The 3-Month Rule: A Practical Guide to Embracing Non-Scalable Solutions in Development
In the tech community, Paul Graham’s mantra, “Do things that don’t scale,” is widely recognized, yet few explore how to effectively integrate this philosophy into their coding practices. After eight months of developing my AI podcast platform, I’ve established a straightforward method: every unscalable solution I implement receives a trial period of three months. At the end of this timeframe, it either proves its worth and gets a robust build, or it is discarded.
As engineers, we are often conditioned to devise scalable systems from the outset—think design patterns, microservices, and distributed architectures capable of accommodating vast numbers of users. However, this mindset often mirrors that of a large organization.
In a startup environment, crafting scalable solutions can turn into costly paralysis, where we waste resources optimizing for non-existent users and addressing problems that may never materialize. My three-month rule encourages me to create straightforward, albeit “inelegant,” code that can be deployed quickly, helping me acquire essential insights into user needs.
My Current Infrastructure Hacks: Smart Choices Behind Non-Scalable Approaches
1. Consolidated Server Resources
All essential services—from databases to web servers—operate on a single virtual machine (VM) costing $40 per month, with no redundancy and manual backups. While this may seem risky, it has provided invaluable insights about my resource requirements in just two months. I found that my platform’s peak usage is only 4GB of RAM—almost leading me to waste time on a complex Kubernetes setup that would have managed idle resources. Additionally, when crashes occur, I gain real data on failures, revealing unexpected vulnerabilities.
2. Hardcoded Configurations for Simplicity
By using hardcoded constants like PRICE_TIER_1 = 9.99 and MAX_USERS = 100 instead of complex configuration files, I can quickly search my codebase and track changes through git history. Notably, I’ve only adjusted these parameters three times in the past three months, demonstrating that the time lost in potential configuration setup vastly outweighs the minimal time spent redeploying for necessary updates.
3. Leveraging SQLite for Performance
Running a multi-user web application on a 47MB SQLite database has been surprisingly effective, supporting 50 concurrent users effortlessly. My analysis of user behavior revealed a staggering 95% read versus 5% write efficiency—ideal for
