The 3-Month Experiment: A Practical Framework for Non-Scalable Solutions

In the world of startups, Paul Graham’s advice to “do things that don’t scale” resonates profoundly, yet the implementation remains a puzzle for many, particularly in the realm of coding. After dedicating eight months to the development of my AI podcast platform, I have crafted a straightforward approach: each non-scalable solution is given exactly three months to demonstrate its merit. If it proves beneficial, we enhance it; if not, it’s time to let go.

As software engineers, we are often trained to prioritize scalable solutions from the outset. Concepts like microservices, design patterns, and distributed systems dominate our thought processes, especially when envisioning systems capable of serving millions. However, this kind of thinking can lead to costly delays in startup environments. Instead of building for hypothetical users who may never arrive, my three-month framework compels me to create straightforward, albeit less elegant, code that gets deployed promptly and teaches me about the actual needs of users.

Current Infrastructure Shortcuts: Insights from My Approach

1. Consolidated System on a Single VM

Currently, everything from the database to Redis runs on one $40/month virtual machine. While this setup lacks redundancy and requires manual backups, it has been a revelation. Over the last two months, I’ve gained a clearer understanding of my resource requirements than any capacity plan could provide. My platform, despite its AI focus, peaks at merely 4GB RAM. The complicated Kubernetes architecture I almost implemented would have resulted in a maintenance nightmare full of idle containers.

Every time there’s a system crash—yes, it has happened twice—I gain insightful data about the actual failure points, which are often unexpected.

2. Hardcoded Configuration Values

In my code, configuration values like:

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

are hardcoded directly within the files. This means every update requires a redeployment, but it’s incredibly efficient. With the ability to search my entire codebase for any configuration value, I can instantly track changes through git history. In just three months, I’ve adjusted these values three times, resulting in a minimal time investment compared to the weeks it would take to build a comprehensive configuration management system.

3. Utilizing SQLite for Production

I chose to deploy SQLite