Embracing the Unscalable: A Practical Approach to Learn and Adapt in Tech Development
In the world of technology startups, the wisdom of Paul Graham rings true: “Do things that don’t scale.” However, when it comes to practical implementation—especially in coding—this advice is often overlooked. After dedicating eight months to developing my AI podcast platform, I’ve established a straightforward framework: any unscalable approach receives a trial period of three months. If it demonstrates its value within that timeframe, it gets further development; if not, it is discarded.
As engineers, we are naturally inclined to devise scalable solutions right from the outset. We gravitate towards sophisticated design patterns, microservices architectures, and distributed systems—designs prepared to support millions of users. While this mindset is suitable for large corporations, for startups, striving for scalability too early can often feel like an exercise in futility. It leads to investing resources into optimizations for users that don’t yet exist, while delaying direct engagement with current problems. My three-month rule encourages me to create straightforward, albeit imperfect, code that can be deployed rapidly, giving me invaluable insights into the true needs of my users.
Current Infrastructure Strategies: Why They’re Smart Choices
1. All Services on a Single VM
Currently, my database, web server, background tasks, and caching all operate on a single, modest $40 monthly virtual machine, offering no redundancy and relying on manual backups. This setup may sound unwise, but it has been incredibly informative. Within just two months, I’ve gained a clearer understanding of my actual resource demands than any extensive capacity planning documentation could have provided. For instance, my platform’s peak usage only requires 4GB of RAM, negating the need for a complex Kubernetes architecture I nearly implemented. Each time there’s a crash—I’ve experienced two—I’m able to gather real data regarding the failures, often revealing issues that weren’t initially on my radar.
2. Hardcoded Configuration Values
Instead of utilizing configuration files or environment variables, I’ve hardcoded key parameters throughout my codebase, such as:
plaintext
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
While this might seem outdated, the advantage lies in the simplicity of searching through the entire codebase for any configuration value quickly. Every price modification is easily tracked in
