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▶️ New video: The complete Studio install on a Mac
If you have been meaning to run Studio and stalled at setup, start here. Apple Silicon, start to finish: the app, the workers, and everything connected. https://www.youtube.com/watch?v=qMoZuu6nz44 The code: https://github.com/selfhosthub/studio What you have by the end: - Studio running locally, API and web UI - Native workers with pip install studio-workers, no Docker needed on Mac - GPU work running on Apple Silicon - A worker registered and picking up jobs This is the foundation video. Every walkthrough after this one starts from a working install and links back here instead of repeating setup, so you install once and never sit through it again. If you're running Windows 11, click here: https://www.skool.com/selfhosthub/new-video-the-complete-studio-install-on-windows-11?p=933a01d8 Console on Pypi: Console on Pypi: https://pypi.org/project/studio-console Hit a step that did not go the way it went on screen? Drop it in the comments. Cheers
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👋 Start Here! + Poll
Welcome to Self-Host Hub. This is the free community built around Studio, a self-hosted AI workflow platform with no per-run fees, no subscription tax, and no limits. Browse the Start Here classroom first. It covers what Studio is, what you can build with it, and how to get running fast. Then, checkout Self-Host Studio - Getting Started it walks you through installing Studio. Then come back and answer the poll below. Your answers help me prioritize the content and guides that matter most to you. How will you run Studio?
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🚗 Community Roadmap - Member requests (updated 9/18/2026)
Welcome to Self-Host Hub. This community is for people building automation infrastructure they own: self-hosted workflows, local AI, and systems you run for yourself or your clients. WHAT IS STUDIO Self-hosted workflow automation platform for AI content pipelines. You own the infrastructure, pick your providers, keep your keys. Four pieces: • App (API + web UI) • Workers (general, video, audio, ComfyUI/GPU), as Docker images or pip install studio-workers for native GPU, Apple Silicon included • Community Catalog (free providers and workflows) • Plus Catalog (advanced production workflows for members) Deploy however you want: single Docker container, Docker Compose, Kubernetes, GPU Host, or hybrid GPU setups. 📋 ROADMAP ✅ MVP: faceless video pipeline (OpenAI, Leonardo, JSON2Video) ✅ Self-hosted video rendering (replaces JSON2Video) ✅ Self-hosted image generation via ComfyUI (replaces Leonardo): Flux.1 Schnell and Flux 2 Klein, generation and image editing ✅ Self-hosted TTS voice cloning (Chatterbox, replaces OpenAI TTS): clone a voice from a ~10 second clip ✅ Bring your own ComfyUI workflows: upload a package, test it privately, publish it to your users 🔜 Self-hosted video generation via ComfyUI Every stage so far eliminated an API bill. The pipeline runs on your own hardware. 📅 UPDATES • 9/18/26 Studio 1.10.0: workers from outside your install wait for your approval before they can join, Wait for Webhook steps pause the run until the callback arrives, uploads get a configurable size limit, and files that share a name or are replaced keep their own contents. • 8/30/26 Studio 1.9.0: give each worker machine its own revocable key limited to the queues you choose, trigger workflows from signed incoming webhooks, 14 new providers including Claude, Gemini, Slack, Discord, Notion and Stripe, and narrate in one language while prompting images in another. • 8/21/26 Studio 1.8.0: the video worker is half the size, runs without a GPU, and transcribes subtitles about 2.3x quicker.
Local speech-to-text that types into any app (free, open source)
For anyone here who likes keeping things on their own hardware: we built FU Flow, a dictation app that runs Whisper on your own PC. Press right Alt, speak, press it again, and the text is typed into whatever app you are in. After setup it works offline. No account, no subscription, unlimited words. Windows 10/11 and Ubuntu 22.04+ (.deb or AppImage), MIT licensed. fuckyouflow.app Which Whisper model size do people here run locally?
Local speech-to-text that types into any app (free, open source)
▶️ New video: Should You Rent or Buy a GPU for AI? I Did the Math
A used RTX 3090 runs $1,000 to $1,600 right now. Renting the same card runs about 10 to 50 cents an hour. So how many hours before buying actually saves you money? New video: I worked it out across the whole range. - Against a typical 20 cent marketplace rental, a used 3090 pays for itself somewhere between 8,000 and 13,000 GPU hours. At four hours a day, that's six to nine years. - Against a 50 cent data center machine, it's 2,400 to 3,800 hours, so owning wins much sooner. - Count the hours the GPU actually works, not the hours the PC is on. Most of us use a card in bursts. - Your power price moves it a lot. At California rates, running your own card barely beats renting at all. - If you rent, keep your models on a volume, and remember a stopped machine still bills for its disk. For most people starting out, renting is the cheaper way in. Prices move every day, so check them yourself before you buy anything. Any questions? Let me know. Cheers, Kawika
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