The 3-Month Rule: A Pragmatic Approach to Tech Development for Startups

In the fast-paced world of tech startups, the advice from Paul Graham—”Do things that don’t scale”—is often echoed but rarely implemented effectively. Having spent the last eight months building my AI podcast platform, I’ve devised a straightforward strategy: any solution I create that isn’t scalable gets a three-month trial period. At the end of this timeframe, I evaluate its effectiveness—if it proves valuable, it gets developed further; if not, it’s put to rest.

As engineers, we are generally inclined to craft scalable systems from the outset. We often obsess over design patterns, microservices, and distributed architectures meant to support millions of users. However, this approach often leads to over-engineering, especially for startups. Optimizing for future users who may never arrive can feel like an inefficient use of resources. My three-month rule encourages me to produce straightforward, albeit imperfect, code that moves quickly and reveals the genuine needs of my users.

Innovative Hacks for Effective Learning

Here’s a rundown of the infrastructure hacks I’m using and the insight they provide:

1. Single-VM Setup

I run my entire operation—database, web server, background jobs, and Redis—on a single $40/month virtual machine. While this approach lacks redundancy and requires manual backups, it has provided substantial learning opportunities. In just two months, I discovered that my “AI-heavy” platform maxes out at 4GB RAM—information I wouldn’t have gleaned from theoretical capacity planning documents. When the system crashes, it offers invaluable insights into the factors that truly affect performance, often revealing unexpected issues.

2. Hardcoded Configurations

Instead of relying on configuration files or environment variables, I use constants throughout my code:

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

This approach facilitates quick searches within my codebase for any configuration value, ensuring that any price change is documented in my Git history and undergoes a review process, albeit informally by myself. Building a configuration management service could have consumed a week of development time; however, I have changed these values only three times in three months. The quick redeploy of 15 minutes proved far more efficient than dedicating 40 hours to a complex system.

**3. SQLite in