Embracing the 3-Month Rule: A Pragmatic Approach to Non-Scalable Development
When it comes to startup development, a common piece of wisdom often shared is Paul Graham’s mantra: “Do things that don’t scale.” While this advice is widely recognized, the challenge lies in executing it effectively, especially in the realm of coding. Over the past eight months, as I’ve been developing my AI podcast platform, I stumbled upon a streamlined methodology: Every temporary, non-scalable solution gets a three-month trial. After this period, it either proves its value and transitions into a more robust implementation or is promptly discarded.
A Shift in Perspective
As developers, we often focus on creating scalable solutions from the outset. We pour our efforts into implementing design patterns, microservices, and distributed systems—beautiful infrastructures designed to manage millions of users. However, in a startup setting, this mindset can be counterproductive, leading us to optimize systems for potential users who may never materialize. My three-month rule compels me to produce straightforward, albeit imperfect, code that actually gets deployed, allowing me to understand the genuine needs of my users.
Current Infrastructure Hacks: Smart Decisions Behind Simple Solutions
1. Consolidated Operations on One Virtual Machine
I currently host my database, web server, background jobs, and Redis services all on a single $40/month VM. While it may seem reckless due to the absence of redundancy and reliance on manual backups, this approach has provided valuable insights. Within just two months, I’ve gained a clearer understanding of my resource requirements. My platform, which I expected to be resource-heavy, only peaks at 4GB of RAM—showing how an intricate Kubernetes setup would have only managed empty containers. When crashes occur (which they have, twice), they reveal insights about failure points, often surprising me.
2. Hardcoded Configuration Values
Configurations are hardcoded throughout my codebase rather than stored in separate files or environment variables:
python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
This seemingly primitive method allows for swift searches across the codebase and keeps configuration history tracked neatly within git. Changing values calls for a minor redeployment process, and over three months, I’ve only adjusted these constants three times—saving me countless hours of potential engineering effort crafting a configuration service.
