There are some videos you watch for entertainment.
There are some you watch for inspiration.
And then there are videos that genuinely change how you think about building products.
Recently, I watched Harkirat Singh’s video, “I Built OpenRouter From Scratch in 4 Hours,” and I found it genuinely valuable, not because it was just another AI project, but because it showed how a real developer product can be broken down, understood, and rebuilt from first principles. The video is available on Harkirat Singh’s YouTube channel, and the corresponding code is published in the code100x/openrouter GitHub repository. ([YouTube][1])
This is not my project, and I am not the creator of the video.
This blog is simply my attempt to explain, in a simple and detailed way, what I learned from it, why the idea is powerful, and what beginner-to-intermediate developers can take away from it.
If you are someone who wants to move beyond “just consuming APIs” and start thinking like a builder of platforms, this topic is worth understanding.
Useful Links
You can watch the original video by Harkirat Singh here: ([YouTube][1])
You can explore the public code repository here: ([GitHub][2])
You can also check OpenRouter’s official documentation to understand what the real product does: its Quickstart describes OpenRouter as a unified API for hundreds of models, and its API docs explain that it normalizes request and response schemas across providers. ([OpenRouter][3])
First, what is OpenRouter in simple words?
Before talking about the video, let’s simplify the core idea.
Normally, if you want to use AI models in your app, you may need to integrate with different providers separately. One provider may have one API format, another may have different authentication, another may expose different model names, and another may return responses in a slightly different structure.
That creates unnecessary complexity.
OpenRouter solves this by acting as a single gateway for many AI models. Its official docs describe it as one API that gives access to hundreds of models through a single endpoint, while handling routing and fallbacks. The docs also say its schema is intentionally similar to the OpenAI Chat API, so developers do not need to learn different request formats for every provider. ([OpenRouter][3])
So in very simple language:
OpenRouter is like a middle layer between your app and multiple AI providers.
Instead of talking to five providers separately, your app talks to one system.
That one idea alone teaches a very important product lesson:
Great developer tools reduce integration pain.
Why this video stood out to me
A lot of AI videos focus on prompts, wrappers, or quick demos.
This one is different.
The interesting part is not “using AI.”
The interesting part is building the infrastructure layer around AI.
That shift matters.
Because once you start building the routing layer itself, you are forced to think about real backend problems like:
- API design
- request normalization
- provider abstraction
- fallback logic
- usage tracking
- security
- dashboard design
- developer experience
That is a much more valuable exercise than building another basic chatbot UI.
What I liked most is that the project naturally pushes you to think like a product engineer, not just a tutorial follower.
What I understood from the project structure
The public GitHub repository already reveals a lot about the architecture. The repo uses a Turborepo monorepo setup and includes apps named api-backend, primary-backend, and dashboard-frontend, along with shared packages like db. The top-level package configuration also shows a TypeScript-based stack using Bun and ElysiaJS. ([GitHub][2])
Even if you do not study every file in the repo, this structure gives a clear idea of how the system is organized.
1. Dashboard Frontend
This is likely the developer-facing UI.
A dashboard in a product like this usually helps with:
- generating API keys
- viewing usage
- checking balance or credits
- exploring models
- managing settings
This is important because when you build a developer product, backend functionality alone is not enough. Developers also need visibility and control.
2. API Backend
This is most likely the public entry point.
This layer probably handles things like:
- receiving incoming API requests
- verifying API keys
- validating request formats
- deciding where the request should go
- returning a normalized response
This is the part external applications actually call.
3. Primary Backend
This likely supports internal operations or orchestration.
In a real-world product, this kind of backend often manages:
- internal business logic
- billing or credit calculations
- analytics
- admin actions
- system-level workflows
4. Shared DB Package
This is a smart monorepo approach.
Instead of duplicating database schemas and types across multiple services, shared packages keep things consistent. For full-stack developers, this is a strong reminder that architecture matters even in small or medium-sized products.
Why Bun and ElysiaJS were smart choices
The video title and repo indicate that the project was built using Bun and ElysiaJS. That stack makes a lot of sense for a fast-moving experimental build. ([YouTube][1])
Bun
Bun is attractive for projects like this because it speeds up development workflow:
- fast installs
- fast startup
- modern TypeScript support
- low setup friction
If your goal is to validate an idea quickly, Bun helps reduce overhead.
ElysiaJS
ElysiaJS is a lightweight backend framework that works well for building APIs quickly. For a project centered around routing requests and exposing clean endpoints, that makes it a practical choice. The value here is not hype. The value is speed, clarity, and developer productivity. ([GitHub][4])
For me, one big lesson here was:
The best stack is not always the biggest stack.
The best stack is the one that helps you ship the architecture clearly.
The main concept I learned: unified APIs are powerful
The biggest lesson from the video is not technical syntax.
It is product thinking.
A unified API sounds simple on paper:
- one endpoint
- one API key
- one request format
- access to many models
But behind that simple experience, there is a lot of hidden engineering.
OpenRouter’s official docs explain that the platform normalizes schemas across different models and providers so the developer only has to learn one interface. They also document automatic fallback behavior when a chosen model fails. ([OpenRouter][5])
That means the product is not just forwarding requests.
It is creating consistency over inconsistency.
And that is what good infrastructure products do.
What actually makes this kind of project difficult?
At first glance, someone might think:
“Okay, so it just takes a request and sends it to another API.”
But the real challenge is not forwarding requests.
The real challenge is handling the fact that each provider behaves differently.
Different providers can vary in:
- authentication format
- model naming
- supported features
- error messages
- response shape
- streaming behavior
- latency
- pricing
- rate limits
So if you want to build a system like OpenRouter, your real task is:
Create one stable developer experience on top of many unstable differences.
That is much harder, and much more interesting.
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