Meta tags:
description= GitHub Copilot works alongside you directly in your editor, suggesting whole lines or entire functions for you.;
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Text of the page (most frequently used words):
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Text of the page (random words):
out spending most of your time spelunking through the docs or searching the web fetch tweets draw a scatterplot memoization goodreads rating javascript fetch_tweets js python fetch_tweets py ruby fetch_tweets rb typescript fetch_tweets ts go fetch_tweets go 1 2 3 4 5 6 7 8 9 10 11 12 13 14 const token process env twitter_bearer_token const fetchtweetsfromuser async screenname count const response await fetch https api twitter com 1 1 statuses user_timeline json screen_name screenname count count headers authorization bearer token const json await response json return json copilot replay 1 2 3 4 5 6 7 8 9 10 11 import tweepy os secrets in environment variables def fetch_tweets_from_user user_name authentification auth tweepy oauthhandler os environ twitter_key os environ twitter_secret auth set_access_token os environ twitter_token os environ twitter_token_secret api tweepy api auth fetch tweets tweets api user_timeline screen_name user count 200 include_rts false return tweets copilot replay 1 2 3 4 5 6 7 8 9 10 11 12 require twitter def fetch_tweets_from_user handle twitter twitter rest client new do config config consumer_key env twitter_consumer_key config consumer_secret env twitter_consumer_secret config access_token env twitter_access_token config access_token_secret env twitter_access_token_secret end tweets twitter user_timeline handle tweets end copilot replay 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 const token process env twitter_bearer_token const fetchtweetsfromuser username string const url https api twitter com 1 1 statuses user_timeline json screen_name username count 20 return fetch url headers authorization bearer token then res res json then tweets tweets map tweet id tweet id text tweet text created_at tweet created_at user id tweet user id name tweet user name screen_name tweet user screen_name profile_image_url tweet user profile_image_url copilot replay 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 package main var apikey os getenv twitter_bearer_token type tweet struct text string func fetchtweetsfromuser user string tweet error url https api twitter com 1 1 statuses user_timeline json screen_name user count 200 req err http newrequest get url nil if err nil return nil err req header set authorization bearer apikey resp err http defaultclient do req if err nil return nil err defer resp body close if resp statuscode 200 return nil fmt errorf bad status d resp statuscode var tweets tweet if err json newdecoder resp body decode tweets err nil return nil err return tweets nil copilot replay javascript draw_scatterplot js python draw_scatterplot py ruby draw_scatterplot rb typescript draw_scatterplot ts 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 import d3 from d3 const drawscatterplot data height width const svg d3 select scatterplot append svg attr height height attr width width svg selectall circle data data enter append circle attr cx d d x attr cy d d y attr r 5 copilot replay 1 2 3 4 5 6 7 8 import matplotlib pyplot as plt def draw_scatterplot x_values y_values plt scatter x_values y_values s 20 plt title scatter plot plt xlabel x values plt ylabel y values plt show copilot replay 1 2 3 4 5 6 7 8 9 10 require gruff def draw_scatterplot x_values y_values g gruff scatter new 400 g title gcd g x_axis_label x g y_axis_label y g data data x_values y_values g write plot png end copilot replay 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 import d3 from d3 const drawscatterplot data array x number y number size number x d3 scalelinear number number y d3 scalelinear number number svg d3 selection svggelement unknown htmlelement any margin top number bottom number left number right number const width svg attr width margin left margin right const height svg attr height margin top margin bottom svg selectall remove const g svg append g attr transform translate margin left margin top const xaxis g append g attr transform translate 0 height call d3 axisbottom x copilot replay javascript memoize js python memoize py ruby memoize rb typescript memoize ts go memoize go 1 2 3 4 5 6 7 const memoize fn const cache return args const key json stringify args return cache key cache key fn args copilot replay 1 2 3 4 5 6 7 8 9 10 def memoize func cache def wrapper args if args in cache return cache args else cache args func args return cache args return wrapper copilot replay 1 2 3 4 def memoize func cache lambda args cache args func call args end copilot replay 1 2 3 4 5 6 7 8 9 10 11 12 const memoize fn function function const cache key string any return args any any const key json stringify args if cache key return cache key const result fn args cache key result return result copilot replay 1 2 3 4 5 6 7 8 9 10 11 package main func memoize fn func int int func int int cache make map int int return func n int int if v ok cache n ok return v cache n fn n return cache n copilot replay javascript rating js python rating py ruby rating rb typescript rating ts go rating go 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 const https require https const parsestring require xml2js parsestring const apikey process env goodreads_api_key const getrating title return new promise resolve reject https get https www goodreads com book title xml key apikey title title res let xml res setencoding utf8 res on data chunk xml chunk res on end parsestring xml err result if err reject err else resolve result goodreadsresponse book 0 average_rating copilot replay 1 2 3 4 5 6 7 8 9 10 import os requests json bs4 key os environ goodreads_api_key def get_rating title get the average rating of the book from goodreads and return a float url https www goodreads com book title xml key key title title response requests get url soup bs4 beautifulsoup response text xml rating soup find average_rating text return float rating copilot replay 1 2 3 4 5 6 7 8 9 10 require goodreads def get_rating_of_book_by_title title client goodreads client new api_key your_api_key api_secret your_api_secret book client book title book rating end copilot replay 1 2 3 4 5 6 7 8 9 10 11 12 13 const apikey process env goodreads_api_key const getrating title string return fetch https www goodreads com book title xml key apikey title title then res res text then res const parser new domparser const xml parser parsefromstring res text xml const rating xml getelementsbytagname average_rating 0 textcontent return rating catch error console log error copilot replay 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 package main import log net http net url os strconv github com beevik etree var apikey os getenv goodreads_api_key getrating gets the average rating of a book using the goodreads api func getrating title string float64 error u url url scheme https host www goodreads com path book title xml rawquery url values key apikey title title encode resp err http get u string if err nil return 0 err defer resp body close doc etree newdocument if _ err doc readfrom resp body err nil return 0 err ratings doc findelements average_rating if len ratings 0 return 0 nil rating err strconv parsefloat ratings 0 text 64 if err nil return 0 err return rating nil copilot replay flight reports hundreds of engineers including our own use github copilot every day this is the single most mind blowing application of machine learning i ve ever seen mike krieger co founder instagram github copilot works shockingly well i will never develop software without it again lars gyrup brink nielsen i was stunned when i started writing clojure with github copilot and it filled an idiomatic namespace require just like i was going to write it gunnika batra senior analyst trying to code in an unfamiliar language by googling everything is like navigating a foreign country with just a phrasebook using github copilot is like hiring an interpreter harri edwards open ai don t fly solo enjoy a 60 day free trial then 10 month or 100 year per user start my free trial explore docs learn about github copilot terms and conditions coming later this year github copilot for companies join the waitlist frequently asked questions general what is github copilot github copilot is an ai pair programmer that helps you write code faster and with less work it draws context from comments and code to suggest individual lines and whole functions instantly github copilot is powered by codex a generative pretrained language model created by openai it is available as an extension for visual studio code visual studio neovim and the jetbrains suite of integrated development environments ides github copilot is not intended for non coding tasks like data generation and natural language generation like question answering your use of github copilot is subject to the github terms for additional product and features how does github copilot work openai codex was trained on publicly available source code and natural language so it works for both programming and human languages the github copilot extension sends your comments and code to the github copilot service and it relies on context as described in privacy below i e file content both in the file you are editing as well as neighboring or related files within a project it may also collect the urls of repositories or file paths to identify relevant context the comments and code along with context are then used by openai codex to synthesize and suggest individual lines and whole functions what data has github copilot been trained on github copilot is powered by codex a generative pretrained ai model created by openai it has been trained on natural language text and source code from publicly available sources including code in public repositories on github does github copilot write perfect code in a recent evaluation we found that users accepted on average 26 of all completions shown by github copilot we also found that on average more than 27 of developers code files were generated by github copilot and in certain languages like python that goes up to 40 however github copilot does not write perfect code it is designed to generate the best code possible given the context it has access to but it doesn t test the code it suggests so the code may not always work or even make sense github copilot can only hold a very limited context so it may not make use of helpful functions defined elsewhere in your project or even in the same file and it may suggest old or deprecated uses of libraries and languages when converting comments written in non english to code there may be performance disparities when compared to english for suggested code certain languages like python javascript typescript and go might perform better compared to other programming languages like any other code code suggested by github copilot should be carefully tested reviewed and vetted as the developer you are always in charge will github copilot help me write code for a new platform github copilot is trained on public code when a new library framework or api is released there is less public code available for the model to learn from that reduces github copilot s ability to provide suggestions for the new codebase as more examples enter the public space we integrate them into the training set and suggestion relevance improves in the future we will provide ways to highlight newer apis and samples to raise their relevance in github copilot s suggestions how do i get the most out of github copilot github copilot works best when you divide your code into small functions use meaningful names for functions parameters and write good docstrings and comments as you go it also seems to do best when it s helping you navigate unfamiliar libraries or frameworks how can i contribute by using github copilot and sharing your feedback in the feedback forum you help to improve github copilot please also report incidents e g offensive output code vulnerabilities apparent personal information in code generation directly to copilot safety github com so that we can improve our safeguards github takes safety and security very seriously and we are committed to continually improving human oversight can github copilot introduce insecure code in its suggestions public code may contain insecure coding patterns bugs or references to outdated apis or idioms when github copilot synthesizes code suggestions based on this data it can also synthesize code that contains these undesirable patterns this is something we care a lot about at github and in recent years we ve provided tools such as github actions dependabot and codeql to open source projects to help improve code quality of course you should always use github copilot together with good testing and code review practices and security tools as well as your own judgment does github own the code generated by github copilot github copilot is a tool like a compiler or a pen github does not own the suggestions github copilot generates you are responsible for the code you write with github copilot s help we recommend that you carefully test review and vet the code before pushing it to production as you would with any code you write that incorporates material you did not independently originate does github copilot recite code from the training set the vast majority of the code that github copilot suggests has never been seen before our latest internal research shows that about 1 of the time a suggestion may contain some code snippets longer than 150 characters that matches the training set previous research showed that many of these cases happen when github copilot is unable to glean sufficient context from the code you are writing or when there is a common perhaps even universal solution to the problem what can i do to reduce github copilot s suggestion of code that matches public code we built a filter to help detect and suppress the rare instances where a github copilot suggestion contains code that resembles public code on github you have the choice to turn that filter on or off during setup with the filter on github copilot checks code suggestions with its surrounding code for matches or near matches ignoring whitespace against public code on github of about 150 characters if there is a match the suggestion will not be shown to you in addition we have announced that we are building a feature that will provide a reference for suggestions that resemble public code on github so that you can make a more informed decision about whether and how to use that code as well as explore and learn how that code is used in other projects other than the filter what other measures can i take to assess code suggested by github copilot you should take the same precautions as you would with any code you write that uses material you did not independently originate these include rigorous testing ip scanning and checking for security vulnerabilities you should make sure your ide or editor does not automatically compile or r...
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