The 3-Month Rule: A Pragmatic Approach to Non-Scalable Solutions in Tech Development
In the world of software engineering, Paul Graham’s mantra to “do things that don’t scale” often hangs in the air, yet it rarely comes with a clear roadmap for implementation, especially in the realm of coding. After dedicating eight months to the development of my AI podcast platform, I’ve devised a straightforward framework that I find effective: any non-scalable workaround is granted a lifespan of three months. If it proves its worth, we refine it for scalability; if not, it’s time to let it go.
As engineers, we frequently feel compelled to create “scalable” solutions from the outset—think design patterns, microservices, and distributed systems that cater to millions of potential users. This approach, while impressive, often mirrors the mindset of larger companies rather than the agile, need-driven environment of startups.
At startups, the push for scalability can actually represent an unnecessary delay. We’re optimizing for users who may never materialize and solving problems that might not exist. My 3-month rule compels me to employ simpler, more straightforward—and yes, sometimes “messy”—solutions that can be deployed quickly and offer insights into what users genuinely require.
Current Non-Scalable Strategies: Why They Actually Add Value
1. Consolidated Infrastructure on a Single VM
Currently, I operate my database, web server, background jobs, and caching all on one $40 per month virtual machine (VM). There’s no redundancy, and I manage backups manually.
This setup, often deemed suboptimal, provides surprising advantages. In just two months, I’ve gained deeper insights into my actual resource requirements than I could have through any capacity-planning document. My so-called “AI-heavy” platform peaks at just 4GB of RAM. The complex Kubernetes architecture I nearly implemented? It would have involved managing idle containers instead of effectively handling user requests.
When outages occur (and they have), I gather tangible data on what really fails—spoiler alert: it’s rarely what I had anticipated.
2. Hardcoded Configuration Values
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
I maintain a straightforward system without configuration files or environment variables. Constants are directly embedded within the code, and changes necessitate a redeployment
