The 3-Month Experiment: A Practical Approach to Unscalable Solutions

In the world of startups, the conventional wisdom often echoes Paul Graham’s famous directive: “Do things that don’t scale.” Yet, when it comes to implementing this advice in the realm of coding, many of us find ourselves at a loss.

After dedicating eight months to building my AI podcast platform, I’ve developed a straightforward methodology: every unscalable solution I implement is given an initial lifespan of three months. At the end of that period, each solution must either prove its worth and evolve into a more robust system, or it will be phased out.

As engineers, we’re conditioned to aim for “scalable” solutions right from the start. We talk about design patterns, microservices, and distributed systems—spectacular architectures designed for millions of users. However, this is a mindset better suited for larger companies.

In a startup environment, focusing on scalability can often lead to costly delays. It’s like preparing for an audience that doesn’t yet exist, addressing potential issues that may never arise. My three-month rule encourages me to write straightforward, perhaps even “bad,” code that is quick to deploy, thus enabling me to discover what users truly need.

Insightful Infrastructure Hacks: A Closer Look

1. Consolidated Operations on One Virtual Machine

In my current setup, everything operates on a singular $40-per-month virtual machine, encompassing the database, web server, background jobs, and caching. While this approach lacks redundancy and relies on manual backups, it has provided invaluable insights into my resource consumption over the past couple of months. I learned that my application, which I initially expected to be resource-intensive, peaks at around 4GB of RAM. The complex Kubernetes infrastructure I almost put in place would have ended up managing idle containers.

When my system has crashed (which has happened twice), I gained real-time insights into what specifically failed—often unpredictable elements.

2. Simplistic Hardcoding of Configuration Values

My approach to configuration is notably straightforward: constants defined within my codebase, such as:

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

This method might seem outdated, but it allows me to search through my codebase for any configuration parameter in mere seconds. Changes made to values take