投稿時間:2023-06-12 17:29:01 RSSフィード2023-06-12 17:00 分まとめ(31件)

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ROBOT	ロボスタ	堤幸彦監督の新作『SINGULA(シンギュラ)』は「人類を存続させるか否か」15体のAIが討論する劇薬映画	https://robotstart.info/2023/06/12/singula-preview.html	堤幸彦監督の新作『SINGULAシンギュラ』は「人類を存続させるか否か」体のAIが討論する劇薬映画シェアツイートはてブ堤幸彦監督の新作映画『SINGULA』シンギュラが年月日、報道関係者向けに公開され、堤幸彦監督、原作・原案を担当した一ノ瀬京介氏、出演者のspi氏が登壇した。	2023-06-12 07:51:31
ROBOT	ロボスタ	【ジェネレーティブAIとファッションの未来】画像生成AIがデザインしたファッション集をプロンプト付きで発表、講演動画も公開	https://robotstart.info/2023/06/12/openfashion-omnis.html		2023-06-12 07:31:46
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	NEC、マルチクラウド間のデータ移動を容易にする月額固定料金のクラウドストレージを販売 | IT Leaders	https://it.impress.co.jp/articles/-/24945	NEC、マルチクラウド間のデータ移動を容易にする月額固定料金のクラウドストレージを販売ITLeadersNECは年月日、クラウド型ストレージサービス「NECCloudStorageSt」を提供開始した。	2023-06-12 16:06:00
python	Pythonタグが付けられた新着投稿 - Qiita	【Python・API制限回避】gspreadで最終行に行を大量追加する方法	https://qiita.com/MOSO1409/items/f0ee5772e1e7241e3c85	gspread	2023-06-12 16:59:10
python	Pythonタグが付けられた新着投稿 - Qiita	【Python】gspreadで最終行数を取得する方法	https://qiita.com/MOSO1409/items/6b010cf9eef4fc0c2d66	wsrowcount	2023-06-12 16:44:57
js	JavaScriptタグが付けられた新着投稿 - Qiita	NCMBの管理画面をGoogle Chrome機能拡張で便利にする（スクリプトログのダウンロード）	https://qiita.com/goofmint/items/d317eb10fa51a8becc93	google	2023-06-12 16:09:53
AWS	AWSタグが付けられた新着投稿 - Qiita	AWS サーバー複製手順(EC2)	https://qiita.com/takatakaryoryo/items/7fcadf463dd123024782	都度	2023-06-12 16:49:56
AWS	AWSタグが付けられた新着投稿 - Qiita	terraformのバージョン Bump upを2年半ぶりに行った	https://qiita.com/ugu/items/a627e217338dd1b17de0	bumpup	2023-06-12 16:16:17
技術ブログ	Developers.IO	WSL2 で md-to-pdf を使うために四苦八苦した話	https://dev.classmethod.jp/articles/how-to-setup-mdtopdf-on-wsl2/	mdtopdf	2023-06-12 07:26:18
海外TECH	DEV Community	AWS open source newsletter, #160	https://dev.to/aws/aws-open-source-newsletter-160-227o	AWS open source newsletter Welcome to of the AWS open source newsletter where we try and share all the important open source news projects events and content that open source builders want This week we have new projects that include tools to help you build data workflows Terraform modules to help you incorporate temporary elevated access controls integrating Tailscale to change your traffic flows a neat AWS Lambda debugging tool Go bindings for Cedar and more We also feature content this week on lots of great open source technologies including Apache Spark Cedar Keycloak Apache Cassandra Ray Apache Airflow deequ Babelfish for Aurora PostgreSQL MySQL PyTorch ROS Kotlin AWS SDK for pandas Apache Kafka Notation and more Finally don t miss the videos and events sections and make sure you check out events happening this week including the open source data meetup in London where I will be speaking FeedbackPlease please please take minute to complete this short survey and bask in the warmth of having helped improve how we support open source Celebrating open source contributorsThe articles and projects shared in this newsletter are only possible thanks to the many contributors in open source I would like to shout out and thank those folks who really do power open source and enable us all to learn and build on top of what they have created So thank you to the following open source heroes Suman Debnath Justin Garrison Aidan Steele Nathan Glover Lars Jacobsson John Milne Abdel Jaidi Anton Kukushkin Leon Luttenberger Jimmy Ray Jesse Butler Stuti Deshpande Jesus Max Hernandez Divya Gaitonde Joseph Barlan Aniket Jiddigoudar Parnab Basak Fernando Gamero Shubham Mehta Michael Raney Meet Bhagdev Lotfi Mouhib Vara Bonthu Praveen Koorse and Mike Hicks Jeremy Cowan and Shane Corbett Latest open source projectsThe great thing about open source projects is that you can review the source code If you like the look of these projects make sure you that take a look at the code and if it is useful to you get in touch with the maintainer to provide feedback suggestions or even submit a contribution Toolsaws ddkaws ddk DataOps Development Kit is an open source development framework to help you build data workflows and modern data architecture on AWS From the README The DDK offers high level abstractions of AWS CDK constructs allowing you to quickly build pipelines that manage data flows on AWS You can create your pipelines using Typescript or Python We want our customers to focus on writing code that adds business value whether that is a data transformation cleaning data to train a model or creating a report We believe that orchestrating pipelines creating infrastructure and creating the DevOps behind that infrastructure is undifferentiated heavy lifting and should be done as easily as possible using a robust framework terraform aws sso elevatorterraform aws sso elevator is a Terraform module for implementing temporary elevated access via AWS IAM Identity Center Successor to AWS Single Sign On and Slack From the README While temporary credentials through SSO IAM Identity Center is a significant improvement compared to static IAM users there is still a risk of IDP credentials compromise It makes creating permanently assigned permission sets a tricky task balancing between allowing enough and mitigating the risk of credentials compromise To address this we ve created this Terraform module AWS SSO Elevator It allows requesting and granting temporary elevated access for AWS SSO through a Slack request approval workflow to reduce security risks And it is now open sourced Despite its recent debut in the open source space it s been used and proven valuable by our customers Check out the rest of the README for additional details and features this provides and you can also check out the demo This is certainly on my todo list to try out And here is a link to the Terraform registry freedatafreedata when it comes to pushing creativity boundaries in what you can do with AWS few people reach AWS Hero Aidan Steele s level Freedata alters the traffic patterns from your webapp hosting EC instances via AWS Systems Manager Session Manager and Tailscale Session Manager streams are limited to about mbit Not to be taken seriously but fun nevertheless lambda debuglambda debug is the latest update from our good friend and AWS Community Builder Lars Jacobsson Lambda Debug is a tool that enables you to invoke AWS Lambda functions in the cloud from any event source and intercept the requests with breakpoints locally There s similar functionality in SST which this is inspired by but is designed to work with AWS SAM and CloudFormation and in a later version CDK As always documentation and examples of how you can get started are bar raising aws greengrass json gzipaws greengrass json gzip is an AWS Greengrass component from Nathan Glover that takes a stream of JSON messages from StreamManager and batches them into a gzip file It uses a JSON Line JSONL format for the messages This is a prototype component and Nathan is experimenting how to approach batching high frequency data direct to S pre partitioned without relying on Cloud services like Kinesis or Firehose cedar gocedar go is a project from Mathis Joffre that provides a Go binding for Amazon Cedar Policy using wasm to embed the Cedar engine with near zero overhead The repo provides an example of how to use the Cedar engine to evaluate a policy inside your Go code Very cool Demos Samples Solutions and Workshopsgame streaming on kubernetesgame streaming on kubernetes this repo from Justin Garrison is an example of how to set up a Kubernetes cluster and an example workload to stream a game from Kubernetes using Sunshine Sunshine is an open source implementation of NVIDIA GameStream and Moonlight is an open source client for playing games This repo heavily builds on the work from Games on Whales GOW They provided the main containers and an example implementation of a Kubernetes deployment The workload in this example folder has been changed but will be upstreamed at some point pycon polarspycon polars is a workshop that my colleague Suman Debnath recently delivered at PyCon In this workshop you will start with Polars basics and compare Polars with Pandas DataFrame You will walk through code exploring functions and features of Polars for example load and transform data from CSV Excel or Parquet perform data analysis in parallel and prepare your data for machine learning pipelines and shall compare with Pandas and Spark The workshop will focus on the following which makes Polars special parallel hashing lazy execution and expresive API AWS and Community blog postsKeycloakAs part of getting familiar with AWS Identity Centre and looking to integrate an identity provider other than Active Directory and the other usual suspects I decided to deploy Keycloak Keycloak is a very feature rich open source alternative for those folks who want to run their own identity and access management service I thought this would be straight forward to setup on my AWS account but it turned out anything other than that I put together a quick post to share my findings and to help future explorers looking to deploy Keycloak on AWS Check it out at Integrating Keycloak as my Identity Provider for IAM Identity Centre Part one deploying Keycloak on AWS hands on AWS SDK for pandasIn the post Advanced patterns with AWS SDK for pandas on AWS Glue for Ray Abdel Jaidi Anton Kukushkin and Leon Luttenberger showcase some more advanced patterns to run your workloads using AWS SDK for pandas In particular these examples demonstrated how Ray is used within the library to distribute operations for several other AWS services not just Amazon S hands on NotationNotation is a CLI project to add signatures as standard items in the registry ecosystem and to build a set of simple tooling for signing and verifying these signatures Jimmy Ray and Jesse Butler have put together this great post Announcing Container Image Signing with AWS Signer and Amazon EKS that walks you through the newly launched AWS Signer Container Image Signing This new service provides the capability for customers to have native AWS support for signing and verifying container images stored in container registries like Amazon Elastic Container Registry Amazon ECR AWS Signer is a fully managed code signing service to ensure trust and integrity of your code and supports signing and verifying container images using Notation The blog post looks at some of the upstream projects that AWS teams have been contributing to such as kyverno notation aws and Ratify Also don t miss Jimmy s sample repo ks notary admission that provides a non production example of how to use the Notation CLI with AWS Signer to verify container image signatures in Kubernetes for container images stored in private Amazon ECR registries Make sure you check out this weeks must read post deequAWS Glue Data Quality allows you to measure and monitor the quality of your data so that you can make good business decisions Built on top of the open source DeeQu framework AWS Glue Data Quality provides a managed serverless experience Last week there was a series of posts that are worth checking out if you are exploring this open source framework The series kicked off with Getting started with AWS Glue Data Quality from the AWS Glue Data Catalog where Stuti Deshpande Jesus Max Hernandez Divya Gaitonde Joseph Barlan and Aniket Jiddigoudar talk about the ease and speed of incorporating data quality rules using AWS Glue Data Quality into your Data Catalog tables and how to run recommendations and evaluate data quality against your tables The post provides links to the other posts that explore different dimensions of how to use AWS Glue Data Quality Make sure you check out this series of posts top notch stuff ecs compose xAWS Community Builder John Milne was a guest on Build on Open Source a while back check out the session talking about his open source journey and showing us some of his open source projects In his latest blog post Journey of creating a new AWS CloudFormation resource shows you how to create custom Cloudformation resources and how to integrate them using ecs compose x and troposphere LLM roundupLast week we saw a number of interesting posts covering the very hot large language model space The pick of these for me were Train a Large Language Model on a single Amazon SageMaker GPU with Hugging Face and LoRA shows you how to train the billion parameter BloomZ model using just a single graphics processing unit GPU on Amazon SageMaker hands on Announcing the launch of new Hugging Face LLM Inference containers on Amazon SageMaker looks at the release of a new Hugging Face Deep Learning Container DLC for inference with Large Language Models LLMs hands on Big data and analyticsBest practices for migrating SQL Server MERGE statements to Babelfish for Aurora PostgreSQL covers one use case of the Babelfish Compass tool to convert them to equivalent Babelfish T SQL code hands on Cross account Amazon Aurora MySQL migration with Aurora cloning and binlog replication for reduced downtime explains the various steps involved to migrate your Aurora MySQL cluster from one AWS account to another AWS account while setting up binary log replication to reduce the downtime hands on Deep dive on Amazon MSK tiered storage dives deep into the underlying infrastructure affects Apache Kafka performance when you use Amazon Managed Streaming for Apache Kafka Amazon MSK tiered storage hands on Stream data with Amazon DocumentDB Amazon MSK Serverless and Amazon MSK Connect is a hands on guide to help you run and configure the open source MongoDB Kafka connector to move data between Amazon MSK and Amazon DocumentDB hands on Other posts and quick readsBuild high performance ML models using PyTorch on AWS Part demonstrates the performance and ease of running large scale high performance distributed ML model training and deployment using PyTorch on AWS hands on Create RESTful APIs on AWS with OpenAPI Specification With No Coding looks at how you can use AWS serverless technologies and the OpenAPI specification hands on Announcing Amazon S checksums support in the AWS SDK for Kotlin shows you how to begin using Amazon S checksums with the AWS SDK for Kotlin hands on Orchestrate NVIDIA Isaac Sim and ROS Navigation on AWS RoboMaker with a public container image dives deep into how to run high fidelity simulations using NVIDIA Isaac Sim and ROS Navigation on AWS RoboMaker hands on Quick updatesApache AirflowAmazon Managed Workflows for Apache Airflow MWAA now supports in place version upgrades for environments version x and later Amazon MWAA is a managed orchestration service for Apache Airflow that makes it easier to set up and operate end to end data pipelines in the cloud With in place version upgrades on Amazon MWAA customers can seamlessly upgrade their existing version x environments to newer available Airflow versions while retaining the execution history and configurations enabling them to leverage the latest capabilities of the Airflow platform Amazon MWAA manages the entire upgrade process from provisioning new Apache Airflow versions to upgrading the metadata database In case of a failure during the upgrade Amazon MWAA is designed to roll back to the previous stable version and the associated metadata database snapshot Make sure you read Introducing in place version upgrades with Amazon MWAA where Parnab Basak Fernando Gamero and Shubham Mehta walk you through this in depth and look at typical use cases and considerations you need to think about Apache CassandraAmazon Keyspaces for Apache Cassandra is a scalable serverless highly available and fully managed Apache Cassandra compatible database service Amazon Keyspaces now supports Multi Region Replication Amazon Keyspaces Multi Region Replication is a new capability that provides you with automated fully managed active active replication across the AWS Regions of your choice You can improve both availability and resiliency from regional degradation while also benefiting from low latency local reads and writes for global applications With Multi Region Replication Keyspaces asynchronously replicates data between Regions and data is typically propagated within a second Multi Region Replication also eliminates the difficult work of resolving update conflicts and correcting for data divergence issues enabling you to focus on your application For those wanting more details then check out this post Announcing Amazon Keyspaces Multi Region Replication where Michael Raney and Meet Bhagdev look at the benefits and use cases of this new feature and demonstrate how to get started using Multi Region Replication in Amazon Keyspaces Apache SparkAmazon EMR on EKS now supports Spark Operator and spark submit as new job submission models for Apache Spark in addition to the existing StartJobRun API With today s launch you now have the flexibility to submit your Apache Spark jobs via your preferred submission model on Amazon EMR on EKS without needing to change your application Prior to today s launch you could only submit Apache Spark jobs via the StartJobRun API including using the AWS CLI and AWS Controllers for Kubernetes ACK Customers with existing Apache Spark applications running Spark Operator or spark submit would have to make changes to their applications in order to use Amazon EMR on EKS With this feature you can now run your applications on EMR on EKS without changing them benefit from the EMR Spark runtime performance and features and save time by using the spark submit and Spark Operator you are already familiar with Read Introducing Amazon EMR on EKS job submission with Spark Operator and spark submit where Lotfi Mouhib and Vara Bonthu walk you through the process of setting up and running Spark jobs using both Spark Operator and spark submit RayAWS Glue for Ray a data integration engine option on AWS Glue is now generally available AWS Glue for Ray helps data engineers and ETL extract transform and load developers scale their Python jobs AWS Glue for Ray combines that serverless capability for data integration with Ray ray io a popular new open source compute framework that helps you scale Python workloads Similar to Apache Spark and Python engines on AWS Glue you only pay for the resources that you use while running code and you don t need to configure or tune the resources AWS Glue for Ray facilitates the distributed processing of your Python code over multi node clusters You can create and run Ray jobs anywhere that you can run AWS Glue ETL jobs This includes existing AWS Glue jobs command line interfaces CLIs and APIs You can select the AWS Glue for Ray engine locally or through notebooks on AWS Glue Studio and Amazon SageMaker Studio Notebook When the Ray job is ready you can run it on demand or on a schedule RubyAWS Lambda now supports Ruby as both a managed runtime and a container base image Developers creating serverless applications in Lambda with Ruby can take advantage of new features such as endless methods a new Data class improved pattern matching and performance improvements For more information on Lambda s support for Ruby see our blog post at Ruby runtime now available in AWS Lambda To deploy Lambda functions using Ruby upload the code through the Lambda console and select the Ruby runtime You can also use the AWS CLI AWS Serverless Application Model AWS SAM and AWS CloudFormation to deploy and manage serverless applications written in Ruby Additionally you can also use the AWS provided Ruby base image to build and deploy Ruby functions using a container image To migrate existing Lambda functions running earlier Ruby versions review your code for compatibility with Ruby and then update the function runtime to Ruby To dive deeper read the post Ruby runtime now available in AWS Lambda where Praveen Koorse goes into more details including stuff you need to know to get started Videos of the weekCedarCheck out Mike Hicks as he demo s features of the Cedar policy language during the Open Source Summit NA in Vancouver last month You should also read the supporting post in the New Stack The Cedar Programming Language Authorization SimplifiedkrrJoin Jeremy Cowan and Shane Corbett from the Containers from the Couch posse as they speak with guest Natan Yellin at how you can optimise your container resources using krr an open source project from Robusta Build on Open SourceFor those unfamiliar with this show Build on Open Source is where we go over this newsletter and then invite special guests to dive deep into their open source project Expect plenty of code demos and hopefully laughs We have put together a playlist so that you can easily access all sixteen of the episodes of the Build on Open Source show Build on Open Source playlist We are currently planning the third series if you have an open source project you want to talk about get in touch and we might be able to feature your project in future episodes of Build on Open Source Events for your diaryIf you are planning any events in either virtual in person or hybrid get in touch as I would love to share details of your event with readers London Open Source Data Infrastructure MeetupHuckletree Shoreditch June th PM PMAs part of London Tech Week Aiven are co hosting the London Open Source Data Infrastructure Meetup on the th of June This event is in collaboration with Hugh Evans from Daemon the AI and Deep Learning for Enterprise group The speaker panel includes Ricardo Sueiras from Amazon Web Services AWS discussing the wonders of Apache Airflow Davies Oludare from Confluent shedding light on the intricate world of Kafka and Ed Shee from Seldon breaking down the nuances of ML powered summarisation Spaces are limited so make sure you reserve your spot by checking and out the meetup pageOpen Source FestivalLagos Nigeria June th thAn established and essential event for all open source developers Open Source Festival promises to be even bigger and better than previous years I feel very privileged to have spoken at this event in and so make sure you check this out if you are in the region or perhaps if you are maybe looking to sponsor an open source event then maybe this is the one you should check out Check out more on their homepage Open Source Festival Live AWS and Apache Hudi WorkshopOnline June nd at am PTNadine Farah and Soumil S are teaming up to deliver a live workshop that will mimic a typical use case in this instance building near real time dashboards for a fictional ride sharing company Make sure you reserve your place by going to the registration page Live AWS and Apache Hudi Workshop Build a ride share lakehouse platformCortexEvery other Thursday next one th FebruaryThe Cortex community call happens every two weeks on Thursday alternating at UTC and UTC You can check out the GitHub project for more details go to the Community Meetings section The community calls keep a rolling doc of previous meetings so you can catch up on the previous discussions Check the Cortex Community Meetings Notes for more info OpenSearchEvery other Tuesday pm GMTThis regular meet up is for anyone interested in OpenSearch amp Open Distro All skill levels are welcome and they cover and welcome talks on topics including search logging log analytics and data visualisation Sign up to the next session OpenSearch Community Meeting Stay in touch with open source at AWSRemember to check out the Open Source homepage to keep up to date with all our activity in open source by following us on AWSOpen	2023-06-12 07:48:21
海外TECH	DEV Community	Harnessing the Power of Machine Learning: Practical Applications in Software Development	https://dev.to/hackmamba/harnessing-the-power-of-machine-learning-practical-applications-in-software-development-3c2d	Harnessing the Power of Machine Learning Practical Applications in Software DevelopmentExplore the practical applications of machine learning in software development leveraging its power to enhance productivity automate tasks and optimize processes OverviewWe are in a fascinating world where machine learning meets software development A realm where intelligent algorithms collaborate with developers optimizing processes automating tasks and unleashing the true potential of software applications In this fast paced digital era software development has evolved into a dynamic field constantly seeking innovative solutions to meet the ever growing demands of users With its ability to analyze vast amounts of data discover patterns and make informed decisions machine learning has emerged as a game changer in this domain From intelligent code completion that anticipates your next move to automated bug detection that uncovers hidden flaws machine learning algorithms are revolutionizing how software is built They empower developers to streamline workflows enhance software quality and deliver exceptional user experiences But it doesn t stop there Machine learning also powers cutting edge applications like natural language processing enabling chatbots and voice assistants to understand and respond to our queries with astonishing accuracy Visual UI testing becomes a breeze with image recognition algorithms that automate the verification process ensuring pixel perfect interfaces across different platforms Join us in exploring practical applications where we ll dive deep into how machine learning transforms software development Get ready to witness the integration between human creativity and artificial intelligence and discover how harnessing the power of machine learning can take your software development skills to unprecedented heights Understanding Machine Learning FundamentalsBefore diving into practical applications it is crucial to grasp the fundamental concepts of machine learning Machine learning can be broadly categorized into supervised unsupervised and reinforcement learning These techniques enable computers to learn from data and make predictions or take actions without being explicitly programmed A machine learning system consists of several key components including data preprocessing and feature engineering model training and evaluation and model deployment and monitoring These components work together to create powerful and accurate machine learning models Practical Applications of Machine Learning in Software DevelopmentPredictive Analytics Machine learning enables software applications to predict future outcomes based on historical data This application finds its use in customer behavior analysis recommendation systems and fraud detection and prevention Natural Language Processing NLP NLP leverages machine learning to understand and process human language Sentiment analysis text classification language translation and chatbots are some practical applications of NLP in software development Computer Vision The software can analyze and interpret visual data using machine learning algorithms Object detection image recognition facial recognition and biometric systems are examples of computer vision applications in software development Anomaly Detection Machine learning models can identify unusual patterns or behaviors in data making them valuable for detecting network intrusions and monitoring system health Challenges and Considerations in Harnessing Machine LearningWhile machine learning offers immense potential there are challenges and considerations to keep in mind when incorporating it into software development Data quality and quantity High quality and diverse datasets are essential for training accurate machine learning models Acquiring and preprocessing relevant data can be a time consuming and complex process Ethical considerations and bias Machine learning models can inadvertently perpetuate bias or discrimination present in the training data Addressing ethical considerations and ensuring fairness when deploying machine learning solutions is crucial Model interpretability and explainability As machine learning models become more complex interpreting and explaining their decisions can be challenging This poses challenges in scenarios where transparency is required Scalability and computational resources Training and deploying machine learning models require significant computational resources Scalability becomes a concern when dealing with large datasets or real time applications Best Practices for Incorporating Machine Learning in Software DevelopmentTo harness the power of machine learning effectively it is important to follow the best practices Clearly define the problem statement and goals Clearly articulate the problem you aim to solve and establish specific goals for your machine learning project Acquire and preprocess relevant data Collect and preprocess data that is relevant diverse and representative of the problem domain Select appropriate machine learning algorithms and techniques Choose the most suitable ones based on the problem type and available data Train and evaluate models effectively Split your data into training and evaluation sets train your models and rigorously evaluate their performance using appropriate metrics Deploy and monitor models in production Once a model is trained deploy it in a production environment and continuously monitor its performance to ensure it remains effective and accurate Continuously iterate and improve the models Machine learning models are not static they can be improved over time Continuously gather feedback retrain models and iterate to enhance performance Real World Examples of Machine Learning in Software DevelopmentReal world examples demonstrate the impact of machine learning in software development Netflix s recommendation system Netflix uses machine learning algorithms to analyze user preferences and provide personalized recommendations enhancing the user experience Google s language translation services Google Translate leverages machine learning to provide accurate translations between languages enabling seamless communication across cultures Uber s surge pricing algorithm Uber s pricing algorithm utilizes machine learning to determine fare prices based on supply and demand optimizing revenue while balancing rider and driver satisfaction Facebook s facial recognition technology Facebook uses machine learning to identify and tag individuals in photos providing users with a convenient and user friendly experience Future Trends and Emerging Technologies in Machine LearningThe future of machine learning in software development holds exciting prospects Reinforcement learning and self learning systems Reinforcement learning where software agents learn from trial and error has the potential to create self learning systems capable of adapting to new situations Generative adversarial networks GANs and synthetic data generation GANs can generate synthetic data that resembles real data expanding possibilities in areas with limited datasets Edge computing and Internet of Things IoT applications Machine learning on edge devices and IoT devices will enable real time and decentralized decision making reducing latency and enhancing privacy Explainable AI and responsible AI practices There is a growing focus on developing models to explain their decisions fostering transparency and ethical use of machine learning Final ThoughtMachine learning has become a game changer in software development unlocking innovative applications and transforming industries By understanding the fundamentals embracing best practices and considering the challenges software developers can harness the power of machine learning to build intelligent and impactful solutions As machine learning continues to evolve its potential to shape the future of software development remains immense Embrace the possibilities and embark on the journey of unlocking the true potential of machine learning in your software projects So embrace the power of machine learning explore its practical applications and embark on the path to creating innovative and impactful software solutions that shape the future The possibilities are limitless and the time to harness the power of machine learning in software development is now ResourcesThe Role of Machine Learning in Software Development Use Cases and ToolsMachine Learning Algorithms Real World Applications and Research DirectionsHarnessing Machine Learning and Big Data Analytics for Real World Applications A Comprehensive Survey	2023-06-12 07:27:18
海外TECH	DEV Community	Vim for The VS Code User: Part 1 - Initial Setup	https://dev.to/aziznal/vim-for-the-vs-code-user-part-1-initial-setup-29la	Vim for The VS Code User Part Initial SetupAhh vim For me it was just a funny meme I saw every once in a while about how people can t seem to exit it But then as most adventures in a dev s life start I went on the internet while procrastinating from doing my real work and I instead watched a former meth addict and Netflix engineer talking about neovim its shortcuts plugins theming etc and it sort of clicked for me I tried it and I ve been hooked ever since In my following article s I want to show you how you can get started with vim specifically Neovim including avoiding some the mistakes I fell into This will help you turn your IDE integrated development environment into a PDE Personal development environment With this article and the ones I plan on making in this series you will make vim into a personalized code editing powerhouse custom tailored for exactly your flow and yourself Here s a link to my config if you re interested Before You StartFor me the vim roller coaster looked something like this The point is to keep pouring in effort and you ll get there but you ll probably have to push through it at some point Once you re used to vim you ll have a hard time going back to your default editor without at least using vim motions because although you can switch your brain to the old ways again you won t want to Also I want to mention some important points about things that I was doing wrong in my opinion that may help you as well Stop using a non qwerty layout unless you know what you re doing Switching to the US QWERTY layout was the best thing I did for my coding speed I used Turkish Q before Don t think you need more experience before you start You are literally as ready as you will ever be right now Ease into it Do things YOUR way at YOUR pace 🫵 Don t stop using your favorite editor cold turkey Your productivity will plummet to the ground and you will feel like crap For starters you could check out this vim extension for VS code for example Don t use Vimscript This is my ️personal opinion ️ but I ve had a much easier time using plain old lua which is how you setup your vim config as I ll show you in an upcoming section One last thing I m using the words vim and neovim interchangeably here but I m strictly talking about neovim in pretty much all instances Why Vim Here is one good explanation Why oh WHY do those nutheads use vi My reasons are Incredibly fast startupClean Simple Nothing more than an editor Minimal hand movement I don t even need to move my hand to the arrow keys Tailored exactly to your needsUnmatched customizabilityBrag rightsIt s not even funny how much quicker neovim is to start than my VS Code Maybe I made my VS Code installation a bloated mess of extensions and other crap or maybe I just want a functional but customized editor that doesn t take half my RAM to edit an HTML file Vim s shortcuts are also all close to the center of the keyboard You don t have to move your hands that much even on the keyboard itself let alone away from the keyboard to the mouse Not to mention the insane amount of customization you can do I cannot stress this enough but you will never run out of ways to make vim your own Most importantly you now get to brag I use arch AND vim btw Installing NeovimThere s no better guide than here The Modes of VimThis is probably one of the biggest differences between your conventional text editing environment and vim I m not going to go into too much depth about these but you can read about them here probably do some more research though In a normal text editor you can just write immediately and you can use combinations of Ctrl Shift Super Opt and Alt Cmd and a key for your shortcuts In vim there are modes you should be familiar with Normal all your keys yes including the letters are shortcuts for text manipulation and many other functions Insert pretty much exactly like a normal text editor Visual Select text and do something with it copy paste delete format etc Command Run a vim command You already know how to use Insert Visual and probably even Command modes VS Code s default is in insert mode you are in Visual mode when selecting any text and Ctrl Shift P opens the command palette which is a sort of command mode in itself What makes vim special for me is its Normal mode Instead of reaching for the bottom left corner of your keyboard your fingers can stay on the home row of your keyboard I won t sugar coat it It s a slow start to get used to it but practice makes perfect Once you know your way around these modes it becomes a matter of wanting to do something and your fingers will simply do it The Most Basic Motions and ShortcutsThere is not a chance in heehaw that I can list all the shortcuts you will need and use in vim in an article Instead I will list some resources where you can get into it yourself To get right into the action you can use vim s built in interactive tutorial by running the  Tutor command Try it it s pretty nice I can also recommend the following resources which personally helped me recommend starting with this one Vim tutor What s a Config Your config is how you tell neovim about your plugins themes shortcut maps and so on Your config will most likely be found under config nvim It can be split into different files each responsible for configuring a different aspect of vim like theme lua for your theme sets lua for your settings etc It all starts with one file init lua which is loaded automatically when you open vim This file loads the rest of your config An init lua file is almost like an index js You can write your program code in it or you can write your code in different files and then export the whole thing through this file which is what we re going to do Here s what the inside of your top level init lua will look like require yourname What s yourname This is just a namespace where you write your config It s a convention people follow in their configs to avoid clashes with the built in neovim config You can replace yourname with anything within reason Next you want to create a few more things a directory called luaa directory below lua called yourname which has to match what you picked before for that an init lua file under lua yourname The lua directory means something in the lua programming language so make sure to name it exactly so Now our top level init lua file makes sense doesn t it It s importing the init lua under lua yourname Piece of cake Here s a cool pic So it s basically the top init file calling the init file below it which in turn calls any files you want the init file under yourname will typically look like this require sets lua require remaps lua any other files What about the packer lua file though Why not load that too The reason is that this file is special It s a config for a plugin manager called Packer which I use to install and manage my plugins It s sort of like your package json I ll show you how you can basically set up this file with your first plugin so you can get started installing plugins as soon as possible Create the packer lua file under lua yourname and add the following into it vim cmd packadd packer nvim return require packer startup function use your plugin installation code will go here end To find plugins I personally just google something like neovim plugin copilot and usually the gets me a github page with instructions I can follow So if you want to install the github copilot extension you would do the following vim cmd packadd packer nvim return require packer startup function use use zbirenbaum copilot lua end Then run the following commands to install the newly added plugin s so to load packer into the context PackerSync to install the newly added pluginRemember you run these commands by clicking shift semicolon type the command without the colon since it would already be there then press enter Many plugins require you to also set them up before you can use them make sure to refer to the documentation Here is a link to the documentation for this copilot extension which requires us to do something before the plugin actually works So let s add the config for the plugin You ll add this under a new folder called after plugin Just like the lua folder this folder is special in that everything in it is executed after your own config is loaded Add a file called copilot lua name is not important and paste what the docs recommends into it require copilot setup Exit vim and start it up again and you should now have a  Copilot command To authenticate your account run  Copilot auth and follow the instructions Now you have copilot in neovim You ve just gone through the basic flow to install a plugin Remember to read the docs for any given plugin well to avoid unnecessary headaches Here s my own config if you re interested Other ResourcesMy own neovim configTips for using vim A game for learning vim in vim Collection of Awesome Plugins Some more popular plugins NextThere are so many topics to cover that I plan on splitting it all up over multiple articles I plan to tackle the following topics ThemingLanguage Servers and AutocompleteMust Have pluginsFinding your filesGitStay tuned for these and let me know if there is anything in specific that you d like me to cover	2023-06-12 07:25:22
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