AskLibra Shorts
A local desktop workflow for turning long videos into short-form content through transcription, clip suggestions, face-aware reframing, caption styling, edit planning, preview rendering, and platform-ready exports.
Role
Product Designer, Desktop Application Architect, AI-Assisted Full-Stack Developer
Platform
Desktop Application
Status
Live
Year
2026
Private desktop application environment
This case study focuses on the product workflow, local media pipeline, interface behavior, and export process without exposing private video files, local paths, or unpublished content.
Overview
AskLibra Shorts is a desktop application designed to reduce the number of tools needed to repurpose long-form video. It combines local media import, audio extraction, transcription, clip analysis, reframing, caption styling, previewing, edit planning, and rendering inside one project-based workspace.
The workflow emphasizes local processing where practical, rather than presenting short-form video production as a cloud-only application.
Problem
Producing a polished short often requires several disconnected tools for transcription, clip selection, reframing, captions, previewing, and rendering.
- Long-form footage requires manual review
- Identifying strong short-form moments is time-consuming
- Transcription often happens in a separate tool
- Vertical reframing can lose the speaker's face
- Captions require separate styling and rendering
- Preview and final export may use different workflows
- Creators move files between several applications
- Render progress and output location can be unclear
- Local projects can break when stored on disconnected drives
- YouTube imports require additional download handling
Solution
AskLibra Shorts brings the most repetitive parts of short-form video production into one desktop workflow. The application analyzes local media, creates a transcript, recommends clip ranges, applies caption and reframing choices, generates an edit plan, and renders the selected result using local media tools.
- Reduce tool switching
- Make clip discovery faster
- Preserve selected caption settings
- Keep faces visible in vertical exports
- Provide reliable preview and final rendering
- Support multiple platform aspect ratios
- Keep users informed during long-running operations
- Maintain stable local project storage
Previous workflow
The process depended on several tools and repeated manual steps. This increased editing time and made it difficult to keep transcription, clip timing, caption styling, preview output, and final rendering synchronized.
Import footage into an editor -> extract or generate a transcript -> review the full video -> manually identify clips -> reframe for vertical video -> add and style captions -> render a preview -> make corrections -> export for each platform.
Core workflow
The desktop workflow moves from source import to local transcription, clip selection, edit planning, preview, and final export.
- 1
Import video
Add a local video file or import supported online video content.
- 2
Prepare media
Copy or register the source inside the project and extract audio for transcription.
- 3
Transcribe locally
Use whisper.cpp to generate a transcript and SRT caption data.
- 4
Analyze transcript
Review transcript segments and identify candidate moments for short-form clips.
- 5
Suggest clips
Present recommended clip ranges with timing and scoring information.
- 6
Select and adjust
Choose a suggested clip or manually refine its start and end points.
- 7
Configure format
Select 9:16, 1:1, or 16:9 output based on the destination platform.
- 8
Apply reframing
Reframe the selected segment while keeping the primary speaker visible.
- 9
Customize captions
Choose caption style, position, chunking, and visual treatment.
- 10
Create edit plan
Save timing, layout, caption, and reframing instructions as a reusable render plan.
- 11
Preview
Render a lower-cost preview so the user can inspect the result before final output.
- 12
Render and export
Produce the final short and save it inside the project's export folder.
Feature areas
The application groups the short-form production workflow into import, transcription, reframing, captions, planning, preview, and export.
Import and project setup
Creates a consistent project workspace for source media, transcripts, edit plans, previews, and final exports.
- Local video import
- Supported YouTube import
- Project folders
- Stable application-support storage
- Source and export organization
Transcription and clip discovery
Converts long footage into searchable transcript data and surfaces candidate moments for short-form editing.
- Local audio extraction
- whisper.cpp transcription
- SRT generation
- Transcript segmentation
- Suggested clips
- Clip scoring
- Timing adjustment
Reframing and format control
Adapts the selected clip for different social platforms while preserving the most important visual subject.
- 9:16 export
- 1:1 export
- 16:9 export
- Face-aware cropping
- Platform-oriented framing
- Manual format selection
Caption workflow
Keeps caption choices synchronized from the interface through preview and final rendering.
- Caption style selection
- Caption chunking
- Words-per-caption control
- Position and layout options
- SRT-based burn-in
- FFmpeg caption rendering
- Selected-style persistence
Edit planning and preview
Separates planning, previewing, and final output so users can validate the edit before committing to the full render.
- Edit-plan generation
- Saved plan state
- Preview rendering
- Per-clip plan status
- Reset plan
- Final render enabled only when a plan exists
Export and feedback
Makes long-running media operations visible and helps users locate completed files without guessing.
- Persistent export status
- Progress feedback
- Success and error states
- Saved-file hints
- Open Exports support
- Modal and inspector feedback
- Final output organization
My role
I defined the product workflow, desktop application structure, media-processing pipeline, interface direction, project-storage behavior, caption and reframing controls, preview strategy, and export feedback. AI-assisted development accelerated implementation, debugging, refactoring, and iteration, while workflow decisions, usability testing, and final validation remained under my direction.
AI-assisted, human-directed development
AI accelerated implementation and troubleshooting across the React, Rust, and media-processing layers. Product direction, workflow design, visual judgment, usability decisions, and final testing remained human-led.
AI accelerated
- React and TypeScript implementation
- Rust and Tauri integration
- FFmpeg command construction
- Filter-chain debugging
- Caption-rendering fixes
- File-path troubleshooting
- UI iteration
- Build error diagnosis
- Documentation
- Test planning
Human direction and ownership
- Product requirements
- Editing workflow
- Interface design
- Local-processing decisions
- Media pipeline behavior
- Caption requirements
- Reframing expectations
- Manual visual review
- Workflow validation
- Release decisions
Architecture and stack
Technology stack
Technical challenges
Coordinating multiple runtimes
Connected a React interface, Tauri commands, Rust application logic, and local media binaries inside one desktop workflow.
FFmpeg filter-chain reliability
Built and debugged media commands involving cropping, scaling, caption burn-in, audio handling, and multiple output formats.
Caption-style persistence
Resolved a bug where captions rendered correctly only when users left the default style unchanged.
Face-aware reframing
Applied dynamic crop behavior intended to keep speakers visible in vertical-video outputs.
Preview versus final rendering
Separated edit-plan creation, preview output, and final rendering so users could validate results before export.
Long-running operation feedback
Added persistent status messaging so transcription, planning, previewing, and exporting did not appear frozen.
Stable local project paths
Moved project storage away from a removable external drive into the application-support directory to prevent broken paths when the drive was disconnected.
Local and online imports
Managed differences between local files and downloaded video sources while keeping the downstream workflow consistent.
Smaller-window usability
Refined layout and overflow behavior so the editor remained usable near the application's minimum supported width.
Dependency and release readiness
Verified which local binaries and runtime dependencies must be present for the desktop application to function after installation.
Key fixes and iterations
Product development focused on making the desktop workflow more reliable across local files, downloaded sources, captions, preview, and export.
- External-drive project path failures
- YouTube filename mismatch
- FFmpeg caption filter issues
- Preview overflow on smaller screens
- Selected caption styles not reaching final render
- Persistent export-status feedback
- Compact start and end clip controls
- Edit-plan state and final-render gating
- Application branding update
- Project storage moved to Application Support
Quality assurance
Build integrity
- TypeScript checking
- Rust compilation
- Tauri development builds
- Production build checks
- FFmpeg command validation
- Dependency-path checks
Functional validation
- Local import testing
- Supported online import testing
- Transcription testing
- SRT generation
- Suggested clip review
- Timing adjustment
- Caption-style testing
- Face-aware reframing review
- Preview rendering
- Final rendering
- Export-folder verification
UI and live validation
- Minimum-window-width behavior
- Side-panel layout
- Modal behavior
- Status feedback
- Error-state visibility
- Project reopening
- Edit-plan persistence
Results
Qualitative workflow outcomes
One local editing workflow
AskLibra Shorts combines the main steps of video repurposing into one desktop workflow.
Local transcription and SRT generation
The application generates transcript and subtitle data for the selected source media.
Transcript-based clip suggestions
Transcript analysis helps surface candidate clip ranges while preserving user control.
Face-aware multi-format exports
Selected clips can be prepared for 9:16, 1:1, and 16:9 output with speaker-aware framing.
Configurable burned-in captions
Caption style, position, chunking, and rendering choices flow into preview and export.
Preview-before-render workflow
Users can inspect a lower-cost preview before committing to the final render.
Persistent export feedback
Long-running media operations show status, success, error, and saved-file feedback.
Project-based local storage
Project storage keeps source media, transcripts, edit plans, previews, and exports organized locally.
2K+ views
One business-negotiation Short generated from the app crossed 2K views during early channel testing.
1.4K+ views in early testing
A second Shark Tank-style business clip reached the 1K+ range within the first few hours, suggesting the app could repeatedly surface usable short-form moments.
Business-decision clips
Early testing showed stronger audience signals around clips involving money, equity, scaling, negotiation, pricing, and investor tension.
Project takeaways
AskLibra Shorts combines the main steps of video repurposing into one desktop workflow, reducing tool switching while preserving user control over timing, framing, captions, previewing, and final export.
Local AI can improve creative workflows
Transcription and analysis can run within a desktop application, reducing dependence on separate cloud tools for every stage.
Preview and final output should share one plan
Saving timing, caption, framing, and format decisions in an edit plan helps prevent preview and final-render mismatches.
Long-running operations need visible state
Transcription and video rendering may take time, so clear persistent feedback is part of the core product experience.
Media applications require path discipline
Stable project and binary paths are essential because removable drives and renamed downloads can break otherwise-correct workflows.
What this project demonstrates
AskLibra Shorts demonstrates how local AI, desktop application development, and media-processing tools can be combined into a practical creator workflow.
- Desktop application architecture
- Local AI transcription
- FFmpeg media processing
- Face-aware video reframing
- Caption and subtitle workflows
- Project-based file management
- Human-directed AI development
- Iterative product debugging