Flowtonik
The AI-assisted DAW built by producers, for producers
What it does
Flowtonik is a macOS digital audio workstation with AI-assisted workflows, built for producers who care about craft and control. Instead of generating finished songs for you, Flowtonik integrates AI directly into a professional DAW. You record, edit, arrange, and mix as usual—while AI helps generate music or MIDI, separate tracks, assist with mixing, and speed up technical edits through natural-language commands. The human stays in control. AI works quietly in the background.
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- IUI used AI to recreate a $4000 piece of audio hardware as a pluginJan 2026 · ▲160
Hi Hacker News, This is definitely out of my comfort zone. I just wanted to show you guys because I'm super proud of it. It's a 100% faithful recreation based off of the schematics, patents, and ROMs that were found online. So please watch the video and tell me what you think https://youtu.be/auOlZXI1VxA The reason why I think this is relevant is because I've been a programmer for 25 years and AI scares the shit out of me. I'm not a programmer anymore. I'm something else now. I don't know what it is but it's multi-disciplinary, and it doesn't involve writing code myself--for…
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I trained a 125M-parameter transformer to autocomplete piano performances in real time (~108 notes/sec on an iPhone 15). The idea is basically GitHub Copilot or Tabnine, except instead of prompting it with code, you prompt it by playing a few notes on a MIDI piano. The model then continues what you played, entirely on-device. The app is free if anyone wants to try it. Happy to answer questions about the model, training, Core ML, or the many things that didn't work.
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Hey HN, Henry from Cactus here! We previously released Cactus Needle, a 14MB agentic LLM for tool call, device use, and structured extraction for phones, wearables, smart homes, small robots and microcontrollers. We got really great feedback here, and have now incorporated the suggestions to release Needle 2. The whole model is a single 14MB binary that runs a full session in 28MB of RAM; 45m parameters at 2bit compression. Needle hits 500 tokens/sec decode speed on a Raspberry Pi 5, sits between 400-1,500 tokens/sec on VR devices like Meta Quest 3S and Apple Vision Pro, and ranges…
AI · 27d ago · cactuscompute.com
