Embracing the 3-Month Rule: A Practical Guide to Unscalable Solutions in Tech

In the rapidly evolving world of technology startups, the mantra of “doing things that don’t scale,” as popularized by Paul Graham, often surfaces. However, the challenge lies in effectively applying this principle in the realm of coding and infrastructure. Over the past eight months, while developing my AI podcast platform, I’ve adopted a straightforward approach: any unscalable method is given three months to prove its worth before a decision is made to either elevate it to a more robust solution or phase it out entirely.

The startup Mindset: Rethinking Scalability

As engineers, we frequently find ourselves conditioned to pursue scalable solutions from the outset. Complex design patterns, microservices, and distributed systems cater to the needs of large organizations and scale effectively across millions of users. However, for startups, this focus can often lead to unnecessary delays and expensive overplanning, as we end up optimizing for users who may not yet exist.

My three-month rule encourages me to focus on delivering straightforward, albeit imperfect, code that ships quickly. This approach allows me to gain valuable insights into the actual needs and behaviors of my users.

Ingenious Infrastructure Hacks: Practical Insights

1. Consolidated Setup on One Virtual Machine

Currently, my entire platform operates on a single virtual machine for just $40 a month, integrating the database, web server, background jobs, and caching system. While this setup lacks redundancy and relies on manual backups to my local machine, it has provided profound insights. In just a couple of months, I’ve learned my resource consumption peaks at 4GB of RAM—knowledge that would have been elusive with an elaborate infrastructure.

When issues arise (which they have), I’m able to gather real data about system failures, discovering unexpected weak points that direct my development focus.

2. Hardcoded Configurations for Swift Adjustments

Instead of employing configuration files or complex environment variables, I’ve opted for hardcoded constants like these:

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

While this may seem rudimentary, it allows for quick searches through the codebase and ensures that every change is logged in the version history. My three changes over the past three months required just 15 minutes of redeployment—far