投稿時間:2022-05-12 01:40:35 RSSフィード2022-05-12 01:00 分まとめ(43件)

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IT	気になる、記になる…	｢Apex Legends Mobile｣は5月18日に配信開始へ	https://taisy0.com/2022/05/12/156829.html	apexlegendsmobile	2022-05-11 15:10:04
AWS	AWS - Webinar Channel	SageMaker Friday episode 3 - Train large deep learning models	https://www.youtube.com/watch?v=zR-xAR61q-A	large	2022-05-11 15:17:06
js	JavaScriptタグが付けられた新着投稿 - Qiita	callback関数ってなんだっけ？	https://qiita.com/TakehiroKATO/items/97bee8971dd9de31ded8	callback	2022-05-12 00:53:24
js	JavaScriptタグが付けられた新着投稿 - Qiita	localStorageデータ操作あれこれ（保存、取得、更新、削除など）	https://qiita.com/shiho97797/items/30c1475363c587b60a75	winlose	2022-05-12 00:36:13
Ruby	Rubyタグが付けられた新着投稿 - Qiita	foo_comserver2をrubyから操作するメモ	https://qiita.com/tamaki-kurenai/items/53b33a1059d68983cf98	requirewinoleolewin	2022-05-12 00:53:38
Ruby	Railsタグが付けられた新着投稿 - Qiita	Rails 7 の便利な ComparisonValidator でちとハマった	https://qiita.com/scivola/items/33b962a497968a4fee5a	comparisonvalidator	2022-05-12 00:45:42
技術ブログ	Developers.IO	Cloudflare Pagesのインフラがパワーアップ。 ビルド時の初期化が2秒まで短縮しました。	https://dev.classmethod.jp/articles/cloudflare-pages-new-features/	pages	2022-05-11 15:51:58
海外TECH	Ars Technica	Windows 11 is bringing back Sound Recorder with a new design	https://arstechnica.com/?p=1853500	voice	2022-05-11 15:44:36
海外TECH	Ars Technica	Judge brings dismissed Steam antitrust lawsuit back from the dead	https://arstechnica.com/?p=1853541	allege	2022-05-11 15:37:08
海外TECH	MakeUseOf	Microsoft's May 2022 Patch Tuesday Contains Some Big Security Fixes	https://www.makeuseof.com/microsoft-may-2022-patch-tuesday/	windows	2022-05-11 15:58:10
海外TECH	MakeUseOf	Can You Convert a Gasoline Vehicle to an Electric Vehicle?	https://www.makeuseof.com/can-you-convert-gasoline-vehicle-to-electric-vehicle/	electric	2022-05-11 15:30:13
海外TECH	MakeUseOf	Unable to Download iTunes for Windows? Here Are 8 Fixes	https://www.makeuseof.com/windows-itunes-download-fix/	library	2022-05-11 15:15:13
海外TECH	DEV Community	MMML | Deploy HuggingFace training model rapidly based on MetaSpore	https://dev.to/qazmkop/mmml-deployment-huggingface-training-model-rapidly-based-on-metaspore-29ig	MMML Deploy HuggingFace training model rapidly based on MetaSporeA few days ago HuggingFace announced a million Series C funding round which was big news in open source machine learning and could be a sign of where the industry is headed Two days before the HuggingFace funding announcement open source machine learning platform MetaSpore released a demo based on the HuggingFace Rapid deployment pre training model As deep learning technology makes innovative breakthroughs in computer vision natural language processing speech understanding and other fields more and more unstructured data are perceived understood and processed by machines These advances are mainly due to the powerful learning ability of deep learning Through pre training of deep models on massive data the models can capture the internal data patterns thus helping many downstream tasks With the industry and academia investing more and more energy in the research of pre training technology the distribution warehouses of pre training models such as HuggingFace and Timm have emerged one after another The open source community release pre training significant model dividends at an unprecedented speed In recent years the data form of machine modeling and understanding has gradually evolved from single mode to multi mode and the semantic gap between different modes is being eliminated making it possible to retrieve data across modes Take CLIP OpenAI s open source work as an example to pre train the twin towers of images and texts on a dataset of million pictures and texts and connect the semantics between pictures and texts Many researchers in the academic world have been solving multimodal problems such as image generation and retrieval based on this technology Although the frontier technology through the semantic gap between modal data there is still a heavy and complicated model tuning offline data processing high performance online reasoning architecture design heterogeneous computing and online algorithm be born multiple processes and challenges hindering the frontier multimodal retrieval technologies fall to the ground and pratt amp whitney DMetaSoul aims at the above technical pain points abstracting and uniting many links such as model training optimization online reasoning and algorithm experiment forming a set of solutions that can quickly apply offline pre training model to online This paper will introduce how to use the HuggingFace community pre training model to conduct online reasoning and algorithm experiments based on MetaSpore technology ecology so that the benefits of the pre training model can be fully released to the specific business or industry and small and medium sized enterprises And we will give the text search text and text search graph two multimodal retrieval demonstration examples for your reference Multimodal semantic retrievalThe sample architecture of multimodal retrieval is as follows Our multimodal retrieval system supports both text search and text search application scenarios including offline processing model reasoning online services and other core modules Offline processing including offline data processing processes for different application scenarios of text search and text search including model tuning model export data index database construction data push etc Model inference After the offline model training we deployed our NLP and CV large models based on the MetaSpore Serving framework MetaSpore Serving helps us conveniently perform online inference elastic scheduling load balancing and resource scheduling in heterogeneous environments Online services Based on MetaSpore s online algorithm application framework MetaSpore has a complete set of reusable online search services including Front end retrieval UI multimodal data preprocessing vector recall and sorting algorithm AB experimental framework etc MetaSpore also supports text search by text and image scene search by text and can be migrated to other application scenarios at a low cost The HuggingFace open source community has provided several excellent baseline models for similar multimodal retrieval problems which are often the starting point for actual optimization in the industry MetaSpore also uses the pre training model of the HuggingFace community in its online services of searching words by words and images by words Searching words by words is based on the semantic similarity model of the question and answer field optimized by MetaSpore and searching images by words is based on the community pre training model These community open source pre training models are exported to the general ONNX format and loaded into MetaSpore Serving for online reasoning The following sections will provide a detailed description of the model export and online retrieval algorithm services The reasoning part of the model is standardized SAAS services with low coupling with the business Interested readers can refer to my previous post The design concept of MetaSpore a new generation of the one stop machine learning platform Offline ProcessingOffline processing mainly involves the export and loading of online models and index building and pushing of the document library You can follow the step by step instructions below to complete the offline processing of text search and image search and see how the offline pre training model achieves reasoning at MetaSpore Search text by textTraditional text retrieval systems are based on literal matching algorithms such as BM Due to users diverse query words a semantic gap between query words and documents is often encountered For example users misspell iPhone as Phone and search terms are incredibly long such as months old baby autumn small size bag pants Traditional text retrieval systems will use spelling correction synonym expansion search terms rewriting and other means to alleviate the semantic gap but fundamentally fail to solve this problem Only when the retrieval system fully understands users query terms and documents can it meet users retrieval demands at the semantic level With the continuous progress of pre training and representational learning technology some commercial search engines continue to integrate semantic vector retrieval methods based on symbolic learning into the retrieval ecology Semantic retrieval modelThis paper introduces a set of semantic vector retrieval applications MetaSpore built a set of semantic retrieval systems based on encyclopedia question and answer data MetaSpore adopted the Sentence Bert model as the semantic vector representation model which fine tunes the twin tower BERT in supervised or unsupervised ways to make the model more suitable for retrieval tasks The model structure is as follows The query Doc symmetric two tower model is used in text search and question and answer retrieval The vector representation of online Query and offline DOC share the same vector representation model so it is necessary to ensure the consistency of the offline DOC library building model and online Query inference model The case uses MetaSpore s text representation model Sbert Chinese QMC domain V optimized in the open source semantically similar data set This model will express the question and answer data as a vector in offline database construction The user query will be expressed as a vector by this model in online retrieval ensuring that query doc in the same semantic space users semantic retrieval demands can be guaranteed by vector similarity metric calculation Since the text presentation model does vector encoding for Query online we need to export the model for use by the online service Go to the q amp A data library code directory and export the model concerning the documentation In the script Pytorch Tracing is used to export the model The models are exported to the export directory The exported models are mainly ONNX models used for wired reasoning Tokenizer and related configuration files The exported models are loaded into MetaSpore Serving by the online Serving system described below for model reasoning Since the exported model will be copied to the cloud storage you need to configure related variables in env sh Build library based on text search The retrieval database is built on the million level encyclopedia question and answer data set According to the description document you need to download the data and complete the database construction The question and answer data will be coded as a vector by the offline model and then the database construction data will be pushed to the service component The whole process of database construction is described as follows Preprocessing converting the original data into a more general JSonline format for database construction Build index use the same model as online sbert Chinese qmc domain v to index documents one document object per line Push inverted vector and forward document field data to each component server The following is an example of the database data format After offline database construction is completed various data are pushed to corresponding service components such as Milvus storing vector representation of documents and MongoDB storing summary information of documents Online retrieval algorithm services will use these service components to obtain relevant data Search by textText and images are easy for humans to relate semantically but difficult for machines First of all from the perspective of data form the text is the discrete ID type of one dimensional data based on words and words At the same time images are continuous two dimensional or three dimensional data Secondly the text is a subjective creation of human beings and its expressive ability is vibrant including various turning points metaphors and other expressions while images are machine representations of the objective world In short bridging the semantic gap between text and image data is much more complex than searching text by text The traditional text search image retrieval technology generally relies on the external text description data of the image or the nearest neighbor retrieval technology and carries out the retrieval through the image associated text which in essence degrades the problem to text search However it will also face many issues such as obtaining the associated text of pictures and whether the accuracy of text search by text is high enough The depth model has gradually evolved from single mode to multi mode in recent years Taking the open source project of OpenAI CLIP as an example train the model through the massive image and text data of the Internet and map the text and image data into the same semantic space making it possible to implement the text and image search technology based on semantic vector CLIP graphic modelThe text search pictures introduced in this paper are implemented based on semantic vector retrieval and the CLIP pre training model is used as the two tower retrieval architecture Because the CLIP model has trained the semantic alignment of the twin towers text and image side models on the massive graphic and text data it is particularly suitable for the text search graph scene The model structure is as follows Due to the different image and text data forms the Query Doc asymmetric twin towers model is used for text search image retrieval The image side model of the twin towers is used for offline database construction and the text side model is used for the online return In the final online retrieval the database data of the image side model will be searched after the text side model encodes Query and the CLIP pre training model guarantees the semantic correlation between images and texts The model can draw the graphic pairs closer in vector space by pre training on a large amount of visual data Here we need to export the text side model for online MetaSpore Serving inference Since the retrieval scene is based on Chinese the CLIP model supporting Chinese understanding is selected The exported content includes the ONNX model used for online reasoning and Tokenizer similar to the text search MetaSpore Serving can load model reasoning through the exported content Build library on Image searchYou need to download the Unsplash Lite library data and complete the construction according to the instructions The whole process of database construction is described as follows Preprocessing specify the image directory and then generate a more general JSOnline file for library construction Build index use OpenAI Clip Vit BASE Patch pre training model to index the gallery and output one document object for each line of index data Push inverted vector and forward document field data to each component server Like text search after offline database construction relevant data will be pushed to service components called by online retrieval algorithm services to obtain relevant data Online Services The overall online service architecture diagram is as follows Multi mode search online service system supports application scenarios such as text search and text search The whole online service consists of the following parts Query preprocessing service encapsulate preprocessing logic including text image etc of pre training model and provide services through gRPC interface Retrieval algorithm service the whole algorithm processing link includes AB experiment tangent flow configuration MetaSpore Serving call vector recall sorting document summary etc User entry service provides a Web UI interface for users to debug and track down problems in the retrieval service From a user request perspective these services form invocation dependencies from back to front so to build up a multimodal sample you need to run each service from front to back first Before doing this remember to export the offline model put it online and build the library first This article will introduce the various parts of the online service system and make the whole service system step by step according to the following guidance See the ReadME at the end of this article for more details Query preprocessing serviceDeep learning models tend to be based on tensors but NLP CV models often have a preprocessing part that translates raw text and images into tensors that deep learning models can accept For example NLP class models often have a pre tokenizer to transform text data of string type into discrete tensor data CV class models also have similar processing logic to complete the cropping scaling transformation and other processing of input images through preprocessing On the one hand considering that this part of preprocessing logic is decoupled from tensor reasoning of the depth model on the other hand the reason of the depth model has an independent technical system based on ONNX so MetaSpore disassembled this part of preprocessing logic NLP pretreatment Tokenizer has been integrated into the Query pretreatment service MetaSpore dismantlement with a relatively general convention Users only need to provide preprocessing logic files to realize the loading and prediction interface and export the necessary data and configuration files loaded into the preprocessing service Subsequent CV preprocessing logic will also be integrated in this manner The preprocessing service currently provides the gRPC interface invocation externally and is dependent on the Query preprocessing QP module in the retrieval algorithm service After the user request reaches the retrieval algorithm service it will be forwarded to the service to complete the data preprocessing and continue the subsequent processing The ReadMe provides details on how the preprocessing service is started how the preprocessing model exported offline to cloud storage enters the service and how to debug the service To further improve the efficiency and stability of model reasoning MetaSpore Serving implements a Python preprocessing submodule So MetaSpore can provide gRPC services through user specified preprocessor py complete Tokenizer or CV related preprocessing in NLP and translate requests into a Tensor that deep models can handle Finally the model inference is carried out by MetaSpore Serving subsequent sub modules Presented here on the lot code Retrieval algorithm servicesRetrieval algorithm service is the core of the whole online service system which is responsible for the triage of experiments the assembly of algorithm chains such as preprocessing recall sorting and the invocation of dependent component services The whole retrieval algorithm service is developed based on the Java Spring framework and supports multi mode retrieval scenarios of text search and text search graph Due to good internal abstraction and modular design it has high flexibility and can be migrated to similar application scenarios at a low cost Here s a quick guide to configuring the environment to set up the retrieval algorithm service See ReadME for more details Install dependent components Use Maven to install the online Serving componentSearch for service configurations Copy the template configuration file and replace the MongoDB Milvus and other configurations based on the development production environment Install and configure Consul Consul allows you to synchronize the search service configuration in real time including cutting the flow of experiments recall parameters and sorting parameters The project s configuration file shows the current configuration parameters of text search and text search The parameter modelName in the stage of pretreatment and recall is the corresponding model exported in offline processing Start the service Once the above configuration is complete the retrieval service can be started from the entry script Once the service is started you can test it For example for a user with userId who wants to query How to renew ID card access the text search service User Entry ServiceConsidering that the retrieval algorithm service is in the form of the API interface it is difficult to locate and trace the problem especially for the text search image scene can intuitively display the retrieval results to facilitate the iterative optimization of the retrieval algorithm This paper provides a lightweight Web UI interface for text search and image search a search input box and results in a display page for users Developed by Flask the service can be easily integrated with other retrieval applications The service calls the retrieval algorithm service and displays the returned results on the page It s also easy to install and start the service Once you re done go to to see if the search UI service is working correctly See the ReadME at the end of this article for details Multimodal system demonstrationThe multimodal retrieval service can be started when offline processing and online service environment configuration have been completed following the above instructions Examples of textual searches are shown below Enter the entry of the text search map application enter cat first and you can see that the first three digits of the returned result are cats If you add a color constraint to cat to retrieve black cat you can see that it does return a black cat Further strengthen the constraint on the search term change it to black cat on the bed and return results containing pictures of a black cat climbing on the bed The cat can still be found through the text search system after the color and scene modification in the above example ConclusionThe cutting edge pre training technology can bridge the semantic gap between different modes and the HuggingFace community can greatly reduce the cost for developers to use the pre training model Combined with the technological ecology of MetaSpore online reasoning and online microservices provided by DMetaSpore the pre training model is no longer mere offline dabbling Instead it can truly achieve end to end implementation from cutting edge technology to industrial scenarios fully releasing the dividends of the pre training large model In the future DMetaSoul will continue to improve and optimize the MetaSpore technology ecosystem More automated and wider access to HuggingFace community ecology MetaSpore will soon release a common model rollout mechanism to make HuggingFace ecologically accessible and will later integrate preprocessing services into online services Multi mode retrieval offline algorithm optimization For multimodal retrieval scenarios MetaSpore will continuously iteratively optimize offline algorithm components including text recall sort model graphic recall sort model etc to improve the accuracy and efficiency of the retrieval algorithm For related code and reference documentation in this article please visit Some images source	2022-05-11 15:15:36
海外TECH	DEV Community	Join LiveChat Incubator and bring your product idea to life!	https://dev.to/livechat/join-livechat-incubator-and-bring-your-product-idea-to-life-4a7g	Join LiveChat Incubator and bring your product idea to life Hello dev community After years of breaking communication barriers and developing successful products Livechat has decided to do something entirely different Even when possessing a huge amount of talent we can only focus on so many projects at the same time We have come to the conclusion that our resources can serve not only us as a company but anyone who thinks the way we do and shares our vision So I m thrilled to announce the beginning of LiveChat Incubator We are looking for projects that have the potential to break down communication barriers between people and businesses and help both parties express themselves in a more personal manner While we remain in the business communication industry it doesn t have to be a text based solution like ours We are looking for technology driven products with the potential for scalability rather than online services If you have an idea for great innovation or you are already developing a promising product we are here to help you And by help we mean more than money but if you join us we will take care of that too In LiveChat Incubator you can count on our Knowledge We are a company of veterans After years of operating in the business communication industry with multiple successes and failures we ve gathered experience that will allow you to avoid the errors that many startups make You will learn from our mistakes Technology Our products are built on a scalable infrastructure that serves thousands of companies to communicate with millions of consumers Every single day You will be able to use the company s Developer Platform with access to Messaging APIs website widgets and other building blocks for modern communication tools available for web and mobile We will also offer you access to comprehensive technological facilities ーIT tools and infrastructure including platforms such as IBM Amazon or Cloudflare and specialist knowledge You will be able to use it freely Distribution Channels ️What is the purpose of creating a great product if no one knows about it Getting in front of customers is the struggle of every startup In LiveChat Incubator we ll support you with our communication channels and know how allowing you to grow your user base quicker than you think giving you access to our paying customers You will also be able to use our Marketplace and get in touch with developers and of our partners and affiliates We will talk about you Loudly An Ecosystem of Products Do you have an idea for a product complementary to one of ours It sounds like destiny We will support you in integrating it into our ecosystem and help leverage and maximize the potential of your project Let s build together Check out more info on our site or you can ask questions directly in the thread below	2022-05-11 15:07:38
海外TECH	DEV Community	Introducing Audium - A "Micro-Podcast" social web app	https://dev.to/dhravya/introducing-audium-a-micro-podcast-social-web-app-18a9	Introducing Audium A quot Micro Podcast quot social web app Overview of My Submission auDiumA simple barebones Micro Podcast web appThis was made as a submission for the Appwrite x Dev Hackathon InspirationShort content is blowing up nowadays with the rise of social media that provide short form content and popular websites making a shift from long to short it s time to make short podcasts This was inspired by Twitter which is considered a micro blogging platform It s a simple way to share your thoughts ideas and stories auDium is a simple web app that does exactly that but with audio It s like spotify twitter Submission Category Web Wizards Link to Code Dhravya audium app A simple barebones Micro Podcast social web app auDiumA simple barebones Micro Podcast web appThis was made as a submission for the Appwrite x Dev HackathonInspirationShort content is blowing up nowadays with the rise of social media that provide short form content and popular websites making a shift from long to short it s time to make short podcasts This was inspired by Twitter which is considered a micro blogging platform It s a simple way to share your thoughts ideas and stories auDium is a simple web app that does exactly that but with audio It s like spotify twitter Screenshots️Built withWhere Appwrite helps Database All the posts are stored in an appwrite database instance Appwrite makes databases very easy with it s python SDKStorage All the audio files are stored in the appwrite storage instance Authentication however is provided by Auth which provides really powerful and easy… View on GitHub Additional Resources InfoFor the purpose of this hackathon the app is very barebones It s not meant to be used as a full fledged social media yet but I ll be constantly working on new features and improvements So for now you can consider it as a proof of concept that worksAlso the app is not hosted because I don t have the funds to host Appwrite on my own and this projects needs quite a bit of storage too which I don t have So for now you can only use it in the development environment on your local machine Screenshots	2022-05-11 15:02:30
海外TECH	DEV Community	Getting started with Markdoc in Next.js	https://dev.to/stripe/getting-started-with-markdoc-in-nextjs-ioj	Getting started with Markdoc in Next jsStripe is open sourcing Markdoc the Markdown based authoring system that powers the Stripe documentation website Whether it s for a simple static site authoring tooling or a full blown documentation website Markdoc is designed to grow with you so you can create engaging experiences no matter how large or complex your documentation becomes Let s have a look at how to get started The rest of this tutorial is implemented in Next js using create next app InstallationTo start using Markdoc you first need to install it with npm install markdoc markdoc saveoryarn add markdoc markdoc saveAs this sample app is using Next js you also need to install the Next js plugin npm install markdoc next js saveFinally add the following lines to the next config js file const withMarkdoc require markdoc next js module exports withMarkdoc pageExtensions js md mdoc These lines allow you to use js md and mdoc files These additions to the config file enable you to write your docs in either JavaScript or Markdown file formats If you would like to use Markdown files with another framework than Next js a plugin will be required Now that you re all set up let s write some content Using MarkdocTo get started locate the pages folder that s automatically generated when spinning up a Next js app with create next app and create a new index md file inside it Markdoc syntax is a superset of Markdown specifically the CommonMark specification so in this file you can write content using this syntax Some title A subtitleThis is a paragraph with a link to your awesome website Markdoc is extensible so you can also use variables functions and create custom tags For these you have to define your content in JavaScript and use Markdoc transform and Markdoc renderers react to render everything Let s look into how to do that VariablesVariables are defined in a config object and can then be used in your content Here s what a complete code sample could look like before we break it down and explain the different pieces config jsexport const config variables user name Justice Ketanji Brown Jackson Page jsimport React from react import Markdoc from markdoc markdoc import config from config js const Page gt const doc Hello user name const content Markdoc transform doc config return lt section gt Markdoc renderers react content React lt section gt export default Page For example if you wanted to display a user s name you would declare a config object like this const config variables user name Justice Ketanji Brown Jackson In Markdoc to reference the variables in your config prepend the variable name with a dollarSign For example you would refer to the user s name like this const content Hello user name Don t forget to prepend the variable with a otherwise it will be interpreted as a tag Then you need to pass these two variables in Markdoc transform and render your content using Markdoc renderers react const content Markdoc transform doc config return lt section gt Markdoc renderers react content React lt section gt Using variables is a powerful feature for instance if you want to display dynamic data such as a user s API key An example of such a feature can be found on the Stripe documentation website Customizing stylesMarkdoc introduces the concept of annotations to allow you to style different nodes which are elements Markdoc inherits from Markdown For example you can add IDs and classes with the following syntax index md My title custom id Another title custom class name here You can then refer to these in your CSS to apply styles styles css custom id color purple custom class name here color darkblue This would generate the following HTML lt h id custom id gt My title lt h gt lt h class custom class name here gt Another title lt h gt And render the content shown below Some style related attributes can also be applied using annotations Function width Example align right TagsMarkdoc comes with four built in tags and also lets you build your own For example you can try the if tag that lets you conditionally render content const config variables tags featureFlagEnabled true const document if tags featureFlagEnabled Here s some secret content if You can build your own tags in three steps Let s go through an example with a custom banner component First you need to start by creating the React component that you want to render The code for a small banner component could look like this Banner jsconst Banner type children gt return lt section className banner type gt children lt style jsx gt alert border px solid red lt style gt lt section gt export default Banner This component will accept a type prop to indicate if the banner should be styled as an alert info warning banner etc The children prop represents the content that will be passed inside that tag in the Markdown file later on To be able to use this component as a tag in a Markdown file first create a “markdoc folder at the root of your Next js app and a “tags js file inside it that will contain all your custom tags This app s architecture would end up looking like this components Banner jsmarkdoc tags jspages index mdInside your custom tag file here tags js you need to import your React component and export a variable containing the component you want to display You would also include any attributes you want to use In this case the type of banner When declaring the attributes you need to specify their data type markdoc tags jsimport Banner from components Banner export const banner Component Banner attributes type type String The final step is to use this custom tag in your Markdown content like this banner type alert This is an alert banner banner If you create a custom tag that does not accept any children you can write it as a self closing tag banner Syntax validationAdditionally Markdoc lets you validate the abstract syntax tree AST generated before rendering If you consider the Banner component written above you can use it as a tag when writing your content in JavaScript and check for any syntax error before rendering For example if a banner tag is used without a type attribute that is required you can implement some error handling to avoid rendering broken content This syntax validation can be implemented with a single line using Markdoc validate const config tags banner const content banner Example banner with a syntax error banner const ast Markdoc parse content const errors Markdoc validate ast config Handle errorsIn this case the error returned will look like this FunctionsYou can extend Markdoc with custom utilities by writing your own functions For example if you wanted to add a way to transform your content to uppercase you would start by creating a file inside your markdoc folder for example functions js In this file add the following helper function markdoc functions jsexport const uppercase transform parameters const string parameters return typeof string string string toUpperCase string Then import this function in the component that needs it and pass it in your config object import uppercase from markdoc functions const config functions uppercase Call it in your content within tags const document uppercase Hello World And finally call Markdoc transform and use the React renderer to render everything const doc Markdoc transform document config Markdoc renderers react doc React So the complete syntax for a small component would look like this config jsexport const config functions uppercase functions jsexport const uppercase transform parameters const string parameters return typeof string string string toUpperCase string Page jsimport Markdoc from markdoc markdoc import config from config js import uppercase from functions js const Page gt const document uppercase Hello World const doc Markdoc transform document config return lt section gt Markdoc renderers react doc React lt section gt export default Page Some built in functions are available such as equals to display some content if two variables are equal not and or to use in an if statement to perform boolean operations default that returns the second parameter passed if the first returns undefined and debug that serializes the values as JSON to help you while debugging ResourcesIf you re interested in learning more here are some useful resources you can check out and if you re interested we welcome your contributions to the repository We can t wait to see what you re going to build with Markdoc and don t hesitate to let us know what you think Official Markdoc documentationMarkdoc repositoryMarkdoc Next js plugin repositoryMarkdoc playgroundNext js boilerplate demo Stay connectedIn addition you can stay up to date with Stripe in a few ways Follow us on TwitterJoin the official Discord serverSubscribe to our Youtube channelSign up for the Dev Digest	2022-05-11 15:00:37
Apple	AppleInsider - Frontpage News	Apple boosts trade-in credit until May 31 - a week after it cut them	https://appleinsider.com/articles/22/05/11/apple-boosts-trade-in-credit-until-may-31---a-week-after-it-cut-them?utm_medium=rss	Apple boosts trade in credit until May a week after it cut themOn the heels of a near across the board trade in price cut Apple has increased trade in values by to for select iPhone iPad Mac and Apple Watch models in the US and UK until May Apple boosts trade in credit for select devices until May The extra trade in credit may range from to for iPhone to for iPad for Mac and to for Apple Watch Apple doesn t offer any additional credit for Android trade in or other Apple devices Read more	2022-05-11 15:51:00
Apple	AppleInsider - Frontpage News	Apple sells out of all 256GB iPod touch colors	https://appleinsider.com/articles/22/05/11/apple-sells-out-of-all-256gb-ipod-touch-colors?utm_medium=rss	Apple sells out of all GB iPod touch colorsA day after announcing the iPod touch would remain on sale only while stocks last the GB versions of every color model have already sold out The venerable iPod touch was discontinued on May and by May stocks online were running out At time of writing it is no longer possible to buy a GB iPod touch in any of its six different colors Certain other configurations have also sold out on the online Apple Store though it s possible some may yet remain in physical stores Read more	2022-05-11 15:07:29
海外TECH	Engadget	'Apex Legends Mobile' arrives on May 17th	https://www.engadget.com/apex-legends-mobile-may-17-155111815.html?src=rss	x Apex Legends Mobile x arrives on May thThe wait is over After a year of regional betas and a delay to the game s initially planned limited launch Respawn Entertainment announced on Wednesday it will release Apex Legends Mobile on May th At that point anyone who wants to check out the battle royale can do so on Android and iOS Fans can pre register to receive a notification as soon as Apex Legends Mobile is available to download And by signing up for a download prompt Respawn says you ll be helping the community unlock special rewards nbsp nbsp nbsp So close Just out of reach Apex Legends Mobile is launching May Help the community unlock the pre registration rewards Android pre register now iOS sign up here for pre reg updates pic twitter com aLpyXwRAnーApex Legends Mobile PlayApexMobile May EA first shared it was bringing Apex to mobile in The Android and iOS release does not feature cross play support with the PC and console versions of the popular first person shooter At launch Apex Legends Mobile will also include a smaller pool of playable characters but one will feature one exclusive legend nbsp	2022-05-11 15:51:11
海外TECH	Engadget	Airbnb's big redesign helps you split stays between homes	https://www.engadget.com/airbnb-2022-redesign-spilt-stays-insurance-150439751.html?src=rss	Airbnb x s big redesign helps you split stays between homesNow that it s relatively safe to travel again Airbnb is unveiling an overhauled experience that includes some much needed features for frequent travellers To start the reworked app now lets you search for categories tied to the home style location or activity You can look for places close to national parks or even book a treehouse Airbnb is clearly hoping you ll book stays beyond the usual tourist hotspots The biggest addition however might be Split Stays As the name implies this lets you split a trip between two homes without having to separately book each location You can split between categories or destinations too This promises to be helpful for long trips or any travel where you won t stay in one place And while Airbnb no longer offers refunds if you get COVID it is providing some extra protections to encourage travel A new AirCover program will either relocate you or provide a refund if the host needs to cancel within days if you can t check in or if the home doesn t match the advertised claims The hour safety phone line has also expanded support to languages and AirCover is built directly into both the app and Airbnb s website The reworked experience is available in the US today and should spread worldwide this week Airbnb may be stretching when it claims this is its largest change quot in a decade quot but it might be enough to prompt a vacation after two years of huddling in place	2022-05-11 15:04:39
Cisco	Cisco Blog	Latest Innovations in Cisco DNA Software for Wireless	https://blogs.cisco.com/networking/latest-innovations-in-cisco-dna-software-for-wireless	Latest Innovations in Cisco DNA Software for WirelessBy deploying the latest innovations in Cisco DNA Advantage software for Wireless along with Cisco DNA Center you can provide your workforce with improved wireless stability performance and security	2022-05-11 15:00:58
海外科学	NYT > Science	Spinal Fluid From Young Mice Sharpened Memories of Older Rodents	https://www.nytimes.com/2022/05/11/science/mice-aging-memory-spinal-fluid.html	Spinal Fluid From Young Mice Sharpened Memories of Older RodentsResearchers identified a protein in the fluid that could boost the cognition of aging animals ーand might lead to future treatments for people	2022-05-11 15:05:38
海外TECH	WIRED	The EU Wants Big Tech to Scan Your Private Chats for Child Abuse	https://www.wired.com/story/europe-csam-scanning-law-chat-encryption	protection	2022-05-11 15:45:20
金融	RSS FILE - 日本証券業協会	株主コミュニティの統計情報・取扱状況	https://www.jsda.or.jp/shiryoshitsu/toukei/kabucommunity/index.html	株主コミュニティ	2022-05-11 15:30:00
金融	金融庁ホームページ	職員を募集しています。（金融庁における国際関連業務に従事する職員（課長補佐級））	https://www.fsa.go.jp/common/recruit/r4/soumu-01/soumu-01.html	課長補佐	2022-05-11 16:00:00
金融	金融庁ホームページ	職員を募集しています。（金融モニタリング業務に従事する職員（弁護士））	https://www.fsa.go.jp/common/recruit/r3/souri-20/souri-20.html	Detail Nothing	2022-05-11 16:00:00
ニュース	BBC News - Home	Maddie Thomas: Police suspect missing Bristol girl has been abducted	https://www.bbc.co.uk/news/uk-england-bristol-61411978?at_medium=RSS&at_campaign=KARANGA	april	2022-05-11 15:41:53
ニュース	BBC News - Home	EuroMillions: UK ticket holder claims £184m jackpot	https://www.bbc.co.uk/news/uk-61402635?at_medium=RSS&at_campaign=KARANGA	holder	2022-05-11 15:42:26
ニュース	BBC News - Home	Ministers pledge votes on neighbours' extensions, but leave question mark over housing target	https://www.bbc.co.uk/news/uk-politics-61400935?at_medium=RSS&at_campaign=KARANGA	Ministers pledge votes on neighbours x extensions but leave question mark over housing targetMichael Gove proposes fresh planning rules in England but leaves a question mark over his party s new homes target	2022-05-11 15:46:23
ニュース	BBC News - Home	Al Jazeera reporter killed during Israeli raid in West Bank	https://www.bbc.co.uk/news/world-middle-east-61403320?at_medium=RSS&at_campaign=KARANGA	shireen	2022-05-11 15:07:50
ニュース	BBC News - Home	Sri Lanka crisis: Ex-PM flees to naval base as arson attacks spread	https://www.bbc.co.uk/news/world-asia-61403510?at_medium=RSS&at_campaign=KARANGA	crisis	2022-05-11 15:33:09
ニュース	BBC News - Home	Adidas sports bra adverts banned over bare breasts	https://www.bbc.co.uk/news/business-61413184?at_medium=RSS&at_campaign=KARANGA	parts	2022-05-11 15:35:23
ニュース	BBC News - Home	Rob Edwards: Watford appoint former Forest Green boss as manager for 2022-23	https://www.bbc.co.uk/sport/football/61413324?at_medium=RSS&at_campaign=KARANGA	championship	2022-05-11 15:46:07
ニュース	BBC News - Home	Giro d'Italia: France's Arnaud Demare wins thrilling sprint finish to take stage five	https://www.bbc.co.uk/sport/cycling/61410185?at_medium=RSS&at_campaign=KARANGA	Giro d x Italia France x s Arnaud Demare wins thrilling sprint finish to take stage fiveMark Cavendish falls away as France s Arnaud Demare wins stage five of the Giro d Italia in a thrilling sprint finish	2022-05-11 15:19:39
ニュース	BBC News - Home	Ukraine war: Snake Island and battle for control in Black Sea	https://www.bbc.co.uk/news/world-europe-61406808?at_medium=RSS&at_campaign=KARANGA	importance	2022-05-11 15:03:14
北海道	北海道新聞	知床遊覧船事故対策検討委員会メンバー	https://www.hokkaido-np.co.jp/article/679633/	五十音順	2022-05-12 00:32:29
北海道	北海道新聞	米、台湾海峡の安定協議へ　ＡＳＥＡＮ首脳と	https://www.hokkaido-np.co.jp/article/679654/	asean	2022-05-12 00:29:00
北海道	北海道新聞	収賄容疑で病院元課長逮捕　国立病院機構運営、警視庁	https://www.hokkaido-np.co.jp/article/679653/	下志津病院	2022-05-12 00:29:00
北海道	北海道新聞	政府、対ロシア新規投資禁止　制裁発動、最大手銀凍結	https://www.hokkaido-np.co.jp/article/679652/	経済制裁	2022-05-12 00:23:00
北海道	北海道新聞	ＦＩＦＡ、エクアドルの調査開始　Ｗ杯予選、無資格選手の起用疑惑	https://www.hokkaido-np.co.jp/article/679651/	国際サッカー連盟	2022-05-12 00:17:00
北海道	北海道新聞	ネイマールら６月の日本戦へ　ブラジル代表２７人発表	https://www.hokkaido-np.co.jp/article/679650/	日本代表	2022-05-12 00:17:00
北海道	北海道新聞	安全運航へ抜本策探る　意識の向上策も焦点　知床事故の有識者委初会合	https://www.hokkaido-np.co.jp/article/679636/	kazui	2022-05-12 00:09:39
仮想通貨	BITPRESS（ビットプレス）	[Bloomberg] 暗号資産交換業者、顧客に不利な取引手掛ける－米SEC委員長	https://bitpress.jp/count2/3_9_13197	bloomberg	2022-05-12 00:08:36