Found this on @tracklib then chopped it on the MPC @Akai_Pro 🔪
Episode 1 of The Cook Up is live on my YouTube.
Full video → link in bio 🍳
Who should I cook up with next? 👇🏽
#mpcsample #tracklib #beatmaker #beatmaking #thecookup
TRACKLIB retweeted
eu amo o @tracklib
eu amo a arte de samplear
TRACKLIB retweeted
New @tracklib is such an upgrade. Stellar work.
TRACKLIB retweeted
Thank you @tracklib app, 🙏🏽‼️just made it ten times easier to sample records 👀💯
TRACKLIB retweeted
really cool to see @tracklib constantly upgrading. sampling is the heart of production so having these tools mobile is a game changer for real.
TRACKLIB retweeted
Producers, I exclusively released all the stems to my "Many Moods.." album via @tracklib
tracklib.com/music/artists/m…
TRACKLIB retweeted
Suno, Udio, and AI music could never give me this feeling.
Human music. Now and forever.
Next year I’m doubling down on my support of @tracklib
TRACKLIB retweeted
Best black Friday plugin deal and its free the least amount of resistance from listening to sampling I experienced. #IYKYK
TRACKLIB retweeted
You need to make sample worthy music.
Because @tracklib helps musicians get paid when they are sampled.
TRACKLIB retweeted
Oh my Jesus THANK YOU @tracklib
TRACKLIB retweeted
Replying to @TheGeniusParty @tracklib
Man I’ve been OG with Tracklib since 2018 fr. Every year I say I wish they had an app and it’s finally here. It simplifies everything tbh
TRACKLIB retweeted
The shit I’ve been cooking lately is FUCKING INSANE.
Shoutout to @tracklib for the clearable samples.
My forte has always been choppin up vintage records and now that I know I have a way to clear the samples, it’s game time!!!
TRACKLIB retweeted
Sample a '70s Soundtrack to an Italian Erotic Thriller. @tracklib feature is a tribute to a killer OST from our catalogue and highlights Maestro Claudio Tallino's work on the score for the 1976 film 'Calamo.' A hidden Psychedelic-Funk gem.
tracklib.com/blog/sample-a-7…
TRACKLIB retweeted
Replying to @LinusEkenstam
Also, @tracklib is rad because a) artists opted in b) ownership/royalty splits for derivative works are predefined, pre-cleared and fair rather than onerous. AI training data set providers could learn from this methodology.




















