We need a way to give each Discord channel its own persistent "computer" running Claude Code CLI, where:
| # 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 |
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
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:
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
Author: Jacques Gariépy, [email protected] Date: January 2025
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.
| # 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 |
| # 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 |