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

In the tech world, there’s a well-known mantra from Paul Graham: “Do things that don’t scale.” This advice is often echoed in startup circles, yet the implementation of this philosophy in the realm of coding is rarely dissected. After dedicating the past eight months to developing my AI podcast platform, I’ve devised a straightforward framework: any unscalable workaround gets a lifespan of three months. At the end of this period, it either proves its worth and is further developed, or it is discarded.

As software engineers, we are conditioned to design scalable architectures from the outset—think intricate design patterns, microservices, and distributed systems aimed at accommodating millions of users. While this approach fits larger corporations, it often leads to wasted efforts in a startup environment. Focusing on scalable solutions can simply mean delaying implementation for problems that may never arise. My 3-month guideline compels me to write straightforward, imperfect code that delivers results and, more importantly, reveals the actual needs of my users.

Current Infrastructure Shortcuts and Their Value

1. Single VM Deployment

Currently, all my operations—including the database, web server, background jobs, and caching—are running on a single $40/month virtual machine. There is no redundancy and backups are performed manually.

This setup may seem inadequate, but it has proven invaluable. In just a couple of months, I’ve gained insights into my real resource usage that any planning document could not have provided. My platform, despite its AI focus, only utilizes about 4GB of RAM at peak times. The complex Kubernetes architecture I had contemplated would have ended up managing a multitude of unused containers.

When the system crashes (which it has done a couple of times), I receive genuine data about the failure points—usually in ways I never anticipated.

2. Hardcoded Configuration

Instead of relying on configuration files or environment variables, I’ve employed hardcoded constants throughout my codebase, such as:

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

While this might not be the most elegant solution, it provides an unexpected advantage: I can quickly search the entire codebase for any configuration value. Each change is tracked in Git history, ensuring a clear understanding of updates,