Embracing the 3-Month Rule: A Pragmatic Approach to Coding in Startups

In the realm of startup development, one principle often echoes in founder circles: “Do things that don’t scale,” as offered by entrepreneur Paul Graham. While the advice is widely known, the implementation within the technical landscape is rarely discussed. After dedicating eight months to creating my AI podcast platform, I’ve arrived at a straightforward yet powerful framework: every unscalable hack remains in place for exactly three months. Post this period, each hack must validate its worth or face removal.

Rethinking Scalability in Startups

Engineers are instinctively trained to prioritize scalable solutions from the outset. Concepts like design patterns, microservices, and distributed systems represent the gold standard for handling extensive user bases. However, this mindset can be counterproductive in a startup environment. Focusing on scalability too early may lead to unnecessary complexity, as you may be envisioning solutions for users that aren’t even present yet.

My 3-month rule encourages me to lean into straightforward, albeit “imperfect,” coding practices that get my product to market swiftly. This approach ultimately helps me gather real insights into user behavior and needs.

Ingenious Hacks: My Current Infrastructure

Here are some unconventional hacks I’ve implemented, each of which serves a strategic purpose:

1. Consolidated Infrastructure on a Single VM

I’ve chosen to operate everything—a database, web server, background jobs, and caching mechanism—on a single $40/month VM. This design lacks redundancy and relies on manual backups.

Why is this an astute choice? In just two months, I have gained insights into my actual resource requirements that far outweigh anything predicted by capacity planning documents. My platform’s peak usage barely exceeds 4GB of RAM. The complex Kubernetes setup I nearly adopted would have ended up overseeing dormant containers.

Each time the system fails (which occurred twice), I receive crucial data on the real pain points—often surprising and educational.

2. Simplified Hardcoded Configuration

Throughout my codebase, you’ll find constants like:

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

Instead of utilizing config files or environment variables, everything is hardcoded. Modifying any value necessitates a redeployment.

This approach unveils a hidden advantage: Searching for specific