The Three-Month Framework: A Pragmatic Approach to Unscalable Solutions

In the tech world, the phrase “Do things that don’t scale,” famously coined by Paul Graham, is often echoed. However, the practical implementation of this advice, especially in programming, is rarely discussed. After eight months of developing my AI podcast platform, I’ve devised a straightforward approach: every unscalable technique is given a lifespan of three months. If it proves valuable, it’ll be polished; if it doesn’t, it will be discarded.

As engineers, we typically aim to create scalable solutions from the outset. We often get caught up in design frameworks like microservices and distributed architectures—all in an effort to accommodate a potentially vast user base. However, this often translates to delays at startups, where optimizing for future users means ignoring the immediate needs at hand. My three-month rule compels me to produce simple, albeit imperfect, code that can be effectively deployed, allowing me to genuinely understand user requirements.

My Current Strategies: Lessons from Unconventional Choices

1. Consolidation on a Single VM

At present, my database, web server, background jobs, and caching are all hosted on a single $40/month virtual machine. This approach offers zero redundancy and relies on manual backups to my local system.

Why is this approach clever? In just two months, I’ve gained a clearer insight into my resource needs than I would have from any theoretical capacity document. My so-called “AI-intensive” platform generally maxes out at just 4GB of RAM. The intricate Kubernetes architecture I almost implemented would have meant managing a plethora of idle containers.

Whenever the system does crash—yes, it’s happened twice—I’m getting actionable data about the real issue, and interestingly, it’s rarely what I anticipated.

2. Hardcoded Configuration Parameters

Consider this sample of my code:
plaintext
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
I’ve opted not to use configuration files or environment variables, choosing instead to hardcode constants throughout my application. Each change requires a redeployment.

The hidden advantage? I can search my entire codebase for any configuration value in mere seconds. Changes to pricing are easily tracked in my Git history and each modification undergoes a self-review process (which, admittedly, involves me reviewing my own pull requests).

Setting