Embracing the 3-Month Rule: A Practical Approach to Experimentation in Development

In the entrepreneurial world, the advice of Paul Graham resonates strongly: “Do things that don’t scale.” Yet when it comes to its application within software development, the specifics can be murky. After eight months of building my AI podcast platform, I’ve established a straightforward framework that has guided my journey: every unscalable innovation is given a trial period of three months. At the end of that period, we either enhance it based on demonstrated value or phase it out entirely.

As engineers, we are generally conditioned to focus on scalable solutions from the outset — incorporating sophisticated design patterns, microservices, and distributed systems into our projects. While these are important considerations for larger organizations, they can quickly become excessive for startups. In reality, pursuing scalability prematurely often leads to wasted resources, as we are optimizing for hypothetical user bases and addressing potential problems that may never surface. By adhering to my 3-month rule, I am compelled to implement straightforward, albeit imperfect, code that can be rapidly delivered. This approach has provided invaluable insights into user needs.

My Current Temporary Infrastructure Hacks and Their Merits:

1. A Single Virtual Machine for All Operations

Hosting my database, web server, background jobs, and caching services on just one $40/month virtual machine may appear reckless, but it’s taught me more about my resource needs in a matter of months than any theoretical planning ever could. The evidence suggests the “AI-focused” nature of my platform only requires about 4GB of RAM at peak. The elaborate Kubernetes architecture I considered would have only managing dormant containers.

When the system has crashed (twice so far), I’ve gathered tangible data about the real points of failure — surprisingly, they weren’t what I anticipated.

2. Hardcoded Configuration Parameters

Instead of utilizing configuration files or environment variables, my constants look something like this:

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

While this lacks elegance, it has a hidden advantage: accessing configuration values across my codebase is instantaneous. Changes are easily tracked in version control, and every adjustment is reviewed — albeit by me. The time saved here is significant; manual changes have occurred just three times in three months, equating to about 15 minutes of deployment versus a projected