MoonshotAI
Featured
Kimi-K3
Kimi K3 is an open-weight, 2.8T-parameter native multimodal agentic model with a 1M-token context window, designed for frontier coding, knowledge work, and reasoning tasks.
AI repository intelligence
FR-AI / ANALYSISWhy it is worth attention
It provides a complete, self-hosted karaoke experience with phone scanning, TV playback, WebSocket sync, AI-assisted classification, hardware transcoding support, and easy Docker deployment, all in one package.
Who it is for
- Home users running a NAS or Linux server
- Karaoke enthusiasts who want a private setup
- Developers interested in building or extending a KTV system
- Android TV owners looking for a free, local karaoke app
Use cases
- Family parties where guests pick songs via WeChat scan on their phones
- Private karaoke room without external services or subscriptions
- Media server integration for managing a personal song library
- Learning and prototyping a full-stack application with Spring Boot, Vue, and Android
Strengths
- Comprehensive feature set: song search, queue, lyrics, sound effects, dual-track support, and remote control
- Simple Docker Compose setup with prebuilt images for NAS and Linux hosts
- Hardware transcoding support (Intel VAAPI, Rockchip MPP) for better performance
- AI-assisted song classification and automatic metadata extraction via FFprobe
Considerations
- Designed only for trusted home networks; must not be exposed to the public internet
- AI-generated accompaniment may retain vocals and cannot match professional studio quality
- Bluetooth microphone latency depends on TV box firmware; USB/wired devices are recommended
README quick start
快速开始
Description
一个局域网的私人ktv系统
Related repositories
Similar projects matched by category, topics, and programming language.
lopopolo
Featured
harness-engineering
Harness Engineering is a methodology for improving coding agent outputs by carefully crafting the environment around them—providing curated context, tools, and executable constraints that encode an organization’s nonfunctional requirements and cumulative lessons.
slvDev
Featured
esp32-ai
A 28.9 million parameter language model runs on an $8 ESP32-S3 microcontroller entirely on-device, generating simple stories at about 9.5 tokens per second.