Embracing the 3-Month Rule: A Practical Approach to Non-Scalable Solutions in Tech Development
In the entrepreneurial world, a common piece of wisdom from Paul Graham encourages startups to “do things that don’t scale.” However, the implementation of this advice in a coding context is often left underexplored. After eight months of developing my AI podcast platform, I’ve established a simple but effective framework: every unscalable hack receives a lifespan of three months. Following this period, it either demonstrates its worth and is properly developed or is phased out completely.
The Challenge of Scalability in Startups
In the realm of engineering, there’s often a strong focus on creating scalable solutions right out of the gate. This includes utilizing design patterns, microservices, and distributed systems—architectural strategies that are well-suited for managing high user volumes. However, for emerging startups, this approach can lead to expensive delays as we optimize for potential users who may never materialize.
This is where my 3-month rule comes into play. It encourages a mindset shift towards writing straightforward, albeit imperfect, code that is deployable and capable of providing insights into user needs.
My Approach to Infrastructure: Innovative Hacks that Inform
1. Consolidated Operations on a Single VM
I run my entire stack—database, web server, and background jobs—on a single virtual machine for just $40 a month. There’s no redundancy, and backups are manually saved to my local drive.
This unconventional choice has proven strategic; monitoring my actual resource use has been far more informative than any capacity planning document. Initially, my “AI-focused” platform required only 4GB of RAM, indicating that a complex Kubernetes environment would have been an unnecessary burden.
When the system crashes—which it has twice—I gain concrete data about the points of failure, often revealing unexpected insights.
2. Direct Configuration Management
I employ hardcoded configurations scattered throughout the codebase, such as:
python
PRICE_TIER_1 = 9.99
MAX_USERS = 100
While this method may appear rudimentary, it allows for rapid adjustments through a straightforward grepping process. Each price alteration is documented in my Git history and subjected to review—albeit self-reviewed. Building an overhaul to implement a configuration service would consume a week’s worth of effort, while I’ve only made three changes in the last three months.
3. Using SQLite in Production
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One Comment
Thank you for sharing this insightful framework. I appreciate how you’ve clearly outlined the importance of deliberately applying unscalable hacks as a learning tool rather than a long-term solution. The 3-month rule is a powerful mental model—it shifts focus from premature perfection to experimental agility, allowing startups to validate assumptions rapidly.
Your practical examples, like consolidating operations on a single VM and employing hardcoded configurations, underscore the value of simplicity and quick iteration in early stages. These approaches enable data-driven decision-making, revealing real resource constraints and user behaviors that often go unnoticed with overly complex infrastructure from the outset.
It might be interesting to explore how to systematically evaluate when a particular hack has exceeded its 3-month window and should be replaced with scalable solutions. Developing a lightweight review cadence could help ensure this process remains disciplined as the project grows. Overall, your approach strikes a healthy balance between pragmatism and experimentation—an inspiring model for early-stage technical decision-making.