The 3-Month Rule: A Practical Approach to Unscalable Solutions
In the startup world, the mantra “do things that don’t scale” is widely recognized, yet the practical implementation of this principle in the coding phase often goes overlooked. After dedicating the past eight months to constructing my AI podcast platform, I’ve adopted an effective framework that aligns with this philosophy: any temporary hack that cannot scale is granted a lifespan of just three months. By the end of this period, it must either demonstrate its value and undergo proper refinement or be discarded entirely.
As developers, we are typically conditioned to think in terms of scalability from the outset. We gear ourselves towards creating architectural masterpieces—utilizing design patterns, microservices, and distributed systems capable of accommodating millions of users. However, this is often a viewpoint more suited to larger corporations than to agile startups.
In a startup environment, focusing on scalable solutions can frequently manifest as costly procrastination. By optimizing for potential users who might never materialize, we can overlook the immediate needs of current users. My three-month framework compels me to produce straightforward, albeit “imperfect,” code that is deployable and teaches me valuable lessons about user requirements.
Current Infrastructure Hacks: Unconventional yet Strategic
1. Consolidated into a Single VM
With all my essential functions—including the database, web server, background jobs, and Redis—running on a single $40-per-month virtual machine, there is no redundancy and backups are manual.
Why is this strategy effective? It has provided me with invaluable insight into my true resource requirements over the past few months. Instead of setting up an elaborate Kubernetes infrastructure that would have managed empty resources, I have gained direct data on real performance under load, discovering the peak usage reaches merely 4GB of RAM. Each system crash has offered unexpected insights, often revealing that the problems were not what I anticipated.
2. Hardcoded Configuration
Instead of utilizing config files or environment variables, I’ve opted for hardcoded constants throughout my codebase:
python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
This may appear primitive, but it allows me swift searches across my entire codebase. Each price change is easily tracked in version history, and every configuration update receives a review. Opting for a dedicated configuration service would have required a week
