The 3-Month Rule: A Technical Strategy for Embracing the Unscalable

In the startup ecosystem, Paul Graham’s sage advice to “do things that don’t scale” often echoes in discussions about growth and innovation. However, the challenge lies in effectively applying this mantra to software development and engineering. After eight months of dedicated work on my AI podcast platform, I’ve formulated a straightforward approach: every temporary hack or unscalable solution is given a three-month trial period. At the end of this timeframe, it either proves its worth by being transformed into a robust feature or is discarded.

As software engineers, we are taught to prioritize scalability from the outset. Techniques such as design patterns, microservices, and distributed systems are heralded for their ability to handle vast user bases. Yet these strategies often reflect the needs of large corporations rather than early-stage startups.

In the startup environment, the pursuit of scalable solutions can sometimes lead to expensive delays, focusing on hypothetical users and nonexistent problems. My three-month guideline compels me to adopt straightforward, albeit rudimentary, coding practices that enable me to deliver functional products and truly understand user needs.

My Current Infrastructure Strategies: Simplistic Yet Effective

1. Consolidated Operations on a Single VM

I run my web server, database, and background jobs—all on a single $40/month virtual machine. This setup lacks redundancy and relies on manual backups to my local system.

The rationale behind this approach is enlightening: I gained invaluable insights into my actual resource requirements much faster than any capacity planning documentation could have provided. I found my AI platform demanding a mere 4GB of RAM at peak times, which made the complex Kubernetes architecture I nearly implemented look foolish. Every crash I experienced (and there have been two) taught me precisely what failed—lessons I wouldn’t have learned without this approach.

2. Direct Configuration Values

Throughout my codebase, configuration is hardcoded directly:

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

There are no separate config files or environment variables; changing a value requires redeploying the code.

The efficiency here is significant: I can easily search for configuration values across my codebase, and all modifications are recorded in my version history. Though creating a dedicated configuration service might take a week, I’ve only changed