The 3-Month Strategy: A Pragmatic Approach to Navigating Unscalable Code
In the world of startups and fast-paced tech development, there’s a well-known piece of advice from Paul Graham: “Do things that don’t scale.” While this wisdom often resonates with entrepreneurs, there is a lack of conversation around how this principle translates to the technical backend of software development.
Having spent eight months developing my AI podcast platform, I’ve crafted a straightforward framework: every unscalable solution is given a lifespan of three months. After this period, it must demonstrate its worth and evolve into a more robust system or face the axe.
The reality is that as engineers, our training emphasizes building scalable solutions right from the outset—think of intricate design patterns, microservices architectures, and distributed systems capable of serving millions. While this big-company mentality serves established enterprises well, it can hinder startups.
In the startup sphere, creating scalable software early on often turns into an exercise of costly procrastination. It leads to optimizing for users who have yet to arrive and tackling issues that may never arise. My three-month rule compels me to adopt a more direct approach, allowing me to write straightforward, “imperfect” code that is deployable and helps me better understand what users truly need.
Current Infrastructure Techniques: Effective and Ingenious
1. Consolidation on a Single VM
I operate my database, web server, background jobs, and caching all on a single $40/month virtual machine—no redundancies, with manual backups to my local storage.
Why is this approach beneficial? In just two months, I’ve gained a clearer understanding of my actual resource requirements than I would have through any capacity planning documentation. My platform, even with its AI functionalities, peaks at 4GB of RAM. The complex Kubernetes infrastructure I almost set up? It would have been a management nightmare filled with idle containers.
Each time the system crashes (which has happened twice), I gather valuable insights on what truly fails—it’s rarely what I initially predicted.
2. Hardcoded Configuration Parameters
python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
I avoid configuration files and environment variables, opting instead for constants embedded throughout my code. Altering any parameter necessitates a redeploy.
The brilliance of this method lies in its simplicity: I can quickly search my
