The 3-Month Rule: A Pragmatic Approach to Non-Scalable Solutions in Development

In the tech startup world, the mantra often repeated is Paul Graham’s insightful advice: “Do things that don’t scale.” However, the practical application of this principle in programming often remains uncharted territory. Having spent the past eight months developing my AI podcast platform, I have honed a straightforward yet effective framework: any unscalable solution I employ is given a lifespan of just three months. At the end of this period, it must either demonstrate its worth and evolve into a robust implementation or be eliminated from the project.

As engineers, we are often conditioned to create scalable systems from the outset, diving deep into design patterns, microservices, and distributed architectures—all geared toward accommodating vast numbers of users. Yet, this approach tends to reflect a mindset suited for larger enterprises.

In a startup environment, developing scalable solutions can frequently lead to costly delays. We find ourselves optimizing for future users who may never materialize, addressing challenges that may not even be applicable. My three-month rule encourages me to prioritize practicality over perfection, enabling me to write straightforward, even “low-quality,” code that is promptly deployed, providing invaluable insights into actual user needs.

My Current Infrastructure Hacks: Simplicity and Smarts

1. Single VM for Everything

I currently host my entire stack—a database, web server, background jobs, and Redis—on a solitary $40/month virtual machine. While this approach lacks redundancy and relies on manual backups to my local machine, it has been surprisingly enlightening. Within just two months, I’ve gained more insights into my resource requirements than I could have gleaned from any planning document. It turns out my platform, which I anticipated would be resource-intensive, only peaks at 4GB of RAM. The complex Kubernetes setup that I nearly implemented would have just been a futile exercise in managing idle resources. When the VM has gone down (twice!), I’ve received concrete data about the actual failure points—none of which aligned with my expectations.

2. Hardcoded Configuration

My configurations are directly embedded in the code, such as:

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

No external config files or environment variables—just constants interspersed throughout the codebase. While this may seem haph