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

In the world of startups, where agility is key and resources are often limited, Paul Graham’s well-known mantra, “Do things that don’t scale,” takes on a different meaning—especially in the realm of software development. It’s one thing to understand the principle; it’s an entirely different challenge to implement it effectively in your code.

I’ve been diligently working on my AI podcast platform for the past eight months, and I’ve crafted a straightforward framework to guide my development: every unscalable solution is given a lifespan of three months. Following this period, it must either prove its worth and be fully developed or face elimination. This strategic approach allows me to prioritize what truly matters.

Why the 3-Month Rule Works

As engineers, we are often conditioned to seek out scalable solutions from the outset. We think in terms of design patterns, microservices, and distributed architectures meant to support a vast array of users. However, this mindset can lead to wasting time and resources on problems that don’t yet exist, especially for startups.

My 3-month rule advocates for embracing minimalist, even “inefficient” solutions that promote immediate learning and validation of user needs.

Current Hacks That Are Surprisingly Effective

1. A Single Virtual Machine for Everything

All aspects of my platform, from the database and web server to background jobs and caching, are managed on a single virtual machine (VM) priced at just $40 per month. There are trade-offs, such as no redundancy or automated backups, but this approach has yielded critical insights.

In two short months, I’ve learned about my actual resource usage far more effectively than any planning document could reveal. It turns out my resource demands are much lower than anticipated, allowing me to avoid over-engineering solutions that don’t fit my actual needs.

2. Hardcoded Configuration Values

I’ve opted for hardcoded constants scattered throughout my codebase instead of utilizing configuration files or environment variables. For example:

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

Changes require a redeployment, yet the benefits are clear. I can quickly search for configuration values across the entire codebase, and alterations are tracked through version control. Ultimately, this approach saves me countless