投稿時間:2022-04-01 11:41:59 RSSフィード2022-04-01 11:00 分まとめ(36件)

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IT	ITmedia 総合記事一覧	[ITmedia ビジネスオンライン] 「プレステの父」久夛良木健氏は、近大でなにをするのか	https://www.itmedia.co.jp/business/articles/2204/01/news081.html	itmedia	2022-04-01 10:12:00
IT	情報システムリーダーのためのIT情報専門サイト IT Leaders	ドイツはIoT利用先進国、EU27カ国中5位─欧州委員会Eurostat調査より：第31回 | IT Leaders	https://it.impress.co.jp/articles/-/22936	ドイツはIoT利用先進国、EUカ国中位ー欧州委員会Eurostat調査より第回ITLeaders欧州委員会の統計担当部局ユーロスタットEurostatは、欧州企業のIoTおよびインターネット全般の利用実態を調査している。	2022-04-01 10:30:00
IT	情報システムリーダーのためのIT情報専門サイト IT Leaders	独Celonis、プロセスマイニングツール「PAFnow」の独Process Analytics Factoryを買収 | IT Leaders	https://it.impress.co.jp/articles/-/22935	PAFnowは、米マイクロソフトのBIツール「MicrosoftPowerBI」のアドオン製品。	2022-04-01 10:01:00
AWS	AWS Japan Blog	AWS Lambda で最大 10 GB のエフェメラルストレージをサポート可能に	https://aws.amazon.com/jp/blogs/news/aws-lambda-now-supports-up-to-10-gb-ephemeral-storage/	AWSLambdaで最大GBのエフェメラルストレージをサポート可能にサーバーレスアプリケーションはイベント駆動型で、ウェブAPI、モバイルバックエンド、ストリーミング分析から機械学習MLや高性能アプリケーションのデータ処理段階まで、エフェメラルなコンピューティング関数を使用します。	2022-04-01 01:30:23
AWS	AWS Japan Blog	AWS Week in Review – 2022 年 3 月 21 日	https://aws.amazon.com/jp/blogs/news/aws-week-in-review-march-21-2022/	AWSWeekinReview年月日AWSWeekinReviewはシリーズとして、毎週AWSからの興味深いニュースやお知らせをダイジェストでお伝えします今週も、過去日間に行われた最も重要なAWSリリースをまとめました。	2022-04-01 01:16:10
python	Pythonタグが付けられた新着投稿 - Qiita	Pythonanywhereのデプロイで　ModuleNotFoundError: No module named ''になった話	https://qiita.com/ReRu-1003/items/739cdaa08cf1c0579722	PythonanywhereのデプロイでModuleNotFoundErrorNomodulenamedxxになった話今回、フロントエンドにReactjsの画面・バックエンドにDjangorestframeworkを使用したポートフォリオだったり自己紹介だったりを掲載するサイトを作成。	2022-04-01 10:39:40
js	JavaScriptタグが付けられた新着投稿 - Qiita	Core Web Vitals（コアウェブバイタル・CWV）改善の施策を考える	https://qiita.com/kooooji/items/328df58988eeb8674c5f	・スコアを改善する施策は色々あるが、手間と後の保守性を考えた施策を選ぶべきスコア改善の施策一覧LCP改善の考えファーストビューユーザーがページを開いて最初に表示される部分の表示速度を上げることでスコアの向上に繋がる傾向があるため、特にページ上部の読み込み速度を上げることが重要となってきます。	2022-04-01 10:53:40
js	JavaScriptタグが付けられた新着投稿 - Qiita	爆速でするNuxtとSkywayの連携	https://qiita.com/abcshotaro616/items/29a8f8350ba0e9e87590	爆速でするNuxtとSkywayの連携こんにちは関西の大学に通うキンジョウですもう春ですね。	2022-04-01 10:03:57
Ruby	Rubyタグが付けられた新着投稿 - Qiita	Ruby on Rails 事始め	https://qiita.com/EasyCording/items/c89acbe85224d6d14dda	つまり、RORはDOCKERの知識とセットで学ぶべき言語なのである。	2022-04-01 10:31:57
Docker	dockerタグが付けられた新着投稿 - Qiita	とりあえず、Dockerが使えるようになる記事	https://qiita.com/kenta-muscle/items/5d0914dcfee747f849ad	コンテナの立ち上げもコマンドつで完了Dockerでコンテナを立ち上げるまででは、Dockerの概要について理解できたところで、続いてはコンテナを立ち上げるまでにどんな作業が必要なのかを見ていきましょう。	2022-04-01 10:37:35
golang	Goタグが付けられた新着投稿 - Qiita	Goのプログラム内でjqを使ってJSONを加工する	https://qiita.com/takayoshi-shiraki/items/02b04068d08fb6d31970	Goのプログラム内でjqを使ってJSONを加工するはじめにJSONデータをフィルタしたり、集計したりといった加工にjqコマンドは便利ですよねGoでプログラムを書いた場合も、プログラムの中でJSONを扱うケースは多々あると思いますが、JSONの構造に合わせて構造体を定義してマッピングしたり、ちょっとした成形をしたりと面倒なことがあると思います。	2022-04-01 10:52:16
Ruby	Railsタグが付けられた新着投稿 - Qiita	Ruby on Rails 事始め	https://qiita.com/EasyCording/items/c89acbe85224d6d14dda	つまり、RORはDOCKERの知識とセットで学ぶべき言語なのである。	2022-04-01 10:31:57
技術ブログ	Developers.IO	EC2をProxyサーバにしてIPv4オンリーの自宅ネットワークをIPv6対応にしてみた	https://dev.classmethod.jp/articles/ipv6-proxy-server/	自宅	2022-04-01 01:02:57
海外TECH	DEV Community	The THREAT HUNTER of your Cloud	https://dev.to/aws-builders/the-threat-hunter-of-your-cloud-36me	The THREAT HUNTER of your Cloud DAY THE THREAT HUNTER OF CLOUD Day Twenty Five days of Cloud on GitHub Read On iCTPro co nz Read on Dev toCollect logs from AWS resourcesuse Machine Learning Statistical analysis and graph theory Detect and investigate threatsA fast and effective way to identify a root cause for security issues Detective can process terabytes of data and comes with data visualization of the vast information from the report AMAZON Detective How to Enable Amazon Detective from the consoleThe Data will be automatically organized into graph model investigate using GaurdDuty and AWS Security Hub Amazon Macie Find the Cause using interactive visualizations Lets talk a bit about Amazon GuardDutyIts a treat detection service from AWS Which continuously monitors malicious activity This is done with the help of Machine Learning amp Anomaly detection Data s from CloudTrail VPC flow logs DNS logs are used for analysis to provide graph view GaurdDuty have to enabled and wait for hours to enable the Detective Why should we use Investigate determine the cause related to incidents Triage determine who should look into it Threat Identification detailed understanding to identify threat Cost Free for daysCheck out this link to understand more about the Detective Pricing on each region Works with ServicesCloudTrail AWS api calls VPC flow logs traffic on VPC TerminologiesBehavior graph Generated from incoming data with the accountDetective source Data information on AWS Flowlogs CloudTrail and GaurdDuty Findings Entity Extracted from source data Finding issues found by guard duty Investigation Finding out root cause for issue Profile Visualizations and supporting information Profile Panel Visualization on profile Relationship what s happening with two individual resources or how they are related You can use a primary account to collect all data to create graph from secondary account Secondary account will only have data that contributed to primary account Important linksAmazon Detective User GuideAmazon Detective FAQsAmazon Detective DocumentationGuardDuty finding typesConnect with me on Twitter Connect with me on Linkedin‍‍Read more post on dev to or iCTPro co nzConnect with me on GitHub ltag user id follow action button background color DB important color FEF important border color DB important Anuvindh SankaravilasamFollow Experienced Cloud Technology Specialist with a demonstrated skillset of working with Emergency NZ Police amp Education industry	2022-04-01 01:45:17
海外TECH	DEV Community	League of Legends rank(ing) guide	https://dev.to/mage_ai/league-of-legends-ranking-guide-4m88	League of Legends rank ing guide TLDRChannel your inner scholar and Ryze to the top leverage machine learning ranking models that can increase your rank in League of Legends through match analysis and predictions free of code GlossaryIntroObjectiveDatasetData cleaningFeature engineeringModel trainingEvaluationDeploying APIConclusion IntroLeague of Legends is a multiplayer online game that is played by millions casually and professionally For a competitive vs game where players fight to take down the others home base it s no sup Ryze that if you choose to play the most over powered “OP champions which are characters you play you ll have a higher likelihood of winning games against players of your skill level Ryze Source Riot Games Thus if you re looking to improve in the competitive League of Legends ranked game mode how about trying an ML analysis to ensure you make the most statistically backed decisions in your next game How about learning more about a buzzword in the tech industry “machine learning ML while enjoying content about your interests Like Hextech in Arcane we at Mage see ML as a tool that can be wielded by anyone and for anything not just a few data scientists who excel at math and know how to code Neither of these are required you just need to understand the concept of a ranking model What is ranking Ranking is an application of machine learning that sorts data based on a parameter like whether you win or lose in a League of Legends game Depending on the order of how the data is sorted we can make predictions on future outcomes of new data In other words if you take a list of Teemo games and rank them based on whether he wins or loses the model will score the champion Teemo as “low on relevancy to “wins This means that Teemo s presence in the game has little to no effect on whether you win or lose giving him a low score in relevance to wins Then later when you ask the model to predict whether you ll win this new Teemo game it ll want more information Teemo the “Beemo skin Source Riot Games ObjectiveOther than making glorified Teemo slander this guide aims to pique your interest in ML concepts through your interests in League If we can begin by teaching gamers like you ML concepts like what ranking models are through your interests you can harness the power of a data driven analysis to show others cool and easy ways AI can be implemented for your projects Therefore in this guide we want to answer the following questions using the ML ranking model we are building Which champions increase my likelihood of winning plat diamond games In other words which champions are meta in my “elo meaning skill level and help me climb Which factors have the greatest impact on whether I win or lose What s the likelihood that I ll win a diamond game playing this champion and earning that amount of gold by minutes Note I generated my dataset using high elo games because I wanted to be taken seriously Check out my Github if you d like to learn how to query the Riot Games API to make your own dataset for a more accurate analysis on players in your elo whether it s higher or lower DatasetMost data scientists know that the best models originate from a strong and suitable dataset For this guide we painstakingly generated rows and columns of actual platinum and diamond tier ranked game data that we carefully selected from parsing responses from the Riot Games API To download it and make your own data analyses simply wish upon this star You can find more details about how we generated our dataset from the publicly available League of Legends ranked game data on my Github repository Stargazer Soraka Source Riot Games At a glance this dataset contains rows each containing a player s match info from a platinum or diamond level ranked game There are columns containing data like the player s KDA champion they picked ban if a teammate went Afk gold at minutes gold by the end of the game and whether they won or lost Now that we have the data our next steps are to prepare our data for the model to train Data cleaningLook out A tsu Nami is incoming to wash away excess data Nami in her Program Nami skin Source Riot Games Data cleaning is a step in the process of machine learning to reduce noise in your data to prepare clear and concise data for your ML model to train on What this means is that most of the time your raw data may contain empty or unrelated rows or columns of data that if left alone would confuse your model when it s looking for trends in the rows that lead to an outcome like winning or losing The most difficult part of data cleaning and the next step feature engineering is that there is no specific formula or order to do things which is why it seems so intimidating to get started Like League of Legends there s a high learning curve but you can definitely do it As you go through the following steps always ask yourself why we are performing this operation and you ll learn things quickly Removing duplicatesAt a glance and from how the data was collected from the Riot Games API we gathered data by choosing a random plat or diamond level player A and generating their recent ranked games However if our list of players that we gathered data from also contained player A s party member B then there will be games containing player A and B s teammates and opponents that are recorded twice Duplicate data can skew prediction results by giving the model an impression that an occurrence of an event happens more frequently Imagine how falsely a model would predict a Yasuo player s victory if we kept duplicates of a time the one trick Yasuo main dominated a game We can remove duplicate instances if any of player A and B s games overlapping by removing rows that contain the same values where players played in the same match on the same team and played the same champion FilterSometimes when we re unsure what data needs to be cleaned we can go through each of the columns and look for empty invalid or incorrect values and filter them There are roles to play in the game but the position feature contains a th value Invalid for when the Riot Games API was unable to guess what role a player was playing Check the right side of the image below to see the unique values of the column position If we kept the rows with the Invalid value in the position column we will be informing the model that players can play the Invalid role We don t want the model to train on inaccurate values as it will reduce the model s accuracy So we will use the filter operation to keep only the rows where the role played could be determined By extension we have a column representing the amount of the gold earned by their lane opponent at the minute mark and it contains values Unfortunately these rows with empty values in the ten min lane opponent gold column need to be filtered out because the data in these rows are incomplete and would result in an inaccurate prediction of an outcome Removing unrelated columnsThinking back on our intention for building a model we re interested in finding out which champions that a player picks that maximize their chances of winning So we will need to remove unrelated columns We will come back to this step later since some of the columns will be removed after extracting relevant information from them Feature engineeringThe point ️of this step is to transform present existing information to be as clear cut as Fiora s rapier so we add columns to specify to the model what good gameplay in ranked matches look like This also generalizes existing data into fewer simpler columns Fiora in her Bewitching skin Source Riot Games Add column based on a conditionalOne method of identifying whether a player is performing well is seeing if they beat their lane opponent at minutes a crucial turning point of the game Although this doesn t guarantee their victory it gives them an edge because it grants them the capacity to use their strength to aid their teammates and pressure the enemy which can influence whether they win or lose Since teamwork is hard to identify and measure we ll simply label instances where players had an advantage over their lane opponents in the boolean meaning true or false column beat lane opponent Replacing the ten min gold column with this boolean column simplifies the numerical range of gold values the range is gold to be exact into a simple True or False for whether the player beat their opponent This value is much easier for the model to understand and determine which factors lead to a favorable outcome To add a new column by comparison we simply compare the columns and return True if we have beat our opponent Extract columns from JSONFor those who have experience working with APIs and JSON a technical term programmers use to describe groups of named textual data we know that occasionally we receive data as a JSON and need to write code to dig the relevant data out If you re wondering why people store data in such a pesky format explore this lesson about how JSON data types are actually useful in storing auxiliary information Rek sai the digging queen Source Riot Games For the scope of this guide we are simply interested in what data from the challenges column that contains JSON would be useful to our goal Here s an example what kind of information a single row of JSON contains Pretty print generated by carbon now shSince we want to predict whether a champion is a carry and fights well all we care about is the teamDamagePercentage Aggregate sumFinally for some Ahri thmatic calculations Academy Ahri skin Source Riot Games One of the main assessments of how well you played in a game is how loaded you are compared to your team a value we dub gold percentage To do this we need to aggregate fancy term for performing an operation for each group of matches and teams to sum the amount of gold earned by the team in total This operation is called aggregate total sum and you can read more about it here We ll aggregate the gold column to find the team s total gold team gold Using the same operation we can find the sum of ten min gold and call it team ten min gold Divide column valuesWith the divisor calculated we can now divide an individual s gold by the team s to find their gold percentage Again we will do the same by dividing ten min gold by team ten min gold to calculate the column gold ten min percentage that tells us how much a player is contributing in gold at the minute mark Subtract column valuesFinally with the gold percentages we re interested in seeing whether a player scaled well as the game went on If a player had a significant gold percentage at minutes did they leverage the funds they had and carry their teammates to victory Or did they throw Ahri throwing out a heart Source Riot Games To see whether the player s gold contribution rose or fell we subtract gold percentage from gold ten min percentage I call this feature scalability Last data cleaning stepWe mentioned in the previous section that we will remove the columns we either finished extracting information from or created new columns that summarized the data better This leaves only columns which are bans beat lane opponent deaths gold gold percentage gold ten min percentage kills picks scalability team damage percentage and win Now that we are finally done preparing our data we get to move onto the model training step Model trainingA recap of what we re doing with the ranking model We are evaluating which champions in the picks column Have the highest likelihood of winning platinum and diamond level ranked gamesBy sorting our rows of data with the wins on top we can score each champion for how many wins they gotUsing the relative positioning of each champion score in our sorted list we can make predictions whether you ll win a ranked game playing a certain champion depending on how high or low a relevant champion placed on the list SetupWe know this is confusing so we created a visualization that helps guide you through the process of selecting what you re ranking and how Train test splitWhen a machine learning model is being trained what it means is that it takes the data we painstakingly put together and split a majority of the rows to learn how to predict whether players would win or lose their games playing a certain champion and fulfilling certain performance goals To fairly calculate the accuracy of the model we use approximately of the data for teaching the model how to predict which champions win games and the remaining to test if the model is correct in its predictions Found in the Review gt Statistics tab of MageIf you d like to know more about the process of splitting and get some Sera tonin by exposing yourself to some K pop idol girls this K folds cross validation lesson is perfect for you Pop star Seraphine Source Riot Games EvaluationNow that the model has been trained we can now interpret the results of the model Generally we evaluate whether a model makes good predictions or not based on the accuracy precision and recall values However since those metrics are general we also use SHAP values to gain insight on how the individual columns affect the outcome This is all in an effort to understand and improve model performance in the next retraining step General metricsThe at a glance values we rely on to evaluate our model performance are Showing accuracy precision recall Now a final test of your thresh hold of enduring my puns Thresh Source Riot Games During the testing step we evaluate the metrics we just mentioned by seeing the following testing examples When testing the outcomes of a Thresh player s games accuracy means that the model correctly predicted the outcome of a Thresh player s game results of the time Including the games they lost Precision is where the model correctly predicts a Thresh player winning a game Thus an recall rate means that in the testing set if there were instances of a Thresh player winning a game we managed to identify of them This is a really technical term but these three metrics are usually linked to a confusion matrix Although this matrix is confusing to some I m sure it s useful to some data scientists out there SHAP valuesTo find out which columns influenced the outcome the most Mage displays a list of columns and the trend in relation to the outcomes in the Review gt Top features tab This list tells us a lot Proof that whether players win lane is not correlated to whether they win the game gt since “beat lane opponent didn t meet the “Top features listColumn that influences win rate the most is actually “deaths gt indicates that decreasing your death count increases your odds of victory more than increasing killsOnly or correctly predicted outcomes available for each champion in the testing set gt rows of data wasn t enough to make predictions on the champions in League of LegendsOur top features list also tells us which columns had a miniscule influence on the outcome Since columns like “bans “beat lane opponent and “team damage percentage did not make the list we can choose to exclude them in our re train step and improve our ranking model Deploying APIIf you re curious whether certain values of gold and deaths affect your win rate you can deploy the API in the Predict gt Playground tab to make custom predictions In the gif below we entered “ deaths and “ gold and wanted to see a ranked list of champions most likely to win As you can see the champions Bard Nautilus and Alistar are “picks that would increase your likelihood of winning with those gold and death counts Conclusion Ghostly rider Hecarim Source Riot Games Were our questions answered After we ran around like headless horsemen Hecarim to prepare train and evaluate a model are the results from our ranking model sufficient to answer the questions we asked in the Objective section Q “Which champions increase my likelihood of winning plat diamond games A Although we don t have enough data to be confident tentatively based on only or instances each we will say with a handful of salt Playing Sett LeBlanc Fiora Lux Shen and Jax andNot playing Yuumi Vex and Yasuo will increase your chances of winning Q “Which factors have the greatest impact on whether I win or lose A Deaths and gold were the most influential columns so if you want to win your diamond or plat games earn gold ーlearn computer science CS and don t feed Wow such million dollar advice Q “What s the likelihood that I ll win a diamond game playing this champion and earning that amount of gold by minutes A Although we cannot make predictions based on champion “picks you can experiment with different inputs like kills gold and deaths in our Mage Playground to see which champions have the highest chance of winning with those stats Displayed in the playground is the assertion that Jax Lucian and Karthus have the highest chance of winning with deaths ish gold kill and etc About myselfSince you read all the way to the end I d like to share a bit about the inspiration and motivation I had for writing this lengthy piece I was a participant at the Riot Games Hackathon where I met Phreak and an amazing data scientist who introduced me to what data analysis is Fangirling over meeting Phreak Since then I strongly believe that there are many like me in who are intimidated by big buzzwords like machine learning but have curiosity You read through this guide so you more or less understand the basic concepts to build and leverage a machine learning model Unleash those cool ideas Begin your data driven journey …with Mage Jk unless Although we used Mage in the above operations we absolutely believe that there are other methods of doing data analytics Visuals are important I would rather display operations with a pretty UI than code to not intimidate non programmers But as shown in the guide Mage makes the process of building models easier so if you are just getting started I recommend Mage Academy We have a plethora of interesting lessons about machine learning concepts From beginner friendly intros and advanced topics like this one becoming an AI expert has never been easier and more fun Whenever you feel ready you can build your first model Whenever you need help or want to share the cool ways you leverage data join our AI community on Discord Lastly hope you enjoyed the article gamers Thank you for sticking around until the end	2022-04-01 01:21:35
海外TECH	DEV Community	Avoiding `let` in TypeScript	https://dev.to/ku6ryo/avoiding-let-in-typescript-4ob4	Avoiding let in TypeScriptI sometimes feel I am forced to use let instead of const in TypeScript even I want to use const to prevent reassignments When we use if or switch there are cases that seems we need to use let However we do not have to use let actually const fruitNum number a number to represent a type of fruitlet fruitName string Unknown switch fruitNum case fruitName Apple break case fruitName Banana break default break Instead of the above code we may want to use a anonymous function to make fruitName a const const fruitNum number a number to represent a type of fruitconst fruitName string gt switch fruitNum case return Apple case return Banana default return Unknown Hope this makes your code more cleaner	2022-04-01 01:11:00
海外TECH	DEV Community	Maintaining feature flags in a product engineering team	https://dev.to/jackmarchant/maintaining-feature-flags-in-a-product-engineering-1d7a	Maintaining feature flags in a product engineering teamI have mixed feelings about feature flags They are part of the product development workflow and you would be hard pressed to find a product engineering team that doesn t use them Gone are the days of either shipping and hoping the code will work first time or testing the life out of a feature so much that it delays the project The benefits of using feature flags certainly outweigh the bad but it doesn t stop teams from cursing them every time a major bug is reported or an incident occurs as a result of enabled or disabled feature flags In this post I will discuss the benefits and some drawbacks of using feature flags to help you learn from some of the lessons I ve personally learned in the hopes that you can avoid the mistakes First let s understand what a feature flag does and why it s there A feature flag is at its simplest there are more advanced controls you can use an on off switch to release some new functionality to your product through the code base Teams can safely ship code knowing the feature can be enabled for small groups of users at a time and released to more customers as confidence in a feature or behaviour grows Before feature flags you only had one shot to ship code to production and make sure it works which meant a longer build up to releasing code for the first time or complicated infrastructure to support canary releases The main problem with feature flags is what happens when you have too many and they start conflicting with each other or you have so many different flows to test that the team spends much longer on a feature than they should This leads me to the first lesson Lesson The number of feature flags you maintain will spiral out of controlEvery time you create a feature flag you re introducing a different behaviour for your code that may only be initially released to parts of your user base meaning you now support two different behaviours feature flag on and feature flag off This is empowering for a growing product and engineering team As the number of feature flags in use in production grows so too does the frustration of testing all of those different cases and receiving bug reports where your first instinct is to check which feature flags are enabled or disabled Keeping track of feature flags means making them attributed to your team so there s ownership of the feature flags and tracking the rollout status including ensuring rollout continues to happen or the flag is retired When feature flags have already spiralled and are out of control the best thing to do is pause development of new features and clean up any flags that are rolled out or no longer required Getting feature flags back under control should be a priority given the impact on development and testing time for related features This is a hard lesson to learn because there s only one way out Clean up the flags Lesson Clean up feature flags regularly make it part of the development cycleAfter some time in production feature flags can become stale and turn into technical debt which must be paid back at some point or risk cumulating over time and being at the point of no return In some ways you will always have to live with feature flags and they become part of the work you do The difference between a feature flag that is new and part of the feature actively being worked on versus a flag that has lost purpose is enormous To avoid this dysfunctional reality we must clean up feature flags after the feature has been rolled out or no longer needed Sometimes this will be a minor piece of work that involves only one person making the code change and testing it and other times it can require re testing an entire feature In my experience how well you have built up your automated testing around the feature will impact whether it s a minor change or a major one Recently in my team we had built up a lot of feature flags for a variety of reasons whether it was changing teams forgotten features or slow rollouts and so we had to take a week or two to clean up around feature flags in a short period of time We ended up doing the work for these in a short time frame then merging and releasing the changes incrementally and over a longer period just in case anything went wrong This proved to be successful in the end and the team swarmed on the work to get it done We re working on keeping track of feature flags more closely now to make it part of our development cycle to clean up feature flags rather than waiting for the eventual build up When we release a new feature with a corresponding flag we document it and ensure it keeps rolling out and create a ticket for the future to clean it up Progressing with the rollout usually means opening up the feature to more customers and this brings me to the final lesson Lesson Always be rolling outFeature flags should be temporary and are meant to increase the velocity of your team by allowing you to ship quickly and get real feedback from smaller groups of users in a safe way Flags therefore should be intended to be rolled out completely to your whole user base at some point It s normal to start with a small group and then build up incrementally to larger groups but this should always be on a timeline Once you forget about it or move on to the next thing and leave the flag there it will become stale and your team falls into the trap of having to maintain it and test both states of the flag should a change to that area of the code base be required There s no one right time frame for a feature flag to exist it will always depend on the feature and the group of customers using it to give you feedback directly or indirectly through usage That s why keeping track of the current state of feature flags in your control is important including managing the continual rollout to more users As you find bugs you can pause the rollout until the bugs are fixed but if you haven t hit any road blocks it s critical to keep forging ahead so removing the feature flag becomes possible once it has been made available for all of your users Feature flags are both a blessing and a curse it s probably no secret to most engineering teams What s missing in my opinion is a framework to manage feature flags over time and throughout engineering organisations They help keep the product working and make it easy to rollback changes without code deployment and give on call engineers peace of mind when they can safely turn off a flag that has caused an incident If left unchecked feature flags can slow engineering teams down to a crawl so create each feature flag with caution and a plan for its eventual removal Feature flags have given product teams the confidence to move fast with a plan to rollback at the click of a button but with great power and flexibility comes a cost which should not be underestimated	2022-04-01 01:09:33
海外TECH	DEV Community	GitHub's notifications on your Discord Server	https://dev.to/garaujodev/githubs-notifications-on-your-discord-server-441h	GitHub x s notifications on your Discord ServerI really love Discord and use it as a communication tool for personal projects with my friends and teammates Sometimes I wonder Do we have an App for Discord equivalent to GitHub App for Slack former Pull Panda So I noticed that actually we don t have it for simple reasons We can integrate GitHub Webhook to send events directly to Discord Webhook Simple and functional So let s do it Create the WebhookThe first step is to create the Webhook on your Discord server to receive the events from GitHub Go to your discord Server Settings Click on the Integrations item on the sidebar Hit the Create Webhook button Fill the name field I used GitHub Integration and select a channel to post the messages Click on Save Changes button Then click on Copy Webhook URL Set up the Webhook on GitHubNow you ll need to configure GitHub to send the events to Discord Webhook Navigate to your organization or repository on GitHubClick the Settings tab Click the Webhooks item on sidebar Hit the Add Webhook button on the top of the page Fill the Payload URL field with the URL copied in the step above Step Add github at the end of the URL It s very important In Content Type select Application JSON You can leave the Secret field empty In Event Triggers if you select Just the push event you ll receive new pushed commits Everything you ll receive everything like new Pull Requests opened workflows etc Let me select individual events you can choose Hit the Add Webhook button You re all set You ll get GitHub events notifications on your Discord channel If you have any questions you can reach me on Twitter or you can just use the comments section below I hope I was helpful and thank you for reading	2022-04-01 01:02:09
Apple	AppleInsider - Frontpage News	Adobe CC Express update brings quick actions and productivity features	https://appleinsider.com/articles/22/04/01/adobe-cc-express-update-brings-quick-actions-and-productivity-features?utm_medium=rss	Adobe CC Express update brings quick actions and productivity featuresAn update to Adobe Creative Cloud Express pushed Wednesday brought quality of life features to its mobile and web apps Customizable basic shapes are now available allowing users to select from a library of shapes that can be resized while keeping the design consistent The new Adobe Color integration allows users access to a large collection of preset palettes and themes Users can also search and see suggestions by Adobe to fit different themes and moods Read more	2022-04-01 01:06:25
医療系	内科開業医のお勉強日記	Covid-19 DNAワクチン：ZyCoV-D 第３相ランダム化二重盲検プラシーボ対照化治験	https://kaigyoi.blogspot.com/2022/04/covid-19-dnazycov-d.html	本研究の付加価値インドの大規模集団でDNAワクチンが第相試験で試験されたのはこれが初めてであり、針を使わない送達装置を用いてDNAワクチンを送達する世界初の第相試験であり、インドで歳の年齢層でCOVIDワクチンが試験されるのもこれが初めてです。	2022-04-01 01:58:00
医療系	内科開業医のお勉強日記	COVID-19のリスク、予防、治療に関する誤った情報により、人命が失われています	https://kaigyoi.blogspot.com/2022/04/covid-19.html	米国では、政府専門家ライセンス委員会を含むは、米国憲法修正第条で保証された言論の自由の権利を侵害することはできません。	2022-04-01 01:25:00
金融	ニッセイ基礎研究所	米個人所得・消費支出（22年2月）－PCE価格総合指数は前年同月比+6.4％と82年1月以来の伸び。物価上昇に歯止めがかからず	https://www.nli-research.co.jp/topics_detail1/id=70716?site=nli	月の個人消費前月比は名目値では前月と前月から大幅に伸びが鈍化したものの、辛うじてプラスを維持した一方、実質ベースでは前月とマイナスに転じた図表、図表。	2022-04-01 10:34:01
金融	ニッセイ基礎研究所	ユーロ圏失業率（2022年2月）－6％台で堅調に推移	https://www.nli-research.co.jp/topics_detail1/id=70715?site=nli	nbsp【ユーロ圏か国失業率年月、季節調整値】・失業率は、市場予想を上回ったが、前月から改善した図表・失業者は万人となり、前月万人から万人減少したnbspbloomberg集計の中央値。	2022-04-01 10:02:21
ニュース	ジェトロ ビジネスニュース（通商弘報）	政策金利の据え置き決定、経済見通しは下方修正	https://www.jetro.go.jp/biznews/2022/04/77d15d5641a0ddc9.html	下方修正	2022-04-01 01:20:00
海外ニュース	Japan Times latest articles	Japan’s business mood sours as Ukraine war and rising fuel price take toll	https://www.japantimes.co.jp/news/2022/04/01/business/tankan-first-quarter/	survey	2022-04-01 10:35:23
海外ニュース	Japan Times latest articles	The Smithsonian unveils Buddhist paragons who put ‘Mind Over Matter’	https://www.japantimes.co.jp/culture/2022/04/01/arts/smithsonian-zen-buddhism/	The Smithsonian unveils Buddhist paragons who put Mind Over Matter The National Museum of Asian Art in Washington D C displays the breadth of its Zen artifacts from both Japan and China for the first time	2022-04-01 10:00:46
ビジネス	ダイヤモンド・オンライン - 新着記事	アマゾン労組結成投票、アラバマの賛否は僅差 - WSJ発	https://diamond.jp/articles/-/300831	賛否	2022-04-01 10:18:00
北海道	北海道新聞	野田こども相、コロナ陽性　公務取りやめ自宅療養	https://www.hokkaido-np.co.jp/article/664080/	取りやめ	2022-04-01 10:34:00
北海道	北海道新聞	帯畜・樽商・北見工大が統合　国立大学機構が発足	https://www.hokkaido-np.co.jp/article/664079/	北見工業大	2022-04-01 10:33:55
北海道	北海道新聞	道東で冷え込み、留辺蘂氷点下１１・６度	https://www.hokkaido-np.co.jp/article/664078/	道東	2022-04-01 10:23:53
北海道	北海道新聞	首都キーウ、不気味な静けさ　目立つ兵士、市民まばら	https://www.hokkaido-np.co.jp/article/664061/	首都	2022-04-01 10:18:10
北海道	北海道新聞	景況感、１年９カ月ぶり悪化　３月の日銀短観、原油高やコロナ	https://www.hokkaido-np.co.jp/article/664054/	企業短期経済観測調査	2022-04-01 10:15:37
北海道	北海道新聞	【道スポ】２年目道産子左腕　根本　初先発初勝利したい　３月３１日１９歳の誕生日	https://www.hokkaido-np.co.jp/article/664058/	日本ハム	2022-04-01 10:01:00
ビジネス	東洋経済オンライン	50代まで万年課長だった男が華麗に転身できた訳 ふとした契機に自分の可能性を信じて踏み出した | ワークスタイル | 東洋経済オンライン	https://toyokeizai.net/articles/-/577579?utm_source=rss&utm_medium=http&utm_campaign=link_back	希望退職	2022-04-01 11:00:00
ビジネス	東洋経済オンライン	漫画｢キングダム｣（第1話）身の丈を超えた野望 壮大な物語の序章｢30話｣を一挙公開！ | キングダム | 東洋経済オンライン	https://toyokeizai.net/articles/-/538044?utm_source=rss&utm_medium=http&utm_campaign=link_back	春秋戦国時代	2022-04-01 11:00:00
IT	週刊アスキー	DMM GAMES、4人協力可能ローグライクアクションゲーム「Conan Chop Chop」の配信を開始	https://weekly.ascii.jp/elem/000/004/088/4088046/	conanchopchop	2022-04-01 10:45:00