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

In the entrepreneurial world, advice often revolves around the mantra: “Do things that don’t scale.” While this is a well-known principle attributed to Paul Graham, the implementation of this advice, especially in the realm of coding, is rarely discussed. I’ve spent the past eight months constructing my AI podcast platform, during which I’ve developed a straightforward framework centered on a critical tenet: each unscalable solution is allowed a three-month lifespan. In that timeframe, it has to demonstrate its worth or face elimination.

As engineers, we’re taught to focus on scalable solutions from the outset—leveraging design patterns, microservices, and distributed systems to accommodate millions of users. However, this mindset often thrives in larger corporate settings. In the early stages of a startup, concentrating too heavily on scalability can lead to costly delays as we optimize for users who may never materialize. By adhering to my three-month rule, I encourage the development of straightforward, even “imperfect” code that can be deployed quickly and effectively, allowing me to better understand actual user needs.

Current Infrastructure Strategies: Why They Work

1. Consolidated Resources on a Single Virtual Machine

Instead of spreading resources across multiple servers, my entire setup—including the database, web server, background jobs, and Redis—operates on a single $40-per-month virtual machine. This may seem risky due to its lack of redundancy and reliance on manual backups, yet it has proven instructive.

In just two months, I gained valuable insights into my platform’s actual resource demands. I learned that my AI-centric application only peaks at 4GB of RAM, revealing that the complex Kubernetes architecture I once considered would have been wasteful and misaligned with my needs. Each time the server crashes, I collect real, unexpected insights about what truly fails.

2. Simplified Configuration Management

My code relies on hardcoded configuration values:

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

While some may deem this approach primitive due to the absence of configuration files or environment variables, it has a hidden benefit: rapid retrieval of configuration settings across my codebase. Each change is effectively logged in git history, and every adjustment is carefully reviewed, albeit