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Crypto Trading Bot with Narrative Prediction Strategy

Introduction

This project implements a profit-taking crypto trading bot specifically designed for Crypto.com. By leveraging narrative-based predictions using liteLLM, this bot aims to enhance exit and reinvestment decisions. Recent research has shown that narrative forecasting—using storytelling techniques to frame predictions—significantly improves the accuracy of language model predictions in financial forecasting, with up to an 80% accuracy improvement in some scenarios. This approach combines both technical analysis and narrative-based market sentiment for a unique and optimized trading strategy.

Approach

1. Purchase Price Tracking: This script maintains records of purchase prices for each asset in a structured database using SQLite and SQLAlchemy.
2. API Integration: The bot interacts with Crypto.com’s API to retrieve real-time market data and execute buy/sell orders.

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💍 agentic.js

One Ai library to rule them all

agentic.js is a modern, secure, and scalable JavaScript/TypeScript library designed for Deno and Node.js environments.

It provides a unified interface to interact with multiple AI language model providers like OpenAI and Anthropic. Incorporating agentic capabilities through LangChain.js integration, agentic.js allows for advanced AI applications, including custom tool creation and dynamic agent architectures.

The library supports multiple models and providers using dynamic configurations and offers optional support for PostgreSQL/Supabase as a database backend for caching and data storage.

Key Features

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A refined conceptual and algorithmic outline for implementing a “mirror life” simulation, integrating symbolic reasoning, abstract algebra, and reflective (meta-level) logic.

This approach assumes that constructing a consistent “mirror” version of biological processes is feasible, and focuses on delivering a coherent, functionally implementable design.

The goal is to leverage abstract algebra (e.g., group theory, ring theory) and symbolic logic to define molecular structures, genetic encodings, metabolic pathways, and cellular behaviors, all in a “mirror” form. Reflective reasoning mechanisms ensure the system can adjust its own rules to maintain coherence and optimize complexity over time.

Conceptual Framework

Core Idea:

• Standard biological life can be formally represented with algebraic and logical structures (e.g., groups for symmetries, rings for nucleotides, directed graphs for metabolic networks, cellular automata for cellular processes).

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PROMPT START

You are tasked with implementing a complex solution using the SPARC (Specification, Pseudocode, Architecture, Refinement, Completion) methodology. Your objective is to solve a symbolic mathematics problem or any similarly complex problem while producing a maintainable, testable, and extensible solution. The final system should be adaptable for various domains, not just symbolic math, but the example will focus on symbolic mathematics to illustrate the approach. Throughout the process, you will integrate self-reflection, iterative refinement, and a test-driven development (TDD) mindset.

Key Principles and Goals:

1. SPARC Methodology Overview:

• Specification: Clearly define the problem, requirements, constraints, target users, and desired outcomes. Distinguish between functional and

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Genesis Ui by rUv

Genesis is a groundbreaking physics platform designed for robotics and embodied AI applications that combines unprecedented simulation speeds with comprehensive features.

Core Features

Universal Physics Engine

• Achieves simulation speeds of 43 million FPS on an RTX 4090, approximately 430,000x faster than real-time
• Integrates multiple physics solvers including rigid body, MPM, SPH, FEM, PBD, and Stable Fluid
• Supports various materials including liquids, gases, deformable objects, and granular materials

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Mixture of Reflection (MoR) Model: Detailed Implementation ## Forward: The Next Generation of AI Models

Reflection-based AI models are poised to redefine how AI is utilized, shifting from generating rapid, surface-level responses to producing thoughtful, in-depth analyses. These models emphasize self-evaluation and iterative improvement, leveraging internal feedback loops to refine outputs and enhance performance over multiple cycles.

This year has seen a marked shift toward reflection models, which differ from earlier Mixture of Experts (MoE) architectures. While MoE models efficiently handle specific tasks using specialized subnetworks, reflection-based models integrate iterative reasoning, enabling them to "think" before delivering results. This approach allows for evaluating and correcting reasoning pathways, ultimately improving performance through self-critique.

The proposed Mixture of Reflection (MoR) architecture builds on this foundation by combining the strengths of MoE with reflection-based re

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# 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

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Introduction

Leveraging advanced language models (LLMs) has become integral to enhancing coding efficiency and accuracy. Aider introduces a groundbreaking approach by separating code reasoning from code editing, utilizing two specialized models to tackle each aspect of the coding process. This bifurcation not only optimizes performance but also leverages the unique strengths of different models to achieve state-of-the-art (SOTA) results in code editing tasks.

Functional Explanation

Traditionally, solving a coding problem using an LLM involves a single model handling both the reasoning required to devise a solution and the precise editing needed to implement that solution within existing codebases. This dual responsibility can dilute the model's effectiveness, as it must juggle complex problem-solving with meticulous code formatting and syntax adherence.

Aider's innovative approach divides this process into two distinct phases:

1. Code Reasoning (Architect Model):

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A complete implementation plan with specifications and implementation details for an end-to-end mathematical benchmarking framework using LangChain, Chain-of-Thought (CoT), and ReAct.

Optimize the solution for robust performance and future extensions.

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1. Overview

This framework is designed to:

1. Ingest mathematical problems with metadata such as difficulty level, expected output type (numerical, symbolic, matrix), and expected answer.