This is a submission for the GitHub Copilot Challenge: New Beginnings
What I Built
Text-to-Content AI is an all-in-one AI content generation platform that transforms ideas into polished content. The platform offers multiple content generation tools including articles, natural speech, custom images, infographics, code snippets, and question-and-answer capabilities.
The application is built using modern web technologies including React, TypeScript, and Vite, while leveraging powerful AI providers such as Google's Gemini, ElevenLabs, and Hugging Face for content generation.
Demo
Live Demo: text-to-content-ai
Example output:
Key Features:
- Real-time content generation
- Secure authentication
- Mobile-first design
- Dark mode support
- Intuitive user experience
Repo
Copilot Experience
Building this app with github copilot was a delightful experience, especially the copilot edits feature helped in generating the code quickly for multiple files and applying the changes to all files in one click.
GitHub Models
Use of GitHub Models: GPT-4o and Claude
For the development of Text-to-content-ai, I leveraged advanced GitHub models like GPT-3.5 and Claude to prototype and implement LLM capabilities seamlessly into the app. Here's how they contributed:
Claude was really helpful in writing quickly the frontend tailwind code and quickly building the UI of the app.
GPT-4o was very helpful in creating the backend logic of the supabase edge functions i am using to send user's request to Gemini AI API and get the actual content.
Google Gemini AI models and other LLMs used
When exploring AI solutions for content generation, while Claude and GPT-4 were compelling options, their paid APIs led me to explore alternatives. Here's how I architected my solution:
For Text Generation:
I implemented Google's Gemini AI API as the core content generation engine. The Gemini model proved highly effective, delivering optimal results for text-based content while being more cost-effective.
For Text-to-Image Generation:
Since Gemini AI doesn't currently offer image generation capabilities, I turned to Hugging Face's platform. After evaluating various options, I selected the FLUX-Schnell model through their inference API. This model stood out for two key reasons:
- Lightning-fast generation speed
- Consistently reliable output quality
For Text-to-Speech Conversion:
Eleven Labs' API emerged as the perfect solution for voice synthesis, completing my content generation pipeline.
This multi-service architecture allowed me to build a comprehensive content generation system while maintaining cost efficiency and performance.
Conclusion
Text-to-Content AI aims to streamline the content creation process by providing a comprehensive suite of AI-powered tools in one platform. By combining multiple AI providers and modern web technologies, the project demonstrates the potential of AI in enhancing content creation workflows and improving user productivity.
The platform's diverse range of features - from article generation to code snippets - makes it a versatile tool for content creators, developers, and anyone looking to leverage AI for their content needs.
Top comments (1)
nice one !