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

In the realm of startup culture, the well-known mantra from Paul Graham, “Do things that don’t scale,” resonates deeply. However, the implementation of this philosophy in the context of software development remains a lesser-discussed topic.

Having dedicated the past eight months to the construction of my AI podcast platform, I’ve formulated a straightforward framework that has served me well: any unscalable workaround has precisely three months to demonstrate its worth. Should it prove beneficial and effective, it transitions into a refined solution; otherwise, it’s retired.

As engineers, we often lean toward crafting scalable solutions from the outset, emphasizing design patterns, microservices, and distributed systems—all essential for managing vast user bases. However, this mindset often diverges from the realities faced by startups, where such preemptive scalability can become a costly form of procrastination. My three-month rule has guided me to produce straightforward, albeit imperfect, code that expedites shipping and ultimately reveals the genuine needs of my users.

Current Infrastructure Strategies: Practical Examples of Smart Simplicity

1. Single VM Architecture

I operate a singular virtual machine that accommodates everything—from the database and web server to background jobs and Redis—costing only $40 per month, without redundancy and supplemented by manual backups.

The genius of this setup lies in its capacity to unveil my actual resource requirements more effectively than any intricate capacity planning document could. Within two months, I’ve established that my “AI-intensive” platform requires a mere 4GB of RAM. The elaborate Kubernetes architecture I nearly implemented would have been an exercise in managing needless complexity.

When crashes occur—and they have—a wealth of insights emerges about the elements that falter, which frequently defies my expectations.

2. Hardcoded Configuration Values

Consider this code snippet:

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

I forego configuration files and environment variables in favor of hardcoded constants. This effective approach means that updating any value necessitates a simple redeployment.

The true power lies in the ability to quickly search my entire codebase for configuration values. Every pricing adjustment is documented in Git history, and updates undergo a self-review process. Creating a dedicated configuration service might take a week, yet I’ve altered