Embracing the 3-Month Rule: A Practical Framework for startup Development

In the realm of entrepreneurship and software development, there’s a popular mantra that resonates with many: “Do things that don’t scale,” a principle championed by Paul Graham. Yet, navigating this advice when it comes to actual implementation, especially in coding, can be a challenge. After eight months of developing my AI podcast platform, I’ve created a straightforward framework that emphasizes experimentation: any unscalable solution is granted a lifespan of just three months. If it proves its worth, it gets a solid foundation; if not, it’s time to move on.

As engineers, we often focus on crafting scalable solutions from the onset. Concepts like design patterns, microservices, and distributed systems can be enticing, especially when envisioning an enterprise-level architecture capable of supporting millions of users. However, in a startup environment, this meticulous planning can sometimes translate into wasteful procrastination, resolving issues that may never arrive while ignoring the needs of current users. By adhering to my 3-month rule, I’m encouraged to write simpler, more direct code that prioritizes real user needs over hypothetical complexities.

Current Strategies and Their Logic

Let’s dive into some of my current infrastructure choices that may seem unconventional but have proven to be effective learning experiences.

1. Single VM for All Operations

I host my entire application—including the database, web server, and background jobs—on a single virtual machine costing only $40 a month. The lack of redundancy and manual backups might sound risky, but this approach has provided invaluable insights into my resource requirements. Within just two months, I found that my “AI-heavy” platform only required 4GB of RAM. The more intricate Kubernetes setup I nearly pursued would’ve only managed idle containers. Moreover, during system crashes (which have happened twice), I gained real-time data about failures—which was often unexpected.

2. Simplified Configuration Management

Instead of utilizing configuration files or environment variables, I’ve opted for hardcoded values scattered throughout my codebase, such as:

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

This method allows me to quickly locate configuration changes using a search tool, with every modification recorded in git history. In three months, I’ve made only three configuration changes, making the quick redeployment a fraction