paulmillr

Most active GitHub users (git.io/top)

The count of contributions (summary of Pull Requests, opened issues and commits) to public repos at GitHub.com from Wed, 21 Sep 2022 till Thu, 21 Sep 2023.

Only first 1000 GitHub users according to the count of followers are taken. This is because of limitations of GitHub search. Sorting algo in pseudocode:

githubUsers
 .filter(user => user.followers > 1000)

rxaviers

People

:bowtie:	😄 :smile:	😆 :laughing:
😊 :blush:	😃 :smiley:	☺️ :relaxed:
😏 :smirk:	😍 :heart_eyes:	😘 :kissing_heart:
😚 :kissing_closed_eyes:	😳 :flushed:	😌 :relieved:
😆 :satisfied:	😁 :grin:	😉 :wink:
😜 :stuck_out_tongue_winking_eye:	😝 :stuck_out_tongue_closed_eyes:	😀 :grinning:
😗 :kissing:	😙 :kissing_smiling_eyes:	😛 :stuck_out_tongue:

gbaman

# An example to get the remaining rate limit using the Github GraphQL API.

import requests

headers = {"Authorization": "Bearer YOUR API KEY"}


def run_query(query): # A simple function to use requests.post to make the API call. Note the json= section.
    request = requests.post('https://api.github.com/graphql', json={'query': query}, headers=headers)
    if request.status_code == 200:

ReemRashwan

import numpy as np
import pandas as pd
import pydicom
import cv2
from sklearn.model_selection import train_test_split
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from keras_preprocessing.image.dataframe_iterator import DataFrameIterator

# tested on tf 2.1 

wjandrea

#!/usr/bin/env python3
"""
Use Unicode flag emoji homoglyphs to re-encode UTF-8.

To encode, a string is first encoded as UTF-8, then each byte is
broken down into bits, and each bit is encoded as a flag:
The more well-known country flag in each pair represents 0, and the
less well-known one represents 1. Pairs are cycled through.

There are 8 pairs total, which is the most I could find without repeats.

veekaybee

ChatGPT Resources

Context

ChatGPT appeared like an explosion on all my social media timelines in early December 2022. While I keep up with machine learning as an industry, I wasn't focused so much on this particular corner, and all the screenshots seemed like they came out of nowhere. What was this model? How did the chat prompting work? What was the context of OpenAI doing this work and collecting my prompts for training data?

I decided to do a quick investigation. Here's all the information I've found so far. I'm aggregating and synthesizing it as I go, so it's currently changing pretty frequently.

Model Architecture

bashbunni

# study stream aliases
# Requires https://github.com/caarlos0/timer to be installed. spd-say should ship with your distro

declare -A pomo_options
pomo_options["work"]="45"
pomo_options["break"]="10"

pomodoro () {
  if [ -n "$1" -a -n "${pomo_options["$1"]}" ]; then
  val=$1

osbm

var geometry = 
    /* color: #ffc82d */
    /* shown: false */
    ee.Geometry.Polygon(
        [[[36.803785364345735, 37.6022824651851],
          [36.84292415829105, 37.569634979372736],
          [36.8896160528223, 37.548134235962486],
          [36.96171383114261, 37.54377889953015],
          [36.99741939754886, 37.565280899470345],
          [36.97819332333011, 37.58759286852694],

veekaybee

Anti-hype LLM reading list

Goals: Add links that are reasonable and good explanations of how stuff works. No hype and no vendor content if possible. Practical first-hand accounts of models in prod eagerly sought.

Foundational Concepts

Pre-Transformer Models

matthen

"""Hello world, with a genetic algorithm.

https://twitter.com/matthen2/status/1769368467067621791
"""

import random
import time
from dataclasses import dataclass
from itertools import chain
from typing import Iterable, List