Embracing the Three-Month Rule: A Pragmatic Approach to startup Development

In the world of startups, the mantra “Do things that don’t scale” often echoes from the lips of influential figures like Paul Graham. However, it’s one thing to recite that wisdom and another to put it into practice, especially in the realm of software development.

After dedicating eight months to building my AI podcast platform, I’ve crafted a straightforward strategy: any non-scalable solution is given a lifespan of just three months. After this period, I assess its performance; it either evolves into a robust feature or gets phased out.

The Challenge of Scalable Solutions

As engineers, we often find ourselves fixated on creating scalable solutions right from the get-go. The allure of sophisticated design patterns, microservices, and distributed systems makes it tempting to idealize architecture that can support millions of users. But in a startup environment, this mindset can lead to unnecessary complexity and delays, effectively delaying actual growth while attempting to appease users who haven’t even arrived yet.

My three-month rule encourages simplicity. It pushes me to produce direct, uncomplicated code that delivers results and reveals what my users truly require.

Current Infrastructure Hacks That Work Wonders

1. Single Virtual Machine Setup

Currently, my entire infrastructure runs on a single virtual machine (VM) costing $40 a month. This includes the database, web server, background jobs, and Redis, with no redundancy and manual backups to my local device.

Why is this approach beneficial? Within just two months, I’ve gained insights into my actual resource requirements that no planning document could ever provide. I’ve discovered that my platform’s peak usage reaches only 4GB of RAM, negating the need for the intricate Kubernetes setup I initially considered—one that would have only managed empty containers.

When my system crashes (which it has twice), I gain tangible insights into what truly fails—often surprising and educational.

2. Hard-Coded Configurations

Consider this configuration system:

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

I’ve opted for hard-coded constants scattered throughout the code instead of sophisticated configuration files or environment variables. While this requires a redeployment for any changes, it allows me to quickly locate any value across my codebase.

Creating a dedicated configuration service would take a week, but I’ve