Embracing the 3-Month Rule: A Pragmatic Approach to Building Unscalable Solutions
In the tech world, it’s common to hear the advice from Paul Graham to “do things that don’t scale.” However, there’s often a lack of guidance on how to effectively apply this principle, particularly when it comes to programming. After spending the last eight months developing my AI podcast platform, I’ve devised a straightforward framework that I call the “3-Month Rule.” This idea is simple: any unscalable technique I implement is given a three-month trial period. At the end of this timeframe, I assess whether it has provided value and deserves a permanent place in my architecture, or whether it should be retired.
As developers, we are often conditioned to prioritize scalability from the outset. We aim to build sophisticated design patterns, utilize microservices, and enhance distributed systems—all to accommodate projected millions of users. However, this method of thinking is frequently better suited for larger companies.
In the startup environment, the focus on scalable solutions can sometimes lead to costly delays where we optimize for users who may never arrive, addressing issues that aren’t pressing at the moment. My 3-Month Rule pushes me to produce straightforward, sometimes “bad,” code that can be tested and iterated upon quickly, allowing me to gain insights into users’ actual needs.
My Pragmatic Infrastructure Hacks: Smart Choices for Learning
1. Simplifying with a Single Virtual Machine
Currently, my entire infrastructure—from the database to the web server and background tasks—runs on a single $40/month virtual machine. This setup lacks redundancy and involves manual backups to my local system. While it might seem unwise, this strategy has provided valuable insights into my resource requirements in a matter of weeks. I’ve discovered that, despite my platform’s AI-intensive nature, it only demands 4GB of RAM during peak usage. Had I implemented a complex Kubernetes architecture, I would have been managing empty containers.
Whenever my system crashes— which has happened a couple of times—I gain concrete data regarding its weak points. Interestingly, the failures are never where I anticipated.
2. Hardcoded Configuration for Efficiency
For configuration, I engage in hardcoding values directly into the codebase:
plaintext
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
There are no configuration files
One Comment
This post offers a refreshingly pragmatic approach to balancing immediacy and scalability, especially relevant for startups and solo developers. The 3-Month Rule resonates as an effective strategy—it encourages rapid experimentation and learning without getting bogged down by premature optimization. I’ve found that this mindset not only accelerates iteration cycles but also provides concrete data to inform future architecture decisions.
Your emphasis on using simple, cost-effective setups—like the single VM and hardcoded configs—aligns well with the principle of “fail fast, learn fast.” It’s a reminder that initially investing in perfect infrastructure can sometimes delay identifying the core user needs and pain points. The key takeaway is that embracing unscalable solutions temporarily can yield valuable insights, enabling more deliberate scaling when the time comes.
One consideration, however, is to monitor the technical debt that might accrue with hardcoded values or minimal infrastructure. Regularly scheduled reviews at the end of each 3-month period seem essential to reassess whether those quick-and-dirty solutions still serve the product’s growth trajectory or need to be replaced with more scalable approaches.
Overall, your framework exemplifies a healthy balance between pragmatic immediate execution and strategic planning—great food for thought for anyone navigating the startup journey!