Embracing the 3-Month Rule: A Pragmatic Approach to Code Development in Startups

In the startup world, the mantra of “doing things that don’t scale” is often echoed, as highlighted by Paul Graham’s famous advice. Yet, when it comes to implementation, particularly in programming, this transition can be complex and poorly understood. After eight months of developing my AI podcast platform, I’ve adopted a practical framework to navigate this challenge: every unscalable solution receives a dedicated three-month trial. After this period, it either proves its worth and is transformed into something robust or is discarded.

As engineers, we are typically conditioned to create scalable solutions right from the outset. We become well-versed in architectural patterns like microservices and distributed systems—strategies designed to support millions of users. However, this perspective often neglects the reality of many startups, where investing in scalability can simply lead to costly delays. By adhering to my three-month rule, I am compelled to focus on straightforward, functional code that helps me uncover genuine user needs.

Innovative Infrastructure Hacks: A Closer Look

1. Consolidated Operations on a Single Virtual Machine

Currently, my entire application ecosystem—including the database, web server, background jobs, and Redis—operates from a single $40/month virtual machine. This approach sacrifices redundancy and relies on manual backups to my local device.

The brilliance behind this strategy lies in the insights I’ve gained regarding resource usage in just two months. My system’s maximum capacity only peaks at 4GB of RAM, revealing that a complex Kubernetes setup would have only managed empty spaces. Each time the server crashes (which has happened twice), I get valuable feedback on unexpected failure points.

2. Hardcoded Configuration Values

My configurations are directly embedded within the codebase, such as:
python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
This method eliminates the use of configuration files or environment variables. While some might deem this approach reckless, it allows me to swiftly search my entire codebase and track configuration changes through git history. With just three adjustments in three months, the time saved in engineering hours is substantial—15 minutes vs. a week spent building a configuration service.

3. Running SQLite in Production

Believe it or not, my multi-user web application is powered by SQLite. With