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AI is not limited by capability anymore. It’s limited by trust. When data and models remain protected during execution, experimentation becomes safer, audits can occur without threat to user data, and real-world applications can scale with confidence. This is true adoption.

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Nesa

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10,098 views • 4 months ago •via X (Twitter)

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IoTeX was selected by Messari as a core case study in its new AI report, cited alongside World, Coinbase 🛡️, Openτensor Foundaτion and others. Messari’s research underscores a bigger shift: real-world AI needs verified data, and IoTeX is the first full stack infrastructure that can supply it. Live device data becomes verifiable insight, ready for real-world agentic deployment. Quicksilver is already proving this in production with DIMO, Nubila Network and more, powering agentic applications built on IoTeX-verified real-world intelligence. Read the full analysis in the report below.
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IoTeX was selected by Messari as a core case study in its new AI report, cited alongside World, Coinbase 🛡️, Openτensor Foundaτion and others. Messari’s research underscores a bigger shift: real-world AI needs verified data, and IoTeX is the first full stack infrastructure that can supply it. Live device data becomes verifiable insight, ready for real-world agentic deployment. Quicksilver is already proving this in production with DIMO, Nubila Network and more, powering agentic applications built on IoTeX-verified real-world intelligence. Read the full analysis in the report below.

IoTeX

56,151 views • 7 months ago

Introducing the Grayscale Walrus Trust $WAL 🧊 Walrus 🦭/acc is redefining decentralized data management, a critical pillar for scaling real-world Web3 applications. 🧠 Programmable Data at Scale: Walrus enables on-chain storage and real-time data management, powering dApps like social networks, games, and productivity tools — all while maintaining privacy and ownership. 🔗 Chain-Agnostic by Design: Walrus can store, share, and manage data across any blockchain (not just Sui), giving developers flexibility and speed. 🏗️ Core Infrastructure: From encrypted file sharing to active deletion and lifecycle controls, Walrus is a foundational piece of Mysten Labs’ decentralized stack. See important disclosures and learn more about the Grayscale Walrus Trust:
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Introducing the Grayscale Walrus Trust $WAL 🧊 Walrus 🦭/acc is redefining decentralized data management, a critical pillar for scaling real-world Web3 applications. 🧠 Programmable Data at Scale: Walrus enables on-chain storage and real-time data management, powering dApps like social networks, games, and productivity tools — all while maintaining privacy and ownership. 🔗 Chain-Agnostic by Design: Walrus can store, share, and manage data across any blockchain (not just Sui), giving developers flexibility and speed. 🏗️ Core Infrastructure: From encrypted file sharing to active deletion and lifecycle controls, Walrus is a foundational piece of Mysten Labs’ decentralized stack. See important disclosures and learn more about the Grayscale Walrus Trust:

Grayscale

16,856 views • 11 months ago

Introducing Grayscale Space and Time Trust $SXT 🪐 , backed by M12 - Microsoft's Venture Fund and integrated with Microsoft Fabric*, connects the power of decentralized blockchains with the speed and flexibility of traditional databases through: 🏢 Enterprise-Readiness: Backed by Microsoft and integrated into Microsoft Fabric*, allowing developers to easily access data from networks like #Bitcoin, Sui, and #Ethereum. 🖊️ Proof of SQL: Verifies queries on tamperproof data using zero-knowledge proofs without needing to rerun or reveal underlying data. ⏳ Decentralized + Scalable: Blends blockchain security with traditional database speed and accessibility for reliable, real-time data access. ✅ Verifiable Data: As we enter the new age of AI, transparency is paramount. Space and Time enables verifiable data, helping ensure we can trust the datasets that power AI systems and smart contract applications. See important disclosures and learn more about the Grayscale Space and Time Trust: *Source: Space and Time press release, May 20, 2025.
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Introducing Grayscale Space and Time Trust $SXT 🪐 , backed by M12 - Microsoft's Venture Fund and integrated with Microsoft Fabric*, connects the power of decentralized blockchains with the speed and flexibility of traditional databases through: 🏢 Enterprise-Readiness: Backed by Microsoft and integrated into Microsoft Fabric*, allowing developers to easily access data from networks like #Bitcoin, Sui, and #Ethereum. 🖊️ Proof of SQL: Verifies queries on tamperproof data using zero-knowledge proofs without needing to rerun or reveal underlying data. ⏳ Decentralized + Scalable: Blends blockchain security with traditional database speed and accessibility for reliable, real-time data access. ✅ Verifiable Data: As we enter the new age of AI, transparency is paramount. Space and Time enables verifiable data, helping ensure we can trust the datasets that power AI systems and smart contract applications. See important disclosures and learn more about the Grayscale Space and Time Trust: *Source: Space and Time press release, May 20, 2025.

Grayscale

81,565 views • 1 year ago

In 2025, the AgentFlayer exploit highlighted a new category of risk in AI systems. It was not a traditional breach involving stolen credentials or broken encryption. Instead, it demonstrated how an autonomous AI agent could be manipulated into executing unintended actions by processing malicious instructions embedded inside content it automatically processes. The incident did not expose a flaw in one specific integration. It revealed a structural weakness in how many modern AI agents are built. Today’s agents are no longer passive language models. They read documents automatically, scan emails, connect to SaaS tools, access cloud storage, and execute actions across multiple systems. To be useful, they are granted meaningful permissions. That capability creates value, but it also expands the attack surface. Most agent environments operate in a trusted, plaintext execution model. Data is encrypted at rest and in transit, but it is typically decrypted during inference so the model can process it. That runtime visibility is where potential risk lies. In a zero-click scenario like AgentFlayer, an attacker can embed hidden instructions inside a document that the AI processes automatically. Because the agent may have access to connected systems such as Google Drive, Slack, or GitHub, it can potentially be influenced to retrieve sensitive information or perform unintended actions. The user does not need to click a malicious link or approve a suspicious request. Therefore, the core issue is that during execution, the system may have access to sensitive data and broad privileges, meaning whoever controls the execution environment ultimately controls access to that data. Now consider a different architectural approach. If a system is designed so that data remains protected during execution, the risk profile changes. On Nesa, privacy is enforced at the execution layer through Equivariant Encryption. Computation can occur on encrypted data, reducing the visibility surface during runtime. Sensitive inputs and models do not need to be exposed in plain text to infrastructure operators for inference to occur. This does not eliminate prompt injection, logic manipulation, or tool misuse. Encryption alone cannot prevent an agent from being instructed to take an unintended action if it has been granted that permission. What it does do is materially reduce confidentiality risk. By limiting access to readable sensitive data during execution and reducing unilateral visibility at the infrastructure layer, the potential blast radius of a successful manipulation attempt is constrained. As AI agents become more autonomous and embedded into enterprise workflows, security must move deeper into architecture. The goal is not to claim invulnerability. It is to reduce trust concentration and contain systemic exposure when failures occur. AgentFlayer was not simply a one-off exploit. It was a reminder that in autonomous systems, execution-layer design determines how risk propagates.
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In 2025, the AgentFlayer exploit highlighted a new category of risk in AI systems. It was not a traditional breach involving stolen credentials or broken encryption. Instead, it demonstrated how an autonomous AI agent could be manipulated into executing unintended actions by processing malicious instructions embedded inside content it automatically processes. The incident did not expose a flaw in one specific integration. It revealed a structural weakness in how many modern AI agents are built. Today’s agents are no longer passive language models. They read documents automatically, scan emails, connect to SaaS tools, access cloud storage, and execute actions across multiple systems. To be useful, they are granted meaningful permissions. That capability creates value, but it also expands the attack surface. Most agent environments operate in a trusted, plaintext execution model. Data is encrypted at rest and in transit, but it is typically decrypted during inference so the model can process it. That runtime visibility is where potential risk lies. In a zero-click scenario like AgentFlayer, an attacker can embed hidden instructions inside a document that the AI processes automatically. Because the agent may have access to connected systems such as Google Drive, Slack, or GitHub, it can potentially be influenced to retrieve sensitive information or perform unintended actions. The user does not need to click a malicious link or approve a suspicious request. Therefore, the core issue is that during execution, the system may have access to sensitive data and broad privileges, meaning whoever controls the execution environment ultimately controls access to that data. Now consider a different architectural approach. If a system is designed so that data remains protected during execution, the risk profile changes. On Nesa, privacy is enforced at the execution layer through Equivariant Encryption. Computation can occur on encrypted data, reducing the visibility surface during runtime. Sensitive inputs and models do not need to be exposed in plain text to infrastructure operators for inference to occur. This does not eliminate prompt injection, logic manipulation, or tool misuse. Encryption alone cannot prevent an agent from being instructed to take an unintended action if it has been granted that permission. What it does do is materially reduce confidentiality risk. By limiting access to readable sensitive data during execution and reducing unilateral visibility at the infrastructure layer, the potential blast radius of a successful manipulation attempt is constrained. As AI agents become more autonomous and embedded into enterprise workflows, security must move deeper into architecture. The goal is not to claim invulnerability. It is to reduce trust concentration and contain systemic exposure when failures occur. AgentFlayer was not simply a one-off exploit. It was a reminder that in autonomous systems, execution-layer design determines how risk propagates.

Nesa

17,038 views • 4 months ago

How can AI agents gain true autonomy? CARV ID (ERC-7231) is transforming AI and blockchain integration by creating trustless, sovereign AI agents. This identity protocol aggregates cross-chain and off-chain data, enabling AI Beings to operate with verified identity, privacy, and autonomy. By unifying fragmented data and enriching it with actionable metrics, CARV ID powers the D.A.T.A Framework, giving AI agents access to contextualized insights while preserving user data sovereignty. This means developers can build AI systems that learn, evolve, and act independently within a secure, decentralized ecosystem, free from centralized control. For businesses and researchers, this opens a new frontier where AI-driven decisions are transparent, verifiable, and privacy-preserved. CARV's integration of Solana scalability and Ethereum security ensures these trustless AI agents perform with speed at scale. The future is clear: AI Beings will no longer be mere tools, they will be sovereign participants in digital economies. What implications do you see for AI autonomy in blockchain?
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How can AI agents gain true autonomy? CARV ID (ERC-7231) is transforming AI and blockchain integration by creating trustless, sovereign AI agents. This identity protocol aggregates cross-chain and off-chain data, enabling AI Beings to operate with verified identity, privacy, and autonomy. By unifying fragmented data and enriching it with actionable metrics, CARV ID powers the D.A.T.A Framework, giving AI agents access to contextualized insights while preserving user data sovereignty. This means developers can build AI systems that learn, evolve, and act independently within a secure, decentralized ecosystem, free from centralized control. For businesses and researchers, this opens a new frontier where AI-driven decisions are transparent, verifiable, and privacy-preserved. CARV's integration of Solana scalability and Ethereum security ensures these trustless AI agents perform with speed at scale. The future is clear: AI Beings will no longer be mere tools, they will be sovereign participants in digital economies. What implications do you see for AI autonomy in blockchain?

CARV

96,077 views • 6 months ago

The next wave of AI adoption is already underway. From biotech to energy, data prediction to robotics, new applications are being built on Nesa and powered by our privacy-first infrastructure. It’s all happening now in the Playground. Join the waitlist today 👉
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The next wave of AI adoption is already underway. From biotech to energy, data prediction to robotics, new applications are being built on Nesa and powered by our privacy-first infrastructure. It’s all happening now in the Playground. Join the waitlist today 👉

Nesa

12,978 views • 3 months ago

📡 Data access may determine which autonomous agents can actually function onchain at scale. Lithosphere explores why continuous access to reliable data, coordination layers, and execution context is becoming essential infrastructure for AI-native systems. Read more 🔍 #Lithosphere #Web4 #AutonomousAgents #AIInfrastructure #OnchainData
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📡 Data access may determine which autonomous agents can actually function onchain at scale. Lithosphere explores why continuous access to reliable data, coordination layers, and execution context is becoming essential infrastructure for AI-native systems. Read more 🔍 #Lithosphere #Web4 #AutonomousAgents #AIInfrastructure #OnchainData

LITHO Foundation

30,249 views • 2 months ago

OpenAI's Deep Research is getting a run for its money. Deep Lake was just released, and it's a different take on an AI system that can do deep research on your own data. You can use Deep Lake to build AI search with reasoning on your private and public data. (Look at the attached videos to get an idea of how it works.) If you want to research proprietary and sensitive data, Deep Research won't help you because it's limited to public data. Deep Lake, however, will allow you to use your private data. On top of that, Deep Lake supports multi-modal retrieval from the ground up. It uses vision language models for data ingestion and retrieval so that you can connect any data (PDFs, images, videos, structured data, etc.) You can even use mixed-data queries! Deep Lake can search your data from S3, Dropbox, and GCP. It learns from your queries over time, making the results as relevant to your work as possible!
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OpenAI's Deep Research is getting a run for its money. Deep Lake was just released, and it's a different take on an AI system that can do deep research on your own data. You can use Deep Lake to build AI search with reasoning on your private and public data. (Look at the attached videos to get an idea of how it works.) If you want to research proprietary and sensitive data, Deep Research won't help you because it's limited to public data. Deep Lake, however, will allow you to use your private data. On top of that, Deep Lake supports multi-modal retrieval from the ground up. It uses vision language models for data ingestion and retrieval so that you can connect any data (PDFs, images, videos, structured data, etc.) You can even use mixed-data queries! Deep Lake can search your data from S3, Dropbox, and GCP. It learns from your queries over time, making the results as relevant to your work as possible!

Santiago

171,340 views • 1 year ago

The future of AI should not be trained by a few and owned by a few. We believe intelligence can be improved collectively. Through federated learning, participants help models learn without giving up raw data. Through DAO coordination, the network can evolve with transparent governance, incentives, and aligned decision-making. Better models. Stronger ownership. Shared upside. That’s the DRIVE369 thesis. #drive #drive369 #ai #agent #fl
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The future of AI should not be trained by a few and owned by a few. We believe intelligence can be improved collectively. Through federated learning, participants help models learn without giving up raw data. Through DAO coordination, the network can evolve with transparent governance, incentives, and aligned decision-making. Better models. Stronger ownership. Shared upside. That’s the DRIVE369 thesis. #drive #drive369 #ai #agent #fl

DRIVE369

14,750 views • 3 months ago

can you still tell what’s real? not anymore. HeyGen just dropped Avatar IV, it can make anyone talk anything with human like expression and movement the lines between real and AI is gone step by step tutorial:
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can you still tell what’s real? not anymore. HeyGen just dropped Avatar IV, it can make anyone talk anything with human like expression and movement the lines between real and AI is gone step by step tutorial:

el.cine

289,393 views • 1 year ago

Laika AI x Nuklai Excited to announce our partnership with Nuklai Nuklai is an innovative data ecosystem that will fuel the next generation of AI and Large Language Models (LLMs). Together, we're revolutionizing on-chain data accessibility and monetization. By combining Laika AI's blockchain analytics with Nuklai's ecosystem, we're creating new opportunities for data utilization powered by $LKI.
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Laika AI x Nuklai Excited to announce our partnership with Nuklai Nuklai is an innovative data ecosystem that will fuel the next generation of AI and Large Language Models (LLMs). Together, we're revolutionizing on-chain data accessibility and monetization. By combining Laika AI's blockchain analytics with Nuklai's ecosystem, we're creating new opportunities for data utilization powered by $LKI.

Laika AI

29,901 views • 1 year ago

The Playground Is the New Frontier ⬟ Every era of computing starts with a frontier. For decentralized AI, it’s called the Playground. This upcoming interface is where decentralized intelligence goes live: real models, real encryption, real rewards and more. Proof you can see, privacy you can trust.
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The Playground Is the New Frontier ⬟ Every era of computing starts with a frontier. For decentralized AI, it’s called the Playground. This upcoming interface is where decentralized intelligence goes live: real models, real encryption, real rewards and more. Proof you can see, privacy you can trust.

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36,220 views • 8 months ago

What if a network could deliver AI results closer to where data is created, instead of sending it to far‑off data centers? In collaboration with NVIDIA and Decart, we’re making that a reality by bringing GPU‑powered computing to the network edge — closer to homes and businesses — so AI applications respond in real-time. This is how we're laying the foundation for the next generation of AI‑driven services:
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What if a network could deliver AI results closer to where data is created, instead of sending it to far‑off data centers? In collaboration with NVIDIA and Decart, we’re making that a reality by bringing GPU‑powered computing to the network edge — closer to homes and businesses — so AI applications respond in real-time. This is how we're laying the foundation for the next generation of AI‑driven services:

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🌱 AI-powered farming, without chemicals. Aigen’s solar-powered robots use vision AI to identify and remove weeds at the plant level—helping farmers reduce herbicides and adopt more sustainable practices. Powered by simulation, real-world data and edge AI, this is what physical AI looks like in agriculture. 📖
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🌱 AI-powered farming, without chemicals. Aigen’s solar-powered robots use vision AI to identify and remove weeds at the plant level—helping farmers reduce herbicides and adopt more sustainable practices. Powered by simulation, real-world data and edge AI, this is what physical AI looks like in agriculture. 📖

NVIDIA Robotics

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With the rise of AI, it’s more important than ever to be able to distinguish real from processed or fake data. From clinical trials to authorship and identification, Algorand can instill trust in every record. Can a blockchain do that? #AlgorandCan
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With the rise of AI, it’s more important than ever to be able to distinguish real from processed or fake data. From clinical trials to authorship and identification, Algorand can instill trust in every record. Can a blockchain do that? #AlgorandCan

Algorand Foundation

83,072,622 views • 1 year ago

🔥Nexera & Aethir: Unleashing AI’s Next Frontier Through Tokenized GPU Power 🤝 Nexera is proud to join forces with Aethir in a strategic partnership to make cutting-edge AI infrastructure globally accessible. By tokenizing fractional GPU ownership, we’re enabling developers, enterprises, and investors everywhere to harness the explosive growth of deep learning and generative AI without being limited by geography, scale, or cost. With transparent tokenization, innovators can access powerful GPUs for faster model training and more advanced applications. GPU providers gain streamlined funding for expansion and upgrades, and investors tap into a high-growth market with secure, compliant opportunities that can provide higher yields than other RWA products. It’s an entirely new ecosystem where everyone can thrive, fueling AI’s evolution at an unprecedented pace. By 2030, the global GPU market is projected to exceed hundreds of billions of dollars, driven by the explosive demand for AI-powered applications, deep learning, and increasingly sophisticated generative models, ensuring that tokenizing these invaluable resources is poised to tap into a massive, rapidly expanding opportunity. $NXRA
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🔥Nexera & Aethir: Unleashing AI’s Next Frontier Through Tokenized GPU Power 🤝 Nexera is proud to join forces with Aethir in a strategic partnership to make cutting-edge AI infrastructure globally accessible. By tokenizing fractional GPU ownership, we’re enabling developers, enterprises, and investors everywhere to harness the explosive growth of deep learning and generative AI without being limited by geography, scale, or cost. With transparent tokenization, innovators can access powerful GPUs for faster model training and more advanced applications. GPU providers gain streamlined funding for expansion and upgrades, and investors tap into a high-growth market with secure, compliant opportunities that can provide higher yields than other RWA products. It’s an entirely new ecosystem where everyone can thrive, fueling AI’s evolution at an unprecedented pace. By 2030, the global GPU market is projected to exceed hundreds of billions of dollars, driven by the explosive demand for AI-powered applications, deep learning, and increasingly sophisticated generative models, ensuring that tokenizing these invaluable resources is poised to tap into a massive, rapidly expanding opportunity. $NXRA

Nexera

27,776 views • 1 year ago

🚨 THE BIGGEST BOTTLENECK IN AI ISN'T COMPUTING POWER ANYMORE IT'S MOVING DATA. Instead of laying new cables, Chinese researchers have upgraded existing fiber infrastructure by doing two things at once: Using three wavelength bands (C + L + S) instead of the usual two. Using four cores inside each fiber instead of one. Each core acts like an independent highway, and each band acts like an extra lane on that highway. Together, they’ve reportedly increased transmission capacity per core by nearly 50% and overall data throughput by up to 5×. This matters enormously for AI. Modern AI clusters move terabits of data per second between thousands of GPUs. The biggest bottleneck is often not the chips themselves, but moving data fast enough between them. If you can push 5× more data through the same physical cables, you can train bigger models faster and reduce network congestion. Why this is significant: • It shows multi-core + extended spectrum technology moving from labs into real-world commercial use • The system has already run over 35 km of existing telecom network • It could be especially useful for submarine cables and large-scale data center interconnects • China is also eyeing it for its “Eastern Data, Western Computing” project The deeper implication: We’re reaching the physical limits of how much data we can push through single-core fibers using traditional methods. By combining spatial multiplexing (multiple cores) with spectral multiplexing (more wavelength bands), engineers are finding new ways to keep scaling bandwidth without having to dig up the planet to lay new cables. This kind of breakthrough is quiet but foundational it’s the kind of infrastructure upgrade that will determine how fast AI and cloud computing can actually grow in the coming years. The future of data movement might not require more cables. It might just require smarter ones. How important do you think multi-core and multi-band fiber will be for keeping up with AI’s exploding data demands? Follow for more frontier networking, photonics, and infrastructure technology.
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🚨 THE BIGGEST BOTTLENECK IN AI ISN'T COMPUTING POWER ANYMORE IT'S MOVING DATA. Instead of laying new cables, Chinese researchers have upgraded existing fiber infrastructure by doing two things at once: Using three wavelength bands (C + L + S) instead of the usual two. Using four cores inside each fiber instead of one. Each core acts like an independent highway, and each band acts like an extra lane on that highway. Together, they’ve reportedly increased transmission capacity per core by nearly 50% and overall data throughput by up to 5×. This matters enormously for AI. Modern AI clusters move terabits of data per second between thousands of GPUs. The biggest bottleneck is often not the chips themselves, but moving data fast enough between them. If you can push 5× more data through the same physical cables, you can train bigger models faster and reduce network congestion. Why this is significant: • It shows multi-core + extended spectrum technology moving from labs into real-world commercial use • The system has already run over 35 km of existing telecom network • It could be especially useful for submarine cables and large-scale data center interconnects • China is also eyeing it for its “Eastern Data, Western Computing” project The deeper implication: We’re reaching the physical limits of how much data we can push through single-core fibers using traditional methods. By combining spatial multiplexing (multiple cores) with spectral multiplexing (more wavelength bands), engineers are finding new ways to keep scaling bandwidth without having to dig up the planet to lay new cables. This kind of breakthrough is quiet but foundational it’s the kind of infrastructure upgrade that will determine how fast AI and cloud computing can actually grow in the coming years. The future of data movement might not require more cables. It might just require smarter ones. How important do you think multi-core and multi-band fiber will be for keeping up with AI’s exploding data demands? Follow for more frontier networking, photonics, and infrastructure technology.

TheNewPhysics

20,485 views • 1 month ago

AI doesn’t scale without energy. Data centers don’t grow without energy. Advanced manufacturing doesn’t happen without energy. The next era of growth will be defined by who builds enough capacity to power it. And who can keep the lights on when conditions get tested. - Jamie Gualy, COO at T1 Energy
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AI doesn’t scale without energy. Data centers don’t grow without energy. Advanced manufacturing doesn’t happen without energy. The next era of growth will be defined by who builds enough capacity to power it. And who can keep the lights on when conditions get tested. - Jamie Gualy, COO at T1 Energy

T1 Energy

23,441 views • 1 month ago

Silencio has surpassed 1 million on-chain devices on peaq This is the live count of real contributors using their smartphones to capture and stream the physical world into digital infrastructure. Each device is opt-in, privacy-preserving, and individually rewarded in $SLC. Together, these contributors have recorded more than 10.6 million hours of real-world acoustic signal. The network spans 190 countries and has generated over 38.8 Billion environmental datapoints. All of it is live. All of it is on-chain. Silencio represents a deployed DePIN system operating at global scale. Its data is already integrated into major enterprise marketplaces. Its token economy is active, deflationary, and fully aligned with participation. This milestone is not just about numbers. It is about trust, coordination, and measurable impact. It proves that decentralized networks can scale without compromising on quality, compliance, or utility. Built on peaq. Secured by $SLC and the Blocksound Foundation. Powered by people. This is not the future of data infrastructure. It is already in motion.
0:16

Silencio has surpassed 1 million on-chain devices on peaq This is the live count of real contributors using their smartphones to capture and stream the physical world into digital infrastructure. Each device is opt-in, privacy-preserving, and individually rewarded in $SLC. Together, these contributors have recorded more than 10.6 million hours of real-world acoustic signal. The network spans 190 countries and has generated over 38.8 Billion environmental datapoints. All of it is live. All of it is on-chain. Silencio represents a deployed DePIN system operating at global scale. Its data is already integrated into major enterprise marketplaces. Its token economy is active, deflationary, and fully aligned with participation. This milestone is not just about numbers. It is about trust, coordination, and measurable impact. It proves that decentralized networks can scale without compromising on quality, compliance, or utility. Built on peaq. Secured by $SLC and the Blocksound Foundation. Powered by people. This is not the future of data infrastructure. It is already in motion.

Silencio | Voice Data for AI

22,863 views • 1 year ago

Exciting news from HYPE3.cool! We’re teaming up with Chainbase (💜,💛), the world’s largest omnichain data network to supercharge AI agents with robust on-chain data. Very soon, users will be able to integrate real-time blockchain insights when configuring their agents—making them smarter and more capable than ever! What this means: - Real-time on-chain data for enhanced decision-making - Greater agent intelligence powered by advanced data analytics - Seamless blockchain integration backed by Chainbase (💜,💛)’s decentralized infrastructure About Chainbase: Chainbase is the world’s largest omnichain data network built to power the AI economy with high-quality on-chain data. Through its innovative four-layer, dual-consensus architecture, Chainbase’s network, anchored by Chainbase AVS, provides secure, interoperable data for AI and Web3. Your AI agents can now tap into rich, reliable on-chain insights—paving the way for more dynamic and intelligent interactions. This is just the start of making AI agents truly Web3-native. Stay tuned as we build the future of intelligent property in partnership with Chainbase (💜,💛)! #Web3 #AI #Blockchain
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Exciting news from HYPE3.cool! We’re teaming up with Chainbase (💜,💛), the world’s largest omnichain data network to supercharge AI agents with robust on-chain data. Very soon, users will be able to integrate real-time blockchain insights when configuring their agents—making them smarter and more capable than ever! What this means: - Real-time on-chain data for enhanced decision-making - Greater agent intelligence powered by advanced data analytics - Seamless blockchain integration backed by Chainbase (💜,💛)’s decentralized infrastructure About Chainbase: Chainbase is the world’s largest omnichain data network built to power the AI economy with high-quality on-chain data. Through its innovative four-layer, dual-consensus architecture, Chainbase’s network, anchored by Chainbase AVS, provides secure, interoperable data for AI and Web3. Your AI agents can now tap into rich, reliable on-chain insights—paving the way for more dynamic and intelligent interactions. This is just the start of making AI agents truly Web3-native. Stay tuned as we build the future of intelligent property in partnership with Chainbase (💜,💛)! #Web3 #AI #Blockchain

HYPE3.cool

17,700 views • 1 year ago