The Three-Month Rule: A Practical Approach to Scaling in startup Development

When it comes to startup development, one piece of wisdom often echoed is Paul Graham’s suggestion: “Do things that don’t scale.” However, implementing this advice in a coding context is seldom discussed. Having spent eight months developing my AI podcast platform, I’ve formulated a straightforward framework to put this principle into practice: every unscalable workaround I create is given a lifespan of just three months. If it proves its worth in that time, I’ll refine it; if not, it gets the axe.

As engineers, we are frequently taught to prioritize “scalability” from the outset. We dream in designs featuring microservices and distributed systems—architectures intended to handle vast numbers of users. Yet, this line of thinking often belongs to larger organizations with extensive resources. In the startup realm, creating scalable code can sometimes be an exercise in delaying the inevitable. By focusing on potential future users and hypothetical problems, we risk wasting precious time. Solving this dilemma, my three-month rule compels me to produce straightforward, albeit imperfect, code that actually gets deployed, allowing me to discover genuine user needs.

Current Infrastructure Hacks: Smart Decisions in Simplicity

1. Consolidated Virtual Machine Deployment

For just $40 a month, I run my database, web server, background jobs, and even Redis on a single virtual machine. Yes, there is zero redundancy, and I manage manual backups to my local machine.

Why is this a clever approach? In just two months, I have gained invaluable insights regarding my resource requirements that no capacity planning document could provide. My AI-driven platform reaches a peak of 4GB RAM usage. The complex Kubernetes setup I initially considered would have involved managing containers that were essentially wasted space.

When the system crashes—as it has twice—I gather tangible data on failures. And unsurprisingly, the breakdowns occur in places I’d never anticipated.

2. Simplified Hardcoded Configurations

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

Instead of relying on configuration files or environment variables, I utilize hardcoded constants throughout my files. Updating these values requires a redeployment.

The strength of this method lies in its simplicity: I can quickly search my entire codebase for any configuration value in moments.