Embracing the Unscalable: A Practical Approach to Rapid Learning in Tech Development

When it comes to building a successful product, the mantra “Do things that don’t scale” from tech luminary Paul Graham is often cited. But how does one translate this wisdom into actionable steps within the realm of coding?

Over the past eight months while developing my AI podcast platform, I have devised a straightforward framework that I am calling the 3-Month Rule. Essentially, any unscalable hack I implement is given a lifespan of three months. After this period, it either demonstrates its value and is transformed into a robust solution, or it is phased out entirely.

The Paradox of Scalability in startup Culture

As software engineers, we are often trained to prioritize scalability from the very beginning. We are enamored with concepts like microservices, distributed systems, and complex design patterns—gorgeous frameworks capable of serving millions of users. Yet, this approach can sometimes be a misguided endeavor, especially for startups.

In a nascent company setting, the obsession with scalable solutions can lead to wasted resources, optimizing for hypothetical users and challenges that may never materialize. My 3-Month Rule encourages me to produce straightforward, less polished code that genuinely serves users’ needs rather than stagnant and inefficient abstractions.

Current Infrastructure Hacks: Essential Lessons Learned

1. One Virtual Machine (VM) for Everything

Currently, my entire setup—database, web server, background jobs, and caching—is operated from a single $40/month VM with no redundancy or automated backups.

While this might seem reckless, the insights gained have been invaluable. In just two months, I’ve learned about my actual resource needs, finding that my AI-centric platform peaks at about 4GB of RAM. The overly complex Kubernetes solution I initially considered would have wasted time and resources managing empty containers.

When the system crashes—twice so far—I gain real-world data about what fails, and interestingly, it’s rarely what I anticipated.

2. Hardcoded Configuration Values

Consider this approach:

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

With no separate configuration files or environment variables, my constants are scattered across the codebase. While it may seem archaic, this method provides speed and efficiency. I can quickly search my code for any