The Three-Month Framework: A Pragmatic Approach to Unscalable Solutions
In the tech landscape, the familiar wisdom often echoed by Paul Graham — “Do things that don’t scale” — is common advice. Yet, a significant gap remains in discussing the practical implementation of this philosophy within software development. After eight months of building my AI podcast platform, I’ve crafted a straightforward framework to navigate this dilemma: I commit to each non-scalable solution for just three months. Within that period, it must demonstrate its value or face the chopping block.
As engineers, we’re frequently trained to think about scalability from the beginning. Concepts like design patterns, microservices, and distributed systems dominate discussions, making it tempting to get caught up in “big company” thinking. However, in a startup environment, pursuing scalable solutions too early often translates to costly procrastination — optimizing for users who might never come and addressing potential problems that may never arise. This three-month rule compels me to produce straightforward, albeit flawed code that actually gets deployed, enabling me to gain valuable insights into true user needs.
Current Infrastructure Innovations: How They Drive Learning
1. Consolidation on a Single VM
Everything from the database to web server runs on a $40/month virtual machine. While this approach lacks redundancy and requires manual backups, its brilliance lies in the learning opportunity it provides. I have quickly grasped my actual resource requirements: my “AI-heavy” platform peaks at just 4GB of RAM. The complex Kubernetes setup I had considered would have resulted in managing underutilized containers. Each crash (which has occurred twice) yields priceless data on failure points — insights that often defy my expectations.
2. Hardcoded Configurations
Parameters like pricing tiers and user limits are hardcoded directly into my application. While it may seem impractical without configuration files, this method offers efficiency. Adjustments spark a quick redeploy, and every change is meticulously tracked through Git history. Instead of spending extensive hours creating a configuration service, I’ve only modified values three times in three months, simplifying deployment to 15 minutes rather than 40 hours of engineering work.
3. Leveraging SQLite for Production
Surprisingly, I’m using SQLite for a web application serving multiple users. Maintaining an entire database of just 47MB, it effortlessly accommodates 50 concurrent users. This choice has revealed my access patterns: predominantly read operations. Had I opted for more complex systems like PostgreSQL, I’d have been
