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

In the world of technology startups, a familiar piece of advice often cited is Paul Graham’s mantra: “Do things that don’t scale.” While the concept is widely acknowledged, the execution—especially in the realm of coding—remains a topic that isn’t discussed as frequently.

Having spent the past eight months developing my AI podcast platform, I’ve established a straightforward framework: every unscalable hack I implement is given a lifespan of three months. After this period, I assess its value; if it demonstrates enough merit, it will be properly developed; otherwise, it gets retired.

Why Scalable Solutions Can Be a Trap

As engineers, we often emphasize building scalable solutions right from the outset. Concepts such as design patterns, microservices, and distributed systems serve as cornerstones in crafting architectures capable of handling millions of users. Yet, this “big company” mindset can lead to unnecessary complexity in the early stages of a startup.

In reality, striving for scale too soon can result in costly delays and wasted resources. My three-month rule encourages me to write straightforward, albeit imperfect, code that can be deployed quickly, allowing me to gain invaluable insights into the needs of my users.

Current Infrastructure Hacks: Simple Yet Effective

1. Utilizing a Single Virtual Machine

My entire stack—database, web server, background jobs, and caching—runs on a single $40/month virtual machine. While it may seem reckless, this approach taught me more about my resource requirements in two months than any complex capacity planning document could have revealed. As a result, I discovered that my platform’s “AI-heavy” operations peaked at just 4GB of RAM. The sophisticated Kubernetes setup I nearly implemented would have been wasted on idle containers.

Each crash (and there have been a couple) has provided me with real-time insights into failure points, exposing issues I never anticipated.

2. Hardcoded Configuration

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

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

While this might seem inefficient at first glance, it allows for rapid changes—redeploying only takes 15 minutes versus the potential