Embracing the 3-Month Rule: A Practical Approach to Early-Stage Development

In the realm of startup culture, one piece of advice resonates strongly: “Do things that don’t scale,” originally articulated by Paul Graham. Yet, transitioning this philosophy into coding practices remains a less discussed topic. After eight months of building my AI podcast platform, I’ve devised an actionable framework that I call the 3-Month Rule. Essentially, every unscalable solution I employ is assigned a lifespan of just three months; at the end of this period, it either gets formalized into a robust feature or is abandoned altogether.

As developers, we often focus on crafting scalable solutions right from the outset—layers of design patterns, engaging with microservices, and deploying distributed systems designed to accommodate millions of users. While this thinking is appropriate for larger enterprises, it can be counterproductive in a startup environment.

In the early stages, developing scalable code can lead to costly procrastination as you’re preparing for a user base that hasn’t yet materialized. My 3-Month Rule urges me to create straightforward, sometimes “imperfect,” code that is still functional, allowing me to glean insights into what my users genuinely require.

My Current Infrastructure Strategies: Lessons from the Field

1. Consolidated Computing on a Single VM

I host my database, web server, background jobs, and cache—all on a single $40/month virtual machine, with no redundancy and manual backup procedures to my local storage.

Why does this approach work? Within two months, I gleaned more about my actual resource requirements than any theoretical capacity-planning document could provide. For example, my resource-intensive platform peaks at just 4GB of RAM. The complex Kubernetes infrastructure I almost established would have only served to maintain empty containers.

Each time the VM crashes—an occurrence that has happened twice—I gain invaluable real-time insights into the system’s vulnerabilities, many of which were not anticipated.

2. Simplistic Hardcoded Configurations

My configurations are straightforward and hardcoded:

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

There’s no clutter of configuration files or environment variables—just constant values spread across my code. While I must redeploy to make any changes, this strategy has its advantages. I can quickly search my entire codebase for any configuration value and