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

In the world of startup culture, Paul Graham’s adage of “doing things that don’t scale” often resonates but lacks practical guidance, especially in the realm of software engineering. After eight months of developing my AI podcast platform, I’ve devised a straightforward framework: every non-scalable trick gets a lifespan of three months. After this trial period, it either proves its utility and gets polished or is promptly discarded.

The Mindset Shift: Challenging Conventional Engineering Practices

As engineers, we are ingrained with the importance of developing scalable solutions right from the outset. We often dive into designing intricate architectures, employing patterns like microservices and distributed systems to accommodate potentially millions of users. While these methods are beneficial in larger corporations, at the startup level, pursuing scalability too early can become a costly form of procrastination.

The 3-month rule encourages me to focus on creating straightforward code that addresses immediate user needs rather than wasting energy on hypothetical problems. Here are some unorthodox strategies I’ve implemented that have proven to be surprisingly effective:

1. Consolidated Operations on a Single VM

Currently, I’ve deployed everything—database, web server, background jobs, and caching—on one $40/month virtual machine. It’s a no-fail strategy with no redundancy and manual backups to my local device.

So, why is this a smart move? In just two months, I’ve gained valuable insights into my actual resource requirements. My “AI-heavy” platform peaks at 4GB of RAM, and the complex Kubernetes infrastructure I had considered would have only led to wasted resources. Each time the system crashes (there have been two episodes), I gather real data on failure points, revealing surprises about what goes wrong.

2. Hardcoded Configuration Values

Instead of creating configuration files or relying on environment variables, I’ve opted for hardcoded constants, like:

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

Every change necessitates a redeployment, but the real advantage is traceability. With a simple grep, I can efficiently locate any config value within seconds. In three months, I have only modified these settings a few times, saving countless hours that would have been consumed by a complex configuration