Title: Embracing the 3-Month Rule: A Framework for Learning in startup Development
In the realm of startup growth, the advice from esteemed entrepreneur Paul Graham often resonates: “Do things that don’t scale.” However, there’s a lack of guidance on translating this philosophy into the technical side of development. After eight months of building my AI podcast platform, I have devised a straightforward approach that I call the 3-Month Rule. This strategy gives any unscalable hack a trial period of three months, post which it either proves its worth and gets developed into a robust solution or it gets scrapped.
As developers, our instinct often pushes us toward creating scalable solutions right from the start. We become enamored with design patterns, microservices, and complex system architectures—engineering marvels that promise to support millions of users. However, this kind of thinking tends to dominate in larger companies, while in a startup environment, pursuing scalability prematurely can be a costly delay. Rather than optimizing for non-existent users, my 3-Month Rule prompts me to embrace simple, pragmatic coding that allows me to ship quickly and understand what users truly need.
My Current Infrastructure Hacks: A Strategic Overview
1. Single VM Operation
I’ve consolidated all my operations—database, web server, background jobs, and Redis—onto one economical $40/month virtual machine. There’s no redundancy or automated backups. While this may seem reckless, it has been revealing: in just two months, I’ve grasped my actual resource requirements far better than I could have with any traditional capacity planning. My platform consistently peaks at 4GB of RAM, leading me to realize that my anticipated Kubernetes setup would have likely just amounted to managing underutilized containers. When crashes do occur (and they have, twice), they offer valuable insights into real failure points, which often surprise me.
2. Hardcoded Configuration
Instead of utilizing configuration files or environment variables, I’ve opted for hardcoded constants sprinkled throughout my code:
python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
This method simplifies tracking changes in my Git history. Each modification necessitates a quick redeployment, saving me from investing significant engineering hours in creating a dedicated configuration service. Over the last three months, I’ve made only three changes, translating to about 15 minutes
