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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.
Because of GitHub search limitations, only 1000 first users according to amount of followers are included. If you are not in the list you don't have enough followers. See raw data and source code. Algorithm in pseudocode:
githubUsers
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: |
| # 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: |
| 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 |
| #!/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. |
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.
| # 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 |
| 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], |
| { pkgs, ... }: | |
| { | |
| # ... | |
| nix.settings.system-features = [ "expose-cuda" ]; | |
| nix.settings.pre-build-hook = pkgs.writers.writePython3 "nix-pre-build.py" { } (builtins.readFile ./nix-pre-build-hook.py); | |
| # ... | |
| } |
