Embracing Imperfection: The 3-Month Rule for Scalable Solutions

In the world of startups and tech innovation, the advice from Paul Graham to “do things that don’t scale” often resonates. However, the execution of this principle, especially in coding, is rarely discussed. Over the past eight months, while developing my AI podcast platform, I’ve created a straightforward method to apply this notion: I allow every unscalable workaround a lifespan of just three months. At the end of this period, each solution must either prove its worth and undergo proper development or be discarded.

This approach stems from a critical realization: as software engineers, we are frequently trained to prioritize scalable solutions from the outset. While it’s tempting to design intricate systems with microservices and sophisticated architecture to accommodate millions of users, this mindset can lead to unnecessary complexity, particularly in a startup environment. Often, focusing too heavily on scalability means delaying progress and avoiding immediate user needs.

The Power of Iteration: My Current Infrastructure Strategies

Here’s a glimpse into the unorthodox strategies I’m employing and the valuable lessons I’ve learned from each:

1. Single-VM Infrastructure

I’m currently running my entire stack—database, web server, background tasks, and caching—on a single $40/month virtual machine. This setup lacks redundancy and relies on manual backups to my own machine.

Why is this effective? Within two months, I’ve gained clearer insights into the actual resource consumption of my platform rather than relying on theoretical capacity planning. For instance, I discovered that my “AI-heavy” platform peaks at 4GB of RAM. The complex Kubernetes architecture I contemplated would have meant managing empty containers instead of addressing real issues.

In the event of system crashes—two occurrences thus far—I have received tangible data on failures, which often differ from my initial expectations.

2. Simplistic Configuration Management

I utilize hardcoded constants for configurations rather than dedicated configuration files or environment variables. For instance:

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

Although this may seem unrefined, it simplifies the process significantly. Every price or configuration change is easily traceable through git, and adjustments require minimal redeployment time compared to developing a full-featured configuration service.

3. SQLite for a Multi-User Application

Running