Embracing the 3-Month Rule: A Practical Approach to Unscalable Solutions in Tech Development
In the world of technology startups, the advice from influential thought leaders like Paul Graham resonates strongly: “Do things that don’t scale.” However, when it comes to applying this concept in the realm of coding, the specifics often remain unspoken. As I’ve been developing my AI podcast platform over the past eight months, I’ve devised a straightforward framework that I’ve dubbed the “3-Month Rule.” This principle allows me to evaluate each unscalable strategy for a period of three months. After this timeframe, the hack either demonstrates its value and gets a proper build-out, or it is discarded.
Why focus on unscalable solutions initially? In our training as engineers, we’re conditioned to construct scalable systems from the get-go. We immerse ourselves in design patterns, microservices, and comprehensive architectures intended to support millions of users. While this approach is vital for large enterprises, startups often find that building scalable solutions too early can lead to costly procrastination. Instead of preparing for hypothetical users, I’ve learned to focus on delivering straightforward, albeit imperfect, code that encourages genuine user insights.
Key Unscalable Strategies: Lessons Learned
1. Consolidating Everything on a Single VM
Running my database, web server, background jobs, and caching system on one $40/month virtual machine might sound reckless, but it’s been enlightening. With zero redundancy and manual backups, I’ve uncovered my real resource needs in a couple of months—far more than any capacity planning document could reveal. My platform occasionally peaks at 4GB RAM, proving that an elaborate setup I nearly implemented would have been a futile effort in managing idle resources.
When the server crashes—an occurrence I’ve faced twice thus far—I gather valuable data on what genuinely fails. Surprisingly, it’s never what I anticipated.
2. Opting for Hardcoded Configurations
Instead of utilizing sophisticated configuration management, I’ve embraced hardcoded values throughout my code—think constants for pricing tiers and user limits. Although this may seem outdated, it offers the advantage of rapid searchability within my codebase and ease of tracking changes through version control.
In the past three months, I’ve made only three configuration changes. The time spent redeploying these adjustments? A mere 15 minutes compared to the potential 40 hours it would take to build an application configuration service.
One Comment
Thank you for sharing your practical approach to balancing rapid iteration with foundational engineering principles. The 3-Month Rule elegantly captures the essence of how startups can leverage unscalable solutions to gain critical insights without getting bogged down in premature architecture. Your example of consolidating everything on a single VM highlights a valuable lesson: real-world resource needs often surface through hands-on experimentation, rather than theoretical planning. Similarly, prioritizing speed over complexity—like hardcoded configs and SQLite in production—underscores the importance of validating assumptions early.
This philosophy reminds me of the “fail fast” mindset, emphasizing feedback loops that inform smarter, scalable solutions down the line. Have you considered documenting key learnings from each unscalable experiment to create a pattern library? That way, when the time comes to scale, you’ll have a curated set of tested heuristics tailored to your specific platform and user behaviors. Overall, your framework is a compelling reminder that sometimes, doing it wrong quickly is the most efficient path to doing it right later.