Embracing the 3-Month Rule: A Pragmatic Approach to Technical Decisions
In the startup world, the advice from Paul Graham to “do things that don’t scale” resonates deeply. While many discuss this concept philosophically, few delve into the practicalities of applying it in a technical landscape. After spending the last eight months developing my AI podcast platform, I’ve adopted a methodology that I refer to as the “3-Month Rule,” which allows unscalable hacks to exist for a limited timeframe. If they aren’t providing value after three months, it’s time for them to go.
Rethinking Scalability in Startups
As engineers, our instinct often leads us to craft scalable solutions right from the outset—considerations of microservices, distributed systems, and other complex architectures emerge as we envision supporting vast user bases. However, in the startup environment, such approaches can lead to expensive delays, as they anticipate user demands that simply don’t exist yet. The 3-Month Rule prompts me to prioritize simplicity and speed in my coding practices, enabling me to quickly learn what my users genuinely need.
Current Infrastructure Hacks: Navigating the Unconventional
Allow me to share some of my current infrastructure choices that may seem unconventional, yet have proven to be surprisingly effective:
1. Consolidated Virtual Machine Infrastructure
For a mere $40 a month, I’m operating everything—from the database to the web server and background jobs—on a single virtual machine (VM). This setup lacks redundancy, and I manually back up data to my local machine.
This choice has yielded invaluable insights about my resource requirements. In just two months, I’ve identified that my “AI-heavy” platform only peaks at 4GB of RAM—information that would have been lost in traditional capacity planning documents. When my system crashes—which has happened twice—I gather real-time data on failures, revealing issues that were often unexpected.
2. Hardcoded Configurations
My configuration management is straightforward: I use hardcoded constants throughout my codebase.
python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
Instead of relying on configuration files or variables, this method enables rapid searches for any configuration value. Making adjustments requires a quick redeployment rather than extensive engineering efforts—a task I’ve only needed to do three times in the last three
One Comment
This is a compelling approach that really highlights the importance of pragmatic experimentation during a startup’s early stages. The “3-Month Rule” effectively balances the need for speed and learning with the discipline to cut losses on unproductive hacks. I especially appreciate your emphasis on low-cost, high-visibility infrastructure choices, like consolidating everything on a single VM and using hardcoded configurations. These tactics seem to encourage rapid iteration without getting bogged down in premature optimization, which can often derail progress.
One thought to add is that documenting these hacks—even as temporary solutions—can be invaluable. Over time, you might find patterns or insights that inform more scalable architectures when the time comes. Also, considering automated testing for these quick-and-dirty setups could help catch issues early, ensuring that transformations into more robust systems are smoother when needed. Overall, your framework underscores the value of intentional, time-boxed experimentation to discover what truly matters—an excellent mindset for early-stage tech product development.