Embracing the Unscalable: My 3-Month Experimentation Framework for Startups

In the startup world, agility and adaptability are crucial. Paul Graham famously advised us to “do things that don’t scale,” but how do we take that advice and apply it directly to software engineering? After eight months of developing my AI podcast platform, I’ve created a straightforward framework for managing unscalable methods: each hack is given a three-month lifespan. At the end of that period, we either build it out properly based on its proven value, or it’s retired.

The startup Dilemma: Scaling Too Soon

As engineers, our training emphasizes the creation of scalable solutions from the outset. Concepts such as microservices, distributed systems, and other advanced architectures are invaluable for accommodating millions of users, but they often represent the mindset of larger corporations. For startups, pursuing scalability too early can be detrimental, as it leads to costly delays and distractions.

My three-month framework aims to break this cycle, allowing me to focus on rapidly deploying straightforward and, yes, sometimes “inelegant” code. The goal is to gain insights into user needs without the burden of over-complicated solutions.

Current Infrastructure Hacks and Their Strategic Value

1. Consolidating Everything on One VM

I operate my entire tech stack—database, web server, background jobs, and Redis—on a single $40/month virtual machine, relying on manual backups. Some might see this as reckless, but here’s why it’s a smart move: in just two months, I gained valuable insights into my actual resource requirements. I learned that my “AI-heavy” platform maxes out at 4GB of RAM, which is far more efficient than the complex Kubernetes architecture I initially considered. Every crash has provided real-time data about system failures, revealing unexpected weak points.

2. Utilizing Hardcoded Configuration

Instead of employing configuration files or environment variables, I use hardcoded constants throughout my codebase:

python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"

While redeploying is necessary for every change, I can quickly search for any configuration element using simple commands. In the past three months, I’ve only made three adjustments, saving me immense engineering time while ensuring that every change is documented in my version control history.

**3. Implementing SQLite in Production