Embracing the 3-Month Rule: A Pragmatic Approach to Non-Scalable Solutions

In the ever-evolving landscape of startup development, the wisdom of Paul Graham, who famously advocated for “doing things that don’t scale,” takes on a vital yet often misunderstood form. While the idea is well-known, the practical implementation in the world of coding remains somewhat elusive.

After eight months of crafting my AI podcast platform, I’ve devised an effective framework that I like to call the 3-Month Rule. This approach allows every unscalable hack I apply to have a lifespan of three months. Following this period, each solution is either validated and properly developed or discarded.

The challenge for many developers stems from a foundational mindset built around creating scalable solutions from the get-go. We often find ourselves entrenched in intricate design patterns, microservices architectures, and distributed systems—concepts that serve large enterprises rather than early-stage startups. In this environment, however, prioritizing scalability can sometimes serve as an expensive form of procrastination, tackling challenges that may never arise. My 3-month framework encourages the development of straightforward, even “imperfect,” code that leads to real-world insights about user needs.

Current Infrastructure Hacks: Smart Solutions in Disguise

1. Consolidation into a Single VM

Currently, I’m running my database, web server, background jobs, and Redis on a single $40/month virtual machine. While this might raise eyebrows regarding redundancy, the reality is that I’ve gained invaluable insight into my usage patterns. I’ve learned my “AI-heavy” platform needs just 4GB of RAM, which means that the elaborate Kubernetes setup I nearly implemented would have been managing mostly idle resources. When issues arise, I receive genuine data on failure points—often unexpected but immensely enlightening.

2. Embracing Hardcoded Configuration

With hardcoded constants throughout my codebase, such as:

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

I might forego the flexibility of configuration files. However, the real advantage lies in the simplicity of tracking changes through Git history. Redeploying for configuration changes has been minimal—just three updates in three months, a stark contrast to the complexity of building a dedicated configuration service.

3. Using SQLite in a Multi-User Environment

Imagine operating