Embracing the 3-Month Rule: A Practical Guide for Startups

In the startup world, the common wisdom often echoes the advice of influential figures like Paul Graham: “Do things that don’t scale.” However, the conversation rarely ventures into how to effectively implement this mindset in coding practices. As I’ve navigated the development of my AI podcast platform over the past eight months, I’ve devised a straightforward framework that I call the 3-Month Rule. This approach allows any unscalable hack to exist for just three months, after which it must demonstrate its value or be phased out.

Why the 3-Month Rule Matters

As engineers, we’re often conditioned to prioritize scalable solutions from day one. This mentality pushes us toward intricate design patterns, microservices, and distributed systems, all crafted to support millions of users. While these designs hold merit for larger companies, they can represent a costly delay for startups. My 3-month rule challenges me to develop straightforward, albeit “imperfect,” code that gets deployed and helps reveal the true needs of my users.

Insights from My Current Infrastructure Hacks

Here are some of the unconventional yet insightful strategies I’ve employed, emphasizing their underlying genius:

1. Consolidated Resources on a Single Virtual Machine (VM)

I run everything—database, web server, background jobs, and caching—on a single $40/month VM without redundancy. While some might consider this reckless, I’ve gained invaluable insights into my resource needs. In just a couple of months, I’ve discovered that my platform peaks at 4GB of RAM, a far cry from the complex Kubernetes setup I almost pursued. When failures occur (and they have), I learn precisely what caused the issue, which often surprises me.

2. Using Hardcoded Configuration Values

Configurations in my project are hardcoded directly into the codebase. This means no configuration files or extensive environment variables—just constants that require redeployment when changes are made. This simplicity allows me to swiftly track any configuration alterations through Git history and ensures that every update is easily reviewed. In three months, I’ve adjusted configurations a mere three times, saving countless hours of development time.

3. Leveraging SQLite in Production

I’ve chosen SQLite for my multi-user web app, with an entire database size of just 47MB. Despite handling 50 concurrent users seamlessly, this choice taught me about my access patterns—primarily read-heavy. Had I opted for a more complex solution like