Embracing the 3-Month Framework: A Technical Approach to Non-Scalable Solutions

In the tech world, we often hear Paul Graham’s well-known advice: “Do things that don’t scale.” However, the intricacies of applying this principle in software development rarely receive the attention they deserve. After eight months of developing my AI podcast platform, I’ve created a straightforward strategy: every unscalable solution receives a trial period of three months. Post this phase, it either demonstrates its worth and is refined into a robust system, or it is discarded.

As engineers, we are conditioned to conceive “scalable” solutions from the get-go, leaning on design patterns, microservices, and distributed systems capable of accommodating vast audiences. However, this mindset often aligns more with established enterprises than with the realities of startup life.

In a startup context, focusing solely on scalability can often lead to costly delays, as we expend resources optimizing for potential users who may never materialize or addressing challenges that might remain hypothetical. The three-month rule compels me to produce straightforward, even “imperfect,” code that ultimately facilitates my learning about what users genuinely require.

Exploring My Temporary Infrastructure Solutions

Let’s delve into the unconventional tactics I’m employing—and why they actually make sense:

1. Consolidated Operations on a Single VM

Everything—database, web server, background tasks, and Redis—is housed on a single $40/month virtual machine. There’s no redundancy, and I manually back up my data to a local system.

This approach isn’t flawed; rather, it’s illuminating. Within just two months, I’ve garnered more insights about my resource needs than any capacity analysis could provide. My “AI-intensive” platform barely nudges over 4GB of RAM. The intricate Kubernetes architecture I nearly implemented would have just been a means to manage idle containers.

When the system crashes (which has occurred twice), I gain valuable insights about the reasons behind the failures—spoiler alert: these aren’t always what I predicted.

2. Hardcoded Configurations for Simplicity

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

Instead of utilizing configuration files, I’ve opted for hardcoded constants distributed throughout my codebase. Any alteration necessitates redeployment.

This approach has its unique benefits: I can quickly search