Embracing the 3-Month Rule: A Practical Approach to Non-Scalable Solutions in Tech Development

In the tech world, it’s a common saying that you should “do things that don’t scale,” popularized by venture capitalist Paul Graham. However, there’s often a gap between this advice and how to effectively apply it in programming.

After dedicating the last eight months to developing an AI podcast platform, I have established a straightforward framework: any unscalable work receives a trial period of three months. At the end of this period, if it demonstrates its worth, we’ll invest in building a robust version; if not, it will be phased out.

Rethinking Scalability in startup Development

As engineers, we typically strive for scalable solutions right from the get-go, leveraging intricate design patterns and architectures that can support millions of users. This level of planning, while sophisticated, often caters more to large corporations than to startups.

In a startup environment, spending time optimizing for scalability can be a form of procrastination, as it often addresses theoretical users and hypothetical issues before any real demand exists. By adhering to my three-month rule, I prioritize rapid, straightforward coding that allows me to deliver features and learn directly from user interaction.

Current Tactical Approaches That Work

1. Single Virtual Machine for All Services

I run my database, web server, background processes, and Redis all from a single $40-per-month virtual machine. This setup offers zero redundancy and relies on manual backups to my local device.

This approach is surprisingly effective: I’ve gained clarity on my actual resource requirements in just two months, insights that no capacity-planning document could provide. It turns out my AI platform only peaks at 4GB of RAM, meaning the Kubernetes architecture I nearly implemented would have simply managed empty containers. Furthermore, when crashes occur (yes, they have happened), I receive invaluable information about the failure points, which are seldom what I had anticipated.

2. Hardcoded Configurations Across the Board

In my code, you’ll find constants like:

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

With no configuration files or environment variables, changing a parameter necessitates a redeployment. The advantage? I can search my entire codebase in seconds to locate any configuration value. Each price alteration gets