Embracing Imperfection: The 3-Month Rule for Building Scalable Solutions

The tech industry is abuzz with the mantra, “Do things that don’t scale,” made famous by Paul Graham. However, the application of this advice, especially in coding, often remains uncharted territory. After eight months of developing my AI podcast platform, I have established a straightforward approach: every unscalable hack receives a fixed lifespan of three months. At the end of this period, it either demonstrates its value and is transformed into a robust solution or is discarded.

Rethinking the Engineering Mindset

As engineers, our training often leans heavily toward creating solutions that scale from the very start. We get caught up in sophisticated design patterns, microservices, and distributed systems—pursuing architectural elegance meant for large-scale operations. Yet, in the startup landscape, overly ambitious, scalable solutions can sometimes be a costly form of procrastination. We find ourselves optimizing for users that aren’t part of the current equation, tackling challenges that may never materialize. My 3-month rule encourages me to embrace simplicity and produce straightforward, albeit “bad,” code that is more likely to lead to valuable insights about user needs.

Current Infrastructure Hacks: Smarter Than They Seem

1. Consolidated Operations on a Single VM

All crucial operations—database, web server, background jobs, Redis—function on a single $40/month virtual machine, with no redundancy and manual backups managed locally.

The brilliance of this setup lies in the lessons it provides. Within just two months, I’ve gained deeper insights into my actual resource requirements than any planning document could have offered. I’ve observed that my platform’s peak requires only 4GB of RAM. The complex Kubernetes architecture I initially contemplated would have meant managing empty containers.

When issues arise—twice so far—my system crashes yield real, actionable data on what fails, revealing insights that often surprise me.

2. Hardcoded Configuration for Simplicity

Configuration settings, such as pricing tiers and user limits, are hardcoded directly within the application. This choice might seem archaic, but it simplifies tracking and deployment. Each adjustment requires only a quick redeployment, a process I can execute efficiently, proving more effective than a week-long development of a configurable service when changes are infrequent.

3. Utilizing SQLite in Production

In an unexpected turn, I have chosen SQLite for my multi