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

In the realm of startups and software development, the well-known advice from Paul Graham to “do things that don’t scale” often meets with skepticism, especially among engineers. While the mantra is widely acknowledged, implementing it effectively can feel elusive—particularly within the coding community.

After eight months of developing my AI podcast platform, I’ve cultivated a straightforward yet powerful framework: every non-scalable solution is given a dedicated three-month trial. If it proves its value, it will be refined into a sustainable model; if not, it will be discarded.

The Challenge of Scalable Solutions

As engineers, we are trained to think in terms of scalable solutions from the outset. We focus on design patterns, microservices, and robust distributed systems—all of which are crucial for handling large user bases. However, this mindset is often more suited to large corporations than to agile startups.

In a startup environment, over-engineering can lead to unnecessary delays and overly complex solutions designed for hypothetical user bases that don’t yet exist. My three-month rule encourages me to prioritize straightforward, sometimes imperfect, code that enables actual deployment and provides critical insights into user needs.

My Strategic Infrastructure Hacks

1. Consolidated Operations on a Single VM

All aspects of my application—database, web server, background jobs—run on a single $40/month virtual machine (VM) without redundancy or extensive backups.

Why is this a smart move? In just two months, I’ve gained invaluable insights into my resource needs, learning that my “AI-intensive” platform typically operates with just 4GB of RAM. The complex Kubernetes setup I nearly pursued would have required management of empty containers, yielding little benefit. When the system crashes (which has happened on two occasions), the data I collect highlights unexpected breaking points.

2. Simplified Hardcoded Configurations

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

Instead of utilizing configuration files, I rely on hardcoded constants dispersed throughout the code. Changing a single value necessitates a redeploy, but this approach has revealed its own benefits. I can quickly search for any configuration across my codebase, and all changes are meticulously tracked. Building a dedicated configuration service would consume about a week; yet, I’ve