Embracing the 3-Month Rule: A Practical Approach to Unscalable Solutions in Software Development
In the tech community, the wisdom of Paul Graham’s maxim, “Do things that don’t scale,” is widely acknowledged, yet the practical application of this advice in software development remains a topic less frequently explored. During the past eight months of constructing my AI podcast platform, I have developed a straightforward framework that I adhere to: each unscalable solution is granted precisely three months before I determine its fate. This strategy either leads to the substantiation of its value and subsequent refinement or its removal from the project entirely.
As engineers, we are often conditioned to prioritize scalable architectures from the outset. We delve into the realms of design patterns, microservices, and distributed systems, all while envisioning solutions capable of accommodating millions of users. However, this mindset is more suited to large organizations rather than agile startups, where optimizing for a user base that doesn’t yet exist can often lead to undue delays. My 3-month guideline compels me to create simpler, more direct code that may not appear ‘ideal’ but ultimately enables me to grasp the true needs of my users.
Current Infrastructure Strategies: Why They’re More Insightful Than You Think
1. Single VM Infrastructure
My entire stack—database, web server, background jobs, and caching—is hosted on a single $40 per month virtual machine without redundancy and backed up manually to my local computer. This setup might not sound wise initially, but it has allowed me to gain a clearer understanding of my resource requirements in just two months. I discovered that my platform’s real memory needs peak at around 4GB. The complex Kubernetes architecture I nearly implemented would have been managing idle containers instead. Whenever a crash occurs (which has happened twice), I gather real data about the actual vulnerabilities—often different from what I anticipated.
2. Hardcoded Configuration Values
Instead of utilizing configuration files or environment variables, I have spread constants throughout my code, such as:
python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
This choice simplifies my workflow; any configuration change necessitates a redeployment. The advantage? I can search my entire codebase for any config parameter in seconds. As a result, modifications are always documented in git history, and despite a small personal
