Embracing Imperfection: A 3-Month Framework for Scalable Learning in Tech
The tech world often reverberates with the advice of prominent figures like Paul Graham, who encourages us to “do things that don’t scale.” However, applying this principle effectively in the coding sphere isn’t frequently discussed. After spending eight months developing my AI podcast platform, I’ve crafted a practical approach: every unscalable solution gets a trial period of three months. At the end of this timeframe, we either solidify its value through proper implementation or phase it out entirely.
As engineers, we typically lean towards crafting scalable solutions from the outset. We’re trained to embrace sophisticated design patterns, microservices, and elaborate distributed systems capable of supporting millions of users. But this is often a mentality that aligns more with larger corporations than startups. In the startup environment, pursuing immediate scale can, in fact, be a form of costly procrastination—addressing issues that may never arise while optimizing for an audience that doesn’t yet exist. My three-month rule compels me to produce straightforward, sometimes rudimentary code that gets deployed, ultimately revealing insights about actual user needs.
Current Infrastructure Strategies: Smart Solutions for Uncharted Problems
1. Unified Operations on a Single VM
I run all components—database, web server, background processes, and Redis—on a single $40/month virtual machine, embracing the absence of redundancy and opting for local backups. This approach has proven insightful; I’ve gleaned more about my resource requirements in a mere two months than any capacity planning document could offer. My “AI-centric” platform typically uses only 4GB RAM, making a complex Kubernetes setup unnecessary. Each crash (which has happened twice) provides valuable data about system weaknesses, revealing surprises along the way.
2. Hardcoded Configuration Values
In my application, configuration values such as pricing tiers and maximum user limits are hardcoded directly into the code, eliminating the use of config files or environment variables. This may appear inefficient, but the efficiency here lies in the speed at which modifications occur—redeploying any changes typically takes just 15 minutes compared to the 40 hours it would require to establish a configuration service. Additionally, tracking changes through Git ensures that I can easily audit my decision-making.
3. Utilizing SQLite in Production
Current operations include employing SQLite for a multi-user web application, with a database size of only 47MB that accommodates 50 users simultaneously. My
