I built two open-source tools faster by letting AI write most of the code
The author built two open-source projects quickly by using AI to write most of the code, while still maintaining control over the architecture, data models, and review process.
Why it matters
This approach demonstrates how AI can be leveraged as a productivity tool for developers, especially solo and open-source projects, by increasing coding throughput while maintaining control over the overall design and quality.
Key Points
- 1The author didn't let AI build the products, but rather used AI to write the code while maintaining control over the overall design and decision-making
- 2The author's workflow involves precisely describing what needs to be implemented, having the AI write the code, and then reviewing and refining it
- 3The author used this approach for two different projects - Ackify, an internal document acknowledgement tool, and SHM, a self-hosted metrics app
Details
The author explains that he was able to build two open-source projects much faster by using AI to write most of the code, while still maintaining control over the architecture, data models, and review process. This is not about using clever prompts or relying on autonomous agents, but rather leveraging the AI's speed and efficiency in execution while the author focuses on the precise definition of requirements, decision-making, and quality control. The author's workflow involves describing exactly what needs to be implemented, having the AI write the code, and then reviewing and refining it. This approach allowed the author to explore ideas faster, kill bad ones earlier, and finish small useful tools instead of polishing one project forever. The author sees AI as a force multiplier, not a decision-maker, and believes this approach is particularly beneficial for solo developers and open-source projects that require small, precise tools with fast iteration.
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