Embracing the 3-Month Rule: A Technical Approach to Unscalable Solutions
In the world of innovation and startups, the mantra inspired by Paul Graham, “do things that don’t scale,” often raises a crucial question: How do we effectively implement this in the realm of coding? After eight months of developing my AI podcast platform, I’ve adopted a straightforward yet powerful framework: every unscalable hack is granted a lifespan of three months. Following this period, each approach must either demonstrate its worth or be discarded.
The Startup Paradigm Shift
As developers, we’re conditioned to prioritize scalable solutions from the outset. We naturally gravitate towards sophisticated design patterns, microservices, and distributed systems that can accommodate vast user bases. However, in the startup environment, this focus can sometimes lead to unnecessary complexity and cost, effectively delaying the realization of actual user needs. My three-month rule encourages straightforward, albeit unrefined coding practices that enable rapid deployment and insight into user behaviors.
Practical Infrastructure Hacks
1. Consolidation on a Single VM
I host my web server, database, background jobs, and Redis on a single virtual machine costing just $40 a month. While this setup lacks redundancy and relies on manual backups, I’ve gleaned invaluable insights into my actual resource requirements over just two months. For instance, my “AI-heavy” platform has revealed a peak RAM usage of only 4GB. Had I opted for a more complex Kubernetes setup, I would have ended up managing an elaborate structure for little return.
2. Hardcoded Configurations
Every setting in my code—such as pricing tiers, user limits, and AI models—is hardcoded as constants, making changes a straightforward redeployment task. Although the thought of a configuration service sounds appealing, I have found that I’ve only updated these values three times in three months. This approach allows me to quickly track changes through Git history and minimizes tedious engineering efforts.
3. Utilizing SQLite in Production
Surprisingly, SQLite powers my multi-user web application effectively, with a database size of only 47MB that easily manages 50 concurrent users. The key lesson here: my usage statistics revealed that 95% of interactions are read operations, aligning perfectly with SQLite’s strengths. If I had opted for a heavier database like Postgres, I would have wasted time optimizing for processes that my application does not require.
4. Simplified Deployment Methodology
My deployment process
One Comment
This is such a compelling approach to balancing hustle with pragmatic engineering. The three-month rule effectively quantifies the typical lifecycle of an unscalable hack, forcing founders and developers to quickly assess value and pivot accordingly. I appreciate how your practical infrastructure hacks—like consolidating on a single VM, hardcoding configurations, and leveraging SQLite—highlight the importance of simplicity during early stages. It’s often tempting to assume more complex, scalable solutions are necessary from the start, but your experience demonstrates that rapid iteration and leaning into what works can provide critical insights without unnecessary overhead.
One aspect worth exploring further is how you plan to transition from these unscalable hacks once they prove valuable. For example, do you schedule a review process at the end of the three months to gradually introduce more scalable approaches, or do you prefer to iterate within the current setup until the need for a more robust infrastructure is undeniable? Your framework reinforces the idea that focusing on immediate learnings and validation saves time and resources, ultimately enabling more informed scalability planning when the time is right. Thanks for sharing such a practical, real-world perspective!