Embracing the 3-Month Rule: A Pragmatic Approach to Non-Scalable Solutions

In the startup world, there’s a common adage from entrepreneur Paul Graham: “Do things that don’t scale.” While this advice often resonates on a philosophical level, many struggle to translate it into actionable steps—especially within the realm of software development. After spending eight months developing my AI podcast platform, I’ve devised a practical framework: I give every non-scalable tactic a lifespan of exactly three months. This approach forces me to either refine these makeshift solutions into robust systems or scrap them altogether.

The Traditional Engineer’s Dilemma

As software engineers, we’re conditioned to design solutions that can effortlessly accommodate an expanding user base from the outset. We dwell in a world of elegant design patterns, microservices architectures, and distributed systems—all built to handle millions. This mindset, however, is often more applicable to established companies than to startups. In early-stage ventures, focusing too much on scalability can lead to wasted resources, as we frequently work on features for users that don’t yet exist. Adopting my 3-month rule compels me to produce simple, straightforward code that is deployable, allowing me to better understand user needs.

Clever Infrastructure Hacks

Here are some of the non-scalable practices I’ve implemented that have proven beneficial to the development process:

1. A Single VM for Everything

Currently, my entire infrastructure runs on a single $40/month virtual machine, serving as the database, web server, and more. While this setup lacks redundancy and relies on manual backups, it has provided invaluable insights into my actual resource requirements. After only two months, I discerned that my AI-driven platform peak usage is around 4GB of RAM. The extensive Kubernetes architecture I almost set up would have required me to manage empty containers—learning from mistakes is much more informative.

2. Hardcoded Configurations

Instead of using configuration files or environment variables, I rely on hardcoded constants throughout my codebase:

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

This unorthodox approach may seem primitive, but it enables rapid modifications and straightforward tracking via Git history. In the last three months, I’ve only needed to modify these values three times, saving me extensive engineering hours that would have been