The 3-Month Experiment: A Pragmatic Approach to Unscalable Solutions in Software Development

In the ever-evolving world of technology startups, there’s a recurring piece of wisdom that has surfaced from industry leaders like Paul Graham: “Do things that don’t scale.” However, the challenge often lies in how to effectively implement this philosophy in coding practices. After eight months of developing my AI podcast platform, I stumbled upon a straightforward yet powerful framework: I dedicate a strict three-month timeline for every unscalable hack I deploy. By the end of this period, each solution either demonstrates its worth and is built out properly, or it is retired from the project entirely.

Understanding the Flaws of Conventional Thinking

Within the engineering realm, we tend to gravitate towards creating “scalable” solutions right from the start. Concepts such as microservices, distributed systems, and intricate architecture lend themselves to accommodating vast user bases. However, for startups, this often transforms into costly procrastination as resources get allocated to hypothetical problems for audiences that haven’t yet materialized. My three-month rule compels me to prioritize straightforward coding that yields real results and offers critical insights into what users genuinely require.

Updates from My Current Infrastructure Hacks

Here’s a rundown of my current strategies for leveraging unscalable solutions, along with the rationale behind them:

1. Simplified Deployment on a Single VM

All components, including the database, web server, background jobs, and caching, run seamlessly on a $40/month virtual machine. While this setup lacks redundancy and relies on manual backups, it has delivered invaluable data regarding my resources. Surprisingly, the platform’s demands peak at just 4GB of RAM. If I had opted for a complex Kubernetes architecture early on, I would have been managing an unnecessary complexity.

2. Hardcoded Configuration Values

Instead of utilizing configuration files or environment variables, I’ve chosen to hardcode critical constants throughout the code:

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

This approach allows for swift searching via grep across the codebase while ensuring every change is documented in git history. With three configuration updates in three months, it’s clear that the conventional route of building an extensive configuration service would have been an unnecessary investment of time.

3. Using SQLite in Production

Yes, I have embraced