Embracing the 3-Month Rule: A Framework for Developing Non-Scalable Solutions

In the startup ecosystem, a common piece of wisdom attributed to Paul Graham urges creators to “do things that don’t scale.” While this advice resonates deeply, particularly when it comes to initial product development, practical implementation can often be overlooked—especially in coding practices.

Having spent the past eight months developing my AI podcast platform, I’ve established a straightforward approach: any non-scalable hack I implement is given a lifespan of just three months. At the end of this period, I assess its impact—if it proves valuable, I invest time and resources to enhance it; if not, it gets discarded.

Rethinking Scalability in Early Stages

As engineers, we’re often conditioned to prioritize scalable solutions from the very beginning. We’re drawn to sophisticated architectures like design patterns, microservices, and distributed systems designed to support high user volumes. However, this mindset can be detrimental in a startup environment, where focusing on scalability can equate to wasting precious time and resources on potential user demands that may never materialize.

My 3-month rule compels me to produce straightforward and often imperfect code that actually gets released. Through this process, I learn what my users genuinely require—an invaluable insight that is difficult to glean from theoretical planning alone.

Current Infrastructure Hacks: Smart Decisions for Learning

Here are some of my current strategies, which might seem unorthodox but have proven effective in my development journey:

1. Consolidated Resources on a Single VM

I run my entire infrastructure—database, web server, background jobs, and Redis—on a single virtual machine costing just $40 per month. While this might appear risky due to a lack of redundancy, it has offered significant learning opportunities. Within two months, I’ve gained critical insights into my resource needs—discovering that my “AI-heavy” platform only peaks at 4GB of RAM. With this knowledge, I can avoid the complexities of a Kubernetes setup that would have required management of empty containers.

2. Hardcoded Configuration Values

Instead of using external configuration files or environment variables, I utilize constants directly in my codebase, such as:

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

This strategy allows me to swiftly track changes through Git and enables