# Hacker News "Show HN" Post
## Title
Show HN: ClipSense MCP – AI debugging for 500MB mobile app crash videos
## Alternative Titles (pick one)
1. Show HN: ClipSense MCP – AI video analysis for mobile debugging (React Native, iOS, Android)
2. Show HN: Analyze mobile app crash videos with AI using Model Context Protocol
3. Show HN: ClipSense – Frame-by-frame AI analysis of mobile app bugs from screen recordings
## Body
Hi HN! I built ClipSense MCP to solve a problem I kept running into as a React Native developer: debugging mobile apps from screen recordings.
**The Problem:**
When users report bugs, they send screen recordings (often 50-500MB). Manually scrubbing through these videos takes hours, and Claude Desktop's 31MB file upload limit makes it useless for this use case.
**The Solution:**
ClipSense uses Model Context Protocol (MCP) to bring AI-powered video analysis directly into your coding workflow. It works with VS Code (via Claude Code), Cursor, Windsurf, and other MCP-compatible AI assistants.
**How it works:**
1. Record your app crash with QuickTime, iOS, or Android screen recording
2. In your IDE: "Analyze Desktop/crash.mp4"
3. Get frame-by-frame analysis + root cause + code fixes in ~2 minutes
Under the hood:
- MCP server reads local video files (up to 500MB, 9 formats supported)
- Uploads to Cloudflare R2 (encrypted)
- Processes with Claude Sonnet 4.5 (vision API for frame-by-frame analysis)
- Returns structured analysis: root cause, timeline, visual evidence, recommended fix
**Example output:**
```
Root Cause: Null pointer exception at ProfileScreen.tsx:142
Timeline:
- 0:15 - User navigates to profile screen
- 0:18 - App crashes (NullPointerException)
Recommended Fix:
const avatarUrl = user?.profile?.avatar ?? DEFAULT_AVATAR;
```
**Tech stack:**
- TypeScript
- Model Context Protocol SDK (@modelcontextprotocol/sdk)
- Node.js 18+
- Cloudflare R2 (video storage)
- Claude Sonnet 4.5 (AI vision)
**What makes it different:**
- 500MB file support (vs Claude Desktop's 31MB limit)
- Works directly in your IDE (no context switching)
- Supports 9 video formats (MP4, MOV, WebM, AVI, MKV, FLV, MPEG, 3GP, WMV)
- Works with React Native, iOS (Swift/Objective-C), Android (Kotlin/Java)
**Pricing:**
- Free tier: 3 analyses/month
- Pro: $29/mo for 50 analyses
- All tiers include video encryption, auto-deletion after 24 hours, no AI training on your data
I'd love feedback from HN:
1. What other video formats should I support?
2. Would you use this for web app debugging too?
3. Any security concerns with video uploads?
Try it:
- Repo: https://github.com/clipsense/-mcp-server
- Install: `npm install -g @gburanda/clipsense-mcp-server`
- Get API key: `curl -X POST "https://api.clipsense.app/api/v1/keys/request" -H "Content-Type: application/json" -d '{"email":"you@example.com"}'`
Happy to answer questions!
---
## Alternative Body (More Technical)
Hi HN! I built an MCP server that lets AI coding assistants analyze 500MB mobile app crash videos.
**Problem:** Claude Desktop has a 31MB file upload limit, making it useless for analyzing realistic screen recordings of mobile app bugs.
**Solution:** ClipSense MCP uses the Model Context Protocol to read local video files (up to 500MB), process them with Claude Sonnet 4.5's vision API, and return structured debugging analysis.
**Architecture:**
```
Your IDE (VS Code/Cursor)
→ MCP Client
→ ClipSense MCP Server (stdio transport)
→ Read local .mp4 file
→ Upload to Cloudflare R2 (encrypted, TLS)
→ Queue analysis job (PostgreSQL)
→ Process with Claude Sonnet 4.5 (frame-by-frame)
→ Return structured analysis
```
**Key technical decisions:**
1. **Why MCP over VS Code extension?**
- Works with multiple AI assistants (Claude Code, Cursor, Windsurf, Continue.dev)
- Leverages existing AI context (codebase, conversation history)
- No need to build separate chat interface
2. **Why Cloudflare R2?**
- Cheaper than S3 for egress ($0 vs $0.09/GB)
- Better global CDN for video streaming
- Built-in presigned URL support
3. **Why Claude Sonnet 4.5?**
- Best vision model for frame-by-frame analysis
- Handles technical screenshots (stack traces, IDE)
- Fast inference (~2 min for 10-min video)
**Supported formats:** MP4, MOV, WebM, AVI, MKV, FLV, MPEG, 3GP, WMV (all converted to MP4 via ffmpeg)
**Security:**
- Videos encrypted in transit (TLS)
- Videos encrypted at rest (R2)
- Auto-deleted after 24 hours
- No AI model training on user data
**Performance:**
- Average analysis time: 2-3 minutes
- Max video: 500MB, 10 minutes
- Frame sampling: ~2 FPS for 10-min video = ~1200 frames
**Open questions:**
1. Should I support web app debugging (browser DevTools recordings)?
2. Should I add Jira/Linear integration for auto-filing bugs?
3. Any interest in team collaboration features (shared analyses)?
**Try it:**
```bash
npm install -g @gburanda/clipsense-mcp-server
curl -X POST "https://api.clipsense.app/api/v1/keys/request" \\
-H "Content-Type: application/json" \\
-d '{"email":"you@example.com"}'
```
Repo: https://github.com/clipsense/-mcp-server
Pricing: Free tier (3 analyses/month), $29/mo for 50 analyses, $99/mo for 300 (team plan).
Looking forward to HN feedback!
---
## Recommended Version to Post
Use the **first body** (not "Alternative Body"). It's more accessible to a broader HN audience while still being technical enough. The alternative is too architecture-heavy and might alienate non-technical readers.
## Best Time to Post
- **Best days**: Monday-Wednesday
- **Best times**:
- 8-9 AM ET (before work, East Coast)
- 12-1 PM ET (lunch break)
- 6-7 PM ET (after work, catching up)
- **Avoid**:
- Friday afternoons (low engagement)
- Weekends (unless you're prepared to respond all weekend)
## Engagement Strategy
1. **First 30 minutes are critical** - respond to ALL comments
2. Upvote thoughtful questions (even critical ones)
3. Provide code examples when asked
4. Be humble about limitations (acknowledge the 31MB thing is Claude's constraint, not yours)
5. Don't be defensive about pricing criticism
6. Thank people for trying it out
7. Ask for specific feedback (e.g., "What other video formats would help?")
## Common HN Objections + Responses
### "Why not just use Claude Desktop directly?"
Response: "Claude Desktop has a 31MB file upload limit. 99% of screen recordings exceed this (a 1-minute iPhone recording at default quality is ~80MB). ClipSense MCP works around this by using the filesystem access that MCP provides."
### "This feels like a thin wrapper around Claude API"
Response: "Fair point! The value isn't just the API call – it's the integration. ClipSense handles video format conversion (9 formats → MP4), chunked upload to R2, presigned URL generation, frame extraction, and structured output parsing. It saves developers from building this themselves."
### "Pricing seems high for API calls"
Response: "The cost breakdown: Claude Sonnet 4.5 vision API + R2 storage + compute. A 5-minute video = ~600 frames at 2 FPS = ~$2-3 in API costs alone. Free tier is truly free (subsidized), and Pro tier margins are thin. Happy to share more details if helpful!"
### "Why not open source the backend?"
Response: "Great question. The MCP server is open source (MIT), but the backend API is closed source to manage costs (prevent abuse of free tier). If there's strong interest in self-hosting, I could explore a Docker Compose setup."
### "Security concerns about uploading videos"
Response: "Totally valid. Videos are: (1) encrypted in transit (TLS), (2) encrypted at rest (Cloudflare R2), (3) auto-deleted after 24 hours, (4) never used for AI training. For extra-sensitive videos, you can self-host once I release the Docker setup."
## Follow-up Posts
If the initial post gets traction:
1. Day 2: "Ask HN: What would make ClipSense MCP more useful for your team?"
2. Week 1: "Show HN: ClipSense now supports web app debugging (follow-up)"
3. Month 1: "Show HN: 1,000 videos analyzed – here's what I learned about mobile bugs"
## Success Metrics
- Front page: 100+ points
- Comments: 30+ substantive comments
- Conversions: 50+ API key requests from HN traffic (track with utm_source=hackernews)
- GitHub stars: 100+ new stars
