Embracing the 3-Month Rule: A Pragmatic Approach to Building an AI Podcast Platform
In the world of startups and tech, the advice to “do things that don’t scale” often echoes through the halls of innovation. However, turning that advice into actionable steps—particularly in coding—remains a lesser-discussed topic. Based on my own experience developing an AI podcast platform over the past eight months, I’ve developed a straightforward framework that I call the “3-Month Rule.”
Understanding the 3-Month Rule
The essence of the 3-Month Rule is simple: any approach or hack I’ve implemented that lacks scalability receives a trial period of three months. During that time, I assess its utility. If it proves its worth, it is then restructured for a more robust application; if not, it is phased out.
As engineers, we often find ourselves aimed at creating scalable solutions from the outset. We envision elaborate architectures using microservices and distributed systems, designed for thousands, if not millions, of users. But in a startup environment, focusing on scalability from day one can lead to costly delays. The 3-Month Rule encourages me to prioritize simple, straightforward coding that fosters real-world learning about user needs.
Current Infrastructure Hacks: My Smart Shortcuts
Here are some of the unorthodox methods I’ve employed that may seem counterintuitive but have enriched my understanding of my platform’s needs:
1. Consolidation on a Single VM
I operate everything—from the database to the web server and background jobs—on a single virtual machine costing just $40 per month. While this setup lacks redundancy and relies on manual backups, it has provided invaluable insights into my actual resource requirements. After two months, I realized my “resource-heavy” platform peaks at just 4GB of RAM. The complex Kubernetes architecture I nearly implemented would have meant managing idle resources instead.
2. Hardcoded Configuration
I opted for hardcoding configuration variables—prices, user limits, and AI model specifications—across my codebase without using configuration files or environment variables. While this approach forces a redeployment for any change, it grants me the ability to swiftly check the values across my entire project and track revisions easily via Git. This method not only saves time but also eliminates unnecessary complexity, allowing agile modifications.
3. Leveraging SQLite in Production
Yes, I’ve chosen SQLite for my multi-user web application. With a database size of
