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

In the startup world, the well-known advice from Paul Graham—”Do things that don’t scale”—is often acknowledged but not fully understood in terms of practical application, particularly in software development. After spending eight months building an AI podcast platform, I’ve created a straightforward framework that has transformed my approach to coding: I allocate a lifespan of three months for every unscalable hack I implement. If it proves its worth, I’ll invest the time and resources to develop it properly; if not, it will be phased out.

As software engineers, our training often leads us to focus on scalable solutions right from the start. Concepts like design patterns, microservices, and distributed systems are central to creating robust architectures capable of supporting millions of users. However, this mindset is often more suited for established enterprises than for startups.

In a startup environment, striving for scalability can often result in costly delays, as it prioritizes hypothetical future users over present needs. My three-month rule compels me to create straightforward, functional code that enhances my understanding of user requirements.

Embracing Pragmatic Infrastructure Hacks: My Insights

1. A Single Virtual Machine (VM) for Everything

Currently, my entire setup—including the database, web server, and background jobs—operates on one $40/month VM without redundancy. While this may sound risky, the insights I’ve gained about my actual resource requirements in just two months are invaluable. For instance, I discovered my “AI-heavy” platform peaks at 4GB of RAM, an observation that likely would have eluded me with a more complex architecture like Kubernetes, which I had almost implemented.

When the system crashes—which has happened twice—I receive immediate and useful data about what truly fails, and it’s rarely what I anticipate.

2. Hardcoded Configurations

My configuration settings are hardcoded directly into the codebase:

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

I have no configuration files or environment variables to manage. While this approach may seem suboptimal, it provides the advantage of rapid retrievability; I can quickly search the entire codebase for specific configuration values. In three months, I’ve only made a few changes, which only required about 15