The 3-Month Framework: A Practical Approach to Embrace the Unscalable

In the world of startups, the mantra “Do things that don’t scale” is often echoed, particularly among tech entrepreneurs. However, translating this concept into actionable strategies for coding can be quite a challenge. Having spent the last eight months developing my AI podcast platform, I’ve realized the importance of a structured approach to tackle this dilemma. I call it the “3-Month Rule”—a straightforward framework where each unscalable solution is given a three-month trial period. After this, it must either prove its worth and be integrated into the system, or it will be discarded.

The Challenge of Scalability in Startups

Many engineers are conditioned to create scalable systems from the outset—thinking in terms of microservices, intricate architectures, and robust databases that are designed for millions of users. However, this mindset can often lead to costly delays, particularly in a startup environment where you’re frequently optimizing for users that don’t even exist yet. This framework encourages me to focus on simplicity and practicality. It pushes me to produce straightforward, even “imperfect” code that can be deployed directly to learn what my users genuinely need.

Ingenious Infrastructure Decisions

Let me share some of the clever yet seemingly unconventional infrastructure choices I’ve made and why they’ve proven to be advantageous:

1. Consolidation on a Single VM

Rather than spreading my resources thin, I run my entire stack—database, web server, background jobs, and caching—on a single, $40/month VM without redundancy. This might seem risky, but my experience has yielded invaluable insights about my actual resource needs. In just two months, I learned that my AI platform has a peak memory usage of 4GB, rendering a complex Kubernetes setup unnecessary. Additionally, when my system does crash (which has happened a couple of times), I gain concrete data about failures that were not anticipated.

2. Hardcoded Configuration Values

My configuration consists of hardcoded values like:

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

By eliminating configuration files and environment variables, I can quickly scan my codebase for any setting using basic search commands. With only three changes in three months, the traditional approach of cultivating a configuration service would have wasted significant engineering hours—especially for