Embracing the 3-Month Rule: A Pragmatic Approach to Development in Startups

In the world of tech startups, there’s a popular mantra often attributed to Paul Graham: “Do things that don’t scale.” While this advice is widely recognized, the practical application of it, especially in the realm of coding, remains less discussed. Over the past eight months of developing my AI podcast platform, I have adopted a simplified yet effective framework that I call the “3-Month Rule.” This principle states that any non-scalable solution I implement will be evaluated after three months. If it proves its worth, we will invest in a robust solution; if not, it will be phased out.

Rethinking Scalability in Startups

As developers, there’s an inherent drive to create scalable solutions from the outset. We often envision elaborate architectures—design patterns, microservices, distributed systems—that can accommodate millions of users. However, this approach is more suited to larger companies with established user bases. In the startup environment, striving for scalability can often lead to unnecessary delays, focusing efforts on hypothetical users while neglecting the immediate needs of the actual user base.

The 3-Month Rule encourages me to write straightforward, albeit potentially “messy,” code that allows for rapid deployment and genuine user feedback. By doing so, I gain a clearer understanding of what truly adds value for my users.

Current Infrastructure Insights: What I’m Learning from Simple Solutions

1. Utilizing a Single Virtual Machine (VM)
   My entire stack, including the database, web server, and background tasks, runs on one $40-per-month VM—no redundancy or complex setups. This approach has provided invaluable insights into my resource requirements. Instead of laboriously planning out capacity, I’ve learned that my platform comfortably operates at 4GB of RAM. When the server experiences outages, I receive direct feedback about the system’s pain points, which often surprises me.

2. Hardcoded Configuration
   My configuration is embedded within the code as constants rather than utilizing separate files or environment variables. While this seems rudimentary, it allows me to efficiently track changes through Git, and modifications require minimal deployment time. Instead of investing time in developing a dedicated configuration service, I’ve streamlined my process, making it possible to adapt quickly based on intermittent needs.

3. SQLite as My Production Database
   Contrary to conventional wisdom, I have deployed SQLite for my application. It currently handles 50 concurrent users