Embracing the Unscalable: My 3-Month Approach to Learning through Code

In the ever-evolving world of technology, the mantra “do things that don’t scale” is often attributed to thought leader Paul Graham. While the advice is widely recognized, the implementation strategies specifically tailored for software development are seldom discussed. Over the past eight months, as I built my AI podcast platform, I’ve conceptualized a simple yet effective framework: every temporary solution—no matter how unscalable—has a lifespan of just three months. After this period, each hack either proves its worth and evolves into a robust solution or is discarded.

As engineers, we are conditioned to prioritize “scalable” solutions from the outset. From design patterns to microservices and distributed systems, we often gravitate towards architecting that can accommodate a vast user base. However, this mindset is typically more relevant to larger corporations.

In a startup environment, the pursuit of scalable architecture can often lead to excessive delays and wasted resources. It means preparing for a user base that might not even exist yet, addressing challenges that may never materialize. My three-month rule encourages me to produce straightforward, if imperfect, code that can be deployed quickly, allowing me to truly understand what users need.

Key Infrastructure Strategies and Their Hidden Benefits

1. A Single Virtual Machine for Everything

I’ve consolidated my database, web server, background jobs, and Redis on a single virtual machine costing just $40 a month. While there’s no redundancy and backups are manual, this approach has proven enlightening. In just two months, I’ve gained insights into my actual resource requirements that no planning document could provide. Surprisingly, my “AI-heavy” platform maxed out at just 4GB of RAM. The complex Kubernetes architecture I nearly implemented would have only served to manage inactive resources.

When the system crashes (and it has twice), I gain valuable insights into the actual failure points—often not where I expected.

2. Hardcoded Values for Configuration

I utilize hardcoded constants across my codebase—no external configuration files or environment variables:

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

Updating a configuration means redeploying, but this simplicity has its advantages. Searching my entire codebase for configuration values takes mere seconds, allowing me to track changes meticulously