Embracing the 3-Month Rule: A Pragmatic Approach to Unscalable Solutions in Tech Development
In the world of startup development, the common mantra from industry luminary Paul Graham rings true: “Do things that don’t scale.” However, the challenge lies in practically applying this philosophy, especially in coding environments. After eight months dedicated to constructing my AI podcast platform, I discovered a method that has proven invaluable: the 3-Month Rule.
This straightforward framework dictates that any unscalable workaround is granted a life span of three months. After this period, the solution must either demonstrate its effectiveness and transition to a sustainable model, or it faces elimination.
While conventional wisdom among engineers emphasizes the creation of scalable architectures from the outset—think design patterns, microservices, and distributed systems—this approach is often ill-suited for the startup mentality. Building for scalability can quickly become a costly exercise in procrastination, especially when you’re tailoring solutions for users that haven’t even arrived yet. Adopting my 3-Month Rule has compelled me to produce straightforward and sometimes imperfect code that ships, allowing me to gain insights into the actual requirements of my users.
Current Infrastructure Hacks: Smart Choices for Real Insights
1. Consolidated on a Single Virtual Machine
Right now, everything operates on one $40/month VM, encompassing the database, web server, background jobs, and Redis. With zero redundancy and manual backups to my local system, this might seem counterintuitive. However, I’ve gained a deeper understanding of my resource requirements in the past two months than through any capacity planning documentation. My application’s peak resource usage is just 4GB of RAM, proving that a complex Kubernetes setup would have been an exercise in managing unused resources. Each crash—yes, there have been two—has provided enlightening data about unexpected failures.
2. Hardcoded Configuration Throughout
In my approach, configurations are hardcoded directly into the codebase:
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
Without external configuration files or environment variables, updates translate to redeployments. This method, while seemingly primitive, offers a unique advantage: I can quickly search the codebase for configuration values, ensuring every price adjustment is documented in git history. Instead of investing a week in building a configuration service when these values only change a
