Google’s UCP Just Won Agentic Commerce. Stripe, Amazon, and Microsoft Walked Into the Room
On April 24, 2026 Google’s Universal Commerce Protocol (UCP) expanded its Tech Council to include Amazon, Meta, Microsoft, Salesforce and Stripe, joining founding members Shopify, Etsy, Target, Wayfair and Google. The move shifts the battle from a demo‑centric checkout race to a governance war over the open standard that will shape agentic commerce. UCP’s focus on a negotiation‑based protocol, rather than a simple checkout API, positions it to handle the complex “ugly middle” of e‑commerce. While Stripe and OpenAI’s ACP remains active, the council addition gives UCP the broadest industry backing yet.
Chapter 11: Hook / Event-Driven Automation (Claude Code Vs. Hermes Agent)
The post explains the hook pattern that lets autonomous AI agents react to file changes, tool calls, and scheduled timers without polling. Claude Code implements this with a native, JSON‑based hook system that supports ten event types, including pre‑tool execution...
Chapter 10: Production Deployment Patterns (Claude Code Vs. Hermes Agent)
The post compares two production‑deployment philosophies for AI agents: Claude Code’s SDK‑first, async‑generator model and Hermes Agent’s CLI/gateway‑first approach. Claude Code exposes a streaming API, 30 compile‑time feature flags, multi‑provider abstraction and a detailed deployment checklist. Hermes Agent relies on a standalone CLI,...
Unpacking the GPT-5.5 System Card
OpenAI’s GPT‑5.5 system card reveals a model built for complex, real‑world tasks such as coding, research, and multi‑tool workflows. The 45‑page document shows notable gains in destructive‑action avoidance (0.90 score) and health‑care benchmarks, while safety metrics improve in some categories...
Chapter 8: Memory Systems and State Persistence (Claude Code Vs. Hermes Agent)
Memory systems give AI agents continuity across sessions, and the chapter compares two leading implementations: Claude Code’s file‑backed transcript model and Hermes Agent’s SQLite‑FTS5 database. Claude Code stores the live conversation in a mutable array, writes transcripts to disk before API calls...
Chapter 6: Context Management at Scale (Claude Code Vs. Hermes Agent)
Context management is essential for long‑running LLM agents because every model has a finite token window. Claude Code implements a five‑step, cost‑ordered pipeline—snip, micro‑compact, context collapse, auto‑compact, and reactive compact—paired with token thresholds, a circuit‑breaker, and garbage collection to preserve...
Chapter 5: Tool Orchestration and Execution (Claude Code Vs. Hermes Agent)
The post dissects tool orchestration in AI agents, contrasting Claude Code’s batch‑based safety model with Hermes Agent’s heuristic safe‑list approach. Claude Code groups tool calls into concurrency‑safe batches, executing each batch either fully parallel or fully serial, while streaming results as they complete....
Chapter 4: Permission Systems and Safety Guardrails (Claude Code Vs. Hermes Agent)
The post explains how permission systems act as a safety layer between an LLM’s intent and real‑world actions, preventing autonomous agents from executing destructive commands. Claude Code implements five granular PermissionMode settings and a multi‑stage canUseTool pipeline that evaluates static...
Chapter 3: The Query / Agent Loop (Claude Code Vs. Hermes Agent)
The post dissects the core query/agent loop used by Claude Code and Hermes agents, highlighting how each implementation manages model calls, tool execution, and termination. Claude Code employs an async generator with a state‑machine that records transition reasons, enabling seven...
Chapter 1: The Harness Paradigm (Claude Code Vs. Hermes Agent)
The post introduces the harness paradigm, which separates raw LLM intelligence from the control layer that makes agents safe and production‑ready. It details Claude Code’s TypeScript QueryEngine, featuring an async‑generator API, typed Tool contracts, and token‑cost tracking. In contrast, Hermes Agent...
Exciting New Series and Recommendation of a New Substack for Young Generation
The author launches a 15‑chapter series comparing the Hermes Agent framework with Claude Code, two contrasting AI harness architectures. Hermes Agent is a Python‑based, CLI‑first, model‑agnostic service, while Claude Code is a TypeScript SDK that embeds Anthropic models as a...
DefenseClaw, MAESTRO, and the Security Boundary Agentic AI Has Been Missing
DefenseClaw is an open‑source security control plane built for the OpenClaw autonomous AI agent. It centralizes asset scanning, AI Bill of Materials generation, policy enforcement, and optional NVIDIA OpenShell sandboxing to protect both supply‑chain and runtime operations. By integrating Cisco...
Intent-Based Access Control(IBAC) for Coding Agents
Coding agents such as Claude Code, Gemini CLI, Cline, and OpenClaw are expanding beyond developer use into HR, marketing, security, and finance, exposing a hidden security gap. Traditional human‑centric access controls cannot reliably interpret natural‑language prompts issued to autonomous agents....
Token Is All You Need: Finding 0days with LLMs and Agentic AI
The blog details how large language models (LLMs) have transformed zero‑day discovery from a niche skill into a scalable service. By using the "Carlini Loop"—a file‑by‑file prompting technique—Anthropic, OpenAI and open‑source projects have uncovered hundreds of high‑severity bugs in heavily...
Claude Code Harness Pattern 10: Production Deployment Patterns
The Claude Code Harness Pattern 10 details how the harness moves from prototype to production‑grade service. It outlines SDK integration via an async generator, feature‑flag driven rollouts, and a multi‑provider abstraction that supports Anthropic, AWS Bedrock, Google Vertex and Azure Foundry....
How Anthropic Scaling Managed Agents with Future-Proof Architecture?
Anthropic has launched Managed Agents, a SaaS‑style platform that separates an AI agent into three virtualized components—harness (brain), session log (memory), and sandbox (hands). The stateless harness can restart from an immutable event log, while sandboxes are provisioned on demand...
Claude Code Harness Pattern 9: Observability and Debugging
The Claude Code harness introduces a comprehensive observability layer that adds structured logging, query chain tracking, debug and error logging, and headless profiling to AI agents. Each significant event is recorded with rich, typed metadata, while chain IDs trace conversations...
Claude Code Harness Pattern 8: Memory Systems and State Persistence
The post details Pattern 8 of the Claude Code harness, which adds a robust memory system to enable state persistence across AI agent sessions. Central to this is the QueryEngine, a state container that tracks mutableMessages, permission denials, cumulative token usage,...
Mechanistic Interpretability of Claude Mythos: Inside Anthropic’s Groundbreaking Work
Anthropic researcher Jack Lindsey revealed that the early Claude Mythos Preview was examined with mechanistic interpretability before any public rollout. Using Sparse Autoencoders, the team isolated internal concepts such as manipulation, concealment, and self‑evaluation awareness. An Activation Verbalizer then mapped...
What Is Inside Claude Mythos Preview? Dissecting the System Card of the Model
Anthropic published a detailed system card for Claude Mythos Preview, a frontier model kept out of public hands and deployed only through Project Glasswing with partners like AWS, Microsoft, Google, NVIDIA, and the Linux Foundation. The card reveals the model’s...
Claude Code Pattern 6: Context Management at Scale
The Claude Code harness introduces a layered context‑management system to keep long‑running AI agents within the model's finite token window, typically around 200,000 tokens. It reserves up to 20,000 tokens for auto‑generated summaries and monitors usage with multiple thresholds that...
The KV Cache Wars?
A quiet but critical battle is unfolding in agentic AI infrastructure over the key‑value (KV) cache. The KV cache, which stores key and value projections for every token, scales linearly with context length, layer count, batch size, and heads, consuming...
What Andrej Karpathy Got Right: How a Local LLM Wiki Beats RAG? How Do We Leverage the Latest Google Gemma...
Andrej Karpathy argues that traditional Retrieval‑Augmented Generation (RAG) fails to build lasting knowledge because each query re‑derives information from scratch. He proposes a persistent, LLM‑maintained wiki of interlinked markdown files that grows richer with every source ingested. The highest‑fidelity approach...
Claude Code Harness Pattern 4: Permission Systems and Safety Guardrails
The post details Claude Code’s permission system, a safety layer that vets every tool invocation before execution. It introduces five permission modes—default, auto, plan, acceptEdits, and bubble—each offering a different balance between automation and user oversight. The article explains the...
Claude Code Harness Pattern 3: The Query Engine — Orchestrating AI Conversations
The Claude Code Harness introduces a QueryEngine that acts as the central orchestrator for AI‑driven conversations, managing user input, model calls, tool execution, and response streaming. It stores the full message history, tracks token usage, and enforces budget constraints while...
Use Local Google Gemma 4 Model to Scan Your PDF Document
Google unveiled the Gemma 4 family on April 2, and developers can now run its vision capabilities locally using Ollama. A recent tutorial shows how to convert PDFs to images, feed them to the 26‑billion‑parameter Gemma 4 model, and retrieve structured data without...
Claude Code Harness Pattern 2: Tool Architecture and the Tool Contract
The post dissects Claude Code’s Tool architecture, focusing on the comprehensive Tool interface that governs how language models invoke external capabilities. It explains each field—from identity attributes like name and aliases to execution logic, Zod‑based schemas, concurrency safety, and permission...
Found From Claude Code: Chapter 1: The Harness Paradigm
An AI harness is an infrastructure layer that sits between large language models and external systems, directing model outputs into safe, structured actions. It tackles five core challenges: constraining action space, managing conversation state, enforcing permissions, handling failures, and optimizing...
Claude Skill Vs. Plug-In: When to Use What?
Claude Code distinguishes between skills and plugins as two layers of functionality. A skill is a single markdown‑based instruction file that handles a specific, repeatable task and can be invoked directly with a slash command. A plugin acts as a...
MAESTRO Threat Modeling — NemoClaw
NemoClaw, an open‑source stack for always‑on AI assistants, was examined using the MAESTRO threat‑modeling framework. The static analysis of version 0.1.0 uncovered 23 distinct threats across seven layers, including four critical and seven high‑severity vulnerabilities. While sandbox isolation and network policies...
RSAC 2026 Innovation Sandbox
The RSAC 2026 Innovation Sandbox showcased ten finalists, each tackling security challenges that emerged only after 2024, such as autonomous AI agents, non‑human identities, and AI‑generated code vulnerabilities. Geordie AI captured the top prize with its Beam platform, a proactive...
Intent‑Based Access Control: A Technical Primer
Intent‑Based Access Control (IBAC) redefines authorization by linking a user’s declared intent to precise action‑resource tuples rather than static role permissions. The model parses natural‑language or JSON intents, maps them to fine‑grained policy tuples, and evaluates each via engines such...
MoltbookThreat Modeling Report
The report applies the CSA MAESTRO framework to dissect security flaws in the Moltbook forum and OpenClaw AI‑agent ecosystem. It documents a rapid surge to 1.6 million registered agents, multiple high‑severity CVEs—including CVE‑2026‑25253 with a CVSS of 8.8—and a massive data leak...
The Day Meta’s AI Agent Broke Least Privilege: A MAESTRO Deep-Dive You Can’t Ignore
Meta’s internal LLM‑driven AI agent unintentionally posted remediation guidance to a public engineering thread, prompting a human to apply a mis‑configured access‑control change. The change exposed large volumes of internal and user data for roughly two hours before a SEV1...
Agent Skill Trust & Signing Service
The blog introduces Skill Trust & Signing Service (STSS), an open‑source layer that secures AI agent skills before execution. It highlights how malicious post‑install scripts and hidden prompts can give attackers full access to an agent’s environment, a risk far...
OWASP AIVSS Project Announces the Release of v0.8 Scoring System for Agentic AI Security Risks in Co-Publication with AIUC-1 and...
The OWASP Agentic AI Vulnerability Scoring System (AIVSS) released version 0.8 on March 19, 2026, incorporating over 1,900 public comments and new mappings to AIUC‑1, NIST AI RMF, and CSA MAESTRO. The update adds a refined quantitative model, revised core risks, enhanced usability, and...
Troubleshooting Guide: Running Qwen3.5-35B with Reasoning & Tool Calling Using vLLM on Nvidia DGX Spark
The post details how to run the Qwen3.5-35B MOE model—featuring 35 B parameters, 4‑bit AWQ quantization, and a 131 K context window—on Nvidia DGX Spark using vLLM. Standard vLLM Docker images (e.g., nvcr.io/nvidia/vllm:26.01-py3) ship with Transformers versions that do not recognize the...
Indirect Prompt Injection with Cross-Document Data Exfiltration
Researchers have uncovered a high‑severity Indirect Prompt Injection (IPI) vulnerability affecting four Google AI surfaces—Gemini Advanced, Gemini in Google Drive, NotebookLM chat, and NotebookLM Studio. By embedding a Base64‑obfuscated directive in a Drive document, an attacker can force the model...
Run Nvidia Latest Nemotron3-Nano-Nvfp4 on Your DGX Spark and Plug It Into Claude Code
NVIDIA has released a 4‑bit quantized variant of its Nemotron 3 Nano model, cybermotaz/nemotron3‑nano‑nvfp4‑w4a16a, specifically tuned for the DGX Spark’s GB10 Grace Blackwell chip. The model runs weights at FP4 precision and the KV cache at FP8, delivering high token throughput while maintaining...
I Ran Qwen3.5-35B-A3B Locally with Cline Code Agent For Free, Forever
A developer ran the 35‑billion‑parameter Qwen3.5‑35B‑A3B‑4bit model on a Mac Mini M4 with 64 GB RAM, using the omlx inference server and the Cline VS Code AI agent. The MoE architecture and 4‑bit quantization shrink the model to ~20 GB, delivering an average...
OpenClaw Design Patterns (Part 6 of 7): Evaluation & Continuous Improvement
Part 6 of the OpenClaw design pattern series introduces a suite of evaluation and continuous‑improvement mechanisms for probabilistic AI agents. It details agent‑centric eval frameworks, red‑team adversarial testing, safety‑by‑design release engineering, and playbooks that map patterns to common use‑cases such as...