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

In the world of startups and technology, the popular mantra by Paul Graham—”Do things that don’t scale”—often inspires entrepreneurs to think outside the box. But how can we effectively implement this strategy, especially when it comes to coding? After spending eight months developing my AI podcast platform, I devised a straightforward framework that I refer to as the 3-Month Rule. This approach allocates a three-month lifespan to any unscalable hack, after which the solution must either prove its worth or be discarded.

As engineers, we frequently find ourselves entrenched in the pursuit of scalable solutions from the outset. We’re trained to think about sophisticated architectures—like microservices and distributed systems—that can support millions of users. However, this perspective can be detrimental in a startup environment. Focusing too much on scalability often leads to unnecessary complexities, essentially causing us to optimize for hypothetical users and problems that may never arise.

My 3-month rule compels me to prioritize simplicity and functionality over idealistic coding practices. By doing so, I can effectively understand user needs through practical experience rather than theoretical assumptions.

Current Infrastructure Hacks: Smart Strategies for Efficiency

Here are some of the unconventional approaches I have taken, each of which has provided surprising insights and advantages:

1. All-In-One VM Utilization

I’m currently running my entire architecture—database, web server, background jobs, and Redis—on a single virtual machine for just $40 a month. This setup has no redundancy and involves manual backups to my local device.

Why is this wise instead of reckless? Because it has allowed me to accurately gauge my resource requirements. In just two months, I learned that my AI-heavy platform only peaks at 4GB of RAM. The complex Kubernetes configuration I nearly implemented would have been superfluous, managing empty resources instead of addressing my actual needs. And when crashes do occur, they reveal unpredicted failure points.

2. Hardcoded Configuration for Simplicity

Configuration values are hardcoded directly into my code, bypassing the need for complex config files or environment variables. For instance:

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

Although this may seem suboptimal, it gives me the ability to quickly