投稿時間:2023-01-23 21:25:11 RSSフィード2023-01-23 21:00 分まとめ(26件)

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IT	気になる、記になる…	Anker、オーディオグラス｢Soundcore Frames｣シリーズの専用充電ケースを発売	https://taisy0.com/2023/01/23/167491.html	anker	2023-01-23 11:52:36
IT	気になる、記になる…	｢Yahoo!ニュース｣アプリのiOS版、記事内の気になったポイントに印をつけられるハイライト機能が利用可能に	https://taisy0.com/2023/01/23/167485.html	yahoo	2023-01-23 11:42:45
IT	気になる、記になる…	明日から最強寒波襲来、｢Nintendo Switch｣の結露にご注意を ｰ 任天堂が公式に注意喚起	https://taisy0.com/2023/01/23/167482.html	nintendo	2023-01-23 11:32:13
IT	ITmedia 総合記事一覧	[ITmedia ビジネスオンライン] 映え意識？　カンロ、“ファンタジーキャンディー”の新商品　透明感ある鉱石を表現	https://www.itmedia.co.jp/business/articles/2301/23/news166.html	itmedia	2023-01-23 20:08:00
AWS	lambdaタグが付けられた新着投稿 - Qiita	[AWS]Lambdaに送られるIot topicのpayloadにtopic名を含める方法	https://qiita.com/to-fmak/items/e9e28bfd0750f71369a1	awslambda	2023-01-23 20:56:05
python	Pythonタグが付けられた新着投稿 - Qiita	【AtCoder入茶記事】入茶しました！	https://qiita.com/TsubakiMint/items/11e4c3300368a593216e	tsuba	2023-01-23 20:20:44
js	JavaScriptタグが付けられた新着投稿 - Qiita	Node.jsの基礎をまとめる2	https://qiita.com/ysk-s/items/70b78911348024876dbc	nodejs	2023-01-23 20:07:51
AWS	AWSタグが付けられた新着投稿 - Qiita	[AWS]Lambdaに送られるIot topicのpayloadにtopic名を含める方法	https://qiita.com/to-fmak/items/e9e28bfd0750f71369a1	awslambda	2023-01-23 20:56:05
AWS	AWSタグが付けられた新着投稿 - Qiita	AWS上に構築した IBM Security Verify Access のリソース監視を行う①	https://qiita.com/fitz/items/30b2fce586b5bf62a630	amazon	2023-01-23 20:27:10
Ruby	Railsタグが付けられた新着投稿 - Qiita	devise導入手順まとめ	https://qiita.com/yukihito_tomo/items/1b595df29cdb38c68d41	devise	2023-01-23 20:46:21
海外TECH	MakeUseOf	5 Reasons Apple Should Bring Face ID to the Mac	https://www.makeuseof.com/why-apple-should-bring-face-id-to-the-mac/	apple	2023-01-23 11:16:15
海外TECH	DEV Community	Building personalised recommendation system (p.2)	https://dev.to/vhutov/building-personalised-recommendation-system-p2-53gk	Building personalised recommendation system p In the first part of the series on building a personalised music recommendation system we discussed the use of machine learning algorithms such as Collaborative Filtering and Co occurrence Counting to create an items similarity matrix This resulted in a set of files each representing the similarities between various artists and tracks As an example one of these files may look like this FQbjTyendYWaNpKwa CZWezQVsURXaXKqx CXWjxzNUsdJxJJdwvnRkJSzxNuaWGqwubyUbaZ WMRPWKqSmrBGDBFSop nFkdlSjzXmRTtwJOzDYBgDigrSkcYWfZHwBETP ducEVhyTKQJzezAC XyouuXCZmMpatFPJHLzCvFAxycGXpf AKFOLvEQQYCBNiQWHq VFTgzYRcCuwLzESiYZfWDAMNwqrjfgOZy zgTcAtWQyJupgbUnj LuNFCkKOjPcnpouEgnyHere the first entry is the key entity followed by N entities that are similar to it In this second part of the series we will delve into the process of building a Node js application that utilises this data and other information to provide real time music recommendations to users based on their individual tastes We will explore the use of different database paradigms to ensure our solution is both scalable and performant If you re just joining and want to follow along as we build this system but don t want to dive into the world of machine learning you ll need the following data The original raw playlist dataset which can be found at Kaggle The similarity matrices which can be found at GitHub and contain artist similarities matrix factorization MLP and co occurrence and track similarities matrix factorization With that out of the way let s get started System ArchitectureThe Node js application we are building will consist of three peripheral services and a high level recommendation service with a fluent API a request handler and an express route to handle requests The following diagram gives a high level overview of the application architecture To build this app we will start by implementing the lower level services before moving on to the higher level ones This approach is known as building bottom up and I hope it will help understand the process better Services TracksServiceTo store and retrieve the track and artist data we will use a relational database paradigm This requires us to create two tables one for storing artists and another for storing the tracks of each artist Here is the SQL code needed to create these tables CREATE TABLE artists uri varchar NOT NULL name varchar NOT NULL PRIMARY KEY uri CREATE TABLE tracks uri varchar NOT NULL name varchar NOT NULL artist uri varchar NOT NULL PRIMARY KEY uri KEY artist uri artist uri CONSTRAINT tracks fk FOREIGN KEY artist uri REFERENCES artists uri To run all the services we need we will use Docker containers One option is to use docker compose with the mysql image to create these tables and store the data persistently Here is an example of a docker compose yml file that can be used to create a MySQL container and set up the necessary environment variables volumes and ports services mysql server image mysql command default authentication plugin mysql native password environment MYSQL ROOT PASSWORD admin MYSQL DATABASE music MYSQL USER user MYSQL PASSWORD user volumes db var lib mysql ports volumes db You need to have a docker compose installed on your machine Using the above docker compose yml file we can easily start a MySQL server and connect to it from our Node js application To populate the data from the raw playlist dataset into the database we will need to write a script I used Python for this task as it is simple to use but since the main focus of this article is the Node js application for recommendations I will not go into the details of how the script works You can find the script in the GitHub repo You can run this script to import the data from the raw dataset into the database Once the data is imported our application can then use this data to query tracks and artists Now let s implement the TracksService class This service will be responsible for handling various queries related to track data this is the service interface class TrackService type knex Knex db constructor db this db db asyncTrackData async trackIds gt asyncGetTracksByArtist async artistIds fanOut randomize gt The first function is a simple query for track data It selects tracks by their URI and renames the uri column to id which we will need later on Gets track data from db param string trackIds returns Promise lt Object lt string any gt gt tracks data asyncTrackData async trackIds gt const rows await this db select from tracks whereIn uri trackIds return rows map uri id rest gt id rest This function accepts an array of track IDs as an input and returns a promise that resolves to an array of track data It uses the knex js library to connect and query the MySQL database The second function of the TracksService class allows us to query track IDs by their authors It may appear complex at first glance but the complexity arises from the fact that we want to limit the maximum number of tracks per author Get list of tracks per artist param string artistIds list of artist ids param Object options configuration param number options fanOut max number tracks per artist param boolean options randomize false when fan out specified if true will randomly shuffle tracks before limiting returns Promise lt Object lt string string gt gt map where key is artist id and value is a list of artist track ids asyncGetTracksByArtist async artistIds fanOut null randomize false gt const rows await this db select track id uri artist id artist uri from tracks whereIn artist uri artistIds const fanOutFun randomize v gt sampleSize v fanOut v gt slice v fanOut const applyLimitToArtist R map k v gt k fanOutFun v const groupByArtist R groupBy R prop artist id const limitPerArtist fanOut R pipe R toPairs split object to a list of tuples applyLimitToArtist per tupple apply the limit function R fromPairs construct the object back R identity if fanout is false do nothing For each value within parent object take field track id Convert artist id track id a artist id artist id artist id track id b artist id artist id to artist id a artist id b const projectTrackId R mapObjIndexed R pluck track id return R pipe groupByArtist limitPerArtist projectTrackId rows It starts by querying an unlimited number of tracks per artist and then depending on the input arguments randomly or deterministically limits the number of tracks We make use of two additional libraries here Ramda and Lodash for object and array manipulations It s important to note that artist uri column in the tracks table is a foreign key which means that MySQL creates a secondary index on this field Having a secondary index ensures that our query will run quickly even when we have a large amount of data in the database as we do not need to perform a full scan of the table SimilarityServiceOur next service helps us to find similar entities As you may recall we previously created similarity matrices which hold the similarity relations The service API is straightforward given a list of entity IDs and an index which serves as an identifier for the ML model used to build a similarity matrix the function returns a list of similar entities class SimilarityService type redis RedisClientType redis constructor redis this redis redis asyncGetSimilar async ids indexName fan out gt While it would be possible to use SQL to model this functionality I propose using a key value based NoSQL solution We can use an in memory database such as Redis to store our similarity matrices as they can fit in RAM For example the artist files use less than KB and the track files need MB Even if we had or more artists and tracks it would still fit within memory Additionally we can also shard if more memory is needed Using RAM based storage type we ensure that request is fulfilled with the least possible delay Redis also has persistence mode which we will use for durability of the data To use our similarity service we will need to update our docker compose file to start a Redis server as well Here is an example of how we can do that services redis image redis ports volumes redis data command redis server save loglevel warningvolumes redis The schema we will use for storing the similarity data in Redis is as follows key list entity id The key consists of two parts lt index name gt lt entity id gt We will also need a script that populates Redis with similarity data This script can be found here Once the script is run it will create four different similarity indices With the setup in place we can now write the implementation for the similarity service For provided entity ids fetches similar entities param string ids param Object options param string options indexName redis index name param number options fanOut limit number of similar entities per entity returns Promise lt Object lt string string gt gt map of similar entities where key is input entity id and values are similar entity ids asyncGetSimilar async ids indexName fanOut gt const key id gt indexName id creates array of promises const pendingSimilarities ids map async id gt const similarIds await this redis lRange key id fanOut if similarIds length return null return id similarIds when promises are awaited we get a list of tuples id similarIds ideally we want to have some error handling here const similarities await Promise allSettled pendingSimilarities filter r gt r value map r gt r value return Object fromEntries similarities The implementation is simple it executes N concurrent requests to Redis to get array slices for the provided entity keys It returns an object where the key is the original entity ID and the value is an array of similar entity IDs Ideally we should have error handling for the promises here to handle any errors that might occur when querying Redis FuzzySearchOur recommendation system is not based on user activities and we do not have access to internal track IDs Instead we will be relying on arbitrary strings of text entered by the user to specify track or artist names In order to provide accurate recommendations we will need to implement a fuzzy search mechanism for tracks and artists Fuzzy search is an approach that can help us build a robust system for finding actual tracks based on user input It allows for a degree of flexibility in the user s search queries making it less prone to errors A standard example of a system which supports such queries is ElasticSearch To begin let s take a look at our docker compose configuration for setting up ElasticSearch services elastic image elasticsearch ports environment xpack security enabled false discovery type single node volumes elastic usr share elasticsearch datavolumes elastic In our implementation we will be using two types of indices in ElasticSearch one for indexing tracks with artists and another for indexing just artists This is similar to a reverse representation of the original SQL model The diagram below illustrates how we are storing and querying data in SQL And this is how we are storing them in ElasticSearch The ElasticSearch indices allow us to query the required track and artist IDs by their names very quickly providing a fast and efficient search process This improves the overall user experience as the system is able to provide recommendations in a timely manner The built in fuzzy search mechanism enables us to handle variations in the user s input such as misspellings or slight variations in the search query This ensures that our system can still provide accurate recommendations even if the user s input is not perfect The FuzzySearch class is responsible for handling the search functionality in our recommendation system class FuzzySearch type Client es constructor es this es es asyncSearchArtist async artists gt asyncSearchTrack async tracks gt The asyncSearchArtists method is responsible for searching for artists in the artists index using the provided name s as the search query The method uses a helper method called matchField to compose the SELECT part of the ElasticSearch query param string fieldName field name which need to match param string value matching value returns elasticsearch query for matching lt fieldName gt field const matchField fieldName value gt match fieldName query value operator AND The asyncSearchArtists method then constructs the full request specifying the artists index and the query for each artist It uses the ElasticSearch client s msearch multiple search method to perform the search param artist string artists array of query strings for matching artists each object must contain artist name returns Promise lt SearchResult gt track and artist ids asyncSearchArtists async artists gt const responses await this es msearch searches artists flatMap artist gt index artists query matchField name artist return maybeGetFirstFrom responses The search results returned by ElasticSearch may contain zero to many matched objects so we need to flatten the result set For simplicity let s select the first result from the result set if it exists const maybeGetFirstFrom responses gt responses flatMap r gt if r hits hits return const id r hits hits return id id When searching for tracks we have two cases when artist name search clause is present or is missing so let s create a helper function for constructing the query param track string artist string value matching value returns match Object array of matching clauses in elastic query dsl const searchTrackTerms track artist null gt const trackTerm matchField name track if artist return trackTerm const artistTerm matchField artist artist return trackTerm artistTerm The asyncSearchTracks method makes use of this helper function to construct the query for searching for tracks in the tracks index param track string artist string tracks array of query strings for matching tracks each object must contain either track name or track and artist names having artist name present increases likelyhood of finding the right track returns Promise lt id string gt track ids for matched queries asyncSearchTracks async tracks gt const responses await this es msearch searches tracks flatMap track gt index tracks query bool must searchTrackTerms track return maybeGetFirstFrom responses This concludes the implementation of peripheral services In summary we have implemented three services which we ll use to build our recommendation system Data retrieval using an SQL model This service allows us to quickly lookup data by its ID providing fast access to the required information Similarity retrieval using an in memory key value model This service provides a blazing fast access to the similarity matrix which is crucial for generating recommendations based on similar items Fuzzy search using a text index This service allows us to quickly find relevant entity IDs even when the user s input is not perfect The built in search mechanism in the text index provides a degree of flexibility in the search process handling variations in the user s input Each of these services targets to decrease latency time in their respective zones of responsibility Together they provide a robust and efficient ground for the recommendation system that can handle variations in user input while providing accurate and timely recommendations Fluent APIIn the remaining sections we will look at how we can integrate the services we have built to create a functional recommendation system While we could start using the services as is building a real world recommendation flow would become tedious and cumbersome To improve the development process we need to build a data model and a service that provides a fluent API for construction recommendation flows The core concepts in this data model will be a dynamic entity and a pipe A dynamic entity represents a piece of data that can change over time such as a user s query or a track s artist A pipe represents a flow of data through the system allowing us to apply different operations on the data as it moves through the system The data model and service will provide a simple and intuitive way to build recommendation systems making it easy to experiment with different approaches and fine tune the system to achieve the desired performance EntityAs mentioned previously a dynamic entity is just a container of properties It is a plain JavaScript object that can hold any properties that are relevant to the recommendation system We won t enforce any specific structure for this entity and the responsibility of type checking will be on the client of this API This allows for maximum flexibility in the data model and makes it easy to add or remove properties as needed PipeA pipe is an asynchronous function that receives an array of entities and returns a modified array of entities It can perform various operations on them such as filtering sorting or transforming the data For example a pipe can filter out entities that do not meet certain criteria sort based on a specific property or transform the entities by adding or removing properties If a pipe creates a new entity it copies over properties from the parent entity This allows for the preservation of relevant information from the original entity while also allowing for the addition of new properties RecommendationServiceWith the concepts of dynamic entities and pipes we can now introduce the RecommendationService API type Config Objecttype Entity string any type OneOrMany lt A gt A A type Pipe OneOrMany lt Entity gt gt Promise lt Entity gt class RecommendationService Peripheral API fuzzySearchTracks Pipe fuzzySearchArtists Pipe enrichTrack Pipe similar options Config gt Pipe artistTracks options Config gt Pipe Util API dedupe by string string gt Pipe diversify by string string gt Pipe sort by string string gt Pipe take limit int gt Pipe set from string to string gt Pipe setVal key string value any gt Pipe Composition API merge pipes Pipe gt Pipe compose pipes Pipe gt Pipe internals similarityService SimilarityService trackService TrackService fuzzyService FuzzySearch The API is split into three main sections peripheral API util API and composition API Peripheral API provides the core services that are necessary to build a recommendation system such as the fuzzySearchTracks fuzzySearchArtists enrichTrack similar and artistTracks methods These methods provide access to the services that were previously created and make it easy to retrieve data from the system Util API provides utility methods for manipulating the data as it moves through the system such as the dedupe diversify sort take set and setVal methods Composition API provides methods for composing and merging pipes These methods allow for the creation of complex data manipulation flows by combining multiple individual pipe functions The full implementation of the RecommendationService service is not included in the article but it can be found in the GitHub repository Let s take a look at the fuzzySearchTracks method as an example of how we can implement one of the peripheral API methods in the RecommendationService The method takes an input of user search requests which are represented as dynamic entities Each entity must contain a track name and optionally an artist name typedef Object lt string any gt Entities param Entity Entity input user search requests Each entity must contain track name and optionally artist name returns Promise lt Entity gt found track ids fuzzySearchTracks async input gt input toArray input const trackInputs input filter v gt v track const searchResults await this fuzzyService asyncSearchTracks trackInputs return searchResults The fuzzySearchTracks method does not preserve any of the parent properties of the input entities as they have no use in the recommendation flow The method serves as an entry point and its primary function is to retrieve the relevant track ids The similar method is another example of how we can implement one of the peripheral API methods It takes an input of entities and an options object and finds similar entities Finds similar entities Copies properties from parent to children typedef Object lt string any gt Entities param Object Function options see SimilarityService asyncGetSimilar options param Entity Entity input returns Promise lt Entity gt similar entities for every entity from input similar options gt async input gt input toArray input options isFunction options options options const ids input map R prop id const similarMap await this similarityService asyncGetSimilar ids options return input flatMap entity gt const similar similarMap entity id return similar map id gt entity id The method then maps over the input entities and for each entity it flattens an array of similar entities by copying over the properties from the parent entity to the child entities which get new ids This allows for the preservation of relevant information from the original entity Let s also overview how composition functions work We have two of those merge and compose The merge takes a list of pipes and runs them in parallel creating a higher level pipe The high level pipe internally feeds input to the composed parts runs them concurrently and then merges their output Merges few pipes into single pipe typedef Object lt string any gt Entities param Entity gt Promise lt Entity gt pipes returns high level pipe merge pipes gt async input gt converging function receives an array of triggered promisses and awaits all of them concurrently const convergingF async flows gt await Promise allSettled flows filter v gt v value missing error handling map v gt v value flat return R converge convergingF pipes input The merge function is a powerful tool that allows us to run multiple pipes concurrently which can greatly speed up the process of generating recommendations The compose function is another composition function in the RecommendationService It creates a sequential execution of pipes where the output of one pipe is fed as input to the next pipe Creates sequential composition of pipes typedef Object lt string any gt Entities param Entity gt Promise lt Entity gt pipes returns high level pipe compose pipes gt async input gt return R pipeWith R andThen pipes input Using the compose function we can create complex recommendation flows FlowBuilderWe can now utilise this API to construct a recommendation flow The flow will be split into two branches we will be recommending tracks based on the user s track preferences and also based on their artist preferences as seen in the flow diagram The following is the interface for the flow builder const buildFuzzySearchFlow config recs RecommendationService gt Importing the fluent API interface const fuzzySearchTracks fuzzySearchArtists similar enrichTrack artistTracks dedupe diversify take set setVal merge compose recs Flow implementation And this is how we can implement the flow based on user search input const artistFlow compose dedupe artist fuzzySearchArtists dedupe id set id recommender setVal flow artist flow similar config recs artist mlp dedupe id artistTracks config artistTracks diversify recommender artist id take const trackFlow compose fuzzySearchTracks dedupe id set id recommender setVal flow track flow similar config recs track dedupe id diversify recommender take return compose merge artistFlow trackFlow dedupe id diversify flow take config limit enrichTrack Let s dive into the specifics of the artist recommendations branch To begin we use the compose function to build a sequential chain of underlying pipes This allows us to perform a series of operations in a specific order First we use the dedupe function to take unique artist names based on the user s search input and then query ElasticSearch to retrieve artist IDs Then we use the dedupe function again to ensure that the results from ElasticSearch are also unique dedupe artist fuzzySearchArtistsdedupe id Next we use the set and setVal functions to create new properties on each entity set id recommender setVal flow artist flow The set function creates a new property called recommender by copying the value of the entity s id property The setVal function creates a new property called flow with a constant value of artist flow These two functions will allow us to trace the origin of recommendations later on when needed We then move on to finding similar artists to the ones provided by the user similar config recs artist mlp dedupe id This is done by querying the similar index with values specified in the config file We then use the dedupe function again to remove any duplicate results Finally the last step in the artist flow is to retrieve the songs for the recommended artists and limit the number of results artistTracks config artistTracks diversify recommender artist id take To ensure a more natural shuffle of the results the diversify function is used which uses a Round Robin shuffling mechanism When we call the full flow we will receive results similar to the example shown below id VpSHBPdKaKYVjHO recommender MCceyCBcFnzjGYQUn flow artist flow artist id FIBURUorzrKF name The Air Near My Fingers artist uri FIBURUorzrKF id lmIRfcnrQVLOE flow track flow recommender mGYvljYAbvDtWqkj name Bailando Spanish Version artist uri qGbQCHVROPvTlw id wJIkAAgwAhUhpEV recommender MCceyCBcFnzjGYQUn flow artist flow artist id ZwdSxdxEREPySFridCfh name Me Against The World artist uri ZwdSxdxEREPySFridCfh Each result will have properties such as id recommender flow artist id name and artist uri These properties provide information about the recommended track as well as where it came from in the recommendation flow The full code that includes a request handler and express app can be found in the GitHub repository That s it I hope you have enjoyed building the recommendation systems and learned something new	2023-01-23 11:25:51
海外TECH	DEV Community	Creating GitHub Template Repository	https://dev.to/rndsetiawan/creating-github-template-repository-3m5l	Creating GitHub Template Repository Table of Content STEP Create a New Repository STEP Create a Template Repository STEP Creating Repository from a Template You can create a new repository on your GitHub account or any organization where you have sufficient permission If you want to create a template from the existing repository go through STEP STEP Create a New Repository In the upper right corner use the drop down menu and select New Repository ーcreate a new repositoryOr click the tab Repositories →New ーcreate a new repositoryIn the Owner drop down select the account on which you wish to create the repository ーchoose repository ownerType a Name for your repository and fill in an optional Description box fill repository name and descriptionChoose repository visibility Public repositories are accessible to everyone on the internet Private repositories are only accessible to you and people who you share access with and for organization repositories specific organization members repository visibilityCheck the Add a README file box You can edit the README file later add a readme file Click Create Repository create a repository buttonYour new repository is ready repository ready to use STEP Create a Template Repository Under your repository name click ️Setting setting repositoryFill in the checkbox Template Repository setting activate template repositoryGo back and refresh your browser If successful Use this template button just added before and after setup of a template repositoryNow your template is ready for editing before you use it STEP Creating Repository from a Template On the GitHub page navigate to the main page of the repository You can use templates from your repository organization repository or another repository Click Use this template and select Create a new repository create a new repository from the templateAnother step to Create New Repository is In the upper right corner use the drop down menu and select New Repository create a new repository from the templateOr click the tab Repositories →New create a new repository from the templateTo create a repository with your template of the directory structure and files of an existing repository use the Choose a template drop down and select a template repository create a new repository from the templateAfter this step the next step is the same as STEP Create New Repository starting from no In the Owner drop down select the account on which you wish to create the repository repository ownerType a Name for your repository and fill in an optional Description box fill repository name and descriptionChoose repository visibility Public repositories accessible to everyone on the internetPrivate repositories are only accessible to you and people who you share access with and for organization repositories specific organization members repository visibilityCheck the Add a README file box You can edit the README file later add a readme fileClick Create Repository create a repository buttonYour newly generate template repository is ready newly generate template repositoryFinish	2023-01-23 11:14:27
海外TECH	DEV Community	5 free resources to learn JavaScript	https://dev.to/catherineisonline/5-free-resources-to-learn-javascript-1la7	free resources to learn JavaScriptJavaScript is a programming language that is primarily used to create interactive and dynamic web pages It allows developers to create things like interactive forms animations and real time updates on web pages It is a client side scripting language which means that the code is executed by the user s web browser rather than on the server JavaScript is an essential technology for web development and it s widely used in creating dynamic web pages and web apps As a beginner learning JavaScript is a great place to start because it is a versatile and widely used programming language It is easy to learn and many resources and tutorials are available to help you get started Additionally JavaScript is compatible with other web development technologies such as HTML and CSS so you can use it to enhance the functionality of your web 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comprehensive curriculum The platform offers beginner to advanced courses and also provides a clear path to follow which can help users to stay on track and motivated Additionally Codecademy has a built in code editor which allows users to write and test their code within the platform which can be very helpful for beginners Codecademy also offers a Pro version that provides additional resources such as quizzes quizzes and real world projects as well as personalized support and feedback from expert instructors This can be an excellent option for those who want to take their learning to the next level In summary Codecademy is a great platform for anyone looking to learn to code With its interactive learning format wide range of languages and technologies comprehensive curriculum and Pro version with additional resources it provides an excellent environment for anyone looking to start or advance their coding journey WSchoolsOne of the main reasons to study at WSchools is its comprehensive coverage of web development technologies WSchools offers tutorials and references on HTML CSS JavaScript SQL PHP and more providing a well rounded education for anyone looking to break into the field of web development The tutorials are written in a clear and easy to understand format making it accessible for beginners and also it offers a Try it yourself feature that allows users to practice and test their code within the platform which is an excellent way to solidify the concepts learned Another benefit of WSchools is its focus on web development standards The website is developed and maintained by the World Wide Web Consortium WC which is an international community that develops open standards to ensure the long term growth of the Web This means that the information and tutorials provided on the website are up to date with the latest web development standards which can be valuable for anyone looking to develop web applications or websites WSchools also offers certification programs that can help users to demonstrate their knowledge and skills to potential employers This can be an excellent way to showcase your abilities to potential employers especially if you re new to the field In summary WSchools is a great platform for anyone looking to learn web development With its comprehensive coverage of web development technologies easy to understand tutorials and focus on web development standards it provides an excellent environment for anyone looking to start or advance their coding journey Additionally the certification programs can be a valuable asset for anyone looking to demonstrate their knowledge and skills to potential employers MDN Web DocsOne of the main reasons to learn JavaScript from MDN is the quality of its documentation MDN provides detailed documentation on JavaScript covering everything from basic concepts to advanced features and is written by experts in the field which ensures that the information provided is accurate up to date and easy to understand The documentation also covers the latest versions of JavaScript including ES and ES which can be valuable for anyone looking to work with the latest technologies Another benefit of learning from MDN is the resources it offers The MDN JavaScript Guide provides a comprehensive introduction to the language and the MDN JavaScript Reference provides a detailed reference for the JavaScript language and its standard libraries which can be an invaluable resource for anyone looking to deepen their understanding of the language Additionally MDN provides interactive examples and code snippets that allow users to test and experiment with the concepts learned which can be a great way to solidify understanding and build skills MDN is also a community driven platform and it s open source which means that the content is frequently updated and improved based on feedback and contributions from developers worldwide This ensures that the information provided is always up to date and relevant There are a lot of other resources I have used like Udemy or Coursera but to be honest without them you can be just fine The resources I have recommended are not written in priority order I sometimes learned the same topics from different platforms simultaneously which helped me understand the subject better I hope you will find your favorite platform to learn from out of these resources to learn JavaScript for free	2023-01-23 11:07:02
Apple	AppleInsider - Frontpage News	Apple shooting for 25% production in India, says trade minister	https://appleinsider.com/articles/23/01/23/apple-shooting-for-25-production-in-india-says-trade-minister?utm_medium=rss	Apple shooting for production in India says trade ministerIndia s trade minister claims that Apple is intending to raise its local manufacturing to from its current to although he did not give any timescale Mumbai IndiaShri Piyush Goyal India s minister of commerce and industry also did not specify what devices he expects Apple to produce in the country However his comments fit with previous reports that Apple intends to make one in four iPhones in India by Read more	2023-01-23 11:38:03
Apple	AppleInsider - Frontpage News	How to add student ID to Wallet in iOS 16	https://appleinsider.com/inside/ios-16/tips/how-to-add-student-id-to-wallet-in-ios-16?utm_medium=rss	How to add student ID to Wallet in iOS In participating colleges and universities students can add IDs to the Wallet app on iPhone and Apple Watch Here s how to do it You re going to have the ability to add your student ID card to Wallet on your iPhone and Apple Watch said Kevin Lynch vice president of Technology at Apple at WWDC You can get access to places like your dorm the library or events on campus Here is how you can add your student ID to the Wallet app where you can use it and what to do if your iPhone s battery runs out Read more	2023-01-23 11:28:11
海外TECH	Engadget	Microsoft's Xbox Elite Wireless Controller Series 2 Core falls to a new low of $100	https://www.engadget.com/microsofts-xbox-elite-wireless-controller-series-2-core-falls-to-a-new-low-of-100-112052109.html?src=rss	Microsoft x s Xbox Elite Wireless Controller Series Core falls to a new low of Microsoft s best Xbox One and Xbox series wireless controller is the Xbox Elite Series and the white Core version is now available at the lowest price we ve seen yet You can pick one up at Amazon owned Woot for just or off the regular price nbsp Buy Xbox Elite Series Core at Woot The Elite Series Core gives you the same exact controller as the Elite Series without the additional accessories It s designed with competitive gamers in mind offering a wrap around rubberized grip shorter hair trigger locks and hours of battery life It feels and plays just as well as the Elite offering Xbox wireless connection with the Xbox One and Series S X consoles reducing latency and letting you use the headphone jack You can also connect it to a PC via Bluetooth The Xbox Accessories app provides customization options like button remapping sensitivity curve adjustments dead zones vibration intensity tweaking and LED colors nbsp If you decide you want the normal Elite Series accessories after all that s no problem ーjust purchase the Complete Component Pack separately That gives you everything missing from the Core model including a carrying case a thumbstick adjustment tool a charging dock two classic thumbsticks one tall thumbstick one dome thumbstick one cross shaped D pad two medium and two mini paddles as well as a USB C cable nbsp With the sale you could buy the component pack and Elite Series Core controller and still save over purchasing the regular Elite Series model Just remember that Woot s return policy isn t quite as generous as its parent Amazon nbsp Follow EngadgetDeals on Twitter and subscribe to the Engadget Deals newsletter for the latest tech deals and buying advice	2023-01-23 11:20:52
医療系	医療介護 CBnews	マイナンバーカード活用で搬送先選定しやすくなる-2022年版消防白書、傷病者の負担軽減も	https://www.cbnews.jp/news/entry/20230123201341	消防白書	2023-01-23 20:22:00
金融	金融庁ホームページ	入札公告等を更新しました。	https://www.fsa.go.jp/choutatu/choutatu_j/nyusatu_menu.html	公告	2023-01-23 11:30:00
ニュース	BBC News - Home	Rishi Sunak says questions remain over Nadhim Zahawi tax affairs	https://www.bbc.co.uk/news/uk-politics-64373509?at_medium=RSS&at_campaign=KARANGA	nadhim	2023-01-23 11:50:08
ニュース	BBC News - Home	Richard Sharp: BBC chairman asks for conflict of interest review	https://www.bbc.co.uk/news/uk-politics-64370668?at_medium=RSS&at_campaign=KARANGA	boris	2023-01-23 11:56:52
ニュース	BBC News - Home	Households will be paid to use less electricity on Monday	https://www.bbc.co.uk/news/business-64367504?at_medium=RSS&at_campaign=KARANGA	electricity	2023-01-23 11:49:47
ニュース	BBC News - Home	Heathrow Airport: Dozens of flights cancelled due to freezing fog	https://www.bbc.co.uk/news/uk-64370940?at_medium=RSS&at_campaign=KARANGA	airways	2023-01-23 11:14:12
ニュース	BBC News - Home	Ukraine war: Germany won't block export of its Leopard 2 tanks, foreign minister says	https://www.bbc.co.uk/news/world-europe-64370165?at_medium=RSS&at_campaign=KARANGA	ukraine	2023-01-23 11:04:52
ニュース	BBC News - Home	Cost of living: Five tips when asking for a pay rise	https://www.bbc.co.uk/news/business-64288791?at_medium=RSS&at_campaign=KARANGA	psychologist	2023-01-23 11:49:48
ニュース	BBC News - Home	Australian Open results: Novak Djokovic beats Alex de Minaur; Andrey Rublev & Ben Shelton through	https://www.bbc.co.uk/sport/tennis/64369956?at_medium=RSS&at_campaign=KARANGA	Australian Open results Novak Djokovic beats Alex de Minaur Andrey Rublev amp Ben Shelton throughNovak Djokovic makes light work of home hope Alex de Minaur as he continues his pursuit of a record extending th men s title at the Australian Open	2023-01-23 11:23:02