Embracing the 3-Month Rule: A Pragmatic Approach to Non-Scalable Solutions in Tech

In the realm of startup development, the advice from industry luminary Paul Graham, “Do things that don’t scale,” echoes with profound significance. However, the challenge often lies in knowing how to effectively translate this guidance into actionable strategies within the coding context.

Having spent eight months crafting my AI podcast platform, I’ve honed a straightforward principle: any non-scalable workaround I implement is given a lifespan of three months. After this period, each solution must either demonstrate its worth and be further developed into a robust system or face elimination.

Rethinking Scalability

As engineers, we’re often conditioned to prioritize scalable architectures from the outset. From microservices to distributed systems, the focus on accommodating a vast number of users can overshadow the needs of a nascent startup. However, in the startup environment, opting for scalability too soon can sometimes lead to unnecessary complexities and inefficiencies. My 3-month rule encourages me to write pragmatic, straightforward code that actually delivers product value and clarifies user needs.

Current Infrastructure Insights: Unconventional Yet Effective Strategies

1. Consolidated Operations on a Single Virtual Machine

My platform’s database, web server, and background processes all operate on a single $40 per month virtual machine (VM). There’s no redundancy—this setup requires manual backups to my local machine.

This approach might seem reckless, but it has proven invaluable. I’ve gained better insights into my resource demands over the past two months than any extensive capacity predictions could have provided. For instance, I discovered that my “AI-intensive” application only peaks at 4GB of RAM, debunking the need for a complex Kubernetes structure that I had been considering.

When the system crashes (which has happened a couple of times), I receive genuine data reflecting what fails, revealing insights I would have otherwise missed.

2. Simple Hardcoded Configurations

Consider this:

python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"

With no reliance on configuration files or environment variables, all configurations are hardcoded constants spread throughout the code. Modifications require a redeployment.

This approach has a hidden advantage: I can quickly search and track changes across my entire codebase. After all, significant configuration changes have only occurred