TinyWow

Converts multiple files across 50+ format combinations (image, video, audio, document, PDF) in a single browser session without server-side account persistence or file storage. Uses client-side or lightweight server-side transcoding pipelines that process files sequentially or in parallel queues, discarding outputs after download without retention. Architecture relies on standard codec libraries (FFmpeg for video/audio, ImageMagick or similar for images) wrapped in web-accessible endpoints that accept multipart form uploads and stream binary responses.

Reduces image file size through lossy or lossless compression algorithms applied either in-browser (via JavaScript libraries like ImageMagick.js or Squoosh) or via minimal server-side processing. Supports JPEG quality reduction, PNG optimization via pngquant, WebP conversion for modern formats, and batch processing of multiple images with uniform compression settings. No machine learning or content-aware compression — uses standard codec parameters (quality slider, color palette reduction) to achieve size reduction.

Encodes and decodes URLs, query parameters, and special characters using standard URL encoding schemes (percent-encoding, base64). Supports batch processing of multiple URLs. Uses standard encoding libraries to handle RFC 3986 compliance. No advanced URL manipulation like parsing, validation, or shortening — focuses on encoding/decoding operations.

Validates JSON, XML, CSV, and YAML syntax and applies formatting operations including minification, pretty-printing, and indentation normalization. Uses standard parsing libraries to detect syntax errors and provide error messages. Supports batch processing of multiple files. No schema validation, data transformation, or semantic analysis — focuses on syntax checking and formatting.

Enables basic PDF operations including conversion to/from image formats (PNG, JPG), text extraction via OCR or embedded text parsing, merging multiple PDFs, splitting PDFs by page range, and reordering pages. Uses standard PDF libraries (likely PDFKit, PyPDF2, or iText equivalents) for manipulation and Tesseract or similar for OCR when text extraction is needed. No form filling, signature verification, or advanced security features — focuses on structural transformations and format conversion.

Converts audio files between formats (MP3, WAV, OGG, M4A, FLAC, AAC) and applies basic transformations including volume adjustment, trimming to specific time ranges, and concatenation of multiple audio files. Uses FFmpeg or similar audio codec libraries to handle format transcoding and basic DSP operations. No advanced audio processing like EQ, compression, noise reduction, or effects — focuses on format compatibility and simple structural edits.

Converts video files between formats (MP4, WebM, AVI, MOV, MKV, FLV) with adjustable codec parameters including bitrate, resolution scaling, and frame rate. Uses FFmpeg or similar video codec libraries to handle transcoding pipelines. Supports batch processing of multiple videos with uniform settings. No advanced video editing (cutting, effects, color grading) or AI-powered enhancement — focuses on format compatibility and codec optimization.

Converts between document formats (DOCX, XLSX, PPTX, ODT, TXT, RTF) and extracts text content from structured documents. Uses document parsing libraries (likely LibreOffice UNO, Pandoc, or similar) to handle format transformations while preserving basic structure (paragraphs, tables, lists). No layout preservation, style retention, or advanced formatting — focuses on content accessibility and format compatibility.

Creates and extracts compressed archive files (ZIP, RAR, 7Z, TAR, GZIP) with support for batch operations on multiple files. Uses standard compression libraries (likely libzip, 7-Zip SDK, or similar) to handle archive operations. Supports password-protected archives for basic security. No advanced compression algorithms or multi-volume archive support — focuses on standard compression formats and simple extraction workflows.

Generates QR codes from text, URLs, or contact information with customizable size and error correction levels. Decodes QR codes from uploaded images using image processing and pattern recognition. Uses standard QR code libraries (likely qrcode.js or similar) for generation and OpenCV or similar for decoding. No advanced features like custom logos, color customization, or dynamic QR codes — focuses on basic generation and reading.

Extracts dominant colors from uploaded images and converts between color formats (HEX, RGB, HSL, CMYK). Uses image processing algorithms (likely k-means clustering or histogram analysis) to identify primary colors in images. Supports batch color format conversion for design workflows. No color theory analysis or accessibility checking — focuses on extraction and format compatibility.

Transforms text between case formats (uppercase, lowercase, title case, camelCase, snake_case, kebab-case) and applies basic text formatting operations including whitespace normalization, line break handling, and character counting. Uses simple string manipulation algorithms without NLP or semantic understanding. Supports batch processing of multiple text inputs with uniform formatting rules.