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
One Comment
This is a compelling approach that highlights the importance of experimentation and rapid iteration in early-stage development. The “3-month rule” serves as an effective discipline to avoid overengineering and premature optimization, which are common pitfalls for many startups. Your emphasis on simplicity—such as using a single VM and hardcoded configurations—allows for tangible learning and quick adjustments based on actual user feedback.
I’d add that this mindset aligns well with the concept of “learning by doing.” By embracing less scalable solutions initially, you free up resources and mental bandwidth to understand real user needs and system behavior. Once the growth is predictable or the product-market fit is validated, then scaling strategies and more robust architectures can be thoughtfully implemented.
Moreover, your experience underscores that building in small, manageable increments, and knowing when to pivot or scale, can dramatically improve product quality over time. It’s a pragmatic reminder that smart, focused experimentation often trumps overly cautious planning—especially in the chaotic, fast-paced environment of startups. Thanks for sharing this insightful framework!