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
