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

In the world of technology and development, a common piece of advice often shared by influential figures like Paul Graham is to “do things that don’t scale.” Yet, few dive into the practicalities of applying such wisdom, particularly in the realm of coding.

After dedicating the last eight months to building my AI podcast platform, I have forged a simple yet effective strategy: each unscalable hack is granted a three-month trial period. At the end of this timeframe, it must either demonstrate its worth and be upgraded into a robust solution or face the chopping block.

As developers, we are accustomed to designing scalable systems from the outset. Concepts such as design patterns, microservices, and distributed architectures become the gold standard—ideal for handling millions of users. However, this mindset is often a reflection of larger corporate priorities rather than the agile reality of startups. In many cases, striving for scalability can become an expensive form of procrastination, as we might be optimizing for customers who have not yet set foot through our doors.

My 3-month rule compels me to write straightforward, albeit imperfect code that can actually be deployed and help clarify real user needs.

Current Infrastructure Strategies: Smart Decisions in Simplicity

1. Consolidated Resources on a Single VM

My entire operation runs on a single $40-per-month virtual machine, encompassing the database, web server, background jobs, and Redis. There’s no redundancy, and I perform manual backups locally.

This may seem reckless, but it has proven insightful. In just two months, I’ve gleaned more about my real resource needs than any capacity planning document could offer. As it turns out, my “AI-heavy” platform peaks at just 4GB of RAM. The intricate Kubernetes framework I nearly implemented would have only led to managing empty containers. Each time the system crashes (happened twice now), I gain concrete data about failures—never quite what I anticipated.

2. Hardcoded Values Throughout the Codebase

Rather than relying on configuration files or environment variables, I use constants like:
python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
Modifying any of these requires redeployment.

The advantage? I can quickly search my entire code