Embracing the 3-Month Rule: A Pragmatic Approach to Non-Scalable Solutions

In the ever-evolving landscape of software development, the advice from renowned entrepreneur Paul Graham—“Do things that don’t scale”—often gets cited without much elaboration on its practical implementation in the realm of coding. After dedicating eight months to building my AI podcast platform, I’ve formulated a straightforward framework that champions this ethos: every non-scalable solution I employ has a lifespan of just three months. At the end of this period, each approach must either prove its worth and evolve into a more sophisticated solution or be retired altogether.

The startup Mindset: Building for Learning, Not for Scale

As engineers, we’re often ingrained with the mantra of crafting “scalable” architectures right from the outset. We indulge in sophisticated patterns, microservices, and distributed systems designed to accommodate millions of users. However, this mindset is typically suited for larger organizations. In the startup environment, striving for scalability can result in costly delays as you prematurely optimize for hypothetical user needs.

By applying my three-month rule, I am encouraged to build straightforward, albeit imperfect, solutions that can be deployed quickly. This approach not only facilitates immediate user feedback but also provides valuable insights into actual user requirements.

Current Infrastructure Hacks That Actually Work

1. Consolidating Everything on a Single VM

In my setup, the database, web server, background jobs, and caching are all hosted on a single $40/month virtual machine with zero redundancy. While this may seem reckless, it has proven to be a strategic decision. Within just two months, I gained clearer insights into my actual resource needs than any capacity planning document could provide.

The platform’s resource usage peaked at just 4GB of RAM, revealing that the complex Kubernetes architecture I was contemplating would have been unnecessary. When the server crashes—something that has happened twice—I am able to gather valuable data about the system’s vulnerabilities, which often differ from my initial expectations.

2. Hardcoded Configuration Values

Here’s a glimpse into my code:

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

Instead of using configuration files or environment variables, I’ve opted for hardcoded constants. While this might appear limiting, it offers an unexpected advantage: I can swiftly search my entire