Embracing Unscalable Solutions: The 3-Month Experiment for Startups

In the world of entrepreneurship, especially within the tech sphere, we often hear the advice from influential figures like Paul Graham: “Do things that don’t scale.” However, the conversation typically doesn’t delve into the practical aspects of implementing this ethos in software development.

After eight months of developing my AI podcast platform, I’ve adopted a straightforward methodology: any unscalable solution is given a three-month trial period. At the end of this period, if the approach proves its worth, it gets a more robust development. If not, it is discarded.

The Dilemma of Scalable Solutions

As engineers, we frequently gravitate towards building scalable architectures from the outset, often inspired by principles like microservices and distributed systems—frameworks designed to handle vast user volumes. This mindset aligns with larger enterprises, but in a startup context, striving for scalability can lead to unnecessary complications.

At a fledgling company, writing scalable code often becomes a form of procrastination. We expend resources on potential user bases that may not materialize, prematurely solving challenges that may never arise. By enforcing my three-month rule, I compel myself to create straightforward, albeit less-than-perfect, code that is functional and reveals the genuine needs of my users.

Current Infrastructure Innovations: Smart Hacks

1. Consolidated Operations on a Single VM

I run my database, web server, background tasks, and Redis all on a single virtual machine for just $40 a month, with no redundancy and manual backups to my drive.

Why is this a savvy decision? In just two months, I gained more insights into my resource requirements than any capacity-planning document could provide. My platform peaks at 4GB of RAM, meaning the complex Kubernetes setup I nearly implemented would have involved managing empty containers. When my system crashed—twice so far—I gathered invaluable data on failure points, revealing issues I hadn’t anticipated.

2. Simplified Configuration Management

Instead of using configuration files or environment variables, I employ hardcoded constants throughout my codebase for essential values like:

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

While this approach may seem primitive, it allows me to easily search any config value using grep and track changes through git history. Each adjustment