The 3-Month Experiment: A Practical Approach to Building Non-Scalable Solutions

In the world of startups, the renowned advice from Paul Graham—“Do things that don’t scale”—is often echoed, yet few discuss how to effectively apply this principle in the realm of software development. After eight months of developing my AI podcast platform, I’ve formulated a unique framework to embrace this concept: every approach that lacks scalability is given a lifespan of just three months. At the end of this period, I evaluate whether it has demonstrated its worth and warrants a more robust implementation, or if it should be discarded.

The Challenge of Engineering for Scale

As engineers, our instinct is to construct scalable solutions from the get-go. We are well-versed in elaborate architectures, such as design patterns and distributed systems, which are intended to accommodate millions of users. However, this mindset often aligns with big corporate strategies, which may not be suitable for a startup’s fast-paced environment.

In smaller ventures, focusing solely on scalability can lead to costly delays, as you’re often optimizing for hypothetical users, tackling non-existent issues. My three-month experiment encourages me to adopt a simpler, more direct coding approach that actually gets deployed, providing a clearer understanding of user needs.

Current Infrastructure Hacks: Insights from Simplicity

1. One Virtual Machine for Everything

Instead of a complex setup, I’ve opted to run my database, web server, background jobs, and caching on a single $40/month virtual machine. While this means I sacrifice redundancy and rely on manual backups, it has yielded valuable insights. In just two months, I discovered that my platform’s peak usage required only 4GB of RAM, disproving the necessity of an intricate Kubernetes configuration.

Furthermore, when my system has crashed—twice—I gathered critical data about the failure points, revealing unexpected insights.

2. Hardcoded Constants for Configuration

I rely on hardcoded constants throughout my code:

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

While this might seem impractical, the simplicity allows me to quickly search and track changes in my configuration. With only three adjustments made in three months, the time spent redeploying is a fraction of what a dedicated configuration service would have demanded.

3. SQLite as a Production Database

Utilizing SQLite for my multi-user