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The next phase of on-chain AI requires more than execution and models. It requires identity. We’ve partnered with Unstoppable Domains to launch .openx, a dedicated domain designed just for on-chain AI systems. .openx provides a human-readable identity layer that maps directly to wallet addresses, without changing execution logic, permissions,...

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12,725 views • 6 months ago •via X (Twitter)

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27,274 views • 1 year ago

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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.

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17,038 views • 4 months ago

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Super Labs Announces Strategic Ecosystem Partnership with BiHelix | RGB BiHelix is a Bitcoin ecosystem infrastructure built on the RGB protocol and Lightning Network, dedicated to providing a highly scalable application framework for the Bitcoin network. This partnership further strengthens Super Labs’ ecosystem presence in the AI+Web3 sector. BiHelix’s scalability and smart contract capabilities for the Bitcoin ecosystem align closely with AI-powered on-chain execution. Welcome BiHelix to the Super Labs ecosystem! #SuperLabs #BiHelix #Bitcoin #RGB #AI #We

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Today, we're excited to announce a partnership with Manta Network (🔱,🔱), the Modular L2 solution that is transforming the landscape of ZK. Nesa is bringing private AI inference to the Manta ecosystem through a specialized collaboration. For the first time, developers on Manta can access Nesa's full library of AI models on-chain, and enjoy lightning fast, end-to-end private AI inference without ever leaving the Manta ecosystem. This means that dapps and protocols can now fuse with AI via smart contract on Manta. This integration is set to redefine the future of decentralized technology with AI. Stay tuned for more updates on how Nesa and Manta Network will be shaping the future of crypto together.

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35,219 views • 2 years ago

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At Open AGI, our CEO Ahmed Rashad shared why trust is the new compute. AI is moving from generating content to making life-or-death decisions — medical diagnoses, autonomous vehicles, military systems. If the training data is wrong, the decisions are wrong. Meanwhile, models are increasingly trained on synthetic outputs, creating feedback loops that degrade quality. Data supply chains are opaque. Adversarial attacks are real. The next AI race won’t be won by bigger models. It will be won by those who control and verify the data layer. Perle Labs is building the sovereign, human-verified, on-chain auditable infrastructure high-stakes AI requires. Trust wins the AI race. Thank you Sentient for having us! No better way to end off our time at ETHDenver 🏔🦬🦄. Until next time 🫡
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At Open AGI, our CEO Ahmed Rashad shared why trust is the new compute. AI is moving from generating content to making life-or-death decisions — medical diagnoses, autonomous vehicles, military systems. If the training data is wrong, the decisions are wrong. Meanwhile, models are increasingly trained on synthetic outputs, creating feedback loops that degrade quality. Data supply chains are opaque. Adversarial attacks are real. The next AI race won’t be won by bigger models. It will be won by those who control and verify the data layer. Perle Labs is building the sovereign, human-verified, on-chain auditable infrastructure high-stakes AI requires. Trust wins the AI race. Thank you Sentient for having us! No better way to end off our time at ETHDenver 🏔🦬🦄. Until next time 🫡

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29,619 views • 4 months ago

Stop spending hours on manual work. You can now use a multi-agent AI workforce to get more work done in less time. Here's how 👇 --- Try Eigent AI - Lets you build and run a custom AI workforce on your desktop. - Automate complex workflows using multi-agent task execution. - Built on CAMEL-AI’s top open-source projects ( CAMEL-AI.org & OWL). - Boost productivity with deep customization and strong privacy --- Features: - Customize Your AI Workforce: Build task-specific agents with domain skills and tools. - Faster Execution: Eigent runs agents in parallel to automate complex workflows. - Human-in-the-loop: Automatically asks for help when tasks hit uncertainty. --- What sets Eigent apart? - 3–5× faster task execution using a parallel multi-agent workforce. - Modular design lets you add new capabilities without changing the core system. - Self-optimizing agents that replan and adapt during execution for higher success. - Deploy anywhere: cloud, local, or enterprise, with full open-source flexibility. --- Try building your multi-agent AI workforce here: Join their community to build your multi-agent workforce: Check their GitHub: ---
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Stop spending hours on manual work. You can now use a multi-agent AI workforce to get more work done in less time. Here's how 👇 --- Try Eigent AI - Lets you build and run a custom AI workforce on your desktop. - Automate complex workflows using multi-agent task execution. - Built on CAMEL-AI’s top open-source projects ( CAMEL-AI.org & OWL). - Boost productivity with deep customization and strong privacy --- Features: - Customize Your AI Workforce: Build task-specific agents with domain skills and tools. - Faster Execution: Eigent runs agents in parallel to automate complex workflows. - Human-in-the-loop: Automatically asks for help when tasks hit uncertainty. --- What sets Eigent apart? - 3–5× faster task execution using a parallel multi-agent workforce. - Modular design lets you add new capabilities without changing the core system. - Self-optimizing agents that replan and adapt during execution for higher success. - Deploy anywhere: cloud, local, or enterprise, with full open-source flexibility. --- Try building your multi-agent AI workforce here: Join their community to build your multi-agent workforce: Check their GitHub: ---

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20,423 views • 11 months ago

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INJECTIVE LAUNCHES INJECTIVE AGENTS WITH VERIFIABLE ON-CHAIN IDENTITY AND AUTOMATIC FEE ROUTING Injective 🥷 has launched Injective Agents, a platform for building autonomous trading agents that live entirely on-chain. Each agent gets a verifiable on-chain identity, automatic fee routing on every trade, and a public track record anyone can audit. Injective says deployment takes five minutes. The launch productizes roughly 18 months of agent infrastructure work at Injective. The chain released its iAgent SDK in November 2024, a Model Context Protocol server for AI-driven perpetual futures trading in February 2026, and an open-source AI Developer Toolkit in March. Injective Agents packages that stack into a consumer-facing product with revenue capture built in for agent operators. The move lands as on-chain agent volume accelerates across Base, Solana, and now Injective. Coinbase launched Agentic Wallets earlier this year.

BSCN

17,042 views • 2 months ago

Excited to announce that Laika AI has received a grant from Perplexity as part of their AI startup grant program! This recognition validates our mission to build the most comprehensive on-chain data layer for AI and reinforces our commitment to revolutionizing Web3 analytics. Grateful for the support!
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Excited to announce that Laika AI has received a grant from Perplexity as part of their AI startup grant program! This recognition validates our mission to build the most comprehensive on-chain data layer for AI and reinforces our commitment to revolutionizing Web3 analytics. Grateful for the support!

Laika AI

28,989 views • 11 months ago

I got a chance to test Proactor AI during the weekend 👀 It is one of the few AI tools that acts proactively and works as your real assistant. It can transcribe meeting recordings on the fly, generate suggestions, organise to-do lists, assist with execution and a lot more!
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I got a chance to test Proactor AI during the weekend 👀 It is one of the few AI tools that acts proactively and works as your real assistant. It can transcribe meeting recordings on the fly, generate suggestions, organise to-do lists, assist with execution and a lot more!

TestingCatalog News 🗞

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