The 3-Month Rule: A Pragmatic Approach to Learning Through Experimentation in Tech
In the world of technology startups, the phrase “do things that don’t scale,” popularized by Paul Graham, is often thrown around but rarely explored in practical terms, especially within the realm of coding. After dedicating eight months to the development of my AI podcast platform, I’ve devised a straightforward framework that I call the “3-Month Rule.” This rule dictates that every non-scalable approach I take will be given a trial period of three months. At the end of this period, each method either proves its effectiveness and gets refined, or it’s discarded.
As developers, we’re often conditioned to prioritize scalable solutions, focusing on microservices, elegant architectures, and distributed systems capable of accommodating vast user bases. However, in a startup environment, striving for scalability can lead to unnecessary delays and complex solutions that address hypothetical user needs rather than the real problems at hand. By adhering to my 3-month rule, I am encouraged to produce straightforward, even imperfect, code that can be deployed quickly, thus providing valuable insights into what users genuinely require.
Current Non-Scalable Strategies: Insights and Advantages
1. Unified Virtual Machine Infrastructure
All of my platform’s components—database, web server, background jobs, and caching—all operate on a single Virtual Machine (VM) costing just $40 a month. This setup is devoid of redundancy and relies on manual backups to my local machine.
Why is this approach beneficial? In just two months, I have gained more clarity regarding my actual resource needs than I ever could from elaborate capacity planning exercises. Surprisingly, the memory usage of my supposedly “AI-heavy” platform peaked at only 4GB RAM. The intricate Kubernetes architecture I nearly built would have simply served to manage empty containers.
Moreover, each time the system encounters issues (which has happened twice), I gather real-time data on what truly fails—often contrary to my initial expectations.
2. Static Configuration Management
My configuration values are hardcoded directly within the application:
python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
There are no external configuration files or environment variables; altering any of these constants necessitates a redeployment.
The advantage? I can rapidly search my entire codebase for any configuration variable. Each pricing adjustment
