Embracing the 3-Month Rule: A Practical Approach for startups to Innovate

In the world of startups, Paul Graham’s well-known advice to “do things that don’t scale” is often reiterated but rarely dissected in practical terms, especially concerning software development. After dedicating eight months to building my AI podcast platform, I’ve created a straightforward framework that I call the “3-Month Rule.” This concept allows me to assign unscalable solutions a limited lifespan of three months. After this period, each solution must either demonstrate its worth and be fully developed, or face elimination.

As engineers, our training typically emphasizes creating scalable systems from the outset, filled with intricate design patterns and complex architectures that accommodate millions of users. However, this mindset can lead to costly procrastination in the startup environment, focusing on scaling for users who are yet to materialize. By applying my 3-month rule, I prioritize writing straightforward, even “inefficient,” code that can be deployed quickly, allowing me to gain real insights into user needs.

Clever Infrastructure Hacks That Drive Learning

Here are some of the infrastructure decisions I’ve made that challenge conventional wisdom but have proven to be quite effective:

1. Unified Virtual Machine Approach

My entire application—covering the database, web server, background tasks, and caching—operates on a single $40/month virtual machine, with no redundancy and manual backups to my local machine. While this setup may seem precarious, it has provided valuable insights into my actual resource requirements. For instance, my AI-driven platform spikes at 4GB of RAM, highlighting how an elaborate setup like Kubernetes would have been managing components that weren’t in use.

Every crash has taught me something new about the application’s vulnerabilities, revealing issues I hadn’t anticipated.

2. Hardcoded Configuration

For now, I’ve opted for hardcoded configuration values such as pricing tiers and user limits directly in my codebase. While this approach might raise eyebrows, it has enabled me to make quick searches across my code and maintain a clear version history with each change. Instead of spending significant time developing a configuration service, I’ve redeployed only three times in three months—saving considerable effort and time for minor adjustments.

3. Utilizing SQLite for Production

My database, a mere 47MB SQLite file, perfectly accommodates a multi-user setup, handling 50 concurrent users without a hitch. The exploration of access patterns revealed that the majority of my database interactions are