Blog Posts

Know Your Agent (KYA): AI Agent Identity, the Standards Race, and What Is Actually On-Chain

When you open a bank account you show an ID. When you sign up for an exchange you take a selfie. That procedure is KYC (Know Your Customer), and since the FATF was founded in 1989 it has been the standard way to keep illicit money out at the entry point of the financial system. Now the entity signing contracts, sending payments and swapping tokens on a DEX is increasingly not a person but an AI agent, and the same question comes back in a new form. The agent that just sent a payment request to my API: who is it, who built it, and what was it actually authorized to do? The trust layer that answers that question is being called KYA (Know Your Agent).

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The A2A (Agent-to-Agent) Protocol: How Agents Delegate to Each Other, and Where Payments Plug In

Every time this blog has covered x402, UCP and MPP or Know Your Agent, the term “A2A” showed up as an assumption. An agent hands work to another agent, pays for it, checks who the counterparty is. All of that sits on a lower layer: how do two agents talk in the first place? The attempt to standardize that layer is the Agent2Agent (A2A) protocol.

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gs-quant: How Goldman Sachs Open-Sourced Its Quant Code, and Where the Line Is

Tell someone that an investment bank’s trading code is on GitHub and you usually get one of two reactions: “why would they do that” or “they kept the important parts, surely.” goldmansachs/gs-quant is a case where both reactions are half right. The repository was created in December 2018, passed 12,000 stars as of August 2026, and had a push on the very day I checked. The README also states plainly that you need to be an institutional client of Goldman Sachs to use the pricing and risk APIs.

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The Failure a Pass Rate Hides: How the YouTube Ads Team Runs Production Evals

The idea that a well-written prompt makes an agent behave holds up right until the demo ends. In production, the same prompt and the same input produce different results run to run. A case that passed yesterday fails today, and one that failed yesterday passes. Deciding whether that system is ready to ship takes measurement, not a feeling.

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Don't Ship Agent Skills Without Evals: Philipp Schmid's Testing Method and SkillsBench

If you use a coding agent like Claude Code, Gemini CLI, or Codex, you eventually end up writing skills: Markdown files that hand the agent your team’s coding conventions, a specific SDK’s usage patterns, or a deployment procedure. The problem is that almost nobody tests them. We would never ship code without tests, yet skills, which directly change how an agent behaves, get shipped after a few manual runs and a gut-level “looks fine.”

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GPT-5.6 Sol, Terra, and Luna: A Model Routing Guide for Coding Agents

A claim like “Luna Max beats Terra High” circulates around GPT-5.6 discussions, and it does not hold up. Sol, Terra, and Luna are separate models, each a different capability tier. Max, high, xhigh, and ultra are settings within a given model that control how much reasoning time it uses and how many agents run in parallel. Collapsing a tier name and a settings name into one ranking compares two different axes as if they were one.

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On-Policy Distillation: Closing the Gap Between RL and SFT

The two standard post-training methods each leave a gap. Supervised fine-tuning (SFT) has the student imitate sequences a teacher already produced, but training happens on states the student may never actually visit, so errors compound over long generations. Reinforcement learning (RL) samples from the student’s own rollouts, which fixes that mismatch, but the reward is usually a single bit or two per episode. A post Thinking Machines Lab published in October 2025 proposes combining the two: sample trajectories from the student, then have a strong teacher score every token in that trajectory.

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AI Self-Improvement Starts Outside the Model

When people talk about AI self-improvement, the usual image is a model rewriting its own weights. Lilian Weng’s July 4, 2026 post on Lil’Log argues the near-term version looks different. The recursive self-improvement (RSI) we can actually observe today shows up first in the system around the model, not inside it. That system is what she calls the harness. This post walks through her argument: what a harness is, where the optimization target is moving, what a striking SWE-bench number actually means, and what breaks if you get the risk boundaries wrong.

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Bitcoin, Ethereum, and the Quantum Computing Question

On March 30, 2026, a joint paper from Google Quantum AI, the Ethereum Foundation, and Stanford shook up the crypto community. It estimated that the physical qubit count needed to recover a private key on secp256k1 (the elliptic curve behind Bitcoin and Ethereum signatures) had dropped nearly 20x from the previous best estimate. A threat that used to sound like “millions of qubits, decades away” now reads as “under 500,000, maybe within this decade.”

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Apify x402 and Coinbase Wallets, in Depth: How Agents Buy Web Automation Tools Directly

Apify, the web scraping platform, announced x402 support: AI agents can now run more than 20,000 Apify Actors by paying with USDC on Base, with no Apify account and no API key. Apify frames the prior x402 ecosystem as roughly 2,000 endpoints, so by its own count this integration expanded the paid-tool surface by about 10x.

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