The 3-Month Rule: A Practical Approach to Building Non-Scalable Solutions in Software Development

In the world of software development, it’s common to hear the advice from Paul Graham: “Do things that don’t scale.” However, the challenge lies in understanding how to apply this principle effectively within your coding practices.

Over the past eight months, as I’ve been constructing my AI podcast platform, I developed a unique framework that I now call the “3-Month Rule.” This approach allows me to engage with unscalable hacks for a designated time frame of three months. If these hacks prove effective, I invest in refining and scaling them. If not, they are discarded.

The Problem with Scalable Solutions from the Start

As engineers, we often have a tendency to create scalable solutions right from the outset. Concepts like design patterns, microservices, and complex architectures are ingrained in our training. We envision systems that can effortlessly handle millions of users, but this is often more suitable for large corporations rather than startups.

In a startup environment, optimizing for scalability may merely lead to expensive delays. Many times, we find ourselves developing features for users who have yet to materialize or solving problems that may never arise. My 3-Month Rule encourages a different mindset—one where I prioritize simple, straightforward code that can be shipped quickly. This practice enables me to understand my users’ actual needs more clearly.

Current Infrastructure Practices: Why They Work

1. Consolidated Operations on a Single VM

I operate my entire infrastructure—database, web server, background jobs, Redis—on a single, affordable $40/month virtual machine. This approach sacrifices redundancy, relying on manual backups.

The takeaway? In just two months, I gained insights regarding my resource demand that no planning document could provide. It turns out, my “AI-focused” platform only utilizes 4GB of RAM at peak times. Had I pursued an elaborate Kubernetes setup, I would have only been managing idle containers.

When my system crashes (which has happened a couple of times), I receive valuable feedback about the points of failure—often surprising ones.

2. Hardcoded Configuration Settings

Configuration values are hardcoded throughout my code, such as:

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

Although this lacks the