Embracing Imperfection: My 3-Month Experimentation Framework for Developing Non-Scalable Solutions

When it comes to building products, especially in the startup space, the common wisdom often leans heavily on the mantra popularized by Paul Graham: “Do things that don’t scale.” However, the challenge lies in translating this philosophy into actionable strategies, particularly when it comes to coding.

Over the past eight months, while developing my AI podcast platform, I’ve crafted a straightforward yet effective framework: every non-scalable solution is given a lifespan of three months. After this period, the solution must either demonstrate its value and be upgraded to a more robust architecture, or it is destined for removal.

The startup Paradox: Scalable Solutions vs. Immediate Needs

As engineers, we are conditioned to prioritize scalable solutions from the outset—think of intricate design patterns, microservices, and distributed systems that cater to millions of users. However, this approach can often lead to overengineering, especially in a startup environment where funds are tight and immediate user feedback is paramount.

In many cases, focusing too much on scalability means preparing for challenges that may never arise, while neglecting the real issues at hand. My 3-month rule compels me to write straightforward, sometimes “imperfect,” code that can be quickly deployed. This method reveals what users genuinely require.

My Current Non-Scalable Strategies and Their Benefits

1. Centralized Operations on a Single VM

I’ve opted to host my database, web server, background jobs, and Redis all on a single, $40-per-month virtual machine. While this setup lacks redundancy and relies on manual backups, it has provided remarkable insights into my actual resource needs in just two months.

I’ve discovered that, contrary to initial expectations, my “AI-heavy” platform only requires a maximum of 4GB of RAM. The complex Kubernetes architecture I was tempted to build would have only served to manage nonexistent capacity. Each crash—two so far—has offered me empirical data about the points of failure. Without fail, the issues consistently surprise me.

2. Hardcoded Configuration

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

I’ve kitted out my code with constants instead of sophisticated configuration files or environment variables. While some may view this as subpar practice, the reality