The 3-Month Experiment: A Practical Approach to Non-Scalable Solutions in Tech Development

In the tech startup world, one guiding principle often emphasized is Paul Graham’s advice to “do things that don’t scale.” However, the challenge lies in translating this concept into actionable implementation, especially in the realm of coding. After eight months of developing my AI podcast platform, I found an effective strategy: each non-scalable hack I employ gets a life span of three months. At the end of this period, it must either demonstrate its value and evolve into a robust solution or be eliminated.

As engineers, we frequently find ourselves crafting scalable solutions from the onset. We dive into design patterns, microservices, and distributed architectures aimed at accommodating millions of users—thinking typical of larger companies. However, for startups, pursuing scalability too early can lead to unnecessary resource wastage. My three-month timeframe encourages me to create straightforward, efficient code that achieves tangible results and provides insights into my users’ true needs.

Recent Infrastructure Choices and Their Strategic Benefits

1. Unified Virtual Machine Setup

I operate my entire stack—database, web server, background processes, and Redis—on a single virtual machine costing just $40 per month. While this lack of redundancy may seem unwise, it has provided enlightening data regarding my actual resource requirements. For instance, my initially conceived “AI-heavy” platform has shown that, at peak usage, I only require 4GB of RAM. Instead of bogging myself down with a complex Kubernetes setup, I’ve acquired valuable insights about real pressures and failures.

2. Hardcoded Configuration Values

Instead of implementing configuration files or environmental variables, I’ve opted for hardcoded constants for settings like pricing and user limits. This approach means that altering any value necessitates a redeployment. However, it offers a significant advantage: I can quickly search for configuration values throughout my codebase, and every change is meticulously tracked in version history. Given that I have modified these settings a mere three times in three months, this method has saved me countless engineering hours.

3. Utilizing SQLite in Production

Surprisingly, I have adopted SQLite as the database solution for this multi-user web app, efficiently managing its 47MB size without difficulty. With 95% of my access patterns being read operations, SQLite has turned out to be an appropriate choice. Had I chosen a more complex solution like Postgres from the beginning, I would have been preoccupied with optimizing connection management for