Embracing the Unscalable: My 3-Month Framework for Rapid Learning in Software Development

In the world of tech startups, the mantra “do things that don’t scale” frequently echoes through the halls of innovation. Pioneered by influential figures like Paul Graham, this advice is often shared without concrete guidance on how to apply it, particularly in software development contexts.

During my journey building an AI podcast platform over the past eight months, I’ve devised a systematic approach that I like to call the “3-Month Rule.” This framework allows me to experiment with unscalable solutions for a limited time—three months. After this period, each hack either proves its worth and evolves into a scalable solution, or it is retired entirely.

Rethinking Scalability

As software engineers, we are primarily trained to design for scalability from the outset. Terms like design patterns, microservices, and distributed systems dominate our thoughts—these grand architectures are all intended to support millions of users seamlessly. However, this mindset sometimes overlooks the tangible needs of early-stage startups, where focusing too much on scale can lead to inefficiencies and misallocated resources.

In fact, I believe that overly ambitious plans for scalability can result in costly procrastination. Often, we find ourselves trying to cater to potential users who don’t exist yet, while the real questions about user experience remain unanswered. My three-month approach compels me to write straightforward, albeit rough code that quickly enters the market and provides valuable insights into user needs.

Tactical Hacks with Purpose

Here’s a rundown of some of my current infrastructure methods that may seem unorthodox at first, but are actually insightful in practice:

1. Consolidated on a Single VM

I run my entire setup—including the database, web server, and background jobs—on one $40/month virtual machine. While this might appear risky due to a lack of redundancy and reliance on manual backups, this method has yielded profound insights. After just two months, I gained clarity on how much resource my platform genuinely requires—peaking at 4GB of RAM—contrary to what I would’ve assumed when considering a more complex Kubernetes architecture.

Notably, when the system does crash (as it inevitably has a couple of times), I gain real feedback on the issues at hand, often revealing surprises that I couldn’t have anticipated.

2. Hardcoded Configuration

My configurations are scattered directly across the codebase—no config files or environment