In an era where artificial intelligence is reshaping every facet of software development, I embarked on a unique journey to make my first open-source contribution with the assistance of ChatGPT. As an AI prompt engineer with years of experience in harnessing the power of large language models, I saw this as an opportunity to not only contribute to a meaningful project but also to explore the symbiosis between human creativity and AI capabilities in the open-source ecosystem.
The Open Source Landscape in 2025: A Data-Driven Overview
Before delving into my personal experience, it's crucial to understand the current state of open source. As we stand in 2025, the open-source world has undergone significant transformations:
- Open source software now powers an astounding 85% of the global technology infrastructure, up from 80% in previous years.
- GitHub hosts over 250 million public repositories, a 25% increase from 2023.
- AI-assisted coding tools have become ubiquitous, with 75% of developers integrating them into their daily workflows.
- The demand for open source contributors has surged by 40% since 2023, reflecting the growing importance of collaborative development.
- According to the latest Open Source Survey, 68% of companies now actively encourage their employees to contribute to open-source projects, recognizing the benefits of community involvement and skill development.
These statistics underscore the pivotal role of open source in modern technology and highlight the increasing accessibility of contribution opportunities, particularly with the aid of AI tools.
Leveraging ChatGPT for Project Selection: An AI Prompt Engineer's Approach
As an AI prompt engineer, I approached the project selection process with a unique perspective. I crafted a series of prompts designed to extract the most relevant and actionable information from ChatGPT. Here's a breakdown of my methodology:
Skill and Interest Mapping:
I provided ChatGPT with a detailed breakdown of my skills, interests, and experience level, using specific keywords and phrases that I knew would trigger relevant responses.Project Suggestion Request:
I asked ChatGPT to suggest suitable open source projects, emphasizing factors such as project health, community engagement, and alignment with emerging technologies.Comprehensive Project Analysis:
For each suggested project, I requested a multi-faceted analysis, including:- Project goals and potential impact
- Technical stack and required expertise
- Contribution guidelines and community dynamics
- Recent activity and future roadmap
AI-Human Collaboration Potential:
I specifically inquired about projects that would benefit from AI-assisted contributions, aligning with my expertise as an AI prompt engineer.
ChatGPT's response was impressively comprehensive:
Based on your profile as an AI prompt engineer, here are three highly suitable open source projects for 2025:
1. TensorFlow Lite for Edge AI:
- Goal: Optimize ML models for next-gen edge devices and IoT
- Tech Stack: C++, Python, Rust, CUDA
- Contribution Areas: Model compression, hardware-specific optimizations, AI-assisted code generation
- AI Collaboration Potential: High (e.g., using LLMs for code optimization)
2. OpenAI Gym 2.0:
- Goal: Provide a comprehensive platform for reinforcement learning research
- Tech Stack: Python, PyTorch, JAX
- Contribution Areas: New environments, AI agent implementations, documentation
- AI Collaboration Potential: Very High (e.g., using GPT-4 for generating test scenarios)
3. Mozilla DeepSpeech:
- Goal: Create an open-source speech-to-text engine
- Tech Stack: C++, Python, TensorFlow
- Contribution Areas: Acoustic modeling, language model integration, multilingual support
- AI Collaboration Potential: High (e.g., using transformers for improved transcription)
Each project has active communities, well-documented contribution processes, and aligns well with your AI expertise. They also present opportunities to leverage your prompt engineering skills in meaningful ways.
This AI-generated overview provided a wealth of information, allowing me to make an informed decision quickly. I was particularly drawn to the OpenAI Gym 2.0 project due to its high AI collaboration potential and alignment with my interests in reinforcement learning.
Finding the Right Issue: AI-Assisted Issue Analysis
With OpenAI Gym 2.0 as my chosen project, I turned to ChatGPT to help me identify a suitable first issue. I developed a multi-step prompt strategy:
Issue Scraping and Analysis:
I shared the project's GitHub issues page and asked ChatGPT to scrape and analyze the open issues.Categorization and Difficulty Assessment:
I requested a categorization of issues based on difficulty, required skills, and potential impact.Beginner-Friendly Recommendations:
I asked for 3-5 beginner-friendly issues that would allow me to make a meaningful contribution while learning about the project.AI Assistance Potential:
For each recommended issue, I inquired about how AI tools could potentially assist in addressing the task.
ChatGPT's analysis was both thorough and insightful:
After analyzing the OpenAI Gym 2.0 issues, here are 4 beginner-friendly options with high AI assistance potential:
1. Issue #3056: "Implement a new classic control environment"
- Difficulty: Medium
- Skills: Python, Basic RL concepts
- Task: Create a new environment based on a classic control problem
- AI Assistance: Use GPT-4 to generate initial environment code and documentation
2. Issue #3078: "Enhance documentation for custom environment creation"
- Difficulty: Low
- Skills: Technical writing, Basic Python
- Task: Improve and expand the guide for creating custom environments
- AI Assistance: Leverage ChatGPT for generating comprehensive documentation drafts
3. Issue #3092: "Develop a tutorial for using Gym with stable-baselines3"
- Difficulty: Medium
- Skills: Python, RL algorithms, Stable-baselines3
- Task: Create a step-by-step tutorial for integrating Gym environments with SB3
- AI Assistance: Use AI to generate code snippets and explain complex concepts
4. Issue #3105: "Implement parallel environment execution for improved performance"
- Difficulty: High
- Skills: Python, Parallel programming, Performance optimization
- Task: Enhance Gym to support parallel execution of multiple environments
- AI Assistance: Utilize code generation models to assist with parallelization strategies
I recommend starting with Issue #3078 as it allows you to leverage your technical writing skills while familiarizing yourself with the project structure. The AI assistance potential is also high for this task.
This AI-powered issue analysis provided me with a clear picture of the available options and their respective challenges and opportunities. I decided to tackle Issue #3078, as it aligned well with my skills and offered a great opportunity to use AI in the documentation process.
Setting Up the Development Environment: AI-Guided Configuration
With my project and issue selected, I enlisted ChatGPT's help to set up my development environment. I crafted prompts to:
- Parse the project's README and installation instructions
- Generate a step-by-step setup guide tailored to my operating system (macOS)
- Anticipate and provide solutions for common setup issues
- Suggest optimal IDE configurations and useful extensions for the project
ChatGPT's response was comprehensive and tailored to my needs:
Here's a step-by-step guide to set up OpenAI Gym 2.0 on macOS, optimized for AI-assisted development:
1. Install dependencies:
brew install python@3.10 cmake swig
2. Clone the repository:
git clone https://github.com/openai/gym.git
cd gym
3. Set up a virtual environment:
python3.10 -m venv venv
source venv/bin/activate
4. Install Python dependencies:
pip install -e .[all]
5. Install additional AI development tools:
pip install black isort pylint
6. Configure your IDE (VSCode recommended):
- Install Python and Jupyter extensions
- Set up Black and isort as formatters
- Configure pylint for linting
7. Set up pre-commit hooks:
pip install pre-commit
pre-commit install
Common issues and solutions:
- If you encounter MuJoCo-related errors, ensure you have the latest version installed
- For rendering issues, install additional dependencies: `brew install ffmpeg freeglut`
- If you face permission errors, use `sudo` for the problematic commands
Optimal IDE Configuration:
- Use VSCode with the "Python" and "Jupyter" extensions
- Enable "Format on Save" and set Black as the default formatter
- Configure the "autoDocstring" extension for easy documentation
- Install the "GitLens" extension for enhanced Git integration
AI-Assisted Development Setup:
- Install the "GitHub Copilot" extension for AI-powered code suggestions
- Set up a custom keybinding to quickly access ChatGPT for code explanations and generation
This tailored guide not only helped me set up the basic environment but also optimized it for AI-assisted development, aligning perfectly with my role as an AI prompt engineer.
Making the Contribution: Collaborative AI-Human Documentation Enhancement
With my environment set up, I was ready to tackle the documentation enhancement issue. I developed a systematic approach to leverage ChatGPT throughout the contribution process:
Content Analysis:
I shared the existing documentation with ChatGPT and asked for a comprehensive analysis, identifying areas for improvement and expansion.Structure Optimization:
I requested suggestions for reorganizing the documentation to improve clarity and flow.Content Generation:
For each section requiring enhancement, I used ChatGPT to generate initial drafts, which I then refined and customized.Code Example Creation:
I prompted ChatGPT to generate relevant code examples to illustrate key concepts.Review and Refinement:
I iteratively shared my progress with ChatGPT, asking for suggestions and improvements.
Here's an example of how ChatGPT assisted with content generation for the "Creating Custom Environments" section:
To create a custom environment in OpenAI Gym 2.0, follow these steps:
1. Define your environment class:
```python
import gym
from gym import spaces
import numpy as np
class CustomEnv(gym.Env):
def __init__(self):
super(CustomEnv, self).__init__()
# Define action and observation space
self.action_space = spaces.Discrete(2)
self.observation_space = spaces.Box(low=0, high=255, shape=(84, 84, 3), dtype=np.uint8)
def step(self, action):
# Implement environment dynamics
# Return observation, reward, done, info
pass
def reset(self):
# Reset environment to initial state
# Return initial observation
pass
def render(self, mode='human'):
# Implement visualization
pass
Register your environment:
from gym.envs.registration import register register( id='CustomEnv-v0', entry_point='path.to.your.module:CustomEnv', )Use your custom environment:
import gym env = gym.make('CustomEnv-v0')
Remember to thoroughly document each method, including parameters, return values, and any specific behaviors or edge cases.
This AI-generated content provided a solid foundation, which I then expanded upon with project-specific details and best practices.
## Submitting the Pull Request: AI-Enhanced Communication
For the final step, I collaborated with ChatGPT to craft a clear, comprehensive, and engaging pull request description. I provided ChatGPT with a summary of my changes and asked it to generate a PR description that would effectively communicate the value and impact of my contribution.
Here's the AI-generated PR description, which I further refined:
Title: Enhance documentation for custom environment creation
Description:
This PR addresses issue #3078 by significantly improving and expanding the guide for creating custom environments in OpenAI Gym 2.0.
Key Enhancements:
- Restructured the documentation for improved readability and logical flow
- Added detailed explanations of core concepts (observation space, action space, reward function)
- Included step-by-step instructions for creating and registering custom environments
- Provided multiple code examples showcasing different environment types
- Added a troubleshooting section addressing common issues
- Included best practices for environment design and implementation
Implementation Details:
- Updated
docs/creating_environments.mdwith new content and structure - Added
docs/examples/custom_environments/directory with sample implementations - Enhanced inline documentation in
gym/core.pyto better explain key methods
Testing:
- Verified all code examples by implementing and running them
- Conducted a readability review with three team members of varying experience levels
- Ensured compatibility with the latest Gym 2.0 API
Impact:
This enhanced documentation will significantly lower the barrier to entry for new contributors looking to create custom environments, fostering innovation and expanding the Gym ecosystem.
Future Work:
- Consider creating video tutorials to complement the written documentation
- Develop a gallery of community-contributed custom environments
I welcome any feedback or suggestions for further improvements!
This AI-assisted PR description ensured that I communicated the value and comprehensiveness of my contribution effectively, increasing the likelihood of a smooth review process.
## Lessons Learned and Future Contributions: An AI Prompt Engineer's Perspective
My experience using ChatGPT to assist with my first open-source contribution as an AI prompt engineer yielded several valuable insights:
1. **AI as a Collaborative Partner**:
AI tools like ChatGPT can serve as invaluable collaborators, offering suggestions, generating content, and providing rapid feedback throughout the contribution process.
2. **The Importance of Effective Prompting**:
As an AI prompt engineer, I found that crafting clear, specific prompts was crucial in extracting the most relevant and useful information from ChatGPT.
3. **Balancing AI Assistance and Human Creativity**:
While AI provided significant support, human creativity, critical thinking, and domain knowledge remained essential in producing a high-quality contribution.
4. **AI-Enhanced Learning**:
Leveraging AI allowed me to quickly grasp complex concepts and project-specific details, accelerating my learning curve.
5. **The Power of Iterative Refinement**:
Using AI to generate initial drafts and then iteratively refining them proved to be an efficient workflow for producing polished content.
For future contributions, I plan to:
- Explore more advanced AI-assisted coding techniques, such as using GPT-4 for code generation and optimization
- Develop custom AI models fine-tuned on project-specific codebases and documentation
- Create AI-powered tools to assist other contributors in navigating complex open-source projects
- Contribute to the development of AI-enhanced contribution guidelines and best practices
## Conclusion: The Symbiosis of AI and Open Source
My journey as an AI prompt engineer contributing to open source with the help of ChatGPT demonstrates the powerful synergy between artificial intelligence and collaborative software development. As we look to the future, we can anticipate:
- Increasingly sophisticated AI coding assistants tailored to specific open-source projects
- AI-driven project management tools that optimize task allocation and contribution workflows
- Advanced natural language interfaces for navigating and understanding complex codebases
- AI-enhanced code review processes that combine automated checks with human expertise
However, it's crucial to remember that the heart of open source remains human collaboration, creativity, and the shared goal of improving technology for everyone. AI tools like ChatGPT are powerful allies in this endeavor, but they complement rather than replace the human element that makes open source so vibrant and innovative.
As we continue to push the boundaries of what's possible with AI-assisted development, let's embrace these tools as a means to lower barriers, increase efficiency, and unleash our collective potential. The future of open source is a harmonious blend of human ingenuity and artificial intelligence, working together to create software that changes the world.
So, whether you're an AI expert or a newcomer to coding, I encourage you to take the plunge into open-source contribution. With AI as your guide and collaborator, you'll find that the world of open source is more accessible and exciting than ever before. Your unique perspective and contributions, enhanced by the power of AI, could be the key to unlocking the next big innovation in the ever-evolving landscape of technology.
