boxabirds

# Project Policy

This policy provides a single, authoritative, and machine-readable source of truth for AI coding agents and humans, ensuring that all work is governed by clear, unambiguous rules and workflows. It aims to eliminate ambiguity, reduce supervision needs, and facilitate automation while maintaining accountability and compliance with best practices.

# 1. Introduction

> Rationale: Sets the context, actors, and compliance requirements for the policy, ensuring all participants understand their roles and responsibilities.

## 1.1 Actors

ruvnet

ruvnet

Design of a Self-Improving Gödel Agent with CrewAI and LangGraph

Introduction:
The Gödel Agent is a theoretical AI that can recursively self-improve, inspired by the Gödel Machine concept ([cs/0309048] Goedel Machines: Self-Referential Universal Problem Solvers Making Provably Optimal Self-Improvements). Our design combines the CrewAI framework (for orchestrating multiple role-based AI agents) with LangGraph (for structured reasoning workflows) to create a provably self-enhancing agent. The agent leverages Generalized Policy Optimization (GSPO) and other reinforcement learning techniques (PPO, A3C, etc.) for policy improvement, while employing formal verification (using tools like Coq, Lean, or Z3) to ensure each self-modification is correct and beneficial. The architecture is modular and state-of-the-art, emphasizing configurability, verifiability, and c

ruvnet

DeepSeek R1 Distill: Complete Tutorial for Deployment & Fine-Tuning

This guide shows how to deploy an uncensored DeepSeek R1 Distill model to Google Cloud Run with GPU support and how to perform a basic, functional fine-tuning process. The tutorial is split into:

1. Environment Setup
2. FastAPI Inference Server
3. Docker Configuration
4. Google Cloud Run Deployment
5. Fine-Tuning Pipeline (Cold Start, Reasoning RL, Data Collection, Final RL Phase)

ruvnet

o1 Pro: Insider Trading Mirroring System

Introduction

In the dynamic world of financial markets, staying ahead of insider movements can provide significant strategic advantages.

The Insider Trading Mirroring System is a sophisticated tool designed to monitor publicly disclosed insider trades and automatically mirror these actions within your investment portfolio. By leveraging cutting-edge technologies like LangGraph and integrating real-time data feeds, this system offers a seamless and automated approach to capitalizing on insider trading activities.

Legal & Ethical Considerations
It's crucial to emphasize that this system only processes publicly available insider trading information, as mandated by regulatory bodies such as the U.S. Securities and Exchange Commission (SEC). Engaging in trading based on material non-public information is illegal and unethical. Users must ensure compliance with all relevant laws and regulations and consult with legal and compliance professiona

JacquesGariepy

HoloGate-Flow: A Multi-Activation Gated Feed-Forward Layer with Affine Flow Coupling

Author: Jacques Gariépy, [email protected] Date: January 2025

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Abstract

ruvnet

Title:

The Emergence of Malicious Large Language Models (LLMs) and the Next Frontier of Symbolic-AI Integration: A Comprehensive Research Paper

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Abstract

This research paper explores the rapid rise of malicious Large Language Models (LLMs)—often termed “Dark LLMs”—designed explicitly for cybercrime.

Building on prior analyses, we update the discourse to address critical gaps in existing research, focusing on model profiling, economic drivers, regulatory challenges, and advanced AI concepts such as symbolic reasoning and consciousness prompts.

ruvnet

# Symbolic Representation of Prompt

# Initialization: Define Universal State
Ψ(t) ∈ H  # Ψ(t): State vector in Hilbert space H

# Field Configuration Space
M = { (g, φ) | g ∈ G, φ ∈ Φ }  # G: Symmetry group, Φ: Field space
μ : M → ℝ^+  # Measure on configuration space

# Complexity Operator

ruvnet

# Step 1: Represent Universe State
Initialize Ψ(t) in Hilbert space H

# Step 2: Define Field Configurations
Define configuration space M with measure μ
For each (g, φ) in M:
    Represent fields as algebraic structures (groups, rings, etc.)

# Step 3: Complexity Operator
Define operator T acting on Ψ(t) to extract complexity

ruvnet

Auto-Fixer Script

Introduction

The Auto-Fixer script is a powerful tool designed to automatically test and fix React components in a project. It leverages the London school of Test-Driven Development (TDD) and uses an AI-powered code assistant to iteratively improve failing tests and component code.

Features

• Automatically runs tests for specified React components
• Analyzes test failures and error messages