投稿時間:2022-05-29 01:14:06 RSSフィード2022-05-29 01:00 分まとめ(17件)

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js	JavaScriptタグが付けられた新着投稿 - Qiita	Railsにjavascriptを導入する	https://qiita.com/ryo-he/items/73ebd8c136347185f022	javascript	2022-05-29 00:43:36
js	JavaScriptタグが付けられた新着投稿 - Qiita	PDFを閲覧＋αするクロム拡張の楽な作り方③	https://qiita.com/largetownsky/items/e61624ba8be7e1d50fed	閲覧	2022-05-29 00:38:58
Ruby	Railsタグが付けられた新着投稿 - Qiita	Railsにjavascriptを導入する	https://qiita.com/ryo-he/items/73ebd8c136347185f022	javascript	2022-05-29 00:43:36
Ruby	Railsタグが付けられた新着投稿 - Qiita	【Rails】学生エンジニアの個人開発日記#01	https://qiita.com/54_chair/items/19bddf6bdb333cff0488	rails	2022-05-29 00:13:23
海外TECH	MakeUseOf	9 Active Recovery Options for Making the Most of Your Rest Days	https://www.makeuseof.com/active-recovery-options-rest-days/	Active Recovery Options for Making the Most of Your Rest DaysWhile rest days are important between workouts you don t have to spend them doing nothing Use these resources for fun active recovery ideas	2022-05-28 15:30:14
海外TECH	MakeUseOf	4 Things to Do Before Selling Your Windows PC or Laptop	https://www.makeuseof.com/windows-things-to-do-before-selling/	Things to Do Before Selling Your Windows PC or LaptopStop Before you sell off your Windows computer perform these steps to ensure you keep all your data safe and secure from whoever you re selling to	2022-05-28 15:15:14
海外TECH	DEV Community	What makes CSS so hard?	https://dev.to/sherrydays/what-makes-css-so-hard-n92	challenging	2022-05-28 15:39:54
海外TECH	DEV Community	How to build your first VR app with React360 today!	https://dev.to/kai_wenzel/how-to-build-your-first-vr-app-with-react360-today-4loi	How to build your first VR app with React today As a tech head you re probably aware of virtual reality and its various applications Video games web and mobile apps and more can reap the benefits of VR s amazing features If your next development goal is creating VR apps and you re familiar with the React ecosystem you re in luck You can now develop amazing VR experiences using React and JavaScript In this tutorial we ll show you how to develop a simple and interactive React virtual reality application using React By the end you should be ready to build your first VR app in React What is React React is a library that utilizes a lot of React Native functionality to create virtual reality apps that run in your web browser It uses Three js for rendering and comes as an npm package By combining modern APIs such as WebGL and WebVR with the declarative power of React React helps simplify the process of creating cross platform VR experiences Learning how to use React is a great way to kickstart your VR development career In this tutorial we ll cover all the basics to help you get started using React Installing React First and foremost you need to install the React CLI This will give you access to all the necessary commands to assist you in developing a virtual reality application Now go to the desired folder through the command terminal and run the following command npm install g react cliThis is a one time install so you don t have to do this every time The benefit of being in the project folder is that it makes the following step easier After installation create a new project called my project creative right and type react init my projectYou have successfully created your first virtual reality application with React To view the app in your browser navigate to the my project folder via the terminal and then run npm start This will guide you to the destination in the browser Alternatively you can access the output by following http localhost index html Here s what the app should look like Now that you have the app up and running let s talk about the code in detail Two important files we ll be working with are client js and index js The index js file consists of four parts ClassImportsStylesComponent RegistryWe re importing React to use its class functionality We ll gather some parts from React to build a VR environment import React from react import AppRegistry StyleSheet Text View from react export default class my project extends React Component render return Welcome to React const styles StyleSheet create panel width height backgroundColor rgba justifyContent center alignItems center greetingBox padding backgroundColor borderColor dda borderWidth greeting fontSize AppRegistry registerComponent my project gt my project The class syntax and components are quite similar to React and React Native respectively The View component allows you to render various aspects of the VR world and if you want to play with the look and feel The Style attribute and StyleSheet will help you do that There are numerous resemblances to React Native and CSS functionality here When it comes to the Text component you can create dynamic text to display all kinds of data for the user In the end the class needs to be registered to the client for rendering You ll notice that the init function in the client js file makes a new instance for your project and then utilizes the class from the index js file to assign a render method to this project After that the project environment is applied to a panoramic image followed by the execution of the init function import ReactInstance from react web function init bundle parent options const r new ReactInstance bundle parent fullScreen true options r renderToSurface r createRoot my project r getDefaultSurface r compositor setBackground r getAssetURL simmes start screen jpg window React init Those are the basics of React functionality and you re good to go with your first VR app development Now that you know how a React VR app is made let s go through some steps to customize it Adding background assetsYou can apply any panoramic image to the VR background using React For this tutorial we re using a Simmes free image you can use any image you like To use a panoramic image for the background add the desired image to the static assets folder This folder contains all the static assets such as pictures music and models and React searches for them here Use the command below to update the background r compositor setBackground r getAssetURL simmes start screen jpg VR interactionsOne of the most important and engaging aspects of any application is interaction Without interaction an app is lifeless You can add this vital functionality to your React VR app by adding the VrButton component to the index js files imports as follows import AppRegistry StyleSheet Text View VrButton from react Next add a counter to record the number of clicks Start by setting the click count to zero state count The next step is to write an increment function incrementCount gt this setState count this state count In the end we ll render the VrButton like so lt View style styles panel gt lt VrButton onClick this incrementCount style styles greetingBox gt lt Text style styles greeting gt You have visited Simmes this state count times lt Text gt lt VrButton gt lt View gt You ve successfully set up the button and you can now see the number of people who visit your VR world Adding immersive soundsNot all apps you create will require sound However when it comes to games videos and other immersive experiences sounds are essential To add sound to our VR app we need to fetch a few more things from React import asset AppRegistry NativeModules StyleSheet Text View VrButton from react The next step is to import the AudioModule from NativeModules and set a new const const AudioModule NativeModules Once the new const is set we can add specific functionality to the way the sound plays For example we can make it start playing upon the click of the button and stop when the button is clicked again To make this work we ll add a Boolean value to the state state count playSound false Finally we ll write another function to manage the way sound plays playSound gt this setState playSound this state playSound if this state playSound AudioModule createAudio sza source asset brokenclocks mp volume AudioModule play sza else AudioModule stop sza Once executed this function upgrades the playSound state which is set to false The way the sound plays will depend on the value assigned to playSound To make it play an audio instance has to be created via the createAudio component Once created you can play the audio via the assigned name This only occurs when playSound is set to true The sound stops playing when it is false That s why we make a new instance every time playSound is true We ll now create a button to start and stop playing the sound Here s what the code will look like lt View style styles panel gt lt VrButton onClick this incrementCount style styles greetingBox gt lt Text style styles greeting gt You have visited Simmes this state count times lt Text gt lt VrButton gt lt VrButton onClick this playSound style styles greetingBox gt lt Text style styles greeting gt You can play the music of your people lt Text gt lt VrButton gt lt View gt Your VR app is now complete Let s run the application to see how it looks Running your React VR applicationYou can view the app via the run npm start command Your first React VR app called “My Project should contain the background that you chose and two buttons One button controls the audio and the other keeps track of the number of users who visit the application You can now invite your friends and family to play with your new VR app and brainstorm new ideas for the app with fellow programmers The possibilities are endless I hope this tutorial helped you understand React a little better Tell us about your experience with React What do you think is the best way to create VR apps We would love to hear from you That s it and if you found this article helpful please hit the ️ button and share the article so that others can find it easily If you want to see more of this content you can support me on Patreon Have a nice day	2022-05-28 15:35:56
海外TECH	DEV Community	Using generators to improve developer productivity	https://dev.to/carloscuesta/using-generators-to-improve-developer-productivity-2i7i	Using generators to improve developer productivityA few weeks ago at N we did get stuff done week during this time the product and engineering teams have the opportunity to try or build anything we like I decided to spend the week adding a code generation tool to the Web projectautomating a bit of the coding work while improving the developer experience In this article I ll walk through the experience and the outcome of implementing a tool to generate code ‍ What is a code generator A code generator is a tool that given a set of rules and inputs creates code files and folders To name a few popular ones create react appcreate next appcreate react native appAll of them will create code based on specific rules taking into account the inputs provided by the user Here s a simple example What s the problem Imagine you start the day working on a new task and you need to create a feature before writing any code you will need to consider a few things Folder structure and architecture Naming files convention Where to put the feature How you should write the tests I m sure the project you re working on has a list of conventions and patterns defined that explains how you should work in the codebase However every time you are going through this process it requires you to think about those conventions to make the decision Where is the source of truth Documentation Architecture Decision Records Similar files on the codebase ️A teammate ‍As software engineers we work very hard to not repeat code building abstractions automating manual workflows What about writing code In the same way we advocate for automating processes such as deployments we should also make an effort to the non creative part of coding such as scaffolding Why you should use them Generating code will save you time and will increase the productivity of the team Developer experienceIn a team that is constantly growing it is important to make sure that everyone is aligned and able to build things efficiently Having a great Developer Experience will boost the confidence a developer has with the codebase Trust empowers people to be more productive and agile ️ to name a few of the many benefits you ll get by using generators Ensure everyone is doing things as expected according to the project conventions Reduce friction when working with the codebase Ease the onboarding of new joiners Decrease the development time Decision fatigueThe most important thing is that your teammates will not have to spend time on low value decisions such as deciding how a component is structured Turns out our decision making process gets worse the more decisions we make This is called decision fatigue For example Steve Jobs limited his everyday clothing down to one outfit to avoid making a decision Using code generatorsSounds good right Let s take a look at how we can implement code generators in our project Choosing the right toolI didn t want to reinvent the wheel my focus was set on the outcome of generating code not building a tool that solves this problem There are a lot of Open Source projects that will do an awesome job generating code Here s the list of the ones I considered HygenPlopYeomanI decided to go with Hygen at N because Great monorepo supportEasy to maintain You ll only need to care about EJS template files and prompts No configuration requiredHigh value with low effort writing a generator is very simple Plop is also a great tool but creating a generator is more complex since you need to spend more time writing code and the tool demands extra time on the configuration part compared to Hygen Yeoman is another valid option but maintaining generators requires you to manage and publish packages to a registry and I would say the use case of this tool is more suited to scaffolding projects instead of smaller parts of a codebase Getting startedIt s time to create our code generator First install Hygen as a devDependency in your project npm install D hygenDefine a script inside the package json to use the script binary scripts generators hygen Now we can run Hygen through the generators script npm run generatorsAfter running the command you ll see that Hygen is telling us we don t have any generators Error please specify a generator Hygen v By default hygen will read the generator files from a folder named templates If you want to change this behavior create a file named hygen js at the root level of your project like this const path require path module exports templates path join dirname generators Creating a generatorA generator is composed of one or more actions every action contains two items Templates ejs Prompts prompt js ️Let s build a simple generator that creates a React component At the end of the post you ll find a GitHub repository with a lot of different examples The first thing we need to create is the generator folder that will contain the action Generator componentAction react mkdir p generators component react TemplatesTemplate files define the code that will be created after running the generator these files are written using a template language called Embedded JavaScript templates Every template starts with a frontmatter header On this header you will define the metadata of the template using the following properties PropertyTypeUsagetoStringThe destination of the file once compiled fromStringUse an external file as template forceBooleanOverwrite existing filesunless existsBooleanExecute the generator unless file already exists injectBooleanInject the contents into an existing file instead of creating a new one afterRegexInject the template after the regexbeforeRegexInject the template before the regexprependBooleanInject the template at the start of the fileappendBooleanInject the template at the end of the fileat lineRegexInject the template at the specified line numberskip ifRegexSkip injection if regex matches eof lastBooleanTrim the newline from the end of the injection shStringTrigger a shell command after compiling the templateNow let s add a template file named index ejs inside the react action folder we created previously touch generators component react index ejsAs I mentioned before we want to create a React component so we need to specify the location of the file where the component is going to be created We can do that using the to property to src components lt h changeCase pascalCase name gt index js As you can see we re using a variable called name on the header This value will be provided by the prompts of the generator ️Then we need to write the body of the template where we define the code that will be generated once the template is compiled I m reusing the name variable in the body as well to create the name and export of the component to src components lt h changeCase pascalCase name gt index js const lt h changeCase pascalCase name gt gt lt section gt lt h gt Hey lt h gt lt h gt lt code gt lt h changeCase pascalCase name gt lt code gt lt h gt lt section gt export default lt h changeCase pascalCase name gt PromptsIn case you need to ask for user input optionally you can use a prompt file This is very useful to customize the output of the generator Prompts are defined using a library named Enquirer Inside of the same react we will create a prompt js file to ask for the variable defined as name in the template module exports type input name name message Write the name of your component There are a ton of different input types available take a look at this link to get the complete list Now it s time to finally run our generator using the script along with the name and the action npm run generators component reactYou ll be asked for the prompts and finally the magic will happen DemoTake a look at carloscuesta codegenerators demo if you want to play and see more complex examples Automate all the things It s time for you to find out repetitive tasks and patterns to extract them into a generator I m very happy with the productivity boost and the consistency that a tool like this can bring to a team ️	2022-05-28 15:22:38
海外TECH	DEV Community	How I built a Deep Learning Powered Search Engine	https://dev.to/ashwinscode/how-i-built-a-deep-learning-powered-search-engine-49mh	How I built a Deep Learning Powered Search EngineWe use search engines everyday Without them we would be using the Internet in a much different way a way which I can t imagine what it would be like I decided to have a go at making my own small search engine using Deep Learning to power the search results Breaking the problem downBecause making a fully fledged search engine is a very long and expensive task I decided that my search engine would be for Wikipedia pages only a k a given a search query it will return any relevant Wikipedia pages for it There were main steps to building this search engine Find Create a database of Wikipedia pagesIndex the database of pagesEfficiently match search queries to pages in the index Creating the DatabaseInstead of trying to find a databse of Wikipedia pages online I decided to create my own simply because I wanted to build a web crawler What is a web crawler A web crawler is initially given a few starting URLs to visit They then visit these URLs and parse through the HTML to find more URLs they can visit Any URLs they find are added to a queue so that they can visit them later Search engines like Google and Bing use web crawlers to keep adding to their index of website links so that they can come up in their search results Since most of the URLs on Wikipedia pages are URLs to other Wikipedia pages all I had to do was write a simple generic web crawler and feed it a few intial Wikipedia pages import requestsimport osfrom tinydb import TinyDBfrom html parser import HTMLParserdb TinyDB results json def get host url while os path dirname url not in http https url os path dirname url return urlclass Parser HTMLParser def init self super Parser self init convert charrefs True self url self urls self meta description self title self paragraph content self paragraph False self set description False self set title False def set url self url if url if os path dirname url not in http https url url self url url def handle starttag self tag attrs if tag meta for attr in attrs if attr name and attr description self set description True if self set description if attr content self meta description attr self set description False elif tag a for attr in attrs if attr href link attr if link if link link get host self url link self urls append link elif http in link and link index http self urls append link elif https in link and link index https self urls append link elif tag p and len self paragraph content lt self paragraph True elif tag title self set title True def handle endtag self tag if tag p self paragraph False def handle data self data if self set title self title data self set title False elif self paragraph self paragraph content data def clear self self urls self meta description self title self paragraph content self paragraph False self set description False self set title Falsedef crawl start queue parser Parser queue start queue seen urls while len queue gt if queue not in seen urls try print queue page requests get queue parser set url queue parser feed page text db insert title parser title description parser meta description content parser paragraph content url queue seen urls append queue queue queue parser urls parser clear except pass queue queue crawl Everytime the crawler visits a page it scans through the HTML for any a tags and adds its href attribute to the queue It also records the page s title found in between the title tags the first characters of the page s article by looking at the content within the p tags and the page s meta description however after crawling I found that none of the Wikipedia pages have meta descriptions After the page is scanned through its URL and recorded details are saved to a local JSON file using TinyDB I ran the crawler for a few mintues and managed to scrape around pages Indexing the DatabaseTo return relevant pages to a user s search query I was planning to use a KNN algorithm to compare the vector encodings of the search query and the vector encodings of the page contents store in the database Vector encodings of natural language are crucial when it comes to understanding what a user is saying As you will see later I decided to use a Transformer model to encode sentences into a vector representation With what I had so far it was possible to build a working search engine with the above method in mind but it would be extremely inefficient since it would involve going through EVERY record in the database vectorising their article s content and comparing it to the query vector Increasing Search EfficiencyIn order to increase the efficiency of the search I had an idea to index preprocess the database in order to reduce the search space for the KNN algorithm The first step was to vectorise the content of all the pages I had scraped and stored in my database from tinydb import TinyDBfrom sentence transformers import SentenceTransformerfrom sklearn cluster import KMeansmodel SentenceTransformer sentence transformers stsb roberta base v db TinyDB results json contents for record in db content record content contents append content embeddings model encode contents As you can see for vectorising sentences I used the SentenceTransformer library and the stsb roberta base v transformer model which was fine tuned for tasks like Neural Search where a query needs to be matched with relevant documents the exact task I had at hand Now that I had all the vector representations of the pages I decided to cluster semantically similar pages together using the K Means clustering algorithm The idea behind this was that when a search query is entered the query would be vectorised and be classified into a cluster Then we could perform KNN with the pages in that cluster only instead of the whole database which should improve efficiency kmeans KMeans n clusters kmeans fit embeddings buckets for record label in zip db kmeans labels if label not in buckets buckets label buckets label append dict record import picklepickle dump kmeans open kmeans model save wb pickle dump buckets open buckets save wb Clustering pagesfrom sentence transformers import SentenceTransformerimport pickleimport numpy as npfrom sklearn neighbors import NearestNeighborsmodel SentenceTransformer sentence transformers stsb roberta base v kmeans pickle load open cluster save rb buckets pickle load open buckets save rb while True query input Search encoded query model encode query encoded query np expand dims encoded query axis bucket kmeans predict encoded query bucket int bucket squeeze embeddings for result in buckets bucket embeddings append result content embeddings model encode embeddings neigh NearestNeighbors n neighbors neigh fit embeddings indexes neigh kneighbors encoded query return distance False for index in indexes T doc buckets bucket int index squeeze print doc title doc url print Matching user query to most relevant pagesIn the code above it shows that I broke the database down into clusters However I did a lot of tinkering with the number of clusters If we group the database into too many clusters searches would be extremely efficient but there would be a huge drop in result accuracy The same goes the other way I found that with clusters the accuracy of the results were really high however it was still extremely slow to return results seconds for each search but the worst was seconds Search how do i compose musicMusical instrument Wikipedia Elements of music Wikipedia Music criticism Wikipedia Contemporary classical music Wikipedia Accompaniment Wikipedia Musical improvisation Wikipedia Musique concrète Wikipedia Programming music Wikipedia music Film score Wikipedia Song Wikipedia Harpsichord Wikipedia Music theory Wikipedia Music industry Wikipedia Definition of music Wikipedia Wolfgang Amadeus Mozart Wikipedia Invention musical composition Wikipedia musical composition Music Wikipedia Music Wikipedia Aesthetics of music Wikipedia Musicology Wikipedia Searches relating to music took the most time possibly because the database contained a lot of music pages and so they were all grouped into one big cluster With anything above clusters I found that results were being returned at a quick speed however the accuracy was poor For example I d search something simple such as Liverpool Football but the engine would fail to return the Liverpool F C page despite it being present in the database A better solutionWith the trade off between speed and accuracy in the above solution being way too sensitive I had to find a better solution After a bit of research I came across ANNOY ANNOY stands for Approximate Nearest Neighbours Oh Yeah and is a small library provided by Spotify to search for points in space that are close to a given query point Spotify themselves use ANNOY for their user music recommendations Approximate Nearest Neighbours You may be thinking why would we want an approximate nearest neighbours alogrithm Why not an exact one For KNN to be exact it has to iterate through each and every datapoint given to it which is obviously extremely inefficient Things can be drastically sped up if a little bit of accuracy is sacrificed but in practice this sacrifice in accuracy does not matter at all A user would not mind if the second closest datapoint and first closest datapoint are swapped around since they are both probably good matches to their query How ANNOY worksHere is a good article explaining how ANNOY works written by the man who built ANNOY himself ANNOY works by building loads of binary trees a forest from the dataset its given To build a tree it selects two random points in the vector space and divides the space into two subspaces by the hyperplane equidistant to the two random points The process repeats again in the new subspaces that were just made This keeps going until there is a certain n number of points in each subspace Points that are near to each other should be in the same subspace since it is unlikely that there would be a hyperplane to separate them into separate subspaces Now that we have all the subspaces a binary tree can be constructed The nodes of the tree represent a hyperplane So when given a query vector we can traverse down the tree telling us which hyperplanes we should go down to in order to find some x most relevant points in the vector space ANNOY builds many of these trees to build a forest The number of trees in the forest is specified by the programmer When given a query vector ANNOY uses a priority queue to search this query through the binary trees in its forest The priority queue allows for the search to focus on trees that are best for the query aka trees whose hyperplanes are far from the query vector After it has finished searching ANNOY looks at all the common points its trees have found which would form the query vector s neighbours Now the k nearest neighbours can be ranked and returned Refactoring Indexing and Search codeIt didn t take long to change the code to use ANNOY thanks to its straightforward API from tinydb import TinyDBfrom sentence transformers import SentenceTransformerfrom annoy import AnnoyIndexmodel SentenceTransformer sentence transformers stsb roberta base v db TinyDB results json descriptions for record in db description record content descriptions append description embeddings model encode descriptions index AnnoyIndex embeddings shape euclidean vec idx for vec in embeddings index add item vec idx vec vec idx index build index save index ann stores the results of the indexingIndexing codefrom tinydb import TinyDBfrom sentence transformers import SentenceTransformerfrom annoy import AnnoyIndexmodel SentenceTransformer sentence transformers stsb roberta base v db TinyDB results json index AnnoyIndex euclidean index load index ann while True query input Search vec model encode query indexes index get nns by vector vec all db db all for i in indexes print all db i title all db i url print user search codeNow let s see the results Search great composersIgor Stravinsky Wikipedia Contemporary classical music Wikipedia Gustav Mahler Wikipedia Symphony Wikipedia Art music Wikipedia Symphony No Beethoven Wikipedia Beethoven Wolfgang Amadeus Mozart Wikipedia Ludwig van Beethoven Wikipedia Music of Central Asia Wikipedia Big band Wikipedia Program music Wikipedia Cello Suites Bach Wikipedia Film score Wikipedia Johann Sebastian Bach Wikipedia Elements of music Wikipedia Music of China Wikipedia Organ music Wikipedia music Toccata and Fugue in D minor BWV Wikipedia BWV Georg Philipp Telemann Wikipedia Sonata form Wikipedia Search what are the rules of cricketNo ball Wikipedia International cricket Wikipedia Toss cricket Wikipedia cricket Match referee Wikipedia Cricket ball Wikipedia Board of Control for Cricket in India Wikipedia India national cricket team Wikipedia Caught Wikipedia Substitute cricket Wikipedia cricket International Cricket Council Wikipedia Portal Cricket Wikipedia CricketDelivery cricket Wikipedia cricket Cricketer disambiguation Wikipedia disambiguation Cricket disambiguation Wikipedia disambiguation Cricket West Indies Wikipedia Zimbabwe Cricket Wikipedia Bowled Wikipedia Pakistan Cricket Board Wikipedia World Cricket League Wikipedia West Indies cricket team Wikipedia As you can see the results returned are pretty accurate It obviously doesn t help that the database I had was pretty small but this did yield some good results despite it On top of that these results were produced almost instantly much much quicker than the previous solution ConclusionThe only thing I could see that stopped from the search results being better was the size of the database If I did set out to build a much bigger search engine in the future I d look to use databases such as Firebase or MongoDB and look into how ANNOY could interface with them Having said that I built this project to investigate how deep learning models could be used in document searching tasks and what can be done to efficiently perform the searches and I think I ve taken a lot away from this project Thank you for reading and I hope you ve learnt something from this too	2022-05-28 15:14:11
ニュース	BBC News - Home	Russia scraps age limit for new troops in Ukraine push	https://www.bbc.co.uk/news/world-europe-61619638?at_medium=RSS&at_campaign=KARANGA	weapons	2022-05-28 15:40:37
ニュース	BBC News - Home	Radio 1's Big Weekend lands in Coventry	https://www.bbc.co.uk/news/newsbeat-61571301?at_medium=RSS&at_campaign=KARANGA	festival	2022-05-28 15:09:51
ニュース	BBC News - Home	Monaco Grand Prix: Charles Leclerc on pole after Sergio Perez crash	https://www.bbc.co.uk/sport/formula1/61619354?at_medium=RSS&at_campaign=KARANGA	monaco	2022-05-28 15:31:02
北海道	北海道新聞	レッドソックス沢村、マイナーへ　メジャー２年目で初降格	https://www.hokkaido-np.co.jp/article/686807/	大リーグ	2022-05-29 00:32:00
北海道	北海道新聞	「平和考える輪を広げたい」　高校生平和大使の道内３人抱負	https://www.hokkaido-np.co.jp/article/686806/	実行委員会	2022-05-29 00:29:00
北海道	北海道新聞	ロシアが金属２７００トン略奪か　マリウポリから海路輸送	https://www.hokkaido-np.co.jp/article/686786/	金属	2022-05-29 00:05:26
海外TECH	reddit	Sergio Pérez hits the barriers causing red flag. Charles Leclerc grabs pole position	https://www.reddit.com/r/formula1/comments/uzp6zi/sergio_pérez_hits_the_barriers_causing_red_flag/	Sergio Pérez hits the barriers causing red flag Charles Leclerc grabs pole position submitted by u magony to r formula link comments	2022-05-28 15:06:03