The Three-Month Rule: A Practical Approach to Unscalable Solutions for Startups

In the startup world, the mantra “Do things that don’t scale,” famously articulated by Paul Graham, is often echoed but seldom dissected, particularly when it comes to its application in coding practices. After dedicating eight months to developing my AI podcast platform, I have embraced a straightforward philosophy: each unscalable hack I implement is given a trial period of just three months. This framework not only allows for rapid prototyping but also determines whether a solution can evolve into a more robust architecture or be discarded altogether.

The startup Paradigm: Rethinking Scalability

As engineers, we are conditioned to develop scalable systems from the outset—crafting intricate designs like microservices and distributed architectures that are primed for vast user bases. However, in the startup landscape, devoting resources to scalable code can be a form of expensive procrastination, as it often addresses theoretical issues for users who may never materialize. My three-month rule compels me to focus on straightforward, albeit imperfect, solutions that can be deployed quickly and reveal genuine user needs.

Insights from My Current Infrastructure Hacks

1. Consolidated Resources on a Single VM

I have chosen to host my entire platform, including the database, web server, and background jobs, on a single $40/month virtual machine, intentionally forgoing redundancy and relying on manual backups. This decision has proven invaluable; in just two months, I’ve gained insights into my actual resource requirements that traditional capacity planning could never measure. For example, my “AI-heavy” platform reaches a memory peak of only 4GB, revealing that the complex Kubernetes setup I nearly implemented would have been pointless.

Whenever the system crashes—an event that has occurred twice so far—I receive invaluable data about the real failure points, which are often surprising.

2. Hardcoded Configurations for Simplicity

Instead of using configuration files or environment variables, my constants are hardcoded throughout the code, such as:

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

This straightforward method enables me to search for configuration values in seconds, track changes via Git history, and conduct code reviews on my deployments. Rather than taking a week to set up a configuration service, my approach has required just a few redeploy