One Keyboard, One Mouse — Control Every Machine Seamlessly

🖥️

One Keyboard, One Mouse — Control Every Machine Seamlessly

Tired of switching between your Mac, Windows PC, and Linux boxes? Beamer lets you use a single keyboard and mouse across all your machines on the same network. Push your cursor off the edge of one screen and it flows right onto the next — just like a multi-monitor setup, but with real computers.

✨ What It Does

• 🖱️ Cross the edge — push your pointer through the screen border and it arrives on the other machine at the same position
• ⌨️ Keyboard follows you — type natively on any connected machine with smart key remapping (Cmd↔Ctrl, Option↔Alt)
• 📋 Clipboard travels — copy text and images on one machine and paste them on another instantly
• 🎯 Mac gestures on Windows — swipe your trackpad for Task View, desktop switching, and more
• 🔒 Your network only — no cloud servers, encrypted P2P connection, paired with a simple 6-digit code

⚡ Quick Setup

• 🍎 macOS: Download the .dmg, drag to Applications, grant permissions and relaunch
• 🪟 Windows: Run the installer — approve SmartScreen warning with "More info" → "Run anyway"
• 🐧 Linux (Flatpak):

flatpak install --user https://flatpak.kalkmancode.co.uk/beamer.flatpakref

💬 FAQ

Is Beamer free? Yes — completely free, open-source under GPL-3.0, no account required.

Can I connect more than two machines? Absolutely! Pair each machine with every other one for a chain of any size.

Does it work between two Macs or two PCs? Yes — Beamer works in any direction and between identical platforms too.

👉 Give it a try today — your workflow will thank you!

🔗 Check out Beamer on GitHub

🔊 Pocket TTS: Speech That Fits in Your Pocket

Tiny TTS for Any CPU

Forget about GPUs and heavy web APIs. Pocket TTS turns text into speech with just a pip install and a function call.

Why Pocket TTS?

• 🚀 Runs on plain CPUs — no GPU required
• 🪶 Ultra-small models, down to 1.9M parameters
• ⚡ Low latency, ~200ms for the first audio chunk
• 🧠 Supports voice cloning and multi-language (english, french, german, italian, etc.)

Try It with the CLI

Use the generate command directly from the command line:

...pocket-tts generate --text "Hello world." --voice marius...

Adjust the voice with --voice and text with --text. Choose a pretrained language model with --language when running generate, export-voice, or serve (default: english).

Quick Tip: Streaming & Server

Generate audio instantly over HTTP with the serve command:

...pocket-tts serve...

Navigate to http://localhost:8000 for a faster web interface where the model stays in memory between requests.

FAQ

Q: Does it work on a laptop CPU?

A: Absolutely — it's designed for CPU-only, even 1.9M parameter models run fast.

Q: Can I use custom voices?

A: Yes, via voice cloning or by providing your own audio files directly.

Q: Is it easy to install?

A: Just run pip install pocket-tts or use uvx pocket-tts — no heavy setup needed.

Get Started Today

🎙️

🔗 https://github.com/kyutai-labs/pocket-tts

🚀 workout.cool: The Modern Fitness Coaching Platform

🎯 A Comprehensive Fitness Platform

A modern fitness coaching platform that lets you create workout plans, track progress, and access a vast exercise database with detailed instructions and video demonstrations.

💡 The Story Behind workout.cool

This project was born to revive what we lost. As the primary contributor to the original workout.lol, I witnessed its abandonment after video licensing costs became prohibitively expensive. Rather than let this valuable work disappear, I created a fresh, modern implementation — an evolution, not just a revival.

👥 Built For You

Whether you're a fitness enthusiast or a developer, this platform is built for the community. Track your progress, manage your routines, and explore a comprehensive exercise database with rich video demonstrations.

📱 Quick Start

🐳 With Docker

1. Copy environment variables
2. Start everything for development

cp .env.example .env
make dev

3. Open your browser

💻 Without Docker

1. Install dependencies
2. Copy environment variables
3. Start the development server

pnpm install
cp .env.example .env
pnpm dev

🔗 Join the Rescue Mission

Star this repo, join our Discord, report issues, and share feature requests — we're listening to every single one.

🚀 [https://github.com/Snouzy/workout-cool](https://github.com/Snouzy/workout-cool)

❓ FAQ

Q: How do I install workout.cool?

A: Clone the repository, run pnpm install, and copy .env.example to .env.

Q: Can I use Docker to run it?

A: Yes! Use the Docker build and run commands to deploy with a PostgreSQL database.

Q: What license applies to this project?

A: It's licensed under the MIT License — free to use and modify freely.

heartlib: Open-Source Music Foundation Models

🎵

heartlib: Open-Source Music Foundation Models

The most powerful open-source music generation model of 2026

🎶

A family of open-sourced music foundation models

• 🎼 HeartMuLa — music language model generating music conditioned on lyrics and tags with multilingual support
• 🎧 HeartCodec — 12.5 Hz music codec with high reconstruction fidelity
• 📝 HeartTranscriptor — whisper-based model tuned for lyrics transcription
• 🔗 HeartCLAP — audio–text alignment model for unified embedding space

🚀

Latest releases — 7B and 3B versions

• 🔥 HeartMuLa-7B — comparable to Suno in musicality, fidelity and controllability
• 🎬 MuLaCover — controllable cover-song and music-remix model
• 🎧 HeartCodec-oss-encoder — audio tokenization and reconstruction

🖥️

Local deployment in one step

... git clone https://github.com/HeartMuLa/heartlib.git

... pip install -e .

⚖️

License & citation

... Apache 2.0 License

❓

FAQ

Q: How do I generate music?

A: ... python ./examples/run_music_generation.py --model_path=./ckpt --version="3B"

Q: What versions are available?

A: ... HeartMuLa-3B and HeartMuLa-7B (7B being the latest)

Q: How do I install it locally?

A: ... git clone ... / pip install -e .

🎵 Start generating music with heartlib today!

... https://github.com/HeartMuLa/heartlib

colibri: Tiny Engines, Immense Models

🐦

colibri: Tiny Engines, Immense Models

Run frontier MoE models on hardware you already own

(/)

A mixture-of-experts model of hundreds of billions of parameters

Pure C, zero dependencies, experts streamed from disk.

🧠

13 engines ready — from 35B to 2.8T parameters

• 🐦 GLM-5.2 / 5.3 (744B MoE)
• 🤖 DeepSeek V4 Flash & V4.1 (284B / 552B)
• 🔥 Qwen3.8-Flash-Next (125B)
• 🎨 Qwen-Image-2.1 (text to picture)
• 🧩 Decision models: Laya, GLiNER2.5, Clef

🖥️

No GPU required — CPU runs everything, GPU accelerates

• 🖥️ CPU: every engine runs with nothing else installed
• 🎮 Vulkan: any GPU with 1.2 driver
• 🚀 CUDA: NVIDIA cards with toolkit support
• 📦 Multi-drive: read experts from NVMe, two drives +37.5%

⚡

One step to get started

... ./start-here.sh — asks for your machine specs, recommends a model, builds or downloads the engine, and opens the dashboard.

... ./start-here.sh --yes — no questions, go straight to the recommendation.

🧩

System One: make models answer choices, not paragraphs

... POST /v1/systemone — gives the model a document and only allowed options, returning probabilities and confidence.

❓

FAQ

Q: Can I run large models on a laptop?

A: Yes — on a 25 GB laptop, every expert streams from disk and runs correctly, though slower.

Q: Do I need a GPU?

A: No — every engine runs on CPU; a GPU is optional but faster for the hot experts.

Q: How do I connect colibri to other apps?

A: Yes — use the OpenAI-compatible API (base URL http://127.0.0.1:8000/v1) or the Anthropic-compatible endpoint; existing clients work unmodified.

🎉 Get started now and watch your own hardware run frontier models!

... https://github.com/JustVugg/colibri