moar

moar is a technical tool designed to optimize the interaction between extensive documents and large language models (LLMs). Its primary function is to transform complex files, such as PDFs, presentations, and spreadsheets, into structured Markdown or CSV formats ready to be processed by tools like ChatGPT, Claude, or Gemini. By converting raw content into clean data, it eliminates common "file too large" errors and overcomes the context window limitations of artificial intelligence applications.

Unlike conventional text extractors, moar analyzes the actual document structure, precisely identifying headers, tables, clauses, and footnotes. This technical approach allows for a reduction in token consumption by up to 95% without any loss of meaning. By providing the model with exactly what it needs, users can get up to five times more useful conversations from the same AI subscription, improving operational efficiency in data management.

The architecture of this application stands out for its focus on data privacy and security. All processing runs 100% locally in the user's browser, meaning documents never leave the computer or get uploaded to external servers. Since there is no telemetry or cloud file uploading, the tool even works without an internet connection, making it a robust solution for professionals working with confidential information.

Key capabilities and practical benefits include:

• Multi-format optimization: Supports up to 9 file types, including DOCX, PPTX, XLSX, JSON, HTML, and plain text files, with a capacity of up to 50MB per document.
• Adaptive platform adjustment: Output content is automatically adjusted based on the specific model and subscription plan the user uses, respecting their context window limits.
• Smart Select: Allows you to describe what information you are looking for so the tool can extract only the relevant sections locally before sending them to the AI.
• Intelligent Chunking: Facilitates the processing of files that exceed any context window by splitting them into ordered, appropriately sized fragments for sequential delivery.
• Library management and merging: Offers the ability to combine related documents and maintain a history of optimized items to streamline recurring workflows.

This utility is essential for analysts, researchers, and AI power users who need to feed their models large volumes of data without wasting resources. By acting as an efficient bridge between traditional file formats and machine-readable formats, moar maximizes response accuracy and data processing fidelity in any artificial intelligence workflow.