video▌

Privacy-first video blur workflows for faces, license plates, and sensitive objects. Covers GDPR/CCPA compliance, anonymization vs blur, motion-tracked redaction, and when to use each BGBlur mode. Use when user mentions face blur, face anonymization, license plate blur, privacy redaction, GDPR video compliance, PII removal, dashcam privacy, or anonymizing footage before publishing.

Quality assurance for AI-blurred videos — detect mask bleeding, temporal flicker, missed detections, edge halos, and motion-tracking failures. Use when user mentions blur quality, QA review, mask artifacts, flickering blur, missed faces, plate tracking failure, blur edge halo, or validating BGBlur output before delivery.

Pre-process videos with FFmpeg before blur pipelines — format conversion, trimming, resolution normalization, metadata stripping, and frame-rate standardization. Use when user mentions ffmpeg, video conversion, trim video, normalize format, strip metadata, prepare video for upload, MP4/MOV/MKV conversion, or pre-processing before BGBlur.

Integrate BGBlur blur APIs into apps and pipelines — face blur, license plate blur, NSFW detection for images and video. Covers REST API patterns, batch processing, webhook delivery, and SDK usage. Use when user mentions BGBlur API, blur API integration, face blur API, license plate API, video blur SDK, embed blur in app, or programmatic blur processing.

Given a YouTube video URL or ID, return title, channel, duration, full timestamped transcript segments, and whether captions are auto-generated or human-authored. Read-only.

Category: test

./scripts/vtd.js can:

Custom video players with adaptive streaming, quality selection, and playback controls. \n \n Supports HLS adaptive bitrate streaming with automatic quality switching and error recovery, plus native Safari fallback \n Includes fullscreen, picture-in-picture, volume control, playback speed adjustment, and subtitle/caption tracks \n Provides quality selection UI, thumbnail preview on hover, and video upload with progress tracking \n Built with React hooks and hls.js library for cross-browser compa

Forensic deconstruction of YouTube videos to extract viral formulas, hooks, and retention mechanics. \n \n Analyzes video transcripts across 11 systematic sections: hook architecture, structural blueprint, retention mechanics, emotional engineering, storytelling, linguistic patterns, algorithm signals, CTAs, and viral coefficient scoring \n Extracts reusable fill-in-the-blank script templates, power phrases, and transition patterns ready for immediate application to new content \n Includes imple

Unified audio generation dispatcher routing TTS, music, sound effects, and voice cloning to optimal models. \n \n Routes requests to ElevenLabs (TTS, voice cloning, SFX) or fal.ai (music) based on request type, with latencies ranging from <1s to ~15s \n Supports five audio capabilities: multilingual text-to-speech with voice selection, low-latency turbo TTS, background music composition, sound effect generation (up to 22 seconds), and voice cloning from audio samples \n Requires only ELEVEN