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

In the world of startups and technology, a common piece of wisdom often circulated by industry influencer Paul Graham is, “Do things that don’t scale.” While this advice is widely recognized, the conversation rarely delves into actionable strategies for implementing it in software development. After dedicating the last eight months to building my AI podcast platform, I devised a straightforward framework: each unscalable hack is allotted a lifespan of three months. At the end of this period, we assess whether it has proven its worth and warrants further development, or if it’s time to let it go.

As engineers, our instinct is often to architect “scalable” solutions right from the outset. We consider design patterns, microservices, and distributed systems—beautiful architectures ready to accommodate millions of users. However, this line of thinking can be detrimental in a startup environment where too much focus on scalability can translate into delays and unnecessary complexity.

The reality is that in a startup, prioritizing scalable code can lead to costly delays, as it often addresses hypothetical user needs rather than real ones. My three-month framework pushes me to focus on creating straightforward, albeit imperfect, code that actually delivers value and helps me understand user requirements more clearly.

Current Infrastructure Hacks: Smart Decisions in Disguise

1. Consolidated Infrastructure on a Single VM

I’ve chosen to consolidate my database, web server, background jobs, and Redis on a single $40 per month virtual machine. This setup lacks redundancy and relies on manual backups to my local machine.

Why is this a strategic move? Over the past two months, I’ve gained invaluable insights into my resource needs that any capacity planning document wouldn’t have revealed. For instance, my platform, which is centered around AI, peaks at just 4GB of RAM. The Kubernetes architecture I nearly implemented would have left me managing idle containers instead of responding to actual user demands and usage patterns.

2. Hardcoded Configuration

Configurable values are hardcoded throughout my codebase. For example:

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

Although this approach lacks flexibility, it has a hidden advantage: I can easily search for any configuration value in moments. Each price change is documented in git history, and every