Rachid Zarouali xinity

How Personal AI Agents and Agent Orchestrators like OpenClaw or GasTown are Made

Over the last few months, projects like Gas Town by Steve Yegge and OpenClaw by Peter Steinberger have made “AI agent orchestrators” feel suddenly mainstream. It is tempting to treat them as a new kind of intelligence, but under the hood they are still a small set of primitives wired together with discipline: an LLM API call, a state loop, tools, memory, and orchestration.

This raises a practical question: what is actually inside an “agent,” and how is it different from ChatGPT (a chat UI over a model) or coding tools like Claude Code (an agentic coding surface)? Gas Town’s README frames it as a “multi‑agent orchest

name	description
orchestrating-swarms	Master multi-agent orchestration using Claude Code's TeammateTool and Task system. Use when coordinating multiple agents, running parallel code reviews, creating pipeline workflows with dependencies, building self-organizing task queues, or any task benefiting from divide-and-conquer patterns.

Claude Code Swarm Orchestration

Master multi-agent orchestration using Claude Code's TeammateTool and Task system.

To deploy, create a Lambda function and enable function URL (no auth - yolo), then use the handler above in your function. That same implementation will also work with API Gateway HTTP (aka v2), if you want to use ALB or API Gateway REST (aka v1) you should swap the schema used for parsing.

Then you can test using a POST request with this body:

{
  "jsonrpc": "2.0",
  "method": "tools/list",
  "id": 2
}

Talos

cat << EOF > patch.yaml
cluster:
  proxy:
    disabled: true
  network:
    cni:
      name: none

Cluster API OpenStack using Flatcar

This is done on devstack environment.

Resources

From an existing Kubernetes cluster deployed with Kind:

Update: Please see Bishop Fox's rapid response post Log4j Vulnerability: Impact Analysis for latest updates about this vulnerability.

Technologies using Apache Log4j

The Cosmos 🌌 team at Bishop Fox 🦊 is currently researching open-source projects that appear to use Log4j by default.

Apache Druid
Apache Dubbo
Apache Flink
Apache Flume

	apiVersion: kubectl.config.k8s.io/v1beta1
	kind: Preference

	# Default output and behavior
	defaults:
	- command: get
	options:
	- name: output
	default: wide
	- command: delete

	---
	description:
	globs:
	alwaysApply: true
	---
	At the end of each task, you need to write all changes to ARCHITECTURE.md.

	And by write all changes, I mean modify the architecture of the entire app. If you've changed a function - modify its params/output or whatever you changed.
	If you've added a new file - add it to the ARCHITECTURE.md.

	#!/bin/bash

	# Function to display usage information
	usage() {
	echo "Usage: $0 /path/to/input.mp4 [ /path/to/output_directory ]"
	exit 1
	}

	# Check if at least one argument (input file) is provided
	if [ $# -lt 1 ]; then

	#! /usr/bin/env python3

	'''
	Needs Requests (pip3 install requests)

	Author: Marcello Salvati, Twitter: @byt3bl33d3r
	License: DWTFUWANTWTL (Do What Ever the Fuck You Want With This License)


	This should allow you to detect if something is potentially exploitable to the log4j 0day dropped on December 9th 2021.