Clipora AI

Architected a comprehensive desktop application completely automating the video repurposing pipeline. The platform solves the bottleneck of manual editing by handling everything from initial transcription to final rendering, allowing creators to focus on content rather than technical editing.

The core engine utilizes advanced speech-to-text technology for high-accuracy transcription and large language models to intelligently analyze content structure, identifying viral-worthy hooks and segments. This semantic analysis ensures clips retain context and engagement.

Implemented an advanced video processing pipeline that automatically detects faces and dynamically crops landscape video into 9:16 vertical format, keeping the active speaker perfectly centered throughout the clip.

Built a secure 'Bring Your Own Credentials' (BYOC) architecture, allowing users to safely manage sensitive API keys and OAuth tokens locally. The system supports direct YouTube integration for one-click publishing of generated shorts.

• Developed a comprehensive desktop application for seamless video processing
• Implemented autonomous 'Active Speaker Tracking' using smart face detection logic
• Integrated high-accuracy offline speech-to-text transcription
• Leveraged advanced AI for semantic video analysis and viral hook detection
• Built an automated pipeline for subtitle burning and intelligent vertical cropping
• Created a custom 'Karaoke-style' subtitle animation system with word-level timing
• Implemented a local task queue system to manage resource-intensive rendering jobs
• Built a secure authentication flow for YouTube integration with persistent login
• Developed a custom video player with real-time preview of AI edits
• Implemented robust data management logic to ensure system integrity
• Created a user-friendly interface to handle background initialization
• Engineered a resilient internal communication system with automatic recovery
• Designed a modular settings system supporting dynamic configuration of AI models