The 3-Month Experiment: A Practical Framework for Non-Scalable Solutions
In the world of startups, Paul Graham’s advice to “do things that don’t scale” resonates profoundly, yet the implementation remains a puzzle for many, particularly in the realm of coding. After dedicating eight months to the development of my AI podcast platform, I have crafted a straightforward approach: each non-scalable solution is given exactly three months to demonstrate its merit. If it proves beneficial, we enhance it; if not, it’s time to let go.
As software engineers, we are often trained to prioritize scalable solutions from the outset. Concepts like microservices, design patterns, and distributed systems dominate our thought processes, especially when envisioning systems capable of serving millions. However, this kind of thinking can lead to costly delays in startup environments. Instead of building for hypothetical users who may never arrive, my three-month framework compels me to create straightforward, albeit less elegant, code that gets deployed promptly and teaches me about the actual needs of users.
Current Infrastructure Shortcuts: Insights from My Approach
1. Consolidated System on a Single VM
Currently, everything from the database to Redis runs on one $40/month virtual machine. While this setup lacks redundancy and requires manual backups, it has been a revelation. Over the last two months, I’ve gained a clearer understanding of my resource requirements than any capacity plan could provide. My platform, despite its AI focus, peaks at merely 4GB RAM. The complicated Kubernetes architecture I almost implemented would have resulted in a maintenance nightmare full of idle containers.
Every time there’s a system crash—yes, it has happened twice—I gain insightful data about the actual failure points, which are often unexpected.
2. Hardcoded Configuration Values
In my code, configuration values like:
python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
are hardcoded directly within the files. This means every update requires a redeployment, but it’s incredibly efficient. With the ability to search my entire codebase for any configuration value, I can instantly track changes through git history. In just three months, I’ve adjusted these values three times, resulting in a minimal time investment compared to the weeks it would take to build a comprehensive configuration management system.
3. Utilizing SQLite for Production
I chose to deploy SQLite
One Comment
This is a compelling approach that aligns well with the principles of rapid iteration and learning in startup environments. Your three-month trial period is a practical way to test ideas without over-investing in non-scalable solutions upfront. I especially appreciate the emphasis on gaining real-world insights through simple, quickly deployable architectures—like your use of a single VM and hardcoded configs—over prematurely optimizing for scalability.
One aspect worth considering as you move forward is how to maintain agility as your system grows. While SQLite has served you well so far, planned transitions to more robust solutions could be mapped out ahead of time, ensuring that scaling efforts are manageable when needed. Additionally, automating some aspects of configuration management or incremental migration might be the next step to reducing manual overhead while retaining your core methodology of rapid experimentation.
Overall, your framework exemplifies an effective balance: it embraces “doing things that don’t scale” to learn fast, while recognizing the importance of laying a foundation that can evolve into scalable solutions when justified. Thanks for sharing such practical insights!