RetroAI

Inspiration

Modern AI systems can access less than 0.001% of the web. The Internet Archive has preserved over 1 trillion webpages, but ChatGPT and other AI assistants cannot access any of them. This includes decades of technical documentation, forum discussions, academic lecture notes, and guides that developers still need today.

71% of enterprises maintain legacy code, yet finding documentation for deprecated technologies requires manual searching through archives. We built RetroAI to make the archived web AI-accessible using the Model Context Protocol.

What it does

RetroAI converts any archived website into an AI-accessible resource by generating custom MCP servers on demand. Users provide a URL, and RetroAI fetches the content, analyzes its structure, and creates specialized tools that allow AI systems to query, search, and synthesize information from that site.

The system handles old forum posts, legacy documentation, academic course materials, and technical guides from the 1990s and 2000s. Once processed, users can ask natural language questions about the content and receive synthesized answers with proper markdown formatting.

How we built it

Backend:

Dynamic MCP server generator that uses Google Gemini Computer to pull data from sites
Google Gemini API integration for content processing and synthesis
Flask API with /initialize and /call endpoints

Frontend:

Windows 98-inspired interface that transforms to modern design
Drag-and-drop functionality for website icons
Real-time API integration with progress tracking
Markdown rendering using marked.js
CSS animations showing the retro-to-modern transformation

Integration:

Model Context Protocol for standardized AI tool creation
Internet Archive Wayback Machine for content retrieval

Challenges we ran into

Context window optimization: Even with large context windows, entire archived sites can exceed limits. We implemented intelligent chunking to preserve critical information while staying within constraints.

Real-time reliability: Ensuring the live demo works consistently with actual API calls, proper error handling, and smooth transitions required extensive testing.

Accomplishments that we're proud of

We solved a real problem: made 1 trillion archived webpages accessible to AI systems for the first time.

Our implementation is production-ready, not just a proof of concept. The system works with any archived website and returns properly formatted markdown responses.

The interface directly embodies the hackathon theme of upgrading retro technology. The visual transformation from Windows 98 to modern design demonstrates the core value proposition.

We built the first known MCP server generator for legacy web content, creating a new category of AI tooling.

What we learned

MCP is powerful for extending AI capabilities, but requires careful design when generating servers dynamically rather than statically.

Content extraction from legacy websites requires significantly more robustness than modern web scraping. Each era of web development has distinct patterns and problems.

Visual presentation matters for hackathon demos. The Windows 98 transformation makes the concept immediately understandable and memorable.

The market for legacy technology support is substantial. Every developer we spoke with has experienced this problem.

What's next for RetroAI

Immediate next steps:

Expand to additional archive sources beyond Internet Archive
Improve parsing for PDFs, old forum formats, and non-English content
Build a registry with Supabase of commonly-accessed sites for faster repeated queries

The fundamental problem is clear: valuable knowledge exists in archives but is inaccessible to AI systems. RetroAI provides the infrastructure to solve this permanently.