Usually it's very fast, no need to remove prior commits from Pd's repositories. You're sure you supplied a different version number when uploading with deken?

Try the mailing list. All the devs are there, including the LibPd devs/maintainers.

What is this /mrpeach/net prefix? AFAIK, [udpsend] is part of the mrpeach library, so [mrpeach/udpsend] should work. Did you try it?

Perhaps you don't even need [symbol]. You can give the patch a try without it, it might work.

Happy that it worked.

You might want to give [serial_print] abstraction a try, I made it especially for communicating with Arduino's Serial.print() function. Your Arduino code seems alright for this case. You can find the abstraction here.
Connect [comport] to [serial_print], and both outlets of [serial_print] to [s ] ([send]) without arguments, like this:

[comport 1 9600]
|
[serial_print]
|           /
|         /
|       /
|      /
[send]

Then use [r ] ([recieve]) objects with the same argument as the string you use in your Arduino code, for example [r p], [r s1], [r f1], [r f2] for your first Arduino sketch, and [r a1] etc. [r dist] for your second sketch.

@KMETE OK, you have pd-cyclone-master in your search paths, but you're trying to load [cyclone/scope~]. What happens if you just try to load [scope~] (no "cyclone" prefix)?

your search path is called pd-cyclone-master, whereas you're trying to load an object from a path called cyclone. Either change the name of the pd-cyclone-master directory to cyclone, or the path in the object name to pd-cyclone-master.

The second edition of "Digital Electronics for Musicians" is out! This book is on Pd combined with the Arduino and occasionally with the Raspberry Pi for the creation of interactive musical projects. This second edition focuses on updated software, introduces the Bela, and adds a chapter on AI with the [neuralnet] object.
It includes an introduction to Pd, an introduction to the Arduino, Linux command line for remotly controlling the Pi, wireless communication between the Arduino and the computer with the XBee modules and WiFi, and examples on embedding computers to create stand alone projects.

Check the book here.

As a side-note, the publisher is looking for readers to write a review on Amazon. If you're interested, DM me your email and I will forward it to the publisher. You'll get a free PDF version of the book so you can read it and then write a review. This is on a first-come first-serve basis, for a limited number of people.

Admins, sorry for this shameless slef-plug, if you think it doesn't fit this forum, please remove.

[neuralnet] update! Version 0.3 has just been released!

• New activation functions added
• Access to the internal structure of a trained network (e.g. the latent space)
• Storing weights and biases during training for visualization
• Save models during training
• Signal-rate version of the object!
• Audio autoencoder example added!

Binaries for Linux, Raspberry Pi 3,4,5, macOS, and Windows (thanks @ben.wes for macOS and Windows) are available through deken.
Souces are available on GitHub https://github.com/alexdrymonitis/neuralnet
Thanks to Ben Wesch (@ben.wes), Dan Wilcox, IOhannes m zmoelnig, Christof Ressi, and others!

Enjoy!