Loopstate
Your own subliminal or affirmations audio, mixed live
What it does
Most subliminal or affirmation tools hand you a fixed script, voice, and frequency — take it or leave it. Loopstate lets you write or upload your own script, record it in your own voice (or pick one of ours), and drag binaural frequency and ambience sliders with the mix changing live in your browser, before you pay. One-time $1.99 per track, no subscription, free remix for 7 days after.
Write your affirmations or subliminal script free, record it in your own voice or pick one of ours, set your binaural frequency and ambience, and hear it mixed live before you pay $1.99.
Write your own affirmation or visualization script, record it in your own voice — or pick one of ours — and set the binaural frequency and ambience around it. Mixed live in your browser, free to preview, $1.99 to keep. A sample voice line, mixed in real time. Your own track works exactly the same way — write your script, pick a voice, then drag these same controls. Type a goal in plain words — confidence, money, love, focus. Pick affirmation lines or a guided "future self" story. AI drafts the script instantly. Read it, edit it, regenerate it — it costs you nothing until you're happy with it. Load a voice — generated or your own recording — then switch binaural bands and drag the volume…from loopstate.space
Does the same job
all alternatives →- BCBulk Creation of Transcripts from YouTube Playlists with Whisper2023 · github.com · ▲125
I know there are various tools that are supposed to make this easy, but I couldn't find anything that did everything I wanted, so I made this today for fun. The web-based offerings all take forever and seem flaky, and you need to process one video at a time, with no control over the transcription settings. In contrast, my script lets you convert a whole playlist in bulk with full control over everything. It's truly easy to use-- you can clone the repo, install to a venv, and be generating a folder full of high quality transcript text files in under 5 minutes. All you need to do is supply the…
- CACloning a musical instrument from 16 seconds of audio2022 · erlj.notion.site · ▲185
In 2020, Magenta released DDSP [1], a machine learning algorithm / python library which made it possible to generate good sounding instrument synthesizers from about 6-10 minutes of data. While working with DDSP for a project, we realised how it was actually quite hard to find 6-10 minute of clean recordings of monophonic instruments. In this project, we have combined the DDSP architecture with a domain adaptation technique from speech synthesis [2]. This domain adaptation technique works by pre-training our model on many different recordings from the Solos dataset [3] first and then…




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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…
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Launched alongside, August 2026
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Life & fun · 9d ago · louisabraham.github.io


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Hello HN! I found that picking out plausible but diverse skin tones for my digital art and game development projects was kind of difficult, and I got curious about if there was a way to define a color space that made it easy. I've built a color picker and procedural generation algorithm based on the space as well as a bunch of other fun js features and demos throughout the page that use the equations. If you find it interesting, I have lots of explanations of how I built it and what properties the space has. The methodology might be a bit shaky, but hopefully the result is as helpful for…
Life & fun · Aug 2026 · toneyalexander.github.io


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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