Harrison First
11.2K posts

Harrison First
@harrisonfirst
“French and Swedish” (out now)
Stockholm Sweden Katılım Haziran 2010
3.1K Takip Edilen5.3K Takipçiler
Harrison First retweetledi
Harrison First retweetledi
Harrison First retweetledi
Harrison First retweetledi

One thing we missed with crypto music ...
We built some amazing tools for artists.
But hardly any apps for music fans.
I like this concept by Piki on @base
Music fans can earn by predicting / curating music you think people will like. Try it out ↓
Piki Music@pikinyc
Introducing Piki! The Music Prediction Market. • Listen and rate songs. • Predict whether the next 10 people will like it. • Earn when you're correct. Live now on @base. Try the mini-app below ↓
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Harrison First retweetledi
Harrison First retweetledi
Harrison First retweetledi

Video Creators, @FlixFanatics are dropping 12,500 $FUN XP in rewards to the 5 top creators!
Tell the $FLIX story in one of 5 epic categories (categories in the video)
Starts TODAY & ends on Feb 21
Post on X, tag @FLIXdotFUN @FlixFanatics + #FLIXvideos & share link on Discord
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Harrison First retweetledi

The AI music landscape in 2026.
This week we mapped 46 generative AI tools … the ethical, the unethical, and everything in between.
Here’s how we break out our categorisation:
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💿 Full song creation - Generate a complete song based on a text description. Some are ethically trained. Others are trained on scraped, unlicensed music.
🎹 DAW-based - Tools that sit inside your DAW, or function like a DAW, to assist music creation and production (generate chord progressions, basslines or drums).
🎤 Vocal tools - These are optimized to generate vocal sounds (choirs or voice-changing outputs).
🎛️ Remix tools - using AI to mashup or remix existing music.
🖼️ Sync and soundtracks - Generates background audio for podcasts, vlogs, adverts.
🧘♀️ Wellness - Generates audio for focus and wellness apps.
🚨 Attribution and enforcement - Detect AI content, track attribution, flag infringements, and process royalties from generative AI.
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The biggest takeaway I have is that lumping generative music AI into one homogenous category and making “good” or “bad” assumptions is not really correct.
The spectrum is WIDE here.
-> Some tools are scraping copyright on a disrespectful level. Others are using clever, ethical new training models.
-> Some tools are killing the creative process with “vending machine” song generation. Others are accelerating creativity in production.
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Full report below.
As always, please tell me what I got wrong, what I’m missing, or where you think this goes next!

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