Embracing the 3-Month Rule: A Pragmatic Approach to Rapid Development

In the world of software development, particularly within the startup ecosystem, Paul Graham’s maxim to “do things that don’t scale” often constitutes a compelling directive. Yet, the conversation rarely goes beyond mere philosophy to actionable implementation, especially in programming. After eight months of developing my AI podcast platform, I’ve introduced a straightforward yet effective framework: every unscalable workaround is granted a lifespan of just three months. During this time, it must either demonstrate its worth and evolve into a robust solution, or it will be discarded.

As engineers, we tend to gravitate toward scalable designs from the outset—envisioning distributed systems, microservices, and architectural marvels that cater to millions of users. However, this perspective is often more suited to large corporations than to startups. In a fledgling company, chasing scalability can lead to costly delays, as it often involves optimizing for potential users who may not even exist yet. My 3-month rule encourages me to produce straightforward, albeit “imperfect,” code that actually functions and provides real insights into user needs.

Current Infrastructure Strategies: Pragmatic Solutions

1. Consolidating Resources on a Single VM

I run my entire setup—including the database, web server, background jobs, and caching—all from a single virtual machine costing just $40 a month. While there’s no failover strategy, I conduct manual backups.

Why is this a smart approach? In just two months, I’ve gained valuable insights into my resource requirements that surpass any capacity-planning documentation. For example, my platform’s maximum RAM usage hovers around 4GB, revealing that the complex Kubernetes framework I nearly deployed would have been an exercise in managing idle resources. Each crash (yes, it has happened twice) reveals critical data about unexpected vulnerabilities.

2. Simplifying Configurations

Instead of complex configuration files or environment variables, I rely on hardcoded constants dispersed throughout my codebase:

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

This method means any changes necessitate a redeployment, but here’s the silver lining: I can quickly search my entire codebase for configuration values and track changes in git history effortlessly. With just three changes in three months, I saved immense hours that would have gone into