投稿時間:2023-04-04 04:25:09 RSSフィード2023-04-04 04:00 分まとめ(35件)

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AWS	AWS Partner Network (APN) Blog	Trek10 Delivers Value Through Managed Services Offerings with Support from the AWS MSP Program	https://aws.amazon.com/blogs/apn/trek10-delivers-value-through-managed-services-offerings-with-support-from-the-aws-msp-program/	Trek Delivers Value Through Managed Services Offerings with Support from the AWS MSP ProgramThe AWS MSP program team spoke with Andy Warzon Chief Technology Officer and Co founder of Trek an AWS Premier Tier Services Partner to learn about their cloud managed services practice and the value they are creating jointly with AWS for the customers they support	2023-04-03 18:23:43
AWS	AWS Partner Network (APN) Blog	InterSystems and the AWS Workload Migration Program: Helping Healthcare Customers Move to the Cloud	https://aws.amazon.com/blogs/apn/intersystems-and-the-aws-workload-migration-program-helping-healthcare-customers-move-to-the-cloud/	InterSystems and the AWS Workload Migration Program Helping Healthcare Customers Move to the CloudTake a dive deep into InterSystems enrollment in the AWS ISV Workload Migration Program WMP as well as the architecture of their Health Connect Cloud offering to explore the benefits of migrating healthcare applications to the cloud	2023-04-03 18:21:55
AWS	AWS Big Data Blog	Generic orchestration framework for data warehousing workloads using Amazon Redshift RSQL	https://aws.amazon.com/blogs/big-data/generic-orchestration-framework-for-data-warehousing-workloads-using-amazon-redshift-rsql/	Generic orchestration framework for data warehousing workloads using Amazon Redshift RSQLTens of thousands of customers run business critical workloads on Amazon Redshift AWS s fast petabyte scale cloud data warehouse delivering the best price performance With Amazon Redshift you can query data across your data warehouse operational data stores and data lake using standard SQL You can also integrate AWS services like Amazon EMR Amazon Athena Amazon SageMaker AWS …	2023-04-03 18:40:58
AWS	AWS Database Blog	Use the compatibility tool for Amazon DocumentDB (with MongoDB compatibility) to improve migrations	https://aws.amazon.com/blogs/database/use-the-compatibility-tool-for-amazon-documentdb-with-mongodb-compatibility-to-improve-migrations/	Use the compatibility tool for Amazon DocumentDB with MongoDB compatibility to improve migrationsAmazon DocumentDB is a fast scalable highly available and fully managed document database service that supports MongoDB workloads You can use the same MongoDB and application code drivers and tools to run manage and scale workloads on Amazon DocumentDB without worrying about managing the underlying infrastructure As a document database Amazon DocumentDB …	2023-04-03 18:07:12
AWS	AWS Machine Learning Blog	Generate a counterfactual analysis of corn response to nitrogen with Amazon SageMaker JumpStart solutions	https://aws.amazon.com/blogs/machine-learning/generate-a-counterfactual-analysis-of-corn-response-to-nitrogen-with-amazon-sagemaker-jumpstart-solutions/	Generate a counterfactual analysis of corn response to nitrogen with Amazon SageMaker JumpStart solutionsIn his book The Book of Why Judea Pearl advocates for teaching cause and effect principles to machines in order to enhance their intelligence The accomplishments of deep learning are essentially just a type of curve fitting whereas causality could be used to uncover interactions between the systems of the world under various constraints without …	2023-04-03 18:59:34
AWS	AWS Machine Learning Blog	Zero-shot prompting for the Flan-T5 foundation model in Amazon SageMaker JumpStart	https://aws.amazon.com/blogs/machine-learning/zero-shot-prompting-for-the-flan-t5-foundation-model-in-amazon-sagemaker-jumpstart/	Zero shot prompting for the Flan T foundation model in Amazon SageMaker JumpStartThe size and complexity of large language models LLMs have exploded in the last few years LLMs have demonstrated remarkable capabilities in learning the semantics of natural language and producing human like responses Many recent LLMs are fine tuned with a powerful technique called instruction tuning which helps the model perform new tasks or generate responses to …	2023-04-03 18:37:07
AWS	AWS Management Tools Blog	Using Open Source Grafana Operator on your Kubernetes cluster to manage Amazon Managed Grafana	https://aws.amazon.com/blogs/mt/using-open-source-grafana-operator-on-your-kubernetes-cluster-to-manage-amazon-managed-grafana/	Using Open Source Grafana Operator on your Kubernetes cluster to manage Amazon Managed GrafanaIntroduction Kubernetes APIs are robust and its control loop mechanism allows us to control the state of resources that are even outside of Kubernetes environments Customers have shifted their focus towards workload gravity and rely on Kubernetes native controllers to deploy and manage the lifecycle of external resources such as Cloud resources We have seen customers …	2023-04-03 18:55:51
python	Pythonタグが付けられた新着投稿 - Qiita	【激ムズ！】pikalangでHello World!出力してみた	https://qiita.com/Naoya_pro/items/19e2fc81fe7888c7baa0	esotericprogramminglangua	2023-04-04 03:21:48
海外TECH	DEV Community	MLOps is 98% Data Engineering	https://dev.to/cpard/mlops-is-98-data-engineering-2bpi	MLOps is Data Engineering MLOps is Mostly Data EngineeringTL DR MLOps emerged as a new category of tools for managing data infrastructure specifically for ML use cases with the main assumption being that ML has unique needs After a few years and with the hype gone it has become apparent that MLOps overlap more with Data Engineering than most people believed Let s see why and what that means for the MLOps ecosystem IntroductionMLOps is a relatively recent term A quick search on Google Trends reveals that the term started being searched for around the end of Upon examining the trend line above we can observe a significant spike that occurred at the end of Since then the interest has remained high ML is not something new though if we check Google Trends for that term we will see that the term exists since and with the interest growing exponentially since Interest Over Time for the term Machine Learning on GoogleMachine learning has made amazing progress in the past years with some of the most important achievements in tech being related to it The rapid growth of machine learning is what sparked the creation of MLOps as a category With the pace of innovation around ML accelerating teams and companies have started to have issues keeping up Building and operating ML products started putting a lot of pressure on the data and ML engineering teams and where s there s pain there s also opportunity More and more people started seeing opportunities for bringing new products to the market promising to turn every company out there with any data into an AI driven organization And just like this we reached to the state of the industry you can see below MLOps category as included in MAD Keep in mind that the above landscape includes only companies labeled as “MLOps and there are overlaps with other categories in the ML category of MAD vendors around B in investments without accounting for public companies like Google and AWS investing in the space What are all these companies offering Let s see What is inside an MLOps platform The MLOps vendors can be split among a number of product categories Deployment amp Serving of models i e OctoMLModel Quality and Monitoring i e Weights amp BiasesModel training i e AWS SagemakerFeature Stores i e TectonIt s important to mention here that the above categories are supplementary in many cases for example if you use a Feature Store you also need a service for model training If you pay attention to the product categories above you will notice that there is nothing particularly unique about them in the grand scheme of things What do I mean by that Deployment and serving of models →This is a common operation found in both data engineering and software engineering People have been deploying pipelines or even better deploying applications of various complexity way before ML was a thing Model quality and Monitoring →This is a unique problem to ML The way you monitor a model for quality is not the same as you do with a software project or a data pipeline But this is only part of the quality problem as we will see later Model training →This is unique to ML but building models is nothing new the question is what has changed in the past five years that requires a completely different paradigm in doing it Feature stores →This is one of the most interesting products of MLOps for the uninitiated the first thing that comes to mind is some kind of specialized data base but feature stores are actually more than that They are a complete data infrastructure architecture that is proposed and attempted to be productized How different it is from the classic data infrastructure architectures We will see Let s see how each one of the above categories overlap or not with Data Engineering and what that means Deployment amp Serving of ModelsThis is one of the most interesting aspects of MLOps in my opinion Mainly because this is the part where the outcome of the work an ML Engineer does gets to the point where concrete value can be generated out of it A recommender can serve recommendations to users and fraud detection can be applied in real time But what is interesting here is that this process doesn t have much to do with ML the engineering problems are more related to product engineering We can think of a model as a function that requires some input and generates some output To deliver value with this function we need a way to add it as part of the product experience we are delivering In engineering terms that means that we have to wrap the model as a service with a clean API that will be exposed to the product engineers Then we need to deploy this service in a scalable and predictable way just like we do with any other service for our product After that we need to operate the service and ensure that it is provisioned the resources needed based on demand We also need to monitor the service for problems and be able to fix them as soon as possible Finally we want to have some kind of continuous deployment integration process to deploy updates to the service Just like we do with any other service of our product As we can see the above process is almost identical to managing the release cycle of any other software component out there while it s primarily the product engineering involved as a stakeholder After all they have to ensure that the new functionality the model provides is integrated in the right way to the product without disrupting its operations There s one specific need that is imposed to the engineering and ops teams because of having to work with ML models and this is related to monitoring the performance of the model itself but we will talk more about this later The question here is if integrating a model to our product doesn t differ than any other feature we release about the product in terms of the release and platform engineering and operations why do we need a whole new category of products My opinion here is that the industry is trying to solve the unique challenges of turning models into services by building complete new platforms but this is less than optimal The true need here is developer tooling that will enrich the existing and proven platforms and methodologies for releasing and operating software at scale for the case of doing that with ML models as the foundational software artifact We don t need MLOps engineers we need tools that will allow ML Engineers to package their work in a way that the platform and release engineers will be able to consume and produce the artifacts needed for the product engineers to integrate into the product A recurrent pattern I see is an attempt from vendors who are trying to become category creators to define a new type of engineer In most cases this is a crossover between existing roles i e analytics engineer where you have someone who s primarily an analyst but also does some part of the data engineering work e g creates pipelines This is probably a smart marketing move but the world doesn t work like that New roles emerge and cannot be forced by a vendor Why we would like ML Engineers to assume responsibilities of a release or platform engineer Why we would like the former to be introduced to a completely new category of tools that sounds alien to their practice Separation of concerns is a good thing both in software architecture and in organizational design Model Quality and MonitoringThis is where things are getting really interesting quality assurance control and monitoring is a huge topic in software engineering In a way and with a bit of exaggeration these are the elements that turn software engineering into…engineering There are many best practices and mature platforms for software quality related tasks The problem is that ML models can easily challenge these You might have heard that quality in data infrastructure is hard and it is It s not just the software that we have to monitor for quality it s also the data And data is a different beast when it comes to applying quality concepts in ML the situation is even worse You pretty much have a black box system generated and you need to monitor its performance by just observing its outputs based on the inputs it gets in production Because of this Model quality and monitoring is usually mentioned together with terms like model drift Where the model is monitored in terms of its “predictive performance over time and if it drops under a threshold we know that we need to retrain it with fresh data Which makes sense right As our product changes and our customers behaviors change the model needs to get retrained to consider these changes I have two main arguments here The first is how different is the observability of model quality metrics like drift different to any product related monitoring In product we keep monitoring the performance of our features do people engage with them in the way we expect If something changed and engagement dropped we should address that right These are all part of what is usually referred as experimentation infrastructure for product and big part of it requires the right data infrastructure and data engineering to exist No matter how unique ML models are at the end we are going to be observing a service feature on how it performs interacting with our users and based on the data we collect figure out if action is needed My feeling is that there s a lot of overlap here between the ML observability and the data infra engineering foundations that the organization is building for product experimentation My other argument is about data quality in general ML models are built on top of data their quality is a direct reflection of the quality of data used to build them This is a serious problem that data engineering is constantly fighting with and I can t see how the replication of this process is helping in any way to solve the problem Data engineers are the people who are monitoring the data from its capture to the point where the ML engineer can use it They have access to the whole supply chain of data and they can monitor and add controls at any point of that chain Adding another platform that is overlapping with both the data engineering and product engineering quality controls is not going to solve the problem and in the worst case it might make it even worse Again the solution here is engineering tooling to enrich the existing architectures and solutions Finding out what quality for data entails and equip the people who s job is to ensure data and product quality to extend their reach into the ML models too Model TrainingThis is a short one to be honest Model Training has more to do with Cloud Computing than anything else and in my opinion this is the space where the big cloud providers are mainly delivering value today The main reason being the need for hardware to exist to do the actual training But in the general case model training is nothing more than a data pipeline Data is read from a number of sources and gets transformed through the application of a training algorithm It doesn t matter that much if this going to happen in the CPU or the GPU This is the bread and butter of Data Engineering the tooling exists already and the main differentiation that I see here is the cloud compute abstraction where we are talking about a completely different category of infrastructure anyway Model training at scale should be part of the data engineering discipline as they have the tooling already they have the responsibility for the SLAs on the data needed and they can control that release lifecycle much better Do the ML people bother with these operations I can t see why to be honest I believe they would prefer to spend more time in building new models than dealing with operations for data crunching at scale I m getting boring at this point but again we don t need new platforms We just need to give the right tooling to DEs to communicate effectively with both ML and production engineers and add model training as another step in their ETL pipelines Feature StoresI left Feature Stores for the end on purpose as they are a great example of the overlap with data engineering while their popularity is a great indication that something is not right with the current state of data infrastructure The above is a feature store architecture as presented by Tecton one of the first and most popular feature store vendors Looking at that we see that we have Stream data sourcesBatch data sourcesTransformationsStorageServingModel serving and trainingFeature stores are similar to a typical data infrastructure architecture used by companies that require both streaming and batch processing capabilities However they specialize in supporting machine learning features by serving only one type of data consumer the ML model Vendors have packaged the feature store architecture into products which has caused some confusion Some may question the need for another Spark or Flink cluster for real time feature generation especially if they are already using those tools for ETL jobs However feature stores are useful because they describe what needs to be added to existing data infrastructure to effectively productize machine learning As a product feature stores should focus on building tooling and practices for data ML and product engineers to work together more effectively Any additional overhead and complexity should be carefully evaluated to ensure that the benefits of using a feature store outweigh the costs Vendors should focus on providing useful tooling to support this rather than duplicating existing data infrastructure Final ThoughtsI hope that by reading this essay you didn t feel like I m trying to dismiss MLOps because I m not I believe that ML and its productization is important and will become even more important in the future and for this to happen the right tooling is needed But it s time for the MLOps industry to mature and understand who the right audience is what the problems are and bring the next iteration of solutions in the market Money and time was spent and lessons should have been learned I can t wait to see what the next iteration of these products will be There s a lot of opportunity ahead	2023-04-03 18:19:05
海外TECH	DEV Community	Relational database vs nosql: A Beginner's Guide	https://dev.to/documatic/relational-database-vs-nosql-a-beginners-guide-37gl	Relational database vs nosql A Beginner x s GuideDatabases are the backbone of modern data driven applications From e commerce websites to social media platforms databases play a crucial role in storing retrieving and managing large amounts of data In fact without databases it would be impossible to manage the vast amount of information that we generate and consume every day But not all databases are created equal In this article I will explore with you the two main types of databases relational and NoSQL Whether you re a complete beginner or an experienced developer understanding the differences between these two types of databases is essential for building fast scalable and reliable applications So let s dive in and discover the similarities and differences between relational and NoSQL databases Relational DatabasesRelational databases are a type of database that organizes data into tables with each table containing rows and columns Each row in a table represents a unique record or instance of the data and each column represents a specific attribute or characteristic of that data How it worksA relational database is a type of database that stores data in structured tables Each table consists of columns and rows where each column represents a particular type of data e g name age address and each row represents a single instance of that data Relational databases are designed to store and manage data using a set of rules known as a schema The schema defines the structure of the database and specifies the types of data that can be stored in each table One of the key benefits of using a relational database is data normalization which refers to the process of organizing data in a way that reduces redundancy and dependency By breaking down data into smaller more manageable tables relational databases can prevent data inconsistencies and make it easier to update and maintain large datasets Relational databases use SQL Structured Query Language to interact with data SQL is a standardized language used for managing relational databases It is used to insert update retrieve and delete data from tables Concept of tables rows and columnsTables are the basic building blocks of a relational database A table consists of rows and columns with each row representing a unique instance of the data and each column representing a specific attribute of the data The relationships between tables are defined by common columns known as keys which allow the data to be joined together when needed Relational databases are designed to efficiently manage structured data that fits into a predefined schema For example in a database for an e commerce website there might be a table called Products that contains information about each product such as the product name price and description Each row in the Products table represents a unique product while each column represents a specific attribute of that product Data normalization and how it s achievedData normalization is an important aspect of relational databases Normalization involves organizing data in a way that reduces data redundancy and ensures data consistency This makes it easier to manage and update data and ensures data accuracy In a normalized database data is organized into tables in such a way that each table represents a single concept or entity For example in the e commerce website database the Products table would contain only information about products while a separate table might contain information about customers Normalization is achieved by dividing data into tables based on the relationships between the data This involves identifying the dependencies between the data and breaking them down into smaller more manageable tables Examples of popular relational databasesSome popular examples of relational databases include MySQL Oracle and SQL Server MySQL is an open source database management system that is widely used for web applications Oracle is a commercial database management system that is used in many large organizations SQL Server is a Microsoft product that is widely used in enterprise environments NoSQL DatabasesNoSQL databases are a type of database that do not use the traditional table based structure of relational databases Instead they use a variety of data models including document based key value and graph based models NoSQL database and how it differs from a Relational databaseA NoSQL database is a type of database that does not use the structured table based approach of relational databases Instead NoSQL databases use a variety of data models to store and access data NoSQL databases are designed to handle unstructured or semi structured data making them particularly useful for handling large volumes of data in real time One key difference between NoSQL and relational databases is that NoSQL databases are designed to scale horizontally meaning that they can easily add more nodes to a cluster in order to handle increased traffic or data volumes Relational databases on the other hand typically scale vertically meaning that they require more powerful hardware to handle increased traffic or data volumes Concept of collections and documentsIn NoSQL databases data is typically organized into collections and documents A collection is a group of related documents and a document is a set of key value pairs that contain the data Unlike tables in a relational database documents in a NoSQL database do not have a fixed schema For example in a document based NoSQL database like MongoDB data might be organized into collections based on the type of data such as customers products or orders Each document within a collection might contain different fields depending on the specific data being stored Types of NoSQL databasesThere are several different types of NoSQL databases each with its own strengths and weaknesses Some of the most common types of NoSQL databases include Document based databases These databases store data in collections of documents typically in JSON or XML format Examples of document based databases include MongoDB and Couchbase Key value stores These databases store data as key value pairs with each value associated with a unique key Key value stores are often used for caching and high speed data access Examples of key value stores include Redis and Riak Graph databases These databases store data in nodes and edges representing the relationships between the data Graph databases are often used for social networking and recommendation systems Examples of graph databases include Neoj and OrientDB Column family databases These databases store data in columns rather than rows allowing for efficient retrieval of large amounts of data Column family databases are often used for storing and analyzing large amounts of data Examples of column family databases include Apache Cassandra and HBase Examples of popular NoSQL databasesSome popular examples of NoSQL databases include MongoDB Cassandra and Couchbase MongoDB is a document based database that is widely used for web and mobile applications Cassandra is a column family database that is designed for high scalability and availability Couchbase is a document based database that is designed for high performance and availability in distributed environments Comparing Relational and NoSQL DatabasesRelational and NoSQL databases have different strengths and weaknesses and each type of database is suited to different use cases The table below shows some key differences between the two types of databasesRelational DatabasesNoSQL DatabasesData ModelUse a structured table based approach to store dataUse a variety of data models such as document based key value graph and column familySchemaHave a fixed schema meaning that the structure of the database is defined in advance and must be adhered toTypically have a flexible or dynamic schema meaning that the structure of the database can evolve over timeScalabilityTypically designed to scale vertically requiring more powerful hardware to handle increased traffic or data volumesDesigned to scale horizontally meaning that they can easily add more nodes to a cluster in order to handle increased traffic or data volumesTransactionsTypically designed to handle transactions which are sets of related database operations that must be completed togetherMay or may not support transactions depending on the specific database and data model being used Query optimizationIn addition to the differences in data models and scalability and others shown in the table above there are other key factors to consider when choosing between a relational database and a NoSQL database One of these factors is Query Optimization which involves designing queries to retrieve data from the database in the most efficient manner possible The approach to query optimization and indexing strategies can vary significantly between relational and NoSQL databases Query optimization and indexing strategies play a critical role in the performance of any database system Relational databases use a declarative language such as SQL to execute queries against the database These queries are optimized by the query optimizer which determines the most efficient way to execute the query by analyzing the schema indexes and statistics of the tables involved in the query Indexing in a relational database involves creating data structures that enable faster lookup and retrieval of data Indexes are usually created on columns that are frequently used in WHERE clauses or JOINs as these columns are used to filter or join rows in the table In contrast NoSQL databases do not use SQL and queries are typically expressed using a query language that is specific to the database Query optimization in NoSQL databases involves choosing the most efficient data model and query language for the application s requirements Since NoSQL databases do not enforce a fixed schema indexing strategies can be more flexible and varied Some NoSQL databases may use automatic indexing while others may require manual configuration It s important to note that the choice of database and indexing strategy should be driven by the specific requirements of the application In general relational databases are more suitable for applications that require complex queries and data consistency while NoSQL databases are better suited for applications that require scalability flexibility and high availability Advantages and DisadvantagesEach type of database has its own advantages and disadvantages Here are some of the pros and cons of each type Relational DatabasesAdvantages Relational databases are well suited to handling structured data and complex queries They also provide strong data consistency and integrity making them a good choice for applications that require ACID Atomicity Consistency Isolation Durability transactions Disadvantages Relational databases can be less flexible than NoSQL databases particularly when it comes to handling unstructured data or scaling horizontally They can also be more expensive to scale and maintain due to the need for powerful hardware and specialized database administrators NoSQL DatabasesAdvantages NoSQL databases are well suited to handling unstructured or semi structured data making them a good choice for handling large volumes of data in real time They can also be highly scalable and flexible and can be less expensive to maintain than relational databases Disadvantages NoSQL databases can be less well suited to handling complex queries and transactions than relational databases They may also be less consistent or durable depending on the specific database and data model being used Use Case ScenariosWhile the advantages and disadvantages of relational and NoSQL databases are well documented it can be difficult to understand how these differences translate to real world use cases Here are some examples of scenarios where one type of database may be more suitable than the other Relational Database Use CasesE commerce Online stores that manage large amounts of customer data order information and inventory levels may benefit from using a relational database By using a fixed schema and enforcing data normalization rules an e commerce company can ensure data consistency and reduce the risk of errors or duplicates Banking Financial institutions that need to manage transactions account balances and customer information may also benefit from a relational database By using transactions and enforcing strict data constraints a bank can ensure the accuracy and integrity of its data which is critical for regulatory compliance Healthcare Hospitals and medical clinics that need to manage patient records appointment schedules and test results may benefit from a relational database By using a fixed schema and enforcing data normalization rules healthcare providers can ensure data accuracy and consistency across multiple systems and applications NoSQL Database Use CasesSocial Media Social media platforms that need to manage large amounts of unstructured data such as user profiles posts and comments may benefit from using a NoSQL database By using a flexible schema and allowing for easy horizontal scaling a social media platform can handle unpredictable spikes in traffic and user data volumes IoT Internet of Things IoT applications that need to manage real time data streams such as sensor readings location data and device statuses may also benefit from a NoSQL database By using a key value or document based data model an IoT application can store and process data quickly and efficiently without being constrained by a fixed schema Gaming Online games that need to manage complex player profiles game states and leaderboards may benefit from a NoSQL database By using a graph or document based data model a gaming company can store and query data quickly and efficiently while also allowing for easy horizontal scaling to accommodate millions of players These examples demonstrate how different types of databases can be used to solve specific business problems and can help you to decide which type of database is best suited for your needs ConclusionTo end with relational databases and NoSQL databases each have their own unique strengths and weaknesses Relational databases use a structured table based approach to store data have a fixed schema and are designed to scale vertically NoSQL databases use a variety of data models have a flexible schema and are designed to scale horizontally Additionally NoSQL databases may or may not support transactions When it comes to choosing between a relational database and a NoSQL database it s important to consider factors such as the specific use case the amount of data being stored and the level of flexibility required Happy Hacking Bentil hereWhich of these two databases do you use What is your experience with either of them I will be glad to hear from you in the comment section This will help other who are yet to use these databases in their projects If you find this content helpful Please Like comment and share	2023-04-03 18:04:42
海外TECH	Engadget	Yale's Assure Lock 2 is down to its lowest price ever	https://www.engadget.com/yales-assure-lock-2-is-down-to-its-lowest-price-ever-182029122.html?src=rss	Yale x s Assure Lock is down to its lowest price everThe Yale Assure Lock can automatically open your door takes voice commands and let guests in with a code Right now at Amazon the keyless configuration of the smart lock is down to its lowest price since its debut with a percent discount that makes it instead of The deal applies to the black finish only ーthe nickel finish is seeing a nine percent or discount and the bronze version is full price The discounted model includes both WiFi and Bluetooth connectivity and has a touchscreen keypad for access for visitors or when you re not using your phone Best Buy is offering the same discount so if you prefer shopping there you can still save nbsp We were impressed with the lock giving it a particularly high score of in our review In most cases it only requires a Phillips head screwdriver to install and it took our reviewer about a half hour and that included some minor troubleshooting with customer service Keep in mind that this replaces your entire deadbolt so it may not be an option for renters Once installed you can assign multiple entry codes for different family members or other visitors and the lock instantly notifies you of anyone who s gained access nbsp Primary users can set up the Assure Lock to open when their Apple Watch is nearby or by using the app on a smartphone or home hub smart display The lock also works with voice controls using any smart home assistant but requires a spoken pin for added security Exposing any system to remote access capabilities can leave room for exploitation so Yale includes two layer encryption two factor authentication and biometric verification to make things more secure nbsp One drawback is that the lock uses disposable AA batteries Due to fire safety regulations there s no option for a rechargeable battery pack If the batteries happen to die when you re out an about you can hold a volt battery up to the bottom of the lock to give it enough juice to let you in so you can swap out the cells nbsp Follow EngadgetDeals on Twitter and subscribe to the Engadget Deals newsletter for the latest tech deals and buying advice This article originally appeared on Engadget at	2023-04-03 18:20:29
海外TECH	Engadget	Sony’s next pair of budget earbuds will reportedly cost $120 when they arrive this month	https://www.engadget.com/sonys-next-pair-of-budget-earbuds-will-reportedly-cost-120-when-they-arrive-this-month-180247649.html?src=rss	Sony s next pair of budget earbuds will reportedly cost when they arrive this monthSony s upcoming WF CN earbuds have leaked courtesy of Best Buy In a listing spotted by WinFuture s Roland Quandt the retailer revealed the true wireless buds will go on sale on April st for That s more than the WF C model they re expected to replace According to Android Police which saw the listing before it was removed by Best Buy the nbsp WF CN will offer active noise cancellation thanks to the inclusion of Sony s “Noise Sensor Technology Bluetooth connectivity means the earbuds will also support two simultaneous connections Additionally the WF CN will carry over a few features from the WF C Expect the inclusion of Sony s Digital Sound Enhancement Engine for restoring lost detail in Bluetooth audio and IPX certified splash protection MySmartPriceBattery life reportedly comes in at hours total with the included charging case It s unclear if that takes ANC use into account For comparison you can get up to hours of total playtime from the WF C but then they don t feature noise cancelation One thing Best Buy didn t reveal is what Bluetooth codecs the WF CN will come with out of the box It would be nice to see aptX and LDAC support but given that the WF C limit you to SBC and AAC that may be hoping for too much Provided the listing is accurate we ll get clarity on those details soon enough This article originally appeared on Engadget at	2023-04-03 18:02:47
海外TECH	CodeProject Latest Articles	How to Programmatically Create HTML, ODT, DOCX & PDFs Documents for Free	https://www.codeproject.com/Articles/5358126/How-to-Programmatically-Create-HTML-ODT-DOCX-PDFs	software	2023-04-03 18:14:00
海外TECH	CodeProject Latest Articles	Simon Says, A Child's Game	https://www.codeproject.com/Articles/5358225/Simon-Says-A-Childs-Game	gamesimon	2023-04-03 18:01:00
海外科学	NYT > Science	Latest Updates: NASA Introduces the 4 Astronauts of the Artemis II Moon Mission	https://www.nytimes.com/live/2023/04/03/science/artemis-nasa-news	artemis	2023-04-03 18:35:34
海外科学	NYT > Science	Canada Earns a Spot Among NASA’s Artemis II Crew	https://www.nytimes.com/2023/04/03/science/artemis-ii-moon-canada.html	gateway	2023-04-03 18:24:56
海外TECH	WIRED	Sonos Era 300 Review: Stunning Spatial, Superior Sound	https://www.wired.com/review/review-sonos-era-300/	speaker	2023-04-03 18:12:39
海外TECH	WIRED	Massive 3CX Supply-Chain Hack Targeted Cryptocurrency Firms	https://www.wired.com/story/3cx-supply-chain-attack-north-korea-cryptocurrency-targets/	precision	2023-04-03 18:03:55
ニュース	@日本経済新聞 電子版	キャッシュレス決済初の100兆円超え　22年、QRが急伸 https://t.co/ek5DQ8UtVy	https://twitter.com/nikkei/statuses/1642951553547141121	キャッシュレス決済	2023-04-03 18:05:49
ニュース	@日本経済新聞 電子版	インド「我々の時代来る」　グローバルサウスの盟主自認 https://t.co/WZPKKnCGnW	https://twitter.com/nikkei/statuses/1642951330141704192	盟主	2023-04-03 18:04:56
ニュース	@日本経済新聞 電子版	トランプ氏、4日に出頭・罪状認否へ　重罪含むとの見方 https://t.co/FRkomgCoMN	https://twitter.com/nikkei/statuses/1642950814846287872	罪状認否	2023-04-03 18:02:53
ニュース	BBC News - Home	Darya Trepova: Russia releases video of suspect in cafe killing of Vladlen Tatarsky	https://www.bbc.co.uk/news/world-europe-65161095?at_medium=RSS&at_campaign=KARANGA	darya	2023-04-03 18:02:55
ニュース	BBC News - Home	Timothy Schofield found guilty of sexually abusing boy	https://www.bbc.co.uk/news/uk-england-bristol-65167274?at_medium=RSS&at_campaign=KARANGA	activity	2023-04-03 18:37:21
ニュース	BBC News - Home	University strikes mandate renewed for six more months	https://www.bbc.co.uk/news/education-65168937?at_medium=RSS&at_campaign=KARANGA	proposals	2023-04-03 18:17:51
ニュース	BBC News - Home	Ros Atkins on… Why were there queues at Dover?	https://www.bbc.co.uk/news/uk-65169959?at_medium=RSS&at_campaign=KARANGA	dover	2023-04-03 18:28:29
ニュース	BBC News - Home	IPL 2023: England's Moeen Ali takes 4-26 as Chennai Super Kings beat Lucknow Super Giants	https://www.bbc.co.uk/sport/cricket/65168691?at_medium=RSS&at_campaign=KARANGA	IPL England x s Moeen Ali takes as Chennai Super Kings beat Lucknow Super GiantsChennai Super Kings bounce back from their opening game IPL defeat to beat Lucknow Super Giants by runs	2023-04-03 18:36:19
ビジネス	ダイヤモンド・オンライン - 新着記事	「新人研修は全員参加、宿泊必須」「不参加で」拒否する新人を会社はクビにできる？ - 組織を壊す「自分ファースト」な社員たち　木村政美	https://diamond.jp/articles/-/320634	「新人研修は全員参加、宿泊必須」「不参加で」拒否する新人を会社はクビにできる組織を壊す「自分ファースト」な社員たち木村政美この春入社した人の新卒社員を対象に、新人社員研修が始まった。	2023-04-04 03:55:00
ビジネス	ダイヤモンド・オンライン - 新着記事	日銀総裁の交代でマンション市場どうなる？「後悔しない物件」の買い方とは - 不動産の新教科書	https://diamond.jp/articles/-/320633	任期満了	2023-04-04 03:50:00
ビジネス	ダイヤモンド・オンライン - 新着記事	【京都】JA赤字危険度ランキング2023、5農協中2農協が赤字転落 - 全国512農協 JA赤字危険度ランキング2023	https://diamond.jp/articles/-/320068		2023-04-04 03:45:00
ビジネス	ダイヤモンド・オンライン - 新着記事	【滋賀】JA赤字危険度ランキング2023、9農協中ワーストは2億円の赤字 - 全国512農協 JA赤字危険度ランキング2023	https://diamond.jp/articles/-/320067		2023-04-04 03:40:00
ビジネス	ダイヤモンド・オンライン - 新着記事	マスク氏、ツイッターで「銀行の夢」再燃 - WSJ PickUp	https://diamond.jp/articles/-/320632	wsjpickup	2023-04-04 03:35:00
ビジネス	ダイヤモンド・オンライン - 新着記事	【社説】金融パニックで責任転嫁のイエレン氏 - WSJ PickUp	https://diamond.jp/articles/-/320631	wsjpickup	2023-04-04 03:30:00
ビジネス	ダイヤモンド・オンライン - 新着記事	トランプ氏の起訴、米国で党派の分断深まる - WSJ PickUp	https://diamond.jp/articles/-/320630	wsjpickup	2023-04-04 03:25:00
ビジネス	ダイヤモンド・オンライン - 新着記事	「貯金できない」あなたは知らずに脳にだまされているかもしれない理由 - ニュースな本	https://diamond.jp/articles/-/319142	「貯金できない」あなたは知らずに脳にだまされているかもしれない理由ニュースな本物価が上がる一方で、賃金が上がらない昨今、「貯金ができない」「お金が増えない」と嘆く人は多いと思いますが、お金が増えないのには理由があります。	2023-04-04 03:20:00
ビジネス	ダイヤモンド・オンライン - 新着記事	「入社したら一生安泰」だった大手企業の離職者が止まらない“深刻な理由” - 佐久間宣行のずるい仕事術	https://diamond.jp/articles/-/320238	「入社したら一生安泰」だった大手企業の離職者が止まらない“深刻な理由佐久間宣行のずるい仕事術「読者が選ぶビジネス書グランプリ」で、『佐久間宣行のずるい仕事術』が総合グランプリとビジネス実務部門賞をダブル受賞。	2023-04-04 03:10:00