Aww Man is an internet radio show hosted by Rory Hinchey, which also books concerts in Prague for musicians who play unusual music.
The next live radio show is scheduled for March 22, 2026 at 11:00 CET with an in-studio performance by LÁZ . The streaming page (which launches in a new window) cycles through a limited number of archived shows otherwise.
The playlists section below has links to all recorded editions of the show in downloadable .mp3 format, shows are available as podcasts on Apple Podcasts, Amazon Music, and TuneIn.
Email: r{@}awwman.net
Facebook: https://www.facebook.com/awwmanradiobooking/
Instagram: https://www.instagram.com/awwmanradiobooking/
Podcast RSS feed: https://awwman.net/rss/awwman-podcast.rss
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S1056 - Doodstream | 99% VALIDATED |
app = Flask(__name__)
@app.route('/recommend', methods=['GET']) def recommend(): # Assume user provides a video ID and we fetch its features video_id = 0 # Example video ID query_features = video_features[video_id].reshape(1, -1) S1056 - DoodStream
nbrs = NearestNeighbors(n_neighbors=3, algorithm='brute', metric='euclidean').fit(video_features) distances, indices = nbrs.kneighbors(query_features) app = Flask(__name__)
@app
from flask import Flask, jsonify from sklearn.neighbors import NearestNeighbors import numpy as np app = Flask(__name__)
@app.route('/recommend'
# Example in-memory video features video_features = np.array([ [1, 2, 3], [4, 5, 6], [7, 8, 9] ])
# Return recommended video IDs return jsonify(indices[0].tolist())
app = Flask(__name__)
@app.route('/recommend', methods=['GET']) def recommend(): # Assume user provides a video ID and we fetch its features video_id = 0 # Example video ID query_features = video_features[video_id].reshape(1, -1)
nbrs = NearestNeighbors(n_neighbors=3, algorithm='brute', metric='euclidean').fit(video_features) distances, indices = nbrs.kneighbors(query_features)
from flask import Flask, jsonify from sklearn.neighbors import NearestNeighbors import numpy as np
# Example in-memory video features video_features = np.array([ [1, 2, 3], [4, 5, 6], [7, 8, 9] ])
# Return recommended video IDs return jsonify(indices[0].tolist())
