Embracing the 3-Month Rule: A Pragmatic Approach to Early Development

In the ever-evolving world of technology, Paul Graham’s advice to “do things that don’t scale” rings true, yet seldom do we discuss the practical implementation of this principle in coding, particularly for startups. Reflecting on my progress while developing an AI podcast platform over the past eight months, I’ve adopted a simple yet effective framework: every unscalable solution has a lifespan of three months. Within this period, it must demonstrate value or be discarded entirely.

As engineers, we often fall into the trap of designing for scalability from day one. We envision sophisticated architectures featuring design patterns, microservices, and distributed systems aimed at accommodating countless users. However, this approach frequently leads to expensive delays in startups, optimizing for hypothetical users and solving non-existent problems. My three-month rule compels me to employ straightforward, albeit less-than-perfect code that ultimately helps me understand genuine user needs.

Innovative Infrastructure Hacks that Work

1. Centralized Virtual Machine Utilization

I run my entire stack—including the database, web server, background jobs, and Redis—on a single virtual machine costing $40 per month. This lack of redundancy might seem reckless, especially with manual backups to my local machine, but here’s the silver lining: I’ve gained invaluable insights regarding my resource requirements. From this minimal setup, I’ve discovered that my AI platform typically peaks at just 4GB of RAM. The Kubernetes configuration I nearly implemented would have resulted in managing empty containers rather than addressing real demands.

Each time the system crashes (yes, twice so far), I gain practical data about what truly fails—and it’s never what I anticipated.

2. Hardcoded Configurations for Simplicity

Instead of using configuration files or environment variables, I opted for hardcoded constants:

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

Though this may appear primitive, the advantage lies in the ease of tracing configuration changes. I can quickly search my codebase for any value, and all modifications are captured in Git history. Since I’ve altered these parameters only three times in the past three months, the quick redeployments of mere minutes significantly outweigh the extensive engineering time required to establish a dedicated configuration service.

3. SQLite as My Production Database

Yes,