Embracing the 3-Month Rule: A Pragmatic Approach to Rapid Learning in Software Development
In the tech startup world, the mantra “Do things that don’t scale,” popularized by Paul Graham, is often cited but rarely implemented effectively in software coding practices. As I embark on my journey of developing an AI podcast platform, I’ve adopted a straightforward yet powerful framework: every unscalable shortcut gets a trial period of three months. After this time, we either build a more robust solution based on the insights gained or phase it out entirely.
The Dilemma of Scalability in Startups
As software engineers, we are conditioned to prioritize scalable solutions from the outset. We gravitate towards elegant architectures like microservices and distributed systems, which may be ideal for larger companies dealing with millions of users. However, in a startup setting, aiming for scalability too soon can lead to costly delays and wasted resources. That’s why my three-month rule encourages me to embrace simplicity and focus on delivering results that matter.
Through this method, I can produce straightforward code that directly addresses user needs rather than getting bogged down in theoretical solutions. Here are some of the infrastructure hacks I’ve implemented that might seem unconventional but have proven to be smart choices:
1. Consolidated Infrastructure on One Virtual Machine
Hosting the database, web server, background jobs, and caching—all on a single $40/month virtual machine—might sound reckless. However, this limited setup has allowed me to accurately gauge my resource requirements. After two months, I discovered that my so-called “AI-heavy” platform only required 4GB of RAM. The complex Kubernetes architecture I nearly deployed would have only resulted in wasted resources and empty containers. Each crash provides invaluable insights into real issues, often highlighting unexpected vulnerabilities.
2. Hardcoded Values Throughout
Instead of relying on configuration files and environment variables, I’ve opted for hardcoded constants:
python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
This approach means that any change necessitates a redeployment, but it comes with benefits. I can quickly search my entire codebase for any configuration value, and every modification is tracked in my Git history. Rather than spending a week developing a configuration service, I’ve only needed to make three changes in three months, representing a fraction of the effort while still
