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.
One Comment
This perspective on the Three-Month Rule offers a compelling counterbalance to the typical emphasis on initial scalability. I appreciate how you’ve internalized the principle of “doing things that don’t scale” by setting a clear timeframe—three months—to test and validate your work. This approach not only accelerates learning but also prevents overinvesting in premature infrastructure that may not align with actual user needs.
Your practical examples resonate deeply. The consolidation of your infrastructure onto a single VM, despite its apparent fragility, provides tangible feedback that can guide smarter future scaling decisions. Likewise, the use of hardcoded configurations enables rapid iteration, which is crucial in a startup environment where speed often trumps perfection.
This framework underscores an essential startup mindset: prioritize quick deployment and real-world validation over complex, “future-proof” designs—at least initially. It reminds us that early simplicity and iterative experimentation are often the most effective pathways to building resilient, scalable systems over time. Looking forward to seeing how these tactics evolve as your platform grows!