Embracing the 3-Month Rule: A Pragmatic Approach to Non-Scalable Solutions in Development

When it comes to advice from renowned figures like Paul Graham, one phrase shines particularly bright: “Do things that don’t scale.” However, translating this wisdom into actionable steps within the realm of coding often remains unaddressed.

Over the past eight months while building my AI podcast platform, I’ve established a practical framework that has been invaluable in my development process: each unscalable approach has a lifespan of just three months. After this period, these methods must either demonstrate their worth and be fully developed, or they risk being phased out.

Rethinking Development Mindsets

As software engineers, we are conditioned to think of scalability from the outset. Our training often centers around creating intricate frameworks—think design patterns, microservices, and distributed systems—designed to accommodate vast user loads. Yet, this is often the mindset of larger corporations, which may not translate well for startups.

In a startup environment, striving for scalable solutions too early can lead to costly delays. We can find ourselves optimizing for imaginary users and addressing issues that may never arise. My three-month rule encourages me to write straightforward, albeit imperfect, code that is quick to deploy and reveals what users genuinely require.

Current Infrastructure Hacks: Practical Insights

1. Unified VM Structure

I’ve chosen to consolidate everything onto a single $40/month virtual machine (VM)—from the database to the web server and background jobs. This setup lacks redundancy and relies heavily on manual backups.

What may seem like a reckless decision is, in fact, brilliantly educational. Within just two months, I’ve gained valuable insights into my actual resource needs, discovering that my “AI-heavy” platform only peaks at 4GB of RAM. The extensive Kubernetes configuration I considered would have resulted in managing idle containers instead.

When failures happen—twice so far—they provide insightful data about unexpected bottlenecks, allowing me to learn from real scenarios.

2. Hardcoded Settings

In my code, I use constants directly instead of configuration files or environment variables:

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

This approach means that any change prompts a redeployment. The advantage? I can quickly search my codebase for configuration values. Adjustments are recorded