The Three-Month Approach: A Pragmatic Framework for Non-Scalable Solutions in Tech Development
When it comes to startup culture, one piece of advice often echoes in entrepreneurial circles: “Do things that don’t scale,” a principle shared by Paul Graham. However, translating this wisdom into actionable steps, particularly in coding, is seldom discussed. After eight months of enhancing my AI podcast platform, I’ve created a straightforward yet effective framework: every unscalable solution gets a trial period of three months. Within this timeframe, it either demonstrates its worth and is developed further, or it is phased out.
In the world of software engineering, we are constantly urged to craft scalable solutions right from the start. We learn about design patterns, microservices, and distributed systems—the robust architectures that accommodate millions of users. However, this mindset is often a reflection of large-scale company thinking.
In a startup environment, aiming for scalability can frequently lead to unnecessary delays. It’s like preparing for an audience that doesn’t yet exist, solving hypothetical problems that might never manifest. My three-month framework compels me to write straightforward, concise, and even “imperfect” code, enabling me to deliver functional products while gaining real insights into user needs.
Current Infrastructure Hacks and Their Surprising Effectiveness
1. Single Virtual Machine Architecture
All aspects of my setup—database, web server, background jobs, Redis—reside on one affordable $40/month virtual machine. It’s lacking redundancy and relies on manual backups to my personal system.
Here’s the brilliance in this approach: In just two months, I’ve gained a clearer understanding of my resource needs than any capacity planning spreadsheet could provide. My “AI-intensive” platform peaks at 4GB of RAM. The complex Kubernetes environment I contemplated would have only managed idle containers.
Each time the system crashes—yes, this has happened twice—I gather invaluable data about what truly went wrong, and it’s rarely what I anticipated.
2. Hardcoded Configuration Values
plaintext
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
I’ve opted against configuration files and environment variables in favor of constants spread throughout my code. Any modification necessitates a full redeployment.
The advantage? I can quickly sift through my entire codebase using grep for any specific value. I track every price adjustment in git history,
One Comment
This framework offers a refreshing perspective on balancing agility with thoughtful experimentation, especially in the early stages of product development. I appreciate how you emphasize rapid iteration and real-world insights over theoretical scalability—far too often, startups get bogged down trying to optimize for scale before validating core assumptions.
Your approach with a single VM and hardcoded configs exemplifies “doing things that don’t scale” pragmatically. It highlights that sometimes the best way to learn what’s truly necessary is by direct experience, even if it means embracing temporary hacks. The key is your willingness to iterate quickly, cut losses, and evolve as insights emerge.
It’s also worth noting that this mindset fosters a culture of continuous learning and resilience—valuable traits for any startup. Once the product-market fit stabilizes, then scaling architectures can be implemented with more confidence, grounded in actual usage data rather than assumptions.
Thanks for sharing this insightful strategy—it’s a practical reminder that, especially in startup environments, prioritizing speed and learning over premature optimization can save time, resources, and stress in the long run.