Embracing the 3-Month Rule: A Practical Approach to Non-Scalable Solutions

In the entrepreneurial world, especially within the tech landscape, the advice from Paul Graham to “do things that don’t scale” resonates deeply. Yet, the implementation of this concept in software development is rarely discussed. After dedicating eight months to building an AI podcast platform, I’ve developed a straightforward framework: every non-scalable solution receives a trial period of just three months. If it proves its worth, it gets the upgrade it deserves; if not, it’s time to let it go.

As engineers, we’re often conditioned to immediately seek out scalable solutions—think design patterns, microservices, and extensive distributed systems. While these architectures are designed to efficiently manage millions of users, they represent the mindset of larger companies. In a startup environment, crafting scalable code from the outset can merely become an expensive form of procrastination, as you’re often fixing issues that may never arise while trying to accommodate users that don’t yet exist.

This is where my three-month rule comes into play. It encourages me to implement straightforward, sometimes “suboptimal,” code that can be deployed rapidly, allowing me to uncover the real needs of my users.

My Current Infrastructure Hacks: Smart Solutions in Action

1. Consolidation into a Single Virtual Machine

Currently, my entire infrastructure—database, web server, background jobs, and Redis—runs on a single $40/month virtual machine. This setup, devoid of redundancy and reliant on manual backups, has proven invaluable. In just two months, I’ve gained insights about my actual resource utilization that no capacity plan could provide. For instance, my “AI-centric” platform typically peaks at 4GB of RAM, making the complicated Kubernetes architecture I almost implemented unnecessary—better to manage real data over empty containers.

When my VM has crashed—twice now—I’ve gathered authentic data about the failure points, revealing unexpected vulnerabilities.

2. Hardcoded Configuration

Instead of using configuration files or environment variables, I’ve employed hardcoded constants throughout my project, such as:

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

This approach might seem rudimentary, but it allows me to efficiently search for configuration values across my entire codebase and track changes via Git. With only three adjustments in three months, the time