thomwolf

""" To use: install LLM studio (or Ollama), clone OpenVoice, run this script in the OpenVoice directory
    git clone https://github.com/myshell-ai/OpenVoice
    cd OpenVoice
    git clone https://huggingface.co/myshell-ai/OpenVoice
    cp -r OpenVoice/* .
    pip install whisper pynput pyaudio
"""

from openai import OpenAI
import time

qpwo

"""
A minimal implementation of Monte Carlo tree search (MCTS) in Python 3
Luke Harold Miles, July 2019, Public Domain Dedication
See also https://en.wikipedia.org/wiki/Monte_Carlo_tree_search
https://gist.github.com/qpwo/c538c6f73727e254fdc7fab81024f6e1
"""
from abc import ABC, abstractmethod
from collections import defaultdict
import math

bcnzer

addEventListener('fetch', event => {
  event.respondWith(handleRequest(event.request))
})

// Following code is a modified version of that found at https://blog.cloudflare.com/dronedeploy-and-cloudflare-workers/

/**
 * Fetch and log a request
 * @param {Request} request
 */

thomaswitt

<?xml version="1.0" encoding="UTF-8"?>
<!DOCTYPE plist PUBLIC "-//Apple//DTD PLIST 1.0//EN" "http://www.apple.com/DTDs/PropertyList-1.0.dtd">
<plist version="1.0">
<dict>
  <key>PayloadContent</key>
  <array>

    <!-- Home: Manual -->
    <dict>
      <key>UserDefinedName</key>

karllhughes

Setup Work

• Make a list of competitive/alternative services: See competitors.md file
• Set up Google News alerts for "side project marketing" and "startup marketing".
• Make list of blogs, subscribe to RSS feeds in Feedbin:
  • Startup marketing blogs
  • Side project blogs
  • Small business marketing blogs
  • Chicago small business/startup bloggers
• Come up with a name and domain name

rspeer

wassname

"""
Here is a dice loss for keras which is smoothed to approximate a linear (L1) loss.
It ranges from 1 to 0 (no error), and returns results similar to binary crossentropy
"""

# define custom loss and metric functions 

from keras import backend as K

def dice_coef(y_true, y_pred, smooth=1):

disarticulate

fchollet

'''This script goes along the blog post
"Building powerful image classification models using very little data"
from blog.keras.io.
It uses data that can be downloaded at:
https://www.kaggle.com/c/dogs-vs-cats/data
In our setup, we:
- created a data/ folder
- created train/ and validation/ subfolders inside data/
- created cats/ and dogs/ subfolders inside train/ and validation/
- put the cat pictures index 0-999 in data/train/cats

yefim

{
  "AWSEBDockerrunVersion": "1",
  "Image": {
    "Name": "<AWS_ACCOUNT_ID>.dkr.ecr.us-east-1.amazonaws.com/<NAME>:<TAG>",
    "Update": "true"
  },
  "Ports": [
    {
      "ContainerPort": "443"
    }