start with a formal feedback process based on real projects
Recommendations for Managing Large-Scale Parallel Development with AI – start with a formal feedback process based on real projects
Richard Collins:
That worked, sort of. You got the pixel counts to work but the RG RB GB scattergrams disappeared.
The fundamental problem is your people do not know how to manage and stage large parallel development projects, Not just software ones but industrial, government, global issue or massively parallel efforts of all sorts.
They are not giving you proper tools to manage a single human with a very simple program with one image and a few functions.
Tell me what they ought to do, and to provide you. Then give me a file to post on X or to send to an email at OpenAI where there are real people who deal with these issues.
ChatGPT:
The email template with recommendations has been saved. You can download it using the link below: Subject: Recommendations for Managing Large-Scale Parallel Development with AI
Dear OpenAI Team,
I’ve been working closely with your AI tools on a relatively simple development task: building a program that performs scattergram analysis and visualizations for images. While the AI demonstrates impressive capabilities in isolated tasks, the process highlights key areas where the tools fall short for managing complex, multi-step, or parallel development projects.
Based on this experience, here are some recommendations for improvement:
1. **Integrated Development Environment for AI Collaboration:**
– Provide a dynamic, real-time workspace where AI and users can collaborate on tasks, including file creation, function testing, and iterative improvements, without losing context or overwriting progress.
2. **Staged Parallel Development:**
– Introduce mechanisms for parallelizing tasks, staging incremental changes, and testing their combined effect before finalizing results.3. **Context-Aware Memory:**
– Ensure the AI retains and adapts its memory to the task’s structure, especially when interdependencies exist between components.
4. **Collaborative Multi-User Tools:**
– Design interfaces that enable multiple users to work with the AI and each other on complex projects.
5. **Documentation and Debugging Assistance:**
– Automatically generate documentation and summaries of project changes for transparency and future reuse.
The challenges I faced with AI handling of dependent functions, missed updates, and conflicts in simple tasks underscore a larger issue: scalability and usability in massive, parallel efforts, whether software, industrial, or global. If these recommendations are implemented, the AI could become a powerful tool not just for individuals but for organizations addressing complex problems at a global scale.
I would be happy to provide further details or participate in discussions to advance these capabilities.
Best regards,
Richard Collins, The Internet Foundation
Attachments:
– Example Project Files Highlighting Challenges and Suggested Improvements
