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Personal Agent Protocol (Poppy for short ) is an open protocol for how personal agents and companies work together. A company uses it to declare how personal agents can interact with it. A personal agent uses it to sign in safely and get access to a person’s account, limited to what that person and the company agree to.

From: Personal Agent Protocol: Introduction.

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The protocol is built on OAuth, JSON Web Tokens (JWTs), HTTPS, OpenAPI and MCP. Because these are open and widely used, teams can implement it with libraries and tools they already have. The core is also designed to be extended, so industries can build their own layers on top.
Protocols such as Universal Commerce Protocol (UCP) focus on shopping, from finding products to checking out and managing orders. Personal Agent Protocol is complementary, covering how a personal agent identifies itself, gets the customer’s permission, and works with a company across any kind of task — from buying a sweater to applying for a mortgage.

From: Sharing a draft of Personal Agent Protocol | Sierra.

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Going “Cashless” through cards, as seen in the West, is a windfall for banks, as cash usage continues to decline about 4% a year.

Every card tap, swipe, or digital wallet use generates interchange, processing fees, and data.

So banks did not fight the death of cash. They profited from it and actively suppressed A2A and other alternative payments.

In North America, that profit is staggering. Credit cards alone account for 39% of the region’s $0.7 trillion payments revenue.

That is $273 billion in credit card revenue, or for better context, nearly 1% of North American GDP.

From: (21) Thank You, Stablecoins: Why 1% of North American GDP Goes to Card Companies.

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Credit Cards bundle a credit line, a ubiquitous payment method and rewards. Alternate payment methods (APMs) generally don’t offer all three. Fintechs have been nibbling around the edges of this bundle. Agents may accelerate the nibbling

Basic math suggests Agentic optimization reduces card margins
Issuers and networks eventually need to reduce acceptance costs to avoid steering at purchase; that may undermine rewards levels
Issuers eventually need to narrow lend margins to avoid adverse selection versus Fintech lenders
This will be a slow drip rather than a flood, but forewarned is forearmed

From: (21) Unbundling the Credit Card – by Andrew M. Dresner.

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Buterin wrote this week that he has lost more money in botched migrations than in all hacks combined.

From: Answers That Cannot Be Questioned – by Timo Hotti.

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Most humans don’t actively optimize. They may take a good deal as they shop for a new service, but then stay with that service once the promo period ends. Banks & Fintechs count on this to justify the promo cost. It takes discipline and effort to optimize. Only a fraction make a habit of it.

Marketing departments know this and try to design propositions that deter optimization.

From: (21) Do banks need optimizer firewalls? – by Andrew M. Dresner.

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Should policymakers seek to enhance the moneyness of stablecoins, we counsel for the adoption of five targeted reforms: Federal Reserve master account access for qualifying issuers, industry-funded insurance for claimholders, a secured interest regime to replace the Act’s flawed bankruptcy provisions, finality rules establishing when stablecoin transfers conclusively discharge obligations, and express tokenization of the stablecoin redemption right.

From: The Moneyness of Stablecoins by Christopher K. Odinet, Andrea Tosato, Yesha Yadav :: SSRN.

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How Private Inference Works

Trusted Execution Environment (TEE)

NEAR AI Cloud combines Intel TDX and NVIDIA TEE technologies to create isolated, secure environments for AI computation:
Intel TDX(Trust Domain Extensions) : Creates confidential virtual machines (CVMs) that isolate your AI workloads from the host system, preventing unauthorized access to data in memory.
NVIDIA TEE : Provides GPU-level isolation for model inference, ensuring model weights and computations remain completely private during processing.
Cryptographic Attestation : Each TEE environment generates cryptographic proofs of its integrity and configuration, enabling independent verification of the secure execution environment.

From: Private Inference – NEAR AI.

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Brave begins this journey by removing the need to trust LLM/API providers, using Confidential LLM Computing on NEAR AI TEEs. Brave uses NEAR AI TEE-enabled Nvidia GPUs to ensure confidentiality and integrity by creating secure enclaves where data and code are processed with encryption.

From: Verifiable Privacy and Transparency: A new frontier for Brave AI privacy | Brave.

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When Sentinel’s decision resolves to ask the user, Sentinel creates a pending approval and execution stops. Sentinel sends that request directly to the Muse client, describing the exact action to be approved. A dialog is presented to the user directly within the client UI — not via their conversation with Muse — and their answer is routed directly back to Sentinel, which acts on the user’s decision. Sentinel then updates its authoritative approval state and allows the operation to proceed or rejects it as appropriate.

From: How We Built Safety Into Muse | Meta AI Research.

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