Embracing the 3-Month Rule: A Practical Approach to Non-Scalable Solutions in Tech Development
In the world of startup culture, the mantra “do things that don’t scale” is often reiterated, yet practical applications of this advice in the realm of coding are seldom discussed. This was a challenge I faced while working on my AI podcast platform over the past eight months. To address it, I’ve developed a straightforward framework: every unscalable hack is given a lifespan of three months. After this period, it either demonstrates its worth and is developed further or it’s discarded.
As engineers, we typically lean toward building scalable solutions from the outset—think design patterns, microservices, and distributed systems. While this approach may be ideal for larger companies, in a startup environment, focusing too much on scalability can often lead to wasted time and resources, delaying crucial progress.
The harsh truth is that in a startup phase, overly optimizing for future users can lead to unnecessary complexity. Implementing my 3-month rule allows me to produce simple, functional (and perhaps less polished) code that delivers real insights into user needs.
Current Infrastructure Strategies and Their Wisdom
1. Consolidated Operations on a Single VM
All components of my platform, including the database, web server, and background jobs, operate on one $40/month virtual machine. There’s no redundancy involved, and I handle backups manually.
Why is this strategic? In just a couple of months, I’ve gleaned far more about actual resource requirements than any comprehensive planning document could provide. My platform peaks at around 4GB of RAM, which means the complicated Kubernetes architecture I nearly implemented would have been managing inactive containers. When the VM has experienced downtime, I’ve noted exactly what fails—something I didn’t predict.
2. Hardcoded Constants
Instead of using configuration files or environment variables, I directly embed constants such as:
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PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
While this means redeployment is necessary for any changes, the benefit is clear: I can swiftly search the codebase for any configuration value. Each price modification ends up in git history and is reviewed—albeit by myself. Rather than spend a week developing a complex configuration service, I’ve invested a mere 15 minutes in redeployment.
