Embracing the 3-Month Rule: A Practical Approach to Unscalable Development
In the world of startups and software engineering, the phrase “Do things that don’t scale,” popularized by Paul Graham, is often echoed. However, the challenge lies not in understanding this principle but in effectively putting it into practice, especially in the realm of coding. After dedicating eight months to developing my AI podcast platform, I’ve devised a straightforward framework: any unscalable hack is granted a lifespan of three months. At the end of this period, it either proves its worth, prompting a proper implementation, or it gets the axe.
As engineers, our training often gears us towards creating scalable solutions right from the outset. We become enamored with concepts like design patterns and microservices, composing elaborate architectures meant to accommodate millions of users. However, this mindset is typically more suited for established businesses than for startups. In early-stage ventures, striving for scalability can lead to costly delays, as we find ourselves optimizing for future users we may never acquire, addressing nonexistent problems. My 3-month rule encourages the development of straightforward, albeit “imperfect,” code that actually gets deployed and reveals what users genuinely need.
Insights from My Current Infrastructure Hacks
1. Unified VM Architecture
All components—from the database to the web server—are consolidated on a single $40/month virtual machine. While this approach sacrifices redundancy, it has provided invaluable insights into my resource needs. Within two months, I determined that my “AI-heavy” platform peaks at 4GB of RAM, rendering the complex Kubernetes setup I nearly initiated unnecessary. Each crash (and there have been two) has yielded actual data on failures, which are often not what I anticipated.
2. Simplified Configuration Management
I rely on hardcoded configurations scattered throughout my code, where price tiers and user limits are defined as constants:
python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
While a dedicated configuration service would take considerable time to build, the current approach has proven efficient for the three configuration changes I’ve made in three months. It’s a matter of 15 minutes of redeployment instead of weeks of engineering effort.
3. SQLite as a Production Database
Running SQLite for a multi-user web app may sound imprudent, but my database is
One Comment
This post wonderfully highlights the importance of embracing rapid experimentation and practical engineering over premature optimization—a mindset crucial for startups and early-stage projects. The 3-month rule offers a tangible framework to prioritize learning, user feedback, and iterative improvement.
I especially appreciate the emphasis on lightweight infrastructure hacks like a unified VM and simplified configurations, which reduce time-to-deploy and allow for real-world testing. These approaches reflect a deep understanding: that gaining empirical insights quickly is often more valuable than meticulously architecting for scalability from day one.
One insight I’d add is that, after the initial 3-month proof of concept, use that data to inform which scalable architectures are genuinely necessary versus those that can wait. Balancing quick wins with strategic planning ensures that as your platform grows, your infrastructure evolves organically instead of prematurely, saving both time and resources.
Thanks for sharing these practical, experience-based strategies—many founders and engineers could benefit from adopting this flexible, trial-driven mindset!