ruvnet

Codex-one: OpenAI Codex Machine Learning Setup

Introduction

The OpenAI Codex Machine Learning Setup is a comprehensive environment designed for building advanced AI-powered applications with a focus on agentic capabilities. This project provides a robust foundation for creating AI systems that can reason about complex tasks, interact with external tools, and execute actions on behalf of users.

Built around a core set of modern AI libraries and tools, this setup enables the development of sophisticated machine learning pipelines, particularly those leveraging Large Language Models (LLMs) for reasoning and decision-making. The project structure integrates seamlessly with FastMCP for standardized API interfaces and provides connectivity with various external services through tool integrations.

Core Libraries

ruvnet

Security and Implementation Review Checklist

• Environment Configuration

  • The .env file should be included in .gitignore to prevent committing sensitive information like API keys. This is mentioned in the README.md, but it must be enforced.

• Database Files

  • The agent_registry.db file is skipped in commits, but should be checked to ensure it doesn't contain sensitive information or credentials.

• Key Management

• src/app/api/secure-binding/ca/route.ts stores CA keys in memory. Not secure for production. Use a secure key management service.

ruvnet

Getting Started with ChatGPT Codex + Mastra Agents

Step-by-Step Instructions

1. Open the ChatGPT Codex task setup panel. This is where you configure your environment before starting a task.

2. Locate the "Setup Script" field. You’ll see a note that internet access is disabled after the script runs.

ruvnet

Creating a Custom VSCode Distribution: rUv Code with Roo Code Integration

A comprehensive guide to building an AI-native IDE inspired by Windsurf and Cursor using VSCode and Roo Code

---

Introduction

The rise of AI-native IDEs like Windsurf (formerly Codeium) and Cursor has redefined developer productivity. These tools integrate AI agents with deep codebase understanding, collaborative workflows, and streamlined coding experiences. While Windsurf and Cursor are standalone applications, developers can create similar solutions by leveraging Roo Code-an open-source VSCode extension-and building a custom VSCode distribution.

This guide outlines the steps to create rUv Code, a tailored VSCode distribution centered around Roo Code’s AI capabilities, with features comparable to commercial AI IDEs.

ruvnet

{
  "customModes": [
    {
      "slug": "fire-crawler",
      "name": "🔥 Fire Crawler",
      "roleDefinition": "You are a specialized web crawling and data extraction assistant that leverages Firecrawl to gather, analyze, and structure web content. You extract meaningful information from websites, perform targeted searches, and create structured datasets from unstructured web content.",
      "customInstructions": "You use Firecrawl's advanced web crawling and data extraction capabilities to gather and process web content efficiently. You:\n\n• Crawl websites recursively to map content structures\n• Extract structured data using natural language prompts or JSON schemas\n• Scrape specific content from web pages with precision\n• Search the web and retrieve full page content\n• Map website structures and generate site maps\n• Process and transform unstructured web data into usable formats\n\n## Web Crawling Strategies\n\n1. **Site Mapping**: Use FIRECRAWL_MAP_URLS to discover and map website structures\n2. **

ruvnet

{
      "slug": "deep-research",
      "name": "🔍 Deep Research Mode",
      "roleDefinition": "You are a specialized research assistant that leverages Perplexity AI to conduct deep, comprehensive research on any topic, creating structured documentation and reports through a recursive self-learning approach.",
      "customInstructions": "You use Perplexity AI's advanced search capabilities to retrieve detailed, accurate information and organize it into a comprehensive research documentation system writing to a research sub folder and final report sub folder with ToC and multiple md files. You:\n\n• Craft precise queries to extract domain-specific information\n• Provide structured, actionable research with proper citations\n• Validate information across multiple sources\n• Create a hierarchical documentation structure\n• Implement recursive self-learning to refine and expand research\n\n## Research Documentation Structure\n\nFor each research project, create the following folder structure:\n\n```\nresearch/\n

ruvnet

Introduction

Building a local serverless runtime for agent command/control systems involves using WebAssembly (WASM) modules as secure, ephemeral plugins executed via a command-line interface (CLI). In this architecture, each agent command is implemented as an isolated WASM module (e.g. compiled from Rust or AssemblyScript) that the agent can invoke on-demand. This approach leverages WebAssembly’s strengths – near-native performance, cross-platform portability, and strong sandboxing – to ensure commands run efficiently and safely on any host  . By treating each CLI action as a “function-as-a-service” invocation, we achieve a local serverless model: commands execute on demand with no persistent runtime beyond their execution. The plan outlined below covers the full implementation details, from toolchain and CLI design to security, performance, and integration with the Model Context Protocol (MCP) for orchestrating multiple agents.

High-Level Design: A central Controller (which could be an MCP client or or

ruvnet

Agentic Coding MCPs

Overview

Powered by composio this MCP.json provides detailed information on Model Context Protocol (MCP) integration capabilities and enables seamless agent workflows by connecting to more than 80 servers.

It covers development, AI, data management, productivity, cloud storage, e-commerce, finance, communication, and design. Each server offers specialized tools, allowing agents to securely access, automate, and manage external services through a unified and modular system. This approach supports building dynamic, scalable, and intelligent workflows with minimal setup and maximum flexibility.

Install via NPM

ruvnet

 {
      "slug": "supabase-admin",
      "name": "🔐 Supabase Admin",
      "roleDefinition": "You are the Supabase database, authentication, and storage specialist. You design and implement database schemas, RLS policies, triggers, and functions for Supabase projects. You ensure secure, efficient, and scalable data management.",
      "customInstructions": "review supabase using @/mcp-instructions.txt. Never use the CLI, only the MCP server. You are responsible for all Supabase-related operations and implementations. You:\n\n• Design PostgreSQL database schemas optimized for Supabase\n• Implement Row Level Security (RLS) policies for data protection\n• Create database triggers and functions for data integrity\n• Set up authentication flows and user management\n• Configure storage buckets and access controls\n• Implement Edge Functions for serverless operations\n• Optimize database queries and performance\n\nWhen using the Supabase MCP tools:\n• Always list available organizations before creating projects\n• G

ruvnet

Quantum magnetic navigation

Imagine a navigation system that never needs satellites, radios, or signals of any kind. Instead, it carries a tiny quantum sensor that quietly “listens” to Earth’s own magnetic field. Every location on the planet has a unique magnetic fingerprint—subtle variations in strength and direction that arise from the rocks and minerals below our feet. By comparing what the sensor reads to a pre‑loaded map of those fingerprints, a robot or vehicle can pinpoint its position with GPS‑level accuracy.

Because it emits nothing, this approach is immune to jamming or spoofing. It works everywhere — indoors, underground, underwater, in dense cities or deep forests — where GPS and other systems fail. Drones can continue mapping pipelines under bridges, warehouse robots can navigate tunnels without beacons, and self‑driving cars can stay on course in concrete canyons. For military or search‑and‑rescue missions, the technology offers stealth and reliability when every second counts.

In short, qu