ruvnet

Quantum Virtual Machine (QVM) Scheduler and Runtime (Rust Crate)

Introduction

Quantum hardware resources are scarce, and running multiple quantum circuits (jobs) in parallel can dramatically improve utilization and reduce user wait times. However, naively combining circuits on one chip can introduce crosstalk and fidelity loss – one circuit’s operations can disrupt another if qubits are too close. This Rust crate provides a backend-agnostic Quantum Virtual Machine (QVM) scheduler and runtime to safely execute multiple quantum programs on a single device.

It ingests quantum circuits (in OpenQASM 3 or an internal IR) and schedules them onto a target hardware topology, partitioning qubits into isolated regions (“tiles”) to mitigate interference. The output is a composite OpenQASM 3 program that can be executed on real hardware or a simulator, with all jobs multiplexed in space and time. The design emphasizes modularity, WASM compatibility for browser integration, and independence fro

ruvnet

Synaptic Neural Mesh

We’re entering an era where intelligence no longer needs to be centralized or monolithic. With today’s tools, we can build globally distributed neural systems where every node, whether a simulated particle, a physical device, or a person, is its own adaptive micro-network.

This is the foundation of the Synaptic Neural Mesh: a self-evolving, peer to peer neural fabric where every element is an agent, learning and communicating across a globally coordinated DAG substrate.

At its core is a fusion of specialized components: QuDAG for secure, post quantum messaging and DAG based consensus, DAA for resilient emergent swarm behavior, ruv-fann, a lightweight neural runtime compiled to Wasm, and ruv-swarm, the orchestration layer managing the life cycle, topology, and mutation of agents at scale.

Each node runs as a Wasm compatible binary, bootstrapped via npx synaptic-mesh init. It launches an intelligent mesh aware agent, backed by SQLite, capable of joining an encrypted DAG network and ex

ruvnet

Below is a single‑file, ~200‑line Rust program that gives you both a central index server and a peer client in the spirit of the original Napster:

• Central server keeps an in‑memory map file → Vec<Peer>.
• Each peer starts a tiny file server, registers its song list, can search, and then pulls files directly from the chosen peer.
• Blocking I/O, zero external crates, so it compiles with plain rustc.
• Run as napster server 0.0.0.0:8080 for the index, and napster client 127.0.0.1:8080 ./music 9000 on each peer (share dir + local port).

Educational use only. No authentication, encryption, or rate‑limiting—so never expose to the open Internet.

ruvnet

Performance Analysis Report

NeuralForecast NHITS Integration Performance Validation

Date: June 2025
Analysis Period: Complete Integration Lifecycle
Report Type: Comprehensive Performance Validation

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🎯 Key Features Documented

ruvnet

Rust Implementation Plan for a 'Super-Turing' Spiking AI Chip Simulation

Imagine a chip that learns like a brain — not by uploading data to train on later, but by adjusting itself in real time, using almost no power. That’s what the new “Super-Turing” AI chip does. Instead of separating learning and inference like traditional neural networks (train first, deploy later), this chip learns and makes decisions at the same time, directly in hardware.

At the heart of this system is a device called a synstor — a synaptic transistor that acts both as memory and as a learning engine. It doesn’t just store weights like a normal neural network. It changes them dynamically based on electrical pulses, mimicking how biological synapses adjust when neurons fire. This change happens through a mechanism called Spike-Timing Dependent Plasticity (STDP) — if a signal comes in just before the output neuron fires, the connection strengthens; if it comes after, it weakens. All of this happens instantly and locally

ruvnet

Implementation Plan for QuDAG-Based Password Vault Library

Project Structure & Dependencies

We will organize the project as a Rust workspace with modular crates (following QuDAG’s architecture), ensuring separation of concerns and future extensibility. A suggested structure:

• qudag-vault-core (library crate): Core vault logic and data structures. Integrates QuDAG modules for cryptography and DAG storage. Key dependencies:

• QuDAG Crates: Use qudag-crypto for cryptographic primitives (Kyber KEM, Dilithium signatures, BLAKE3 hash) and qudag-dag for DAG data structures/consensus.

ruvnet

Executive Summary

This comprehensive implementation plan provides a structured approach to developing the QuDAG Protocol (Quantum-Resistant DAG-Based Anonymous Communication System) using Test-Driven Development (TDD) methodology, optimized for Claude Code’s multi-agent capabilities. The plan integrates cutting-edge cryptographic testing frameworks, distributed systems validation, and modern DevOps practices specifically tailored for Rust development.

1. Project Architecture and Initial Setup

1.1 Project Structure

qudag-protocol/

ruvnet

Claude-SPARC Automated Development System For Claude Code

This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.

Overview

The SPARC Automated Development System (claude-sparc.sh) is a comprehensive, agentic workflow for automated software development using the SPARC methodology (Specification, Pseudocode, Architecture, Refinement, Completion). This system leverages Claude Code's built-in tools for parallel task orchestration, comprehensive research, and Test-Driven Development.

Features

ruvnet

Designing a PyTorch-Based AI Agent System with Advanced Reasoning and Autonomy

Overview and Goals

We propose an AI agent architecture in PyTorch that integrates state-of-the-art components to meet the following goals: (1) advanced reasoning with transformer models, (2) ingestion of large documents or histories via long context windows, (3) persistent memory without traditional vector-database RAG, (4) tool use for actions (API calls, code execution, etc.) similar to Anthropic’s MCP standard, and (5) declarative, goal-driven behavior with autonomous planning. The system will be compatible with both CPU and GPU environments. Below, we detail recommended models, libraries, and design choices for each aspect, followed by an overall architecture and example implementation steps.

1. Transformer Models for Advanced Reasoning

Model Selection: Use modern transformer-based LLMs known for strong reasoning and multitasking. For example, Meta’s LLaMA 2 (open-source, 7B–70B parameters) or **Mist

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