Uneditor

Uneditor: From Academic Bloat to Implementation-Ready Insights The Problem: Academic papers are 80% "social glue" (citations, acknowledgments, colleague references) and only 20% core thesis and proof. This academic formatting significantly slows down engineers who need to implement the actual protocols and algorithms. Our Solution: An AI-powered editor that transforms academic papers by:

Extracting core thesis, proofs, and essential mathematical foundations Simplifying unnecessarily complex mathematical apparatus Removing academic bloat (citations, lengthy reviews, acknowledgments) Providing on-demand explanations for mathematical concepts instead of uneven theoretical backgrounds

The Metaphor: Academic papers are like "The House That Jack Built" - a recursive story with many iterations. Engineers don't need the full tale; they need a clear map of the house. Impact: Dramatically reduces time-to-market for protocol implementation by giving engineers exactly what they need: clean, implementable insights without academic overhead. Target Users: Software engineers, protocol developers, and technical teams who need to quickly understand and implement research findings without wading through academic formatting.

Here is our pitch deck: https://docs.google.com/presentation/d/1S3wKqtsvaTCmzihkiq6I9BTDqoZQaPQ7VZeOJKhQYog/edit?slide=id.g36d4e56edc1_0_67#slide=id.g36d4e56edc1_0_67

uneditor is built on a modern stack for efficient document processing and analysis:

PDF Conversion: marker for robust PDF-to-Markdown conversion. RAG Pipeline: voyageai for high-quality embeddings and reranking. langchain for intelligent document chunking. faiss for lightning-fast similarity search. LLM Core: grok-3-mini: For budget-friendly, high-speed inference (perfect for hackathons! 🏆). gemini-2.5-pro: For production-grade, in-depth analysis.

https://github.com/auditdbio/uneditor-ethcannes?tab=readme-ov-file#-tech-stack