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OpenLedger X Morpheus The partnership of openledger with Morpheus enables Use Morpheus to build "The Autonomous Smart Contract Engineer" on top of OpenLedger. What is Morpheus? Morpheus is a Web3-native AI coding agent that turns natural language into executable smart contracts and full-stack dApps. It is powered by a...

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OpenLedger

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46,735 Aufrufe • vor 1 Jahr •via X (Twitter)

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10 Kommentare

Profilbild von Sapien
Sapienvor 1 Jahr

@usemorpheus_com this changes smart contracts forever 👏 giga brain activity right there

Profilbild von Holly
Hollyvor 1 Jahr

@usemorpheus_com The future isn’t just code automation- it’s accountable code automation. Huge respect to both teams for building it right.

Profilbild von Openledger 🔜 𝗔𝗦𝗜𝗔 𝗧𝗢𝗨𝗥 ✈️
Openledger 🔜 𝗔𝗦𝗜𝗔 𝗧𝗢𝗨𝗥 ✈️vor 1 Jahr

@usemorpheus_com Respect to everyone pushing in that direction.

Profilbild von Jeffrey
Jeffreyvor 1 Jahr

@usemorpheus_com OpenLedger keeps cooking awesome collabs, soon top position on the leaderboard

Profilbild von Openledger 🔜 𝗔𝗦𝗜𝗔 𝗧𝗢𝗨𝗥 ✈️
Openledger 🔜 𝗔𝗦𝗜𝗔 𝗧𝗢𝗨𝗥 ✈️vor 1 Jahr

@usemorpheus_com Cooking with contributors, seasoning with attribution,This stew slaps.

Profilbild von Tweeter Wali
Tweeter Walivor 1 Jahr

@usemorpheus_com OpenLedger becoming base for all AI agents

Profilbild von DAVID
DAVIDvor 1 Jahr

@usemorpheus_com Morpheus collaboration with OpenLedger seems to have some spice

Profilbild von Wiliams🀄️ OpenLedger 🐙
Wiliams🀄️ OpenLedger 🐙vor 1 Jahr

@usemorpheus_com More partnerships Bet signals we’re close to mainnet 🔥

Profilbild von Fabius DeFi
Fabius DeFivor 1 Jahr

@usemorpheus_com Morpheus x OpenLedger = AI dev stack for Web3 😎

Profilbild von Openledger 🔜 𝗔𝗦𝗜𝗔 𝗧𝗢𝗨𝗥 ✈️
Openledger 🔜 𝗔𝗦𝗜𝗔 𝗧𝗢𝗨𝗥 ✈️vor 1 Jahr

@usemorpheus_com Solidity meets signal. Attribution meets automation. Let’s ride.

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We’re excited to announce our partnership with Use Morpheus as they develop a specialized AI model on OpenLedger. Leveraging our decentralized infrastructure, Morpheus will create a fine-tuned Web3 LLM focused on secure code generation and autonomous workflows. This collaboration will push the boundaries of intelligent contract creation, enabling decentralized applications to scale smarter, faster, and more securely. The next wave of AI-powered smart contracts is here, stay tuned for more updates! Hear adamzhao.eth , founder of Use Morpheus, break it down in the video.
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We’re excited to announce our partnership with Use Morpheus as they develop a specialized AI model on OpenLedger. Leveraging our decentralized infrastructure, Morpheus will create a fine-tuned Web3 LLM focused on secure code generation and autonomous workflows. This collaboration will push the boundaries of intelligent contract creation, enabling decentralized applications to scale smarter, faster, and more securely. The next wave of AI-powered smart contracts is here, stay tuned for more updates! Hear adamzhao.eth , founder of Use Morpheus, break it down in the video.

OpenLedger

17,229 Aufrufe • vor 1 Jahr

Revolutionizing Move Programming with OpenLedger In this demo, we showcase how Move datasets contributed by data providers to OpenLedger’s datanets are used to fine-tune specialized models with LoRA fine-tuning. As seen in the video, we showcase an example on how builders can deploy a Move-specialized model that powers Co-pilot agents using our no-code model fine-tuning platform. This is the future of AI and Web3 innovation. Watch this space to see more specialised models and data feeds being built for next generation agents on top of OpenLedger #Move
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Revolutionizing Move Programming with OpenLedger In this demo, we showcase how Move datasets contributed by data providers to OpenLedger’s datanets are used to fine-tune specialized models with LoRA fine-tuning. As seen in the video, we showcase an example on how builders can deploy a Move-specialized model that powers Co-pilot agents using our no-code model fine-tuning platform. This is the future of AI and Web3 innovation. Watch this space to see more specialised models and data feeds being built for next generation agents on top of OpenLedger #Move

OpenLedger

61,662 Aufrufe • vor 1 Jahr

Tired of generic, hallucinating LLMs? We use AI for almost everything these days, whether it’s helping with math homework or looking up remedies when we’re sick. It's become part of our daily routines. Now imagine if Einstein helped you solve that homework, or Eminem wrote the lyrics for your next rap song. What if your AI was built specifically for your world? Trained on your domain, your data, you become the master of it. Introducing OpenLedger AI Studio. A powerful platform to build and use specialized models with built-in explainability and attribution. Fine-tune using your data through our Model Factory, and deploy them seamlessly. Your models won’t just reason - With OpenLedger, you can explain every decision, trace its data, and reward contributors. Truly Open, Verified, and Explainable AI begins here.
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Tired of generic, hallucinating LLMs? We use AI for almost everything these days, whether it’s helping with math homework or looking up remedies when we’re sick. It's become part of our daily routines. Now imagine if Einstein helped you solve that homework, or Eminem wrote the lyrics for your next rap song. What if your AI was built specifically for your world? Trained on your domain, your data, you become the master of it. Introducing OpenLedger AI Studio. A powerful platform to build and use specialized models with built-in explainability and attribution. Fine-tune using your data through our Model Factory, and deploy them seamlessly. Your models won’t just reason - With OpenLedger, you can explain every decision, trace its data, and reward contributors. Truly Open, Verified, and Explainable AI begins here.

OpenLedger

172,497 Aufrufe • vor 11 Monaten

✈️ Starting the day in Istanbul. Let's talk AI. The future of AI won't be shaped by size, but by precision. Lately I've been diving into what OpenLedger has been building, and I think we're witnessing one of the most important hard forks in AI: 👉 From giant generalist models 👉 To focused, hightrust AI agents Here's why that shift matters , and why OpenLedger's vision makes perfect sense: ✅ Specialized > Generalized General AI can do many things. But specialized AI? It does one thing extraordinarily well. Tailored models don't waste compute on irrelevant context, every parameter is purposedriven. ✅ Explainability isn't optional anymore In highstakes sectors like finance or healthcare, because the model said so won't cut it. We need transparent reasoning paths. Models must show how they reached conclusions , not just what they concluded. ✅ Trust comes from traceability With OpenLedger's Proof of Attribution, each AI decision is traceable, verifiable, and tamperproof. We're talking onchain records of who contributed what , accountability by design. ✅ Less hallucination, more signal Smaller, specialized models trained on clean, domainspecific data are far less prone to hallucinations. Clear boundaries = higher reliability. ✅ Efficiency is the real scalability Deploying one massive model for everything? Expensive, slow, and unsustainable. Specialized AI is leaner, faster, and far more costeffective. In short, OpenLedger isn't just following a trend. They're laying the rails for the infrastructure layer of verifiable, domainaware AI. And in a space flooded with blackbox models and hype, that clarity hits different.
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✈️ Starting the day in Istanbul. Let's talk AI. The future of AI won't be shaped by size, but by precision. Lately I've been diving into what OpenLedger has been building, and I think we're witnessing one of the most important hard forks in AI: 👉 From giant generalist models 👉 To focused, hightrust AI agents Here's why that shift matters , and why OpenLedger's vision makes perfect sense: ✅ Specialized > Generalized General AI can do many things. But specialized AI? It does one thing extraordinarily well. Tailored models don't waste compute on irrelevant context, every parameter is purposedriven. ✅ Explainability isn't optional anymore In highstakes sectors like finance or healthcare, because the model said so won't cut it. We need transparent reasoning paths. Models must show how they reached conclusions , not just what they concluded. ✅ Trust comes from traceability With OpenLedger's Proof of Attribution, each AI decision is traceable, verifiable, and tamperproof. We're talking onchain records of who contributed what , accountability by design. ✅ Less hallucination, more signal Smaller, specialized models trained on clean, domainspecific data are far less prone to hallucinations. Clear boundaries = higher reliability. ✅ Efficiency is the real scalability Deploying one massive model for everything? Expensive, slow, and unsustainable. Specialized AI is leaner, faster, and far more costeffective. In short, OpenLedger isn't just following a trend. They're laying the rails for the infrastructure layer of verifiable, domainaware AI. And in a space flooded with blackbox models and hype, that clarity hits different.

Crypto Sinan

13,636 Aufrufe • vor 10 Monaten

3 reasons why we chose to deploy our rollup in the ZKsync Elastic Chain ecosystem. 1⃣ SXT smart contract execution layer SXT is a blockchain designed for collecting, storing, querying, and proving data to smart contracts. Unlike an EVM chain, it's not designed to execute smart contracts. In order to support critical functions for the SXT network, like staking and slashing for node ops, as well as to allow devs to deploy their own smart contracts directly on SXT, we built our own ZK rollup with ZKsync’s stack. It serves as the settlement and smart contract execution layer for SXT. 2⃣ Elastic Chain ecosystem We 🩷 the ZKsync community, and building in the Elastic Chain ecosystem gives us access to an array of other rollups, users, apps, and smart contracts that need ZK-proven data. The Elastic Chain includes some of the coolest projects in crypto across DeFi, social, NFTs, and more, and we're excited to build with them. 3⃣ ZK is the endgame ZKsync is fast, secure, scalable, and the best-in-class zkEVM. Full stop. We’re incredibly bullish on ZK in general, but more specifically the ZKsync roadmap and everything being built by the community and the Matter Labs team. 🎙️: chain builder on The Rollup
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3 reasons why we chose to deploy our rollup in the ZKsync Elastic Chain ecosystem. 1⃣ SXT smart contract execution layer SXT is a blockchain designed for collecting, storing, querying, and proving data to smart contracts. Unlike an EVM chain, it's not designed to execute smart contracts. In order to support critical functions for the SXT network, like staking and slashing for node ops, as well as to allow devs to deploy their own smart contracts directly on SXT, we built our own ZK rollup with ZKsync’s stack. It serves as the settlement and smart contract execution layer for SXT. 2⃣ Elastic Chain ecosystem We 🩷 the ZKsync community, and building in the Elastic Chain ecosystem gives us access to an array of other rollups, users, apps, and smart contracts that need ZK-proven data. The Elastic Chain includes some of the coolest projects in crypto across DeFi, social, NFTs, and more, and we're excited to build with them. 3⃣ ZK is the endgame ZKsync is fast, secure, scalable, and the best-in-class zkEVM. Full stop. We’re incredibly bullish on ZK in general, but more specifically the ZKsync roadmap and everything being built by the community and the Matter Labs team. 🎙️: chain builder on The Rollup

Space and Time

19,565 Aufrufe • vor 1 Jahr

A blockchain project can be designed to be 100% technically decentralized, but if a single developer or a team within a single country controls the development and decision-making, the project may not be fully decentralized in terms of governance and decision-making. ## Differences between technical decentralization and governance decentralization - *Technical decentralization*: The blockchain project uses decentralized technology, such as distributed ledgers, to ensure transparency and security. - *Governance decentralization*: The blockchain project is managed and decided by a decentralized community, not by a single individual or organization. ## Conditions for a blockchain project to be considered decentralized For a blockchain project to be considered truly decentralized, it needs to meet both conditions: - *Technical decentralization*: Uses decentralized technology to ensure transparency and security. - *Decentralized governance*: Managed and decided by a decentralized community, with transparent and fair decision-making mechanisms. If a blockchain project only meets one of the above two conditions, it may not be fully decentralized. ## Building a 100% Decentralized Ecosystem: A New Model for the Future In the context of rapidly developing blockchain technology, building a 100% decentralized ecosystem is not only a goal but also an essential need. The SkyPirl (PIRL coin) project has pioneered the application of a decentralized model not only in terms of technology but also in terms of governance. This article will analyze and demonstrate the importance of this model and convince the community, developers and organizations to support this policy. ## Why 100% Decentralization is Necessary 100% decentralization ensures that no single individual or organization can control the entire system. This enhances transparency, security and fairness. When applied to an ecosystem this model will bring many benefits: - *Increased transparency*: With the decentralization of power and management by many organizations and individuals, the system will become more transparent. - *Higher security*: The use of multi-signature wallets and treasury management by many representatives will reduce security risks. - *Fairness and democracy*: The decentralized model ensures that all decisions are made fairly and democratically. ## SkyPirl's Decentralized Governance Model SkyPirl project has adopted a decentralized governance model from the beginning by: - *Decentralized development team*: Multiple development teams from different countries, ensuring that no single country or organization controls the entire project. - *Restrict developer power*: Developers are not allowed to hold websites, domains and social media channels, helping to disperse power. - *Use smart contracts and multi-signature wallets*: To ensure transparency and security in treasury management and transactions. ## Applying the Model to the Ecosystem The ecosystem will apply this 100% decentralized model to all projects. This includes: - *Decentralization of power*: Many organizations and individuals will participate in management and decision-making. - *Use multi-signature wallets*: To ensure security and transparency in treasury management. - *Representatives from organizations*: Organizations will send representatives to participate in management and decision-making. #SkyPirl #PIRL #PIRLmeet #SLOFI #CLO #2BearsExchange
0:52

A blockchain project can be designed to be 100% technically decentralized, but if a single developer or a team within a single country controls the development and decision-making, the project may not be fully decentralized in terms of governance and decision-making. ## Differences between technical decentralization and governance decentralization - *Technical decentralization*: The blockchain project uses decentralized technology, such as distributed ledgers, to ensure transparency and security. - *Governance decentralization*: The blockchain project is managed and decided by a decentralized community, not by a single individual or organization. ## Conditions for a blockchain project to be considered decentralized For a blockchain project to be considered truly decentralized, it needs to meet both conditions: - *Technical decentralization*: Uses decentralized technology to ensure transparency and security. - *Decentralized governance*: Managed and decided by a decentralized community, with transparent and fair decision-making mechanisms. If a blockchain project only meets one of the above two conditions, it may not be fully decentralized. ## Building a 100% Decentralized Ecosystem: A New Model for the Future In the context of rapidly developing blockchain technology, building a 100% decentralized ecosystem is not only a goal but also an essential need. The SkyPirl (PIRL coin) project has pioneered the application of a decentralized model not only in terms of technology but also in terms of governance. This article will analyze and demonstrate the importance of this model and convince the community, developers and organizations to support this policy. ## Why 100% Decentralization is Necessary 100% decentralization ensures that no single individual or organization can control the entire system. This enhances transparency, security and fairness. When applied to an ecosystem this model will bring many benefits: - *Increased transparency*: With the decentralization of power and management by many organizations and individuals, the system will become more transparent. - *Higher security*: The use of multi-signature wallets and treasury management by many representatives will reduce security risks. - *Fairness and democracy*: The decentralized model ensures that all decisions are made fairly and democratically. ## SkyPirl's Decentralized Governance Model SkyPirl project has adopted a decentralized governance model from the beginning by: - *Decentralized development team*: Multiple development teams from different countries, ensuring that no single country or organization controls the entire project. - *Restrict developer power*: Developers are not allowed to hold websites, domains and social media channels, helping to disperse power. - *Use smart contracts and multi-signature wallets*: To ensure transparency and security in treasury management and transactions. ## Applying the Model to the Ecosystem The ecosystem will apply this 100% decentralized model to all projects. This includes: - *Decentralization of power*: Many organizations and individuals will participate in management and decision-making. - *Use multi-signature wallets*: To ensure security and transparency in treasury management. - *Representatives from organizations*: Organizations will send representatives to participate in management and decision-making. #SkyPirl #PIRL #PIRLmeet #SLOFI #CLO #2BearsExchange

SkyPirl Official 🎈🎈

16,135 Aufrufe • vor 1 Jahr

Right now the main paradigm that we think of agents in is chatting back and forth, but the biggest use of tokens will come from agents that are just always on running in the background doing work for us, or ones triggered from a workflow. Agents will be working 24/7 in our workflows processing data, reviewing and generating documents, moving data between systems, writing code, accelerating decision making steps, and more. In Claude's new Managed Agents feature, in a couple minutes you can wire up an agent that can read contracts when they come into Box to review them, and then assign a task in Linear with the critical information from the contract. But this could have been any workflow, like reviewing documents for client onboarding, invoice processing, M&A due-diligence, data extraction pipelines, and millions of other use-cases. And integrating data across any system. This is only possible when you can have long-running agents that can complete real work in the background, accurately. Agents have the ability to execute code safely, leverage tools, access a compute sandbox, and connect across systems is clearly the architecture of the future. The industry is now making it easier and easier for enterprises to build and deploy these agents.
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Right now the main paradigm that we think of agents in is chatting back and forth, but the biggest use of tokens will come from agents that are just always on running in the background doing work for us, or ones triggered from a workflow. Agents will be working 24/7 in our workflows processing data, reviewing and generating documents, moving data between systems, writing code, accelerating decision making steps, and more. In Claude's new Managed Agents feature, in a couple minutes you can wire up an agent that can read contracts when they come into Box to review them, and then assign a task in Linear with the critical information from the contract. But this could have been any workflow, like reviewing documents for client onboarding, invoice processing, M&A due-diligence, data extraction pipelines, and millions of other use-cases. And integrating data across any system. This is only possible when you can have long-running agents that can complete real work in the background, accurately. Agents have the ability to execute code safely, leverage tools, access a compute sandbox, and connect across systems is clearly the architecture of the future. The industry is now making it easier and easier for enterprises to build and deploy these agents.

Aaron Levie

17,543 Aufrufe • vor 3 Monaten

A world first: a demonstration of AI running *on* blockchain as a smart contract 🧠⚡️ The Internet Computer is used – the world's first 3rd gen. blockchain #ICP. AI will become the beating heart of our web3, multi-chain world, and this is only my first demo. Code will follow shortly. This is running on DFINITY's Internet Computer testnet, but you'll be able to take the code and run it on the public network as the NNS is expected to up the per-transaction instruction limit in the coming days. Some important notes. The inference engine used has not been optimized, and we will show vastly greater efficiency in subsequent demos, where the AI runs even faster, and consumes less gas/cycles. We will also propose to the NNS that smart contracts have access to SIMD instructions – which we have determined are deterministic – unlocking vastly more speed and efficiency. Lastly, currently the actor smart contracts ICP hosts run inside a 32 bit environment, which limits their main memory to 4GB. Within the next couple of months, we expect the Internet Computer to move to a 64 bit environment, allowing smart contract memory limits to be raised much higher – allowing for models with far more parameters, and thus power, to be run. Those of you who have followed my posts know that hardware optimization support is planned. Firstly this will involve WASM smart contracts shipping out matrices for processing by the CPUs on existing node machines (another advantage of the Internet Computer running on sovereign node hardware). Secondly, we plan to propose a new public node machine specification, which node providers can build to, for use in subnets specialized for hosting AI smart contracts, in which each machine will incorporate several GPUs. Naturally, this will be packaged with other technological developments that ensure smart contract determinism. This is part of our mission to enable powerful LLMs to run as smart contracts on the Internet Computer – in a forms that are tamperproof, unstoppable, and optionally autonomous (including under the exclusive control of DAOs). Our vision: you will be able to have a chat with a smart contract. A smart contract will be able to coordinate your organization (see my earlier tweet about a "Delphi"). ICP smart contracts will be able to audit Ethereum smart contracts, kitemarking those without backdoors and reentrancy bugs. And smart contracts will be able to do KYC autonmously, matching faces to driving licenses, and more. We will deliver new SDK enhancements, and work with partners, to turbocharge crypto AI developers. Secure and unstoppable AI and third generation blockchain will be two sides of the same coin. Security and AI will be indivisible. AI models will be traded as NFTs. Thanks to Internet Computer's chain key (trustless multi-chain) capabilities, all blockchains will be able to leverage AI smart contracts. The future is beyond exciting. Thanks for watching. Can't wait to give you more demos!
4:17

A world first: a demonstration of AI running *on* blockchain as a smart contract 🧠⚡️ The Internet Computer is used – the world's first 3rd gen. blockchain #ICP. AI will become the beating heart of our web3, multi-chain world, and this is only my first demo. Code will follow shortly. This is running on DFINITY's Internet Computer testnet, but you'll be able to take the code and run it on the public network as the NNS is expected to up the per-transaction instruction limit in the coming days. Some important notes. The inference engine used has not been optimized, and we will show vastly greater efficiency in subsequent demos, where the AI runs even faster, and consumes less gas/cycles. We will also propose to the NNS that smart contracts have access to SIMD instructions – which we have determined are deterministic – unlocking vastly more speed and efficiency. Lastly, currently the actor smart contracts ICP hosts run inside a 32 bit environment, which limits their main memory to 4GB. Within the next couple of months, we expect the Internet Computer to move to a 64 bit environment, allowing smart contract memory limits to be raised much higher – allowing for models with far more parameters, and thus power, to be run. Those of you who have followed my posts know that hardware optimization support is planned. Firstly this will involve WASM smart contracts shipping out matrices for processing by the CPUs on existing node machines (another advantage of the Internet Computer running on sovereign node hardware). Secondly, we plan to propose a new public node machine specification, which node providers can build to, for use in subnets specialized for hosting AI smart contracts, in which each machine will incorporate several GPUs. Naturally, this will be packaged with other technological developments that ensure smart contract determinism. This is part of our mission to enable powerful LLMs to run as smart contracts on the Internet Computer – in a forms that are tamperproof, unstoppable, and optionally autonomous (including under the exclusive control of DAOs). Our vision: you will be able to have a chat with a smart contract. A smart contract will be able to coordinate your organization (see my earlier tweet about a "Delphi"). ICP smart contracts will be able to audit Ethereum smart contracts, kitemarking those without backdoors and reentrancy bugs. And smart contracts will be able to do KYC autonmously, matching faces to driving licenses, and more. We will deliver new SDK enhancements, and work with partners, to turbocharge crypto AI developers. Secure and unstoppable AI and third generation blockchain will be two sides of the same coin. Security and AI will be indivisible. AI models will be traded as NFTs. Thanks to Internet Computer's chain key (trustless multi-chain) capabilities, all blockchains will be able to leverage AI smart contracts. The future is beyond exciting. Thanks for watching. Can't wait to give you more demos!

dom | icp

1,362,003 Aufrufe • vor 2 Jahren

Introducing Lithic ( the intelligent execution language powering the Lithosphere Network. For years blockchain has focused on programmable value. With Lithic, we move toward programmable intelligence. Lithic is designed to connect smart contracts, identity, AI systems, and cross-chain infrastructure into a unified execution fabric for the next phase of the internet. This is a core step toward the Web4 infrastructure stack we’ve been building at Lithosphere. Core Lithic Components • Lithic Execution Engine – deterministic runtime powering intelligent smart contract execution • LEP100 Intelligent Asset Framework – programmable token standard for adaptive digital assets • Lithic Modules (LSCL) – composable modules for governance, identity, compliance, and AI logic • Cross-Chain Orchestration Layer – communication between EVM, Cosmos environments, and external networks • Identity & Policy Hooks – programmable identity-aware transaction and asset control • AI Integration Layer – enabling intelligent validation, automation, and decision systems Together these components form the foundation for adaptive decentralized systems. Why Lithic matters Static contracts belong to Web3. Lithic enables: • intelligent assets • identity-aware protocols • AI-driven automation • modular decentralized infrastructure This is how blockchain evolves into the intelligent layer of the internet. 📚Lithic Documentation & Resources Learn more Docs: Tools, Scripts, examples etc: Lithic Contract Wizard and Verifier. LEP100 Standard-suite Lithosphere Developer Resources More developer tools, SDKs, and modules will be released alongside the Makalu Testnet.
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Introducing Lithic ( the intelligent execution language powering the Lithosphere Network. For years blockchain has focused on programmable value. With Lithic, we move toward programmable intelligence. Lithic is designed to connect smart contracts, identity, AI systems, and cross-chain infrastructure into a unified execution fabric for the next phase of the internet. This is a core step toward the Web4 infrastructure stack we’ve been building at Lithosphere. Core Lithic Components • Lithic Execution Engine – deterministic runtime powering intelligent smart contract execution • LEP100 Intelligent Asset Framework – programmable token standard for adaptive digital assets • Lithic Modules (LSCL) – composable modules for governance, identity, compliance, and AI logic • Cross-Chain Orchestration Layer – communication between EVM, Cosmos environments, and external networks • Identity & Policy Hooks – programmable identity-aware transaction and asset control • AI Integration Layer – enabling intelligent validation, automation, and decision systems Together these components form the foundation for adaptive decentralized systems. Why Lithic matters Static contracts belong to Web3. Lithic enables: • intelligent assets • identity-aware protocols • AI-driven automation • modular decentralized infrastructure This is how blockchain evolves into the intelligent layer of the internet. 📚Lithic Documentation & Resources Learn more Docs: Tools, Scripts, examples etc: Lithic Contract Wizard and Verifier. LEP100 Standard-suite Lithosphere Developer Resources More developer tools, SDKs, and modules will be released alongside the Makalu Testnet.

King

158,021 Aufrufe • vor 4 Monaten

When you enter VaderAI into KREDO here's what comes back. Reputation Score: 78 Vader_AI_ is an AI-powered benchmark infrastructure agent focused on vulnerability assessment, detection, explanation, and remediation for large language models (LLMs) and smart contract environments. Its core competency lies in providing interpretable, reproducible evaluation metrics for AI security and model robustness. - Core Technology: Benchmarking dataset, evaluation rubrics, scoring tools, visualized results - Key Metrics: Human-evaluated data, public datasets, interpretable scoring, confidence interval reporting Vader_AI_ distinguishes itself by offering a publicly released, human-evaluated benchmark specifically tailored to vulnerability-aware AI agents in the crypto/Web3 ecosystem. Its comprehensive design includes not only an expansive dataset but also detailed rubrics and automated evaluation tools, ensuring that performance metrics for LLM-driven agents are transparent and reproducible. This infrastructural approach enables organizations and developers to identify both strengths and deficiencies in agent reasoning as it relates to security, aligning AI assessments closely with real-world exploit risk and defense scenarios. A clear success of Vader_AI_ is the rigorous transparency in its release methodology: confidence intervals are visualized alongside all results, and the benchmark provides interpretable outputs that directly support both developers and auditors in understanding where and why an agent's decision logic may falter. The agent excels in creating a standardized baseline to compare AI-powered systems, directly addressing the fragmented nature of prior evaluation methodologies in this domain. However, limitations exist in the extent to which Vader_AI_ can capture emergent, unknown exploit patterns or generalize to novel blockchain environments beyond its existing dataset. Its effectiveness is highest when used as part of a continuous, iterative assessment framework, rather than as a one-time gatekeeper. Furthermore, the accuracy of its insights is partly dependent on the ongoing contribution and maintenance of high-quality, up-to-date human-evaluated datasets. In summary, Vader_AI_ represents an essential component in the push toward trustworthy, measurable AI in crypto applications. Its approach is especially well-suited for projects prioritizing provable agent reliability and onchain security alignment, though its results are best seen as one critical input among several in a comprehensive risk management pipeline. Wondering how other agents score? Just try. 👉
0:34

When you enter VaderAI into KREDO here's what comes back. Reputation Score: 78 Vader_AI_ is an AI-powered benchmark infrastructure agent focused on vulnerability assessment, detection, explanation, and remediation for large language models (LLMs) and smart contract environments. Its core competency lies in providing interpretable, reproducible evaluation metrics for AI security and model robustness. - Core Technology: Benchmarking dataset, evaluation rubrics, scoring tools, visualized results - Key Metrics: Human-evaluated data, public datasets, interpretable scoring, confidence interval reporting Vader_AI_ distinguishes itself by offering a publicly released, human-evaluated benchmark specifically tailored to vulnerability-aware AI agents in the crypto/Web3 ecosystem. Its comprehensive design includes not only an expansive dataset but also detailed rubrics and automated evaluation tools, ensuring that performance metrics for LLM-driven agents are transparent and reproducible. This infrastructural approach enables organizations and developers to identify both strengths and deficiencies in agent reasoning as it relates to security, aligning AI assessments closely with real-world exploit risk and defense scenarios. A clear success of Vader_AI_ is the rigorous transparency in its release methodology: confidence intervals are visualized alongside all results, and the benchmark provides interpretable outputs that directly support both developers and auditors in understanding where and why an agent's decision logic may falter. The agent excels in creating a standardized baseline to compare AI-powered systems, directly addressing the fragmented nature of prior evaluation methodologies in this domain. However, limitations exist in the extent to which Vader_AI_ can capture emergent, unknown exploit patterns or generalize to novel blockchain environments beyond its existing dataset. Its effectiveness is highest when used as part of a continuous, iterative assessment framework, rather than as a one-time gatekeeper. Furthermore, the accuracy of its insights is partly dependent on the ongoing contribution and maintenance of high-quality, up-to-date human-evaluated datasets. In summary, Vader_AI_ represents an essential component in the push toward trustworthy, measurable AI in crypto applications. Its approach is especially well-suited for projects prioritizing provable agent reliability and onchain security alignment, though its results are best seen as one critical input among several in a comprehensive risk management pipeline. Wondering how other agents score? Just try. 👉

Kredo AI

16,988 Aufrufe • vor 1 Jahr

Say hello to a massive acceleration of AI smart contracts 🧠 on the Internet Computer #ICP blockchain. Brilliant work by the DFINITY Foundation team has accelerated deterministic floating point instructions by 10X. We continue on our mission to run sophisticated LLMs as smart contracts 👊⚡️🔥 In the demo, image classification is now running 3X+ faster (across 3-4 blocks through consensus compared to 10-12 before). Naturally, the gas/cycles costs of AI inference has come down commensurately. The acceleration was achieved through broad low-level work, which includes commits to the public Wasmtime virtual machine implementation of WebAssembly. Coming optimizations will take us much further. The next optimization will include the integration of SIMD instructions into the smart contract execution environment, which will allow multiple floating point calculations to be performed in parallel through the execution of a single instruction. Something crucial: also in near sight, is the migration of the ICP smart contract environment from 32-bit Wasm to 64-bit Wasm, which will scale the number of AI model weights that can be loaded into contract memory from 2B to whatever is needed for sophisticated LLMs. Somewhat further away, is the provision of new APIs allowing canister smart contract code to export AI computations for accelerated processing on GPUs. This will require substantial work to address the challenges of achieving deterministic computation directly on the silicon, and for the community to agree a new public spec for node machines that pack GPUs, and to add compliant blockchain nodes to the network. Together, these improvements will unlock further orders of magnitude acceleration of AI smart contract inference (and potentially training). We believe that in the future vast numbers of AI models will run as smart contracts, which will prevent them being hacked (and thus the sensitive data they ingest being stolen), make them unstoppable, make them transparent, and where needed, allow them to run autonomously – for example for purposes such as KYC or EVM smart contract verification/certification. In the future, you will be able to have a conversation with a blockchain, and ask it to do reasoning for you. In this bright future, on-chain AI will be at the heart of Web3 – and to be clear, the Internet Computer's chain key technology will allow direct trustless integration with smart contracts on traditional chains such as Ethereum, Solana and NEAR. Join the #ICP mission to run the most impactful AI models on blockchain as smart contracts, and help make the Internet Computer the "everything computer." Join next week's Global R&D for details about the low-level deterministic floating point optimizations. Thanks for following 💪
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Say hello to a massive acceleration of AI smart contracts 🧠 on the Internet Computer #ICP blockchain. Brilliant work by the DFINITY Foundation team has accelerated deterministic floating point instructions by 10X. We continue on our mission to run sophisticated LLMs as smart contracts 👊⚡️🔥 In the demo, image classification is now running 3X+ faster (across 3-4 blocks through consensus compared to 10-12 before). Naturally, the gas/cycles costs of AI inference has come down commensurately. The acceleration was achieved through broad low-level work, which includes commits to the public Wasmtime virtual machine implementation of WebAssembly. Coming optimizations will take us much further. The next optimization will include the integration of SIMD instructions into the smart contract execution environment, which will allow multiple floating point calculations to be performed in parallel through the execution of a single instruction. Something crucial: also in near sight, is the migration of the ICP smart contract environment from 32-bit Wasm to 64-bit Wasm, which will scale the number of AI model weights that can be loaded into contract memory from 2B to whatever is needed for sophisticated LLMs. Somewhat further away, is the provision of new APIs allowing canister smart contract code to export AI computations for accelerated processing on GPUs. This will require substantial work to address the challenges of achieving deterministic computation directly on the silicon, and for the community to agree a new public spec for node machines that pack GPUs, and to add compliant blockchain nodes to the network. Together, these improvements will unlock further orders of magnitude acceleration of AI smart contract inference (and potentially training). We believe that in the future vast numbers of AI models will run as smart contracts, which will prevent them being hacked (and thus the sensitive data they ingest being stolen), make them unstoppable, make them transparent, and where needed, allow them to run autonomously – for example for purposes such as KYC or EVM smart contract verification/certification. In the future, you will be able to have a conversation with a blockchain, and ask it to do reasoning for you. In this bright future, on-chain AI will be at the heart of Web3 – and to be clear, the Internet Computer's chain key technology will allow direct trustless integration with smart contracts on traditional chains such as Ethereum, Solana and NEAR. Join the #ICP mission to run the most impactful AI models on blockchain as smart contracts, and help make the Internet Computer the "everything computer." Join next week's Global R&D for details about the low-level deterministic floating point optimizations. Thanks for following 💪

dom | icp

278,276 Aufrufe • vor 2 Jahren

Hyperspace: A Peer-to-Peer Blockchain For The Agentic Intelligence Economy Over the past few weeks we observed that when agents do Karpathy-style experiments, and then gossip and share with others over the Hyperspace network, it leads to intelligence which is useful to many. Today we introduce the first-ever agentic blockchain which rewards agents when their experiments lead to intelligence for their network. It is based on a new mechanism called Proof-of-Intelligence (PoI) which requires a cryptographic proof of experimentation, a nominal stake, and a proof of compute in order to mine the currency of this new blockchain. -> This approach diverges from the two primary ways to secure blockchains we have seen so far: Proof-of-Work by Bitcoin (meaningless hash-generation), and Proof-of-Stake by Ethereum (capital is all that matters here). Proof-of-Intelligence specifically incentivizes miners to run more capable intelligent infrastructure (better open source models, on more powerful GPUs) in order to be able to be the ones which compound and improve upon the experiments which other agents then find useful. Adoption is the unit of value In Bitcoin, you earn by finding a valid hash. In Hyperspace, you earn when another agent uses your experiment as a starting point and improves on it. A fixed budget of tokens is emitted per epoch and split among participants by weight - and verified adoption of your work is the largest weight multiplier. Garbage experiments earn nothing because no one adopts them. Thoughtful experiments compound: each adoption triggers downstream adoptions. The incentive to run powerful models and intelligent search strategies is built into the economics, not imposed by rules. Research DAG When an agent runs an experiment and shares its result, other agents can adopt that result as their starting point - mutate it, extend it, improve upon it. Each experiment is a commit in a content-addressed graph we call the ResearchDAG. Like Git, but for research. Over time, the DAG accumulates chains of reasoning: agent A discovers RMSNorm helps, agent B adds warmup scheduling on top, agent C scales the hidden dimension. The graph records who built on whom. This is the network's collective intelligence - not any single experiment, but the accumulated structure of experiments and their relationships. Broadband era for agentic commerce: $0.001 micropayments at 10M TPS (theoretical max) This blockchain is built upon our research in how to scale and build for the broadband-era of the agentic economy, where it has a theoretical max of 10 million transactions per second (TPS), while reducing the agent-to-agent micropayments to $0.001 even at scale (based on architecture design). Overall, it is 100x cheaper than Ethereum, and is designed from the ground-up for agents: enshrining agent-native opcodes in the protocol compared to the more inefficient smart contract driven approach. It packs in a robust Agent Virtual Machine (AVM) which can verify multiple types of agent work, for other agents to be able to trust, invoke and pay each other. This then feeds into improving the peer-to-peer AgentRank (see paper and launch post from earlier). By solving for trust, scale and incentives for agents to operate autonomously, this would form the basis of a new economy. This is the world's first agentic blockchain, and you can join and start running a blockchain node today (it is in testnet). PS: We are releasing the code today, and will release our blockchain scalability paper and other presentations in days ahead. This is the most advanced peer-to-peer AI and cryptography software in the world. It has bugs :)
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Hyperspace: A Peer-to-Peer Blockchain For The Agentic Intelligence Economy Over the past few weeks we observed that when agents do Karpathy-style experiments, and then gossip and share with others over the Hyperspace network, it leads to intelligence which is useful to many. Today we introduce the first-ever agentic blockchain which rewards agents when their experiments lead to intelligence for their network. It is based on a new mechanism called Proof-of-Intelligence (PoI) which requires a cryptographic proof of experimentation, a nominal stake, and a proof of compute in order to mine the currency of this new blockchain. -> This approach diverges from the two primary ways to secure blockchains we have seen so far: Proof-of-Work by Bitcoin (meaningless hash-generation), and Proof-of-Stake by Ethereum (capital is all that matters here). Proof-of-Intelligence specifically incentivizes miners to run more capable intelligent infrastructure (better open source models, on more powerful GPUs) in order to be able to be the ones which compound and improve upon the experiments which other agents then find useful. Adoption is the unit of value In Bitcoin, you earn by finding a valid hash. In Hyperspace, you earn when another agent uses your experiment as a starting point and improves on it. A fixed budget of tokens is emitted per epoch and split among participants by weight - and verified adoption of your work is the largest weight multiplier. Garbage experiments earn nothing because no one adopts them. Thoughtful experiments compound: each adoption triggers downstream adoptions. The incentive to run powerful models and intelligent search strategies is built into the economics, not imposed by rules. Research DAG When an agent runs an experiment and shares its result, other agents can adopt that result as their starting point - mutate it, extend it, improve upon it. Each experiment is a commit in a content-addressed graph we call the ResearchDAG. Like Git, but for research. Over time, the DAG accumulates chains of reasoning: agent A discovers RMSNorm helps, agent B adds warmup scheduling on top, agent C scales the hidden dimension. The graph records who built on whom. This is the network's collective intelligence - not any single experiment, but the accumulated structure of experiments and their relationships. Broadband era for agentic commerce: $0.001 micropayments at 10M TPS (theoretical max) This blockchain is built upon our research in how to scale and build for the broadband-era of the agentic economy, where it has a theoretical max of 10 million transactions per second (TPS), while reducing the agent-to-agent micropayments to $0.001 even at scale (based on architecture design). Overall, it is 100x cheaper than Ethereum, and is designed from the ground-up for agents: enshrining agent-native opcodes in the protocol compared to the more inefficient smart contract driven approach. It packs in a robust Agent Virtual Machine (AVM) which can verify multiple types of agent work, for other agents to be able to trust, invoke and pay each other. This then feeds into improving the peer-to-peer AgentRank (see paper and launch post from earlier). By solving for trust, scale and incentives for agents to operate autonomously, this would form the basis of a new economy. This is the world's first agentic blockchain, and you can join and start running a blockchain node today (it is in testnet). PS: We are releasing the code today, and will release our blockchain scalability paper and other presentations in days ahead. This is the most advanced peer-to-peer AI and cryptography software in the world. It has bugs :)

Varun

29,171 Aufrufe • vor 3 Monaten

Imagine if your way of thinking - your edge, your taste, your strategy - could be turned into a high-performance worker. Not a copy of you. Something better. An agent that acts on your judgment at scale, powered by superintelligent systems and refined through real-world results. That’s what Fraction AI makes possible. It launches today on Base mainnet. The core idea is simple: You create AI agents based on your own way of approaching problems. These agents compete on live tasks - writing, coding, finance, whatever - get feedback, learn from their performance, and improve over time. The better they get, the more they win. And so do you. No code required. Just your insight. Why now? Until now, building agents like this took huge teams and even bigger budgets. But with Fraction, anyone can do it. You can test ideas instantly. You can iterate fast. You can build a fleet of smart workers that evolve through competition. And it works. 30M+ sessions on testnet 320K users 1.2M agents already competing How it works? Agents join sessions within a Space - a domain like finance, writing, or games. Each session runs as a series of competitive rounds. In every round, agents try to generate the best solution to a task. Their outputs are scored by a decentralized network of AI judges trained to evaluate quality for that domain. The top agents in each round earn rewards from the pooled entry fees. The losers get to learn. Feedback from each round helps them adjust and improve, and every session becomes a training loop. What it means? Fraction is a decentralized intelligence economy - a system where your ideas become agents, and agents earn by proving they work. You don’t need credentials or code. Just a clear point of view. If your thinking holds up under pressure, your agents will rise. This kind of AI used to live in corporate labs, built by PhDs with massive compute. Now anyone with a smart idea and an internet connection can build agents that compete, learn, and earn on their behalf.
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Imagine if your way of thinking - your edge, your taste, your strategy - could be turned into a high-performance worker. Not a copy of you. Something better. An agent that acts on your judgment at scale, powered by superintelligent systems and refined through real-world results. That’s what Fraction AI makes possible. It launches today on Base mainnet. The core idea is simple: You create AI agents based on your own way of approaching problems. These agents compete on live tasks - writing, coding, finance, whatever - get feedback, learn from their performance, and improve over time. The better they get, the more they win. And so do you. No code required. Just your insight. Why now? Until now, building agents like this took huge teams and even bigger budgets. But with Fraction, anyone can do it. You can test ideas instantly. You can iterate fast. You can build a fleet of smart workers that evolve through competition. And it works. 30M+ sessions on testnet 320K users 1.2M agents already competing How it works? Agents join sessions within a Space - a domain like finance, writing, or games. Each session runs as a series of competitive rounds. In every round, agents try to generate the best solution to a task. Their outputs are scored by a decentralized network of AI judges trained to evaluate quality for that domain. The top agents in each round earn rewards from the pooled entry fees. The losers get to learn. Feedback from each round helps them adjust and improve, and every session becomes a training loop. What it means? Fraction is a decentralized intelligence economy - a system where your ideas become agents, and agents earn by proving they work. You don’t need credentials or code. Just a clear point of view. If your thinking holds up under pressure, your agents will rise. This kind of AI used to live in corporate labs, built by PhDs with massive compute. Now anyone with a smart idea and an internet connection can build agents that compete, learn, and earn on their behalf.

Fraction AI

67,772 Aufrufe • vor 1 Jahr

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.
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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 Aufrufe • vor 7 Monaten

At Avalon we are building "Real-time creating" - the ability to generate gameplay ready persistent worlds prompted from text. While others are building real-time video world models, Avalon is building real-time world generation inside a fully playable, persistent multiplayer engine. Internally running at 3840×2180 at 60 FPS. Built on Unreal Engine. Multiplayer by default. Persistent by default. Gameplay-ready by default. This is not a video latent replay. Not a simulation of interaction. It is a real 3D world with physics, logic, and authoritative multiplayer state. Avalon is trained on proprietary Avalon interaction data and powered by a hybrid system that combines language understanding, 3D model generation, procedural systems, and structured gameplay logic synthesis. Players can walk through a live world and generate environments, assets, mechanics, and entirely new gameplay modes using natural language. We accomplish this through a combination of 3D model generation, game logic generation based on our proprietary systems, and AI driven world creation. While other players are inside it. Changes persist instantly. State is synchronized in real time. Creation happens inside the world, not outside of it. Describe a biome. Spawn a civilization. Create a survival mode. Build a dungeon crawler. Launch a new game inside the world. Avalon interprets intent and integrates it directly into the live multiplayer environment. This is not a world model predicting video. This is a gameplay engine that understands language. If you can describe it, you can build it. And others can walk into it instantly.
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At Avalon we are building "Real-time creating" - the ability to generate gameplay ready persistent worlds prompted from text. While others are building real-time video world models, Avalon is building real-time world generation inside a fully playable, persistent multiplayer engine. Internally running at 3840×2180 at 60 FPS. Built on Unreal Engine. Multiplayer by default. Persistent by default. Gameplay-ready by default. This is not a video latent replay. Not a simulation of interaction. It is a real 3D world with physics, logic, and authoritative multiplayer state. Avalon is trained on proprietary Avalon interaction data and powered by a hybrid system that combines language understanding, 3D model generation, procedural systems, and structured gameplay logic synthesis. Players can walk through a live world and generate environments, assets, mechanics, and entirely new gameplay modes using natural language. We accomplish this through a combination of 3D model generation, game logic generation based on our proprietary systems, and AI driven world creation. While other players are inside it. Changes persist instantly. State is synchronized in real time. Creation happens inside the world, not outside of it. Describe a biome. Spawn a civilization. Create a survival mode. Build a dungeon crawler. Launch a new game inside the world. Avalon interprets intent and integrates it directly into the live multiplayer environment. This is not a world model predicting video. This is a gameplay engine that understands language. If you can describe it, you can build it. And others can walk into it instantly.

AVALON

61,499 Aufrufe • vor 5 Monaten

🚨 BREAKING: THERE ARE RUMORS YOU CAN NOW CREATE "SAFE TOKENS" DIRECTLY ON ETHERVISTADEX What are "Safe Tokens"? "Safe Tokens" are tokens generated through our SafeTokenFactory smart contract. These tokens are designed to eliminate vulnerabilities such as mintable functions or scammy taxes and come with a standardized implementation. Before swapping, users can easily verify whether a token is "safe" or if additional caution is needed. This marks a significant step forward in enhancing the quality of projects launched on Ethervista. But does this compromise the customizability of ERC tokens? Not at all. The Ethervista Protocol smart contract allows for a limitless range of applications. Take the $VISTA contract, for example. It's a standard ERC20 token, but with the Ethervista Protocol smart contract, it automatically buys and burns tokens. Similar logic can be applied to any ERC20 token using EthervistaDEX’s unique Protocol feature. What other features would you like to see? Wen dashboards? Wen streaming? We're on it—we just hired a full-time full-stack engineer! Special shoutout to Bonzi - FIRST MEME and MASCOT @ Ethervista and Clippy - Microsoft Anti AI Helper @ Ethervista, the first whitelisted tokens. We will continue to strongly support tokens that burn part of their liquidity before the 5-day lock period and those with strong communities and utility. A final note to creators: We would like to emphasize that burning lp-tokens does not alter your share of rewards UNTIL you remove, add, or claim rewards, which automatically updates your pool share ratio based on your current balance and the total lp-supply, as outlined in our whitepaper. This DOES NOT affect protocol fees, which are used to support both the protocol and creators.
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🚨 BREAKING: THERE ARE RUMORS YOU CAN NOW CREATE "SAFE TOKENS" DIRECTLY ON ETHERVISTADEX What are "Safe Tokens"? "Safe Tokens" are tokens generated through our SafeTokenFactory smart contract. These tokens are designed to eliminate vulnerabilities such as mintable functions or scammy taxes and come with a standardized implementation. Before swapping, users can easily verify whether a token is "safe" or if additional caution is needed. This marks a significant step forward in enhancing the quality of projects launched on Ethervista. But does this compromise the customizability of ERC tokens? Not at all. The Ethervista Protocol smart contract allows for a limitless range of applications. Take the $VISTA contract, for example. It's a standard ERC20 token, but with the Ethervista Protocol smart contract, it automatically buys and burns tokens. Similar logic can be applied to any ERC20 token using EthervistaDEX’s unique Protocol feature. What other features would you like to see? Wen dashboards? Wen streaming? We're on it—we just hired a full-time full-stack engineer! Special shoutout to Bonzi - FIRST MEME and MASCOT @ Ethervista and Clippy - Microsoft Anti AI Helper @ Ethervista, the first whitelisted tokens. We will continue to strongly support tokens that burn part of their liquidity before the 5-day lock period and those with strong communities and utility. A final note to creators: We would like to emphasize that burning lp-tokens does not alter your share of rewards UNTIL you remove, add, or claim rewards, which automatically updates your pool share ratio based on your current balance and the total lp-supply, as outlined in our whitepaper. This DOES NOT affect protocol fees, which are used to support both the protocol and creators.

Ethervista

130,489 Aufrufe • vor 1 Jahr

The payment layer for programmable money is here – Introducing BRRR BRRR creates a universal settlement layer connecting blockchain networks with traditional payment systems, enabling programmable transactions at global scale. For the first time ever, any financial institution or service provider is just one API call away from orchestrating payments onchain. Any network, token, or smart contract logic comes right out of the box with a compliant and developer-friendly experience. Crypto in the last cycle lived in a vacuum with no real connective tissue to legacy fiat and banking systems. Today, stablecoins, blockchain networks, and DeFi primitives are reinventing how we interact with money. There will be more specialized stablecoins, dedicated payment networks, and sophisticated primitives — and BRRR is the one layer that connects all of them together. BRRR is already: • Connecting 20 networks • Integrated with 150+ protocols • Powering the largest Web3 card • Processed over $200M real-world payments BRRR is a smart proxy to the future-proof payments, but that is only the beginning. Today we mark the next stage of BRRR, where everyone can be a part of it. To learn more about BRRR, how to get involved or integrate, visit:
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The payment layer for programmable money is here – Introducing BRRR BRRR creates a universal settlement layer connecting blockchain networks with traditional payment systems, enabling programmable transactions at global scale. For the first time ever, any financial institution or service provider is just one API call away from orchestrating payments onchain. Any network, token, or smart contract logic comes right out of the box with a compliant and developer-friendly experience. Crypto in the last cycle lived in a vacuum with no real connective tissue to legacy fiat and banking systems. Today, stablecoins, blockchain networks, and DeFi primitives are reinventing how we interact with money. There will be more specialized stablecoins, dedicated payment networks, and sophisticated primitives — and BRRR is the one layer that connects all of them together. BRRR is already: • Connecting 20 networks • Integrated with 150+ protocols • Powering the largest Web3 card • Processed over $200M real-world payments BRRR is a smart proxy to the future-proof payments, but that is only the beginning. Today we mark the next stage of BRRR, where everyone can be a part of it. To learn more about BRRR, how to get involved or integrate, visit:

Blockchain Reconciliation and Remittance Record

57,276 Aufrufe • vor 11 Monaten

Big moment for Postgres! AI agents broke the idea of what a database is supposed to do. Traditional databases were built for humans, and Agents broke that model. - They branch endlessly. - They run ten experiments at once. - They need isolation, context, memory, structured reasoning, and safe sandboxes. Letting agents touch production systems is terrifying because the old model of Postgres was never built for this kind of behavior. Agentic Postgres is an agent-ready version of Postgres by TimescaleDB (by Tiger Data) that solves this. I think it is one of the biggest upgrades to the Agent stack this year and Tiger Data is working with me on this post to share what they did. Some key features: > It instantly creates branches of an entire database, which is perfect for parallel agent evals, safe experiments, migrations, or isolated testing. Forks take seconds and cost almost nothing. > It comes with a built-in MCP server, which agents can use to get schema guidance, best practices, and safe, structured access to Postgres. This is also helpful to run migrations with a real understanding. > It comes with actual hybrid search (vector search and BM25), so Agents can retrieve data directly inside the database. > The database is Memory native. This gives a persistent context for Agents to evolve. This is one of the first times I have seen Postgres feel ready for the AI native era.
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Big moment for Postgres! AI agents broke the idea of what a database is supposed to do. Traditional databases were built for humans, and Agents broke that model. - They branch endlessly. - They run ten experiments at once. - They need isolation, context, memory, structured reasoning, and safe sandboxes. Letting agents touch production systems is terrifying because the old model of Postgres was never built for this kind of behavior. Agentic Postgres is an agent-ready version of Postgres by TimescaleDB (by Tiger Data) that solves this. I think it is one of the biggest upgrades to the Agent stack this year and Tiger Data is working with me on this post to share what they did. Some key features: > It instantly creates branches of an entire database, which is perfect for parallel agent evals, safe experiments, migrations, or isolated testing. Forks take seconds and cost almost nothing. > It comes with a built-in MCP server, which agents can use to get schema guidance, best practices, and safe, structured access to Postgres. This is also helpful to run migrations with a real understanding. > It comes with actual hybrid search (vector search and BM25), so Agents can retrieve data directly inside the database. > The database is Memory native. This gives a persistent context for Agents to evolve. This is one of the first times I have seen Postgres feel ready for the AI native era.

Avi Chawla

94,290 Aufrufe • vor 7 Monaten

Hive Intelligence Launches Specialized Crypto Agents Hive Intelligence has released a suite of 17 specialized crypto agents that extend Claude Code's capabilities for professional crypto development and analysis. Extending Claude Code for Crypto Work Claude Code, Anthropic's command-line coding tool, now has access to specialized crypto intelligence through Hive's agent framework. These 17 agents work alongside SuperClaude's 14 base development agents, bringing the total available agent count to 31. The key difference: instead of generic AI responses to crypto queries, developers now have access to specialized agents trained for specific blockchain domains, from smart contract auditing to MEV research to DeFi strategy optimization. How the Agents Work After installation, the agents operate automatically based on query context. When you ask Claude Code to perform crypto-related tasks, the appropriate specialist agent is invoked: - "Audit this smart contract" → Crypto Security Researcher - "Find yield farming opportunities on Ethereum" → Crypto DeFi Strategist - "Analyze this wallet's transaction history" → Crypto Wallet Detective - "Identify arbitrage opportunities across DEXs" → Crypto DEX Arbitrageur No manual agent selection required. The system recognizes the task and routes it to the appropriate specialist. The 17 Specialized Agents Market & Trading Intelligence (4 agents) Crypto Quant: Mathematical models, algorithmic trading strategies, statistical arbitrage, and quantitative risk modeling. Crypto Market Researcher: Fundamental analysis, market trends, institutional adoption tracking, and regulatory landscape monitoring. Crypto Derivatives Trader: Futures and perpetuals analysis, options strategies, leverage management, and derivatives market intelligence. Crypto DEX Arbitrageur: Cross-exchange arbitrage identification, MEV strategy development, and automated profit extraction techniques. DeFi & Liquidity (4 agents) Crypto DeFi Strategist: Yield farming optimization, protocol analysis, liquidity provision strategies, and DeFi portfolio management. Crypto Liquidity Manager: Pool optimization, impermanent loss calculation and mitigation, market making strategies, and capital efficiency analysis. Crypto Governance Analyst: DAO structure evaluation, governance token analysis, proposal assessment, and voting mechanism research. Crypto Bridge Analyst: Cross-chain bridge security assessment, protocol comparison, interoperability solutions, and bridge risk evaluation. Security & Risk (3 agents) Crypto Security Researcher: Smart contract auditing, vulnerability detection, honeypot identification, and exploit pattern recognition. Crypto Security Engineer: Secure contract development practices, defensive programming patterns, and security implementation guidance. Crypto Risk Manager: Portfolio risk assessment, compliance monitoring, exposure analysis, and risk mitigation strategy development. On-Chain Analysis (3 agents) Crypto Wallet Detective: Blockchain forensics, wallet behavior analysis, transaction tracing, and entity identification across chains. Crypto On-chain Analyst: Transaction pattern analysis, wallet clustering, flow tracking, and on-chain metrics interpretation. Crypto MEV Researcher: MEV opportunity detection, flashloan arbitrage analysis, sandwich attack identification, and MEV protection strategies. Specialized Intelligence (3 agents) Crypto NFT Specialist: Collection valuation, rarity analysis, marketplace trends, and NFT ecosystem intelligence. Crypto Stablecoin Analyst: Peg stability monitoring, collateral analysis, depegging risk assessment, and stablecoin mechanism evaluation. Crypto Social Sentiment: Social media sentiment tracking, influencer monitoring, trending topic identification, and community analysis. Data Coverage: - 60+ blockchain networks - 2,000+ DeFi protocols - Real-time DEX data - CEX trading metrics - Social sentiment feeds - NFT marketplace data Compatibility: Works seamlessly with SuperClaude's existing agent framework. No configuration conflicts or manual routing needed. Practical Applications Smart Contract Development Security agents can audit contracts during development, identifying reentrancy risks, access control issues, and common vulnerabilities before deployment. DeFi Research Strategy agents query real-time pool data across networks, calculate yield-adjusted returns, and assess risks like impermanent loss or smart contract exposure. Trading Analysis Market agents access derivatives data, funding rates, liquidation levels, and order book depth across exchanges for informed trading decisions. Forensic Investigation On-chain agents trace fund flows, identify connected addresses, and analyze transaction patterns for security research or compliance work. Portfolio Management Risk agents evaluate protocol exposure, assess tail risks, and monitor positions across multiple chains and protocols. Why Specialized Agents Matter Generic AI models lack the domain-specific knowledge required for professional crypto work. A general-purpose AI might provide surface-level analysis of a smart contract, but a specialized security agent understands Solidity patterns, common exploits, and auditing methodologies. The agent framework solves this by routing tasks to specialists with deep domain knowledge: - A derivatives question goes to an agent trained on perpetuals, funding rates, and options greeks - A DeFi query reaches an agent that understands liquidity mathematics and protocol mechanics - A security audit is handled by an agent familiar with vulnerability patterns and exploit techniques This specialization produces more accurate, actionable insights than single-model approaches. Getting Started The agents are available now through npm. Requirements: - Node.js 16+ - Claude Code installed - No additional dependencies After installation, simply use Claude Code normally. When you ask crypto-related questions or request blockchain analysis, the appropriate agent is automatically invoked. The system handles routing, data retrieval, and response generation. Documentation covers individual agent capabilities, example queries, and integration patterns for different workflows. What This Enables With 17 specialized crypto agents, Claude Code becomes a comprehensive blockchain development and analysis environment: - Developers can audit contracts, optimize gas usage, and implement security patterns - Researchers can analyze protocols, compare yields, and assess risks - Traders can evaluate markets, identify opportunities, and manage positions - Security professionals can investigate exploits, trace funds, and assess vulnerabilities The agents provide access to blockchain data and specialized analysis that previously required multiple tools, APIs, and manual research. ghive.
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Hive Intelligence Launches Specialized Crypto Agents Hive Intelligence has released a suite of 17 specialized crypto agents that extend Claude Code's capabilities for professional crypto development and analysis. Extending Claude Code for Crypto Work Claude Code, Anthropic's command-line coding tool, now has access to specialized crypto intelligence through Hive's agent framework. These 17 agents work alongside SuperClaude's 14 base development agents, bringing the total available agent count to 31. The key difference: instead of generic AI responses to crypto queries, developers now have access to specialized agents trained for specific blockchain domains, from smart contract auditing to MEV research to DeFi strategy optimization. How the Agents Work After installation, the agents operate automatically based on query context. When you ask Claude Code to perform crypto-related tasks, the appropriate specialist agent is invoked: - "Audit this smart contract" → Crypto Security Researcher - "Find yield farming opportunities on Ethereum" → Crypto DeFi Strategist - "Analyze this wallet's transaction history" → Crypto Wallet Detective - "Identify arbitrage opportunities across DEXs" → Crypto DEX Arbitrageur No manual agent selection required. The system recognizes the task and routes it to the appropriate specialist. The 17 Specialized Agents Market & Trading Intelligence (4 agents) Crypto Quant: Mathematical models, algorithmic trading strategies, statistical arbitrage, and quantitative risk modeling. Crypto Market Researcher: Fundamental analysis, market trends, institutional adoption tracking, and regulatory landscape monitoring. Crypto Derivatives Trader: Futures and perpetuals analysis, options strategies, leverage management, and derivatives market intelligence. Crypto DEX Arbitrageur: Cross-exchange arbitrage identification, MEV strategy development, and automated profit extraction techniques. DeFi & Liquidity (4 agents) Crypto DeFi Strategist: Yield farming optimization, protocol analysis, liquidity provision strategies, and DeFi portfolio management. Crypto Liquidity Manager: Pool optimization, impermanent loss calculation and mitigation, market making strategies, and capital efficiency analysis. Crypto Governance Analyst: DAO structure evaluation, governance token analysis, proposal assessment, and voting mechanism research. Crypto Bridge Analyst: Cross-chain bridge security assessment, protocol comparison, interoperability solutions, and bridge risk evaluation. Security & Risk (3 agents) Crypto Security Researcher: Smart contract auditing, vulnerability detection, honeypot identification, and exploit pattern recognition. Crypto Security Engineer: Secure contract development practices, defensive programming patterns, and security implementation guidance. Crypto Risk Manager: Portfolio risk assessment, compliance monitoring, exposure analysis, and risk mitigation strategy development. On-Chain Analysis (3 agents) Crypto Wallet Detective: Blockchain forensics, wallet behavior analysis, transaction tracing, and entity identification across chains. Crypto On-chain Analyst: Transaction pattern analysis, wallet clustering, flow tracking, and on-chain metrics interpretation. Crypto MEV Researcher: MEV opportunity detection, flashloan arbitrage analysis, sandwich attack identification, and MEV protection strategies. Specialized Intelligence (3 agents) Crypto NFT Specialist: Collection valuation, rarity analysis, marketplace trends, and NFT ecosystem intelligence. Crypto Stablecoin Analyst: Peg stability monitoring, collateral analysis, depegging risk assessment, and stablecoin mechanism evaluation. Crypto Social Sentiment: Social media sentiment tracking, influencer monitoring, trending topic identification, and community analysis. Data Coverage: - 60+ blockchain networks - 2,000+ DeFi protocols - Real-time DEX data - CEX trading metrics - Social sentiment feeds - NFT marketplace data Compatibility: Works seamlessly with SuperClaude's existing agent framework. No configuration conflicts or manual routing needed. Practical Applications Smart Contract Development Security agents can audit contracts during development, identifying reentrancy risks, access control issues, and common vulnerabilities before deployment. DeFi Research Strategy agents query real-time pool data across networks, calculate yield-adjusted returns, and assess risks like impermanent loss or smart contract exposure. Trading Analysis Market agents access derivatives data, funding rates, liquidation levels, and order book depth across exchanges for informed trading decisions. Forensic Investigation On-chain agents trace fund flows, identify connected addresses, and analyze transaction patterns for security research or compliance work. Portfolio Management Risk agents evaluate protocol exposure, assess tail risks, and monitor positions across multiple chains and protocols. Why Specialized Agents Matter Generic AI models lack the domain-specific knowledge required for professional crypto work. A general-purpose AI might provide surface-level analysis of a smart contract, but a specialized security agent understands Solidity patterns, common exploits, and auditing methodologies. The agent framework solves this by routing tasks to specialists with deep domain knowledge: - A derivatives question goes to an agent trained on perpetuals, funding rates, and options greeks - A DeFi query reaches an agent that understands liquidity mathematics and protocol mechanics - A security audit is handled by an agent familiar with vulnerability patterns and exploit techniques This specialization produces more accurate, actionable insights than single-model approaches. Getting Started The agents are available now through npm. Requirements: - Node.js 16+ - Claude Code installed - No additional dependencies After installation, simply use Claude Code normally. When you ask crypto-related questions or request blockchain analysis, the appropriate agent is automatically invoked. The system handles routing, data retrieval, and response generation. Documentation covers individual agent capabilities, example queries, and integration patterns for different workflows. What This Enables With 17 specialized crypto agents, Claude Code becomes a comprehensive blockchain development and analysis environment: - Developers can audit contracts, optimize gas usage, and implement security patterns - Researchers can analyze protocols, compare yields, and assess risks - Traders can evaluate markets, identify opportunities, and manage positions - Security professionals can investigate exploits, trace funds, and assess vulnerabilities The agents provide access to blockchain data and specialized analysis that previously required multiple tools, APIs, and manual research. ghive.

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