Embracing the 3-Month Rule: A Practical Framework for Scalable Learning in Tech

In the tech world, the mantra “do things that don’t scale,” popularized by entrepreneur Paul Graham, is well-known. However, the challenges of implementing this philosophy—especially in coding—are often overlooked. Over the past eight months, while developing my AI podcast platform, I’ve adopted a simple yet effective framework: every unscalable solution is granted a lifespan of three months. After this period, if it proves to be valuable, it will be transformed into a robust solution; otherwise, it will be phased out.

As engineers, our instinct often drives us to prioritize scalable solutions from the outset. We envision complex architectures, from microservices to distributed systems, capable of supporting millions of users. However, this reflects a mindset better suited for larger organizations. In the startup environment, focusing on scalable code can sometimes act as a costly form of procrastination—developing solutions for hypothetical users with potential needs that may never arise. My three-month rule compels me to create straightforward, less polished code that can be deployed, thus providing real-time insights into users’ actual requirements.

Ingenious Infrastructure Hacks That Work

1. Consolidating Resources on a Single VM

Currently, every aspect of my infrastructure—database, web server, background jobs, and Redis—is hosted on a single virtual machine for $40 per month. There’s no redundancy, and I manually back up everything to my local system.

This approach has allowed me to grasp my resource needs far better than any capacity planning document ever could. Within just two months, I discovered my platform, which relies heavily on AI, only peaks at 4GB of RAM. Contemplating a complex Kubernetes deployment would have only led to managing underutilized resources. When issues arose (twice so far), I gained immediate insights into actual failure points—often contrary to my expectations.

2. Hardcoding Configurations for Efficiency

My codebase features constants rather than configuration files or environment variables. For instance:

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

While devoid of flexibility, this setup enables a straightforward grep search across my files to find any configuration value quickly. Each change in pricing is tracked in my Git history, ensuring that any updates are thoroughly reviewed—al