投稿時間:2021-11-13 20:27:36 RSSフィード2021-11-13 20:00 分まとめ(28件)

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python	Pythonタグが付けられた新着投稿 - Qiita	いらすとやの画像をスクレイピングしよう！	https://qiita.com/satelightccb/items/26a1724e3649893ddbc9	いらすとやの画像をスクレイピングしよういらすとやの画像をスクレイピングしてみよう資料作成などで「いらすとや」さんの画像をよく利用しますが、ガバっと取り出したいときがあります。	2021-11-13 19:05:20
Program	[全てのタグ]の新着質問一覧｜teratail（テラテイル）	AWS：Cognito＋Google認証でユーザー名とメールアドレスを取得したい	https://teratail.com/questions/369146?rss=all	AWSCognitoGoogle認証でユーザー名とメールアドレスを取得したいAWSnbspnbspECnbspnbspUbuntunbspnbspnbspnginxnbspでWebアプリケーションを開発しています。	2021-11-13 19:59:56
Program	[全てのタグ]の新着質問一覧｜teratail（テラテイル）	【wordpress】メディアブロックである「メディアと文章」の画像にキャプションを表示させるには？	https://teratail.com/questions/369145?rss=all	【wordpress】メディアブロックである「メディアと文章」の画像にキャプションを表示させるには前提・実現したいこと『実現したいこと』wordpressで作るブログ記事を編集するために使うメディアブロックである「メディアと文章」の画像にキャプションを表示させたいです。	2021-11-13 19:48:36
Program	[全てのタグ]の新着質問一覧｜teratail（テラテイル）	一覧画面と投稿画面を同じページに表示する際のエラー解消	https://teratail.com/questions/369144?rss=all	一覧画面と投稿画面を同じページに表示する際のエラー解消rails学習始めて週間の初心者です。	2021-11-13 19:44:37
Program	[全てのタグ]の新着質問一覧｜teratail（テラテイル）	TensorflowからPytorchへの書き換え	https://teratail.com/questions/369143?rss=all	pytorch	2021-11-13 19:34:14
Program	[全てのタグ]の新着質問一覧｜teratail（テラテイル）	javaで時刻を表示したい	https://teratail.com/questions/369142?rss=all	javaで時刻を表示したい前提・実現したいこと画像のような左側に地名、右側にその場所の時刻を表示させたい。	2021-11-13 19:28:37
Program	[全てのタグ]の新着質問一覧｜teratail（テラテイル）	Regexp.last_matchの意味を教えてください	https://teratail.com/questions/369141?rss=all		2021-11-13 19:05:35
技術ブログ	Developers.IO	QuickSightにてシート切り替え時のビジュアルロードが最小限になりました	https://dev.classmethod.jp/articles/quicksight-smooth-load-visual/	quicksight	2021-11-13 10:54:01
技術ブログ	Developers.IO	Route53 단순 라우팅과 장애 조치 라우팅	https://dev.classmethod.jp/articles/route53-simple-routing-and-failover-routing/	Route 단순라우팅과장애조치라우팅안녕하세요클래스메소드김재욱 Kim Jaewook 입니다 이번에는Route 단순라우팅과장애조치라우팅에대해정리해봤습니다 Route 도메인발급에대한내용은아래블로그를	2021-11-13 10:05:51
海外TECH	DEV Community	Day 36 of 100 Days of Code & Scrum: Fifth Week Retrospective - No Internet Edition	https://dev.to/rammina/day-36-of-100-days-of-code-scrum-fifth-week-retrospective-no-internet-edition-2592	Day of Days of Code amp Scrum Fifth Week Retrospective No Internet EditionHappy weekend everyone Or at least I would have been if I had my Internet back This objectively definitely has been the most unproductive week for me because of things that were out of my control Not having Internet is a HUGE impediment for a modern developer who relies a lot on Google searches Stack Overflow solutions online resources and anything else that has to do with data I tried to do whatever I could to remain productive but it s really hard to not lose my momentum when I m thrown off by this Typically I navigate through my daily life expecting everything to be structured and routinized I m pretty bad at responding to unusual things that seem to come out of nowhere Either way thank you all for the continued support and the encouraging words I received from some of you Special thanks to my wife and editor May for editing images and posting the blog on my behalf Anyway I m not going to skip my retrospective so let s move on to my daily report Weekly Sprint Goalsbuild my company website continued to learn Next js go through the Typescript documentation at least minutes each day continue studying for Professional Scrum Master I PSM I certification continue networking but allocate less time to this coding is more important Weekly ReviewUh oh here are the things I ve done this week company homepage is not even halfway done I did a small portion of the services page until I remembered that I should aim for a single page MVP first and foremost I learned and reviewed Next js a bit here and there mostly about react css modules I read through the first four chapters of the Typescript Handbook until Object Types I started learning about how to use Amazon SES and Lambda to implement an e mail contact form for my website studied Scrum for Professional Scrum Master I PSM I certification continue to network and expand my connections I still made some progress but this is definitely unfocused Weekly RetrospectiveMoving on let s tackle what I ve managed to do well what my shortcomings are and what I could do better next time What Went GreatI was consistent with studying Next js and Typescript for the most part continued to review Scrum even if it wasn t the main focus for this week I got some sections of the homepage have a finished skeleton made some progress with learning how to implement an email contact form reached out to multiple people on different platforms but without getting too distracted by social media finally I can live without checking the bell icon too much Some Mistakes I ve MadeI started a lot of things but finished almost nothing the lack of focus is really bad and goes against Scrum principles because there is no such a thing as partially done I may as well have done nothing At least Scrum says so I also forgot about my I blogged for days straight retrospective post AGAIN forgot to install a website blocker to stop myself from checking off topic content subconsciously I could be avoiding this Things I Could Improve Onhave some backup in case I have outage that way I don t lose too much productivity just focus on one thing at a time and switching around too much stop procrastinating writing literally anything I m planning on writing I should DEFINITELY install something that blocks me from checking certain sites at a specific time prioritize tasks that are more likely to help me meet my weekly goals Thank you once again everyone Have a great weekend Resources Recommended ReadingsOfficial Next js tutorialThe Typescript HandbookThe Scrum GuideMikhail Lapshin s Scrum Quizzes DISCLAIMERThis is not a guide it is just me sharing my experiences and learnings This post only expresses my thoughts and opinions based on my limited knowledge and is in no way a substitute for actual references If I ever make a mistake or if you disagree I would appreciate corrections in the comments Other MediaFeel free to check me in other media and reach out to me	2021-11-13 10:14:26
海外TECH	DEV Community	40+ Nodejs Projects for Beginners – Easy Ideas to Get Started Coding Nodejs [Beginners - Advance]	https://dev.to/jdksa/40-nodejs-projects-for-beginners-easy-ideas-to-get-started-coding-nodejs-beginners-advance-4375	Nodejs Projects for Beginners Easy Ideas to Get Started Coding Nodejs Beginners Advance If you have decided to build your career in NodeJS development we have gathered a list of nodeJS projects for beginners for all node enthusiasts to build highly scalable web applications These best nodeJS projects for beginners will help you gain knowledge about core node concepts train your skill level build a superb work portfolio and get hired for your dream job When you start building your first projects it s highly recommended for beginners in coding to create a repository for each of your projects so you will be able to learn git and show your coding expertise to future employers  Read More Nodejs Projects for Beginners Easy Ideas to Get Started Coding Nodejs	2021-11-13 10:10:06
海外TECH	DEV Community	Hybrid Machine Learning | AWS Whitepaper Summary	https://dev.to/awsmenacommunity/hybrid-machine-learning-aws-whitepaper-summary-1k15	Hybrid Machine Learning AWS Whitepaper SummaryThis article aims to discuss the outline known considerations design patterns and solutions that customers should know when considering integrations between local compute and the AWS cloud across the machine learning lifecycle AWS purpose hybrid ML patterns as an intermediate step in their cloud and ML journey The patterns involve a minimum of two compute environments typically local compute resources such as personal laptops or corporate data centers and the cloud This article is intended for individuals who already have a baseline understanding of machine learning in addition to Amazon SageMaker Stage One BasicsDeveloping technology that applies machine learning is challenging since it depends on data Datasets vary from bytes to petabytes objects to file systems text to vision tables to logs Software frameworks supporting machine learning models evolve rapidly undergoing potentially major changes multiple times a year if not quarter or month Nowadays data science projects require different skill levels in team from business stakeholders quality and availability of datasets and models and customer adoptionCompanies who adopt a cloud native approach realize its value of compute capacity with the needs of their business technical resources to focus on building features rather than taking on the burden of managing and maintaining their own underlying infrastructure But for those companies born before the cloud and even for newer companies founded more recently potentially those that made an informed decision to build on premises how can they realize the value of newly launched cloud services when the early requirements that were once infeasible on the cloud are now within reach For customers who want to integrate the cloud with existing on premises ML AWS propose a series of tenets to guide our discussion Seamless management experience Customers need end to end ML across multiple compute environments limiting the burden of administrative while successfully operate complex tasks Tunable latency The customers enjoy using applications that respond within the timelines of their moment to moment expectations and designers of these applications understand the criticality of time bound SLA s Engineers want to work with ML models that can respond to an app s request within in milliseconds regardless of the hosted While not every customer requires response times at low latency levels but faster is better Fast time to value Customers expect solutions to be easy to use with simple interfaces and not requiring significant amounts of platform specific engineering to execute a task Flexible Customers need compute paradigms that provide the flexibility their business demands ML applications may need to serve real time responses to billions of users worldwide Service providers should anticipate for deploying all environments Low Cost Customers want transparency in their cost structure they need to see a clear economic advantage to computing in the cloud relative to developing locally Service providers need to anticipate this and compete on cost with respect to local compute options End in the cloud If there was any doubt that cloud computing is the way of the future the global pandemic of put that doubt to rest AWS also call out the final state of that design helping customers understand which cloud technologies to leverage in the long runleverage in the long run There are two very different approaches to hosting one type of pattern trains in the cloud with the intention of hosting the model itself on premises while another hosts the model in the cloud to applications deployed on premises Finally a key pillar in applying these patterns is security What is hybrid At AWS we look at “hybrid capabilities as those that touch the cloud in some capacity while also touching local compute resources Local compute such as laptops hosting Jupyter notebooks and Python scripts HDFS clusters storing terabytes of data AWS Outposts stored on premises or AWS Outposts stored on premises The hybrid architectures are having a minimum of two compute environments what we will call “primary and “secondary environments The primary environment as where the workload begins and secondary environment is where the workload ends Depending on your case for instance if you are packaging up a model locally to deploy to the cloud you might call your local laptop “primary and your cloud environment “secondary However if you are training on cloud and want to deploy locally you might call your cloud environment “primary and your local environment “secondary What hybrid is not There are some container specific tools that provide a “run anywhere experience such as EKS and ECS In those contexts we will lean into prescriptive guidance for building training and deploying machine learning models with these services Stage Two Hybrid patterns for developmentDevelopment refers to the phase in machine learning when customers are iteratively building models This may or may not include exploratory data analysis deep learning model development and compilation software package installation and management Jupyter kernels visualization Docker image building and Python driven data manipulation There are two major options for hybrid development that customer can apply one or both Laptop and desktop personal computers Self managed local servers utilizing specialized GPUs colocations self managed racks or corporate data centers Develop on personal computers to train and host in the cloudCustomers can use local development environments such as PyCharm or Jupyter installations on their laptops or personal computer and then connect to the cloud via AWS Identity and Access Management IAM permissions and interface with AWS service APIs through the AWS CLI or an AWS SDK ex boto Having connected to the cloud customers can execute training jobs and or deploy resources Develop on local servers to train and host in the cloud Stage Three Training Hybrid patterns for trainingHybrid pattern for training comes down to one of two paths Either you train locally and deploy on the cloud Or the data sitting on local resources and want to select from that to move into the cloud to train Training locally to deploy in the cloudDuring enterprise migrations training locally may be advantageous as a first step to develop a model There are two key actions here First if you are training locally then you will need to acquire the compute capacity to train a model and think about size of dataset and models that you want to use When you are training on premises you need to plan for that well in advance and acquire the compute resources ahead of time After your model is trained there are two common approaches for packaging and hosting it in the cloud One Simple path is Docker using a Docker file you can build your own custom image that hosts your inference script model artifact and packages Register this image in the Elastic Container Registry ECR and point to it from your SageMaker estimator Another option is using pre built containers within the SageMaker Python SDK Bring your inference script and custom packages upload your model artifact to Amazon S and import an estimator for your framework of choice In the following diagram we outline how to do this from your laptop The pattern is similar for doing the same from an enterprise data center with servers as outlined above How to monitor your model in the cloud A key feature for hosting is model monitor or the ability to detect data bias feature and model quality drift It is ability to capture data hitting your real time endpoint and programmatically compare this to your training data Enabling model monitor is easy in SageMaker Upload your training data to an Amazon S bucket and use our pre built image to learn the upper and lower bounds on your training data This job uses Amazon Deequ to perform “unit testing for data and you will receive a JSON file with the upper and lower statistically recommended bounds for each feature You can modify these thresholds After confirming your thresholds schedule monitoring jobs in your production environment The jobs run automatically comparing your captured inference requests in Amazon S with your thresholds CloudWatch will alert you when your inference data is outside of your pre determined thresholds and you can use those alerts to trigger a re training pipeline How to handle retraining retuning SageMaker makes train and tuning jobs easy to manage because all you need to bring is your training script and dataset Ensure your new dataset is loaded into an Amazon S bucket or other supported data source Once you have defined a training estimator it is trivial to extend this to support hyperparameter tuning Define your tuning job configuration using tuning best practices and execute Having defined a tuning job you can automate this in a variety of ways While AWS Lambda may seem compelling upfront to use the SageMaker Python SDK and not boto with Lambda sadly you need to create an executable layer to upload within your function You may consider SageMaker Pipelines an MLOps framework that uses your SageMaker Python SDK job constructs as argument and creates a step driven framework to execute your entire pipeline How to serve thousands of models in the cloud at low cost You may consider Multi model endpoints give you the ability to serve thousands of models from a single endpoint invoking the name of the model when calling predict Create the multi model endpoint pointing to Amazon S and load your model artifacts into the bucket Invoke the endpoint from your client application eg with AWS Lambda and dynamically select the right model in your application It allows to host up to containers on a single SageMaker endpoint invoking the endpoint with the name of the model you want to use Storing data locally to train and deploy in the cloudwhen and how do I move my data to the cloud Schedule data transfer jobs with AWS DataSyncIt is a data transfer service that simplifies automates and accelerates moving data between on premises storage systems and AWS storage services as well as between AWS storage services It can be easily moved petabytes of data from your local on premises servers up to the AWS cloud It can be deployed can easily move petabytes of data from your local on premises servers up to the AWS cloud It can be connected to your local NFS resources and deploy directly into Amazon S buckets or EFS volumes or both Migrating from Local HDFSAs customers explore migrating data stored in local HDFS clusters typically they find themselves somewhere between two extremes On the other you might wholly embrace HDFS as your center and move towards hosting it within a managed service Amazon Elastic Map Reduce EMR Best practices•Use Amazon S intelligent tiering for objects over KB •Use multiple AWS accounts and connect them with Organizations •Set billing alerts •Enable SSO with your current Active Directory provider •Turn on Studio Develop in the cloud while connecting to data hosted on premisesCustomers who see the value of outsourcing management and upkeep of their enterprise ML development platforms i e through using managed services like Amazon SageMaker can still connect in to their on premises data store at the beginning and middle phases of their enterprise migration Data Wrangler amp SnowflakeData Wrangler enables customers to browse and access data stores across Amazon S Amazon rd Athena Amazon Redshift and party data warehouses like Snowflake This hybrid ML patten provides customers the ability to develop in the cloud while accessing data stored on premises as organizations develop their migration plans Train in the cloud to deploy ML models on premisesYou can download whatever type of model artifact you need but if you are deploying on premises you need to develop and host your own local web server This scenario builds on your previous experience developing and training in the cloud with the key difference of exporting your model artifact to deploy locally AWS recommends using dev and or test endpoints in the cloud to give your teams the maximum potential to develop the best models they can If you are using a managed deep learning container also known as “script mode for training and tuning but you still want to deploy that model locally plan on building your own image with your preferred software version scanning maintaining and patching this over time If you are using your own image you will need to own updating that image as the software version such as TensorFlow Note that the best practice is to decouple hosting your ML model from hosting your application As models grow and shrink in size hitting potentially billions of parameters and hundreds of gigs in byte size or shrinking down to hundreds of parameters and staying under a few MB in size you want the elasticity of the cloud to seamlessly map the state of the art model to an efficient hardware choice Stage Four Deployment Monitor ML models deployed on premises with SageMaker Edge ManagerCustomers can train ML models in the cloud deploy these on premises and monitor and update them in the cloud using SageMaker Edge Manager SageMaker Edge Manage makes it easy for customers to manage ML models deployed on Windows Linux or ARM based compute environments While customers do still need to provision manage procure and physically secure the local compute environments in this pattern Edge Manage simplifies the monitoring and updating of these models by bringing the control plane up to the cloud However you can bring your own monitoring algorithm to the service and trigger retraining pipelines as necessary using the service the redeploy that model back down to the local device This is particularly common for technology companies developing models for personal computers such as laptops and desktops Hybrid patterns for deploymentHybrid ML patterns around deployment can be interesting and complex Choosing the “best local deployment option has a lot of variety You want to think about where your customers sit geographically then you want to get your solution as close to them as you can You want to balance speed with cost cutting edge solutions with ease of deployment and managing In this section will discuss the architecture for hosting an ML model via SageMaker in an AWS region serving responses to requests from applications hosted on premises After that we ll look at additional patterns for hosting ML models via Lambda at the Edge Outposts Local Zones and Wavelength Serve models in the cloud to applications hosted on premisesThe most common use case for a hybrid pattern like this is enterprise migrations You might have a data science team with tens of models if not more than one hundred ready to deploy via the cloud while your application team is still refactoring their code to host on cloud native services Host ML Models with Lambda at Edge to applications on premisesThis pattern takes advantage of a key capability of the AWS global network the content delivery network known as Amazon CloudFront Deploying content to CloudFront is easy customers can package up code via AWS Lambda and set it to trigger from their CloudFront distribution What s elegant about this approach is that CloudFront manages which of the points of presence will execute your function Once you ve set your Lambda function to trigger off CloudFront you re telling the service to replicate that function across all available regions and points of presence This can take up to minutes to replicate and become available This is a huge value add for global companies looking at improving their digital customer experience worldwide AWS Local ZonesLocal Zones are a way to extend your cloud resources to physical locations that are geographically closer to your customers You can deploy ML models via ECS or EKS to serve inference with ultra low latency near your customers using AWS Local Zones AWS WavelengthWavelength is ideal when you are solving applications around mobile G devices either anticipating network drop offs or serving uses real time model inference results Wavelength provides ultra low latency to G devices and you can deploy ML models to this service via ECS or EKS Wavelength embeds storage and compute inside the telecom providers which is the actual G network Training with a rd party SaaS provider to host in the cloudThere are a lot of great SaaS providers for ML out there in the market today like H DataRobot Databricks SAS and others rd Hosting a model in Amazon SageMaker that was trained from a party SAAS provider is easy Ensure your provider allows export of proprietary software frameworks such as with jars bundles images etc Follow steps to create a Docker file usingthat software framework port into the Elastic Container Registry and host on SageMaker Keep in mind that providers will have different ways of handling software in particular images and image versions Control plane patterns for hybrid MLAWS uses the concept of a “control plane or set of features dedicated to operations and management while keeping this distinct from the “data plane or the datasets containers software and compute environments Customers need it for operationalizing ML workloads are as varied and diverse as the businesses they exist within Today it is not feasible for a single workflow orchestration tool to solve every problem so most customers standardize on one workflow paradigm while keeping options open for others that may better solve given use cases One such common control plane is Kubeflow in conjunction with EKS Anywhere SageMaker offers a native approach for workflow orchestration known as SageMaker Pipelines It is ideal for advanced SageMaker users especially those who are already onboarded to the IDE SageMaker Studio The Studio also offers a UI to visual workflows built with SageMaker Pipelines Apache Airflow is also a compelling option for ML workflow orchestration Orchestrate Hybrid ML Workloads with Kubeflow and EKS AnywhereIn this example we re demonstrating training within local on premises resources and orchestrating it using Kubeflow Stage Five Other Services Auxiliary services for hybrid ML patterns AWS OutpostsOutposts is a keyway to enable hybrid experiences within your own data center Order AWS Outposts and Amazon will ship install and manage these resources for you You can connect into these resources however you prefer and manage them from the cloud Outposts helps solve cases where customers want to build applications in countries where there is not currently an AWS Region or for regulations that have strict data residency requirements like online gambling and sports betting AWS InferentiaA compelling reason to consider deploying your ML models in the cloud is the ease of accessing custom hardware for ML inferencing specifically AWS Inferentia You can use SageMaker s managed deep learning containers to train your ML models compile them for Inferentia with Neo host on the cloud and develop retrain and tune pipeline as usual Using AWS Inferentia Alexa was able to reduce their cost of hosting by AWS Direct ConnectAbility to establish a private connection between your on premises resources and your data center Remember to establish a redundant link as wires do go south Amazon ECS EKS AnywhereBoth Amazon ECS and Amazon EKS feature “Anywhere capabilities This means that you can use the cloud as your control plane to define manage and monitor your deployed applications while executing tasks both in the Region and on premises The customers can use ECS Anywhere to deploy their models both in the cloud and on premises at the same point in time The Final Stage Use Cases Hybrid ML Use Cases Enterprise MigrationsOne of the single most common use cases for hybrid patterns is enterprise migrations For some of the largest and oldest organizations on the planet without a doubt there is going to be a difference in ability and availability in moving towards the cloud across their teams ManufacturingApplications within agriculture industrial and manufacturing are ripe opportunities for hybrid ML After companies have invested tens and sometimes hundreds of thousands of dollars in advanced machinery it is simply a matter of prudence to develop and monitor ML models to predict the health of that machinery GamingCustomers who build gaming applications may see the value in adopting advanced ML services like Amazon SageMaker to raise the bar on their ML applications but struggle to realize this if their entire platform was build and is currently hosted on premises The AWS global delivery network to minimize end user latency Mobile application developmentWith the introduction of AWS Wavelength customers can deploy ML models directly inside of the G network To solve applications such as anticipated network drop off or hosting ML models in the cloud for real time inferencing with G customers you can use ML models hosted on ECS to deploy and monitor models onto AWS Wavelength This becomes a hybrid pattern when customers develop and train in a secondary environment wherever that may be with the intention to deploy onto AWS Wave AI enhanced media and content creationCustomers can host these billion plus parameter models via ECS on AWS Local Zones responding to application requests coming from on premises data centers to provide world class experiences to content creators Depending on where customers develop and retrain their models using Local Zones with SOTA models may or may not be a true hybrid pattern but used effectively it can enhance content generator s productivity and ability to create Autonomous VehiclesCustomers who develop autonomous machinery vehicles or robots in multiple capacity by default require hybrid solutions This is because while training can happen anywhere inference must necessarily happen at the edge Amazon SageMaker Neo is a compelling option here Key questions for hybrid ML AV architectures include monitoring at the edge retraining and retuning pipelines in addition to efficient and automatic data labelling ConclusionIn this document we explored hybrid ML patterns across the entire ML lifecycle We looked at developing locally while training and deploying in the cloud We discussed patterns for training locally to deploy on the cloud and even to host ML models in the cloud to serve applications on premises At the end of the day we want to support customer success in all shapes and forms We firmly believe that most workloads will end in the cloud in the long run but because the complexity magnitude and length of enterprise migrations may be daunting for some of the oldest organizations in the world we propose these hybrid ML patterns as an intermediate step on customer s cloud journey References If you are interested in learning how to migrate from local HDFS clusters to Amazon EMR please see this migration guide Original AWS Whitepaper	2021-11-13 10:09:32
海外科学	BBC News - Science & Environment	COP26: New draft deal aims to close lingering divisions	https://www.bbc.co.uk/news/science-environment-59269886?at_medium=RSS&at_campaign=KARANGA	financial	2021-11-13 10:28:42
海外科学	BBC News - Science & Environment	What is COP26 and what is being agreed at Glasgow climate conference?	https://www.bbc.co.uk/news/science-environment-56901261?at_medium=RSS&at_campaign=KARANGA	everyday	2021-11-13 10:18:40
海外ニュース	Japan Times latest articles	U.N. climate negotiators go into overtime to save 1.5 Celsius goal	https://www.japantimes.co.jp/news/2021/11/13/world/climate-change-cop26-extension/	extra	2021-11-13 19:34:19
ニュース	BBC News - Home	COP26: New draft deal aims to close lingering divisions	https://www.bbc.co.uk/news/science-environment-59269886?at_medium=RSS&at_campaign=KARANGA	financial	2021-11-13 10:28:42
ニュース	BBC News - Home	Covid: 'No reason' not to give boosters to under-50s	https://www.bbc.co.uk/news/uk-59273273?at_medium=RSS&at_campaign=KARANGA	levels	2021-11-13 10:49:24
ニュース	BBC News - Home	Scottish Conservative leader Douglas Ross in job 'sleaze' row	https://www.bbc.co.uk/news/uk-scotland-scotland-politics-59270798?at_medium=RSS&at_campaign=KARANGA	conservative	2021-11-13 10:40:30
ニュース	BBC News - Home	What is COP26 and what is being agreed at Glasgow climate conference?	https://www.bbc.co.uk/news/science-environment-56901261?at_medium=RSS&at_campaign=KARANGA	everyday	2021-11-13 10:18:40
LifeHuck	ライフハッカー［日本版］	Amazonのアカウントを安全に保つために、今すぐチェックしたい10項目	https://www.lifehacker.jp/2021/11/10-privacy-settings-every-amazon-user-should-enable-rig.html	amazon	2021-11-13 20:00:00
北海道	北海道新聞	国内最古の縄文土面出土、青森　従来より千年古く	https://www.hokkaido-np.co.jp/article/611362/	縄文時代	2021-11-13 19:18:00
北海道	北海道新聞	元日本代表トンプソンが浦安入り　ラグビーＷ杯４大会出場	https://www.hokkaido-np.co.jp/article/611344/	元日本代表	2021-11-13 19:02:55
北海道	北海道新聞	晩秋に浮かぶ日光・華厳の滝　ＬＥＤでライトアップ２３日まで	https://www.hokkaido-np.co.jp/article/611361/	日本三名瀑	2021-11-13 19:17:00
北海道	北海道新聞	鉄道模型　ファンを魅了　安平・道の駅　巨大ジオラマも	https://www.hokkaido-np.co.jp/article/611360/	鉄道模型	2021-11-13 19:17:00
北海道	北海道新聞	体操橋本選手らに栄誉賞、千葉県　五輪・パラで金メダルの７人	https://www.hokkaido-np.co.jp/article/611357/	東京五輪	2021-11-13 19:03:00
北海道	北海道新聞	メコン流域国へ海洋情報提供延期　政府、ミャンマー情勢に懸念	https://www.hokkaido-np.co.jp/article/611356/	情報提供	2021-11-13 19:03:00
北海道	北海道新聞	日胆の感染者ゼロ　新型コロナ	https://www.hokkaido-np.co.jp/article/611355/	新型コロナウイルス	2021-11-13 19:02:00
海外TECH	reddit	立憲民主党の松木謙公さん「北海道の小選挙区で、れいわ新選組の協力で勝たせていただいた、北海道は野党共闘がなかったら1議席も取れなかった」「本当なら山本太郎さんにうちの党に入ってもらって代表選に出てもらいたいくらい」	https://www.reddit.com/r/newsokuexp/comments/qsy8h2/立憲民主党の松木謙公さん北海道の小選挙区でれいわ新選組の協力で勝たせていただいた北海道は野党共闘がな/	ornewsokuexplinkcomments	2021-11-13 10:05:39