投稿時間:2021-09-11 00:34:35 RSSフィード2021-09-11 00:00 分まとめ(40件)

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Program	[全てのタグ]の新着質問一覧｜teratail（テラテイル）	[Rust][thread_local]スマートポインタについて助言がほしい。	https://teratail.com/questions/358858?rss=all	Rustthreadlocalスマートポインタについて助言がほしい。	2021-09-10 23:45:12
Program	[全てのタグ]の新着質問一覧｜teratail（テラテイル）	Unity 選手の保存とソートを行う方法	https://teratail.com/questions/358857?rss=all	その際に、選手の情報を保存したいと思っています。	2021-09-10 23:42:47
Program	[全てのタグ]の新着質問一覧｜teratail（テラテイル）	(++count)による同一のカウント数値を使いまわしたいです	https://teratail.com/questions/358856?rss=all		2021-09-10 23:42:18
Program	[全てのタグ]の新着質問一覧｜teratail（テラテイル）	グラフに合わせて位置を変えて文字を表示したい	https://teratail.com/questions/358855?rss=all	グラフに合わせて位置を変えて文字を表示したい前提・実現したいことグラフのすぐ右横にpropsで受け取った数字を出力する事を実現したいです。	2021-09-10 23:41:55
Program	[全てのタグ]の新着質問一覧｜teratail（テラテイル）	cloudfrontの設定について	https://teratail.com/questions/358854?rss=all	cloudfront	2021-09-10 23:41:14
Program	[全てのタグ]の新着質問一覧｜teratail（テラテイル）	Manifest merger failed with multiple errors, see logsのエラーの解決法を知りたい	https://teratail.com/questions/358853?rss=all	Manifestmergerfailedwithmultipleerrorsseelogsのエラーの解決法を知りたい発生している問題・エラーこちらの記事を参考に写経し、AndroidnbspStudioでビルドしたところ、Manifestnbspmergernbspfailednbspwithnbspmultiplenbsperrorsnbspseenbsplogsというエラーが発生しました。	2021-09-10 23:35:10
Program	[全てのタグ]の新着質問一覧｜teratail（テラテイル）	Node.js + Socket.IO のxhr-polling が不安定	https://teratail.com/questions/358852?rss=all	iFilterは、websocketを遮断してしまうので、xhrpollingで運用しているのですが、以下のようなエラーが出て、頻繁に接続が切断されます。	2021-09-10 23:29:48
Program	[全てのタグ]の新着質問一覧｜teratail（テラテイル）	サーブレットで文字化けが発生する	https://teratail.com/questions/358851?rss=all	しかし、そのように記述したうえでも文字化けが起きてしまいます。	2021-09-10 23:19:20
Program	[全てのタグ]の新着質問一覧｜teratail（テラテイル）	'Sequential' object has no attribute 'predict_classes'　Pythonエラーの解決	https://teratail.com/questions/358850?rss=all	xSequentialxobjecthasnoattributexpredictclassesxPythonエラーの解決Pythonで機械学習のコードを書いているのですが、下記のエラーが発生しており、うまく実行できません。	2021-09-10 23:01:53
Program	[全てのタグ]の新着質問一覧｜teratail（テラテイル）	C# paiza上でのエラーメッセージの解決	https://teratail.com/questions/358849?rss=all	Cpaiza上でのエラーメッセージの解決前提・実現したいことpaizaラーニングの問題を解いています。	2021-09-10 23:00:51
Ruby	Rubyタグが付けられた新着投稿 - Qiita	Rails Tutorialで詰まったところのまとめ　第１章	https://qiita.com/dmcocoro/items/bf3a8df1361b257355fd		2021-09-10 23:15:44
Ruby	Rubyタグが付けられた新着投稿 - Qiita	【初心者向け】Ruby on Railsのform_withを分かりやすく書き直してみる	https://qiita.com/kobo_q/items/d3f002f9c9e8706100df	引数は普通なら順番通りに入れないとだめですが、挿入先を指定することで順番を意識することなく引数に値を入れることができるようになります。	2021-09-10 23:02:15
AWS	AWSタグが付けられた新着投稿 - Qiita	Amazon CloudWatch LogsのデータをS3にExportする時にハマった	https://qiita.com/tomokon/items/df16913996bbeedfb35c	AmazonCloudWatchLogsのデータをSにExportする時にハマったはじめにAWSのログサービスである「AmazonCloudWatchLogs」内のテキストログをダウンロードするためにSにエクスポートしようとした際に、しょーもないことでハマったので共有します。	2021-09-10 23:27:49
Docker	dockerタグが付けられた新着投稿 - Qiita	M1 Mac + Dockerの環境でTensorflowを使う	https://qiita.com/tsukushibito/items/a5384e920c8ce6cc99fd	まとめAArch環境でTensorflowをビルドする場合は以下の事項に注意ビルド環境に合わせてBazelのビルドオプションを指定するlocalramresourcesだけでなくjobsも使うnumpyのバージョンはTensorflowのバージョンに合わせたものをインストールしないといけないhpyはaptgetでインストールした上で、pipのnobinaryでインストールビルドできてしまえば大したことないのですが、結構大変でした。	2021-09-10 23:06:19
Ruby	Railsタグが付けられた新着投稿 - Qiita	Rails Tutorialで詰まったところのまとめ　第１章	https://qiita.com/dmcocoro/items/bf3a8df1361b257355fd		2021-09-10 23:15:44
技術ブログ	Developers.IO	[GitHub] Pull RequestのBaseブランチが自動で変更されるようになっていた	https://dev.classmethod.jp/articles/base-branch-of-pull-request-was-supposed-to-be-changed-automatically-in-github/	develop	2021-09-10 14:56:01
海外TECH	DEV Community	System Design- Tinder | Cost to develop | How to earn revenue	https://dev.to/mukulalpha/system-design-tinder-cost-to-develop-how-to-earn-revenue-4mno	System Design Tinder Cost to develop How to earn revenueIn this article we ll look at the design system architecture of dating apps like Tinder Bumble Happen OkCupid Hinge How much does it cost to develop a dating app like Tinder To get started let s understand the app and the features that we will talk about in this article This article mainly focuses on the architecture of Tinder Tinder s technology may look simple but when a user left or Right swipe in the app and a match is found But Behind the scenes however a giant infrastructure consisting of thousands of services and terabytes of data supports each and every swipe and match on the platform What is Tinder As the modern generation of young people embrace the dating lifestyle the online dating segment continues to grow It requires a special technological spark to cultivate the feeling of using such dating apps and achieve long term use With the transformation of dating apps Tinder is a good example of how dating apps can be transformed into a highly profitable business The Tinder dating app is different from all other types of dating apps because it has a high matching conversion rate Because of its user friendly features it is a hot topic among the younger generation You can select your communication partner by swiping When this sliding is mutual communication opens a two way channel Hence developing a dating app like Tinder is the same thing as developing a relationship and it takes time and investment to make it unique The uniqueness of the tinder applies to the geographical proximity as the key aspect and makes it for individuals smoother to produce with other individuals of their preferences You can simply view the possible matches and left NO or right yes initiates the process The app analyzes user data with geographic locations mutual friends or similar interests The users must call their age and gender to the individual with which they use a tinder to inform the other users with the same interests in their environment In Short Tinder is a social dating mobile app designed by UX UI designers that allows individuals to search and find the right person to connect with the right person through assistance Support for chat options is consolidated within the app Sign In to Tinder app requires Facebook login so location and interests are used to find the right person Users can make their profile look exciting by adding extra details and descriptions to their profile SCALE at which TINDER operates million members million active users Billion matches Million downloads billion swipes every day left swipe right swipe super like Language supportDesign needs to be scalable to support million userbases Tinder supports more than languages which means that users are spread all over the world Hence this cannot be a simple application hosted on a single continent but needs to be well distributed to provide the best performance to all users worldwide Tinder is entirely hosted in the AWS Cloud There are no web applications other than IOS and Android Tinder uses AWS amplify to develop and test mobile applications MongoDB for DB and Redis for caching and in memory databases Features Login using OAuth Facebook The method of logging in via Facebook or phone number is very simple The dating app algorithm extracts basic user information from Facebook skipping the same old methods of filling out forms and creating personal profiles Swipes Left Right Swipe may be a distinctive sense resolution This feature is designed to improve the method of finding dates Swiping to the right means you like it and swiping to the left means you don t like it The swipe option interface makes online dating clearer and more exciting Matching The users would be obligated to acknowledge if they need some match for the appliance This shall support them to approach different users whom they like Also pairs facilitate in monitoring that the two users have joined to each other with their agreement Also the feature includes un matching that the user isn t any longer fascinated by communicating This supports system in avoiding negative spamming stalking etc chat In order to talk to each other candidates need to establish a means of communication with one another Basics would be to possess a to chat messenger where they will send text messages audio call or video call through the application push notification The users are notified on a real time basis just in case the person is found nearby their set criteria super likes The user can swipe up or send a heart or a rose different application provide different methods of super like to other profile to prioritize them in the selection queue Recommendation EngineAn engine which provides several hundreds thousands of profile when a person logs into the Tinder let s talk about the features of the recommendation algorithm that tinder is using Tag Collecting When a person performs OAuth using FB Tinder collects a lot of important information like location age distance gender preferences places they ve visited likes dislikes etc It also extracts a lot of information from photos and what we write in our profile to better match Cluster User Base when a person enters logs in to Tinder they get a random point from Tinder and based on that point they fall into some basket let s say we have a basket from to this grouping helps to select these people people in basket prefer more match people from buckets and This is mainly due to the high probability of matching based on your likes and people who have similar tastes Active Use Tinder s main goal is to connect people establish meaningful relationships so if one of the parties is inactive it doesn t add up to Tinder s main goal Therefore it is important to know how actively the person is using the app Your pickiness Bad actors If one is doing too much of right swipe it s bad you may not be shown recommendation of other people Also if one is not doing left swipe at all still one is not gonna shown in the recommendation of others as they are not contributing towards the objective of this dating application Do you reply How willingly a person is replying after a match If the user don t engage in longer conversation or messages are not exchanged than those profiles are penalized and not shown in recommendation of other people Progressive taxation If one is getting too much of matches attention to make it fair for others Tinder normalizes this by not showing that profile to many other users At the same time if someone is not getting much attention tinder starts bringing that profile to other users Recommendation Engine properties This recommendation engine brings up the profile of other people based on the above mentioned points Low latency When a person logs in to the application we need to load profiles potential matches profiles real quickly Therefore our Recommendation Engine needs to have low latency able to load profile faster Not real time It s okay if it s not real time ie if someone newly joins tinder it s okay if it takes some time to show this person s profile on other accounts Easy to shard distributed Since we have tons of profiles from across the globe this recommendation engine should be able to shard the data as we can t keep it in one system Full text search we need to search through the whole profile of an individual considering different parameters location age distance gender preferences to provide better recommendations HTTP interface or web socket to get the data and send it to the application Structure data XML JSONWhat Tinder uses for storing and searching through data is “Elastic search which is basically a search system Initially tinder was started with one cluster and couple of shards but after gaining popularity they did distributed system Elasticsearch is able to achieve fast search responses because instead of searching the text directly it searches an index instead Additionally it supports full text search which is completely based on documents instead of tables or schemas Data are clustered for a given location The whole point of dating apps is to meet people in real If I am a user from location X India I will obviously like to get a match with someone who is from location X km depends of users preference So how to achieve this How to shard data to make elastic search queries faster Shard the data by geographical location We here are dividing the whole world map into small boxes We can place each server in these boxes to serve any requests originating from these boxes ie particular lat log within that box will get served by servers in that location Ideally these servers can be at any physical location but for each of these boxes cells there is one designated server Now there are certain boxes where the population is high there one server won t be able to serve all the requests So how can we divide the world into boxes and distribute the load across our servers The size of the boxes in different areas is determined by Unique user count active user count and query count from these regions These points decides the size of the box cell We have to find a balance score on the basis of the above factors to get the optimal size of the box cell for which we use Google s library to save these cells and see the latency performance for that area Whenever a person wants to open tinder his phone makes a query to a system This system is basically a mapper system which based on the lat log of the user gives information to the application user that all of your data is stored on which server This server is the server where users information lies as well as this can be the server where user s potential matches lies As mentioned before servers can be in any physical location but all the data belongs to that particular cell will reside on that one server Now consider this above map let s concentrate on cells and Information belongs to there cells will be store on ser ser ser ser and ser So if a Tinder user is residing at cell and has set range as km i e user want to know all potential matches within km range from user s location The radius of km includes all these cells from cell to cell Mapper will know to query data from all the cells which rely in km range and gather recommendationThat s how recommendation works on Geosharding Recommendation Engine As soon as the new user sign in to the tinder app using FB OAuth his profile details go to the ES feeder service using HTTP WebSocket A copy will be store in DB also by user creation service which adds it to the persistence and another copy to the elastic search as we need a fast search for the recommendation Kafka consumes these messages as need to index these data asynchronously ES workers pick up the message and send it to the location to the cell mapper which uses the s library and has lat long information It returns the shard to which this information was written The ES Worker then notifies the ES and uses the ES API to write the information to that particular shard User information is now saved in Elastic search and he is now ready to do left right swipe Then it calls the recommendation engine and which in turn call to the location to cell mapper again with lat log and it returns multiple shards to which it makes parallel calls to Shards and gets couples of documents profile and send them via HTTP web sockets Now all the profiles are being rendered to the user and he s ready for left right swipe MATCHMAKING Let s consider that there are two profiles X and YThere can be three situations possible X and Y right swipe each other at the same time X does right swipe to Y and Y doesn t Y does right swipe X and X doesn t until now There are millions of matches that occur every day We can have one matching service one cell or We can group couple of cells together with one matchmaking service so there will be couple of matchmaking service up and running there will be lots of queries for recommendation queries so to balance out queries per location and each matchmaking service belongs to couple of cells instead of just one cell as was in case of geosharding Match also works in the same manner Match won t happen between countries It will happen in the cell where a profile is recommended to a user For eg if we recommend profiles to user chances are there will be on an average swipes so we don t need one matchmaking service per cell Whenever a user do the right swipe a message is send to the matchmaking service preferably by web socket where the location manager determines to which shard or matchmaking service this message will go and redirects message to the gateway which connects to Kafka The message is now in the queue Depending on the number of shards we have got as a result form location manager service there will be one or many matchmaking services to which this information will be broadcasted to Information captured here is who is right swiping whom location and other metadata There can be parallel workers which keep reading message coming from the Kafka queue If X happens to right swipe Y then an entry like “X Y enters into Redis and leaves it as it is Now when Y right swipe X then again the same process happens match worker picks the message and checks in Redis weather “X has ever right swiped Y i e we will definitely find key “X Y and check for the metadata which means a match has happened and message will enter in the matched queue which gets picked by match notification and through web socket sends it to both X and Y saying “It s a match If for some reason X has never right swiped Y then what will happen Then just a record “Y X will enter into Redis and that s it when X right swipe back Y then before adding the key it will check for the key Passport Feature When a user moves from one Region location to another could be travelling or moving to different places This could be happening with in the city state or country When user open the app from new location a request is send to the server and with the help of the location mapper Data of the user from previous location cell s shard if transferred to new Location cell s shard User login profile for tinderWe already know the ES stores user info that is already geosharded why don t we just have one more API expose from ES to provide specific user profile info The only optimization we can do is to have one more layer of cache in form of ES so that we can have better performance We can store user related info in a database as well We can have RDBMS as we won t have too many of records also it needs to be geosharded so if geosharding is taken care of we can have our details in RDBMS We can also link order table info with the user table We can also opt for NoSQL as it s auto sharding it automatically scales itself We can go with MongoDB as well as it provides ACID property and sharding by geo How to enable user login A user can log in using FB OAuth by registering our application in FB API We can get lots of information like places user has ever visited likes dislikes close friends etc As Tinder wants to build relationship app we need to have legitimate profile and decide should we really need to show this profile to other or not We don t need to implement sessions in here Since we are trying to write an app in native android or apple SDK we don t need to have sessions all we need to maintain is authentication token MONITORING Without monitoring we don t know what s happening with our system and also to check system performance and SLA compliance One such tool is Prometheus which provides features like altering write queries and also stores time series data It can be used to monitor the application collect logs and monitor system s performance All the user events get forwarded to Kafka which then gets read by Prometheus where we write aggregators to identify latency in any geoshard for eg Suddenly our app will get trending by one tweet and lots of users start login in traffic increase in that geo shard ーASG All these information gets captured in dashboard Kafka is like an event sink where we can push any kind of data which internally has lots of topics and we can read it at Prometheus The same system can leverage to consume other logs which generated by other application and these files get read by filebeat or logstash and get forwards to Kafka and can use the same system to track system performance Filebeat is a lightweight shipper for forwarding and centralizing log data Installed as an agent on your servers Filebeat monitors the log files or locations that you specify collects log events and forwards them either to Elasticsearch or Logstash for indexing Content moderation Constantly keeping eye on content For eg one can use celeb pictures or write bad status what if everyone is doing this and tinder is not suppressing this then engagement goes down Therefore moderating content is important How to achieve this Every action performed by an user is an event like user updates the picture updates the status or does a left right swipe these event needs to get pushed in event sink and get stored in persistence There we need to use some technology like map reduce or Kafka streams or spark to get the useful info from event run Machine Learning algorithm on recent changes to check if the profile pic is user s profile pic or is copied using celeb pic No swipe only right swipe We need to detect all these event we also need to keep an eye on the rate at which the user is doing the right swipe whether he s really reading it or blindly doing the right swipe How to make an app like Tinder When researching how to make a dating app like Tinder you need to understand its exciting features In addition to the simple registration and geolocation functions there are also some exciting features such as rewind options super likes unlimited rights swipes and change location according to subscription plans There are some other additional features such as in app purchases social media integration prevention of abuse and uncensored content anonymous users and matching suggestions using advanced matching formulas How much money do dating apps make If you are thinking ー“how to make money on a dating app then you have to look at the options below In app purchases In app purchase is a regular way of earning profits employed by different types of apps by offering additional features The same trend has been employed by tinder to offer additional features like icons emoji etc This trend has been successful across mobile apps of different niches Features can only be unlocked when users pay the fixed amount and subscribe to the plan Subscriptions This is an option to give a scheduled trial period for users and charge a fixed subscription fee after the completion of the trial period for using the services This is a highly used method for earning through the apps Tinder Plus It is helpful for users who want to subscribe to the freemium model that offers extra options Tinder app allows the users to start free for certain periods and then charges for using its extra features like a change of location unlimited swipes etc The subscription plans are divided into two parts namely Tinder Plus and Tinder basic Tinder plus consists of all the exciting features that will make the users excited once they know the usefulness of the app If you are looking to build a similar dating app like tinder then it is wiser to follow the same trend and make all the exciting features chargeable after a scheduled trial period Tinder has also differentiated the costs depending on the age of the user Tinder gold This plan is exclusively for new members of the app and varies according to the age group of the users Sponsored content Many business enthusiasts have partnered with Tinder to present their sponsored content to the users Profile Lift Tinder asks for a fixed amount from the users to boost the profiles to the top that will enhance the profile views along with some additional features Ads Ads are given to third party agencies for marketing on the apps This will be useful for other business owners to show their products or services while giving you good profits How much does it cost to make a dating app The cost of developing a dating app significantly depends upon important factors like app size app platform Native or Cross platform design elements level of functionality enclosed in it Incorporating the fundamental above features may decrease your investment value however incorporating further functionalities could need additional funds and development costsIn this article we ve discussed the design architecture of Tinder with Recommendation Engine Geosharding Matchmaking Engine User profile login and content moderation Factors to keep in mind while developing a dating app and ways to earn revenue…Stay connected for more content like this	2021-09-10 14:08:51
Apple	AppleInsider - Frontpage News	iPhone 13 event preview, 2022 'iPhone 14' leaked, CSAM delayed on the AppleInsider podcast	https://appleinsider.com/articles/21/09/10/iphone-13-event-preview-2022-iphone-14-leaked-csam-delayed-on-the-appleinsider-podcast?utm_medium=rss	iPhone event preview x iPhone x leaked CSAM delayed on the AppleInsider podcastOn this week s episode of the AppleInsider Podcast Apple announces its September event for iPhone Apple Watch Series and renders of the iPhone are leaked Apple officially announced its first Fall event taking place on September at a m Pacific We expect to see the launch of Apple s iPhone lineup in addition to a redesigned Apple Watch Series New features coming to the iPhone line may include an always on Hz ProMotion display and camera upgrades Rumored health features such as glucose monitoring are not expected to launch with this year s Apple Watch but the physical redesign will sport a new chassis with flat edges and a slightly larger display Read more	2021-09-10 14:23:23
Apple	AppleInsider - Frontpage News	Apple's iPhone 12, 'iPhone 13' to command a third of global 5G shipments	https://appleinsider.com/articles/21/09/10/apples-iphone-12-iphone-13-to-command-a-third-of-global-5g-shipments?utm_medium=rss	Apple x s iPhone x iPhone x to command a third of global G shipmentsThe forthcoming iPhone together with the existing iPhone will increase Apple s total share of G smartphone sales to in claims new research Apple introduced G to all models in the iPhone rangeCounterpoint Research previously said that the iPhone was the best selling G smartphone in October its first month of release now expects strong sales for the iPhone Between the two models Apple is predicted to sell almost million of the G phones Read more	2021-09-10 14:14:46
Apple	AppleInsider - Frontpage News	Apple thought low-cost TV dongle would ruin its premium reputation	https://appleinsider.com/articles/21/09/10/apple-thought-low-cost-tv-dongle-would-ruin-its-premium-reputation?utm_medium=rss	Apple thought low cost TV dongle would ruin its premium reputationA new report has detailed some of the inner workings of Apple TV including how the company shelved plans to build a low cost dongle because it didn t want to tarnish its premium reputation Credit AppleOn Friday The Information published a deep dive into Apple s streaming service and the company s plan for growth Alongside the company s ramp up the report also revealed behind the scenes details about Apple TV Read more	2021-09-10 14:02:42
Apple	AppleInsider - Frontpage News	China not issuing new game licenses, may impact App Store earnings	https://appleinsider.com/articles/21/09/09/china-not-issuing-new-game-licenses-may-impact-app-store-earnings?utm_medium=rss	China not issuing new game licenses may impact App Store earningsRepeating a scenario that impacted iOS developers and Apple itself three years ago China is again not issuing licenses for online game releases including those hosted on the App Store App StoreFollowing China s restriction in the number of hours children can play video games the country has reportedly now stopped approving the release of new games Read more	2021-09-10 14:07:15
Apple	AppleInsider - Frontpage News	Sell your used Apple device, get a 10% cash bonus ahead of the iPhone 13	https://appleinsider.com/articles/21/09/08/sell-your-used-apple-device-get-a-10-cash-bonus-ahead-of-the-iphone-13?utm_medium=rss	Sell your used Apple device get a cash bonus ahead of the iPhone Lock in the best trade in value for your used iPhone ahead of the September California Streaming event with an exclusive cash bonus at leading buyback providers Exclusive iPhone trade in dealsWith less than a week to go ahead of the expected launch of the iPhone and Apple Watch Series and perhaps even new iPads and AirPods now is the time to lock in the best price for your used devices Read more	2021-09-10 14:21:10
海外TECH	Engadget	Automakers dial up the wattage on the future of EVs at Munich's auto show	https://www.engadget.com/iaa-mobility-2021-munich-auto-show-video-143031508.html?src=rss	Automakers dial up the wattage on the future of EVs at Munich x s auto showAfter over a year of canceled auto shows due to the pandemic Munich s IAA Mobility auto show is the first big opportunity for automakers to display their upcoming vehicles to the masses Companies including Mercedes Benz BMW Volkswagen and Porsche dropped new electric concepts and even showed off some production vehicles Engadget has been in Germany this week and here are four of our favorite new models Mercedes Benz EQE sedanRight out of the gate Mercedes took no time to unveil its latest electric sedan the EQE While the EQS is the top of the luxury heap the EQE gives potential buyers the opportunity to slide into Mercedes luxury without dropping as much cash The EQE shares many of the same design elements and features as the more expensive EQS including the optional inch Hyperscreen and rear wheel drive Mercedes Benz EQG electric conceptNot stopping at sedans Mercedes also unveiled a near production concept version of the iconic G Class aka G Wagon The EQG has the same distinctive look of the rugged expensive offroader but with a whole lot of lighting flourishes nbsp How many of those will make it to production is unknown and there s also no word on what it ll cost and how long its range will be But if Mercedes can pull off with the G Wagon what it did with the EQS then the future of offroading ーor driving around while shopping for high end clothing ーwill be electrified BMW i Vision CIrcular Concept EVBMW already has two electric production vehicles the i and iX headed to the US In Munich it took the opportunity to unveil the i Vision Circular concept a vehicle that s more about the future of manufacturing than it is about the future of driving Built entirely out of recycled materials the Circular is manufactured from mono materials that are easy to recycle and take apart Porsche Mission R electric concept race carFor those excited about motorsports and the future of sports cars the Porsche Mission R concept race car gave fans of the German brand a peek at what the future has in store The all wheel drive vehicle uses a volt architecture that supports DC fast charging at up to kW and its battery is tuned for intense track days rather than cruising around town While it s currently a concept the technologies found in the Mission R will likely make their way to the motorsports division and potentially into a future Cayman electric	2021-09-10 14:30:31
海外TECH	Engadget	New 'Metroid Dread' trailer is full of things for Samus to fight	https://www.engadget.com/metroid-dread-overview-trailer-enemies-weapons-abilities-amiibo-141001527.html?src=rss	New x Metroid Dread x trailer is full of things for Samus to fightThe long awaited next entry in the Metroid series is just a few weeks away and Nintendo has offered another taste of what s in store for Samus with a new overview trailer The five minute Metroid Dread clip delves into Samus abilities the enemies she ll face and more The trailer takes time to explain the moves Samus has in her tool belt including the slide mechanic her jumping prowess and a melee counter which should come in very handy as long as you get the timing right Unlockable abilities include targeting missiles flash shift a dash move a tether to pull large obstacles out of the way and a way to morph into a ball to help Samus squeeze through narrow passages Hunter robots called EMMI will pursue Samus if they hear or see her If they catch Samus it s game over Your best bet is to use her abilities to avoid detection since you can t damage them with regular weapons The clip offers a look at some of the many many other enemy types Samus will face on planet ZDR too The trailer also shows how Metroid Dread Amiibo will work if you re fortunate enough to get your hands on them If you tap Samus against your Joy Con you ll gain an extra energy tank that increases your energy reserves Use the EMMI Amiibo instead and you ll get another missile tank to boost missile capacity by You can use each of these once per day to replenish resources Metroid Dread arrives on Nintendo Switch on October th It s the first mainline Metroid game since Metroid Fusion all the way back in Not much longer to wait now though	2021-09-10 14:10:01
金融	RSS FILE - 日本証券業協会	公社債発行額・償還額等	https://www.jsda.or.jp/shiryoshitsu/toukei/hakkou/index.html	発行	2021-09-10 15:00:00
金融	金融庁ホームページ	審判期日の予定を更新しました。	https://www.fsa.go.jp/policy/kachoukin/06.html	期日	2021-09-10 16:00:00
金融	金融庁ホームページ	第一商品（株）における有価証券報告書の虚偽記載に対する課徴金納付命令の決定について公表しました。	https://www.fsa.go.jp/news/r3/shouken/20210910.html	有価証券報告書	2021-09-10 16:00:00
ニュース	BBC News - Home	MI5: 31 late-stage terror plots foiled in four years in UK	https://www.bbc.co.uk/news/uk-58512901?at_medium=RSS&at_campaign=KARANGA	security	2021-09-10 14:26:08
ニュース	BBC News - Home	Lucy-Anne Rushton murder detective jailed for witness signature forgery	https://www.bbc.co.uk/news/uk-england-hampshire-58516654?at_medium=RSS&at_campaign=KARANGA	woman	2021-09-10 14:44:04
ニュース	BBC News - Home	Benjamin Mendy: Manchester City footballer in court on rape charges	https://www.bbc.co.uk/news/uk-england-manchester-58516300?at_medium=RSS&at_campaign=KARANGA	cheshire	2021-09-10 14:12:28
ニュース	BBC News - Home	Sturgeon: Comedian's tweet apology was 'dignified'	https://www.bbc.co.uk/news/uk-scotland-scotland-politics-58513670?at_medium=RSS&at_campaign=KARANGA	tweets	2021-09-10 14:56:29
ニュース	BBC News - Home	Wallace and Gromit: Nick Park unveils comedy duo's Preston statue	https://www.bbc.co.uk/news/uk-england-lancashire-58516735?at_medium=RSS&at_campaign=KARANGA	comedy	2021-09-10 14:47:37
ニュース	BBC News - Home	Was it right to cancel the fifth Test? And what happens now?	https://www.bbc.co.uk/sport/cricket/58517891?at_medium=RSS&at_campaign=KARANGA	Was it right to cancel the fifth Test And what happens now After the England India series finale is called off BBC Sport asks whether the Indian Premier League is to blame and what it means for the Ashes	2021-09-10 14:41:18
ニュース	BBC News - Home	'Rafiq was victim of racial harassment & bullying' - Yorkshire release summary of report findings	https://www.bbc.co.uk/sport/cricket/58514665?at_medium=RSS&at_campaign=KARANGA	x Rafiq was victim of racial harassment amp bullying x Yorkshire release summary of report findingsAzeem Rafiq was the victim of racial harassment and bullying according to the findings of a report released by his former club Yorkshire	2021-09-10 14:02:29
ニュース	BBC News - Home	Man Utd players have 'no place to hide' since Ronaldo arrival - Solskjaer	https://www.bbc.co.uk/sport/football/58512713?at_medium=RSS&at_campaign=KARANGA	Man Utd players have x no place to hide x since Ronaldo arrival SolskjaerManchester United boss Ole Gunnar Solskjaer says there is no place to hide for his players following the return of Cristiano Ronaldo	2021-09-10 14:32:20
ニュース	BBC News - Home	Covid: Will I need a jab and what will university be like this term?	https://www.bbc.co.uk/news/explainers-52753913?at_medium=RSS&at_campaign=KARANGA	covid	2021-09-10 14:20:55
LifeHuck	ライフハッカー［日本版］	今ならセールで20％オフ！ 10000mAhで最大25W出力のAnker新型モバイルバッテリー	https://www.lifehacker.jp/2021/09/amazon-anker-powercore10000pdredux25w.html	今ならセールでオフmAhで最大W出力のAnker新型モバイルバッテリーAnkerの新モデル「AnkerPowerCorePDReduxW」は、最大Wの高出力USBCポートを搭載。	2021-09-10 23:30:00
サブカルネタ	ラーブロ	まぐちゃんラーメン＠東京ラーメンショーセレクション 極み麺（南越谷ラクーン）　「濃厚煮干しラーメン、半チャーハン」	http://feedproxy.google.com/~r/rablo/~3/2CeocCJdJyo/single_feed.php	実行委員会	2021-09-10 15:01:54
北海道	北海道新聞	レバノン元首相が組閣発表　１年の政治空白に打開	https://www.hokkaido-np.co.jp/article/588019/	政治空白	2021-09-10 23:15:00
北海道	北海道新聞	甲府のトヨタ系列店で不正車検　必要な項目の実施怠る	https://www.hokkaido-np.co.jp/article/588017/	山梨甲府市	2021-09-10 23:10:00