The Three-Month Framework: A Practical Approach to Unscalable Development

In the realm of startup culture, Paul Graham’s motto of “Do things that don’t scale” resonates deeply with many founders and developers. However, the challenge often lies in understanding how to apply this principle effectively in software development. After spending the past eight months creating my AI podcast platform, I have devised a straightforward technical framework: any unscalable workaround I implement is given a lifespan of three months. At the end of this period, it must either demonstrate its value and transition into a robust solution or be discarded.

As engineers, we frequently prioritize building scalable solutions from the outset. We rely on sophisticated design patterns, microservices, and distributed systems — the hallmarks of large-scale applications capable of handling millions of users. However, this mindset can be counterproductive in a startup environment, where the reality is that scalable code can often amount to expensive delays. By honing in on immediate user needs through my three-month rule, I am compelled to write simple, rudimentary code that can be deployed quickly, allowing me to gather real insights about what my users actually require.

Current Infrastructure Strategies That Pay Off

1. Consolidated VM Usage

I run all components of my application — the database, web server, background jobs, and caching via Redis — on a single virtual machine, costing just $40 per month. While this setup lacks redundancy, it allows me to experience and analyze my resource requirements in real-time. Within just two months, I discovered that my platform peaks at 4GB of RAM usage. The complex Kubernetes architecture I nearly implemented would have led to wasted resources managing idle containers instead of learning. Each crash (there have been two so far) provides valuable insights into actual failures, none of which align with my original expectations.

2. Direct Configuration Integration

Instead of using external configuration files or environment variables, I employ hardcoded constants within my code:

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

This approach allows me to rapidly search through my codebase for configuration values, track every change through Git history, and maintain a review process, albeit a self-review. Building a comprehensive configuration service might take a week, but I’ve only needed to make three configuration changes in three months, translating to a mere 15 minutes of