The 3-Month Framework: A Practical Approach to Unscalable Solutions in Tech

In the tech world, the mantra “Do things that don’t scale” popularized by Paul Graham has become foundational advice for startups. However, implementing this principle can be a challenge, particularly for engineers accustomed to developing scalable code from the get-go.

After spending the past eight months crafting my AI podcast platform, I unearthed a practical methodology that revolves around a fundamental concept: any unscalable hack I employ has a lifespan of three months. After this period, I evaluate its effectiveness; if it proves valuable, I invest the time to scale it appropriately; if not, I let it go.

Rethinking Scalability

As engineers, we often find ourselves trapped in the mindset of designing for large-scale solutions. We envision microservices and distributed architectures, perfect for handling potential millions of users. However, within the startup realm, such thinking can lead to costly delays—devoting resources to optimize for demands that may never materialize. My three-month rule encourages me to write straightforward, less-than-perfect code that not only gets deployed but also reveals the real needs of my users.

Ingenious Hacks That Work for My Platform

1. Consolidated Infrastructure on a Single VM

I run my entire operation—a database, a web server, a background job processor, and Redis—on a single $40 monthly virtual machine. While this may seem reckless due to the lack of redundancy and reliance on manual backups, it has provided insights into my actual resource needs. In just two months, I discovered that my platform, heavily reliant on AI, only requires 4GB of RAM at peak usage. The complex Kubernetes infrastructure I initially considered could have wasted resources managing empty containers.

Furthermore, when crashes occurred (twice so far), I received invaluable information about failure points, which often differed from my initial assumptions.

2. Hardcoded Configurations

My codebase is devoid of configuration files or environment variables; instead, essential values are hardcoded within the files. For instance, constants like:

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

Although it means redeploying for changes, this approach allows me to quickly search through my codebase and track modifications through version control. Building a separate configuration service would have