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

In the tech world, a piece of advice often echoed by experts like Paul Graham is to “Do things that don’t scale.” However, when it comes to implementation, particularly in coding, this principle tends to be under-discussed. Drawing from my own experience over the past eight months while developing my AI podcast platform, I’ve crafted a method that aligns perfectly with this advice: the 3-Month Rule. This framework allows me to evaluate every non-scalable hack based on a trial period of three months. At the end of that timeframe, we either invest in building it out based on demonstrated value, or we let it go.

As developers, we are often groomed to think in terms of scalability from the outset. We focus on implementing sophisticated architectures—using microservices, distributed systems, and elegant design patterns—designed to handle extensive usage. However, in the startup ecosystem, these scalable solutions can become an expensive means of procrastination, as they often cater to hypothetical users and problems that may never arise. By adhering to my 3-Month Rule, I am compelled to write straightforward, albeit “messy,” code that gets deployed and provides immediate insights into real user needs.

Current Infrastructure Hacks: Smart Simplifications

1. Consolidated Operations on a Single VM

All components—including the database, web server, background jobs, and caching—operate on a single $40/month virtual machine. While this setup lacks redundancy and relies on manual backups to my local machine, it has led to valuable insights. Within just two months, I gained a clearer understanding of my actual resource requirements. For instance, my AI-driven platform typically uses only 4GB of RAM. The intricate Kubernetes architecture I nearly implemented would have resulted in unnecessary overhead without any benefit.

When the system crashes (which has happened a couple of times), I can analyze genuine failure points—data that often contradicts my initial assumptions.

2. Hardcoded Configurations

My configuration settings are defined within the code itself, such as:

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

This methodology eliminates the need for external configuration files or environment variables. While any change necessitates a redeployment, this approach allows for rapid