BTCopilot Subnet

Welcome to BTCopilot Subnet, a pioneering Bittensor-based subnet designed to revolutionize project generation through advanced AI models. BTCopilot aims to transform diverse prompts—ranging from text and voice to images and Figma designs—into fully functional, ready-to-deploy projects. This subnet is tailored for developers, designers, and innovators who seek to accelerate their project development process with high-quality, AI-generated outputs.

Table of Contents

• Overview
• Features
• Incentive Mechanism
• Roadmap

Overview

BTCopilot Subnet leverages state-of-the-art AI models to interpret and convert various types of prompts into complete, deployable projects. Whether you're starting with a simple HTML/CSS framework or aiming to develop a complex React application, BTCopilot can generate the entire codebase, ensuring it meets your specified requirements and is ready for immediate deployment.

Vision

BTCopilot envisions a future where project creation is seamless, automated, and efficient, empowering developers to focus more on innovation and less on repetitive coding tasks. By harnessing the capabilities of the Bittensor network, BTCopilot fosters a competitive environment that drives continuous improvement in AI-generated outputs.

Purpose

The primary purpose of BTCopilot is to:

• Automate Project Generation: Provide a platform that can autonomously generate high-quality projects from diverse input prompts.
• Enhance Productivity: Reduce the time and effort required for project development, enabling developers to quickly bring their ideas to life.
• Promote Innovation: Encourage innovative solutions and optimizations in project generation through competitive incentivization.

Features

• Text Prompt: Generate projects by describing them in text.
• Voice Prompt: Create projects by giving voice commands.
• Image Prompt: Upload an image of a website or app, and BTCopilot will generate a pixel-perfect project.
• Figma Prompt: Convert Figma designs into functional projects.
• Automated Downloads: Directly download the generated projects as complete folders.

Incentive Mechanism

The BTCopilot subnet incentivizes miners and validators to ensure high-quality outputs. Here’s how it works specifically for this subnet:

• Task Assignment: Subnet miners are assigned tasks related to generating and improving machine learning models based on various prompts (text, voice, image, Figma).
• Performance Evaluation: Validators evaluate the outputs produced by miners. The evaluation criteria include accuracy, efficiency, and innovation.
• Ranking and Rewarding: Validators rank the miners according to their performance. The Bittensor blockchain’s Yuma Consensus mechanism determines the TAO rewards distribution based on these rankings.

Evaluation Process

• For Miners:

  Miners in the BTCopilot subnet are tasked with generating project outputs based on various types of prompts. Their outputs are evaluated based on the following criteria:

  1. Accuracy:

     Correctness: The generated code must accurately reflect the requirements stated in the prompt.

     Functionality: The output should be fully functional with minimal to no errors.

  2. Efficiency:

     Resource Utilization: The output should be produced using the least amount of computational resources without compromising on quality.

     Speed: Faster generation times are favored, provided the output meets all other criteria.

  3. Innovation:

     Novelty: Unique and creative approaches to solving the prompt are rewarded.

     Optimization: Innovative optimizations that improve the performance or usability of the generated project are highly valued.

• For Validators:

  Validators play a crucial role in ensuring the quality of outputs. They are responsible for evaluating and ranking the miners’ contributions. The evaluation process involves:

  1. Initial Review: Validators perform an initial check to ensure that the submitted outputs meet basic functional requirements.
  2. Detailed Assessment: Each output is thoroughly reviewed against the criteria of accuracy, efficiency, and innovation.
  3. Feedback Provision: Validators provide detailed feedback to miners, highlighting strengths and areas for improvement.
  4. Ranking Submission: Validators rank the outputs from different miners. These rankings are submitted to the Bittensor blockchain.

Example Scenario

• Prompt: A miner receives a text prompt to create a React-based TodoList application.
• Generation: The miner generates the code for the application and submits it.
• Evaluation: Validators review the submission:
  • Accuracy: Does the application have all the features mentioned in the prompt?
  • Efficiency: Is the code optimized for performance?
  • Innovation: Does the application include any additional features or optimizations not explicitly requested but beneficial?
• Ranking: Validators rank this submission against others.
• Rewarding: Based on the ranking, the miner receives TAO rewards.

Roadmap

Phase 1: Generate HTML/CSS projects from text prompts.

Phase 2: Enable voice prompts for project generation.

Phase 3: Support image prompts to generate pixel-perfect projects.

Phase 4: Integrate Figma designs as input for project generation.

Phase 5: Automate the downloading of fully functional project folders.

Phase 6: Expand to generate full framework-based projects like React, Angular, etc.