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

In the world of startups, the mantra “do things that don’t scale” is often shared, yet the path to achieving this in the realm of software development remains largely uncharted. Inspired by the insights of Paul Graham, I have developed a practical framework during my eight-month journey building an AI podcast platform: the 3-Month Rule. This approach not only encourages experimentation but also sets a definitive timeline for evaluating the viability of unscalable hacks.

As engineers, we’re primarily trained to favor scalable solutions from the outset. We gravitate towards intricate architectural designs, such as microservices and distributed systems, that are prepped for a multitude of users. However, in a startup environment, such foresight can morph into costly procrastination, addressing potential future needs while neglecting current user demands. My 3-Month Rule compels me to write straightforward, albeit imperfect, code that facilitates immediate deployment and teaches me valuable lessons regarding user requirements.

Proven Hacks: Why They Make Sense

1. Consolidation on One Virtual Machine

All components of my infrastructure—including the database, web server, background jobs, and Redis—operate on a single virtual machine costing just $40 per month. While this setup lacks redundancy and relies on manual backups, it has illuminated my actual resource requirements within just two months.

Instead of getting lost in the complexities of an elaborate Kubernetes setup, I’ve discovered that my “AI-heavy” application requires a maximum of 4GB of RAM. When server crashes occur—as they inevitably do—I gain genuine insights into the points of failure, which are often unexpected.

2. Hardcoded Configuration

Using constants directly in my code, such as:

python
PRICE_TIER_1 = 9.99
MAX_USERS = 100

eliminates the need for cumbersome configuration files. While some might view this as shortsighted, this method allows me to instantly locate and track configuration changes through Git history. In just three months, I’ve redeployed these constants only three times, saving an immense amount of engineering hours.

3. SQLite as a Production Database

Running SQLite in a multi-user web environment may raise eyebrows, yet my entire database is a mere 47MB and handles 50 concurrent users effortlessly. This experience revealed that my access patterns predominantly consist of reads rather than writes, making SQLite the ideal