The 3-Month Experiment: A Practical Approach to Non-Scalable Solutions

In the world of startups, we often encounter the sage advice from Paul Graham: “Do things that don’t scale.” While many acknowledge the wisdom behind this statement, the challenge lies in effectively implementing it within the technical realm.

Having spent the last eight months developing my AI podcast platform, I’ve established a straightforward approach: every unscalable tactic is given a lifespan of three months. At the end of that period, it either demonstrates its worth and is transformed into a more robust solution, or it is set aside.

As engineers, our instinct is to pursue scalable solutions from the outset. We think in terms of design patterns, microservices, and distributed systems — a mindset often cultivated by larger enterprises. However, this perspective can lead to costly delays in startup environments, where we may find ourselves preemptively optimizing for issues that may never arise.

My 3-month rule encourages me to embrace simple and sometimes inefficient coding practices that can be quickly deployed. This method not only allows for immediate feedback but also provides insight into user needs.

Current Infrastructure Strategies That Work

1. Unified Virtual Machine Setup

My entire application runs on a single, $40/month virtual machine that hosts the database, web server, background processing tasks, and caching with Redis. While lacking redundancy, this setup has proven invaluable.

By operating in this simplified arrangement, I’ve gained insights into my resource utilization that documentation never could provide. My platform only peaks at 4GB of RAM, revealing that the complex Kubernetes infrastructure I almost implemented would have been unnecessary.

When system failures have occurred—twice thus far—I gained firsthand knowledge of the root causes, which were never what I anticipated.

2. Directly Hardcoded Configurations

In my codebase, you’ll find constants like:

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

By avoiding configuration files and environment variables, any adjustments require redeploying the application. The advantage is clear: I can swiftly search my entire code for a specific configuration, and any change is easily tracked in Git history.

Investing a week in building a configuration service would yield limited returns — I’ve modified these values merely three times in the past three months. Hence, 15 minutes of redeployment outweighs 40 hours of development