Embracing the 3-Month Rule: A Practical Approach to Non-Scalable Development

When it comes to the startup world, one often cited piece of wisdom comes from entrepreneur Paul Graham: “Do things that don’t scale.” Yet, translating this philosophy into tangible coding practices is often overlooked. Having dedicated nearly eight months to developing my AI podcast platform, I’ve created a straightforward framework that guides my approach: every unscalable workaround is given a lifespan of just three months. At the end of this period, these temporary solutions either prove their worth and evolve into robust features, or they are discarded.

The startup Mindset: Rethinking Scalability

As engineers, we’re often conditioned to think in terms of scalability from the outset—embracing design patterns, microservices, and distributed architectures capable of supporting millions. However, this is a mentality more suited to larger corporations. For startups, investing heavily in scalable solutions can amount to costly procrastination, as it focuses on potential users that don’t yet exist and preempts challenges that may never arise. My three-month rule encourages me to embrace simpler coding practices that allow me to expedite delivery and gain real insight into user needs.

Ingenious Yet Simple Infrastructure Hacks

Here are some of the unconventional methods I’ve employed in my development process, demonstrating that “bad” code can be a strategic asset:

1. Consolidated Operations on a Single VM

My entire service—database, web server, background jobs, and caching—runs on a single $40/month virtual machine. While this might sound reckless and lacking in redundancy, it has provided me keen insights into my actual resource consumption. I’ve quickly discovered that my AI-driven platform peaks at just 4GB of RAM. The complex Kubernetes setup I nearly implemented would have been an exercise in futility, managing idle containers. The few crashes I’ve experienced offered valuable data on breaking points, often revealing surprises.

2. Hardcoded Configuration for Simplicity

Instead of using configuration files or environment variables, I’ve opted for hardcoded constants:

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

Altering these values requires a redeployment, but this deliberate setup allows for unprecedented simplicity. I can swiftly locate any config value across my codebase, and all changes are tracked within my version history. A configuration service that might take