The 3-Month Rule: A Pragmatic Approach to Unscalable Solutions
In the realm of startups, the iconic advice from Paul Graham to “do things that don’t scale” often sparks engagement, yet the practical implementation of this concept in software development remains less discussed. After eight months dedicated to developing my AI podcast platform, I’ve crafted a straightforward framework that involves embracing unscalable hacks for a limited duration—three months, to be exact. At the end of this period, each hack must either demonstrate its value for further expansion or be consigned to oblivion.
Embracing a Learning-Centric Model
As software engineers, we’re often conditioned to prioritize scalable architectures right from the start. We dive into advanced design patterns, microservices, and distributed systems, crafting intricate infrastructures designed to accommodate millions of users. Yet, this ‘big company’ mentality can often lead to inefficiencies in the startup ecosystem, where scalability might simply represent a costly diversion.
The three-month rule compels me to adopt a more straightforward coding style that prioritizes shipping over perfection, guiding me to understand my users’ genuine needs.
Insights from My Current Infrastructure Hacks
1. Unified Virtual Machine Setup
Instead of spreading my database, web server, and background jobs across multiple instances, I operate everything on a single virtual machine costing $40 per month. While this setup lacks redundancy and necessitates manual backups, it has provided invaluable insights into my actual resource demands. Within two months, I discovered that my “AI-intensive” platform peaks at just 4GB of RAM, something that a more elaborate Kubernetes architecture would have obscured. Each crash (and there have been a couple) has revealed critical lessons about system failures—none of which aligned with my expectations.
2. Hardcoded Configurations
My configuration philosophy is simple: keep everything hardcoded, from pricing tiers to user limits. This means that updates require a redeployment, but the trade-off is highly efficient tracking. I can swiftly search my codebase for any parameter and have a complete history of changes at my fingertips. What might take a week to build into a configuration service ends up being a 15-minute redeployment task that I’ve only needed to execute three times in three months.
3. Leveraging SQLite for Multitasking
Despite the conventional wisdom against using SQLite for multi-user applications, my decision to do so has proven fruitful. My entire database is a mere 47MB, effortlessly managing
One Comment
This post provides a compelling perspective on the pragmatic use of unscalable hacks within a defined timeframe to accelerate learning and product validation. I appreciate the emphasis on “shipping over perfection” — often, startups get bogged down trying to architect the perfect scalable system from day one, which can delay valuable user feedback. Your approach of intentionally embracing unscalable solutions for three months allows for rapid experimentation and informed decision-making about what truly warrants investment.
The example of consolidating infrastructure on a single VM and using hardcoded configs highlights how understanding the real demands and behaviors of your system can prevent unnecessary complexity early on. This incremental, learning-centric methodology reminds me of the “fail fast” and “build small, learn fast” principles that are critical in early-stage development.
I’m curious—how do you plan to evolve these hacks as your platform grows, and what criteria will you use to determine when to replace them with scalable solutions? Your framework seems like a practical blueprint for balancing agility and scalability in a startup environment.