投稿時間:2022-09-01 00:47:22 RSSフィード2022-09-01 00:00 分まとめ(47件)

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IT	気になる、記になる…	Microsoft、今秋に｢Surface Studio 3｣を発売か − ｢Surface キーボード｣や｢Surface ペン｣の新モデルの写真が流出	https://taisy0.com/2022/08/31/160795.html	central	2022-08-31 14:04:24
AWS	AWS Architecture Blog	Integrating Salesforce with AWS DynamoDB using Amazon AppFlow bi-directionally	https://aws.amazon.com/blogs/architecture/integrating-salesforce-with-aws-dynamodb-using-amazon-appflow-bi-directionally/	Integrating Salesforce with AWS DynamoDB using Amazon AppFlow bi directionallyIn this blog post we demonstrate how to integrate Salesforce Lightning with Amazon DynamoDB by using Amazon AppFlow and Amazon EventBridge services bi directionally This is an event driven serverless based microservice allowing Salesforce users to update configuration data stored in DynamoDB tables without giving AWS account access from AWS Command Line Interface or AWS Management Console This …	2022-08-31 14:47:44
AWS	AWS Management Tools Blog	Find the most evaluated AWS Config rules using AWS CloudTrail Lake	https://aws.amazon.com/blogs/mt/find-the-most-evaluated-aws-config-rules-using-aws-cloudtrail-lake/	Find the most evaluated AWS Config rules using AWS CloudTrail LakeIn this post I ll show you how to find most evaluated AWS Config rules to dive deep into AWS Config charges on your invoice by using AWS CloudTrail Lake The solution uses the new AWS CloudTrail feature CloudTrail Lake to analyze CloudTrail events AWS Config is a service that enables you to assess audit and …	2022-08-31 14:53:34
python	Pythonタグが付けられた新着投稿 - Qiita	Kaggle の Titanicを今更やってみる（初心者）	https://qiita.com/wakakami/items/ad3f45563cdfad2524c0	welcome	2022-09-01 00:00:00
python	Pythonタグが付けられた新着投稿 - Qiita	【Python】引張試験結果のcsvファイルからグラフ作成までを自動化した	https://qiita.com/yuki_2020/items/e0352e2c6a56133d2267	電圧	2022-08-31 23:40:36
python	Pythonタグが付けられた新着投稿 - Qiita	python 使いの ruby メモ	https://qiita.com/Simossyi/items/5759690edee280e935ed	随時	2022-08-31 23:36:00
Ruby	Rubyタグが付けられた新着投稿 - Qiita	python 使いの ruby メモ	https://qiita.com/Simossyi/items/5759690edee280e935ed	python	2022-08-31 23:36:00
AWS	AWSタグが付けられた新着投稿 - Qiita	★★★ EC2（ElasticCompute Cloud）	https://qiita.com/kangaezaru/items/637f7ed2d76f009efd89	ecelasticcomputecloud	2022-08-31 23:20:56
Azure	Azureタグが付けられた新着投稿 - Qiita	azure-functions-core-tools @3 → @4 にバージョンアップしてみました	https://qiita.com/turupon/items/9c973328f702b0debc37	macosmontereypyt	2022-08-31 23:24:41
Git	Gitタグが付けられた新着投稿 - Qiita	ブランチを切り忘れてmasterで作業してしまった時の対処法を解説！	https://qiita.com/KazuyaInoue/items/90d1a185c01a0e9e7317	master	2022-08-31 23:10:14
Ruby	Railsタグが付けられた新着投稿 - Qiita	【Rails】CanCanCanでリソースが特定の条件を満たすときのみ権限を付与する方法	https://qiita.com/m-ito27/items/c959a30b2cdb6b41368a	cancancan	2022-08-31 23:28:02
技術ブログ	Developers.IO	【Tips集】 Pandas,OpenPyXL を利用したエクセル読み書き	https://dev.classmethod.jp/articles/tips-for-using-pandas-and-openpyxl-1/	macosbigsu	2022-08-31 14:41:52
技術ブログ	Developers.IO	タイポから始めるドキュメントコントリビューティング活動	https://dev.classmethod.jp/articles/document-contribution-activities-starting-with-typo/	takakuni	2022-08-31 14:41:27
技術ブログ	Developers.IO	Cloudformation의 함수를 정리했습니다. ( 2 )	https://dev.classmethod.jp/articles/lim-cloudformation-function-kr-2/	Cloudformation의함수를정리했습니다 안녕하세요 임채정입니다 저는요즘에Cloudformation 을사용해서리소스작성을많이하고있습니다 이때 템플릿에서사용하는함수에대해서정리해두면도움이될것같아서이번블	2022-08-31 14:34:40
技術ブログ	Developers.IO	Cloudformation의 함수를 정리했습니다. ( 1 )	https://dev.classmethod.jp/articles/lim-cloudformation-function-kr-1/	Cloudformation의함수를정리했습니다 안녕하세요 임채정입니다 저는요즘에Cloudformation 을사용해서리소스작성을많이하고있습니다 이때 템플릿에서사용하는함수에대해서정리해두면도움이될것같아서이번블	2022-08-31 14:34:23
技術ブログ	Developers.IO	Amazon S3 의 스토리지 클래스를 알아보고 설정해봤습니다.	https://dev.classmethod.jp/articles/lim-s3-storage-classes-kr/	Amazon S 의스토리지클래스를알아보고설정해봤습니다 안녕하세요 임채정입니다 이번블로그에서는스토리지클래스에대해서조사해봤습니다 또 S 버킷의객체에스토리지클래스를설정하는방법을정리해보겠습니다 아젠다스토리지클래스란 스토리지	2022-08-31 14:34:06
技術ブログ	Developers.IO	AWS CLI로 Amazon EC2 인스턴스의 메타데이터를 확인해봤습니다.	https://dev.classmethod.jp/articles/lim-ec2-matadata-kr/	AWS CLI로Amazon EC 인스턴스의메타데이터를확인해봤습니다 안녕하세요 임채정입니다 이번블로그에서는EC 인스턴스의메타데이터를확인해보겠습니다 아젠다메타데이터의기능정의메타데이터확인마무리 메타데이터의기능정의인스턴스메타데이터메	2022-08-31 14:14:35
海外TECH	Ars Technica	Apple quietly revamps malware scanning features in newer macOS versions	https://arstechnica.com/?p=1877183	products	2022-08-31 14:55:37
海外TECH	Ars Technica	Real money, fake musicians: A million-dollar Instagram verification scheme	https://arstechnica.com/?p=1877211	check	2022-08-31 14:41:38
海外TECH	MakeUseOf	Free vs. Paid DAWs: Is It Worth Buying One?	https://www.makeuseof.com/free-vs-paid-daw/	audio	2022-08-31 14:31:14
海外TECH	MakeUseOf	Does the Right-Click Context Menu Randomly Open on Windows 11? Here's How to Fix It	https://www.makeuseof.com/windows-11-context-menu-randomly-open/	windows	2022-08-31 14:16:15
海外TECH	DEV Community	5 Essential Machine Learning Algorithms For Business Applications	https://dev.to/anastasiiamolodoria/5-essential-machine-learning-algorithms-for-business-applications-5dam	Essential Machine Learning Algorithms For Business ApplicationsBusinesses from market giants like Amazon and Netflix to a small retail store somewhere in the heart of Ohio strive to grow and improve their efficiency Incorporating AI and Machine Learning into operational activity is one of the ways to achieve this But due to the diversity of ML it s hard to choose the right method and clearly understand what benefits it can bring So in this article we re going to overview basic Machine Learning algorithms explain their business application and highlight a step by step guide to choosing an appropriate algorithm that will meet your business needs RegressionRegression is a rudimental ML algorithm for finding the relationship between at least two variables These variables can be dependent target and independent predictor An understanding of how variables affect each other allows for building forecasts while also identifying times series cause and effect relationships and serving as a predictor of strength The goal of regression techniques is typically to explain or predict a specific numerical value while using historical data And the variety of the regression model depends on the type and number of input data variables In total there are more than such models Simple linear and multiple linear regression are the most popular of them Simple linear regression consists of only one independent and one dependent variable Multiple linear regression is much more common in practice It foresees numerous explanatory independent variables that influence one dependent variable Here a specific example can better illustrate the differences between simple and multiple linear regression Assume that we re dealing with an ice cream business With a simple linear regression we can find dependency between the number of sales dependent variable and the storage temperature of an ice cream independent variable Multiple linear regression covers clarifying deeper patterns For instance we can check how independent variables the storage temperature pricing and number of flavors and staff affect the sales dependent variable Linear regression is easy to comprehend yet it is rarely used in practice because not all of the features variables in the world are perfectly generalized with a linear trend Usually non linear interconnections are more frequent since they depict a curvy trend in the data change occurring in real life projects Time series information in such projects allows us to work with regression tasks by not only finding key factors affecting the target variable but predicting future values based on historically gathered data including timestamps This is one of the reasons why regression has found a wide application in areas such as retail business processes optimization recommendation systems and etc BUSINESS USE CASE FOR REGRESSION ALGORITHMLet s walk through an example of applying a regression model in a restaurant business Were you a restaurateur you d probably think about cost optimization You can satisfy this need by minimizing the number of spoiled products and by leveraging precise planning of goods purchases We can develop a regression model that will be able to predict when and how many products to buy considering the expiration date of different products To make a workable model we d need to feed it with the following historical data The number of restaurant dishes that were sold during the past periods grouped by days weeks etc Holiday info these days have other specifics Marketing campaign infoThe benefits are obtained through the regression model adoption that explains or predicts a numerical value while using historical data from a previous data set After you implement the described solution you can plan purchases more accurately ClassificationClassification is an ML algorithm of categorizing unstructured or structured data Its application remains effective in such areas as spam filtering document classification auto tagging and defect detection Classes here may be perceived as labels or targets By analyzing the input the model learns how to classify new information mapping labels or targets to the data At the same time binary multiclass and multilabel are the main types of classification algorithms Binary classificationWe train the model to classify new data into categories spam or non spam emails has or doesn t have a lung disease buy or don t buy a product alpaca or llama image which depicts a bit more complicated case of a few shot learning classification The binary model training requires a dataset which is labeled with and After the model analyzes the sorted dataset it is capable of predicting labels for new data At the core of learning lies the ability to recognize patterns Multiclass classification We train a model to classify into more than two categories For instance a classifier can learn how to identify cats dogs lizards and other animals To achieve the reliable accuracy of recognition the model should determine and grasp the features that enable classification into categories However in multiclass as well as binary classification only one category can be assigned to the data sample Multilabel classification In multilabel classification zero or more labels can be assigned to different objects A classifier here may recognize cats dogs and other animals that are depicted in one picture A prominent example of multilabel classification is auto tagging blog articles can be marked with relevant tags like “AI “ML techniques “Healthcare and so on While completing classification tasks the model makes the prediction with the probability from to describing the confidence with which it has delivered this or that verdict regarding the category The number means total uncertainty and represents confidence in the performed classification Therefore depending on the specifics of the business and the client s tasks this threshold can be customized not necessarily more than means “yes less than means “no you can adjust these numbers to the business needs BUSINESS USE CASE FOR CLASSIFICATIONHelpdesk assigns tags to each of the conversations with the customers This is done for quick and easy navigation between previous customers requests and for grouping conversations by topics This process should be automated to reduce manual work The business solution in this case is based on previously tagged client s data We can develop a multi label classification model that will be able to automate the process of assigning several tags to the new conversations with the customers So call center specialists will not spend time on this activity focusing on the other priority tasks instead ClusteringClustering is an ML method that allows us to identify and group data points in organized structures These structures represent large datasets which can be seamlessly grasped and manipulated and new insights can be achieved from the grouped data after clustering modeling Unlike classification clustering doesn t require labeled data After all it tries to find patterns by identifying shared or similar properties and then applies these patterns to create separate groups clusters Grouping or clustering techniques are particularly useful in business applications where there is a need to segment or categorize large volumes of data Typical cases include segmenting customers by different characteristics to better target marketing campaigns and recommending news articles that certain readers will enjoy Clustering is also effective in discovering patterns in complex datasets that may not be obvious to the human eye which makes it one of the most used AI techniques in marketing Clustering Machine Learning models differ depending on the approach Sometimes we start with randomly initialized center points like in K Means and other centroid based algorithms other times we apply hierarchical density or distribution based methods All these algorithms open up opportunities for business usage in anomaly detection image segmentation social network analysis improving marketing campaigns and fraud detection BUSINESS USE CASE FOR CLUSTERINGRetail business can serve as an example here Imagine that the business owner intends to analyze employees performance and identify who is not working very hard Every day a lot of employees work in different supermarket chains with money so the owner wants to get a full picture of employees performance being able to evaluate the efficiency of operational costs In order to help with the solution we can develop a clustering model for anomaly detection In our case the anomaly activity is fixed if employees behavior is uncommon differs from all of the rest By applying the clustering algorithm we identify groups of employees whose behavior differs significantly from the majority of the staff Clustering is the first step towards tackling performance issues and productivity optimization though a business has enough room for the adoption of other ML algorithms Deep LearningDeep learning DL is a field of AI that partially emulates the approaches taken by human beings while learning DL algorithms substitute a neural network with at least three layers that breaks problems into levels of data and then solves them These algorithms resemble the functioning of our brains when we start to comprehend the world learn words and recognize new objects In this way being a branch of ML deep learning substitutes algorithms that lie on multi layer neural networks but differ from traditional AI ML techniques see the picture below The key difference is that deep learning models do not require data with a set of relevant features it s enough just to provide them with raw data giving the algorithm a chance to define relevant features on its own DL models are amplifying along with the increasing amount of data applied for the training So the development of deep learning looks as follows layers of a neural network consist of neurons that transmit information to the neurons of the subsequent layer and the model arrives at a decision when data gets to the output layer Deep learning models are used for a wide variety of business applications In healthcare they help analyze medical images speed up diagnostic procedures and search for drugs In the telecommunications and media industry neural networks can be used for machine translation fraud detection and virtual assistant services The financial industry uses them for anomaly fraud detection portfolio management and risk analysis Summing up we can state that DL is capable of text summarization new image generation speech to text conversion emotion detection movement recognition BUSINESS USE CASE FOR DEEP LEARNINGImagine a shoe business with a team providing customer support services via chats and phone A business owner wants to be able to briefly analyze the quality of services granted by employees and check the level of customer satisfaction The business solution may be built on top of the text summarization that allows us to extract the most relevant information from the chat support text In order to be able to process audio conversations as well we can apply speech to text models for extracting text information from the audio Also aiming generally to analyze the level of customers satisfaction we can work on sentiment analysis models that identify the tone of the conversations positive or negative dialogues Dimensionality ReductionDimensionality reduction techniques involve reducing the number of input features variables or attributes while maintaining as informative a dataset as possible Why do we need it if usually we are aiming to have the maximum amount of data for training the perfect model It quite frequently happens that the performance of machine learning algorithms can degrade with too many input variables A greater number of features increases the chance to overfit the model which is fraught with poor quality results Thanks to the dimensionality reduction we are able to shorten the duration of the training shun overfitting and apply the algorithm for data preparation performed prior to modeling Business usage of dimensionality reduction isn t limited to data preparation before modeling and includes the following areas visualization of high dimensional data image compression models runtime optimization and reducing models complexity BUSINESS USE CASE FOR DIMENSIONALITY REDUCTIONTo better understand this algorithm we will look at a case study The company produces and monitors a lot of different sensors and has plenty of data that needs to be analyzed A prediction model based on existing data may serve them well It should analyze historical data from a bunch of sensors and predict some information taking into account original data Sensor data is sparse so applying ordinary machine learning algorithms without preprocessing steps will result in low quality model performance So one of the best options is to use dimensionality reduction methods before modeling thus being able to reduce the number of features and leave only the most relevant ones for obtaining reliable model quality Then after retrieving important data we can apply regression or classification models for performing predictive modeling based on the target feature we need to predict From dimensionality reduction to regression choosing among Machine Learning techniques may be tough Get acquainted with our recommendations to solve this issue How to Choose ML Algorithm for Your Business AppAccording to ML classification there are supervised unsupervised and reinforcement learning options to be utilized for this business need In supervised learning we encounter an idea of training based on labeled input and output data Regression and classification are algorithms of this group As for unsupervised learning models they require data with input features but without labeled output and are capable of finding structures within the given data Segmentation and clustering belong to this category In the case of Reinforcement learning ML models solve a task by improvising and through further analysis of the feedback regarding taken actions and solutions Once you ve learned about the types of ML algorithms you can take a look at a step by step guide to choosing an appropriate algorithm for business application Define the business problem and algorithms that are the most suitable for tackling itCheck available data amount characteristics type and behavior Think about optimal evaluation metric and speedDecide on a suitable number of features and parametersStick to a baseline model or more sophisticated solution if simple linear algorithms work well there is no need to complicate the work With all the diversity of top Machine Learning algorithms you might get confused about what method to choose Try to adhere to a data related or problem related approach Remember that better data is of greater significance than an algorithm which can be easily enhanced by extending the training time	2022-08-31 14:35:16
海外TECH	DEV Community	How to Build a Custom Field Plugin for Webiny Headless CMS	https://dev.to/menard_codes/how-to-build-a-custom-field-plugin-for-webiny-headless-cms-4o75	How to Build a Custom Field Plugin for Webiny Headless CMSThe concept of Headless CMS has gained a steady increase in popularity as the way people consume data evolves Today data and content are delivered not just through websites but also in mobile apps smart watches VR smart assistants IoT devices and more As a result more and more Headless CMS providers have stepped in to fulfill the demand each offering common and unique features to fulfill your content and data management needs Many of these Headless CMS platforms are proprietary As a result you re locked into whatever feature and functionality there is your Headless CMS provider offers If you need a certain feature or you want to customize something you have no choice but to hope and wait for your provider to implement it if they ever do Alternatively you can use open source Headless CMS platforms like Webiny Webiny is an open source framework that allows you to build serverless applications and websites and as mentioned it comes with a Headless CMS Webiny s composable architecture allows you to customize and extend its built in features and functionalities giving you full control over your entire software such as your websites GraphQL API s resolvers data types schemas Infrastructure as Code with Pulumi etc and data Most of Webiny s functionality is built with plugins When you need to extend Webiny s built in features you ll also need to use plugins or build new ones that best fit your needs So if you need a custom field type for your CMS content model you can create a new one through plugins To demonstrate this article will guide you through building a custom field type for Webiny s Headless CMS content model And to build that custom field we will use a set of built in plugins in Webiny for constructing a new content model field type We ll do this through a step by step process with code samples and screenshots to help you along the way so feel free to follow along In this article you ll learn What are the plugins used to construct a new CMS content model field typeHow to create your custom field plugin Building a custom CMS model field typeLike other Headless CMS platforms Webiny comes with built in content model fields You can get pretty far with these built in fields But depending on your needs you might need to have a content model that requires a property that doesn t come with Webiny s built in field types And in cases like this you ll need to create your custom CMS content model field type Plugins for Creating a Custom CMS FieldTo build your custom field you ll need to use several plugins that all come with Webiny These built in plugins will help you define and build your new content model field type There are five plugins related to building new content model field types where three of which are required and two are optional CmsEditorFieldTypePlugin This is a required plugin used to define some properties of your new content model field from its name type description label the icon to associate with this new field and more Registering the plugin you created with CmsEditorFieldTypePlugin will render your new field type to the content model UI editor CmsEditorFieldRendererPlugin This is another required plugin used to define the renderer for your new field type With this plugin you ll define the component to render the field you re creating It can be as simple as an input or as complex as a widget CmsModelFieldToGraphQLPlugin This is the third required plugin used to define the new field s schema resolver filters etc to the GraphQL API CmsModelFieldToStoragePlugin This is an optional plugin that sits in between your storage and API layer With this plugin you can manipulate the data before it s sent to the storage layer for example censoring profanity in a blog post or comment or after retrieving the data from the storage and do things like decrypting an encrypted text CmsModelFieldToElasticSearchPlugin This is also an optional plugin that is similar to the CmsModelFieldToStoragePlugin for being an intermediary But in this case instead of the storage layer this plugin sits in between the API layer and ElasticSearch With this plugin you can manipulate the data before it is sent to the index or after retrieving it PrerequisitesBefore we begin with the steps on building a custom content model field make sure you have the following if you want to follow along the tutorial Node js or higherEither of the two yarn classic or higher yarn berry or higherAn AWS account and pre configured user credentials to your machineAnd a pre deployed Webiny project to AWS Check this guide to learn how We ll skip the Webiny app installation and deployment process in this article So if you don t have an up and running Webiny project yet be sure to follow this installation and deployment guide Step by Step How To Build a Custom CMS Model Field TypeLet s say we have an e commerce store selling mainly wrist watches The products in this store are grouped according to their watch type namely Mechanical WatchQuartz WatchDigital WatchSmart Watchand HybridThe content model needed for our PDP Product Detail Page requires an enumeration field type to select which kind of wrist watch a given product is This new enumeration field will help categorize the products in the store Since there is no built in content model field in Webiny for our use case we need to create a new one ourselves So in this tutorial we ll build a simple dropdown menu as our new content model field type This dropdown menu should contain all the mentioned watch types so that whenever we add a new product to the store we can choose from the dropdown menu which category of watch it is And with all that said open your Webiny project in the code editor of your choice and let s build this dropdown menu field type You can check the full version of the code in this repo Step Define a New Field TypeWhen building a new content model field type you should first create a plugin that defines this new field type you re making In this new plugin you ll give your new field a name a field type label description the icon for this new field to be rendered on the list of content model fields in the content model UI editor and more To define our new field we ll be using the CmsEditorFieldTypePlugin So first create this new directory fields dropdown under the apps admin src plugins headlessCMS directory from the root of your Webiny project mkdir p apps admin src plugins headlessCMS fields dropdownThen create a new file called productCategoryFieldPlugin tsx under this new directory you created touch apps admin src plugins headlessCMS fields dropdown productCategoryFieldPlugin tsxIn this new file we ll write the plugin to define our new content model field type using the plugin CmsEditorFieldTypePlugin Copy the code below and paste it inside the file productCategoryFieldPlugin tsx apps admin src plugins headlessCMS fields dropdown productCategoryFieldPlugin tsximport React from react import CmsEditorFieldTypePlugin from webiny app headless cms types const DropdownIcon React FunctionComponent gt lt i gt ICON lt i gt const plugin CmsEditorFieldTypePlugin type cms editor field type name cms editor field type product category field type product category label Product Category description Product Category icon lt DropdownIcon gt allowMultipleValues false allowPredefinedValues false multipleValuesLabel Use as a list of multiple categories createField return type product category validation renderer name export default plugin Before we proceed let s first dissect this plugin and understand what it does Under the field property of the CmsEditorFieldTypePlugin we defined the properties of this new dropdown field we re building Here we specified that the type of this new plugin is product category which we will use later to identify this new field type We also defined its label description and icon You can assign your values here if you want to And the rest of the properties inside the field property only relates to the rules regarding this new plugin After defining our new field type we need to include this in the list of content model field types of the headless CMS Under the directory apps admin src plugins open the file headlessCMS ts and import our newly created plugin then add this plugin to the exported array apps admin src plugins headlessCMS ts rest of the importsimport richTextEditor from headlessCMS richTextEditor My custom pluginsimport productCategoryFieldPlugin from headlessCMS fields dropdown productCategoryFieldPlugin lt import the pluginexport default headlessCmsPlugins richTextEditor textField objectFieldRenderer productCategoryFieldPlugin lt the product category field plugin Now it s time to check our new custom field type in the content model UI editor Run this command to start a development server for your Webiny project s admin workspace yarn webiny watch app admin env devAnd then create a sample content model in Webiny s Headless CMS just name it Product and check out the field types list You should be able to see the new field type named PRODUCT CATEGORY create content model JPG Step Define and Build the Renderer for the New FieldNow that we defined our new field type it s time to build the renderer for it The custom field s renderer is what will be shown when you add or edit your content For the renderer of our product category field type we are going to define a dropdown menu And to simplify things we ll use a third party dependency called react dropdown to skip the part of implementing a dropdown menu from scratch So with that said open your terminal and install react dropdown yarn workspace admin add react dropdown Take note of the workspace admin since we only want to install react dropdown in the admin area This is because every Webiny project is a monorepo that has several workspaces in it including the mentioned admin workspace which we re working on right now After installing the dependency we can now define and build the renderer for product category field We will be using the CmsEditorFieldRendererPlugin to define our renderer s properties like rendererName description canUse or when should it be rendered and most importantly the component that will be rendered Under the directory apps admin src plugins headlessCMS fields dropdown add another file named productCategoryFieldRendererPlugin tsx touch apps admin src plugins headlessCMS fields dropdown productCategoryFieldRendererPlugin tsxThen write the renderer plugin inside this file using the CmsEditorFieldRendererPlugin Use the react dropdown to create the dropdown component apps admin src plugins headlessCMS fields dropdown productCategoryFieldRendererPlugin tsximport React from react import CmsEditorFieldRendererPlugin from webiny app headless cms types import ReactDropdown from react dropdown import react dropdown style css export default CmsEditorFieldRendererPlugin gt type cms editor field renderer name cms editor field renderer product category renderer rendererName product category must be the same as the field type defined in CmsEditorFieldTypePlugin name Product Category description Product Category canUse field only render this on fields with the type product category return field type product category the component to render for the field type product category render getBind const Bind getBind the product categories in the e commerce store const options Mechanical Watch Quartz Watch Digital Watch Smart Watch Hybrid const defaultOption options return lt Bind gt bind gt lt ReactDropdown options options onChange value gt bind onChange value value bind value defaultOption placeholder Select an option gt lt Bind gt From the code above the product category field s renderer was defined in the renderer property of the CmsEditorFieldRendererPlugin The rendererName is the same with the custom field s type product category which is required in order to associate the renderer to its corresponding field type The canUse property indicates that this renderer should only be used when the field type is product category And finally the render property is where we defined the dropdown menu component which will be rendered when creating or editing contents that has product category properties in them Also you might have noticed that our dropdown component is nested inside that Bind component This Bind component is hooked into a React Context and helps abstract managing the state of our field renderer s component That s why we don t have a useState hook inside our renderer component Instead we pass the value from the dropdown to bind and use the onChange handler of bind to pass changed data from the component to the context object After you defined the renderer import this plugin to headlessCMS ts and add it to the exported list next to the field type definition plugin from earlier Remember to call this function upon adding it to the list apps admin src plugins headlessCMS ts rest of importsimport richTextEditor from headlessCMS richTextEditor My custom pluginsimport productCategoryFieldPlugin from headlessCMS fields dropdown productCategoryFieldPlugin import productCategoryFieldRendererPlugin from headlessCMS fields dropdown productCategoryFieldRendererPlugin lt import the renderer pluginexport default headlessCmsPlugins richTextEditor textField objectFieldRenderer productCategoryFieldPlugin productCategoryFieldRendererPlugin lt the product category field renderer plugin Now that you added this field renderer plugin you can preview it in the content model UI editor and check the new dropdown field type we just defined Step Define the Field in the GraphQL APIAfter you defined the new field type and its renderer it s time to define this new field in the GraphQL API For this we ll need to use the CmsModelFieldToGraphQLPlugin to define our product category field type on the GraphQL API to handle all incoming requests from this field type Since we will be working on the API layer we ll leave the admin area for now and switch to the Headless CMS API workspace First off add the directory fields dropdown under the directory apps api headlessCMS src from the root of your Webiny project mkdir p apps api headlessCMS src fields dropdownThen create the file named productCategoryFieldPlugin ts under the directory you just created touch apps api headlessCMS src fields dropdown productCategoryFieldPlugin tsInside the file productCategoryFieldPlugin ts define the product category field type to GraphQL apps api headlessCMS src fields dropdown productCategoryFieldPlugin tsimport CmsModelFieldToGraphQLPlugin from webiny api headless cms types const plugin CmsModelFieldToGraphQLPlugin name cms model field to graphql product category type cms model field to graphql fieldType product category lt Must be the same as the field type defined in CmsEditorFieldTypePlugin isSortable true isSearchable true read createTypeField field if field multipleValues return field fieldId String return field fieldId String createGetFilters field return field fieldId String createListFilters field return field fieldId String field fieldId not String field fieldId in String field fieldId not in String field fieldId contains String field fieldId not contains String manage createListFilters field return field fieldId String field fieldId not String field fieldId in String field fieldId not in String field fieldId contains String field fieldId not contains String createTypeField field if field multipleValues return field fieldId String return field fieldId String createInputField field if field multipleValues return field fieldId String return field fieldId String export default plugin In here we defined the fieldType as product category because the fieldType in this plugin must match the field s type we defined in CmsEditorFieldTypePlugin That way Webiny knows this specific plugin is for the product category field type The properties isSearchable and isSortable are set to true since we want this field to be searchable and sortable via the GraphQL API In the read property we defined the field type for product category in the Read API through createTypeField as well as some filters Similarly in the manage property we defined the field type of product category in the Manage API through the createTypeField We also defined the input GraphQL field type through createInputField as well as a filter Now that this plugin is defined we can now import this to the apps api headlessCMS src index ts file and add the productCategoryFieldPlugin plugin to the plugins used by the Headless CMS API apps api headlessCMS src index ts the rest of the importsimport scaffoldsPlugins from plugins scaffolds My custom pluginsimport productCategoryFieldPlugin from fields dropdown productCategoryFieldPlugin lt import the GraphQL plugin for the product category field export const handler createHandler plugins createWcpContext scaffoldsPlugins productCategoryFieldPlugin lt add here the field to GraphQL plugin of the product category field http debug And finally deploy the changes in the API and run the dev environment of your Headless CMS API using this command yarn webiny watch apps api headlessCMS env devIf everything worked as expected you should now be able to save your content model that has the product category field type and create new product entries using this new custom field We will do all these in the next and final step of this tutorial Step Testing the New FieldFor the final step we will test our new custom product category plugin by making a simple content model for the PDP Product Detail Page of our products create entries or product page contents using the PDP content model and finally query the Headless CMS API for the product content entries And if our custom plugin works properly we should be able to use it on the PDP content model use the dropdown menu to categorize the product entries and get the product category when querying the products through the Headless CMS API So to begin with first go to Webiny s Headless CMS and create a new content model or use the Product model from earlier for our PDPs You can use the following fields in your content model Text For the product name Set it as the TitleProduct Category Our custom field type Used for categorizing the watches on the store File This is where we ll put the product images Set it to accept multiple values and to only accept image files Number For the price of the productRich Text For the product descriptionYour Product content model including our very own product category field should look something like this Save this content model and create a new product entry for the Products content model For this example I ll add a mechanical watch and set the category using the dropdown menu we created earlier to Mechanical Watch When querying the products lists we expect that the value we get for this product is Mechanical Watch Save and publish this product entry Make sure you press the orange button from the top right that says Save amp Publish and then click Confirm so that we can query it from the Read API If everything works well you should be able to publish your first product content entry with the custom field type we created Feel free to add as many product entries as you want And now for the final test we will query the Headless CMS API for all our product entries Make sure you re on the Read API s tab or that your API endpoint is cms read locale You can also query the product entries in the Preview API but since we already have a published content entry we can use the Read API instead Now remove the pre populated query and replace it with our products list query listProducts data productName productCategory productPrice productImages productDescription Finally hit the play button to run the GraphQL query And if our implementation works flawlessly and as expected we should be able to retrieve the data we entered on the product content entries Most importantly we ll see the value of the product category obtained from our custom product category dropdown field type And there you have it You just created your own custom content model field type in Webiny s Headless CMS Feel free to play around and experiment with this new custom field type or better yet create your own field and use your imagination to build the field type you want ConclusionIn this article you learned how you can extend Webiny s built in features through plugins You learned the different built in plugins in Webiny for creating custom field types in Webiny s Headless CMS how to use these plugins and how to define the field s type renderer and field to GraphQL plugins to create the custom content model field type We made a simple dropdown menu for our hypothetical e commerce store to help categorize products We also tested our custom field type by creating a content model with the dropdown menu creating content entries using this field type and querying the results in the API Playground Webiny s composable architecture allows you to have full control and freedom over your Headless CMS platform Webiny s plugins power most of its built in features and plugins are also what you need when you re extending Webiny s functionalities With the world of technology rapidly changing year over year having a dynamic and easy to scale Headless CMS platform like Webiny is crucial to the success of your business If you want to have more freedom and control over your Headless CMS platform and your software and data in general be sure to check out Webiny and build your next website with them	2022-08-31 14:10:21
Apple	AppleInsider - Frontpage News	Enter to win an iPhone 14 Pro & metal print from Aluminyze	https://appleinsider.com/articles/22/08/31/enter-to-win-an-iphone-14-pro-metal-print-from-aluminyze?utm_medium=rss	Enter to win an iPhone Pro amp metal print from AluminyzeApple s iPhone Pro is on the horizon and to celebrate Aluminyze is awarding the winner of the Aluminyze x AppleInsider Photo Contest the new device in addition to a inch by inch metal print to showcase your work Enter today for your chance to win s widely anticipated smartphone Submit your best photo to enter to win an Apple iPhone Pro and metal print from Aluminyze Aluminyze has partnered with AppleInsider to run a photo contest in anticipation of Apple s iPhone Pro and its exceptional camera capabilities To enter to win a new iPhone Pro and a inch by inch metal print from Aluminyze submit your favorite photo taken on your iPhone or any other smartphone and sign up for the Aluminyze and AppleInsider newsletters via the widget below Rights to the photo must be your own Read more	2022-08-31 14:58:16
Apple	AppleInsider - Frontpage News	Benks Grand Pro Headphone Stand review: show off AirPods Max with a minimal stand	https://appleinsider.com/articles/22/08/31/benks-grand-pro-headphone-stand-review-show-off-airpods-max-with-a-minimal-stand?utm_medium=rss	Benks Grand Pro Headphone Stand review show off AirPods Max with a minimal standThe Benks Grand Pro Headphone Stand for AirPods Max is a welcome upgrade over the original However it introduces complexity where there was none before ーwhile also ignoring an obvious problem The Benks Grand Pro Headphone Stand has a wireless charger in its baseThe original Benks AirPods Max headphone stand looks nearly identical to the Grand Pro but there is a big difference The Grand pro has a wireless charging pad in the base of the headphone stand Read more	2022-08-31 14:46:08
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