Introduction
I recently developed a task management module called task-orchestrator. I created this module as a base for building an algorithmic trading framework. It can manage two types of tasks:
- Continuous tasks that run in the background.
- Event-triggered tasks that start when a specific event happens.
During the development process, I used AI tools for planning, coding, testing, and writing documentation.
Planning
At the beginning, I was not sure which architecture to use for the framework. I considered the actor model and the event-driven model. I asked ChatGPT, Claude, and DeepSeek for advice. They suggested a hybrid approach, combining both models. Based on this idea, I wrote the specifications and improved them through discussions with AI. Finally, I asked Claude to generate a basic code structure using my final specifications.
Coding
During implementation, I used Claude's code as a base. I also used aider to make necessary changes and add features. Some specific tasks I did:
- Making small adjustments
- Renaming the project
- Creating unit tests
For documentation, aider generated most of it, so I only needed to make small edits, like removing unnecessary links.
Conclusion
Through this process, I realized how useful AI tools are. They helped me write test code and documentation efficiently.
Here are the tools I used:
- Editor: Helix (with lsp-ai)
- AI tools: Aider (for coding, testing, documentation) and DeepSeek API
- Specification discussions: ChatGPT, Claude, and DeepSeek (Web-based)
Top comments (1)
This is such an interesting look at integrating AI into indie development! I've been experimenting with similar tools for my own side projects, and I totally agree that AI can be a game-changer for streamlining workflows—especially for solo developers who have to wear so many hats. I'd love to hear more about how you balanced AI assistance with maintaining your own creative direction and architectural decisions. Great post!