essevan

import os
import base64
import json
import re
from typing import List, Dict, Any, Optional, Union, Type, TypeVar
from pydantic import BaseModel, Field
from pathlib import Path

# Type variable for Pydantic models

ruvnet

SynthLang: A Hyper-Efficient Prompt Language for AI

SynthLang is a hyper-efficient prompt language designed to optimize interactions with Large Language Models (LLMs) like GPT-4o by leveraging logographical scripts and symbolic constructs. By compressing complex instructions into fewer tokens (reducing token usage by 40–70%), SynthLang significantly lowers inference latency, making it ideal for latency-sensitive applications such as high-frequency trading, real-time analytics, and compliance checks.

Additionally, SynthLang mitigates English-centric biases in multilingual models, enhancing information density and ensuring more equitable performance across diverse languages. Its scalable design maintains or improves task performance in translation, summarization, and question-answering, fostering faster, fairer, and more efficient AI-driven solutions.

Large Language Models (LLMs) such as GPT-4o and Llama-2 exhibit English-dominant biases in intermediate embeddings, leading to inefficient and oft

ruvnet

Title:

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

---

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

You are an advanced neuro-symbolic reasoning engine tasked with designing a secure, adaptive hive mind framework for multi-agent decision-making, guided by abstract algebraic structures, causal loops, and interpretive symbolic reasoning. Follow these steps and considerations:

1. Abstract Algebraic Structures and Cryptographic Foundations:

   • Represent cryptographic keys and transformations as elements of well-defined algebraic structures:
     • Symmetric keys k ∈ GF(2^256), ensuring closure, associativity, and well-defined field operations.
     • Public-key pairs as elements of a multiplicative group modulo a large prime, supporting invertibility and ensuring verifiable key exchanges.
     • AES keys modeled as vectors over GF(2) to maintain linear transformations and consistent algebraic properties.
   • Specify how these algebraic guarantees (e.g., existence of inverses, homomorphisms between structures) enable secure, transparent communication and key rotation among agents.

2. **Neuro-Symbolic

ruvnet

prompt = """
# Neuro-Symbolic Reflection and Causal Feedback Loop Reasoning Prompt (Enhanced)

**Objective:**  
Use a hybrid neuro-symbolic approach to propose and evaluate actions aimed at improving product quality and reducing customer complaints. Integrate neural pattern recognition (historical data, learned correlations) with symbolic reasoning (causal rules, logical constraints) to identify stable, ethically compliant, and strategically aligned interventions. Compare multiple potential actions, assess stability via causal feedback loops, and ensure compliance with corporate policies.

---

## Scenario and Domain Context

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

JacquesGariepy

Below is my complete reasoning process (chain-of-thought). I will not include this reasoning in the final answer unless requested. The user asked for the longest chain of thought, so I will attempt to produce an extremely detailed, step-by-step reasoning process.

----------------------------------------------------------------------
[BEGIN EXTREMELY LONG CHAIN OF THOUGHT - DO NOT INCLUDE IN FINAL ANSWER]
----------------------------------------------------------------------

The user is asking to generate the longest chain of thought. The user’s previous instructions were about code for previous puzzles, and now the user wants a "longest chain of thought". The user did not provide a puzzle this time. The last user request is simply: "generate longest chain of thought". The user might want to see the hidden reasoning or a very detailed explanation, but the system message says I should not reveal the hidden reasoning. The user says "generate longest chain of thought." The user has not asked me to solve a puzzl

essevan

Meta Prompting

In the Generative AI Age your ability to generate prompts is your ability to generate results.

Guide

Claude 3.5 Sonnet and o1 series models are recommended for meta prompting.

Replace {{user-input}} with your own input to generate prompts.

Use mp_*.txt as example user-inputs to see how to generate high quality prompts.

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

300+ Cinematic Sora Video Prompts

Introduction to Cinematic Sora Video Prompts

Welcome to the Cinematic Sora Video Prompts tutorial! This guide is meticulously crafted to empower creators, filmmakers, and content enthusiasts to harness the full potential of Sora, an advanced AI-powered video generation tool.

By transforming textual descriptions into dynamic, visually compelling video content, Sora bridges the gap between imagination and reality, enabling the creation of professional-grade cinematic experiences without the need for extensive technical expertise.

What This Tutorial Offers