Embracing the 3-Month Rule: A Practical Approach to Non-Scalable Development

In the world of startup development, the common adage from Paul Graham rings true: “Do things that don’t scale.” However, the challenge lies in translating this advice into actionable steps within coding. After eight months of developing my AI podcast platform, I’ve devised a straightforward framework that I call the “3-Month Rule.” This method gives every unscalable hack a three-month trial period to prove its worth. If it demonstrates value, it gets a proper upgrade; if not, it gets retired.

As engineers, we have been conditioned to prioritize scalability from the outset. We often find ourselves immersed in design patterns, microservices, and distributed systems—all of which cater to the needs of large-scale applications. Yet, for startups, focusing on scalable solutions too early can lead to wasted resources as we chase users that may never join our platform. My 3-Month Rule encourages me to write simple, albeit less-than-perfect code that can be deployed quickly, allowing me to understand what users truly need.

Current Infrastructure Strategies That Work for Me

1. Consolidation on a Single Virtual Machine

Everything—from the database to the web server and background jobs—resides on a single $40/month virtual machine. This setup lacks redundancy, and I only perform manual backups to my local machine.

The brilliance of this approach lies in the insights I’ve gained about my actual resource needs in just a couple of months. My platform, which I initially perceived as resource-intensive, rarely requires more than 4GB of RAM. In hindsight, the complex Kubernetes architecture I nearly implemented would have meant managing empty containers.

When my system has gone down (twice so far), I’ve received invaluable information about failure points—often unexpected ones.

2. Simplistic Hardcoded Configurations

My configurations look something like this:

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

Instead of using configuration files or environment variables, I’ve opted for constants embedded throughout my code. This means that any change requires a redeployment, making every adjustment a tracked event in my git history.

While establishing a configuration service would take time, I’ve made only three changes in the last three months, translating into 15 minutes of redeployment versus 40 hours of development.

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