Embracing the 3-Month Rule: A Practical Guide to Implementing Unscalable Solutions

In the startup world, the widely recognized advice from Paul Graham—”Do things that don’t scale”—is often mentioned, yet the nuances of how to incorporate this principle in a technical context seem to be overlooked. After spending eight months developing my AI podcast platform, I’ve crafted a straightforward framework: every unscalable approach gets a life cycle of three months. At the conclusion of this period, each method either demonstrates its value and evolves into a robust solution or it’s discarded.

The challenge lies in the mindset of engineers trained to prioritize scalable solutions from the outset. From design patterns to microservices and distributed systems, the focus tends to be on architectures that support millions of users. Nevertheless, in a startup environment, a fixation on scalability can lead to unnecessary delays and expenses, as it often involves addressing hypothetical issues that may never arise.

The Power of Simplicity: My Current Infrastructure Strategies

Here are some of the unorthodox approaches I’ve adopted, accompanied by insights on why they’ve proven effective:

1. Consolidated Operations on a Single VM

All crucial components—database, web server, background jobs, and Redis—operate on a single $40/month virtual machine, without redundancy and relying on manual backups.

Why It Works: In just two months, I’ve gained more clarity about my actual resource requirements than any formal capacity planning document could provide. My platform, which I anticipated would demand heavy computational resources, peaks at just 4GB of RAM. The intricate Kubernetes framework I considered would have resulted in managing dormant containers. Furthermore, when crashes have occurred, they revealed surprising insights about system vulnerabilities.

2. Hardcoded Configuration Values

Instead of separate configuration files or environment variables, I use hardcoded constants:

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

The Advantage: With this method, I can swiftly search my entire codebase for configuration values. Changes are easily tracked through Git history, and each update undergoes code review—even if it’s just my own evaluation. Developing a dedicated configuration service would have consumed an entire week, yet I’ve made changes three times in three months, saving countless hours in engineering resources.

3. Utilizing SQLite in Production

Surprisingly, I