Embracing the 3-Month Rule: A Technical Approach to Unscalable Solutions
In the realm of startups and innovative projects, Paul Graham’s adage, “Do things that don’t scale,” resonates strongly. However, the challenge lies in translating that philosophy into actionable steps, particularly within the coding landscape. After dedicating eight months to developing my AI podcast platform, I’ve established a straightforward yet effective framework: every unscalable approach gets a three-month trial period. If it proves its worth, it gets a proper development upgrade; if not, it’s time to move on.
The startup Mindset: Why Scale Early Can Backfire
As engineers, our instinct is often to aim for scalability right from the beginning—considering design patterns, microservices, and distributed systems. This is the mentality that larger companies leverage, but in the startup environment, focusing too heavily on scalability can morph into costly procrastination. We might find ourselves preparing for an influx of users that hasn’t materialized yet and addressing potential issues that may never arise. My 3-month rule pushes me to craft simple, direct code—code that is not necessarily elegant but effectively communicates what our users truly require.
A Look at My Current Infrastructure Strategies
1. Single VM Deployment
Currently, my entire backend—database, web server, background tasks, and Redis—is running on one virtual machine costing only $40 a month. This setup has zero redundancy and relies on manual backups.
Why it works: Over the past two months, I’ve uncovered invaluable insights regarding my resource needs, which would have otherwise eluded me via traditional capacity planning documents. My “AI-centric” platform peaks at just 4GB of RAM, meaning the complex Kubernetes structure I nearly implemented would have only managed empty containers. Each crash (occurring twice so far) provides real data on what truly fails—often surprising insights that I couldn’t foresee.
2. Hardcoded Configuration Values
In my project, configuration settings are hardcoded directly into the files—think constants like:
python
PRICE_TIER_1 = 9.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
The benefit: This approach allows me to search my entire codebase quickly for configuration values. Every change to price points or settings is diligently tracked in git history and reviewed (by myself, in a sense!). Rather than spending a week on establishing a configuration
