompting Workflow execution Once activated the skill executes a predefined multi step process These workflows can include data analysis document generation automation scripts or coordination across external tools Consistency and reliability Because workflows are encoded directly in the skill instructions Claude performs tasks using consistent methodology rather than ad hoc prompting Testing and iteration Skills are continuously refined through triggering tests functional validation and performance comparisons to ensure reliable execution Automation edge Instead of solving tasks from scratch each time the system repeatedly applies optimized workflows Over time this dramatically reduces prompt complexity improves output consistency and scales productivity across thousands of tasks

winkle.

53,951 次观看 • 5 个月前

INTERLINK ROLLS OUT FIVE STRUCTURAL UPGRADES TO ACCELERATE VERIFICATION InterLink Labs (InterLink Labs 👤 + 🌐) is rolling out five structural upgrades to its Verified $ITLG verification pipeline as the Human Network passes 7 million users and prepares for InterLink Chain mainnet, $ITL listing, and the Verified $ITLG migration. The team is expanding its Curator network across more time zones to move verification to a continuous 24-hour rhythm. The AI snapshot layer is being enhanced to clear high-confidence profiles faster and route only edge cases to human review. A new multi-track queue structure splits processing by profile complexity so straightforward verifications no longer wait behind deeper review cases. Users will get a Force Sync option every 12 hours to refresh their own metrics, replacing the 2-week AI snapshot cycle. Where regulatory frameworks allow, InterLink will integrate with trusted partner verification providers to eliminate duplicate KYC work without lowering compliance standards. The compliance bar stays fixed. AMLO, SFC VASP, and FATF Recommendation 15 are non-negotiable. Every verified profile becomes a legally compliant human node designed to plug directly into global financial infrastructure for payments and Real World Assets.
0:14

INTERLINK ROLLS OUT FIVE STRUCTURAL UPGRADES TO ACCELERATE VERIFICATION InterLink Labs (InterLink Labs 👤 + 🌐) is rolling out five structural upgrades to its Verified $ITLG verification pipeline as the Human Network passes 7 million users and prepares for InterLink Chain mainnet, $ITL listing, and the Verified $ITLG migration. The team is expanding its Curator network across more time zones to move verification to a continuous 24-hour rhythm. The AI snapshot layer is being enhanced to clear high-confidence profiles faster and route only edge cases to human review. A new multi-track queue structure splits processing by profile complexity so straightforward verifications no longer wait behind deeper review cases. Users will get a Force Sync option every 12 hours to refresh their own metrics, replacing the 2-week AI snapshot cycle. Where regulatory frameworks allow, InterLink will integrate with trusted partner verification providers to eliminate duplicate KYC work without lowering compliance standards. The compliance bar stays fixed. AMLO, SFC VASP, and FATF Recommendation 15 are non-negotiable. Every verified profile becomes a legally compliant human node designed to plug directly into global financial infrastructure for payments and Real World Assets.

BSCN

20,871 次观看 • 4 个月前

How ICP Token Holders Can Literally Vote To Upgrade The Internet Computer? Most blockchains rely on developers and node operators to coordinate when the protocol needs a major upgrade. The Internet Computer (DFINITY Foundation) takes a different approach. Its Network Nervous System, or NNS, puts governance directly onchain. Here is how it works: 1. ICP HOLDERS LOCK TOKENS INTO NEURONS internet-computer:native holders can lock their ICP into a “neuron.” A neuron is essentially a governance position that gives the holder voting power over NNS proposals. Voting power depends mainly on: • The amount of ICP locked • The neuron’s dissolve delay • The age of the neuron The longer a holder commits their ICP, the greater their potential voting power. 2. NEURONS VOTE ON NETWORK PROPOSALS The NNS allows neuron holders to vote on proposals affecting the Internet Computer. These can include: • Protocol upgrades • Subnet changes • Network configuration • Node provider decisions • Governance parameters • Changes to the network’s underlying software This is where the system becomes particularly interesting. NNS governance is not simply deciding how a community treasury should spend money. Some proposals can directly affect how the blockchain operates. 3. VOTING CAN TRIGGER ACTUAL PROTOCOL CHANGES A successful proposal can instruct the network to adopt an approved change. For example, NNS proposals can be used to upgrade the replica software running across Internet Computer nodes. Once the proposal is approved, the NNS can coordinate the upgrade across the network. That means token holder voting can ultimately result in the protocol itself changing. 4. HOLDERS DO NOT HAVE TO VOTE ON EVERYTHING The NNS also uses a system known as liquid democracy. Neuron holders can choose to follow other neurons for particular proposal categories. When the followed neuron votes, the follower can automatically vote in the same direction. This creates a delegation system without requiring users to give up ownership of their ICP. In simple terms: • Stake ICP • Create a neuron • Choose your voting preferences • Vote yourself or follow another neuron • Earn rewards for participating 5. GOVERNANCE PARTICIPATION CAN EARN REWARDS The NNS gives users an economic incentive to participate. Neurons can accumulate maturity through governance participation. That maturity can later be used to generate new ICP. This turns governance participation into more than just a voting mechanism. It becomes part of the network’s economic design. 6. THE NNS IS ITSELF PART OF THE INTERNET COMPUTER This is arguably the most important part. The NNS is not simply a website where the community discusses proposals. The governance system itself runs on the Internet Computer. Its rules, proposals, neurons and governance decisions are handled through onchain infrastructure. That allows governance decisions to become executable actions. 7. WHY THIS MATTERS FOR PROTOCOL UPGRADES Traditional blockchain upgrades can require significant coordination. Developers may need to release new software. Node operators need to install it. Validators or miners need to support it. Exchanges and infrastructure providers may also need to update their systems. If coordination fails, competing versions of the blockchain can emerge. The NNS is designed to reduce some of that coordination problem. The community can approve a proposal through onchain governance, and the network can then execute the approved change. 8. IT IS NOT ONE ICP TOKEN, ONE VOTE Simply holding ICP does not automatically give someone governance power. Users need to commit their tokens through a neuron. And voting power is not based solely on the number of ICP held. Factors such as dissolve delay and neuron age also influence voting power. This means the system rewards committed participation rather than treating every wallet as an identical vote. 9. SO WHAT MAKES THE NNS DIFFERENT? The key difference is that governance is built into the protocol itself. On many blockchains, governance can look like: Community discussion → Vote → Developers implement the decision. The Internet Computer aims for something closer to: Stake ICP → Vote through NNS → Proposal passes → Network executes the change. The NNS is designed to make governance part of the Internet Computer’s operating machinery. ICP holders therefore have a direct role in deciding how the network evolves. Under the right proposal, their votes can ultimately determine which software the network runs.
0:15

How ICP Token Holders Can Literally Vote To Upgrade The Internet Computer? Most blockchains rely on developers and node operators to coordinate when the protocol needs a major upgrade. The Internet Computer (DFINITY Foundation) takes a different approach. Its Network Nervous System, or NNS, puts governance directly onchain. Here is how it works: 1. ICP HOLDERS LOCK TOKENS INTO NEURONS internet-computer:native holders can lock their ICP into a “neuron.” A neuron is essentially a governance position that gives the holder voting power over NNS proposals. Voting power depends mainly on: • The amount of ICP locked • The neuron’s dissolve delay • The age of the neuron The longer a holder commits their ICP, the greater their potential voting power. 2. NEURONS VOTE ON NETWORK PROPOSALS The NNS allows neuron holders to vote on proposals affecting the Internet Computer. These can include: • Protocol upgrades • Subnet changes • Network configuration • Node provider decisions • Governance parameters • Changes to the network’s underlying software This is where the system becomes particularly interesting. NNS governance is not simply deciding how a community treasury should spend money. Some proposals can directly affect how the blockchain operates. 3. VOTING CAN TRIGGER ACTUAL PROTOCOL CHANGES A successful proposal can instruct the network to adopt an approved change. For example, NNS proposals can be used to upgrade the replica software running across Internet Computer nodes. Once the proposal is approved, the NNS can coordinate the upgrade across the network. That means token holder voting can ultimately result in the protocol itself changing. 4. HOLDERS DO NOT HAVE TO VOTE ON EVERYTHING The NNS also uses a system known as liquid democracy. Neuron holders can choose to follow other neurons for particular proposal categories. When the followed neuron votes, the follower can automatically vote in the same direction. This creates a delegation system without requiring users to give up ownership of their ICP. In simple terms: • Stake ICP • Create a neuron • Choose your voting preferences • Vote yourself or follow another neuron • Earn rewards for participating 5. GOVERNANCE PARTICIPATION CAN EARN REWARDS The NNS gives users an economic incentive to participate. Neurons can accumulate maturity through governance participation. That maturity can later be used to generate new ICP. This turns governance participation into more than just a voting mechanism. It becomes part of the network’s economic design. 6. THE NNS IS ITSELF PART OF THE INTERNET COMPUTER This is arguably the most important part. The NNS is not simply a website where the community discusses proposals. The governance system itself runs on the Internet Computer. Its rules, proposals, neurons and governance decisions are handled through onchain infrastructure. That allows governance decisions to become executable actions. 7. WHY THIS MATTERS FOR PROTOCOL UPGRADES Traditional blockchain upgrades can require significant coordination. Developers may need to release new software. Node operators need to install it. Validators or miners need to support it. Exchanges and infrastructure providers may also need to update their systems. If coordination fails, competing versions of the blockchain can emerge. The NNS is designed to reduce some of that coordination problem. The community can approve a proposal through onchain governance, and the network can then execute the approved change. 8. IT IS NOT ONE ICP TOKEN, ONE VOTE Simply holding ICP does not automatically give someone governance power. Users need to commit their tokens through a neuron. And voting power is not based solely on the number of ICP held. Factors such as dissolve delay and neuron age also influence voting power. This means the system rewards committed participation rather than treating every wallet as an identical vote. 9. SO WHAT MAKES THE NNS DIFFERENT? The key difference is that governance is built into the protocol itself. On many blockchains, governance can look like: Community discussion → Vote → Developers implement the decision. The Internet Computer aims for something closer to: Stake ICP → Vote through NNS → Proposal passes → Network executes the change. The NNS is designed to make governance part of the Internet Computer’s operating machinery. ICP holders therefore have a direct role in deciding how the network evolves. Under the right proposal, their votes can ultimately determine which software the network runs.

BSCN

18,482 次观看 • 19 天前

Anthropic dropped 33 pages for Claude trading bots Last night I decided to try writing one and it worked out for me In 10 hours this script made me $561 The bot has a win rate of about 71% Wallet: Copytrade: Here is the full strategy: The system builds automated workflows for Claude by packaging domain expertise into structured skills that activate automatically when relevant tasks appear Skill architecture Each skill is structured as a modular package containing instructions, scripts, and reference materials This allows Claude to apply specialized workflows without requiring the user to repeat instructions in every conversation Progressive context loading Skills follow a three-layer architecture where only minimal metadata is loaded initially Full instructions and supporting files are accessed only when needed, reducing token usage while maintaining specialized expertise Trigger detection Skills activate when the user request matches defined trigger phrases or workflows This ensures the correct workflow loads automatically without requiring manual prompting Workflow execution Once activated, the skill executes a predefined multi-step process These workflows can include data analysis, document generation, automation scripts, or coordination across external tools Consistency and reliability Because workflows are encoded directly in the skill instructions, Claude performs tasks using consistent methodology rather than ad-hoc prompting Testing and iteration Skills are continuously refined through triggering tests, functional validation, and performance comparisons to ensure reliable execution Automation edge Instead of solving tasks from scratch each time, the system repeatedly applies optimized workflows Over time this dramatically reduces prompt complexity, improves output consistency, and scales productivity across thousands of tasks
0:11

Anthropic dropped 33 pages for Claude trading bots Last night I decided to try writing one and it worked out for me In 10 hours this script made me $561 The bot has a win rate of about 71% Wallet: Copytrade: Here is the full strategy: The system builds automated workflows for Claude by packaging domain expertise into structured skills that activate automatically when relevant tasks appear Skill architecture Each skill is structured as a modular package containing instructions, scripts, and reference materials This allows Claude to apply specialized workflows without requiring the user to repeat instructions in every conversation Progressive context loading Skills follow a three-layer architecture where only minimal metadata is loaded initially Full instructions and supporting files are accessed only when needed, reducing token usage while maintaining specialized expertise Trigger detection Skills activate when the user request matches defined trigger phrases or workflows This ensures the correct workflow loads automatically without requiring manual prompting Workflow execution Once activated, the skill executes a predefined multi-step process These workflows can include data analysis, document generation, automation scripts, or coordination across external tools Consistency and reliability Because workflows are encoded directly in the skill instructions, Claude performs tasks using consistent methodology rather than ad-hoc prompting Testing and iteration Skills are continuously refined through triggering tests, functional validation, and performance comparisons to ensure reliable execution Automation edge Instead of solving tasks from scratch each time, the system repeatedly applies optimized workflows Over time this dramatically reduces prompt complexity, improves output consistency, and scales productivity across thousands of tasks

winkle.

334,967 次观看 • 5 个月前

Multi-robot learning is getting a serious boost! 📚 Researchers have extended Isaac Lab to train heterogeneous multi-agent robotic policies at scale. The new framework supports high-resolution physics, GPU-accelerated simulation, and both homogeneous and heterogeneous agents working together on coordination tasks. They benchmarked different approaches (MAPPO: Multi-Agent Proximal Policy Optimization and HAPPO: Heterogeneous Agent PPO) across six challenging scenarios and showed that large-scale multi-robot training is not only feasible, but efficient. It’s an important step for real-world robotic collaboration, where teams of robots need to coordinate, split tasks, adapt roles, and interact dynamically, not just operate as identical clones. The code is open-source, and it pushes Isaac Lab closer to what robotics actually needs: scalable, physics-driven environments where many different robots can learn to work together. Here's the project page: ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →
0:16

Multi-robot learning is getting a serious boost! 📚 Researchers have extended Isaac Lab to train heterogeneous multi-agent robotic policies at scale. The new framework supports high-resolution physics, GPU-accelerated simulation, and both homogeneous and heterogeneous agents working together on coordination tasks. They benchmarked different approaches (MAPPO: Multi-Agent Proximal Policy Optimization and HAPPO: Heterogeneous Agent PPO) across six challenging scenarios and showed that large-scale multi-robot training is not only feasible, but efficient. It’s an important step for real-world robotic collaboration, where teams of robots need to coordinate, split tasks, adapt roles, and interact dynamically, not just operate as identical clones. The code is open-source, and it pushes Isaac Lab closer to what robotics actually needs: scalable, physics-driven environments where many different robots can learn to work together. Here's the project page: ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

38,997 次观看 • 9 个月前

🚨 CRYPTO: IMF DECLARES TOKENIZATION A "STRUCTURAL SHIFT" IN FINANCIAL ARCHITECTURE The International Monetary Fund has published a formal note stating that tokenization is reshaping regulated finance and constitutes a structural shift rather than a marginal efficiency improvement. The note, authored by Tobias Adrian, the IMF's Financial Counsellor and head of its Monetary and Capital Markets Department, describes how permissioned shared ledgers, programmable financial assets, and smart contract-based risk management are fundamentally altering how settlement, liquidity, and systemic risk operate. The IMF says the most consequential transformation is happening within the regulated financial system itself, including banks, asset managers, and market infrastructure providers, where tokenization enables atomic settlement, continuous liquidity management, and embedded compliance. Tokenized real-world assets have already reached approximately $27.5 billion as of early April, with US Treasury products accounting for over $12 billion of that total. However, the IMF also warned that the same features making tokenized markets efficient could amplify instability. Automated margin calls, real-time settlement, and programmable financial flows could accelerate liquidity stress during volatility. Traditional systems have built-in delays that act as shock absorbers. Tokenized systems may transmit stress instantly across participants. The note calls for clear policy frameworks, robust code governance, legal certainty, and international coordination.
0:14

🚨 CRYPTO: IMF DECLARES TOKENIZATION A "STRUCTURAL SHIFT" IN FINANCIAL ARCHITECTURE The International Monetary Fund has published a formal note stating that tokenization is reshaping regulated finance and constitutes a structural shift rather than a marginal efficiency improvement. The note, authored by Tobias Adrian, the IMF's Financial Counsellor and head of its Monetary and Capital Markets Department, describes how permissioned shared ledgers, programmable financial assets, and smart contract-based risk management are fundamentally altering how settlement, liquidity, and systemic risk operate. The IMF says the most consequential transformation is happening within the regulated financial system itself, including banks, asset managers, and market infrastructure providers, where tokenization enables atomic settlement, continuous liquidity management, and embedded compliance. Tokenized real-world assets have already reached approximately $27.5 billion as of early April, with US Treasury products accounting for over $12 billion of that total. However, the IMF also warned that the same features making tokenized markets efficient could amplify instability. Automated margin calls, real-time settlement, and programmable financial flows could accelerate liquidity stress during volatility. Traditional systems have built-in delays that act as shock absorbers. Tokenized systems may transmit stress instantly across participants. The note calls for clear policy frameworks, robust code governance, legal certainty, and international coordination.

BSCN

11,818 次观看 • 5 个月前

What is the best video editing agent for short form social? Does it actually work? We watched professional video editors, step by step, as they built short-form social reels in Adobe Premiere Pro. Today we're open-sourcing this preview dataset on Hugging Face, to make AI agents better at editing videos. The data set is 234 annotated steps across 4 computer-use trajectories. Editors narrated their reasoning aloud as they worked, so every step pairs a screenshot with the expert's own thought, a structured action, and executable grounding: >a Premiere MCP tool call, keyboard shortcut, menu path, or coordinate click. >The format follows the AgentNet trajectory schema, extended with a Premiere action taxonomy and multi-path execution. ***That makes it directly usable for computer-use agent SFT, reasoning mid-training, tool-use and function calling, and benchmarking agents against a human expert baseline. Enjoy!
0:13

What is the best video editing agent for short form social? Does it actually work? We watched professional video editors, step by step, as they built short-form social reels in Adobe Premiere Pro. Today we're open-sourcing this preview dataset on Hugging Face, to make AI agents better at editing videos. The data set is 234 annotated steps across 4 computer-use trajectories. Editors narrated their reasoning aloud as they worked, so every step pairs a screenshot with the expert's own thought, a structured action, and executable grounding: >a Premiere MCP tool call, keyboard shortcut, menu path, or coordinate click. >The format follows the AgentNet trajectory schema, extended with a Premiere action taxonomy and multi-path execution. ***That makes it directly usable for computer-use agent SFT, reasoning mid-training, tool-use and function calling, and benchmarking agents against a human expert baseline. Enjoy!

ben

39,640 次观看 • 1 个月前

LangGraph. CrewAI. Agno. Which one to pick? The good news is that this will not matter soon! Finally, we have a full picture of how the industry is solving this with just three open protocols that work across ALL frameworks. It's not about picking the best framework. Instead, it's about understanding how protocols create interoperability. The Agent Protocol Landscape shows how three complementary protocols are creating a universal language for Agents: > AG-UI (Agent-User Interaction): - The bi-directional connection between agentic backends and frontends. - This is how agents become truly interactive inside your apps, not just as chatbots, but collaborative co-workers. > MCP (Model Context Protocol): - The standard for how agents connect to tools, data, and workflows. > A2A (Agent-to-Agent): - The protocol for multi-agent coordination. - How agents delegate tasks and share intent across systems. These aren't competing standards. They're layers of the same stack and have handshakes with each other. So instead of building point-to-point integrations, you build to protocols. Moreover, you can integrate LangGraph, CrewAI, or Agno into the same frontend, without rewriting your UI logic. These protocols let everything work together. For instance: - Your LangGraph agent pulls data via MCP. - It delegates analysis to a CrewAI agent via A2A. - Results stream to your React app via AG-UI. - Users see real-time collaboration in your interface. This way, you can focus on building agent capabilities instead of integration mechanics. The protocols handle interoperability automatically. CopilotKit unifies this entire stack into one framework so you can build "Cursor for X" style apps without implementing each protocol from scratch. It gives you all three protocols, generative UI support, and production-ready infrastructure in one framework. I have shared this playbook in the replies! It breaks down handshakes, misconceptions, and real examples and shows exactly how to start building.
0:19

LangGraph. CrewAI. Agno. Which one to pick? The good news is that this will not matter soon! Finally, we have a full picture of how the industry is solving this with just three open protocols that work across ALL frameworks. It's not about picking the best framework. Instead, it's about understanding how protocols create interoperability. The Agent Protocol Landscape shows how three complementary protocols are creating a universal language for Agents: > AG-UI (Agent-User Interaction): - The bi-directional connection between agentic backends and frontends. - This is how agents become truly interactive inside your apps, not just as chatbots, but collaborative co-workers. > MCP (Model Context Protocol): - The standard for how agents connect to tools, data, and workflows. > A2A (Agent-to-Agent): - The protocol for multi-agent coordination. - How agents delegate tasks and share intent across systems. These aren't competing standards. They're layers of the same stack and have handshakes with each other. So instead of building point-to-point integrations, you build to protocols. Moreover, you can integrate LangGraph, CrewAI, or Agno into the same frontend, without rewriting your UI logic. These protocols let everything work together. For instance: - Your LangGraph agent pulls data via MCP. - It delegates analysis to a CrewAI agent via A2A. - Results stream to your React app via AG-UI. - Users see real-time collaboration in your interface. This way, you can focus on building agent capabilities instead of integration mechanics. The protocols handle interoperability automatically. CopilotKit unifies this entire stack into one framework so you can build "Cursor for X" style apps without implementing each protocol from scratch. It gives you all three protocols, generative UI support, and production-ready infrastructure in one framework. I have shared this playbook in the replies! It breaks down handshakes, misconceptions, and real examples and shows exactly how to start building.

Avi Chawla

30,932 次观看 • 9 个月前

🚨 BREAKING: DIGITAL ID IS NOW OFFICIALLY ON THE UK GOVERNMENT AGENDA During the King’s Speech, King Charles confirmed: “My ministers will also proceed with the introduction of Digital ID…” That single sentence will send shockwaves through millions of people already concerned about surveillance, privacy, and state control. The government says Digital ID will “modernise” access to public services. Critics warn it could become the foundation for something far bigger: • Centralised identity systems • Expanded state surveillance • Financial tracking integration • Restricted access to services • Behaviour monitoring through linked databases • The infrastructure for programmable digital governance For years, many dismissed concerns about Digital ID as conspiracy theory. Now it is being openly announced from the floor of Parliament during the King’s Speech itself. And once these systems are embedded into banking, healthcare, taxation, travel, and public services… they become very difficult to reverse. The question is no longer whether Digital ID is coming. The question is how far it eventually goes.
0:12

🚨 BREAKING: DIGITAL ID IS NOW OFFICIALLY ON THE UK GOVERNMENT AGENDA During the King’s Speech, King Charles confirmed: “My ministers will also proceed with the introduction of Digital ID…” That single sentence will send shockwaves through millions of people already concerned about surveillance, privacy, and state control. The government says Digital ID will “modernise” access to public services. Critics warn it could become the foundation for something far bigger: • Centralised identity systems • Expanded state surveillance • Financial tracking integration • Restricted access to services • Behaviour monitoring through linked databases • The infrastructure for programmable digital governance For years, many dismissed concerns about Digital ID as conspiracy theory. Now it is being openly announced from the floor of Parliament during the King’s Speech itself. And once these systems are embedded into banking, healthcare, taxation, travel, and public services… they become very difficult to reverse. The question is no longer whether Digital ID is coming. The question is how far it eventually goes.

Jim Ferguson

133,751 次观看 • 3 个月前

February 2025 at G.A.M.E: Autonomous Commerce, Scalability, and Expansion 1/ AGENT COMMERCE PROTOCOL(ACP) Demo ▸ Open standard for multi-agent commerce and coordination on blockchain ▸ Enables AI agents to collaborate without centralized control ▸ Build Autonomous Commerce (hedge funds, media empires, healthcare) ▸ Details: 2/ X ENTERPRISE API & MEDIA GALLERY ▸ X Enterprise Plugin: Use G.A.M.E’s credentials for higher rate limits ▸ Media Gallery: Upload agent demos (mp4, webm, images). ▸ Tap into 550M+ users for explosive growth 3/ Solana AGENT SUPPORT (G.A.M.E CLOUD) ▸ Test/deploy Solana agents in-sandbox ▸ Unified multi-chain workflows ▸ Shatter siloed testing 4/ Mind Network PLUGIN (G.A.M.E SDK) ▸ FHE-encrypted voting for DAOs ▸ Track vFHE rewards natively ▸ First SDK with on-chain governance 5/ CHAT AGENT MODULE (G.A.M.E SDK) ▸ Llama 3.3 70B via Groq API ▸ Engage in dynamic AI-driven interactions with the ability to trigger functions. ▸ Conversational AI with Action Execution ▸ Short-term memory for context awareness 6/ CoinGecko PLUGIN (G.A.M.E SDK) ▸ Real-time crypto prices/market data ▸ Built-in error handling ▸ Community-contributed 7/ Elfa AI PLUGIN (G.A.M.E SDK) ▸ Real-Time Crypto Intelligence ▸ Track whale wallets & trending tokens ▸ Live smart money insights ▸ Front-run markets with API data 8/ MULTI-MODEL SUPPORT ▸ 5 new models: Llama_3_1_405B, Qwen_2_5_72B_Instruct, DeepSeek_R1, etc. ▸ Match models to tasks: speed vs. creativity ▸ Optimize cost/performance 9/ Farcaster PLUGIN ▸ Post casts to 300K+ decentralized users ▸ Engage Web3-native communities ▸ On-chain social interactions 10/ GAME SDK UPGRADES ▸ X Username-Based Payments ▸ Multi-worker task management ▸ Fix loops/hallucinations with memory reset 11/ Coinbase 🛡️ CDP PLUGIN ▸ Wallet Management ▸ Gas-less USDC transfers ▸ ETH/USDC trading on Base ▸ Web-hook Integration 12/ IMAGE GENERATION ▸ Generate custom AI images from text-based prompts. ▸ Customizable dimensions up to 1440x1440. ▸ Receive images as temporary URLs, making it easy to share and store outputs. ▸ Powered by Together AI 13/ MODEL UPGRADES & AI ROUTER ▸ Dynamic AI Model Switching based on use case ▸ Smart AI Router: 2x performance/stability via Chasm collaboration. 14/ Why February Redefined Autonomy ▸ ACP Demo through G.A.M.E: Multi-agent economies are programmable, competitive, and decentralized. ▸ Social x Crypto Fusion: = Viral growth loops. ▸ Chain Agnosticism: Building the future where agents thrive on any network. Build → Fund → Launch →
0:15

February 2025 at G.A.M.E: Autonomous Commerce, Scalability, and Expansion 1/ AGENT COMMERCE PROTOCOL(ACP) Demo ▸ Open standard for multi-agent commerce and coordination on blockchain ▸ Enables AI agents to collaborate without centralized control ▸ Build Autonomous Commerce (hedge funds, media empires, healthcare) ▸ Details: 2/ X ENTERPRISE API & MEDIA GALLERY ▸ X Enterprise Plugin: Use G.A.M.E’s credentials for higher rate limits ▸ Media Gallery: Upload agent demos (mp4, webm, images). ▸ Tap into 550M+ users for explosive growth 3/ Solana AGENT SUPPORT (G.A.M.E CLOUD) ▸ Test/deploy Solana agents in-sandbox ▸ Unified multi-chain workflows ▸ Shatter siloed testing 4/ Mind Network PLUGIN (G.A.M.E SDK) ▸ FHE-encrypted voting for DAOs ▸ Track vFHE rewards natively ▸ First SDK with on-chain governance 5/ CHAT AGENT MODULE (G.A.M.E SDK) ▸ Llama 3.3 70B via Groq API ▸ Engage in dynamic AI-driven interactions with the ability to trigger functions. ▸ Conversational AI with Action Execution ▸ Short-term memory for context awareness 6/ CoinGecko PLUGIN (G.A.M.E SDK) ▸ Real-time crypto prices/market data ▸ Built-in error handling ▸ Community-contributed 7/ Elfa AI PLUGIN (G.A.M.E SDK) ▸ Real-Time Crypto Intelligence ▸ Track whale wallets & trending tokens ▸ Live smart money insights ▸ Front-run markets with API data 8/ MULTI-MODEL SUPPORT ▸ 5 new models: Llama_3_1_405B, Qwen_2_5_72B_Instruct, DeepSeek_R1, etc. ▸ Match models to tasks: speed vs. creativity ▸ Optimize cost/performance 9/ Farcaster PLUGIN ▸ Post casts to 300K+ decentralized users ▸ Engage Web3-native communities ▸ On-chain social interactions 10/ GAME SDK UPGRADES ▸ X Username-Based Payments ▸ Multi-worker task management ▸ Fix loops/hallucinations with memory reset 11/ Coinbase 🛡️ CDP PLUGIN ▸ Wallet Management ▸ Gas-less USDC transfers ▸ ETH/USDC trading on Base ▸ Web-hook Integration 12/ IMAGE GENERATION ▸ Generate custom AI images from text-based prompts. ▸ Customizable dimensions up to 1440x1440. ▸ Receive images as temporary URLs, making it easy to share and store outputs. ▸ Powered by Together AI 13/ MODEL UPGRADES & AI ROUTER ▸ Dynamic AI Model Switching based on use case ▸ Smart AI Router: 2x performance/stability via Chasm collaboration. 14/ Why February Redefined Autonomy ▸ ACP Demo through G.A.M.E: Multi-agent economies are programmable, competitive, and decentralized. ▸ Social x Crypto Fusion: = Viral growth loops. ▸ Chain Agnosticism: Building the future where agents thrive on any network. Build → Fund → Launch →

G.A.M.E

89,973 次观看 • 1 年前

Microsoft presents Windows Agent Arena Evaluating Multi-Modal OS Agents at Scale discuss: Large language models (LLMs) show remarkable potential to act as computer agents, enhancing human productivity and software accessibility in multi-modal tasks that require planning and reasoning. However, measuring agent performance in realistic environments remains a challenge since: (i) most benchmarks are limited to specific modalities or domains (e.g. text-only, web navigation, Q&A, coding) and (ii) full benchmark evaluations are slow (on order of magnitude of days) given the multi-step sequential nature of tasks. To address these challenges, we introduce the Windows Agent Arena: a reproducible, general environment focusing exclusively on the Windows operating system (OS) where agents can operate freely within a real Windows OS and use the same wide range of applications, tools, and web browsers available to human users when solving tasks. We adapt the OSWorld framework (Xie et al., 2024) to create 150+ diverse Windows tasks across representative domains that require agent abilities in planning, screen understanding, and tool usage. Our benchmark is scalable and can be seamlessly parallelized in Azure for a full benchmark evaluation in as little as 20 minutes. To demonstrate Windows Agent Arena's capabilities, we also introduce a new multi-modal agent, Navi. Our agent achieves a success rate of 19.5% in the Windows domain, compared to 74.5% performance of an unassisted human. Navi also demonstrates strong performance on another popular web-based benchmark, Mind2Web. We offer extensive quantitative and qualitative analysis of Navi's performance, and provide insights into the opportunities for future research in agent development and data generation using Windows Agent Arena.
0:18

Microsoft presents Windows Agent Arena Evaluating Multi-Modal OS Agents at Scale discuss: Large language models (LLMs) show remarkable potential to act as computer agents, enhancing human productivity and software accessibility in multi-modal tasks that require planning and reasoning. However, measuring agent performance in realistic environments remains a challenge since: (i) most benchmarks are limited to specific modalities or domains (e.g. text-only, web navigation, Q&A, coding) and (ii) full benchmark evaluations are slow (on order of magnitude of days) given the multi-step sequential nature of tasks. To address these challenges, we introduce the Windows Agent Arena: a reproducible, general environment focusing exclusively on the Windows operating system (OS) where agents can operate freely within a real Windows OS and use the same wide range of applications, tools, and web browsers available to human users when solving tasks. We adapt the OSWorld framework (Xie et al., 2024) to create 150+ diverse Windows tasks across representative domains that require agent abilities in planning, screen understanding, and tool usage. Our benchmark is scalable and can be seamlessly parallelized in Azure for a full benchmark evaluation in as little as 20 minutes. To demonstrate Windows Agent Arena's capabilities, we also introduce a new multi-modal agent, Navi. Our agent achieves a success rate of 19.5% in the Windows domain, compared to 74.5% performance of an unassisted human. Navi also demonstrates strong performance on another popular web-based benchmark, Mind2Web. We offer extensive quantitative and qualitative analysis of Navi's performance, and provide insights into the opportunities for future research in agent development and data generation using Windows Agent Arena.

AK

19,684 次观看 • 1 年前

OpenClaw setup made me $23,472 Literally overnight my $100 turned into $2,411 Average bot win rate 71% Copytrade: Here is the full strategy: The system builds automated workflows for trading by turning domain expertise into structured skills that activate automatically when specific market conditions appear Skill architecture Each skill is a modular package that includes instruction scripts and reference data This allows the system to apply specialized workflows without needing manual input for every trade Progressive context loading Skills use a three layer structure Only minimal metadata loads at first Full instructions historical data and supporting resources load only when required This reduces resource usage while keeping advanced trading capability Trigger detection Skills activate automatically when market conditions match predefined triggers such as volatility levels orderflow behavior or news sentiment This ensures the right workflow is used at the right time without manual action Workflow execution Once activated each skill runs a predefined multi step process including Real time price tracking and order book analysis Factor generation and backtesting Signal aggregation from machine learning models news sentiment and orderflow Risk assessment and capital allocation Trade execution with retries and position splitting Consistency and reliability All workflows are embedded directly into the system which ensures consistent execution instead of random decision making Every factor signal and risk rule is applied in a structured way Testing and iteration Skills are continuously improved using historical backtesting simulated trading and live performance tracking to maintain reliability in real market conditions Automation edge Instead of creating new strategies every time the system repeatedly uses optimized workflows This reduces complexity increases consistency and scales performance across thousands of trades Performance snapshot Started one month ago with $500 Current daily profit $2,300 per day Morning profit today $71,452 The system runs fully autonomously constantly scanning markets generating signals auditing trades managing risk and executing orders to maximize compounding returns
0:14

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

winkle.

44,158 次观看 • 5 个月前

🚨What Happened in the Gulf That Satellite Images Had to Be Delayed 🚨Something catastrophic just happened in the Gulf. The American satellite company Planet Labs has imposed a 96 hour blackout on new satellite imagery covering the Gulf states, Iraq, and Kuwait. Why would imagery suddenly be delayed? Because real time satellite photos would immediately reveal the scale of destruction to military facilities. Reports confirm Iran launched missile and drone strikes targeting the U.S. Fifth Fleet headquarters at Naval Support Activity Bahrain in Manama. Explosions were seen across the base. Buildings damaged. Infrastructure hit. Communications facilities struck. And now the satellite feed over the entire region just went dark. When imagery gets delayed during wartime… It usually means the damage is massive and the full picture isn’t ready to be revealed. Four days. That’s how long the world may have to wait to see what really happened. #SilentMajoritySpeaks #AStoneGroove
0:12

🚨What Happened in the Gulf That Satellite Images Had to Be Delayed 🚨Something catastrophic just happened in the Gulf. The American satellite company Planet Labs has imposed a 96 hour blackout on new satellite imagery covering the Gulf states, Iraq, and Kuwait. Why would imagery suddenly be delayed? Because real time satellite photos would immediately reveal the scale of destruction to military facilities. Reports confirm Iran launched missile and drone strikes targeting the U.S. Fifth Fleet headquarters at Naval Support Activity Bahrain in Manama. Explosions were seen across the base. Buildings damaged. Infrastructure hit. Communications facilities struck. And now the satellite feed over the entire region just went dark. When imagery gets delayed during wartime… It usually means the damage is massive and the full picture isn’t ready to be revealed. Four days. That’s how long the world may have to wait to see what really happened. #SilentMajoritySpeaks #AStoneGroove

A Gene Robinson

80,645 次观看 • 5 个月前