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The 3-Month Rule: My Technical Framework for Doing Things That Don’t Scale Variation 383

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

In the startup world, the mantra “Do things that don’t scale” popularized by Paul Graham is often shared yet rarely dissected, especially in technical contexts. After spending eight months developing my AI podcast platform, I’ve crafted a straightforward framework inspired by this principle: every unscalable shortcut is given three months to prove its worth. At the end of that period, it either becomes a foundational component or is discarded.

The startup Engineer’s Dilemma

As software engineers, we frequently lean towards creating scalable systems right from the outset. We’re drawn to robust and complex architectures, including microservices, distributed systems, and other solutions designed for handling substantial user volumes. However, for startups, these scalable solutions can often turn into costly delays, addressing hypothetical future issues rather than current user needs.

My three-month rule compels me to adopt a pragmatic approach. I focus on writing straightforward, albeit “less refined,” code that can be deployed quickly, thereby gaining insights into what users genuinely require.

Examples of My Current Infrastructure Tweaks

Here are a few of my key decisions and the reasons why they have been beneficial:

1. Consolidation of Resources on a Single VM

I operate my entire platform—database, web server, background jobs, and Redis—on a single virtual machine costing just $40 per month. While this approach lacks redundancy and requires manual backups, it has provided invaluable lessons about my real needs. Within two months, I’ve learned that my “AI-intensive” platform barely uses 4GB of RAM, saving me from the complexities of setting up Kubernetes that wouldn’t have served any real purpose given my user load. Each crash has delivered genuine insights into what breaks and why.

2. Hardcoded Configuration Values

Configuration settings are embedded directly in my code as constants rather than stored in separate files or environment variables. While this may seem outdated, it allows me to swiftly search for any config value throughout my entire codebase and keeps a clear history via Git. With changes occurring only a few times in three months, this method has significantly reduced the time spent redeploying compared to building a configuration management service.

3. Using SQLite in Production

Currently, my application runs on SQLite, a choice that supports its lightweight, efficient nature. With a database size of only 47MB and the capability to manage 50 concurrent

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