Embracing Non-Scalability: My 3-Month Development Framework

In the tech world, advice such as “Do things that don’t scale” is often bandied about, especially in startup circles, yet the process of applying such guidance in real-world coding is seldom explored. After eight months of developing my AI podcast platform, I’ve implemented a straightforward framework: every unscalable approach or workaround is given a lifespan of just three months. At the end of this period, it either proves its worth, advancing to a more sophisticated implementation, or it is discarded.

The core principle for engineers usually revolves around crafting scalable solutions right from the outset. We’re drawn to design patterns, microservices, and distributed systems—the sophisticated architectures meant to support millions of users. This mindset, however, is often tailored for larger organizations, leading startups to focus on scalability prematurely. In truth, optimizing for hypothetical future users can merely serve as an expensive diversion. My 3-month rule compels me to produce straightforward, albeit rudimentary, code that can be launched quickly, ultimately revealing what actual users need.

Current Efficiency Strategies I’ve Employed

1. Single VM Environment

I’ve opted to run everything—my database, web server, background jobs, and caching—on one $40/month virtual machine with no redundancy and only manual backups. While this may seem simplistic, it has provided valuable insights into my actual resource requirements over just two months. For instance, I’ve established that my “AI-heavy” platform peaks at 4GB of RAM, which suggests that the elaborate Kubernetes orchestration I had nearly created would have involved wasted resources managing dormant containers. Each crash I’ve encountered has yielded real-world data on what fails, often in ways I never anticipated.

2. Hardcoded Configurations

With constants scattered throughout my codebase, changing configurations means redeployment—think PRICE_TIER_1 = 9.99. While lacking traditional configuration files may sound inefficient, it allows me to locate any configuration value across my entire codebase in seconds. As a result, I’ve modified these values a mere three times in three months, which translates to a quick redeployment process instead of lengthy configuration service development.

3. Using SQLite in Production

Remarkably, I’m harnessing SQLite for a multi-user web application where the database size is just 47MB and it seamlessly accommodates up to 50 concurrent users. This experience has revealed that my access patterns are overwhelmingly read-d