Embracing the Unscalable: A 3-Month Experimental Framework for Startups
Many entrepreneurs have come across the renowned advice from Paul Graham: “Do things that don’t scale.” While this concept is well-acknowledged, the practical application, especially in coding, is seldom discussed. Drawing from my own journey in developing an AI podcast platform over the past eight months, I have created a straightforward framework: every unscalable method I implement has a lifespan of exactly three months. Within this time, the approach must either demonstrate its value for further investment or be discarded without hesitation.
As engineers, we’re often conditioned to chase after “scalable” solutions right from the outset. We delve into intricate design patterns and adopt complex architectures geared towards accommodating millions of users. This mindset is typical for larger organizations, but in a startup environment, investing in scalability can turn into a costly delay, causing us to focus on optimizing for an audience that may not even exist yet.
Enter my 3-month rule. It pushes me to produce straightforward, sometimes “imperfect” code that delivers tangible results and reveals the true needs of my users.
My Current Infrastructure Strategies and Their Undeniable Wisdom
1. Consolidated Environment on a Single VM
I host my entire architecture—a database, web server, background jobs, and Redis—on a single, cost-effective $40/month virtual machine, without redundancy and relying on manual backups.
Why is this a smart move? Within just two months, I’ve gained deeper insights into my resource requirements than any formal capacity planning could offer. I discovered that my “AI-heavy” platform typically operates with a peak memory usage of 4GB. The intricate Kubernetes setup I nearly implemented would’ve just been a management task for idle containers.
When crashes do occur (yes, they’ve happened), I receive critical real-time data about the actual causes, which often surprise me.
2. Hardcoded Configuration Across the Board
plaintext
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
Instead of using configuration files or environment variables, I have constants directly in the code. Any changes necessitate a redeployment.
The brilliance of this approach? I can quickly search my codebase for configuration values, and every price adjustment is accounted for in git history. While creating a dedicated configuration service might take a week to develop
