Hello everyone! This is my first time posting on Dev, so I’d appreciate any guidance or feedback you might have.
Getting straight to the point: I’ve built a data API system—a platform from which you can retrieve verified data. It's called Alrithm API (alrithmapi.vercel.app), and it's designed to stream clean, structural datasets directly into AI training loops or data pipelines.
But here is the catch: I am a solo developer operating with a bank account and MetaMask wallet that both sit at exactly $0. Because of this, every single line of code, backend architecture, and database schema for this platform was generated 100% by AI under my architectural direction.
Here is why I built it, how the tech works, and what I learned from shipping a production-ready system with zero budget.
The Problem I Wanted to Fix
If you’ve ever trained a machine learning model, you already know the nightmare of dirty data. Platforms like Scale AI exist, but they are built for massive corporate enterprise budgets. Independent researchers and indie hackers usually get left out.
More importantly, most datasets available online are just static files dumped on a server. There is no built-in way to prove that the data hasn't been tampered with or corrupted while moving through the pipeline.
I wanted a plug-and-play API that gives you clean data, but with an absolute guarantee of integrity.
The Technical Part: Merkle Roots & Web3 Ecosystem
To keep costs at zero, the platform is hosted on Vercel. Instead of just serving basic JSON rows, I worked with the AI to integrate cryptographic security directly into the data engineering layer:
• Row-Level Merkle Root Verification: This is the core feature. The system hashes the data schema and embeds a Merkle Root verification into every single row. If even one byte of data gets corrupted or altered during transit, the cryptographic proof breaks immediately. Data engineers can verify the total integrity of their data source in real-time.
• Web3 Integration: The platform features native compatibility with Ethereum wallets via MetaMask. It's built to support decentralized data contributors and Web3 funding down the line, completely avoiding traditional legacy banking systems.
What it’s Like Orchestrating a 100% AI Codebase
When you let AI write 100% of the code, your job changes completely. You stop being a line-by-line coder and become a System Architect.
I spent hours refining prompts, reviewing data schemas, and catching edge cases. The hardest part wasn't getting the AI to write code, but keeping the system modular and optimized enough to run smoothly on serverless infrastructure without breaking.
Check it out and tear it apart!
The platform is live right now at alrithmapi.vercel.app.
Since this is my first release, I would love for this community to test it out. Hit the endpoints, look at the data structure, and feel free to criticize or give feedback on the technical setup. What data schemas or features should we add next?
Drop a comment below—I'll be hanging around to answer any technical questions!
Top comments (0)