Embracing the 3-Month Rule: A Strategic Approach to Building Non-Scalable Solutions in Tech
In the startup world, the guidance often given by Paul Graham stands out: “Do things that don’t scale.” Yet, there’s a curious silence surrounding the practical execution of this principle, particularly in the realm of technology development. Having spent eight months building my AI podcast platform, I’ve crafted a straightforward yet effective framework for integrating this concept into my coding practices: a strict three-month lifespan for every unscalable solution. After this trial period, I evaluate whether a solution demonstrates its worth and deserves a robust build, or if it will be retired.
As engineers, we are conditioned to focus on scalable architectures from the very beginning, often diving into sophisticated design patterns, microservices, and distributed systems aimed at accommodating millions of users. However, this mentality can be a misstep for startups. In many cases, focusing on scalable code can be a form of costly procrastination, hindering progress while we pre-emptively solve problems that may never arise. By adhering to my three-month rule, I can prioritize producing straightforward, less refined code that actually delivers tangible insights about user needs.
My Pragmatic Infrastructure Hacks and Their Value
1. Unified Virtual Machine Deployment
Currently, my database, web server, background jobs, and Redis operate on a single $40-per-month virtual machine (VM). This setup lacks redundancy and relies on manual backups to my local machine.
Why is this strategic? It has provided unparalleled insights into my genuine resource requirements. Within two months, I’ve learned that my AI-focused platform peaks at just 4GB of RAM. The comprehensive Kubernetes architecture I almost implemented would have resulted in managing empty containers rather than addressing real-time needs. When the system crashes, which has occurred twice, I gain valuable information about actual points of failure—often surprising in their nature.
2. Simple Hardcoded Configurations
For instance, I may have constants such as:
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PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
Instead of relying on separate configuration files, these values are integrated into the codebase, necessitating a redeployment for any adjustments.
The upside? I can quickly locate any configuration across the code via a single search command. Modifications are documented in Git history, and I personally
