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

j4w8n

Implement user API keys with Supabase

This is pretty much my first crack at this. I'm sure things could be improved or done differently.

Rationale

JWTs are at the heart of Supabase authorization, but sometimes we wanna build an app that also gives users access via API keys; or perhaps only exclusively via API keys. As you may know, using JWTs as API keys makes them difficult to revoke and therefore a security issue.

We also want to ensure this doesn't significantly add to RLS polices, if at all.

Finally, we'd love to have this handled by Supabase and do as little as possible in our framework. This simplifies our code and reduces third-party libraries.

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