The 3-Month Experiment: A Pragmatic Approach to Unscalable Solutions

In the startup landscape, the adage from Paul Graham, “Do things that don’t scale,” often echoes in the halls of innovation. However, translating this advice into actionable coding practices can be a challenge. After dedicating eight months to developing my AI podcast platform, I’ve stumbled upon a straightforward yet effective framework: any unscalable solution I implement is granted a lifespan of three months. At the end of this period, it either proves its worth and gets a solid foundation, or it’s time to say goodbye.

As engineers, we are instinctively drawn to crafting scalable solutions from the outset. We embrace design patterns, microservices, and distributed architectures, envisioning systems that can effortlessly handle vast numbers of users. While this mindset resonates with larger organizations, it can become an exercise in futility for startups. Often, investing time in scalable solutions means we’re delaying the inevitable, worrying about potential users who aren’t yet on board. My three-month framework encourages me to write straightforward, albeit “imperfect,” code that delivers real insights into my users’ needs.

A Look at My Current Infrastructure Hacks

Let’s dive into the specific strategies I’ve implemented, which may seem unconventional, but are surprisingly insightful and effective:

1. Streamlined Architecture on a Single VM

Right now, my entire setup—including the database, web server, background processes, and caching—lives on a single $40/month virtual machine. There’s no redundancy, and I manage manual backups to my local system.

Why is this beneficial? In just two months, I’ve garnered a deeper understanding of my resource needs than I could have from any elaborate capacity planning report. My “AI-driven” platform peaks at only 4GB of RAM, making my initial thoughts of a complex Kubernetes architecture seem unnecessary. Each crash has provided me with valuable data on failure points—often not where I expected.

2. Directly Hardcoded Configuration

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

By opting for hardcoded values rather than complex configuration files or environment variables, I can quickly search my entire codebase for any particular configuration. This approach allows me to wisely track price changes in git history and conduct code reviews—albeit self-reviews—for each update.

Rather than