Embracing the 3-Month Rule: A Pragmatic Approach for Startups

In the entrepreneurial landscape, it’s common to hear Paul Graham’s advice to “do things that don’t scale.” However, the challenge arises in translating that guidance into actionable steps, particularly within the realm of coding. After spending eight months developing my AI podcast platform, I discovered a straightforward framework that I now apply: each non-scalable hack is given a lifespan of three months. Beyond this period, it either proves its worth and is properly built out or is scrapped.

As engineers, we’re often conditioned to conceive scalable solutions right from the start—think meticulous design patterns, microservices, and distributed systems. While these frameworks are essential for large organizations handling large user bases, they can become pitfalls for startups.

Focusing on scalability too soon can lead to unnecessary complexities, often delaying progress. My three-month guideline encourages me to write simpler, albeit imperfect code that allows for quicker deployment and real-time learning about user needs.

How I Implement This Framework: Current Infrastructure Hacks

1. Consolidating Resources on a Single VM

My entire stack—database, web server, background jobs, and Redis—operates from a single virtual machine (VM) at a reasonable $40 per month. This setup intentionally lacks redundancy, relying on manual backups to my local machine.

The brilliance of this approach lies in the insights I’ve gained regarding my resource usage within just two months. I discovered that my AI-centric platform only requires 4GB of RAM at peak times. Had I pursued a more complicated Kubernetes arrangement, I would have merely ended up managing idle containers. Moreover, any crashes provide valuable lessons about what truly fails—often surprising elements that I wouldn’t have predicted.

2. Hardcoded Configurations

I maintain crucial configurations directly within my codebase, such as:

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

By avoiding complex configuration files or environment variables, making changes necessitates a redeployment—but this has proven efficient. In three months, I’ve only altered these constants three times, resulting in a mere 15 minutes of redeployment versus a potential 40 hours of engineering effort to create a configuration service.

3. Using SQLite for a Multi-User App

Surprisingly, I’m running SQLite in a web