投稿時間:2021-11-30 07:26:55 RSSフィード2021-11-30 07:00 分まとめ(27件)

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TECH	Engadget Japanese	TwitterのCEOジャック・ドーシーが(再び)辞任。後任は現CTO	https://japanese.engadget.com/twitter-dorsey-resign-210806114.html	pragagrawal	2021-11-29 21:08:06
Google	カグア！Google Analytics 活用塾:事例や使い方	ポッドキャストの収益化＝企業案件や協賛をもらう方法と事例とは。音声読み上げのホストリード広告と企業案件エピソードを配信する人気ポッドキャスト番組がすでにあります。	https://www.kagua.biz/marke/podcast/20211130a1.html	音声	2021-11-29 21:00:35
Program	[全てのタグ]の新着質問一覧｜teratail（テラテイル）	vim編集中に特定のwindowの特定の文字に下線が引かれる原因がわからない	https://teratail.com/questions/371569?rss=all	setcursorline	2021-11-30 06:40:26
技術ブログ	Developers.IO	[新サービス]大幅に使いやすくなりECRの脆弱性診断にも対応したAmazon Inspector v2が発表されました！ #reinvent	https://dev.classmethod.jp/articles/amazon-inspector-v2-released/	amazoninspector	2021-11-29 21:39:35
技術ブログ	Developers.IO	CloudWatch にクライアントアプリのモニタリング機能 CloudWatch Realtime User Monitoring(RUM) が追加されたので試してみた #reinvent	https://dev.classmethod.jp/articles/cloudwatch-rum-overview/	cloudwatch	2021-11-29 21:37:34
海外TECH	Ars Technica	Google Play apps downloaded 300,000 times stole bank credentials	https://arstechnica.com/?p=1816768	malicious	2021-11-29 21:25:36
海外TECH	MakeUseOf	How to Wrap Text in Excel	https://www.makeuseof.com/how-to-wrap-text-in-excel/	excelwrapping	2021-11-29 21:30:12
海外TECH	DEV Community	The definitive guide to Accuracy, Precision, and Recall for product developers	https://dev.to/mage_ai/the-definitive-guide-to-accuracy-precision-and-recall-for-product-developers-4ahg	The definitive guide to Accuracy Precision and Recall for product developers TLDRAccuracy tells you how many times the ML model was correct overall Precision is how good the model is at predicting a specific category Recall tells you how many times the model was able to detect a specific category OutlineIntroductionAccuracyPrecisionRecallHow to determine if your model is doing wellWhen to use precision or recallConclusion IntroductionMost machine learning ML problems fit into groups classification and regression The main metrics used to assess performance of classification models are accuracy precision and recall Source Final FantasyTo demonstrate each of these metrics we ll use the following example We re a mage on a quest to save the villagers from a horde of monsters There are monsters attacking the village We re going to use machine learning to predict what element each monster is weak against There are possible elements that monsters are weak against fire water and wind AccuracyAfter summoning our machine learning model we approach the village and begin the battle After hours of intense combat we are victorious and the villagers are all saved We sit down by the fire have a few refreshments e g boba from the local village tea shop and review how the battle unfolded Magic vs MonstersFor every monster we fought our model predicted an element for us to use that would make the monster the weakest possible Out of predictions our model predicted the correct element times Therefore the accuracy of our model is correct predictions out of predictions made PrecisionMeasuring our model s accuracy told us overall how many times we were correct when predicting a monster s weakness However if we want to know how many times we were correct when predicting a monster was weak against fire we need to use the precision metric Source PokemonLet s say we predicted monsters were weak against fire At the end of the battle we saw that of those monsters of them were actually weak against water and of them were actually weak against wind That means our precision for predicting fire is In other words we predicted fire weaknesses correctly out of fire predictions Source NarutoAfter further review of the battle we predicted monsters were weak against water and monsters were weak against wind Out of the monsters who we predicted were weak against water we were correct times resulting in precision Out of the monsters who we predicted were weak against wind we were correct times resulting in precision The following chart is called a confusion matrix The left y axis is what was predicted The top x axis is what the monster was truly weak against Look at row and column It s saying that out of the monsters that the model predicted a fire weakness for of those monsters were actually weak against water For more information on how to read a confusion matrix check out this article Here are helpful images Source Anuganti SureshSource NillsF blog RecallNow that we know how many times our model predicted correctly overall and how good our model is at predicting a specific weakness e g fire water wind it s time to find out how good our model is at finding monsters with a particular weakness Source Avatar The Last AirbenderFor all the monsters who were actually weak against wind there are of them how many did our model find out Our model found monsters who were weak against wind That means our model has recall when predicting monsters who are actually weak against wind How to determine if your model is doing wellBefore evaluating how good a model is doing we must establish a baseline score A baseline score is a value that our model should perform better than For example if we have a baseline score of accuracy and our model achieved an accuracy greater than that e g then the model is good enough to use in real life scenarios e g production environment There are many ways to establish a baseline score for your model In an upcoming tutorial we ll show you multiple other methods that can be used For our monster weakness prediction model we ll use a baseline score method called mode category This method takes the most abundant weakness category and divides that by the number of predictions Specifically more monsters are weak against wind than any other element only monsters are weak against fire and only monsters are weak against water Our baseline accuracy score will be e g monsters weak against wind divided by monsters in total Source BoratOur model had a accuracy score That is well above the baseline score We can conclude that our model is performing well Note you may want to have different baseline scores and use different metrics other than accuracy e g precision recall etc to assess whether your model performed well or not Depending on your use case precision or recall may be more important When to use precision or recallOn our quest to save the village from monsters our goal was to defeat all the monsters If we can predict their weakness we have a higher chance of victory Therefore we want a model with the highest accuracy in other words a model with the highest chance of predicting the weakness correctly However there are scenarios where you may want a model with higher precision For example you may have a fire spell that drains a lot of your energy and you want to use it sparingly In that case you ll want a model that has high precision when predicting a monster is weak against fire That way when you use your fire spell you know with high certainty that the monster is weak against it Source LifehackThe scenario in which you want to choose a model with high recall is when the predicted outcome leads to a critical decision For example let s say we had a model that predicts whether a villager has been poisoned by a monster and needs immediate medical treatment We want a model that can find every villager that is poisoned even if it incorrectly labels a healthy villager as needing medical treatment The reason why we want this model to have high recall is because it s better to give a healthy person unnecessary treatment than to accidentally pass over someone who was poisoned and needs treatment In other words better safe than sorry ConclusionAccuracy precision and recall mattersAccuracy precision and recall are used to measure the performance of a classification machine learning model there are other metrics for regression models read more here The metrics alone aren t enough to determine if your model is usable in real life scenarios You must establish a baseline score and compare your model s performance against that baseline score In a future tutorial we ll show you how to assess a regression machine learning model a model predicting a continuous numerical value e g housing prices forecasting sales customer lifetime value etc	2021-11-29 21:21:28
海外TECH	DEV Community	Exploring Google Analytics Realtime Data with Python	https://dev.to/martinheinz/exploring-google-analytics-realtime-data-with-python-2n9n	Exploring Google Analytics Realtime Data with PythonGoogle Analytics can provide a lot of insight into traffic and about users visiting your website A lot of this data is available in nice format in web console but what if you wanted to build your own diagrams and visualizations process the data further or just generally work with it programmatically That s where Google Analytics API can help you and in this article we will look at how you can use it to query and process realtime analytics data with Python Exploring The APIBefore jumping into using some specific Google API it might be a good idea to first play around with some of them Using Google s API explorer you can find out which API will be most useful for you and it will also help you determine which API to enable in Google Cloud console We will start with Real Time Reporting API as we re interested in realtime analytics data whose API explorer is available here To find other interesting APIs check out the reporting landing page from where you can navigate to other APIs and their explorers For this specific API to work we need to provide at least values ids and metrics First of them is so called table ID which is the ID of your analytics profile To find it go to your analytics dashboard click Admin in bottom left then choose View Settings where you will find the ID in View ID field For this API you need to provide the ID formatted as ga lt TABLE ID gt The other value you will need is a metric You can choose one from metrics columns here For the realtime API you will want either rt activeUsers or rt pageviews With those values set we can click execute and explore the data If the data looks good and you determine that this is the API you need then it s time enable it and set up the project for it Setting UpTo be able to access the API we will need to first create a project in Google Cloud To do that head over to Cloud Resource Manager and click on Create Project Alternatively you can do it also via CLI with gcloud projects create PROJECT ID After a few seconds you will see new project in the list Next we need to enable the API for this project You can find all the available APIs in API Library The one we re interested in Google Analytics Reporting API can be found here API is now ready to be used but we need credentials to access it There are couple different types of credentials based on the type of application Most of them are suited for application that require user consent such as client side or Android iOS apps The one that is for our use case querying data and processing locally is using service accounts To create a service account go to credentials page click Create Credentials and choose Service Account Give it some name and make note of service account ID second field we ll need it in a second Click Create and Continue no need to give service account accesses or permissions Next on the Service Account page choose your newly created service account and go to Keys tab Click Add Key and Create New Key Choose JSON format and download it Make sure to store it securely as it can be used to access your project in Google Cloud account With that done we now have project with API enabled and service account with credentials to access it programmatically This service account however doesn t have access to your Google Analytics view so it cannot query your data To fix this you need to add the previously mentioned service account ID XXXX some project name iam gserviceaccount com as user in Google Analytics with Read amp Analyse access a guide for adding users can be found here Finally we need to install Python client libraries to use the APIs We need of them one for authentication and one for the actual Google APIs pip install google auth oauthlibpip install google api python client Basic QueriesWith all that out of the way let s write our first query import osfrom googleapiclient discovery import buildfrom google oauth import service accountKEY PATH os getenv SA KEY PATH path to secrets json TABLE ID os getenv TABLE ID credentials service account Credentials from service account file KEY PATH scoped credentials credentials with scopes with build analytics v credentials credentials as service realtime data service data realtime get ids f ga TABLE ID metrics rt pageviews dimensions rt pagePath execute print realtime data We begin by authenticating to the API using the JSON credentials for our service account downloaded earlier and limiting the scope of the credentials only to the read only analytics API After that we build a service which is used to query the API the build function takes name of the API it s version and previously created credentials object If you want to access different API then see this list for the available names and versions Finally we can query the API we set ids metrics and optionally dimensions as we did with API explorer earlier You might be wondering where did I find the methods of service object data realtime get they re all documented here And when we run the code above the print will show us something like this trimmed for readability query ids ga lt TABLE ID gt dimensions rt pagePath metrics rt pageviews profileInfo profileName All Web Site Data totalsForAllResults rt pageviews rows blog blog blog blog blog That works but considering that the result is dictionary you will probably want to access individual fields of the result print realtime data profileInfo profileName All Web Site Dataprint realtime data query metrics rt pageviews print realtime data query dimensions rt pagePathprint realtime data totalResults The previous example shows usage of the realtime method of the API but there are more we can make use of First of them is ga with build analytics v credentials credentials as service ga data service data ga get ids f ga TABLE ID metrics ga sessions dimensions ga country start date yesterday end date today execute print ga data totalsForAllResults ga sessions rows Angola Argentina This method returns historical non realtime data from Google Analytics and also has more arguments that can be used for specifying time range sampling level segments etc This API also has additional required fields start date and end date You probably also noticed that the metrics and dimensions for this method are a bit different that s because each API has its own set of metrics and dimensions Those are always prefixed with the name of API in this case ga instead of rt earlier The third available method mcf is for Multi Channel Funnels data which is beyond scope of this article If it sounds useful for you check out the docs One last thing to mention when it comes to basic queries is pagination If you build queries that return a lot of data you might end up exhausting your query limits and quotas or have problems processing all the data at once To avoid this you can use pagination with build analytics v credentials credentials as service ga data service data ga get ids f ga TABLE ID metrics ga sessions dimensions ga country start index max results start date yesterday end date today execute print f Items per page ga data itemsPerPage Items per page print f Total results ga data totalResults Total results These only have values if other result pages exist if ga data get previousLink print f Previous Link ga data previousLink if ga data get nextLink print f Next Link ga data nextLink Next Link lt TABLE ID gt amp dimensions ga country amp metrics ga sessions amp start date yesterday amp end date today amp start index amp max results In the above snippet we added start index and max results to force pagination This causes the previousLink and nextLink to get populated which can be used to request previous and next pages respectively This however doesn t work for realtime analytics using realtime method as it lacks the needed arguments Metrics and DimensionsThe API itself is pretty simple The part that is very customizable is arguments such as metrics and dimensions So let s take a better look at all the arguments and their possible values to see how we can take full advantage of this API Starting with metrics there are most important values to choose from rt activeUsers rt pageviews and rt screenViews rt activeUsers gives you number of users currently browsing your website as well as their attributesrt pageviews tells you which pages are being viewed by usersrt screenViews same as page views but only relevant within application e g Android or iOSFor each metric a set of dimensions can be used to break down the data There s way too many of them to list here so let s instead see some combinations of metrics and dimensions that you can plug into above examples to get some interesting information about visitors of your website metrics rt activeUsers dimensions rt userType Differentiate currently active users based on whether they re new or returning metrics rt pageviews dimensions rt pagePath Current page views with breakdown by path metrics rt pageviews dimensions rt medium rt trafficType Page views with breakdown by medium e g email and traffic type e g organic metrics rt pageviews dimensions rt browser rt operatingSystem Page views with breakdown by browser and operating system metrics rt pageviews dimensions rt country rt city Page views with breakdown by country and city As you can see there s a lot of data that can be queried and because of the sheer amount it might be necessary to filter it To filter the results filters argument can be used The syntax is quite flexible and supports arithmetic and logical operators as well as regex queries Let s look at some examples rt medium ORGANIC show only page visits from organic searchrt pageviews gt show only results that have more than page viewsrt country United ga country Canada show only visits from countries starting with United UK US or Canada acts as OR operator for AND use For complete documentation on filters see this page Finally to make results a bit more readable or easier to process you can also sort them using sort argument For ascending sorting use you can use e g sort rt pagePath and for descending you will prepend e g sort rt pageTitle Beyond Realtime APIIf you can t find some data or you re missing some features in Realtime Analytics API then you can try exploring other Google Analytics APIs One of them could be Reporting API v which has some improvements over older APIs It however also has a little different approach to building queries so let s look at an example to get you started with build analyticsreporting v credentials credentials as service reports service reports batchGet body reportRequests viewId f ga TABLE ID dateRanges startDate yesterday endDate today dimensions name ga browser metrics expression ga sessions execute print reports As you can see this API doesn t provide large number of arguments that you can populate instead it has single body argument which takes request body with all the values that we ve seen previously If you want to dive deeper into this one then you should check out the samples in documentation which give complete overview of its features Closing ThoughtsEven though this article shows only usage of analytics APIs it should give you general idea for how to use all Google APIs with Python as all the APIs in client library use same general design Additionally the authentication shown earlier can be applied to any API all you need to change is the scope While this article used google api python client library Google also provides lightweight libraries for individual services and APIs at At the time of writing the specific library for analytics is still in beta and lacks documentation but when it becomes GA or more stable you should probably consider exploring it	2021-11-29 21:08:57
海外TECH	DEV Community	How to build a customer-facing roadmap with React	https://dev.to/canonic/how-to-build-a-customer-facing-roadmap-with-react-1gal	How to build a customer facing roadmap with ReactA product roadmap summarizes how a product strategy leads to the actual implementation and charts out your product s vision and direction This article will guide you through each step you need to build a roadmap tool using React for your SaaS startup Let s get started Step Installing ReactWe ll start by creating a new react project using create react app npx create react app roadmapStep Create a roadmap componentWe ll create a component Roadmap that ll contain our display and API logic src components Roadmap Create respective Roadmap js index js and Roadmap css files Add following code in respective order import React from react import Roadmap css function Roadmap const columnMap title Exploring tickets title In Progress tickets title Done tickets title Leaving it for now tickets return lt div className roadmap gt columnMap map column i gt lt div className roadmap column key column title i gt lt div className roadmap column heading gt column title lt div gt lt div className roadmap cards gt column tickets map t i gt lt div key exploring i className roadmap cards item gt lt div className roadmap cards item heading gt t title lt div gt lt div className roadmap cards item content dangerouslySetInnerHTML html t description gt lt svg xmlns x px y px viewBox style enableBackground new maxWidth px cursor pointer gt lt g gt lt g gt lt path d M Hl c lc L c v c l c hc l c v C z gt lt path d M Hc vc hc v C z gt lt g gt lt g gt lt svg gt lt div gt lt div gt lt div gt lt div gt export default Roadmap export default from Roadmap roadmap display flex width max width px min height px roadmap gt div not last child margin right px roadmap column display flex flex direction column flex background eeffc border radius px padding px px roadmap column heading color d font size px font weight padding px margin bottom px text align center roadmap cards display flex flex direction column roadmap cards gt div not last child margin bottom px roadmap cards item background fff border radius px padding px box shadow px e roadmap cards item heading font size px font weight roadmap cards item upvote display flex font size px line height px roadmap cards item upvote count margin left px roadmap cards item p font size px margin top px line height svg not filled path fill fff stroke stroke width px media max width px roadmap flex direction column roadmap gt div not last child margin right margin bottom px Now that our display component is created let s app it to App js and see how our component looks ‍Step Add component to AppWe import our component in App js and remove all the unnecessary files and code Our code App js App css and component should look like below Add following code to App js and App css respectively import React from react import Roadmap from components Roadmap import App css function App return lt div gt lt div className wrapper heading gt Roadmap lt div gt lt Roadmap gt lt div gt export default App import url wght amp display swap html body font family Roboto sans serif wrapper heading padding px text align center margin bottom px max width px color d font weight text transform uppercase letter spacing px Now run yarn start in root of our project and our component should look like this Now it s time to integrate this with a backend to fetch our roadmap tickets and display them ‍‍Step Get your APIsFollow the below stated pointers and get your APIs Cloning the sample project To get the APIs you can visit this link and click on the top right button to clone the project Deploy and get the backend URL Having cloned the project it s now time to deploy it so that we can get our APIs and backend hosted Open the cloned project if you are not already there and you ll see a Deploy button on the top right Click Deploy →Select an environment →Hit deploy Upon completing the project it will provide an API URL Checking the documentationBefore we get started with backend integration let s move to the documentation by clicking on docs on the left sidebar so that we get a better understanding of the APIs involved Backend integration with GraphQL Once you have your APIs ready we can start by installing graphql Step Install GraphQL packagesWe will need two packages for this step since we ll be using graphql to pull our data from the backend Apollo Client and GraphQL yarn add apollo client graphqlStep Configure graphql Configure the Apollo Client in the project directory inside App js so it would communicate with the backend Be sure to replace the uri with the one you ll get from Canonic import React from react import ApolloProvider InMemoryCache ApolloClient from apollo client import Roadmap from components Roadmap import App css Connecting with the backend using apollo client const client new ApolloClient make sure you update the URI here to point to your backend uri cache new InMemoryCache function App return lt ApolloProvider client client gt lt div gt lt div className wrapper heading gt Roadmap lt div gt lt Roadmap gt lt div gt lt ApolloProvider gt export default App ‍Step Querying the dataWe store our graphql queries inside a directory src gql Inside which we create a file and name it queries js This is where we will write the graphql queries for querying the data import gql from apollo client gql query to get roadmap The query parameters we got straight from Canonic autogenerated documentation export const GET ROADMAP gql query roadmaps id title description upvotes count stage value The next step is Querying data and displaying our roadmap tickets We ll execute the graphql query in the Roadmap component and display the tickets fetched according to their status We modify Roadmap js to achieve the above mentioned logic and get the tickets import React from react import useQuery from apollo client import GET ROADMAP from gql queries import Roadmap css function Roadmap const data loading useQuery GET ROADMAP const roadmaps data dividing tickets into their respective categories const exploringTickets roadmaps filter t gt t stage value EXPLORING const inProgressTickets roadmaps filter t gt t stage value IN PROGRESS const doneTickets roadmaps filter t gt t stage value DONE const leavingItForNowTickets roadmaps filter t gt t stage value LEAVING IT FOR NOW const columnMap title Exploring tickets exploringTickets title In Progress tickets inProgressTickets title Done tickets doneTickets title Leaving it for now tickets leavingItForNowTickets return lt div className roadmap gt loading lt svg xmlns style margin auto background none display block shapeRendering auto maxWidth px marginTop px width px height px viewBox preserveAspectRatio xMidYMid gt lt circle cx cy fill none stroke d strokeWidth r strokeDasharray gt lt animateTransform attributeName transform type rotate repeatCount indefinite dur s values keyTimes gt lt animateTransform gt lt circle gt lt svg gt lt gt columnMap map column i gt lt div className roadmap column key column title i gt lt div className roadmap column heading gt column title lt div gt lt div className roadmap cards gt column tickets map t i gt lt div key exploring i className roadmap cards item gt lt div className roadmap cards item heading gt t title lt div gt lt div className roadmap cards item content dangerouslySetInnerHTML html t description gt lt svg xmlns x px y px viewBox style enableBackground new maxWidth px cursor pointer gt lt g gt lt g gt lt path d M Hl c lc L c v c l c hc l c v C z gt lt path d M Hc vc hc v C z gt lt g gt lt g gt lt svg gt lt div gt lt div gt lt div gt lt gt lt div gt export default Roadmap BONUS Adding upvote to Roadmap ticketsAs a bonus let s add a feature for the users to upvote the tickets By doing so we can gain a deeper understanding of what our users are thinking and what they expect from us Find out more about the benefits of having a public roadmap here Step Mutating DataWe create a new file to store our mutations in src gql mutations js Taking a reference from auto generated documentation we can specify the mutation parameters import gql from apollo client gql query to get roadmap The query parameters we got straight from Canonic autogenerated documentation export const UPVOTE gql mutation Upvote ticketId ID createUpvote input ticket ticketId id Step Add upvote logic in the ComponentWe can now connect our mutation in Roadmap component In this case we will save the upvote not only on the backend but also in the local storage since we will not be creating a user on the backend and we want to retain the upvote information We make the following changes in Roadmap js and add the mutation logic Our final file looks like below import React from react import useQuery useMutation from apollo client import GET ROADMAP from gql queries import UPVOTE from gql mutations import Roadmap css function Roadmap const data loading useQuery GET ROADMAP const upvoteTicket useMutation UPVOTE context headers Authorization bdcfcddc ccacaf c bc ac bdefee const roadmaps data const upvotes setUpvotes React useState dividing tickets into their respective categories const exploringTickets roadmaps filter t gt t stage value EXPLORING const inProgressTickets roadmaps filter t gt t stage value IN PROGRESS const doneTickets roadmaps filter t gt t stage value DONE const leavingItForNowTickets roadmaps filter t gt t stage value LEAVING IT FOR NOW const columnMap title Exploring tickets exploringTickets title In Progress tickets inProgressTickets title Done tickets doneTickets title Leaving it for now tickets leavingItForNowTickets const handleUpvoteTicket React useCallback async id gt setUpvotes upvotes gt upvotes id localStorage setItem id true upvoteTicket variables ticketId id upvoteTicket const isTicketUpvoted React useCallback id gt upvotes find t gt t id localStorage getItem id upvotes return lt div className roadmap gt loading lt svg xmlns style margin auto background none display block shapeRendering auto maxWidth px marginTop px width px height px viewBox preserveAspectRatio xMidYMid gt lt circle cx cy fill none stroke d strokeWidth r strokeDasharray gt lt animateTransform attributeName transform type rotate repeatCount indefinite dur s values keyTimes gt lt animateTransform gt lt circle gt lt svg gt lt gt columnMap map column i gt lt div className roadmap column key column title i gt lt div className roadmap column heading gt column title lt div gt lt div className roadmap cards gt column tickets map t i gt lt div key exploring i className roadmap cards item gt lt div className roadmap cards item heading gt t title lt div gt lt div className roadmap cards item content dangerouslySetInnerHTML html t description gt lt svg xmlns x px y px viewBox style enableBackground new maxWidth px cursor pointer className isTicketUpvoted t id amp amp not filled onClick gt handleUpvoteTicket t id gt lt g gt lt g gt lt path d M Hl c lc L c v c l c hc l c v C z gt lt path d M Hc vc hc v C z gt lt g gt lt g gt lt svg gt lt div gt lt div gt lt div gt lt gt lt div gt export default Roadmap Note be sure to create access tokens for your canonic APIs for mutating the data Read more about it here And that s it you ve built the roadmap Conclusion Roadmaps for your products offer several advantages including a better understanding of the strategy and vision guidance for executing the strategy facilitation of discussion and opinions etc Follow this step to step guide and build Roadmaps for your own SaaS startups Check out the live demo link hereand you can checkout the sample code hereYou can also check out our other guides here Join us on discord to discuss or share with our community Write to us for any support requests at support canonic dev Check out our website to know more about Canonic Let us know in the comments below what you think about the guide Thank you	2021-11-29 21:01:12
Apple	AppleInsider - Frontpage News	Cyber Monday Apple deals are selling out at Amazon, grab these discounts while supplies last	https://appleinsider.com/articles/21/11/29/cyber-monday-apple-deals-are-selling-out-at-amazon-grab-these-discounts-while-supplies-last?utm_medium=rss	Cyber Monday Apple deals are selling out at Amazon grab these discounts while supplies lastWith inventory shortages affecting numerous Apple products these Amazon Apple deals offer Cyber Monday savings on units that are still available for purchase Amazon has slashed prices on Apple gear for Cyber Monday delivering AirPods Apple Pencils Apple Watch discounts triple digit Mac discounts and more Read more	2021-11-29 21:41:17
海外TECH	WIRED	The 40 Best Cyber Monday Deals at Target Today	https://www.wired.com/story/best-target-cyber-monday-deals-2021	security	2021-11-29 21:42:00
海外TECH	WIRED	A Hacking Spree Against Iran Spills Out Into the Real World	https://www.wired.com/story/hacking-iran-critical-infrastructure-israel	israel	2021-11-29 21:11:24
ニュース	BBC News - Home	Labour reshuffle: Yvette Cooper becomes shadow home secretary	https://www.bbc.co.uk/news/uk-politics-59461674?at_medium=RSS&at_campaign=KARANGA	labour	2021-11-29 21:11:02
ニュース	BBC News - Home	Ghislaine Maxwell accused of preying on young girls for Epstein to abuse	https://www.bbc.co.uk/news/world-us-canada-59455605?at_medium=RSS&at_campaign=KARANGA	british	2021-11-29 21:18:08
ニュース	BBC News - Home	Facial recognition firm faces possible £17m privacy fine	https://www.bbc.co.uk/news/business-59466803?at_medium=RSS&at_campaign=KARANGA	delete	2021-11-29 21:00:43
ニュース	BBC News - Home	Ballon d'Or: Lionel Messi wins award as best player in world football for seventh time	https://www.bbc.co.uk/sport/football/59454640?at_medium=RSS&at_campaign=KARANGA	Ballon d x Or Lionel Messi wins award as best player in world football for seventh timeParis St Germain and Argentina forward Lionel Messi wins the Ballon d Or awarded to the best footballer of the year for a seventh time	2021-11-29 21:55:59
ニュース	BBC News - Home	'It's an unfortunate reality' - Woods does not expect to return full-time	https://www.bbc.co.uk/sport/golf/59469217?at_medium=RSS&at_campaign=KARANGA	february	2021-11-29 21:05:19
ニュース	BBC News - Home	Barcelona's Putellas wins Women's Ballon d'Or	https://www.bbc.co.uk/sport/football/59468815?at_medium=RSS&at_campaign=KARANGA	female	2021-11-29 21:50:51
ビジネス	電通報 | 広告業界動向とマーケティングのコラム・ニュース	DOOHの新潮流～ヘアサロン・サイネージ編	https://dentsu-ho.com/articles/7987	doohdigitaloutofhome	2021-11-30 06:25:02
LifeHuck	ライフハッカー［日本版］	「オンライン営業」にこれから必要となる5つの営業スキルとは？	https://www.lifehacker.jp/2021/11/246937book_to_read-887.html	株式会社	2021-11-30 07:00:00
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北海道	北海道新聞	熱海土石流、遺族へ補償検討　起点所有者「努力」主張か	https://www.hokkaido-np.co.jp/article/617121/	静岡県熱海市	2021-11-30 06:13:00
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北海道	北海道新聞	世界の核被害者フォーラム開催へ　「締約国会議の出発点に」	https://www.hokkaido-np.co.jp/article/617119/	非政府組織	2021-11-30 06:03:00
ビジネス	東洋経済オンライン	ANAが黒字目標の｢未達｣を悔やまない意外な理由 国内線は急回復､ローンで手元資金は潤沢に | エアライン・航空機 | 東洋経済オンライン	https://toyokeizai.net/articles/-/471876?utm_source=rss&utm_medium=http&utm_campaign=link_back	手元資金	2021-11-30 06:30:00
マーケティング	MarkeZine	良い会社の「免罪符」ではない「Bコーポレーション」が打ち出す、ベネフィットの概念	http://markezine.jp/article/detail/37823	bcorporation	2021-11-30 06:30:00