The 3-Month Framework: A Practical Approach to Unscalable Solutions in Tech
When it comes to startup innovation, we often hear the wise words of Paul Graham: “Do things that don’t scale.” However, actionable strategies for implementing this philosophy in technical development are not frequently discussed. Over the last eight months, as I’ve developed my AI podcast platform, I’ve crafted a simple yet effective framework: every unscalable solution is given a lifespan of three months. After this period, I assess whether it warrants a more robust build or if it should be discarded.
Understanding the Mindset
As developers, there’s a tendency to jump straight into creating scalable solutions. We gravitate towards sophisticated design patterns, microservices, and distributed systems—ideal for accommodating millions of users. However, in a startup context, focusing on scalability too soon can often lead to costly delays.
My three-month rule encourages me to embrace simplicity and directness in my coding, allowing me to ship functional products quickly. This hands-on experience reveals real user needs rather than hypothetical scenarios.
Key Infrastructure Decisions: Smart Hacks
1. Consolidated Operations on a Single VM
I currently run my entire platform—from the database to the web server—on a single $40/month virtual machine. This setup may seem risky, lacking redundancy and relying on manual backups, but the insights I’ve gained are invaluable.
In just two months, I’ve gathered critical information about my actual resource consumption: my “AI-heavy” platform peaks at 4GB RAM. I avoided the complexity of Kubernetes, which would have required management of idle resources, and during a couple of crashes, I learned exactly what failures occurred—often not what I anticipated.
2. Simplified Configuration Management
My configuration is straightforward with hardcoded constants, such as:
plaintext
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
This approach negates the need for complex config files or environment variables. Changes require a simple redeploy, and I can easily trace every adjustment in my git history. Over the past three months, I’ve made only three changes, saving me countless hours of engineering effort.
3. Using SQLite in Production
Surprisingly, I have leveraged SQLite for my multi-user web application, and it performs excellently with a mere 47MB database
