Embracing the 3-Month Innovation Rule: A Pragmatic Approach to Scalable Solutions
In the realm of startups and innovation, the mantra of “doing things that don’t scale” is a well-known piece of advice from Paul Graham. However, few delve into the practicalities of applying this principle within the coding domain. After dedicating eight months to developing my AI podcast platform, I’ve adopted a straightforward framework: every unscalable hack is given a lifespan of just three months. At the end of this period, we assess its effectiveness to determine whether it should be formalized into a robust solution or abandoned altogether.
The common challenge for engineers is their inclination towards building scalable solutions from the outset. While sophisticated architecture—like microservices and distributed systems—may cater to millions of users, this kind of thinking often serves larger organizations better than startups. For a new venture, the pursuit of scalability can lead to costly delays, as resources are allocated to theoretical users and issues that may never arise. My 3-month rule emphasizes the necessity of crafting straightforward, albeit imperfect, code that is delivered quickly, ultimately revealing the genuine needs of our users.
Insightful Infrastructure Decisions: My Current Hacks
1. Consolidated Operations on a Single Virtual Machine
Right now, my entire operation—comprising the database, web server, background jobs, and Redis—functions on a single $40/month virtual machine. While this setup lacks redundancy and relies on manual backups to my local machine, it has allowed me to gain invaluable insights into my actual resource consumption within just two months. Interestingly, my AI-driven platform requires only 4GB of RAM at peak usage. The complex Kubernetes architecture I contemplated would have been merely managing empty containers. Each time the system experiences downtime (which has happened twice), I receive critical data on what fails—often not what I had anticipated.
2. Simplified Configuration Management
In my current configuration, constants such as prices and user limits are hardcoded directly into the codebase:
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
This approach, while seemingly archaic, offers a surprising efficiency. Without config files or environment variables, I can swiftly search the entire codebase for any configuration value. Each price modification is meticulously documented in git history and reviewed personally, ensuring strong oversight without the complexity of
