The 3-Month Rule: A Practical Approach to Non-Scalable Solutions in Tech Development
In the realm of startup advice, Paul Graham’s maxim, “Do things that don’t scale,” is often referenced. Yet, few delve into actionable steps for tech implementation. Over the past eight months of developing my AI podcast platform, I’ve adopted a straightforward but effective strategy: any temporary, non-scalable solution is given a lifespan of three months. After this period, it either establishes its value and is properly integrated or is retired.
The Common startup Dilemma
As engineers, we often fall into the trap of crafting solutions with scalability in mind from the get-go. We get entangled in sophisticated design patterns, microservices, and distributed systems—all aimed at supporting an expansive user base. However, in a startup environment, chasing scalability too soon can lead to nothing more than delayed progress. Instead of addressing immediate users’ needs, we end up building a framework for demands that may never materialize.
My three-month rule compels me to write more straightforward, less refined code that can be deployed quickly, enabling me to genuinely understand what my users require.
My Current Infrastructure Strategies and Their Surprising Insights
1. Single VM for Everything
I currently operate a database, web server, background jobs, and Redis all on one $40/month Virtual Machine (VM). There’s no redundancy, and backups are manual.
Why this works: My resource usage understanding has skyrocketed in just two months, providing insights that no elaborate capacity planning could offer. My platform, predominantly reliant on AI, peaks at a mere 4GB of RAM. The complex Kubernetes infrastructure I nearly implemented would have spent resources on idle containers.
When the system fails—twice thus far—I’ve gathered data on actual points of failure. Notably, the issues are rarely what I anticipated.
2. Hardcoded Settings Across the Codebase
My configurations look something like this:
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
There are no separate config files; simply constants dotted throughout the code. Any adjustments necessitate a redeployment.
The advantage: I can quickly search my entire code for any configuration value. Each price modification is logged in Git history, and I review every change myself.
Creating a dedicated configuration service might take
