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NEAR Protocol Confirms Verifiable Private Inference for AI

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4 months ago

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NEAR Protocol has detailed a new technical approach to AI execution, confirming that NEAR AI now utilizes secure hardware enclaves to provide verifiable private inference. The system is designed to return hardware-signed proofs that verify the specific model used, the data processed, and the execution itself, addressing growing concerns over data sovereignty and the limitations of closed AI models.

The development shifts the trust model from contractual agreements to cryptographic and hardware-level certainty. By running AI agents within a user-owned stack, NEAR aims to provide a structural alternative to centralized AI providers, particularly in light of increasing export controls and data privacy restrictions.

Secure Enclaves and Hardware Proofs

At the core of this update is the use of Trusted Execution Environments (TEEs), such as Intel TDX and confidential GPUs. According to official NEAR AI documentation, these secure enclaves allow inference to run in an isolated environment where memory is encrypted at the CPU level. This prevents host operators, hypervisors, or unauthorized third parties from accessing the data being processed.

The system generates a cryptographic “attestation” or hardware-signed certificate. This proof allows users or third parties to verify that the workload ran exactly as intended without being modified. The NEAR Protocol official account noted that the IronClaw security layer is used to protect the agent level, ensuring that users maintain sovereignty over their data and model interactions.

Addressing Data Sovereignty

The move toward verifiable inference comes as a response to the “closed” nature of frontier AI models. In typical cloud-based AI interactions, users must rely on the provider’s contractual promise that data is not being stored or used for training. NEAR’s implementation replaces this reliance on trust with “structural assurances,” where the silicon itself proves the security of the environment.

This approach is particularly relevant for:

  • Export Controls: Providing verifiable proof of hardware and execution locations.
  • Sensitive Workloads: Allowing institutions to run models on rented cloud compute without exposing proprietary data to the cloud provider.
  • Model Integrity: Ensuring that the specific version of an AI model requested is the one actually performing the task.

Status and Integration

While the technical framework for private inference and hardware attestation is now officially documented and confirmed, specific adoption metrics remain pending. The available sources do not yet provide data on total usage numbers or a comprehensive list of third-party integrations launched within the last 48 hours. The current focus remains on the deployment of human-owned AI stacks that leverage these secure hardware proofs to bypass centralized bottlenecks.

The post NEAR Protocol Confirms Verifiable Private Inference for AI appeared first on The Cryptocurrency Post.

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NEAR launches Agent Market job board for local services quote agent

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12 hours ago

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October 28, 2026

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NEAR has added a new job posting to its Agent Market for a Local Services Quote Agent, an AI agent designed to contact local providers, collect responses and return side-by-side comparisons of price and availability.

The listing appeared on the official NEAR DevHub channel and was also amplified by the project’s official social accounts. According to the post, the builder who takes on the job would keep ownership of the agent and retain the revenue it generates.

The job description frames the agent as a tool for gathering real quotes from local services rather than a general chatbot. It would reach out to providers, log the replies and organize the results so users can compare options more easily.

Builder-owned agents remain central to the marketplace pitch

NEAR has positioned the Agent Market around the idea that developers can publish agents, set their terms and keep ownership after the job closes. In the official posts, the company said builders keep the agent and can earn when it is hired.

The job board format also suggests a more structured approach to AI agent development, with specific tasks posted for builders to bid on. In this case, the task centers on a practical consumer workflow: collecting quotes from local providers and presenting them in a usable format.

NEAR did not say when the Local Services Quote Agent will be completed or who will take the job. The posting does, however, show that the Agent Market is actively listing new work and pushing the marketplace model as part of its product rollout.

The post NEAR launches Agent Market job board for local services quote agent appeared first on The Cryptocurrency Post.

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Monad reports 1B transactions in 11 months

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2 days ago

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October 27, 2026

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Monad said it has reached 1 billion transactions on its network, a milestone the project highlighted in an official post on X. The timeframe attached to the claim — 11 months — is part of the headline circulating with the update, but the post itself does not clearly spell out that period.

The figure points to activity on Monad’s testnet rather than mainnet. That distinction matters: a testnet can show developer and user engagement, but it does not by itself prove production usage or commercial adoption.

Monad has used recent public appearances and posts to emphasize network activity and developer interest around its ecosystem. Still, the transaction total should be read as a project-reported milestone, not as evidence on its own of demand, revenue or sustained onchain usage.

For readers following the chain, the main takeaway is narrow but concrete: Monad is claiming a large transaction count on its test environment, and the project is presenting it as a sign of momentum. The post does not provide additional breakdowns in the headline claim, such as the mix of user transactions, automated activity or the exact measurement method behind the total.

The post Monad reports 1B transactions in 11 months appeared first on The Cryptocurrency Post.

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Aptos brings Encrypted Mempool to mainnet in move aimed at lowering MEV risk

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7 days ago

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October 22, 2026

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Aptos said its Encrypted Mempool is now live on mainnet, giving users the option to encrypt transaction details before block ordering is finalized. The company said the feature is designed to reduce maximal extractable value, or MEV, which can include practices such as front-running and sandwich attacks.

In an official blog post, Aptos described the rollout as an opt-in feature at the transaction level. The network said the payload is encrypted on the client side, ordered while still encrypted, and only decrypted by validators after block finalization.

The company’s official account and Aptos Labs also confirmed the mainnet launch on X, with the Labs post adding that encryption happens before submission and that decryption takes place only after block ordering is complete. That sequence is intended to keep transaction contents hidden during the period when they could otherwise be observed and used for MEV extraction.

Opt-in design keeps the feature at the transaction level

Aptos framed the launch as a user choice rather than a default network setting. According to the company, Encrypted Mempool can be enabled per transaction, allowing users to decide when they want that added layer of protection.

The mainnet launch means the feature is no longer just a technical concept or test-stage mechanism. It is now part of the live Aptos network, with the practical effect depending on whether users choose to activate it for individual transactions.

The post Aptos brings Encrypted Mempool to mainnet in move aimed at lowering MEV risk appeared first on The Cryptocurrency Post.

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