The 3-Month Approach: A Practical Framework for Non-Scalable Coding
In the world of startups, the popular mantra from tech luminary Paul Graham, “Do things that don’t scale,” is frequently touted, yet seldom explored in terms of coding practices. As I navigate the development of my AI podcast platform over the past eight months, I have crafted a straightforward framework: every approach that lacks scalability has a lifespan of three months. After this period, it must either demonstrate its value and be transformed into a robust solution or be discarded entirely.
Why Embrace Non-Scalable Solutions?
As software engineers, we often find ourselves trained to create scalable systems from the outset—implementing microservices, adopting distributed architectures, and developing design patterns meant to manage thousands, perhaps millions, of users. However, in the startup arena, attempting to build for future scalability can sometimes be an exercise in costly procrastination, focusing resources on problems that may never materialize. My 3-month framework encourages the creation of straightforward, if imperfect, code that can be quickly deployed, allowing me to glean insights into the needs of my users.
Current Infrastructure Hacks: Smarter Than They Seem
1. Single Virtual Machine Deployment
I run my entire stack—including the database, web server, background jobs, and caching—on a single virtual machine costing $40 a month. This design comes with zero redundancy and relies on manual backups.
Why is this approach beneficial? Within just two months, I’ve garnered more understanding about my resource requirements than any capacity planning document could provide. My “AI-focused” platform typically operates with a peak usage of only 4GB of RAM. The complex Kubernetes architecture I initially considered would have involved managing unused containers, wasting valuable time.
When my system crashes (which has happened twice), I gather real data on unexpected failure points—information that is far more enlightening than I anticipated.
2. Hardcoded Configuration Values
python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
With constants embedded throughout my code and no use of configuration files or environment variables, updating any setting necessitates a redeployment.
The hidden advantage? I can quickly search my codebase for any configuration value using grep. Changes to pricing are easily trackable through git history, and each alteration is subject to my own code review.
