Embracing Unscalable Solutions: My 3-Month Experimentation Framework

In the world of startups, we often encounter the advice from Paul Graham: “Do things that don’t scale.” While this is a well-known principle, the challenge lies in effectively executing it, especially when it comes to coding and technical development.

After dedicating eight months to building my AI podcast platform, I’ve devised a straightforward yet powerful framework: each unscalable approach is given a lifespan of three months. By the end of this period, we determine whether the solution has demonstrated its worth and warrants further development, or if it’s time to let it go.

Why Unscalable Solutions Matter

Engineers are typically trained to focus on scalability from the outset—crafting intricate architectures, employing design patterns, and utilizing distributed systems to accommodate millions of users. This mindset, while useful in large organizations, can hinder the agility needed in a startup environment. In fact, writing scalable code too soon can result in costly delays and inefficiencies as you may end up optimizing for future users who may never materialize.

The value in my three-month rule is its encouragement of writing straightforward and, yes, sometimes “imperfect” code that gets deployed quickly. This approach has provided invaluable insights into what my users actually require.

Current Technical Strategies and Their Benefits

1. Consolidated Infrastructure on a Single VM

I host my entire setup—a database, web server, background jobs, and caching—all within a single $40/month virtual machine, eliminating unnecessary redundancy. Manual backups to my local machine have revealed more about my resource needs in just two months than any formal capacity planning exercise could. My platform, which I anticipated to be resource-intensive, peaks at only 4GB of RAM. The complex Kubernetes architecture that I nearly built would have only led to managing idle resources.

When outages occur (yes, it’s happened twice), I gain real-time data on what fails—a learning experience that has proved far more enlightening than I had expected.

2. Hardcoded Configuration Values

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

By placing constants directly into my code rather than relying on configuration files or environment variables, changes require mere redeployment rather than extensive rework. This approach means I can quickly search my entire codebase for any configuration parameter