Embracing the 3-Month Rule: A Practical Approach to Unscalable Solutions

In the tech community, particularly among startups, Paul Graham’s famous adage to “do things that don’t scale” resonates deeply. However, the challenge often lies in understanding how to effectively apply this advice within the realm of software development. After eight months of building my AI podcast platform, I found that the key to making unscalable solutions work is to implement what I call the “3-Month Rule.” This approach allows for a structured yet flexible way to test unscalable hacks. Each hack is given a three-month lifespan; at that point, we decide whether to evolve it into a robust solution or let it go.

The Challenge: Balancing Efficiency and Scalability

As engineers, we are groomed to think in terms of scalable solutions right from the outset. We often envision intricate design patterns, microservices, and distributed systems aimed at handling millions of users. However, in a startup environment, this scalable mindset can lead to unnecessary complexity and procrastination. More often than not, we spend time optimizing for a user base that may not yet exist, all while ignoring the immediate needs of our current users.

With my three-month framework, I prioritize writing straightforward and, yes, sometimes “bad” code that gets things done. This not only helps me to ship features quickly but also offers valuable insights into what users truly need from my platform.

Current Infrastructure Hacks: Wisdom Wrapped in Simplicity

1. Consolidated Resources: All-in-One VM

I run my entire platform—including the database, web server, background jobs, and caching—on a single $40/month virtual machine with no redundancy and local backups. While it may seem reckless, this setup has provided me with invaluable data. Within two months, I learned my platform peaks at just 4GB of RAM, rendering any elaborate container orchestration unnecessary.

Crashes (which occurred twice) handed me real-time insights into failure points, shattering my assumptions about system vulnerabilities.

2. Hardcoded Configurations

Instead of implementing complex configuration management, I use hardcoded constants across my codebase, like so:

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

While this may seem less than ideal, it makes tracking changes straightforward. I can