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

In the startup world, we’ve all heard the familiar advice from Paul Graham: “Do things that don’t scale.” However, the challenge lies in translating this advice into practical coding strategies. After eight months of developing my AI podcast platform, I’ve established a guiding principle that has significantly shaped my approach to software development: each unscalable solution I implement is given a three-month trial period. At the end of this timeframe, I assess its performance; it either becomes a solidified part of my codebase or is phased out.

As engineers, we’re often trained to focus on creating scalable architectures from the outset. Concepts like microservices, distributed systems, and elegant design patterns are impressive and allow applications to support millions of users. However, this mindset can lead to expensive delays, especially in a startup environment where scalable solutions may address problems that aren’t even relevant at present. My three-month rule encourages me to write straightforward, sometimes subpar code that can be deployed quickly. This practice reveals insights about real user needs rather than hypothetical scenarios.

My Practical Infrastructure Inventions: Lessons Learned

1. Single Virtual Machine for Everything

Instead of distributing my database, web server, background jobs, and caching all across multiple servers, I’ve opted for a single, cost-effective virtual machine ($40/month). While this approach sacrifices redundancy and relies on manual backups, it has granted me invaluable insights into my resource demands over two months. For instance, I discovered that my AI platform only peaks at 4GB of RAM. The complex Kubernetes setup I considered would have only resulted in managing idle containers. Interestingly, when the system fails, I gain real data on what went wrong—information that often surprises me.

2. Hardcoded Configuration Values

My configuration is straightforward, with values like:

python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"

While it might seem primitive to scatter constants across files without dedicated configuration management, this method allows me to quickly search the codebase for any parameter. Given that I’ve altered these values only three times in three months, the time spent redeploying is minimal compared to what it would take to implement a full-featured configuration service.

3. Leveraging SQLite in Production

Running SQLite for a multi-user application