Embracing the 3-Month Rule: A Pragmatic Approach to Non-Scalable Solutions in Software Development
When embarking on a tech journey, one piece of wisdom often touted is Paul Graham’s mantra: “Do things that don’t scale.” It’s a phrase that many of us hear but few take the time to translate into actionable steps, especially when it comes to coding. I’ve spent the last eight months developing an AI podcast platform, and through trial and error, I’ve crafted a straightforward framework that I’ve come to rely on: every unscalable solution I’ve implemented is allotted a mere three months of testing. If it proves its worth, it gets a permanent fixture; if not, it’s phased out.
As software engineers, we often gravitate toward constructing solutions meant to scale from the very beginning. We become enamored with complex architectures, microservices, and distributed systems — the ideal setup for accommodating vast user bases. However, this “big company” mentality can be premature at the startup phase. Often, striving for scalability can equate to delaying progress and sacrificing crucial learning opportunities.
My three-month rule compels me to write straightforward, albeit imperfect, code that enables swift deployment and actual interaction with users. This directly informs what my audience truly needs.
Insightful Infrastructure Hacks Worth Exploring:
1. Consolidating Resources on a Single VM
Think about managing your database, web server, background tasks, and caching all on a $40/month virtual machine. While it seems risky to rely on a single point of failure with no redundancy, this strategy has provided invaluable insights into my real resource consumption. I’ve discovered that my AI podcast platform operates optimally with only 4GB of RAM. Instead of implementing a complicated Kubernetes configuration that would ultimately be wasted on idle resources, I’ve gleaned actionable data from crash reports that highlight unexpected vulnerabilities.
2. Utilizing Hardcoded Configurations
I’ve opted for hardcoded constants like this:
python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
By avoiding config files or environment variables, any adjustments involve a straightforward redeployment, allowing me to quickly track changes via version control. While the development of a sophisticated configuration service might be a week-long endeavor, given that I’ve only made three modifications in the last three months, the time saved significantly outweighs the benefits of complexity.
