Embracing the Unscalable: The 3-Month Experimentation Framework for Startups
Paul Graham famously advised entrepreneurs to “do things that don’t scale,” yet few address the practical aspects of applying this wisdom to the world of coding and technology development. After eight months of constructing my AI podcast platform, I’ve devised a straightforward methodology: every makeshift, unscalable solution gets a trial period of three months. If it demonstrates its worth, it gets developed further; if not, it’s phased out.
As software engineers, we’re often conditioned to prioritize scalable solutions from the outset, focusing on intricate design patterns, microservices, and distributed frameworks designed to accommodate a vast user base. This mindset, however, aligns more closely with corporate environments than with lean startups. In many cases, pursuing a scalable approach too soon can be equivalent to delaying crucial decisions, particularly when we’re optimizing for potential users that may never materialize.
My 3-Month Rule pushes me to deploy simpler, more straightforward code that can be released quickly, allowing me to glean insights into user needs through direct experience rather than speculation.
Current Infrastructure Strategies: Why They Work
1. Consolidated Operations on a Single VM
I operate my entire backend—database, web server, background jobs, Redis—on a single virtual machine that costs just $40 per month. While this setup lacks redundancy, it has provided invaluable learning experiences. Within two months, I’ve gained a clearer understanding of my resource requirements, discovering that my platform’s peak usage hovers around 4GB of RAM. The sophisticated Kubernetes framework I nearly constructed would have involved managing unused containers.
Each crash I’ve experienced has revealed insights about system vulnerabilities, although the points of failure were often unexpected.
2. Hardcoded Configurations Across the Application
Instead of using configuration files or environment variables, I opt for hardcoded constants like this:
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PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
This approach enables me to quickly search my codebase for configuration values, leading to efficient version control of pricing changes. Revising any of these constants necessitates a simple redeployment, which takes only 15 minutes versus the 40 hours it would have taken to develop a configuration management service.
3. Utilizing SQLite in Production
Although SQLite is typically not used for
One Comment
This is a compelling perspective on embracing unscalable, immediate solutions early in a startup’s lifecycle. I appreciate how you highlight the value of quick experimentation and learning over premature optimization. Your example of consolidating operations on a single VM is a practical reminder that, especially in the early stages, simplicity can lead to faster insights and more agile development.
I’ve found that hardcoded configurations, while seemingly “quick and dirty,” can indeed streamline iteration when your environment is still evolving. However, as you scale, it might be worthwhile to gradually introduce more flexible configuration management to facilitate smoother transitions.
Regarding SQLite for production, it’s a bold choice but understandable in certain contexts—particularly in early MVPs or when dealing with low to moderate traffic. It underscores the point that technology choices should align with current needs rather than future projections, especially when those choices enable rapid deployment and learning.
Overall, your framework exemplifies the importance of balancing practicality with strategic planning—acknowledging that sometimes, doing things that don’t scale initially can set a stronger foundation. Looking forward to hearing how your approach evolves as your platform matures!