Embracing the 3-Month Rule: A Practical Approach to Coding for Startups

In the realm of startup development, the well-known mantra from Paul Graham—”Do things that don’t scale”—often sparks debate. However, the challenge lies in understanding how to effectively apply this principle, particularly when it comes to coding. After eight months of developing my AI podcast platform, I have established a straightforward framework that I’ve dubbed the “3-Month Rule”. Essentially, any unscalable solution I implement is given a trial period of three months. After this timeframe, it must demonstrate its worth to either evolve into a robust solution or be discarded entirely.

The conventional wisdom for developers typically prioritizes scalable solutions from the outset. Concepts like design patterns, microservices, and distributed systems are frequently celebrated for their ability to handle large numbers of users. However, this perspective often belongs to established companies, not startups. In many cases, preparing for scale can lead to needless complications and wasted efforts when you’re vastly outpacing your present user base. My approach aims to address this disconnect by encouraging the use of simpler, more direct coding practices that can expedite learning about real user needs.

Insights from My Current Infrastructure Strategies

1. Unified VM Operation

Currently, my entire infrastructure—database, web server, background jobs, and caching—resides on a single virtual machine that costs just $40 a month. Although this setup lacks redundancy and requires manual backups, it provides invaluable insights into my actual resource requirements. Over two months, I’ve learned that my “AI-centric” platform only peaks at 4GB of RAM. An elaborate setup with Kubernetes would have wasted resources managing idle containers. Each crash provides actionable data about my system’s vulnerabilities, often revealing unexpected problem areas.

2. Hardcoded Resource Configurations

I’ve opted for hardcoded constants throughout my code, such as pricing tiers and user limits. While this method lacks the efficiency of configuration files, it allows for rapid access and change tracking. I’ve only altered these settings three times in three months, equating to less than 15 minutes of redeployment instead of the weeks it would take to implement a dedicated configuration service.

3. SQLite as My Database

Remarkably, my multi-user web application is powered by an SQLite database that is just 47MB in size. It supports up to 50 concurrent users effortlessly. This experience has taught me that my usage patterns are predominantly reads, minimizing the need for