Embracing the 3-Month Experimentation Framework: A Technical Guide for Startups
In the world of startups, the mantra of “doing things that don’t scale” is often touted, particularly by influence-makers like Paul Graham. However, the challenge lies in translating this wisdom into practical, coding strategies. After dedicating eight months to developing my AI podcast platform, I’ve established a straightforward framework: every unscalable tactic is granted a lifespan of just three months. If it demonstrates its value, we’ll refine it; if not, we let it go.
As engineers, we are frequently conditioned to create scalable architectures from the outset—think in terms of elegant design patterns, microservices, and complex distributed systems. Yet, focusing on scalability is typically a luxury, especially in the startup environment. Here, constructing scalable solutions can often become an expensive form of procrastination, as it entails preparing for user bases that may not yet exist and addressing problems that might never arise. My three-month rule encourages me to produce straightforward, albeit “imperfect,” code that aids in genuinely understanding user needs.
Current Infrastructure Hacks and the Genius Behind Them
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Single VM for Everything
Utilizing a single $40/month virtual machine to manage my database, web server, background jobs, and Redis may seem reckless, but it has revealed significant insights. Within two months, I’ve gleaned more about my actual resource needs than any capacity planning document could offer. The platform peaks at 4GB RAM, which would have made an intricate Kubernetes deployment unnecessary. Each crash provides genuine data about real issues, which often differ from my initial expectations. -
Hardcoded Configurations
Instead of employing configuration files or environment variables, I’ve chosen to hardcode constants directly within my application. For instance, pricing tiers and user limits are readily embedded in the code. This may appear primitive, but it grants me instant access to configuration values and tracks changes in a transparent manner via git history. Rather than dedicating a week to create a configuration service, I’ve only needed about 15 minutes for redeployment after minimal changes over three months. -
SQLite for Production
Running SQLite for a multi-user web application might seem counterintuitive, yet my compact database of 47MB handles fifty concurrent users seamlessly. This setup has illuminated my access patterns—95% read and 5% write. Had I opted for PostgreSQL, I would be bogged down in optimizing
