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
One Comment
This is a compelling approach that resonates deeply with the mindset needed for startup agility. The 3-month rule serves as a pragmatic checkpoint, allowing teams to test and iterate quickly without getting bogged down by premature scalability concerns. I particularly appreciate the emphasis on learning from real-world usage—running a single VM to gather concrete data on resource needs is a smart, cost-effective way to inform future infrastructure decisions. It also highlights how initial simplicity not only accelerates deployment but can uncover hidden bottlenecks early, reducing technical debt down the line.
Your point about custom constants for configuration is a great example of a “just enough” approach—keeping things simple and flexible while maintaining control. In early-stage environments, this approach often outweighs the rigidity of more complex configuration management systems.
This framework encourages a mindset shift: prioritize actionable insights over theoretical best practices, and embrace temporary hacks as valuable learning tools rather than aesthetic flaws. Thanks for sharing this practical perspective; it’s an inspiring reminder that fundamental progress often comes from unscaling efforts and pragmatic experimentation.