Quriosity

Generates full-length essays, research papers, and academic documents from user prompts or topic specifications using underlying language models. The system accepts natural language requests describing content requirements (topic, length, style, format) and produces structured written output with multiple paragraphs, citations placeholders, and thematic coherence. Generation happens server-side with results streamed back to the client for real-time preview.

Enables multiple users to simultaneously view, edit, and refine AI-generated content in a shared document workspace with live cursor tracking and change synchronization. Uses operational transformation or CRDT-based conflict resolution to merge concurrent edits from multiple collaborators without data loss. Changes propagate to all connected clients within milliseconds, with version history preserved for rollback.

Exports generated or edited documents in multiple formats (PDF, DOCX, Markdown, plain text, HTML) with preservation of formatting, citations, and structure. Export process handles format-specific requirements such as PDF page breaks, DOCX heading styles, and Markdown link syntax. Batch export allows multiple documents to be exported simultaneously as a ZIP archive.

Generates multiple distinct versions of the same content by varying input parameters such as tone (formal/casual), length (short/long), perspective (pro/con), or academic level (high school/graduate). Each variation is produced independently by the underlying LLM with different temperature or prompt engineering strategies, allowing users to compare approaches and select the best fit. Variations are stored and compared side-by-side in the UI.

Generates hierarchical document outlines and structural frameworks for essays, research papers, and reports based on topic input. The system produces multi-level outline structures (I. Main Point → A. Sub-point → 1. Detail) with brief descriptions for each section, helping users understand content organization before writing. Outlines can be used as templates to guide full document generation or manual writing.

Allows users to queue multiple content generation requests and process them sequentially or in parallel, with built-in quota tracking and rate limiting. The system manages API consumption, prevents quota overages, and provides visibility into remaining generation capacity. Batch operations are tracked with status indicators (queued, processing, completed, failed) and results are aggregated for bulk export.

Synthesizes background research and contextual information for a given topic by combining knowledge from the underlying LLM's training data. The system generates summaries of key concepts, historical context, relevant theories, and current debates related to a topic without requiring external web search. Output is formatted as research notes or background sections suitable for inclusion in academic work.

Applies consistent formatting, citation styles, and structural conventions to generated or user-provided content. The system supports multiple citation formats (APA, MLA, Chicago, Harvard) and document styles (essay, research paper, report, article). Formatting is applied automatically to generated content or can be applied to user-uploaded text, with options for font, spacing, margins, and heading hierarchy.

Analyzes generated or user-provided content for readability metrics, writing quality indicators, and structural coherence. The system calculates metrics such as reading level (Flesch-Kincaid grade level), sentence complexity, vocabulary diversity, and paragraph organization. Assessment results are presented as a quality score with specific recommendations for improvement (e.g., 'simplify sentence 3', 'add transition between paragraphs 2 and 3').

Provides suggestions for improving user prompts to generate higher-quality content. The system analyzes user input prompts and recommends specific modifications such as adding context, clarifying requirements, specifying tone, or including examples. Guidance is presented as interactive suggestions that users can accept to auto-refine their prompt before generation, or as educational tips explaining why certain prompt structures produce better results.

Organizes generated content and collaborative documents into user-created workspaces and projects with folder hierarchies, tagging, and search. Each workspace can contain multiple projects, and each project can contain multiple documents. Workspaces support role-based access control (owner, editor, viewer) for sharing with collaborators. Search functionality indexes document content and metadata for quick retrieval.