Embracing the 3-Month Rule: A Pragmatic Approach to Non-Scalable Solutions in Development
In the realm of startup dynamics, advice from Paul Graham often echoes: “Do things that don’t scale.” However, the implementation of this principle within the coding landscape is seldom discussed.
After dedicating the last eight months to the development of my AI podcast platform, I’ve established a straightforward framework for navigating these unscalable tasks: every makeshift solution holds a lifespan of just three months. At the end of this period, each hack must either demonstrate its worth and be refined for optimal performance, or it will be discarded.
As engineers, we often find ourselves trained to create scalable systems from the outset. Concepts like design patterns, microservices, and distributed architecture aim to accommodate millions of users. However, this perspective often aligns more with larger corporations than with startup culture.
In the startup environment, designing for scalability can become a costly form of procrastination—one that anticipates users who do not yet exist while attempting to solve theoretical issues. My three-month rule compels me to produce straightforward, albeit imperfect, code that is deployable and reveals the genuine needs of users.
A Look at My Current Infrastructure Approaches and Their Hidden Advantages
1. Centralized Operations on a Single Virtual Machine
All elements of my platform—database, web server, background processes, and Redis—function on a single $40/month virtual machine. While there’s no redundancy and backups are done manually, this design teaches me about my resource needs far more effectively than any theoretical document could. Surprisingly, my “AI-heavy” system utilizes only 4GB of RAM at peak times. Had I pursued a complex Kubernetes architecture, I would have been managing idle containers instead.
The two times the system crashed revealed invaluable insights into what truly fails—never what I anticipated.
2. Hardcoded Configuration for Efficiency
Consider these lines of code:
python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
With no config files or environment variables in sight, all constants are interspersed throughout the codebase. Changes necessitate redeployment, but herein lies a strategic advantage. I can rapidly search through the code for any config value, documenting every price alteration through Git’s history. Since these values have been modified merely three times in
