The 3-Month Strategy: A Practical Approach to Unscalable Development
In the realm of startup development, Paul Graham’s famous advice to “do things that don’t scale” is often quoted but rarely mapped out into actionable steps. As a software engineer launching an AI podcast platform over the past eight months, I’ve created a straightforward framework: each unscalable solution is put to the test for three months. After this period, it either proves its worth and receives a proper build-out or is discarded.
In startup culture, there’s a tendency to prioritize scalable solutions, often rooted in the mindset of larger organizations. Concepts like microservices and intricate architectures designed to accommodate millions of users are compelling, but for early-stage startups, they can significantly delay product delivery. My three-month rule has pushed me to prioritize simple, functional code that actually gets deployed, allowing me to understand user needs without getting bogged down in unnecessary complexity.
My Current Strategies and Their Unexpected Benefits
- Consolidated Hosting on a Single VM
Everything—database, web server, background jobs, Redis—operates on a single virtual machine costing just $40 per month. While this setup boasts zero redundancy and requires manual backups, the insights I’ve gained far surpass the predictions from any capacity planning document. My platform, which I initially thought would be resource-heavy, only needs around 4GB of RAM. The complex Kubernetes architecture I contemplated would have been overkill for an empty use case.
- Hardcoded Configuration for Quick Adjustments
Configuration settings like pricing tiers and user limits are hardcoded and spread across my codebase. Admittedly, this requires redeploying to make changes, but it enables me to quickly search through my entire project. Git history neatly tracks adjustments, and each change is reviewed—even if it’s just by me. This has proven far more efficient than developing a dedicated configuration service, as my adjustment frequency has remained low.
- Utilizing SQLite in a Production Environment
My multi-user web app currently runs on SQLite, which has comfortably handled up to 50 concurrent users with a database size of just 47MB. This choice led to valuable insights, revealing that my access patterns favor reads over writes. Had I opted for a more complex system like Postgres from the start, I would have unnecessarily fixed issues that weren’t present.
- Direct Git Pushes to Production
Deployment is achieved with a single command: `
