Embracing the 3-Month Rule: A Technical Approach to Unscalable Solutions
In the realm of startups and innovative projects, Paul Graham’s adage, “Do things that don’t scale,” resonates strongly. However, the challenge lies in translating that philosophy into actionable steps, particularly within the coding landscape. After dedicating eight months to developing my AI podcast platform, I’ve established a straightforward yet effective framework: every unscalable approach gets a three-month trial period. If it proves its worth, it gets a proper development upgrade; if not, it’s time to move on.
The Startup Mindset: Why Scale Early Can Backfire
As engineers, our instinct is often to aim for scalability right from the beginning—considering design patterns, microservices, and distributed systems. This is the mentality that larger companies leverage, but in the startup environment, focusing too heavily on scalability can morph into costly procrastination. We might find ourselves preparing for an influx of users that hasn’t materialized yet and addressing potential issues that may never arise. My 3-month rule pushes me to craft simple, direct code—code that is not necessarily elegant but effectively communicates what our users truly require.
A Look at My Current Infrastructure Strategies
1. Single VM Deployment
Currently, my entire backend—database, web server, background tasks, and Redis—is running on one virtual machine costing only $40 a month. This setup has zero redundancy and relies on manual backups.
Why it works: Over the past two months, I’ve uncovered invaluable insights regarding my resource needs, which would have otherwise eluded me via traditional capacity planning documents. My “AI-centric” platform peaks at just 4GB of RAM, meaning the complex Kubernetes structure I nearly implemented would have only managed empty containers. Each crash (occurring twice so far) provides real data on what truly fails—often surprising insights that I couldn’t foresee.
2. Hardcoded Configuration Values
In my project, configuration settings are hardcoded directly into the files—think constants like:
python
PRICE_TIER_1 = 9.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
The benefit: This approach allows me to search my entire codebase quickly for configuration values. Every change to price points or settings is diligently tracked in git history and reviewed (by myself, in a sense!). Rather than spending a week on establishing a configuration
One Comment
This is a compelling approach that balances pragmatism with the bootstrap mentality—especially the emphasis on rapid experimentation within a defined timeframe. The 3-month rule serves as a disciplined way to test unscalable solutions without falling into the trap of over-engineering early on. I particularly appreciate how you’ve grounded these principles in practical infrastructure choices, like starting with a single VM, which minimizes initial complexity and cost while providing real-world data to inform future scaling decisions.
Your strategy also highlights an important lesson: early-stage startups benefit immensely from simplicity and quick iteration cycles. Hardcoded configs, while not ideal long-term, enable faster development and clearer understanding of what truly needs flexibility. It’s a reminder that oftentimes, “perfect” technical solutions should be deferred until validated by actual user and system data, rather than designed upfront in anticipation of future needs.
Looking ahead, I wonder if integrating automated monitoring during these 3-month trials might help identify bottlenecks or failures even more quickly, enabling data-driven decisions on whether to iterate or pivot. Overall, your framework offers a pragmatic roadmap for balancing speed, learnings, and resource constraints—an approach I believe many early-stage engineers and founders will find invaluable.