投稿時間:2021-11-14 03:18:54 RSSフィード2021-11-14 03:00 分まとめ(21件)

カテゴリー等	サイト名等	記事タイトル・トレンドワード等	リンクURL	頻出ワード・要約等/検索ボリューム	登録日
Program	[全てのタグ]の新着質問一覧｜teratail（テラテイル）	フォームの一部をキャプチャする	https://teratail.com/questions/369189?rss=all	フォームの一部をキャプチャするVBNETでフォームの一部をキャプチャするプログラムを作りましたが、入力したデータやラベルの文字が表示できません。	2021-11-14 02:57:30
Program	[全てのタグ]の新着質問一覧｜teratail（テラテイル）	LaTexで、VSCodeの自動コンパイルを使うとき、あるファイルを開いたまま別のファイルをコンパイルしたい。	https://teratail.com/questions/369188?rss=all	latex	2021-11-14 02:22:22
技術ブログ	Developers.IO	[Terraform]EC2 Linux作成時のユーザデータにtemplatefile関数を使用する	https://dev.classmethod.jp/articles/terraform-ec2-linux-userdata-templatefile/	terraf	2021-11-13 17:14:59
海外TECH	MakeUseOf	8 Ways the iPhone 13 Is Environmentally Friendly	https://www.makeuseof.com/how-the-latest-iphone-is-environmentally-friendly/	benefits	2021-11-13 17:30:22
海外TECH	MakeUseOf	How to Split One Windows PC Between Two People With Multiseat Software	https://www.makeuseof.com/split-one-windows-pc-between-two-people-multiseat/	windows	2021-11-13 17:16:12
海外TECH	DEV Community	CSS variables: Everything you need to know about	https://dev.to/thatanjan/css-variables-everything-you-need-to-know-about-3jjn	CSS variables Everything you need to know aboutIn this blog you will learn about css variables also known as custom properties You will learn Why do Css variables exist How to use it How cascading works with Css variables How to access css variables in javascript How to change css variables with javascript For example You have containers on your webpage All of the containerthe background color will be red container background red container background red container background red container background red container background red Ok That works fine But after some time you started to think that the background color should be blue Now you want to change the color But now you have to every single container and apply the change It will work but that is too much work So What is the solution What if we can change the color once and that will be applied everywhere Yes we can do that with the help of css variables Css variables are officially known as custom properties So How to use Css variables We normally define variables at the root At the top of your css file select the root like this root Inside the root you have to declare the variables just like you use properties and value in CSS root primary bg color red SyntaxVariable name has to start will double dash For example primary bg color secondary bg color primary font colorNow you can use those variables as a value in any valid css property Let s apply it to our example root primary bg color red container background var primary bg color container background var primary bg color container background var primary bg color container background var primary bg color container background var primary bg color Now if you want to change the color just change the value of the variable And changes will be applied everywhere root primary bg color blue container background var primary bg color container background var primary bg color container background var primary bg color container background var primary bg color container background var primary bg color By the way this blog is originally published on cules coding website I would be glad if you give it a visit How cascading works with Css variables You can re declare the value for any css selector Like this root primary bg color blue container background var primary bg color container primary bg color green background var primary bg color container background var primary bg color container background var primary bg color container background var primary bg color Now the container will have a background color of green But other containers background colors will not change But why All of the places that you are using CSS variables are just inheriting values from the root But when you redeclare the variable for container all of the elements inside the container are just inheriting the value from the container If the container would look like below what would be the color of subcontainer root primary bg color blue container primary bg color green background var primary bg color container subcontainer background teal container subcontainer subcontainer background var primary bg color Will it be green or blue It will be green Why BecauseThe CSS parser will check inside subcontainer first Then it will not find anything Then it will check subcontainer s parent subcontainer Again it will find anything Then it will check subcontainer s parent container This time the parser will see avariable declaration that has the value green It will not check anything So this is how it works I know it sounds complex If you know javascript it is like scope chain in javscript How to access CSS variables in javascript Yes you can access css variables in javascript and also you can manipulate them const allStyles getComputedStyle document documentElement returns all css styles as an objectconst bgColor allStyles getPropertyValue primary bg color console log bgColor green or whatever the value is How to change css variables with javascript Get the root elementconst root document querySelector root changing the value of variableroot style setProperty primary bg color red And that s all you need to know about css variables Shameless PlugI have made a video about how to build a carousel postcard with React Material UI and Swiper js If you are interested you can check the video You can also demo the application form herePlease like and subscribe to Cules Coding It motivates me to create more content like this If you have any questions please comment down below You can reach out to me on social media as thatanjan Stay safe Goodbye About me Why do I do what I do The Internet has revolutionized our life I want to make the internet more beautiful and useful What do I do I ended up being a full stack software engineer What can I do I can develop complex full stack web applications like social media applications or e commerce sites What have I done I have developed a social media application called Confession The goal of this application is to help people overcome their imposter syndrome by sharing our failure stories I also love to share my knowledge So I run a youtube channel called Cules Coding where I teach people full stack web development data structure algorithms and many more So Subscribe to Cules Coding so that you don t miss the cool stuff Want to work with me I am looking for a team where I can show my ambition and passion and produce great value for them Contact me through my email or any social media as thatanjan I would be happy to have a touch with you ContactsEmail thatanjan gmail comlinkedin thatanjanportfolio anjanGithub thatanjanInstagram personal thatanjanInstagram youtube channel thatanjanTwitter thatanjanFacebook thatanjanBlogs you might want to read Eslint prettier setup with TypeScript and react What is Client Side Rendering What is Server Side Rendering Everything you need to know about tree data structure reasons why you should use NextjsVideos might you might want to watch	2021-11-13 17:39:59
海外TECH	DEV Community	How to learn AWS for free ?	https://dev.to/aws-builders/how-to-learn-aws-for-free--2inc	How to learn AWS for free 𝑻𝒘𝒐𝑮𝒓𝒆𝒂𝒕𝒂𝒏𝒅𝑭𝒓𝒆𝒆𝑨𝑾𝑺𝒍𝒆𝒂𝒓𝒏𝒊𝒏𝒈𝒓𝒆𝒔𝒐𝒖𝒓𝒄𝒆𝒔below AWS skill builder Free AWS trainings at amazon com I just buy free one and watching dataanalytics original LI post at li activity	2021-11-13 17:18:41
海外TECH	DEV Community	Data Science toolset summary from 2021	https://dev.to/amananandrai/data-science-toolset-summary-from-2021-1dbi	Data Science toolset summary from The year is about to end so let us recall and recollect what different tools have been used by Data Professionals throughout the entire year I am using the term Data Professionals to refer to all different jobs associated with data like Data Scientists Data Analysts Data Engineers To become a better Data Professional we need to have knowledge of different domains but the most important skill set required are knowledge of Databases and SQL languages like Python R Julia JavaScript etc experience in Data Visualization tools like Tableau and PowerBI and knowledge of Bigdata and Cloud Technologies In this post I am going to give a list of different tools and technologies which have been used extensively by Data Professionals throughout the year and the expertise of these can make you one of the best in the industry This list is based on a survey conducted by Kaggle the biggest community of Data Scientists I have used the term toolset because it is a comprehensive list of tools from different domains IDEThe most common languages used for Data Science are Python R JavaScript MATLAB Julia along with SQL These languages are used for data analysis and visualization building machine learning algorithms implementing data pipelines and various other things related to Data science The most important tool we require are IDEs Integrated Development Environments where we write code compile them and then execute them to view the output Here is a list of most common IDEs used by different Data professionals for development which makes their life easier Jupyter Notebook Jupyter Notebook is a web based interactive computational environment for creating Jupyter notebook documents It supports several languages like Python IPython Julia R etc and is largely used for data analysis data visualization and further interactive exploratory computing Visual Studio Code Visual Studio Code VS Code is a source code editor made by Microsoft for Windows Linux and macOS Features include support for debugging syntax highlighting intelligent code completion snippets code refactoring and embedded Git It can be used for writing code in many languages and is one of the most popular IDE among Software engineers as well for its wide variety of features Jupyter Lab JupyterLab is the next generation user interface including notebooks It has a modular structure where you can open several notebooks or files e g HTML Text Markdowns etc as tabs in the same window It offers more of an IDE like experience PyCharm PyCharm is an IDE used specifically for the Python language It is developed by the Czech company JetBrains It provides code analysis a graphical debugger an integrated unit tester integration with version control systems and supports web development with Django as well as data science with Anaconda PyCharm is cross platform with Windows macOS and Linux versions R Studio RStudio is an IDE for R a programming language for statistical computing data science and data visualization It is available in two formats RStudio Desktop is a regular desktop application while RStudio Server runs on a remote server and allows accessing RStudio using a web browser Spyder Spyder is an open source cross platform IDE for scientific programming in the Python language Spyder integrates with a number of prominent packages in the scientific Python stack including NumPy SciPy Matplotlib pandas IPython SymPy and Cython as well as other open source software Notepad Notepad is a text and source code editor for use with Microsoft Windows It supports tabbed editing which allows working with multiple open files in a single window Sublime text Sublime Text is a commercial source code editor It natively supports many programming languages and markup languages Users can expand its functionality with plugins typically community built and maintained under free software licenses To facilitate plugins Sublime Text features a Python API Vim or Emacs Vim is a free and open source screen based text editor program for Unix Emacs or EMACS Editor MACroS is a family of text editors that are characterized by their extensibility The manual for the most widely used variant GNU Emacs describes it as the extensible customizable self documenting real time display editor These two are used in the UNIX and LINUX based systems and are one of the oldest text editors MATLAB MATLAB is a proprietary multi paradigm programming language and numeric computing environment developed by MathWorks MATLAB allows matrix manipulations plotting of functions and data implementation of algorithms creation of user interfaces and interfacing with programs written in other languages AlgorithmsMachine Learning is an integral part of Data Science and most of us are fascinated by the type of things it is doing in our day to day life like Self driven cars Robots and AI assistants talking in almost human language detection of diseases like cancer facial recognition etc All these things are only possible because of data and the ML algorithms which work on this data ML algorithms which are most widely used by Data scientists is listed below It includes a wide variety of algorithms from most basic algorithms like regression and decision trees to high profile Deep Learning algorithms like Transformers GANs and RNNs Linear and Logistic Regression These are the most basic algorithms of the ML ecosystem Almost every data scientist learns these two algorithms as their first ML algorithm Linear regression algorithm is basically a curve fitting algorithm which is used to determine trends and predict the value of dependent variable from independent variables Logistic regression is used for classification tasks and finds probability of class Decision Trees or Random Forests Decision Trees are another popular ML algorithm where based on certain decisions the possible consequences are defined It can be used for both classification and regression task It is an ensemble technique that combines many different decision trees to give the output For classification tasks the output of the random forest is the class selected by most trees For regression tasks the mean or average prediction of the individual trees is returned Gradient Boosting Machines Gradient boosting is a machine learning technique used in regression and classification tasks among others It gives a prediction model in the form of an ensemble of weak prediction models which are typically decision trees Convolutional Neural Networks A convolutional neural network CNN is a class of artificial neural network most commonly applied to analyze visual imagery They have applications in image and video recognition recommender systems image classification image segmentation medical image analysis natural language processing brain computer interfaces and financial time series CNNs are regularized versions of multilayer perceptrons Bayesian Approaches Bayesian inference is a method of statistical inference in which Bayes theorem is used to update the probability for a hypothesis as more evidence or information becomes available Dense Neural Networks It is another class of neural networks that is connected deeply which means each neuron in the dense layer receives input from all neurons of its previous layer The dense layer is found to be the most commonly used layer in the models Recurrent Neural Networks Recurrent Neural Network RNN are a type of Neural Network where the output from previous step are fed as input to the current step It is the first algorithm that remembers its input due to an internal memory which makes it perfectly suited for machine learning problems that involve sequential data The different variants of RNN architecture are Bidirectional recurrent neural networks BRNN Long short term memory LSTM and Gated recurrent units GRUs Transformer Networks A transformer is a deep learning model that adopts the mechanism of attention differentially weighting the significance of each part of the input data It is used primarily in the field of natural language processing NLP and in computer vision CV Like recurrent neural networks RNNs transformers are designed to handle sequential input data such as natural language for tasks such as translation and text summarization Some famous Transformer architectures are BERT and GPT Generative Adversarial Network Generative Adversarial Networks or GANs for short are an approach to generative modeling using deep learning methods such as convolutional neural networks Generative modeling is an unsupervised learning task in machine learning that involves automatically discovering and learning the regularities or patterns in input data in such a way that the model can be used to generate or output new examples that plausibly could have been drawn from the original dataset The GAN model architecture involves two sub models a generator model for generating new examples and a discriminator model for classifying whether generated examples are real from the domain or fake generated by the generator model Evolutionary Approaches Evolutionary algorithms are a heuristic based approach to solving problems that cannot be easily solved in polynomial time such as classically NP Hard problems and anything else that would take far too long to exhaustively process Genetic Algorithm is the most common evolutionary algorithm It is used in Optimization of the neural networks and ML models Machine Learning FrameworksThere are many frameworks built in many languages but mostly Python which have the code for implementing the various ML algorithms discussed above These frameworks make the life of Data scientists quite easier as they have to just call a simple Python function to implement the most complex of ML algorithms without getting into the nitty gritty of them Some of the most prominent ML frameworks are listed below Scikit learn It is one of the most widely used frameworks for Python based Data science tasks It features various classification regression and clustering algorithms including support vector machines random forests gradient boosting k means and DBSCAN and is designed to interoperate with the Python numerical and scientific libraries NumPy and SciPy Link Tensorflow It is mainly used for training ML models which are based on Neural networks and Deep Learning TensorFlow was developed by the Google Brain team for internal Google use It can be used in a wide variety of programming languages most notably Python as well as Javascript C and Java This flexibility lends itself to a range of applications in many different sectors Link Xgboost XGBoost is an open source software library which provides a regularizing gradient boosting framework for C Java Python R Julia Perl and Scala It implements machine learning algorithms under the Gradient Boosting framework It provides a parallel tree boosting also known as GBDT GBM that solve many data science problems in a fast and accurate way The same code runs on major distributed environment Hadoop SGE MPI and can solve problems beyond billions of examples Link Keras Keras is an open source software library that provides a Python interface for artificial neural networks Keras acts as an interface for the TensorFlow library Link PyTorch PyTorch is an open source machine learning library based on the Torch library used for applications such as computer vision and natural language processing primarily developed by Facebook s AI Research lab It is free and open source software released under the Modified BSD license Link LightGBM LightGBM short for Light Gradient Boosting Machine is a free and open source distributed gradient boosting framework for machine learning originally developed by Microsoft It is based on decision tree algorithms and used for ranking classification and other machine learning tasks Link Catboost CatBoost is an open source software library developed by Yandex It provides a gradient boosting framework which attempts to solve for Categorical features using a permutation driven alternative compared to the classical algorithm Link Huggingface It is open source library for building transformer based language models It is used in the field of Natural Language Processing Large language models like BERT GPT etc are implemented using this library Link Prophet It is a time series forecasting library built by Facebook Prophet is a procedure for forecasting time series data based on an additive model where non linear trends are fit with yearly weekly and daily seasonality plus holiday effects It works best with time series that have strong seasonal effects and several seasons of historical data Prophet is robust to missing data and shifts in the trend and typically handles outliers well Link Caret The caret package short for Classification And REgression Training is a set of functions that attempt to streamline the process for creating predictive models The package contains tools for data splitting pre processing feature selection model tuning using resampling variable importance estimation as well as other functionality Link Pytorch Lightning PyTorch Lightning is an open source Python library that provides a high level interface for PyTorch a popular deep learning framework It is a lightweight and high performance framework that organizes PyTorch code to decouple the research from the engineering making deep learning experiments easier to read and reproduce It is designed to create scalable deep learning models that can easily run on distributed hardware while keeping the models hardware agnostic Link Fast ai It is open source library for deep learning called fastai without a period sitting atop PyTorch Link Tidymodels The tidymodels framework is a collection of packages for modeling and machine learning using tidyverse principles It is built using R language Link H HO is an open source in memory distributed fast and scalable machine learning and predictive analytics platform that allows you to build machine learning models on big data and provides easy productionalization of those models in an enterprise environment Link MXNet Apache MXNet is an open source deep learning software framework used to train and deploy deep neural networks Link JAX JAX is NumPy on the CPU GPU and TPU with great automatic differentiation for high performance machine learning research JAX is Autograd and XLA brought together for high performance machine learning research What s new is that JAX uses XLA to compile and run your NumPy programs on GPUs and TPUs Link Cloud Data Storage ProductsThe most important aspect of Data science is data without it nothing is possible We need resources to store data With the advent of Cloud technologies it has become easier to store data and manage it smoothly The below list has the best Cloud Data Storage Products from the best in the business tech giants like Google Amazon and Microsoft Google Cloud Filestore Amazon Elastic Filesystem Microsoft Azure Disk Storage Microsoft Azure Data Lake Storage Google Cloud Storage Amazon Simple Storage Service Enterprise Machine Learning ToolsThese are the tools used by large business organizations Amazon Sagemaker Amazon SageMaker is a cloud machine learning platform that was launched in November SageMaker enables developers to create train and deploy machine learning models in the cloud SageMaker also enables developers to deploy ML models on embedded systems and edge devicesLink Databricks Databricks is an enterprise software company founded by the creators of Apache Spark The company has also created Delta Lake MLflow and Koalas open source projects that span data engineering data science and machine learning Link Azure Machine Learning Studio Azure Machine Learning studio is a web portal in Azure Machine Learning that contains low code and no code options for project authoring and asset management Link Google Cloud Vertex AI Vertex AI brings together the Google Cloud services for building ML under one unified UI and API In Vertex AI you can now easily train and compare models using AutoML or custom code training and all your models are stored in one central model repository These models can now be deployed to the same endpoints on Vertex AI Link DataRobot DataRobot the Boston based Data Science company enables business analysts to build predictive analytics with no knowledge of Machine Learning or programming It uses automated ML to build and deploy accurate predictive models in a short span of time Link Rapidminer RapidMiner is a data science software platform developed by the company of the same name that provides an integrated environment for data preparation machine learning deep learning text mining and predictive analytics Link Alteryx Alteryx empowers analysts to prep blend and analyze data faster with hundreds of no code low code analytic building blocks that enable highly configurable and repeatable workflows Link Dataiku Dataiku enables teams to create and deliver data and advanced analytics using the latest techniques at scale The software Dataiku Data Science Studio DSS supports predictive modelling to build business applications Link Database ProductsThe databases are very important for Datascience in this list there are SQL and No SQL databases along with big data related database products These are profoundly used databases MySQL PostgreSQL Microsoft SQL Server MongoDB Google Cloud BigQuery Oracle Database Microsoft Azure SQL Database Amazon Redshift Snowflake Google Cloud SQL Amazon DynamoDB Microsoft Azure Cosmos DB Google Cloud Bigtable IBM Db Google Cloud Firestore Amazon Aurora Google Cloud Spanner Machine Learning Experiment ToolsThe list below shows tools which are used for machine learning explainability and helping us better understand the ML algorithms like Tensorboard It also contains tools for MLOPs like Weights and Biases ClearML Neptune ai etc They are used to measure performance of models keep logs optimize ML pipelines automate pipelines and tune hyperparameters TensorBoard MLflow Weights amp Biases Neptune ai ClearML Guild ai Polyaxon Comet ml Domino Model Monitor Automated Machine Learning FrameworksAutomated machine learning AutoML is the process of applying machine learning ML models to real world problems using automation More specifically it automates the selection composition and parameterization of machine learning models These frameworks help in implementing AutoML The different steps in traditional ML are data pre processing feature engineering feature extraction feature selection algorithm selection and hyperparameter optimization AutoML helps automate this entire pipeline AutoML dramatically simplifies these steps for non experts Google Cloud AutoML Azure Automated Machine Learning Amazon Sagemaker Autopilot HO Driverless AI Databricks AutoML DataRobot AutoML	2021-11-13 17:13:52
海外TECH	DEV Community	Magento Tips - Pentest with sqlmap	https://dev.to/rhuaridh/magento-tips-pentest-with-sqlmap-1cn0	Magento Tips Pentest with sqlmap Pentest MagentoMagento is popular and hard to upgrade This creates the perfect breeding ground for insecure eCommerce stores which hackers love to exploit A common tool used by penetration testers to detect insecure sites is sqlmap In a nutshell sqlmap is an open source tool that automates the process of detecting and exploiting SQL injection flaws Install sqlmapFirst we need to install sqlmap locally this assumes that you have python installed already git clone depth sqlmap devcd sqlmap devIt should go without saying that you should only ever use sqlmap against your own websites Create a sample SQL injection flaw to testFor testing purposes on our local site we can create a SQL injection flaw to test this against It is important that you never deploy this code live for obvious reasons In my case I just added this to my test controller connection this gt resourceConnection gt getConnection year GET year rows connection gt fetchAll SELECT count as total FROM sales order WHERE created at year total rows total echo Hello World There are total orders on this site exit As you can see we are bypassing the ORM and failing to escape and validate the year input variable This should never be done but yet it is not uncommon to see this in third party extensions Here is what our vulnerable extension looks like Finding a vulnerable parameterFind our magento store url I will use So on our local machine we can now run sqlmap python sqlmap py u dbms mysql sql shellThis command will quickly identify that year is vulnerable The sql shell will then open a shell for us to run queries in Retrieving dataFor example to pull a list of admin e mail addresses you can run SELECT email FROM admin user And that s it That is how easy it is We now have a list of all the admin e mail addresses on the magento store How do I stop SQL injection Always make sure you use the ORM never pass a variable into a query string and always validate user supplied input It s a simple as that Best practice exists for a reason	2021-11-13 17:12:05
海外TECH	DEV Community	How to Migrate Environment Variables (ENV) to Rails Credentials	https://dev.to/thomasvanholder/how-to-migrate-environment-variables-env-to-rails-credentials-15ha	How to Migrate Environment Variables ENV to Rails CredentialsWhy Should You Move From Env to Credentials How it WorksMigrate From Env to Rails CredentialsHow to Use Credentials in Multiple File FormatsHow to Share Keys With a TeamInitially published on Medium on Jan th One key rules them allRails credentials are the new gold standard ENV files are an insecure ancestor In this article you ll learn why and how to migrate how to use API keys in Ruby YML and js erb and how to share a single key once with your team DHH tweeted about its arrival nearly three years ago but new technology often takes time to catch up A wake up call is when you find yourself too frequently juggling API keys between developers in your team It might be time to take a second look at how to implement credentials in a rails app Why Should You Move From Env to Credentials The further a project gets in its development cycle the more services are integrated Every external service has its API key It usually doesn t take too long before developers start hunting teammates for the latest API key How annoying Or just imagine when an API key gets refreshed Every developer individually has to update it into local dotenv files That seems anti automation and anti programmatic ーand it is Stop throwing API keys through Slack or email and avoid a security breach of your keys Luckily rails credentials offer an easy and welcoming successor Uploading your keys to Github Uploading to Github Yes uploading to Github A small annotation is that the API keys are fully encrypted The big win is that there is only a single key to share with your team It never changes Any new API keys added by your fellow developers as rails credentials are pulled from Github as you pull the latest main prev master You can find the key in the config master key folder How it WorksRunning bin rails credentials edit in rails creates two files needed in the config folder credentials yml enc stores all your API keys In case you were wondering the enc extension signifies encryption master key is the key use to decrypt the encrypted file Make sure to check the inclusion of the master key in your gitignore yml file Credentials yml enc is safe and secure sent along with your repository to Github The master key however is never sent along ーguard it like your life depends on it Migrate From Env to Rails CredentialsOpen the credentials file by running the following in your terminal EDITOR code wait bin rails credentials edit Depending on the editor you currently use replace code VS Code For example vim or vi Vimatom Atomsubl or stt SublimeThe credentials file automatically opens in the editor and waits to for you to update and close the file again Migrate the ENV keys you are using in the env file to the credentials yml file Turn your legacy ENV file STRIPE PUBLISHABLE KEY pk test VGLlUNDcZScAOJVyWyIRJwzYZkqMKctSTRIPE SECRET KEY sk test VGLlUNDcZScAOJVyWyIRJwzYZkqMKctgAAYFSTRIPE WEBHOOK SECRET KEY whsec cZpBVGcZpBVGcZpBVGUrgAgcZpBVGcZpBCLOUDINARY URL cloudinary XOrXQ DcZdBoan DcZBoanUGOOGLE API KEY ScAOJVyWyScAOJVyWyIRAOJVyWyIReInto a credentials yml stripe publishable key pk test VGLlUNDcZScAOJVyWyIRJwzYZkqMKctgAAYF secret key sk test VGLlUNDcZScAOJVyWyIRJwzYZkqMKctgAAYF web hook secret key whsec cZpBVGcZpBVGcZpBVGUrgAgcZpBVGcZpBgoogle api key ScAOJVyWyScAOJVyWyIRAOJVyWyIRecloudinary cloud name abcdefg api key api secret abc VGLlVGLlLnote Cloudinary API key is split up as per documentation You are now all set View credentials can run in the terminal Run bin rails credentials show How to Use Credentials in Multiple File Formats Ruby nested keyRails application credentials stripe publishable key single keyRails application credentials google api key YMLcloudinary service Cloudinary api key lt Rails application credentials dig cloudinary api key gt api secret lt Rails application credentials dig cloudinary api secret gt for Cloudinary an additional config cloudinary yml file is needed JavaScript ruby code only possible with js erb formatconst abc lt Rails application credentials google api key gt ERB lt interpolate in script tag gt lt script src lt Rails application credentials google api key gt lt script How to Share Keys With a TeamShare the key in master key with fellow developers to enable decryption Each team member creates a master key file locally in the config folder and pastes it in the shared key ConclusionCoding is more fun without the hassle of chasing the correct API keys Your app is up to date with security best practices Share a master key once and be free of tedious copy pasting Thanks for reading	2021-11-13 17:03:06
海外TECH	Engadget	Tesla is delivering some EVs without USB ports due to chip shortages	https://www.engadget.com/tesla-delivers-ev-without-usb-ports-173616014.html?src=rss	Tesla is delivering some EVs without USB ports due to chip shortagesTesla may be thriving despite chip shortages but those shortcomings are apparently making an impact on the cars people get Electrek has learned numerous Model and Model Y buyers are receiving their electric vehicles without USB C ports in the center console or rear seating areas Some customers said they were alerted in advance but others only found out when they took their EVs home Delivery specialists and others at Tesla have pinned the missing USB ports on chip shortages Some customers have heard Tesla would install the missing connectors in December but it s not clear if this applies to every affected owner Tesla has long stopped responding to requests for comment and is believed to have disbanded its PR team This isn t the first time brands have shipped cars without parts including Tesla BMW recently removed touchscreen features from some models while that company and Tesla have both removed passenger lumbar support options And there aren t many great alternatives ーautomakers have delayed orders halted production and otherwise asked customers to wait longer than usual Even so this could leave more than a few Tesla buyers upset The absence of USB ports breaks not only connectivity but wireless charging That s a luxury to be sure but it s one you d expect given Tesla s price tags It might also sour customers worried Tesla might be sacrificing quality to meet its quarterly delivery targets	2021-11-13 17:36:16
海外科学	BBC News - Science & Environment	COP26: Climate summit approaches 'moment of truth'	https://www.bbc.co.uk/news/science-environment-59269886?at_medium=RSS&at_campaign=KARANGA	climate	2021-11-13 17:02:58
ニュース	BBC News - Home	COP26: Climate summit approaches 'moment of truth'	https://www.bbc.co.uk/news/science-environment-59269886?at_medium=RSS&at_campaign=KARANGA	climate	2021-11-13 17:02:58
ニュース	BBC News - Home	Nazanin Zaghari-Ratcliffe: Husband ends hunger strike after 21 days	https://www.bbc.co.uk/news/uk-59275772?at_medium=RSS&at_campaign=KARANGA	child	2021-11-13 17:26:50
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 17:35:04
ニュース	BBC News - Home	Ecuador prison riot: New fighting at Guayaquil jail kills 58	https://www.bbc.co.uk/news/world-latin-america-59276428?at_medium=RSS&at_campaign=KARANGA	guayaquil	2021-11-13 17:39:21
ニュース	BBC News - Home	Sao Paulo Grand Prix: Lewis Hamilton disqualified from qualifying, Max Verstappen fined 50,000 euros	https://www.bbc.co.uk/sport/formula1/59276919?at_medium=RSS&at_campaign=KARANGA	Sao Paulo Grand Prix Lewis Hamilton disqualified from qualifying Max Verstappen fined eurosLewis Hamilton has been disqualified from qualifying at the Sao Paulo Grand Prix for a technical infringement on his Mercedes car	2021-11-13 17:36:35
ニュース	BBC News - Home	Ireland 29-20 New Zealand: Brilliant Irish claim statement win over All Blacks	https://www.bbc.co.uk/sport/rugby-union/59246484?at_medium=RSS&at_campaign=KARANGA	Ireland New Zealand Brilliant Irish claim statement win over All BlacksIreland outplay New Zealand to claim a statement win in a sensational match played in front of a raucous crowd of in Dublin	2021-11-13 17:19:22
サブカルネタ	ラーブロ	21/303　肉厚わんたん麺と手作り焼売　ら麺亭：特製らーめん（全部入り）、ワンタン（2ケ）	http://ra-blog.net/modules/rssc/single_feed.php?fid=193641	全部入り	2021-11-13 17:00:46
海外TECH	reddit	Lewis Hamilton Car 44 is disqualified from the results of qualifying (Art. 12.4.1 m of the FIA International Sporting Code) for Breach of Article 3.6.3 of the FIA Formula One Technical Regulations.	https://www.reddit.com/r/formula1/comments/qt5fi8/lewis_hamilton_car_44_is_disqualified_from_the/	Lewis Hamilton Car is disqualified from the results of qualifying Art m of the FIA International Sporting Code for Breach of Article of the FIA Formula One Technical Regulations submitted by u magony to r formula link comments	2021-11-13 17:05:18
海外TECH	reddit	Post Match Thread - Ireland v New Zealand	https://www.reddit.com/r/rugbyunion/comments/qt5nhj/post_match_thread_ireland_v_new_zealand/	Post Match Thread Ireland v New Zealand Home FT Away Ireland New Zealand Match Thread Ireland v New Zealand End of Year Internationals Venue Aviva Stadium Dublin Officials Luke Pearce Matthew Carley Christophe Ridley Tom Foley tmo When UTC submitted by u RugbyBot to r rugbyunion link comments	2021-11-13 17:16:17