Embracing Unscalable Approaches: My 3-Month Experimentation Framework for Development

In the tech world, we often hear the sage advice from Paul Graham: “Do things that don’t scale.” While this wisdom is widely acknowledged, the practical implementation of such strategies—especially in coding—remains under-discussed.

Over the past eight months, while constructing my AI podcast platform, I crafted a straightforward yet effective framework: each unscalable approach is given a trial period of three months. At the end of this timeframe, I evaluate its worthiness; if it proves its value, I invest in building a more robust solution. Otherwise, it’s time to part ways.

As engineers, our instinct is often to rush towards scalable solutions. We dive headfirst into complex design patterns, microservices, and distributed systems, envisioning a future of thousands—if not millions—of users. While that is essential thinking for large corporations, in the startup environment, pursuing scalable code can frequently morph into costly procrastination. Rather than focusing on hypothetical users with non-existent problems, my three-month rule compels me to embrace straightforward, even “imperfect,” code that delivers quickly and helps uncover the genuine needs of users.

Insights from My Current Development Practices

1. Consolidated Infrastructure on a Single VM

My entire operation—database, web server, background jobs, and Redis—exists on a single $40/month virtual machine. With no redundancy and manual backups, this setup often raises eyebrows.

Yet, this approach has been enlightening. In just two months, I have gained invaluable insights into my actual resource requirements that capacity planning documents would never yield. My “AI-heavy” platform only peaks at 4GB RAM, revealing that the intricate Kubernetes architecture I nearly employed would merely have managed a multitude of empty containers. Each crash (which has happened twice) provides real data about failure points, which is often not what I anticipated.

2. Hardcoded Configuration

Here’s how I set my constants:

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

With no configuration files or environment variable complexities, I have an efficient system in place. Any change requires redeployment, but this also offers a distinct advantage: I can quickly search my entire codebase for any configuration value, and every price adjustment is