Embracing the 3-Month Rule: An Innovative Approach to Scalable Development

When it comes to startup culture, one piece of advice resonates with budding entrepreneurs and developers alike: “Do things that don’t scale,” as highlighted by Paul Graham. While this idea is widely acknowledged, understanding how to effectively implement it in technical contexts often remains elusive.

After eight months of painstakingly constructing my AI podcast platform, I have devised a straightforward system: any unscalable solution or hack is granted three months to prove its worth. After this period, we either transition it to a robust implementation or retire it altogether.

Rethinking Scalability in Startups

In the engineering world, we frequently strive to design solutions that can handle vast user bases right from the outset—think microservices, distributed systems, and intricate architectures. However, this approach often caters to big companies rather than the nimble environment of a startup. Frequently, the pursuit of scalability can hinder progress, leading us to optimize for users who may never materialize and tackle issues that might not even exist.

My three-month rule mandates that I focus on simple, straightforward code—code that may not be “best practice” but actually gets deployed. This iterative approach allows me to discern user needs in real-time.

Current Infrastructure Strategies: Smart Hacks that Work

1. Consolidated Operations on a Single VM

At present, my entire platform functions on a single $40/month virtual machine that hosts everything from the database to background jobs. While lacking redundancy and relying on manual backups, this arrangement has provided invaluable insights into my actual resource requirements. I have learned, for instance, that my platform’s peak resource consumption is only 4GB of RAM. An intricate Kubernetes configuration would have been overkill.

When the server experiences a crash (which has happened a couple of times), I gain concrete data about the failure points—often surprising insights into what actually goes wrong.

2. Simple Hardcoded Configurations

My configuration approach is refreshingly simplistic: constants are hardcoded directly into the codebase without the complications of config files or environment variables. Any change necessitates a straightforward redeployment, which I’ve done only three times in three months. This allows me to track every modification through version history and keeps my focus on the code rather than on deployment intricacies.

3. Favoring SQLite for Production Use

Surprisingly, I have chosen SQLite as the backbone of my multi-user application. Despite being