The Three-Month Rule: A Pragmatic Approach to Non-Scalable Solutions

In the startup world, we often hear the famous advice from Paul Graham: “Do things that don’t scale.” While this principle is widely acknowledged, its practical application in software development is rarely discussed. After eight months dedicated to building my AI podcast platform, I’ve instituted a straightforward framework: each unscalable solution gets a lifespan of exactly three months. At the end of this period, it either demonstrates its value and evolves into a sustainable solution, or it’s discarded.

As engineers, we are instinctually trained to develop scalable architectures—think complex design patterns, microservices, and distributed systems that cater to millions of users. However, this approach can be detrimental in a startup setting, where scalability often equates to expensive procrastination aimed at hypothetical future users and problems. My three-month rule has empowered me to produce straightforward, albeit imperfect, code that delivers real insights into user needs.

Clever Infrastructure Hacks

Here’s a look at my current technical shortcuts and why they are surprisingly effective:

1. Consolidated on a Single VM

Everything from the database to the web server and background jobs operates on a single $40/month virtual machine, with no redundancy but manual local backups. This approach has granted me invaluable insights into my actual resource requirements in just two months. For instance, my AI platform requires only 4GB of RAM during peak times. The sophisticated Kubernetes setup I considered would have wasted resources managing empty containers. Plus, when crashes occur, I gather authentic data on what truly breaks—often unexpected elements.

2. Hardcoded Configuration Values

With constants such as:

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

There are no configuration files or environment variables—just scattered constants across the codebase. Any changes necessitate redeployment, which has proven efficient. I’ve only modified the configurations three times in three months, translating to 15 minutes of redeployment compared to the week it would have taken to develop a configuration service. This approach allows me to quickly locate and track changes through Git history.

3. Using SQLite in Production

Currently, my multi-user web application operates on SQLite, which is surprisingly adequate with its 47MB database that comfortably supports 50 concurrent users. Through this experience, I’ve realized that my usage patterns lean