Embracing the Unscalable: My 3-Month Framework for Smart Engineering

Paul Graham famously advises entrepreneurs to “do things that don’t scale,” a nugget of wisdom that resonates deeply within startup culture. While many embrace this philosophy conceptually, few delve into the practical implementation, especially in the realm of coding.

After dedicating eight months to developing my AI podcast platform, I’ve crafted a straightforward yet effective framework: any unscalable hack I adopt is granted a lifespan of three months. At the end of this period, each hack either proves its merit and evolves into a robust solution, or it gets retired.

Here’s an important insight: as engineers, we are often conditioned to engineer “scalable” solutions from the outset. We gear ourselves towards sophisticated design patterns, microservices, and distributed systems, constructing architectures that can handle millions of users. Yet, this approach tends to align more with the mindset of large corporations rather than nimble startups.

In a startup environment, aiming for scalability can often translate to delays and unnecessary expenditures. We may find ourselves optimizing for hypothetical users and addressing problems that may never arise in our early stages. By adhering to my three-month rule, I prioritize rapid prototyping with straightforward, functional—albeit imperfect—code that delivers immediate insights into user needs.

Current Infrastructure Innovations: Why They Work for Me

1. Consolidated Infrastructure on One VM

I decided to run all critical operations—database, web server, background jobs, and Redis—on a single $40/month virtual machine. This choice may seem reckless, but it has provided invaluable insights into my resource requirements. Within two months, I’ve frequently observed that my “AI-heavy” platform only demands around 4GB of RAM. The Kubernetes setup I nearly pursued? It would have meant managing an architecture that was largely unnecessary.

When the system crashes—twice to date—I gain firsthand understanding of which components fail. Interestingly, it’s rarely what I anticipated.

2. Hardcoded Variables Throughout the Codebase

My coding style includes placing configuration parameters directly in the code, such as:

python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
This approach eliminates the need for configuration files and environment variables. Modifying any setting necessitates a full redeployment.

The beauty of this method lies in its