Embracing the 3-Month Rule: A Strategic Approach to Unscalable Solutions

In the world of startups and software development, Paul Graham’s adage, “Do things that don’t scale,” is often quoted but rarely operationalized within coding practices. After spending eight months developing my AI podcast platform, I have formulated a straightforward strategy: every unscalable hack I implement is given a lifespan of three months. At the end of this period, each solution must either demonstrate its worth and evolve into a more robust system, or it will be discarded.

Engineers are typically conditioned to prioritize “scalable” solutions from the outset. We are taught to utilize design patterns, microservices, and distributed systems, creating architectures capable of accommodating millions of users. While this is certainly a valuable mindset for larger organizations, it can lead startups to invest in complex solutions prematurely. More often than not, pursuing scalability at the beginning translates to expensive delays, as we prepare for potential users who may never materialize. My three-month rule compels me to write direct, straightforward code that allows me to learn what my users genuinely require.

Current Infrastructure Hacks: A Smart Approach to Learning

1. All-in-One Virtual Machine Setup

I run my database, web server, background tasks, and Redis on a single virtual machine costing $40 a month. This setup lacks redundancy and depends on manual backups to my local system.

Why is this advantageous? The experience has revealed my actual resource needs far more accurately than any theoretical capacity planning could have. I learned that my “AI-heavy” platform typically peaks at just 4GB of RAM. The complex Kubernetes framework I almost implemented would have only involved managing idle containers.

When my system crashes—something that has occurred twice—I gain concrete insights into the specific failures. Interestingly, the causes have consistently been unexpected.

2. Simplified Hardcoded Configuration

My approach to configuration is straightforward:

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

With no config files or environmental variables, these hardcoded constants mean that any updates necessitate redeployment.

The advantage? I can quickly search my entire codebase for configuration values. Each price modification can be traced through git history, ensuring that every change receives code review—even if it’s just by myself.

While constructing a dedicated configuration service would take a