投稿時間:2022-12-05 01:20:51 RSSフィード2022-12-05 01:00 分まとめ(24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
python	Pythonタグが付けられた新着投稿 - Qiita	顔画像にk-meansを使用してピクセル単位で色のクラスタリングしてみた！	https://qiita.com/Kentea/items/41f9706400e23c07e083	adventcalendar	2022-12-05 00:59:24
python	Pythonタグが付けられた新着投稿 - Qiita	pythonで特異値分解を左右特異ベクトルと特異値行列を計算して再現を確認	https://qiita.com/eiraku/items/92ec0c948a3db2f87f45	importnumpyasnpxnpar	2022-12-05 00:53:31
python	Pythonタグが付けられた新着投稿 - Qiita	streamlit を使って WOL を投げるWebアプリを作ってみる	https://qiita.com/rai_suta/items/1f6b00639e79a6064bf0	litpyyamlpingpipinstallr	2022-12-05 00:37:50
js	JavaScriptタグが付けられた新着投稿 - Qiita	DynamoDBの低レベルAPIをNode.jsから叩いてみた	https://qiita.com/the_red/items/1d16827cd41c6a49e5b6	dynamod	2022-12-05 00:20:09
AWS	AWSタグが付けられた新着投稿 - Qiita	[vSAN技術検証]VMware Cloud on AWS (VMC on AWS)を触ってみた	https://qiita.com/ayumu__/items/f3b80afb98a74e324c40	vmcon	2022-12-05 00:42:42
AWS	AWSタグが付けられた新着投稿 - Qiita	aws 無限列車発進！！！	https://qiita.com/yoshii0110/items/18b73856c156549a5fe3	ampdesigneradventcalendar	2022-12-05 00:21:00
AWS	AWSタグが付けられた新着投稿 - Qiita	DynamoDBの低レベルAPIをNode.jsから叩いてみた	https://qiita.com/the_red/items/1d16827cd41c6a49e5b6	dynamod	2022-12-05 00:20:09
AWS	AWSタグが付けられた新着投稿 - Qiita	AzureとAWS比較 IaaS ネットワークセキュリティの違いについて	https://qiita.com/hidekko/items/98e68fd2fa10036da749	aclaccesscontrollist	2022-12-05 00:10:18
Azure	Azureタグが付けられた新着投稿 - Qiita	ユーザー定義ルートと使用手順	https://qiita.com/ss12345/items/a29c916539b21d4ef11e	azure	2022-12-05 00:42:02
Azure	Azureタグが付けられた新着投稿 - Qiita	App Service で Tomcat アプリケーションをデプロイする際の注意点	https://qiita.com/hyatsu/items/83003ce90b4fa538c72b	appservice	2022-12-05 00:18:10
Azure	Azureタグが付けられた新着投稿 - Qiita	AzureとAWS比較 IaaS ネットワークセキュリティの違いについて	https://qiita.com/hidekko/items/98e68fd2fa10036da749	aclaccesscontrollist	2022-12-05 00:10:18
技術ブログ	Developers.IO	Alteryx Serverのコレクションの管理 – Alteryx Serverアドベントカレンダー2022	https://dev.classmethod.jp/articles/alteryxserver-adventcalendar-20221205/	developersioalteryxs	2022-12-04 15:45:07
技術ブログ	Developers.IO	[レポート]Splunkでサーバーサイドリクエストフォージェリを検知する (sponsored by Splunk) #PRT325 #reinvent	https://dev.classmethod.jp/articles/splunk-ssrf-reinvent2022/	advanced	2022-12-04 15:32:13
技術ブログ	Developers.IO	ラスベガスでカーレースを体験してきた #reinvent	https://dev.classmethod.jp/articles/car-racing-reinvent/	momento	2022-12-04 15:31:20
技術ブログ	Developers.IO	Snowflake Marketplace上でM Data社が提供する日本のテレビ番組とテレビ広告に関するメタデータのサンプルを試してみた #SnowflakeDB	https://dev.classmethod.jp/articles/snowflake-marketplace-mdata-japan-tv-metadata/	adventcalendar	2022-12-04 15:30:43
技術ブログ	Developers.IO	AWS re:Invent 2022に関するDevelopersIO投稿まとめ(2022年12月04日分) #reinvent	https://dev.classmethod.jp/articles/aws-reinvent-2022-devio-entries-matome-2022-12-04/	awsreinvent	2022-12-04 15:09:26
技術ブログ	Developers.IO	Amazon SNS で Message だけを配信する方法を教えてください	https://dev.classmethod.jp/articles/tsnote-amazon-sns-raw-message-delivery/	amazonsns	2022-12-04 15:07:39
海外TECH	DEV Community	Notes on the Monorepo Pattern	https://dev.to/david_whitney/notes-on-the-monorepo-pattern-5egc	Notes on the Monorepo PatternMonorepos meaning a singular repository is a term coined by Facebook to describe a single repository that contains all the code for a project It is a pattern that has been used by many large companies including Google Facebook Twitter and Microsoft It is also used by many smaller companies including GitHub and by many open source projects including the Linux kernel It is frequently misinterpreted to mean all of the software that we build and I want to share some notes that clarify where monorepos succeed and fail in organisations of various sizes Where do monorepos work Monorepos work well in an inverse bell curve if productivity related to the size of the software and teams that you have when your repo is just really one app and a component library just the bits of the app in some bad directory layout when you have a very low number of apps you are coupling together via source controlwhen you have apps that either change super infrequently or are all sharing dependencies that churn all the time that must be in lockstep when you ve really just got your app and a few associated tools that s very same as it ever was because so few repos ever had just one tiny piece of a system in them to start with Unfortunately the zone of productivity for these organisational patterns in my opinion is a trap that folks fall into Most software doesn t fit those three categories mentioned above Software tends to moves at medium speed with SME shaped teams and in those situations monorepos are hell fraught with problems that only occur once you ve opted in wholesale to that organisational structure Alternatives that match those probem spacesIn most of those cases when the software is really just one app you should use directories instead of complicated build toolswhen it s all for some shared libraries you re going to reach a point where you want to version in distinctly because the blast radius of change is going to start hard coupling your teams together over timeIt s trivially easy to end up in the bad place where teams end up with tightly coupled deployments that get extremely slow and have to be resolved with tools like nx that frequently take over your entire development workflow bad But the biggest red flag with them is obvious we ve been here before and it sucked Just an old solutionThe first decade of my career before DVCS distributed version control systems was all effectively big monorepo source trees and it was absolutely horrible and fraught with the same coupling risks So we changed Git is designed for narrower slices and doing the monorepo dance in medium to large orgs with all your software will leave you inevitably fighting your tools both in build deployment and source control scenarios The sane approach is this Software that versions together deploys together and changes together should be collocated In the case of the thin end of the wedge with web apps this is often just the app a few shared libraries and a backend admin thing perhaps a few tools Monorepos are fine here At least until you need to split ownership of those things between team boundaries where things creek TL DR This is all about Conway s Law and change frequency that charts the success of software organisation and hard team coupling is more dangerous than software coupling Monorepos in massive organisationsLet s briefly talk about the other end of the spectrum the massive organisations that have a lot of software and a lot of teams and all claim to use monorepos There are notable examples Google Facebook Twitter Microsoft GitHub Firstly none of those organisations use a monorepo as it is frequently interpreted by smaller orgs and the community It s easy to verify this because they all operate open source repositories that are public and distinct from any internal monorepos they may have What they do tend to have is application centric repositories where a single application and it s associated tools and libraries are colocated This makes absolute sense and is no different from your existing non monorepo In fact the majority of the famous monorepos Windows the Linux kernel which of course isn t the same as Linux and Facebook all have entire tooling teams dedicated to making collaborating on them work at scale with the communities they serve It s very important that you don t apply logic from organisations of a scale that you aren t with resources that you do not have to your own problem space without strong consideration If you don t have the budget for entire teams working on source control and collaboration nor tens of thousands of developers to fit around your codebase perhaps don t mimic the patterns of those who do Should I use a monorepo Application centric repositories with associated tools and libraries Yeah Knock yourself out makes lots of sense Putting all your applications spread across multiple teams and ownership boundaries into a single repository Absolutely not this way leads to madness and coupling hell	2022-12-04 15:19:57
海外TECH	Engadget	Hitting the Books: AI is already reshaping air travel, will airports themselves be next?	https://www.engadget.com/hitting-the-books-power-and-prediction-agrawal-gans-goldfarb-harvard-business-review-press-153019603.html?src=rss	Hitting the Books AI is already reshaping air travel will airports themselves be next The holiday travel season is once again upon us It s the magical time of the year that combines standing in airport security lines with incrementally losing your mind as the hands of your watch perpetually tick closer to a boarding time that magically moved up minutes since you left the house and the goober in front of you is in the year of our lord still somehow confused about why we have to take our shoes off in security and goddamit dude stop arguing with the TSA and untie your laces already these tickets are nonrefundable Ai can help fix this It can perhaps even give regular folks a taste of the effortless airport experience that more well heeled travelers enjoy ーthe private jet set who don t ever have to worry about departure times or security lines like the rest of us schmucks stuck flying Spirit nbsp In their latest book POWER AND PREDICTION The Disruptive Economics of Artificial Intelligence University of Toronto economists and professors Ajay Agrawal Joshua Gans and Avi Goldfarb examine the foundational impact that AI ML systems have on human decision making as we increasingly rely on automation and big data predictions In the excerpt below they posit what the airports of tomorrow might look like if AI eliminates traffic congestion and security delays nbsp Harvard Business Review PressReprinted by permission of Harvard Business Review Press Excerpted from POWER AND PREDICTION The Disruptive Economics of Artificial Intelligence by Ajay Agrawal Joshua Gans and Avi Goldfarb Copyright Ajay Agrawal Joshua Gans and Avi Goldfarb All rights reserved Ajay Agrawal Joshua Gans and Avi Goldfarb economists and professors at University of Toronto s Rotman School of Management Their previous book is PREDICTION MACHINES The Simple Economics of Artificial Intelligence The Alternative Airport UniverseBefore considering the threat AI prediction may pose to airports as with everything there is an alternative system that can show us what the other side looks like One example is the alternative universe of the very very wealthy They don t fly commercial and so have no occasion to deal with either the old or newly designed public airport terminals Instead they fly privately and go through private terminals Normally glitz glamour nice restaurants and art galleries are going to be where the very rich are But in the world of airports private terminals are positively spartan The reason there is no investment in making private terminals better places is that the very uncertainty that plagues the rest of us doesn t plague the rich With a commercial plane you are tied to a schedule and those planes will leave late passengers behind With a private plane the schedule is more flexible or even nonexistent If the passengers aren t there the plane doesn t leave until they arrive If the passengers are there earlier the plane leaves then The whole system is designed so there is no waitingーat least on the part of the passengers No waiting means no need to invest in making waiting more pleasant At the same time the rich don t have rules about when they need to leave for the airport They leave when they want If more people could have that experience then surely the optimal terminal would be more spartan than cathedral You don t have to be rich however to see this alternative universe Instead just compare the world on the other side of the arrival gates to those at departure When arrival areas are separated from departure areas they are spartan You might find some light food outlets but everything else is designed to get you out of the airport The critical issue is how close the taxi and parking facilities are even though you may not be in a stressful rush Do you even remember any details of arrivals at your regular airport other than how best to get out The AI Airport ThreatAirports are no strangers to AI Air traffic control has adopted AI based systems to better predict aircraft arrivals and congestion At Eindhoven Airport a new AI baggage handling system is being piloted whereby passengers simply photograph their bags drop them off and pick them up at their destinationーno labels required Subject to privacy requirements it hopes to do the same with people All this will help you get to your flight more quickly None of these things however hit at the key drivers of uncertainty in your travel to your flight ーtraffic and security Change however is already here with regard to traffic Navigational apps such as Waze account for traffic conditions and can reasonably estimate how long it takes to get to any airport based on the time of day The apps aren t perfect but they keep getting better The apps free passengers from having rules that tell them how early they need to leave for the airport Instead they can add that flight time to their calendar and an app tells them the best time to depart and schedule their time accordingly Even better in the near future the uncertainty in the actual time a flight leaves will be taken into account Rather than just telling you when you need to leave based on a scheduled departure the app will tell you when to leave depending on the flight s predicted actual departure Again there is residual uncertainty but the leap from having no information to having more precise information could save hours of waiting time Similarly many Uber riders who previously thought they wouldn t care about knowing the predicted arrival time of their taxi now cite that information as one of the most valuable features of the service Uber uses AI to make that prediction AI could also predict security line wait times Put it all together and you can use the AI to decide when to leave for the airport rather than rely on rules As with everything there will be some who leap at this possibility ahead of others At Incheon and many other airports waiting isn t bad anymore so maybe you don t need to make an informed decision Those developing an AI driven navigation app or flight departure predictor have no direct interest in the earnings of in terminal airport activities However the value of their AI applications depends critically on how many people do not want to wait at airports Thus if airports are currently less costly to wait in the value of those apps is diminished The security line prediction is another matter Airports claim that they want to improve security times and reduce uncertainty But as economists we don t think their incentives are aligned with passengers Yes improving security times leaves more time to spend at the facilities past security But at the same time it will reduce uncertainty and cause people to tighten their airport arrival times Combined with AI that solves the other uncertainty for passengers in getting to the terminal will the airports want to eliminate the uncertainty under their own control Accommodating RulesOur broader point is not about airports but about rules Rules arise because it is costly to embrace uncertainty but they create their own set of problems The so called Shirky Principle put forth by technology writer Clay Shirky states that “institutions will try to preserve the problem to which they are the solution The same can be said of businesses If your business is to provide a way to help people when they wait for a plane what s the chance you are going to ensure they don t have to wait for planes If you want to find opportunities by creating new AI enabled decisions you need to look beyond the guardrails that protect rules from the consequences of uncertainty and target activities that make bearing those costs easier or to reduce the likelihood of bad outcomes that the rules would otherwise have to tolerate We can see this in the long standing protection farmers employ in England ーbuilding hedgerows A hedgerow is a carefully planned set of robust trees and plants that serve as a wall between fields It is extremely useful if your field is full of farm animals and you do not want to employ a person to ensure they do not wander off It is also useful if you do not want heavy rainfall to erode soil too quickly or if you want to protect crops from strong winds Given all this protection against risky events we are not surprised that this practice was the origin of the term “hedging which evolved to have a broader insurance meaning But hedgerows come at a cost By dividing farmland they make it impossible to use certain farming techniques ーincluding mechanization ーthat are only efficient for large swathes of land After World War II the British government actually subsidized the removal of hedgerows although in some cases that removal was excessive given their role in risk management Today there is a movement to restore hedgerows led most prominently by the Prince of Wales In many situations costly investments are made to cover or shelter a would be decision maker from risk Miles of highways are cocooned with guardrails to prevent cars from going down embankments hills or into oncoming traffic Most are fortunately never used but each allows a road to be built in a way that might have otherwise not been sufficiently safe given the fallibility of human drivers More generally building codes precisely specify various measures to protect those inside buildings from uncertain events These include fire but also damage from weather weak building foundations and other natural phenomena like earthquakes What these protection measures have in common is that they typically generate what looks like over engineered solutions They are designed for a certain set of events ーthe once in a lifetime storm or the once in a century flood When those events occur the engineering seems worthwhile But in their absence there is cause to wonder For many years Freakonomics authors Steven Levitt and Stephen Dubner pointed out how life vests and rafts on aircraft ーnot to mention the safety demonstrations of each ーappeared wasteful given that no aircraft had successfully landed on water Then in Captain Sullenberger landed a US Airways plane with no working engines on the Hudson River Does that one example of a low probability event make the precautionary life vests worth it It is hard to know But we cannot conclude that the absence of a possible outcome causes us to assess the probability of that outcome at zero Levitt and Dubner s main point however is that while it is often possible when protection measures are employed to assess the likelihood or change in the likelihood of underlying uncertainty over time it is not possible to measure whether the investments made to reduce the probability of a consequence are excessive as the very risk management strategy employed takes away that information It is entirely possible that too much is wasted on something that for other reasons is no longer high risk at all	2022-12-04 15:30:19
海外科学	NYT > Science	Photos: Mauna Loa’s Eruption Offers Rare Glimpse Into the Earth	https://www.nytimes.com/2022/12/03/science/mauna-loa-volcano-eruption-hawaii.html	Photos Mauna Loa s Eruption Offers Rare Glimpse Into the EarthThe world s largest active volcano erupted for the first time in years raising excitement among scientists who are eager to unlock its many mysteries	2022-12-04 15:53:06
金融	◇◇　保険デイリーニュース　◇◇（損保担当者必携！）	保険デイリーニュース(12/05)	http://www.yanaharu.com/ins/?p=5091	交通事故	2022-12-04 15:33:03
ニュース	BBC News - Home	Cyril Ramaphosa: South African leader leaves future in ANC hands	https://www.bbc.co.uk/news/world-africa-63850913?at_medium=RSS&at_campaign=KARANGA	african	2022-12-04 15:36:19
ニュース	BBC News - Home	Currys has stopped using Royal Mail due to strikes	https://www.bbc.co.uk/news/business-63850899?at_medium=RSS&at_campaign=KARANGA	alternative	2022-12-04 15:51:43
ニュース	BBC News - Home	Patrick Tambay: Former Formula 1 Ferrari driver Tambay dies aged 73	https://www.bbc.co.uk/sport/formula1/63851584?at_medium=RSS&at_campaign=KARANGA	ferrari	2022-12-04 15:18:34