Embracing Imperfection: The 3-Month Rule for startup Development

In the world of startups, the conventional wisdom often revolves around the idea of building scalable solutions from day one. Paul Graham famously advised entrepreneurs to “Do things that don’t scale.” However, practical strategies for implementing this advice in the realm of coding are seldom discussed.

After eight months of developing my AI podcast platform, I have devised a framework that has proven to be invaluable: each unscalable approach is given a lifespan of three months. Following this period, it either demonstrates its value and evolves into a more robust solution or is discarded.

Rethinking Engineering Approaches

As engineers, our training typically focuses on creating solutions suited for scale, complete with sophisticated architecture capable of supporting millions of users. However, in the startup environment, this mindset can lead to unnecessary complications and, at worst, procrastination. Optimizing for future users who may never materialize can lead to wasted resources. Adopting my 3-month strategy compels me to produce straightforward, albeit imperfect, code that swiftly brings my ideas to fruition while providing crucial insights into what users truly need.

My Current Infrastructure Strategies and Their Benefits

1. Single VM Deployment

I operate my entire application—from the database to the web server—on a single virtual machine costing just $40 a month, with no redundancy and manual backups to my local drive. This seemingly risky setup has been a valuable learning experience. Within a mere two months, I’ve gained a clearer understanding of my resource requirements. My “AI-heavy” platform peaks at 4GB of RAM, which makes me realize that the complex Kubernetes environment I initially considered would have only managed empty containers. Each time the server crashes, I glean real insights about the actual causes—insights that often surprise me.

2. Hardcoded Configurations

Imagine constants like:

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

I keep all configurations hardcoded without relying on external files or environment variables. While this may seem inefficient, it enables quick searches and straightforward tracking in git history. Changing configurations entails only a quick redeployment rather than the extensive build time required for a dedicated configuration service, which would have taken a week.

3. SQLite for Production Use

Surprisingly, I run SQLite for my multi-user web application