Embracing the 3-Month Experiment: A Technical Framework for Learning in startup Development

In the world of startups, the mantra “Do things that don’t scale,” as famously advised by Paul Graham, is often recited, yet seldom discussed in practical terms—especially when it comes to coding. Drawing from my eight months of experience building an AI-driven podcast platform, I’ve developed a straightforward but effective framework: any temporary, non-scalable hack gets a lifespan of just three months. At the end of this period, it will either demonstrate its value and be transformed into a robust solution, or it will be discarded.

Rethinking Scalability

As developers, we’re often trained to implement scalable systems from the onset—think design patterns, microservices, and distributed architectures capable of supporting massive user bases. However, in a startup environment, focusing on scalability prematurely can lead to costly delays and wasted resources. Often, creating a scalable solution means optimizing for hypothetical users and addressing issues that may never arise. By applying my three-month rule, I deliberately prioritize simplicity and speed in my coding. This approach allows me to release a product that provides authentic insights into user needs rather than assumptions.

My Pragmatic Infrastructure Hacks

Here are some of the unconventional choices I’ve made and the valuable lessons they’ve imparted:

1. Consolidated Infrastructure on One VM

I run everything—databases, web servers, background jobs, and caches—on a single virtual machine for a modest subscription fee. While this creates zero redundancy and relies on manual backups, it has offered invaluable insights into my resource usage. In merely two months, I learned that my AI-centric platform typically requires only 4GB of RAM. The intricate Kubernetes setup I almost constructed would have merely led to managing idle resources.

When outages happen—and they have—the data I gather reveals unexpected breaking points.

2. Hardcoded Configuration Values

My project utilizes straightforward constants for configuration, with no separate configuration files or environment variables. Making changes requires a redeployment, yet this simplicity offers a significant advantage: I can swiftly search my entire codebase for configuration settings. Each alteration is logged in version history, and I conduct self-reviews on changes. Instead of spending weeks developing a sophisticated configuration management solution, I’ve saved time by reallocating roughly 15 minutes for redeployment over three months.

3. Employing SQLite in Production

I’ve chosen to implement SQLite for my multi-user web application. Surprisingly, it manages my entire