The 3-Month Experiment: A Strategy for Embracing Non-Scalable Solutions in Startups
In the startup world, Paul Graham’s advice to “do things that don’t scale” is widely acknowledged but rarely put into practice, especially in technical development. After immersing myself in building an AI podcast platform for the past eight months, I have devised a straightforward yet effective framework: every unscalable method has a three-month lifespan. During this window, it must either demonstrate its value and evolve into a solid solution or be discarded.
The natural inclination for engineers often leans toward creating scalable systems right from the beginning. Concepts like design patterns, microservices, and distributed architectures are the holy grail, enabling software to potentially manage millions of users. However, in a startup environment, focusing on scalability too early can lead to unnecessary complexities and delays, especially when you are merely delaying the inevitable.
Instead, my three-month rule compels me to develop straightforward, even “imperfect” code that can be deployed swiftly. This approach not only allows me to understand the actual needs of my users but also fosters genuine learning about the system itself.
Insightful Infrastructure Hacks Beneath the Surface
1. Consolidating Everything on One Virtual Machine
My entire stack—database, web server, background jobs, and caching—operates on a single $40/month virtual machine. This setup includes no redundancy and relies on manual backups.
You might wonder why this is a savvy approach. Simply put, I’ve gained unparalleled insights into my resource requirements in just two months. Initially labeled as “AI-heavy,” my platform only peaks at 4GB of RAM, thus confirming that my plan for a complex Kubernetes cluster would have been a waste of resources.
When crashes occur (yes, they’ve happened twice), I’m presented with real insights into what actually fails. Spoiler alert: it’s always something unexpected.
2. Relying on Hardcoded Configuration
Consider this snippet of code:
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
There are no configuration files or environment variables; just constants scattered throughout the codebase. This method requires redeployment for any changes, yet it has its advantages.
The significant benefit? I can quickly search for any configuration value across my entire codebase. Each pricing update is meticulously recorded in
