Embracing the Three-Month Experiment: A Pragmatic Approach to Non-Scalable Solutions

In the world of tech startups, the mantra from industry experts like Paul Graham is well-known: “Do things that don’t scale.” However, translating this philosophy into actionable coding practices can be challenging. After spending eight months developing my AI podcast platform, I’ve cultivated a straightforward framework: any unscalable solution is given three months to prove its worth. After this period, it either evolves into a robust feature or is discarded.

As engineers, our inclination is often to design for scalability right from the start. We dive into complex architecture involving design patterns, microservices, and distributed systems, which are ideal for serving millions of users. But this line of thinking is more suited for large corporations than agile startups.

In a startup environment, obsessing over scalability can lead to unnecessary delays and resource misallocation. You might find yourself optimizing for users who haven’t yet materialized, addressing challenges that may never arise. My three-month rule compels me to employ straightforward, albeit “imperfect,” code that can be deployed quickly, allowing me to uncover the genuine needs of my users.

Ingenious Hacks That Reveal Valuable Insights

1. Consolidating on a Single VM

My entire platform—database, web server, background jobs, and caching—operates on one budget-friendly $40/month virtual machine (VM). While this approach has no redundancy and relies on manual backups to my local system, it comes with a silver lining: I’ve gained invaluable insights into my resource requirements far more effectively than any capacity report could provide. I’ve learned that my application peaks at 4GB of RAM, confirming that my original plans for a complex Kubernetes architecture were unnecessary.

Yes, there have been crashes (two so far), but these incidents have given me real-time data about where issues arise—often in unexpected areas.

2. Embracing Hardcoded Configurations

Instead of relying on external configuration files or environment variables, I opted for hardcoded constants:

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

This may sound inefficient, but the efficiency lies in my ability to quickly search my codebase for any specific value using simple command-line tools. Changes necessitate a redeployment, but my frequency of updates has only been