D2sage

The gateway drug is alcohol.

He is busy with the ladies.

Found this.

How do they look?

Any good educational source to learn Python? Maybe something interactive. There are so many tools online now that charges you monthly. And a lot of them are powered by Chat GPT API. Also, Python seems to be a high value skill to have in the near future.

Yeah but most people who fap are looking to porn while doing it. And nofap can cure porn addiction.

Copium. https://www.yourbrainonporn.com/research/ There are tons of neuroscience behind this. Brain scans included.

This video, blindfolded on mushrooms, is neat.

Your brain ain't wired for the amount of dopamine you can get from porn today. You can only watch porn once a month if you want a healthy brain. Porn leads to mental health issues, that's why so many men are depressed today.

is a picture of a burger like real burger?

Pop 2g dried mushrooms and do your thing.

This movement has screwed up many kids as well. You have kids in UK that identifies as animals. https://www.dailymail.co.uk/news/article-12213579/How-schools-allowing-kids-identify-cats-horses-dinosaurs.htm

This code works. You feed it with MIDI files or musicxml files. And then you hit generate to make the music. Do we got any python people here? What can we do with this code to make it optimal. https://easyupload.io/kqac2x <--- Project file Got 50 000 midi files but not sure if the code is optimal for that large data? import os import random import tkinter as tk from tkinter import filedialog from music21 import converter, instrument, note, chord, stream import tensorflow as tf from tensorflow.keras.models import Sequential from tensorflow.keras.layers import LSTM, Dropout, Dense import numpy as np # Global variables training_dir = "" model_dir = "C:/Users/adria/OneDrive/Desktop/Model" output_dir = "C:/Users/adria/OneDrive/Desktop" sequences = [] model = None def select_training_files(): global training_dir training_dir = filedialog.askdirectory(title="Select Training MIDI Files Directory") print("Training files directory selected:", training_dir) def select_model_directory(): global model_dir model_dir = filedialog.askdirectory(title="Select Model Directory") print("Model directory selected:", model_dir) def select_output_directory(): global output_dir output_dir = filedialog.askdirectory(title="Select Output Directory") print("Output directory selected:", output_dir) def transfer_learning(): global model, sequences transfer_model_path = "C:/Users/adria/OneDrive/Desktop/Model" if not os.path.exists(transfer_model_path): print("Transfer learning model not found. Please check the path.") return def train_model(): global training_dir, model_dir, sequences, model if not training_dir: print("Please select the training files directory.") return if not model_dir: print("Please select the model directory.") return model_path = os.path.join(model_dir, "model.h5") # Convert MIDI files to music21 stream objects training_files = [os.path.join(training_dir, file) for file in os.listdir(training_dir) if file.endswith((".mid", ".musicxml"))] sequences = [] # Clear previous sequences for file in training_files: midi = converter.parse(file) notes = [] chords = [] for element in midi.flatten(): if isinstance(element, note.Note): notes.append(str(element.pitch)) elif isinstance(element, chord.Chord): notes.append('.'.join(str(n) for n in element.normalOrder)) sequences.append(notes) # Create input and output sequences sequence_length = 100 pitch_names = sorted(set(note for sequence in sequences for note in sequence)) note_to_int = dict((note, number) for number, note in enumerate(pitch_names)) network_input = [] network_output = [] for sequence in sequences: for i in range(len(sequence) - sequence_length): sequence_in = sequence[i:i + sequence_length] sequence_out = sequence[i + sequence_length] network_input.append([note_to_int[char] for char in sequence_in]) network_output.append(note_to_int[sequence_out]) network_input = np.array(network_input) # Specify dtype as object network_output = np.array(network_output) n_patterns = len(network_input) # Check if network_input is empty if n_patterns == 0: print("No training sequences found.") return # Reshape the input network_input = np.reshape(network_input, (n_patterns, sequence_length, 1)) network_input = network_input / float(len(pitch_names)) # Check if the pretrained model exists, if not create a new model if os.path.exists(model_path): model = tf.keras.models.load_model(model_path) print("Pretrained model loaded successfully.") else: # Create the model model = Sequential() model.add(LSTM(256, input_shape=(network_input.shape[1], network_input.shape[2]), return_sequences=True)) model.add(Dropout(0.3)) model.add(LSTM(512, return_sequences=True)) model.add(Dropout(0.3)) model.add(LSTM(256)) model.add(Dense(256)) model.add(Dropout(0.3)) model.add(Dense(len(pitch_names), activation='softmax')) model.compile(loss='sparse_categorical_crossentropy', optimizer='adam') print("New model created.") # Train the model print("Training started...") model.fit(network_input, network_output, epochs=50, batch_size=32) print("Training completed.") # Save the model model.save(model_path) print("Model saved successfully.") # Save the sequences np.save(os.path.join(model_dir, "sequences.npy"), sequences) print("Sequences saved successfully.") def load_model(): global model_dir, model, sequences if not model_dir: print("Please select the model directory.") return model_path = os.path.join(model_dir, "model.h5") # Check if the model file exists if not os.path.exists(model_path): print("Trained model not found. Please train the model first.") return # Load the trained model model = tf.keras.models.load_model(model_path) print("Model loaded successfully.") # Load the sequences if os.path.exists(os.path.join(model_dir, "sequences.npy")): sequences = np.load(os.path.join(model_dir, "sequences.npy"), allow_pickle=True).tolist() print("Sequences loaded successfully.") else: print("Sequences not found. Please train the model first.") return def generate_music(): global model, sequences, output_dir if not model: print("Please load or train the model.") return if not output_dir: print("Please select the output directory.") return # Rest of the code for generating music... # Create input sequences sequence_length = 100 pitch_names = sorted(set(note for sequence in sequences for note in sequence)) note_to_int = dict((note, number) for number, note in enumerate(pitch_names)) int_to_note = dict((number, note) for number, note in enumerate(pitch_names)) network_input = [] for sequence in sequences: for i in range(len(sequence) - sequence_length): sequence_in = sequence[i:i + sequence_length] network_input.append([note_to_int[char] for char in sequence_in]) # Check if network_input is empty if not network_input: print("Please train the model first.") return # Generate music for file_number in range(10): # For creating multiple files start = np.random.randint(0, len(network_input) - 1) pattern = network_input[start] prediction_output = [] for _ in range(8 * 4): # Generate 8 bars (4/4 time signature) prediction_input = np.reshape(pattern, (1, len(pattern), 1)) prediction_input = prediction_input / float(len(pitch_names)) predicted_probs = model.predict(prediction_input, verbose=0) predicted_index = np.argmax(predicted_probs) predicted_note = int_to_note[predicted_index] prediction_output.append(predicted_note) pattern = np.append(pattern, predicted_index) pattern = pattern[1:len(pattern)] # Rest of the code... # Create music21 objects from the generated output offset = 0 output_notes = [] for pattern in prediction_output: if ('.' in pattern) or pattern.isdigit(): notes_in_chord = pattern.split('.') notes = [] for current_note in notes_in_chord: new_note = note.Note(int(current_note)) new_note.offset = offset new_note.storedInstrument = instrument.Piano() output_notes.append(new_note) else: new_note = note.Note(pattern) new_note.offset = offset new_note.storedInstrument = instrument.Piano() output_notes.append(new_note) offset += 0.5 # Create a music21 stream object and add the generated notes midi_stream = stream.Stream(output_notes) # Save generated sequence to a MIDI file file_number = random.randint(1000, 9999) output_path = os.path.join(output_dir, f"output_{file_number}.mid") midi_stream.write('midi', fp=output_path) print(f"Music generated successfully: {output_path}") def main(): # Create a Tkinter window window = tk.Tk() window.title("MIDI Music Generation") # Create buttons for each action btn_select_training_files = tk.Button(window, text="Select Training Files", command=select_training_files) btn_select_model_directory = tk.Button(window, text="Select Model Directory", command=select_model_directory) btn_select_output_directory = tk.Button(window, text="Select Output Directory", command=select_output_directory) btn_train_model = tk.Button(window, text="Train Model", command=train_model) btn_load_model = tk.Button(window, text="Load Model", command=load_model) btn_generate_music = tk.Button(window, text="Generate Music", command=generate_music) # Place the buttons on the window btn_select_training_files.pack() btn_select_model_directory.pack() btn_select_output_directory.pack() btn_train_model.pack() btn_load_model.pack() btn_generate_music.pack() # Start the Tkinter event loop window.mainloop() if __name__ == "__main__": main()

The Bolivian government passed a law to lower the minimum working age to 10 in 2014 contrary to international standards. Child labor exist. Probably some child labor worked for your smartphone, computer and other electronics or clothes you wear. He is in it for the long run. There is a reason he is a billionaire and you not.

Leo Gura has mild narcissism or is it just Hubris? Biden. The deputy director of FBI. Hillary Clinton. Bill Clinton. Those guys behind BLM. pickup "artists" Epstein and all those who went to his ugly island. Putin CNN Except that he has genuine love for humanity and it has shown again and again by his actions. He has shown true empathy. Without Elon, you would have massive sociopaths running Twitter or the Ukrainian army would not be able to communicate.

Its all about your thoughts and belief system. Get yourself a wife and stop doing pickup like a thirsty little boy who wants his mommys tit milk.

If there are any aliens we would have seen them by now.

I only use soap and deo.

Learn to eat pussy and she will find you irresistible.

https://www.forbes.com/sites/bernardmarr/2023/05/31/the-future-of-generative-ai-beyond-chatgpt/?sh=3f23d5783da9 This is interesting. The article: Next-generation language models: Future models are expected to understand factors like psychology and the human creative process in more depth, enabling them to create written copy that’s deeper and more engaging. Generative Visual AI: Advanced generative AI may soon be able to create images and videos that are nearly indistinguishable from reality. This could have significant implications for content creation, but also for the potential spread of disinformation through deepfakes. Generative AI in the Metaverse: Generative AI is expected to play a role in the development of virtual and augmented reality experiences, including the creation of more lifelike avatars for these environments. Generative Audio, Music, and Voice AI: Future AI models may be capable of creating novel music compositions, adaptive soundtracks, and more expressive synthetic voices. Generative Design: AI tools are being developed to assist designers in creating new products, with the ability to generate step-by-step instructions for engineering the final product based on the given parameters. Generative AI in Video Games: AI is expected to significantly impact game design, with the potential to create dynamic and responsive environments and characters that enhance player immersion. Imagine the games that will come out in the near future. Right now, most games are boring because we've seen it all. Can't really enjoy a game as a 30 year old unless you're on psychedelics or heavenly baked. https://www.descript.com/overdub <<--- With this tool, you can take one of Leos videos, clone his voice and write anything you want "Leo" to say. Gaming industry will do a quantum leap soon. Graphics will peak sooner than you think: Later, we will probably have something connected to our brain that produce some artificial smell that's in the game world. You will also see a birth of a new category of software: AI detection. With AI you can fake and do harm. But Intel is working on tools and your future PC will have a software installed that can detect an AI generated picture or audio. Similar to antivirus.

What channels and platforms are you getting your news from?

@Danioover9000 Hustlers university can output guys like this guy above. The "alpha" doesn't have to listen to teacher and get an education.

As relevant as 2pac and biggie

@ZenAlex He is famous for many things not just womens. He speaks for mens mental health, which is ignored by society. Also because there is an incel pandemic.