Embracing the 3-Month Rule: A Practical Approach to Learning and Development in Startups

In the entrepreneurial world, there’s a well-known piece of advice from Paul Graham: “Do things that don’t scale.” However, many often overlook how to effectively integrate this philosophy into coding practices. After dedicating eight months to building my AI podcast platform, I’ve established a straightforward framework to manage these unscalable ideas: every temporary hack is granted a lifespan of three months. Following this period, each hack is evaluated for its effectiveness—if it proves valuable, it gets refined; if not, it’s discarded.

As developers, we frequently find ourselves pressured to create solutions that can handle significant growth right from the start. Concepts like design patterns, microservices, and distributed systems often dominate our thinking. Unfortunately, this mindset can lead to decisions that may not be beneficial for a startup environment.

The reality is that striving for scalability can often turn into an expensive form of procrastination. We find ourselves optimizing for hypothetical users and addressing challenges that may never emerge. My 3-month rule encourages the creation of straightforward, even subpar code that can be deployed quickly, offering real insights into what users genuinely need.

My Practical Hacks and Their Unexpected Benefits

1. Unified Virtual Machine Management

Currently, my entire infrastructure operates on a single virtual machine (VM) costing just $40 per month. This setup runs everything—from the database to background tasks and caching—without redundancy. While it may seem risky, this approach has provided invaluable insights into my actual resource requirements. I’ve discovered that my AI-driven platform operates efficiently with just 4GB of RAM, leading me to realize that complex solutions such as Kubernetes would have been managing underutilized resources. Each crash (yes, they happened twice) provided concrete data on weaknesses I hadn’t anticipated.

2. Simplified Configuration Management

Consider my configuration approach, involving hardcoded constants for key parameters:

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

Rather than utilizing configuration files or environment variables, I keep constants throughout my project. This method allows for easily searchable values across the entire codebase, and each change is neatly recorded in version control history. I’ve altered these constants three times in three months—proving why dedicating a week to develop a configuration service was unnecessary