The 3-Month Framework: A Pragmatic Approach to Unscalable Solutions in Software Development

In the fast-paced world of startups, the mantra “Do things that don’t scale,” as popularized by Paul Graham, often rings true. However, discussions around the practical implementation of this advice, particularly in coding practices, are often sparse. After eight months of building my AI podcast platform, I’ve established a straightforward framework: every unscalable endeavor is given a limited lifespan of three months. After this period, it either validates its worthiness and transitions into a robust solution, or it gets phased out.

Unpacking the Scalable Solution Myth

Traditionally, engineers are trained to take a scalable approach right from the outset. We get immersed in design patterns, microservices, and distributed systems—crafting intricate architectures capable of supporting millions of users. However, in a startup landscape, prioritizing scalability can often lead to costly delays. In my experience, the 3-month rule has encouraged me to develop simpler, more immediate solutions that reveal genuine user needs instead of preemptively addressing problems that may not exist.

Current Infrastructure Hacks: Smart Solutions for Learning

Here’s a glimpse into my unconventional infrastructure choices and the valuable lessons they offer:

1. Consolidated Operations on a Single VM

At present, all aspects of my application—from the database to background tasks—operate on a single $40/month virtual machine with no redundancy and manual local backups. This approach has allowed me to glean insights into my resource requirements far more effectively than formal capacity planning reports could. For instance, I learned that my “AI-heavy” platform consistently peaks at 4GB of RAM, thereby avoiding the complications of a complex Kubernetes setup that would have required managing unutilized containers. Each time my server crashes (which has happened a couple of times), I gain firsthand data about failure points, which are often different from my initial assumptions.

2. Simplified Hardcoded Configuration

I’ve opted for hardcoded configuration across my codebase:

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

There are no external configuration files or environment variables—just constants embedded in the code. When changes are needed, redeployment is the only requirement. This strategy may seem crude, but it provides an unparalleled ease of tracking and quick updates through version control,