{"id":912032,"date":"2026-07-21T22:55:16","date_gmt":"2026-07-21T22:55:16","guid":{"rendered":"https:\/\/cri-one.com\/blog\/2026\/07\/21\/wonderphi-library-25-recipes-for-the-programs-you-already-use\/"},"modified":"2026-07-21T23:15:22","modified_gmt":"2026-07-21T23:15:22","slug":"wonderphi-library-25-recipes-for-the-programs-you-already-use","status":"publish","type":"post","link":"https:\/\/cri-one.com\/blog\/2026\/07\/21\/wonderphi-library-25-recipes-for-the-programs-you-already-use\/","title":{"rendered":"WonderPhi Library \u2014 25 Recipes for the Programs You Already Use"},"content":{"rendered":"

Software acceleration for existing hardware.<\/p>\n

You have 500 videos to transcode. You don’t sit down and write a parallel execution plan from scratch. You run one line:<\/p>\n

python wpc-recipe.py ffmpeg-h264 .videos<\/pre>\n
…and out comes videos.plan.wpc.txt<\/code> \u2014 one tuned command per file, ready to feed straight to WonderPhi Compute<\/a>, which runs them across every core you own.<\/p>\n
WonderPhi Library<\/strong> is the recipe book for WonderPhi Compute. It ships 25 pre-optimized recipes for the most popular command-line tools. Point a recipe at a folder, and it generates a WPC plan with the right flags already filled in \u2014 the ones you’d otherwise have to look up on Stack Overflow.<\/p>\n
Three Steps<\/h2>\n
    \n
  1. python wpc-recipe.py --list<\/code> \u2014 see every recipe<\/li>\n
  2. python wpc-recipe.py <recipe> <folder><\/code> \u2014 generate the plan<\/li>\n
  3. wpc.exe that-plan.plan.wpc.txt<\/code> \u2014 WonderPhi Compute runs it<\/li>\n<\/ol>\n
    Or, with the always-on watcher installed, add --inbox<\/code> and the plan drops straight into the pipeline to run itself.<\/p>\n
    The Full Recipe Index<\/h2>\n
    Media<\/h3>\n\n\n\n\n\n\n\n\n
    Recipe<\/th>\nWhat It Does<\/th>\nNeeds<\/th>\n<\/tr>\n<\/thead>\n
    ffmpeg-h264<\/code><\/td>\nTranscode each video to H.264 MP4<\/td>\nffmpeg<\/td>\n<\/tr>\n
    ffmpeg-audio<\/code><\/td>\nExtract audio from each video to MP3<\/td>\nffmpeg<\/td>\n<\/tr>\n
    ffmpeg-thumb<\/code><\/td>\nGrab a thumbnail from each video<\/td>\nffmpeg<\/td>\n<\/tr>\n
    ffmpeg-gif<\/code><\/td>\nConvert each video to an optimized GIF<\/td>\nffmpeg<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n
    Images<\/h3>\n\n\n\n\n\n\n\n\n
    Recipe<\/th>\nWhat It Does<\/th>\nNeeds<\/th>\n<\/tr>\n<\/thead>\n
    img-resize<\/code><\/td>\nResize each image to max 1920px<\/td>\nImageMagick<\/td>\n<\/tr>\n
    img-webp<\/code><\/td>\nConvert each image to WebP<\/td>\nImageMagick<\/td>\n<\/tr>\n
    img-thumb<\/code><\/td>\nMake a 256×256 thumbnail of each<\/td>\nImageMagick<\/td>\n<\/tr>\n
    img-compress<\/code><\/td>\nStrip metadata and recompress<\/td>\nImageMagick<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n
    Documents<\/h3>\n\n\n\n\n\n\n\n\n
    Recipe<\/th>\nWhat It Does<\/th>\nNeeds<\/th>\n<\/tr>\n<\/thead>\n
    pdf-text<\/code><\/td>\nExtract text from each PDF<\/td>\npdftotext<\/td>\n<\/tr>\n
    pdf-compress<\/code><\/td>\nCompress each PDF<\/td>\nGhostscript<\/td>\n<\/tr>\n
    pandoc-pdf<\/code><\/td>\nConvert documents to PDF<\/td>\npandoc<\/td>\n<\/tr>\n
    pandoc-docx<\/code><\/td>\nConvert documents to Word .docx<\/td>\npandoc<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n
    Archives and Integrity<\/h3>\n\n\n\n\n\n\n\n\n
    Recipe<\/th>\nWhat It Does<\/th>\nNeeds<\/th>\n<\/tr>\n<\/thead>\n
    zip-each<\/code><\/td>\nCompress each file into its own .7z<\/td>\n7-Zip<\/td>\n<\/tr>\n
    unzip-each<\/code><\/td>\nExtract each archive into its own subfolder<\/td>\n7-Zip<\/td>\n<\/tr>\n
    sha256<\/code><\/td>\nSHA-256 each file with a combined manifest<\/td>\ncertutil (built-in)<\/td>\n<\/tr>\n
    md5<\/code><\/td>\nMD5 each file<\/td>\ncertutil (built-in)<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n
    Web and Dev<\/h3>\n\n\n\n\n\n\n\n\n\n
    Recipe<\/th>\nWhat It Does<\/th>\nNeeds<\/th>\n<\/tr>\n<\/thead>\n
    curl-check<\/code><\/td>\nHEAD health-check each URL in a list<\/td>\ncurl (built-in)<\/td>\n<\/tr>\n
    curl-get<\/code><\/td>\nDownload each URL into the output folder<\/td>\ncurl (built-in)<\/td>\n<\/tr>\n
    pytest-dirs<\/code><\/td>\nRun pytest in each subdirectory<\/td>\npytest<\/td>\n<\/tr>\n
    git-gc<\/code><\/td>\nRun git gc on each repo subdirectory<\/td>\ngit<\/td>\n<\/tr>\n
    git-pull<\/code><\/td>\ngit pull –ff-only in each repo<\/td>\ngit<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n
    GPU Recipes<\/h3>\n\n\n\n\n\n\n\n\n
    Recipe<\/th>\nWhat It Does<\/th>\nNeeds<\/th>\n<\/tr>\n<\/thead>\n
    ffmpeg-nvenc<\/code><\/td>\nGPU H.264 transcode via NVENC<\/td>\nffmpeg + NVIDIA<\/td>\n<\/tr>\n
    ffmpeg-nvenc-hevc<\/code><\/td>\nGPU HEVC transcode (smaller files)<\/td>\nffmpeg + NVIDIA<\/td>\n<\/tr>\n
    whisper<\/code><\/td>\nSpeech-to-text transcription on GPU<\/td>\nWhisper + CUDA<\/td>\n<\/tr>\n
    realesrgan<\/code><\/td>\n4x image upscaling on GPU<\/td>\nReal-ESRGAN<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n
    Every recipe uses one of three input models: files<\/strong> (scan a folder), dirs<\/strong> (one job per subfolder), or lines<\/strong> (one job per line in a text file). GPU recipes auto-detect your NVIDIA card and fall back to the CPU equivalent when no GPU is present.<\/p>\n
    No Lock-In<\/h2>\n
    The Library generates plain-text plan files. You can read them, edit them, combine them, or write your own. Nothing is hidden. Nothing is compiled. The recipe is just the recipe \u2014 WonderPhi Compute is the kitchen.<\/p>\n
    Zero dependencies. Python standard library only. No network. No telemetry.<\/p>\n
    Get WonderPhi Library \u2014 $9.99 \u2192<\/a><\/strong><\/p>\n
    Browse the full WonderPhi product line<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"
    Software acceleration for existing hardware. You have 500 videos to transcode. You don’t sit down and write a parallel execution plan from scratch. You run one line: python wpc-recipe.py ffmpeg-h264 .videos …and out comes videos.plan.wpc.txt \u2014 one tuned command per file, ready to feed straight to WonderPhi Compute, which runs them across every core you […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[353,357,354,1],"tags":[],"class_list":["post-912032","post","type-post","status-publish","format-standard","hentry","category-computing","category-product-insights","category-technology","category-uncategorized"],"_links":{"self":[{"href":"https:\/\/cri-one.com\/blog\/wp-json\/wp\/v2\/posts\/912032","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/cri-one.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/cri-one.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/cri-one.com\/blog\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/cri-one.com\/blog\/wp-json\/wp\/v2\/comments?post=912032"}],"version-history":[{"count":1,"href":"https:\/\/cri-one.com\/blog\/wp-json\/wp\/v2\/posts\/912032\/revisions"}],"predecessor-version":[{"id":912035,"href":"https:\/\/cri-one.com\/blog\/wp-json\/wp\/v2\/posts\/912032\/revisions\/912035"}],"wp:attachment":[{"href":"https:\/\/cri-one.com\/blog\/wp-json\/wp\/v2\/media?parent=912032"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/cri-one.com\/blog\/wp-json\/wp\/v2\/categories?post=912032"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/cri-one.com\/blog\/wp-json\/wp\/v2\/tags?post=912032"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}