投稿時間:2023-06-09 22:13:26 RSSフィード2023-06-09 22:00 分まとめ(19件)

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python	Pythonタグが付けられた新着投稿 - Qiita	【AzureOpenAI】Embedding(埋め込み化)で熊本のおいしいお店を探してみた	https://qiita.com/kkawano_neko/items/c6c47d8bb6838b187f7b	finefoodreviews	2023-06-09 21:50:39
python	Pythonタグが付けられた新着投稿 - Qiita	Windows11 に Python のインストール（ほんとうに初心者用？）	https://qiita.com/ignorant/items/dcc00b68e602961b5744	windows	2023-06-09 21:25:35
js	JavaScriptタグが付けられた新着投稿 - Qiita	JavaScriptの三項演算子とtruthy・falsyな値について	https://qiita.com/wakasagi135/items/9cd15064f0ce9009f311	falsy	2023-06-09 21:56:57
AWS	AWSタグが付けられた新着投稿 - Qiita	AWS Organizationsを有効化したときのメモ	https://qiita.com/zumax/items/97a7e0dd9c28c89c141c	awsorganizations	2023-06-09 21:18:36
Azure	Azureタグが付けられた新着投稿 - Qiita	【AzureOpenAI】Embedding(埋め込み化)で熊本のおいしいお店を探してみた	https://qiita.com/kkawano_neko/items/c6c47d8bb6838b187f7b	finefoodreviews	2023-06-09 21:50:39
海外TECH	MakeUseOf	20 Amazing Discounts You Can Get With a Free EDU Email Address	https://www.makeuseof.com/tag/discounts-edu-email-address/	student	2023-06-09 12:15:19
海外TECH	DEV Community	Asked ChatGPT and other models to write a simple script	https://dev.to/zakharsmirnoff/asked-chatgpt-and-other-models-to-write-a-simple-script-3kjf	Asked ChatGPT and other models to write a simple scriptHello I continue my journey in the world of deep learning ai and language models I was once tasked to write a simple PowerShell script that gets all groups from Azure Active Directory and extracts all the members from the groups The trick here is that there might be nested groups For an average system administrator or coder it s a trivial task just write a recursive function then get all the groups and process via that function I was interested in how well our language models can accomplish this basic task For all models I was using default settings and the same prompt I removed all the comments generated by model and added mine evaluating the solution The results are not so obvious I must say Let me present THE PROMPT Please write a PowerShell script which gets all groups from Azure AD then iterates over all of the groups and extracts all members Some of the groups can have nested groups Members of those should be extracted as well Let s now check the outputs of our models GPT Import Module AzureAD That s good a separate function to get group members recursively is niceFunction Get GroupMembers GroupId members Get AzureADGroupMember ObjectId GroupId Where Object ObjectType eq User Correct ObjectType is the property we are looking for though no All true Bad foreach member in members Write Host Member DisplayName member DisplayName UserPrincipalName member UserPrincipalName nestedGroups Get AzureADGroupMember ObjectId GroupId Where Object ObjectType eq Group foreach nestedGroup in nestedGroups Write Host Nested Group nestedGroup DisplayName Get GroupMembers GroupId nestedGroup ObjectId Although usually I m already connected it s nice that GPT suggested to handle the exception Yet it could make it better because right now it gives you this which is funny Get AzureADUser You must call the Connect AzureAD cmdlet before calling any other cmdlets try if not Get AzureADUser Connect AzureAD catch Write Host Failed to connect to Azure AD ForegroundColor Red exit groups Get AzureADGroup It forgot to add All true without it we get only first Bad Iterate over each groupforeach group in groups Write Host Group group DisplayName Get GroupMembers GroupId group ObjectId Overall very good Almost a working solution from the very first attempt Pros the logic and cmdlets are correctCons it didn t get ALL groups and ALL members although I specified this in the prompt Also it just printed out the info It could be nice to have an array which I could turn into CSV later but that s fine didn t ask for it in the prompt Actually it did tell me after writing the script This script simply writes the details to the console If you want to save the details to a file or do something else with them you will need to modify the script accordingly GPT Nice That s a simplier solution comparing to one provided by gpt At least it works Connect AzureAD It specified all Already better than gpt groups Get AzureADGroup All true I hate this param stuff in PS but the logic is correctfunction Get NestedGroupMembers param Parameter Mandatory true string GroupId nestedGroupMembers Get AzureADGroupMember ObjectId GroupId forgot all here foreach nestedGroupMember in nestedGroupMembers if nestedGroupMember ObjectType eq Group Get NestedGroupMembers GroupId nestedGroupMember ObjectId yep that s okay else nestedGroupMember foreach group in groups Write Host Group Name group DisplayName Write Host Members groupMembers Get NestedGroupMembers GroupId group ObjectId foreach groupMember in groupMembers Write Host groupMember UserPrincipalName Write Host Disconnect AzureADI must say it s already quite better that gpt for some reason At least we got ALL groups but not all members Pros it connects you to azure ad correctly and the logic is correctCons again not writing to any array and not getting all the members Koala B Wrong from the start We are in cloud dude this cmdlet is for on prem active directory groups Get ADGroup Filter foreach group in groups members Get ADGroupMember Identity group Name foreach member in members why do you need to check if the user is external or not if member MembershipType eq External email member EmailAddress I don t understand the logic at all It creates an array then add something weird and on line it checks whether the array is empty or not What isNested isNested member GroupName like group Name if isNested nestedGroups Get ADGroupMember Identity member GroupName Where Object MembershipType eq External foreach nestedGroup in nestedGroups email nestedGroup EmailAddress Write Output email Very bad in general I will not write pros and cons as I really don t understand what it s trying to do Nous Hermes B groups Get ADGroup Filter Select Object Name Sort Object Name foreach group in groups members Get ADGroupMember Identity group Name Recursive Where Object ObjectClass eq user Select Object SamAccountName Write Output group Name has the following members members join Again Active Directory is used not Azure Active Directory But I checked Get ADGroupMember has a parameter Recursive I can t try it out but probably it works Anyway not for my needs StarChat beta good getting aaaaal the groups groups Get AzureADGroup All true foreach group in groups Write Host Processing group group DisplayName members Get AzureADGroupMember ObjectId group ObjectID Value I don t know what it does the Value is empty foreach member in members if not processedMembers Contains member processedMembers member this variable was never declared nor initialized so I assume it s just null not sure isGroupMember Get AzureADUser ObjectId member memberof Where Object like group displayName so it assumes that nested group should have a similar name if isGroupMember Process NestedGroups GroupId member else outputMembers member function Process NestedGroups string groupId nestedGroups Get AzureADGroupMember ObjectId groupId value foreach ngroup in nestedGroups if not processedGroups contains ngroup processedGroups ngroup Process NestedGroups GroupId ngroup nestedGroupMembes get azureadgroupmember objectid ngroup objectId select ExpandProperty id outputMembers nestedGroupMemebrs Write Output Total Number of Groups groups count Write Output Number of unique members outputMembers Measure Object Unique Count the script fails here no unique propertywrite host List of Unique Members outputMembers sort object unique even if you get there you will get emptinessOverall the logic seems to be almost there but the script is overcomplicated and doesn t actually achieve what it s supposed to Anthropic Claude why in the world you forget to add all groups Get AzureADGroupforeach group in groups members Get AzureADGroupMember ObjectId group ObjectId foreach member in members if member ObjectType eq Group nestedMembers Get AzureADGroupMember ObjectId member ObjectId members nestedMembers Write Host Group group DisplayName members Format Table members null why A very neat solution It worked from the very first run Pros less code easy to readCons not all groups it didn t write to the variable just printed to the screen ConclusionFirst of all commercial models are way better at this Secondly sometimes even smaller and less powerful models can produce a more readable and better code Thank you for reading If you like this post please check my other two posts on how to work with LLaMa based models See you	2023-06-09 12:43:05
海外TECH	DEV Community	CQL Trace Viewer: Visualizing CQL Traces with Dash	https://dev.to/frosnerd/cql-trace-viewer-visualizing-cql-traces-with-dash-405a	CQL Trace Viewer Visualizing CQL Traces with Dash IntroductionWith the rapid growth and demand in data the management and understanding of large volumes of data have become increasingly crucial Cassandra Query Language CQL a language for communicating with the Apache Cassandra database plays an instrumental role in this process CQL has many similarities with SQL the standard language for managing data held in a relational database management system It allows you to interact with Cassandra such as creating updating and deleting tables inserting and querying data However in handling voluminous data performance is key That s where tracing in CQL comes in With tracing you can track the journey of a query as it gets executed within a cluster This includes details about the stages a query passes through the duration for each stage and which nodes are involved This detailed information can help diagnose problems and optimize performance by identifying bottlenecks or areas for improvement But interpreting the raw output of CQL traces can be daunting due to its verbosity and complexity This is why visualizing these traces becomes incredibly beneficial Visualization can simplify the interpretation of these details by providing a more intuitive and user friendly representation Visualizing the output allows developers and administrators to better understand the execution of queries and pinpoint performance issues more easily For the first version of CQL Trace Viewer we opted for a scatter plot visualization that shows the tracing events of each involved node on a timescale This is how it looks In the upcoming sections we will explore CQL tracing in detail discuss the benefits of using the Dash Python framework for building our visualization tool CQL Trace Viewer and provide an overview of the implementation process What is CQL Tracing CQL tracing is a built in feature of Apache Cassandra a powerful tool that developers can leverage to understand how their CQL queries are being processed internally It provides a detailed breakdown of a query s execution path helping you see how much time is spent at each stage and which nodes are involved in the process Turning on tracing is as simple as running the TRACING ON command in the CQL shell before executing your query Let s try it out in the CQL Console of our database created in Astra cqlsh gt TRACING ON cqlsh gt SELECT FROM testks keyvalue limit After the query results we can see the following trace table activity timestamp source source elapsed client Execute CQL query b e c fd dda e af a Parsing SELECT FROM testks keyvalue limit CoreThread b e c fd dda e af a Preparing statement CoreThread b e c fd dda e af a Computing ranges to query CoreThread b e c fd dda e af a Submitting range requests on ranges with a concurrency of rows per range expected CoreThread b e c fd dda e af a Submitted concurrent range requests CoreThread b e c fd dda e af a Sending READS RANGE READ message to size bytes CoreThread b e c fd dda e af a Sending READS RANGE READ message to size bytes CoreThread b e c fd dda e af a READS RANGE READ message received from CoreThread b e c fd dda e af a Executing seq scan across sstables for min min CoreThread b e c fd dda e af a Read live rows and tombstone ones CoreThread b e c fd dda e af a Enqueuing READS RANGE READ response to CoreThread b e c fd dda e af a Sending READS RANGE READ message to size bytes CoreThread b e c fd dda e af a READS RANGE READ message received from CoreThread b e c fd dda e af a READS RANGE READ message received from CoreThread b e c fd dda e af a Processing response from CoreThread b e c fd dda e af a Executing seq scan across sstables for min min CoreThread b e c fd dda e af a Read live rows and tombstone ones CoreThread b e c fd dda e af a Enqueuing READS RANGE READ response to CoreThread b e c fd dda e af a Sending READS RANGE READ message to size bytes CoreThread b e c fd dda e af a READS RANGE READ message received from CoreThread b e c fd dda e af a Processing response from CoreThread b e c fd dda e af a Didn t get enough response rows actual rows per range remaining rows new concurrent requests CoreThread b e c fd dda e af a Request complete b e c fd dda e af aHere the activity column describes what is happening the timestamp column provides the time at which the activity started the source column indicates which node in the cluster performed the activity and source elapsed shows the time in microseconds since the start of query execution at the source node While this detailed output can be valuable it can also be overwhelming This is where our CQL Trace Viewer comes in helping make sense of this output by presenting it in a more approachable and visual format In the next section we ll explore why we ve chosen the Dash framework for this task Why Dash When it comes to building a web application for data visualization choosing the right framework can make all the difference In the case of CQL Trace Viewer we ve opted for Dash a Python framework for building analytical web applications Dash is built on top of Flask Plotly js and React js harnessing the power and flexibility of these libraries This means that it benefits from the well known and simple back end capabilities of Flask the interactive and high quality data visualization of Plotly js and the reactive component based UI build of React js The combination of these technologies gives Dash a strong foundation for building data intensive applications Furthermore Dash is designed specifically for creating analytical web applications making it an ideal choice for our visualization tool Its interactive Plotly js charts and graphs enable users to understand and interact with their data in a much more meaningful way than raw trace outputs can provide Additionally Dash allows us to create our application solely in Python This can significantly simplify the development process especially for those already well versed in Python No JavaScript or HTML knowledge is required although these can be used if desired This Python centric approach means that the app can easily integrate with the scientific Python ecosystem Libraries such as Pandas for data manipulation and NumPy for numerical computations can be used seamlessly within the application further expanding its capabilities Moreover Dash applications are inherently web based and can be deployed on servers and shared over the internet This makes CQL Trace Viewer accessible to anyone who needs to understand the inner workings of their CQL queries anywhere and at any time In the next section we will delve into the implementation details Implementation SetupBefore we can start implementing our app we ll need to setup the development environment Assuming the following requirements txt file dash pandas We can create a virtual environment and install the dependencies using the following commands python m venv venvsource venv bin activatepip install r requirements txtNow let s write some code Application SkeletonAt a high level implementing a Dash application involves creating a Dash instance defining the layout of the application and starting the server Let s create a minimal Dash application in a file called main py to see this in action from dash import Dash htmldash app Dash name title CQL Trace Viewer app dash app serverdash app layout html Div if name main dash app run server debug True The dash app is the main variable to interact with our Dash application dash app server references the embedded Flask application which we are exposing in a seemingly unused variable app This will come in handy later when we deploy our application on Google Cloud Platform GCP The dash app layout defines the layout of the application Here we are using the html Div component to create a container for our application The empty list indicates that the container has no children We will add components to this container later The conditional if name main allows us to start the server in debug mode when we execute the script from the command line The debug mode enables hot reloading meaning the server will automatically update whenever it detects a change in your source code and it displays more detailed error messages in the UI Now that we have the skeleton of our Dash app the next step is to populate the layout with components to interact with the CQL traces and to display the visualizations Adding UI ComponentsThe first UI component we need to add is an input field that allows users to paste their CQL trace Let s create a text area for this purpose To make the start screen more appealing we will store our example trace in a file called trace txt and read it into the text area when the page is loaded initially from dash import Dash dcc htmlwith open trace txt r as trace file trace input dcc Textarea id cql trace value trace file read style width height dash app layout html Div trace input When starting the server using python main py and navigating to in our browser we can see the result Next let s add a scatter plot component that will later be used to plot the parsed trace trace scatter dcc Graph id trace scatter dash app layout html Div trace input trace scatter And this is how the empty scatter plot looks in action Now that we have the basic UI components in place let s add some interactivity Adding CallbacksIn Dash we can process user input via callbacks In our case we need to define a callback function that takes the CQL trace as input parses the output and returns the plotly figure for the scatter plot This function will be called whenever the user changes the value of the input field import tracebackimport pandas as pdfrom dash dependencies import Input Outputfrom io import StringIO dash app callback Output trace scatter figure Input trace input value def parse trace raw trace try if raw trace df pd read csv StringIO raw trace sep s s header skiprows engine python scatter fig build scatter fig df return scatter fig else return except Exception traceback print exc The dash app callback decorator defines the callback function The Output and Input objects specify the components and their corresponding properties that are used as input and output for the callback In our case the trace input is the input component and the trace scatter is the output component The figure property of the trace scatter component is used as the output value The callback function takes the raw trace as input and parses it into a Pandas DataFrame We can use the read csv function to parse the table As a separator we use s s which splits the values based on and trims any whitespaces at the same time The table header is in the first line and we need to skip the second line because it does not contain any data The build scatter fig function is a helper function that creates the plotly figure for the scatter plot We will implement this function in the next subsection Building the Plotly FigureTo build our scatter plot we have to perform some data transformations Specifically we need to compute an absolute timestamp for each activity that we can plot on the x axis as well as build a value to show on the y axis To show the activities over time we could plot the timestamp column on the x axis However the timestamp has only millisecond accuracy and is not updated on every activity The source elapsed column on the other hand is a monotonic clock with microsecond accuracy There is a catch however because source elapsed represents the elapsed time in each node from the start of its tracing activity To get absolute microsecond accurate timestamps for each activity we need to calculate a root timestamp for each node We can do this by subtracting the source elapsed from the timestamp of the first activity in each node While this does not take clock drift between the nodes into account it is the best we can do To build the y axis value we can simply concatenate the source and activity columns This will give us a unique value for each activity in the trace Finally we want to group activities by their source and color them accordingly This is achieved by collecting the activities for each source in the dictionary trace activities and then flattening the output into a single list which we can transform back into a dataframe and use as the input for the scatter plot Here goes the code import plotly express as pximport datetimedef build scatter fig df source root timestamps trace activities for index row in df iterrows source row source row source activity format row source row activity activity timestamp datetime datetime strptime row timestamp Y m d H M S f elapsed micros try elapsed micros int row source elapsed except ValueError pass do nothing this is probably just a value if source not in source root timestamps source root timestamps source activity timestamp datetime timedelta microseconds elapsed micros if source not in trace activities trace activities source activity elapsed timestamp source root timestamps source datetime timedelta microseconds elapsed micros trace activity activity row activity timestamp activity timestamp source activity row source activity start activity elapsed timestamp source source trace activities source append trace activity flattened activities item for sublist in list trace activities values for item in sublist fig df pd DataFrame from records flattened activities fig px scatter data frame fig df x start y source activity color source fig update yaxes autorange reversed return figNow let s see it in action Note that this basic version does not include the code to generate the arrows for messages between nodes which you could see in the screenshot as part of the introduction If you are interested in this feature please take a look at the complete source code DeploymentTo deploy the app we can use Google Cloud App Engine which is specifically built for server side rendered websites After we create a new project in the Google Cloud Console we have to configure the cql trace viewer application Next we have to create a file called app yaml In our case it is enough to specify the Python runtime and App Engine will figure out the rest based on the conventions of an existing file called main py which defines an app variable that points to a Flask application app yamlruntime pythonWe can then use the Google Cloud SDK to deploy the app gcloud app deployAnd it s live on DiscussionWhile parsing the traces with dash was a fun exercise we d like to address a couple of significant discussion points that arose during this process Firstly numerous tracing tools already exist many of which come with powerful capabilities and well designed user interfaces One potential improvement could be for Cassandra to offer an option to output traces in a standardized machine readable format Such a format could be readily imported into any of these existing tools simplifying the process and expanding the range of visualization and analysis options The second point of discussion revolves around the variability of the tracing output particularly concerning the activities which depends heavily on the database implementation and configuration It s worth noting that the activities listed in tracing outputs can differ significantly between different versions or distributions of Cassandra For instance the trace outputs between DataStax Enterprise DSE and Apache Cassandra have considerable differences These variations imply that the parser for the CQL Trace Viewer may need to be adapted or updated according to the specific version or distribution of Cassandra being used SummaryThroughout this blog post we ve journeyed through the conception design and development of the CQL Trace Viewer a web application built using the Dash framework that visualizes the output of traced CQL queries We began by diving into CQL tracing is exploring how it provides insights into the internal processing of CQL queries We highlighted the need for visualization underscoring the value of transforming raw complex trace outputs into an intuitive and user friendly format The choice of Dash as the development framework was then justified We detailed how Dash s built in capabilities its roots in Flask Plotly js and React js and its compatibility with the scientific Python ecosystem made it an ideal candidate for the task at hand We provided a high level overview of the implementation demonstrating how to create a Dash application with a text input a plotly scatter plot figure and a callback function that parses the input and generates the figure We then showed how to deploy the application to Google Cloud App Engine Finally we discussed the potential for future improvements including the standardization of tracing output and the need to adapt the parser to different versions and distributions of Cassandra ReferencesAstra DBTRACING in DSE Cover image by Anne Nygård on Unsplash	2023-06-09 12:09:50
海外TECH	DEV Community	How to Create a YouTube Android App Clone: Step-by-Step Guide	https://dev.to/dhruvjoshi9/how-to-create-a-youtube-android-app-clone-step-by-step-guide-3ec1	How to Create a YouTube Android App Clone Step by Step GuideYouTube has become an integral part of our lives providing us with an endless stream of videos on various topics Have you ever wondered how such an app is built In this blog post I will walk you through the process of creating a YouTube Android app clone from scratch By the end you ll have a basic understanding of how to implement key features and functionalities So let s dive in These steps and and whole blog are just for education purpose only Prerequisites Basic knowledge of Java programming language Familiarity with Android Studio and Android development A working Android device or emulator Step Setting up the ProjectOpen Android Studio and create a new project Choose an appropriate name for your project and set the package name Select the minimum SDK version Choose an Empty Activity template for the project Step Designing the User InterfaceOpen the activity main xml file Design the layout of your YouTube clone using various UI components such as RecyclerView CardView ImageView and TextView Customize the layout to resemble the YouTube app including the header search bar video thumbnails and other relevant elements Step Fetching Data from YouTube APIGo to the Google Developers Console and create a new project Enable the YouTube Data API for your project Generate an API key to authenticate your requests In your Android project add the necessary dependencies in the build gradle file Implement a network request to fetch data from the YouTube API using the API key Parse the JSON response and extract the required data such as video titles thumbnails and video IDs Step Populating the RecyclerViewCreate a new Java class for the RecyclerView adapter Extend the RecyclerView Adapter class and implement the required methods Inflate the item layout for the RecyclerView rows and bind the data fetched from the API Set up the RecyclerView in your activity and attach the adapter to it Test the app to ensure the RecyclerView displays the YouTube videos correctly Step Handling Video PlaybackImplement a click listener for the RecyclerView items to handle video playback Create a new activity or fragment for playing the selected YouTube video Use the YouTube Player API to embed the video player within the activity fragment Pass the selected video ID to the player and start the playback Customize the video player controls and handle user interactions Step Implementing Additional FeaturesAdd a search functionality to allow users to search for specific YouTube videos Implement pagination to load more videos as the user scrolls through the RecyclerView Integrate user authentication to enable features like liking commenting and subscribing to channels Incorporate recommended videos based on user preferences or viewing history Conclusion Congratulations You have successfully created a basic YouTube Android app clone Through this tutorial you ve learned how to set up the project design the user interface fetch data from the YouTube API populate a RecyclerView handle video playback and implement additional features Building a complete YouTube app with all its functionalities requires more advanced techniques and a deep understanding of Android development However this tutorial serves as a starting point for your journey into creating feature rich Android applications Experiment explore and keep learning to enhance your app development skills Remember creating an app like YouTube involves complying with legal and copyright regulations Ensure that you understand and adhere to the terms and conditions of YouTube s API usage and any applicable laws Now it s your turn to take what you ve learned and expand upon it Happy coding and best of luck with your YouTube app clone project	2023-06-09 12:01:03
Apple	AppleInsider - Frontpage News	macOS 14 feature roundup: Presenter Overlay, Reactions, widgets, more	https://appleinsider.com/articles/23/06/09/macos-14-feature-roundup-presenter-overlay-reactions-widgets-more?utm_medium=rss	macOS feature roundup Presenter Overlay Reactions widgets moreThe new macOS Sonoma may have been drowned out in WWDC s focus on the Apple Vision Pro but it has received a startling number of improvements ーincluding ones Apple didn t even mention With Craig Federighi starting with the new screensavers it didn t sound like a banner year for macOS updates Yet ultimately Apple spent minutes of that keynote presentation talking up macOS Sonoma and in actual use we ve been finding more and more excellent new touches Screensavers and the lock screen Read more	2023-06-09 12:46:09
海外TECH	Engadget	Engadget Podcast: Apple WWDC 2023 wrap-up	https://www.engadget.com/engadget-podcast-apple-wwdc-2023-wrap-up-123029415.html?src=rss	Engadget Podcast Apple WWDC wrap upAfter tons of typing and running around Apple s campus we re ready to wrap up WWDC This week Devindra chats with Editor in Chief Dana Wollman and Senior Writer Sam Rutherford about Apple s foray into spatial computing with the Vision Pro the inch MacBook Air and the company s many many software announcements Also we discuss Spotify s latest podcast flubs Diablo and the new Genndy Tartakovsky animated series Unicorn Warriors Eternal Listen below or subscribe on your podcast app of choice If you ve got suggestions or topics you d like covered on the show be sure to email us or drop a note in the comments And be sure to check out our other podcasts the Morning After and Engadget News Subscribe iTunesSpotifyPocket CastsStitcherGoogle PodcastsTopicsApple s AR Headset iOS MacOS and everything else we expect to see at WWDC Meta Quest details drop ahead of Apple s developer conference next week Oppo debuts MR Glass Developer Edition AI leaders issue dire warning on its risks neglecting their own responsibility in its development Chatbot rolled out by National Eating Disorders Association taken offline after giving bad advice Working on Listener mailbag Pop culture picks LivestreamCreditsHosts Devindra Hardawar and Sam RutherfordGuest Dana WollmanProducer Ben EllmanMusic Dale North and Terrence O BrienLivestream producers Julio BarrientosGraphic artist Luke Brooks and Joel ChokkattuThis article originally appeared on Engadget at	2023-06-09 12:30:29
海外TECH	Engadget	Porsche's Mission X concept offers a glimpse at its electric super car future	https://www.engadget.com/porsches-mission-x-concept-offers-a-glimpse-at-its-electric-super-car-future-121518030.html?src=rss	Porsche x s Mission X concept offers a glimpse at its electric super car futurePorsche has unveiled its latest concept vehicle called the Mission X and it says the model gives us quot a glimpse into what the sports car of the future could look like quot The Mission X is supposed to be an electric hypercar with a design that takes inspiration from the automaker s old models including the the Carrera GT and the Spyder With a body that s inches long inches wide and inches high it s a relatively low slung compact vehicle nbsp The model Porsche has presented has a quot Rocket Metallic quot finish though big parts of the vehicle feature a lightweight glass dome with an exoskeleton made of carbon fiber reinforced plastic Those parts include half of the passenger doors which are made in the style of Le Mans sports cars that open forwards and upwards Inside there are two seats made of leather and carbon fiber reinforced plastic an open top steering wheel as well as multiple cameras and clocks on the instrument panel that can display lap times and the driver s vital stats PorschePorsche has also designed the vehicle with staggered tires ー inch wheels at the front and inch wheels at the rear ーfor aerodynamic purposes If the Mission X ever goes into production the automaker intends to make it the fastest road legal vehicle around the Nürburgring Nordschleife a popular circuit in Germany Its battery will be installed centrally behind its seats and it will feature a volt system architecture that can charge the vehicle as twice as fast as the Taycan Turbo S It if ever becomes a real product people can buy of course For now it s just a concept and you can look at more photos of the vehicle at Porsche s Mission X portal nbsp PorscheThis article originally appeared on Engadget at	2023-06-09 12:15:18
海外TECH	WIRED	Apple’s VisionOS Makes a Bold Leap in Computer Interface	https://www.wired.com/story/plaintext-apple-visionos-makes-a-bold-leap-in-computer-interface/	Apple s VisionOS Makes a Bold Leap in Computer InterfaceWe ve come a long way from the command line Now we re being asked to take the next step in human computer interaction whether we re ready or not	2023-06-09 13:00:00
海外TECH	WIRED	‘One Margarita’ Is the Song of Summer—and the Moment	https://www.wired.com/story/one-margarita-song-of-summer-ai/	couldn	2023-06-09 13:00:00
ニュース	BBC News - Home	Salford e-bike rider, 15, killed in ambulance crash named	https://www.bbc.co.uk/news/uk-england-manchester-65854332?at_medium=RSS&at_campaign=KARANGA	cookson	2023-06-09 12:45:31
ニュース	BBC News - Home	Labour shadow minister suspended over complaint	https://www.bbc.co.uk/news/uk-politics-65859023?at_medium=RSS&at_campaign=KARANGA	labour	2023-06-09 12:53:25
ニュース	BBC News - Home	Mirror Group apologises to soap star for using investigators	https://www.bbc.co.uk/news/uk-65856719?at_medium=RSS&at_campaign=KARANGA	nikki	2023-06-09 12:46:51
ニュース	BBC News - Home	Blundell's School students seriously injured in assault	https://www.bbc.co.uk/news/uk-england-devon-65856111?at_medium=RSS&at_campaign=KARANGA	school	2023-06-09 12:26:56
ニュース	BBC News - Home	'No-one to blame' for death of schoolboy Hamdan Aslam	https://www.bbc.co.uk/news/uk-scotland-edinburgh-east-fife-65855800?at_medium=RSS&at_campaign=KARANGA	heart	2023-06-09 12:48:05