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

In the tech world, a well-cited piece of advice from Paul Graham resonates deeply: “Do things that don’t scale.” However, translating this philosophy into tangible processes, particularly in coding, is rarely discussed. After dedicating eight months to developing my AI podcast platform, I’ve devised a straightforward framework that has transformed my approach to seemingly unscalable solutions: the 3-Month Rule.

The Core Concept of the 3-Month Rule

As software engineers, we are often conditioned to aim for scalable solutions from the very beginning. We fall into the trap of employing sophisticated architecture—complete with design patterns, microservices, and distributed systems—tailored for applications with millions of users. However, in a startup environment, this prioritization of scalability can lead to costly delays and frustration. Instead of creating solutions for non-existent users, my 3-Month Rule encourages the development of straightforward and sometimes imperfect code that actually delivers results and provides critical insights into user needs.

Unpacking My Current Infrastructure Hacks

1. Consolidation on a Single Virtual Machine (VM)

At the moment, my entire system operates on one $40/month VM hosting the database, web server, background jobs, and Redis. While this setup lacks redundancy and involves manual backups to my local machine, it has yielded invaluable insights regarding my resource consumption over the past couple of months. Surprisingly, my “AI-focused” platform typically utilizes only 4GB of RAM, negating the need for an elaborate Kubernetes infrastructure I once contemplated. Each system crash (which has occurred twice) has provided me with critical data on failure points, often revealing unexpected problems.

2. Hardcoded Configuration Values

My codebase is filled with hardcoded constants—no 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"

While this may seem primitive, the approach has a hidden advantage: I can quickly search my entire codebase for configurations in seconds. Keeping track of price adjustments in git history simplifies oversight, turning changes into brief redeployment tasks instead of extensive engineering projects.

3. Utilizing SQLite in Production

Yes, SQLite is the database powering my multi-user web application. With a modest size of