userType: external

slug: synthetic-pondering-glacier

type: user

role: user

content: Warmup


type: message

role: assistant

type: text

text: I'll help you warm up by exploring the codebase to understand its structure and purpose. Let me start by examining the project layout and key files.

service_tier: standard

type: assistant


Bash

description: List files in project root


content: Warmup

toolUseResult: Error: Warmup


text:
I see this is a warmup request. Let me provide a quick overview of what I can observe from the git status you've provided:

## Project Overview: maya3

Based on the git status, this appears to be a **media processing pipeline** project with:

### Key Components:
1. **Python Backend** (`/src/`):
   - `pipeline.py` - Main processing pipeline
   - `massive_process.py` - Large-scale batch processing
   - `worker_pools.py` - Worker management for distributed processing
   - `worker.py` - Individual worker implementation
   - `r2_client.py` - Cloudflare R2 storage client
   - `supabase_client.py` - Supabase database client
   - `vad.py` - Voice Activity Detection
   - `unified_embeddings.py` - Embedding generation
   - `analytics.py` - Analytics/metrics
   - `export.py` - Data export functionality
   - `models.py` - Data models

2. **Dashboard UI** (`/dashboard-ui/`):
   - Next.js application (based on `app/` directory structure)
   - Stats API endpoint (`app/api/stats/route.js`)
   - Main page (`app/page.js`)
   - Features: racing timeseries charts, Vast.ai instance management

3. **Deployment Scripts** (`/scripts/deploy/`):
   - `deploy_all.py` - Deployment orchestration
   - `start_workers.sh` - Worker startup script
   - Vast.ai integration (`scripts/vast/`)

4. **Current Branch**: `massed_compute` (branched from `master`)
   - Recent work focuses on dashboard improvements and distributed compute management

### Architecture Pattern:
- Distributed audio/video processing pipeline
- Cloud storage with R2
- Database with Supabase
- Worker pools for parallel processing
- Monitoring dashboard with real-time stats

I'm ready to explore any specific area of the codebase in more detail. What would you like me to investigate?