React VS streamlit

Official React Documentation: react.dev MDN Javascript Documentation: developer.mozilla.org/JavaScript

Early in my career, I struggled to understand how React’s state updates actually worked. Docs gave me the basics, but edge cases around batching were confusing. One weekend, I pulled up the React source code on GitHub and traced how state changes bubbled through the scheduler. Suddenly, the “weird bugs” I hit at work made sense because I wasn’t guessing anymore, I’d seen the engine.

3. Next.js and React (Frontend framework)

My primary passion is backend development, but I wanted to build a user-friendly frontend to complete this project. Given my experience with React, Next.js was the obvious choice due to its excellent documentation and ease of use.

Check out the React 19 Upgrade Guide for more details. Have fun coding, and share in the comments what you’re building with React 19! 🎉

React Official Documentation — Start here for official guides and tutorials

At IVAO, we partially follow micro-services best practices. This means each business group (e.g., Flight tracking, Flight planning, Air Traffic controlling, Training) has its own NestJS API and ReactJS frontend. But we took a shortcut on the DB side, as we have quite some references across systems, especially for user IDs, airports, etc... We decided to have a single MariaDB instance while having a dedicated logical database for each API, so we still have a separation of concerns but we can guarantee data integrity with foreign keys across those databases.

React.js (fast, large ecosystem)

Tools: V0 by Vercel, shadcn/ui, React, Tailwind CSS V0 turns natural language prompts into ready-to-use React + Tailwind UI code. Ask for “a responsive SaaS dashboard with a dark theme and collapsible sidebar” and you get production-ready code instantly, accessible, tweakable, and not locked into rigid templates.

The original CourseCast 2024 was prototyped and deployed as a Streamlit application written in Python. Students would input their course utilities and submit their simulation request to the Streamlit Community Cloud where:

Streamlit → Front-end & deployment. LangChain → LLM integration & chaining workflows. Hugging Face → Pre-trained AI models (e.g., Mixtral-8x7B). Supabase → Vector database for storing PDF embeddings.

Streamlit is a framework to quickly build and share data apps with simple Python scripts.

... and if you (the parent comment author) wants a really easy tool for creating a UI for the Python code, I'd recommend looking at Streamlit: https://streamlit.io/

Streamlit

Use Streamlit to visualize and test predictions interactively:

The only thing left to do then was to build something that could showcase the power of code ingestion within a vector database, and it immediately clicked in my mind: "Why don't I ingest my entire codebase of solved Go exercises from Exercism?" That's how I created Code-RAGent, your friendly coding assistant based on your personal codebases and grounded in web search. It is built on top of GPT-4.1, powered by OpenAI, LinkUp, LlamaIndex, Qdrant, FastAPI and Streamlit. The building of this project was aimed at providing a reproducible and adaptable agent, that people can therefore customize based on their needs, and it was composed of three phases:

Streamlit.io: Great documentation and reusable components to integrate with your AI application for rapid python front-end AI development

GitHub: https://github.com/streamlit/streamlit