The Three-Month Rule: A Pragmatic Approach to Learning Through Simple Solutions

In the tech world, we’re often reminded of Paul Graham’s famous adage: “Do things that don’t scale.” However, the challenge lies not just in understanding this advice but in translating it into actionable steps within our coding endeavors.

After dedicating eight months to developing my AI podcast platform, I’ve devised a straightforward framework to apply this principle: I give every unscalable workaround a lifespan of three months. At the end of this period, it either validates its worth and receives proper development or it gets the ax.

As engineers, we’re naturally inclined to aim for scalable solutions from the outset, focusing on sophisticated architectures and expansive microservices suitable for a large user base. However, in the startup realm, chasing after scalability too soon can turn into a form of costly procrastination. Often, we’re developing features for non-existent users and solving problems that may never appear. My three-month rule compels me to embrace simple, even “subpar” code that can be deployed quickly, providing real insights into user needs and behaviors.

Current Infrastructure Innovations: Why They Work

1. Consolidated Operations on One Virtual Machine

All my essential services—database, web server, background jobs, and caching—run on a single $40/month virtual machine with no redundancy and manual backups. This unconventional setup has provided insights into my resource requirements that far exceed any projected capacity planning. For instance, I discovered that my AI-driven platform requires only 4GB of RAM at peak usage. The complex Kubernetes framework I nearly implemented would have been overkill. Each crash has revealed unexpected breakdowns, offering invaluable data for refinement.

2. Hardcoded Configuration Parameters

Instead of maintaining separate configuration files, I use hardcoded constants distributed throughout my codebase. Making changes necessitates a redeployment, which might seem cumbersome but has its advantages. This approach allows me to quickly search for and track modifications, ensuring that every price change is documented in my version history. Over three months, I’ve only had to adjust these values a handful of times, saving countless hours of development work.

3. Utilizing SQLite for Production

Yes, my multi-user web application runs on SQLite, which handles up to 50 concurrent users seamlessly. My total database size is a mere 47MB, allowing for significant efficiency. I’ve learned that my access patterns lean heavily toward reads rather than writes, making SQLite an ideal choice. Had