Autosmart manufactures and delivers market-leading vehicle cleaning and hygiene products via a network of franchisees to a vast market place.

Vehicle Cleaning & Professional Products - Autosmart International

Our innovative products transform workplaces to build the digital ecosystem of the future · Digital solutions for operations involved in after-sales dealer workshops, aiding workflow automation and cost reduction

AutoSmart | For Smart Products

We use cookies to make your experience better. To comply with the new e-Privacy directive, we need to ask for your consent to set the cookies. Learn more · Free shipping on all orders

Autosmart - The Professionals Choice!

Search over 2 million vehicles or research, build, and price the car of your dreams on the only auto shopping site that offers financing from BECU

AutoSMART - BECU

AutoSmart Australia was founded in 1996 and started franchising in 1999. It has since grown to be Australia&#x27;s largest trade supplier with 35 franchisees nationwide. We are the only manufacturer to market in every state.

Autosmart Australia - Simply the Best Automotive Cleaning ...

AUTOSMART INTERNATIONAL LTD - Free company information from Companies House including registered office address, filing history, accounts, annual return, officers, charges, business activity

AUTOSMART INTERNATIONAL LTD overview - Find and update company ...

AutoSmart&#x27;s exceptional growth is a testament to this fact. They are currently serving large automotive companies such as Toyota Saudi Arabia (Abdul Latif Jameel), Toyota Bahrain (Ebrahim K. Kanoo), Mitsubishi MEA (Principal Company), Kia Saudi Arabia (Aljabr), and Petromin Express (Nissan).&quot;

AutoSmart Company Profile, information, investors, valuation & Funding

Since 1979, Autosmart has created products that clean vehicles more quickly, effectively and efficiently. Today, we lead the field as the UK and Ireland’s largest manufacturer and distributor of professional vehicle and food processing cleaning products for trade customers. No other company has ...

Autosmart - Elite Car Care

&lt;img alt=&quot;Autosmart International&quot; src=&quot;https://autosmart.co.uk/wp-content/themes/autosmart/assets/images/logo-autosmart.png&quot; /&gt; · We manufacture and deliver market leading vehicle cleaning products and make our customers lives easier

Products - Autosmart International

AutoSmart | 174 followers on LinkedIn. Every Certified Vehicle For Less Nationwide! | We are dedicated to providing the ultimate automobile buying experience. Our dealership is your #1 source for buying quality Carfax-certified vehicles. We have extensive relationships in the dealer community ...

AutoSmart | LinkedIn

AutoSmart, Inc. | 61 followers on LinkedIn. Whether you’re looking for your next new or used car, or a pre-owned vehicle trade-in appraisal, we are here to help! We have over 30 years of experience in the automotive industry, and our company was created to provide consumers with a hassle-free ...

AutoSmart, Inc. | LinkedIn

The premium automotive detailing products choice for the professional detailer. Ceramic coating, Si02 sealants, wax, interior cleaner, car wash foam cannon products, trim shine, interior dressing and tire shine, microfiber cloth and detail brushes and other detailing accessories.

Autosmart America

Information on acquisition, funding, investors, and executives for Autosmart International. Use the PitchBook Platform to explore the full profile.

Autosmart International Company Profile: Acquisition & Investors ...

AutoSmart&#x27;s exceptional growth is a testament to this fact. We are currently serving large automotive companies such as Toyota Saudi Arabia (Abdul Latif Jameel), Toyota Bahrain (Ebrahim K. Kanoo), Mitsubishi MEA (Principal Company), Kia Saudi Arabia (Aljabr), and Petromin Express (Nissan).

AutoSmart Technology | LinkedIn

Find out where you can buy Autosmart&#x27;s market leading professional vehicle cleaning and hygiene products. Use the postcode search tool.

Find your local Autosmart Franchisee

Find company research, competitor information, contact details &amp; financial data for Autosmart, Inc. of Apex, NC. Get the latest business insights from Dun &amp; Bradstreet.

Autosmart, Inc. Company Profile | Apex, NC | Competitors, Financials ...

View customer reviews of AutoSmart. Leave a review and share your experience with the BBB and AutoSmart.

AutoSmart | Reviews | Better Business Bureau® Profile

Buy, Sell, or Finance a Car Shop Now Choose between popular models of every kind at affordable prices. Sell A Car AutoSmart will buy your vehicle, even if you don’t buy one from us. Get Approved AutoSmart helps drivers like you get approved every day. Make Payment Make a payment through our ...

AutoSmart | Buy Here Pay Here Used Cars in Longview & Texarkana

Provides Specialized Ready-made applications which include Automobile Field Audit Application, Diagnosis FIR System, Final Inspection App, Kaizen...

AutoSmart: Digitalizing Workplace Environment of Automotive Companies

AutoSmart is an automotive digital solution provider that aims to transform and reimagine automotive ecosystems. Our vision is to transform automobile workplaces into digital ecosystems of the future. Our emphasis is placed on process improvement, cost optimization, quality improvement and lead time reduction. With a dream to transform automobile workplaces into digital ecosystems of the future, AutoSmart was founded in 2017. The first product was an innovative first of a kind real-time field audit management application. It was during the development of this product, we realised that the aftersales and service industry of automobiles is in need of a digital revolution. Today, after several revolutionary products and with an impressive client list, AutoSmart is carving out a name for itself in the industry. The digital revolution in the automobile industry has always been on the automobile. AutoSmart was quick to realise that there was hardly the use of technology in other aspects of the industry. This dire need of the industry was a great opportunity. AutoSmart's exceptional growth is a testament to this fact. We are currently serving large automotive companies such as Toyota Saudi Arabia (Abdul Latif Jameel), Toyota Bahrain (Ebrahim K. Kanoo), Mitsubishi MEA (Principal Company), Kia Saudi Arabia (Aljabr), and Petromin Express (Nissan).

AutoSmart

Smart AI applications can help businesses audit complex international trade documents, significantly benefiting work efficiency and risk control. Documents are a crucial part of international trade, as well as notoriously complex. For trading staff who routinely deal with large amounts of these documents in formats such as scanned images, it makes or breaks their ability to effectively process key information and control risks. Now, with Smart Audit technology that processes information even from crumpled documents, Alibaba is showing how AI solutions can improve efficiency and reduce pain points in international trade. Background International trade is a set of complex procedures, especially for B2B trade. To control risks, cross-validation of multiple documents and document-based risk control strategies are implemented at every step. Examples include the cross-validation of corporate information and bank accounts, audit of risks related to letters of credit, B/Ls, insurance policies, packing lists, invoices, and customs declaration forms. Consider the example of the letter of credit. For this type of document, various clauses must be audited and documents must be mutually consistent and conforming to the terms of the letter of credit. This process is typically carried out by highly skilled and experienced people. This lengthy, risky audit process is the main driver for the development of Alibaba’s Smart Audit technology. Using machine learning and artificial intelligence, Smart Audit processes documents in a more efficient, less costly, and less risky way. The benefits of such technology are numerous. Smart Audit serves more SMEs in international trade by providing order decision reports, terms/credit/trade risk reports, and solutions for preparing and auditing documents. Moreover, it uses AI technology to reduce costs and risks, improve efficiency and customer experience, and boost the optimization and upgrade of core e-businesses. Solution The overall technical solution is abstracted into four parts: image processing, natural language processing, domain knowledge graph, and unified technical architecture. Main components of Alibaba’s Smart Audit solution. · Image processing service Fuzzy test Deformation recovery Documents, Card , Facial recognition Layout analysis · Natural language processing Text categorization (noise included) Words correction and sorting Text parsing, KV categorization Semantic understanding · Domain knowledge graph Domain knowledge: Terms, Port, Cargo description Expert strategies: Terms and conditions, Financing Map of risks: Nation, Bank, Region, Enterprise · Unified technical architecture Leveraging, Innovation, Scale-out Data storage, Decision-making engine, Model service Monitoring and warning Image processing When image quality is good, the existing image recognition technologies can achieve high accuracy. Unfortunately, this is not always the case. Images are usually more complex, and the direct recall of existing technologies only works well in less than 50% of cases. Moreover, common recognition technologies are weak in comprehension. For instance, even if OCR recognizes the characters on a distorted image, it cannot organize them to deliver the correct semantics; nor can it analyze and judge which parts of the images are useful. Therefore, the bigger challenge for image processing, in addition to leveraging Alibaba’s recognition technologies, is how to properly pre-process and post-process images to suit specific needs. Natural language processing Due to the varied formats of documents, and with over half of them being images, the best OCR solutions cannot keep incorrect recognition rate below 10%. Therefore, a robust text classification model is needed. Also, even a low frequency of incorrect character recognition cannot guarantee direct reading and unmanned use if there is no domain-specific optimization and word segmentation. This means that word correction and segmentation based on domain knowledge is of utmost importance. Then, through the parsing engine for content parsing and key-value relationship reconstruction, combined with text-based domain knowledge graph and risk control strategies, semantic understanding and smart audit are completed. Domain knowledge graph The knowledge graph constructed here primarily accumulates knowledge from three fields: domain knowledge, expert strategies, and risk map. Domain knowledge includes terms, abbreviations, and port information commonly used in international trade. Expert strategies include term strategies, conflict strategies, financing strategies, and audit opinions. Risk map includes information on nations, banks, regions, and enterprises. The domain knowledge graph is the foundation of Smart Audit. All advanced processing procedures are intended to combine with it to realize smart audit and risk control in a real sense. Unified technical architecture The unified technical architecture is abstracted out of the existing approaches. First, all service interfaces are gathered under the unified task engine. The next job is to get the most out of Alibaba’s existing technologies and platforms, such as Leiyin (OCR technologies), Alibaba Cloud (certificate and face recognition), MTEE (real-time decision engine), and PAI (model training and deploying platform), to name a few. The various online and offline technologies and platforms that are incorporated into Smart Audit Once these technologies and platforms are incorporated, efforts are focused on digging deeper into the algorithms and models to work out creative solutions for specific problems. Algorithms Some creative algorithms and models described in this article mainly regard image processing and natural language processing. It includes blur detection, unwarping, and word correction and segmentation. The creative algorithms and models used in Smart Audit Blur detection Blur detection, or Image Quality Assessment, aims at reaching the target in a quick, light-weighted way. It can mean smart processing if image quality is good, or prompting to re-upload and process manually. Many traditional solutions can deal with specific types of fuzziness. An example is the Laplacian algorithm that determines whether an image is blurred based on the threshold value obtained by calculating second derivative and variance. The Laplacian algorithm determines whether an image is blurred or not Nevertheless, traditional solutions do not work well in feature extraction and representation. To overcome this restraint, this article presents an improved MobileNetV2 network structure. Blur detection requires particular attention to nuanced differences between image details. Therefore, a set of samples is generated first using random slicing and HSV color space screening, followed by dividing positive and negative samples based on the OCR recognition rate. The original MobileNetV2 network contains 17 bottleneck layers, with each layer further scaling out. This model is large and difficult to converge in the training stage. It is thus clipped and improved into a new architecture that only contains two convolution layers, two pooling layers, two bottleneck layers, and one fully connected layer, in a shallower and narrower network with fewer parameters. During the latest round of testing, the accuracy of this algorithm is about 93.4%. The original model’s size is only 2 MB. In comparison, the trained model using the original MobileNetV2 model is around 26 MB. Layers of the blur detection model Unwarping Image may distort in many ways, such as rotation, folding, and curling. These problems affect not just the OCR recognition, but also, to an even worse result, semantic understanding. Image unwarping is crucial for reaching the stage of auto audit. Many traditional methods can solve specific warping problems. For instance, Hough Transform can be used to detect the straight line, and rotate the image by the correct angle. In recent years, deep learning models, such as FCN, STN, and Unet, have also been used to deal with image distortion. Combined with knowledge of semantic segmentation, Alibaba has proposed a new unwarping algorithm based on improvement of existing methods. First, data synthesis technology is used to create samples. Different forms of are utilized to simulate multiple deformation types, such as images with folded lines and curled curves; and to simulate different levels of deformation by size changes. Interpolation and image inpainting methods are then applied to pad missing pixels of the simulated images. Process of data synthesis The use of existing Stacked Unet-based methods can lead to cracks, distorted lines of texts, and badly deformed characters, among others issues. The Alibaba team has improved the network structure based on Dilated Convolution, and proposed a new unwarping method by adjusting the loss function and smoothing the prediction. Unwarping method in Smart Audit The metric MS-SSIM (Multi-Scale Structural Similarity) is used to evaluate the effect of unwarping algorithms. Alibaba’s proposed algorithm’s MS-SSIM is 0.693, which represents an obvious improvement compared to 0.490 for the previous state-of-the-art one. Examples of unwarping algorithms Word correction and segmentation The HMM model that directly combines word correction with segmentation is not efficient in terms of the large search space in the prediction stage due to long texts. Therefore, Alibaba addresses this problem from a new perspective: word segmentation is treated as a particular case of word correction, and blank is also considered a valid character; correction is treated as a translation task, where a wrong character sequence is translated into a correct one. This way, word correction and segmentation are abstracted into a sequence to sequence process. Word correction and segmentation using Seq2Seq Data synthesis (adding, deleting, and modifying characters based on the probabilistic transfer matrix) and transfer learning are used to train and obtain the target model. Currently, the error rate (edit distance) between the OCR recognition result and the Ground Truth is 15.91% (2.91% if blank is ignored). Applying the new correction and segmentation model decreases the error rate to 2.24% and increases word accuracy to 93.56%. Example of word correction and segmentation Applications Wherever Smart Audit is applied, working efficiency improves by at least 50%, with cost and risk greatly reduced. This section describes two application examples in real scenarios. Audit LC In this example, the user uploads a photographed or scanned LC file. After a series of image processing and natural language processing procedures, Smart Audit reviews each term, marks risk information, and returns the review and decision report. Example of document audit Verify documents The user uploads a photographed or scanned document (such as an insurance policy, B/L, customs declaration). Smart Audit parses and verifies each field, marks information with different colors (purple for consistency, yellow for suspicious information, and red for missing information), and returns a verification and suggestion report. Conclusion This article summarizes the business background and technical solutions of Smart Audit, describing some of the implemented and innovative algorithms and models, as well as several applications. As a new mode of international trade, Smart Audit uses machine learning and artificial intelligence technology to provide risk and decision reports, as well as overall solutions. It is also pushing forward the implementation of other technologies (such as blockchain technology) to better serve more SMEs in international trade. Alibaba Tech First hand and in-depth information about Alibaba’s latest technology → Facebook: . Twitter: . “Alibaba Tech” “AlibabaTech”

Smart Audit: Using AI in International Trade

Laravel Zero - How to Build a TCP Server

Critical Vulnerability in Swedish BankID Exposes User Data

সুইডিশ ব্যাংকআইডি-তে জটিল দুর্বলতা ব্যবহারকারীর ডেটা প্রকাশ করে

Kritische Sicherheitslücke in Swedish BankID legt Benutzerdaten offen

Vulnerabilidad crítica en BankID sueco expone datos de usuario

Une vulnérabilité critique dans Swedish BankID expose les données des utilisateurs

स्वीडिश बैंकआईडी में गंभीर भेद्यता उपयोगकर्ता डेटा को उजागर करती है

スウェーデンの BankID の重大な脆弱性によりユーザーデータが漏洩

스웨덴 BankID의 심각한 취약점으로 인해 사용자 데이터 노출

Vulnerabilidade crítica no BankID sueco expõe dados do usuário

Критическая уязвимость в шведском BankID приводит к раскрытию пользовательских данных

İsveç BankID'deki Kritik Güvenlik Açığı Kullanıcı Verilerini Açığa Çıkardı

Lỗ hổng nghiêm trọng trong BankID Thụy Điển làm lộ dữ liệu người dùng

瑞典 BankID 存在严重漏洞，导致用户数据泄露

Who wants to get a nice and shiny infrastructure diagram?
We are!
Who wants to maintain it?
… Intro That’s right. You work at a company with a lot of infrastructure to manage. There are many services, complex networking, multi-cloud setups, and several CI/CD tools. You have a lot of code written. Maybe with comments. Perhaps even some documentation. Eventually, you start creating diagrams to show your infrastructure at a glance. Everybody loves diagrams! But as soon as you press ‘Save’ button the clock starts ticking, showing how outdated your diagram is. Unfortunately, no one loves outdated stuff. And no one has time to update it. You spent a lot of time being an infrastructure-as-a-code gitops hero. Everything is already here, in the code! There are plenty of tools that transform code into UML diagrams. Is there anything for infrastructure, please? And there is! Let’s take a closer look What’s in the review To make it more practical we are going to use a real repo. The repo has a minimalistic Dockerfile for a simple Spring Boot app. A pinch of Terraform, and a GitHub Actions workflow to glue everything together. It builds the app with Gradle, creates a Docker image, and deploys it to AWS Elastic Container Service. Tools under review Terraform graph
Inframap
Rover
NoReDraw
Others Terraform graph First, let's start with Terraform graph. Terraform provides an embedded option for making a diagram. It creates a Graphviz .dot file. To see a diagram as an image you have to install Graphviz. terraform graph | dot -Tpng > /tmp/graph.png This gives you next diagram Looks acceptable for an out-of-the-box solution. It’s hardly readable, has no coloring scheme and has no icons. I’m not sure if it provides a better sense of architecture than the actual code. Yet, it could be a nice addition to a pull request analysis by demonstrating the actual plan (specify -plan=plan.out). Inframap If the Terraform graph is too basic for your needs, let's move on to Inframap. This tool reads your tfstate or HCL to generate a graph specific to each provider. Instead of showing everything, the tool shows the most important resources. Developers of Inframap created a human-readable version of terraform graph. inframap generate . | dot -Tpng > inframap.png This gives the next diagram Well, that’s way more readable. If you want more app-centric representation that’s the way to go. It doesn't have too much detail (subnets, security groups, IAM roles, etc) and looks more concise. We have all main components like the load balancer, ECS service, and the cluster. A few nitpicks: I would put client instead of im_out. And make the ECS cluster a container (rectangle) with the service inside. Anyway, it’s pretty decent. Rover Next up is Rover, another Terraform visualizer. There are several options for how to run Rover. Unfortunately, I wasn’t able to make Rover run terraform under the hood. Image generation with the specified plan didn’t work for me either. So I had to run Rover in standalone mode and explicitly specify previously generated TF plan: terraform plan -out=plan.out
terraform show -json plan.out > plan.json

docker run --rm -it -p 9000:9000 -v $(pwd):/src im2nguyen/rover:latest -planJSONPath=/src/plan.json The UI on http://localhost:9000/ appeared to be pretty nice. It might be helpful to click around to get more details on what’s going on. Again, mostly for PRs and a general understanding of resources. This is how generated diagram looks like I'm pleased with the color legend and grouping. The information seems to be excessive for the needs of an architecture diagram. Again, it’s a terraform visualizer, it shows everything defined in the plan, with no surprises. Alright, you’ve got the idea of terraform visualizers. They try to represent the state of resources defined in .tf files. Is there anything else left overboard? Right, the applications. The whole point of infrastructure is to serve a set of applications. Without showing the apps it all doesn’t make a lot of sense. Inframap did a great job with its app-centric approach. Are Terraform files not enough for the big picture? Let’s fall down the rabbit hole. NoReDraw This tool takes a step further. Instead of dealing with Terraform, NoReDraw focuses on… everything. Well, currently Gradle, Docker, GitHub Action, and Terraform AWS resources are supported. And ‘supported’ is a bit of a strong word as the project is still in beta. NoReDraw takes a source folder or git repo URL as an input. Then parses and cross-matches everything it can. Want to add more? Implement your own provider as the tool is highly customizable. Don’t like how components are linked together? Create your own LinkStrategy. Another cool benefit is that you can generate a diagram in Plant UML or Mermaid. This gives you an option to embed the drawing directly into your docs as text. wget https://github.com/uladzimirFilipchanka/noredraw/raw/main/noredraw.sh && chmod +x noredraw.sh

./noredraw.sh \
--source . \
--export PNG \
--output noredraw.png Let’s see, from bottom to top. Gradle builds a Jar named arch-diagram-sample. Then it gets containerized by Docker and ends up being a Docker image. The image is assigned to the task which is deployed into Amazon ECS via a GitHub Actions workflow. All correct! The diagram definitely lacks in style, but if you plan to embed it you’ll have to suffer enjoy it anyway. At least some extra coloring wouldn’t hurt. The attributes in blocks nicely describe each artifact and are very handy. I really hope NoReDraw gets more traction in the near future. More providers and features can make it production-ready. Others Finally, let's look at some other tools and services. They didn’t make it to the shortlist but are worth mentioning. Pluralith This service helps to visualize and manage cloud infrastructure by parsing Terraform code. Pluralith goes beyond 'regular' visualizers and offers real-time updates. With nicely done CI/CD integrations (GitHub and GitLab as of now). Lucidscale The older sibling of Lucidchart. It’s a super-powerful tool for managing cloud infrastructure. Lucidscale offers automated and detailed visualizations but works another way. It connects to your cloud account and generates diagrams of the architecture. Currently, AWS, Azure, and GCP are supported. In this case, you don’t even need to adhere to infrastructure-as-a-code. Lucidscale provides all that you may or may not need. Including live sync, filtering, customization, and collaboration. Cloudcraft Like Lucidscale it connects to your cloud account. It offers deep integration with AWS (and now Azure), but focuses on simplicity.
Cloudcraft has a minimalistic and user-friendly interface. It allows you to quickly create cloud architecture diagrams. You might miss some minor features in Cloudcraft, but the beauty of diagrams compensates for it. Outro Yes, there are definitely more tools available on the market (let me know about your favorite ones in the comments). Maintaining up-to-date infrastructure diagrams can be a challenge. But with the right tools, it becomes much more manageable. The key is to find the one that best fits your needs and workflow. Who wants to get a nice and shiny infrastructure diagram? We are! Who wants to maintain it? … Who wants to get a nice and shiny infrastructure diagram? We are! Who wants to maintain it? … Intro That’s right. You work at a company with a lot of infrastructure to manage. There are many services, complex networking, multi-cloud setups, and several CI/CD tools. You have a lot of code written. Maybe with comments. Perhaps even some documentation. Eventually, you start creating diagrams to show your infrastructure at a glance. Everybody loves diagrams! But as soon as you press ‘Save’ button the clock starts ticking, showing how outdated your diagram is. Unfortunately, no one loves outdated stuff. And no one has time to update it. You spent a lot of time being an infrastructure-as-a-code gitops hero. Everything is already here, in the code! There are plenty of tools that transform code into UML diagrams. Is there anything for infrastructure, please? infrastructure-as-a-code And there is! Let’s take a closer look What’s in the review To make it more practical we are going to use a real repo. The repo has a minimalistic Dockerfile for a simple Spring Boot app. A pinch of Terraform, and a GitHub Actions workflow to glue everything together. It builds the app with Gradle, creates a Docker image, and deploys it to AWS Elastic Container Service. Tools under review Terraform graph Inframap Rover NoReDraw Others Terraform graph Terraform graph Inframap Inframap Rover Rover NoReDraw NoReDraw Others Terraform graph First, let's start with Terraform graph . Terraform provides an embedded option for making a diagram. It creates a Graphviz .dot file. To see a diagram as an image you have to install Graphviz . Terraform graph Graphviz terraform graph | dot -Tpng > /tmp/graph.png terraform graph | dot -Tpng > /tmp/graph.png This gives you next diagram Looks acceptable for an out-of-the-box solution. It’s hardly readable, has no coloring scheme and has no icons. I’m not sure if it provides a better sense of architecture than the actual code. Yet, it could be a nice addition to a pull request analysis by demonstrating the actual plan (specify -plan=plan.out ). -plan=plan.out Inframap If the Terraform graph is too basic for your needs, let's move on to Inframap . This tool reads your tfstate or HCL to generate a graph specific to each provider. Instead of showing everything, the tool shows the most important resources. Developers of Inframap created a human-readable version of terraform graph . Inframap terraform graph inframap generate . | dot -Tpng > inframap.png inframap generate . | dot -Tpng > inframap.png This gives the next diagram Well, that’s way more readable. If you want more app-centric representation that’s the way to go. It doesn't have too much detail (subnets, security groups, IAM roles, etc) and looks more concise. We have all main components like the load balancer, ECS service, and the cluster. A few nitpicks: I would put client instead of im_out . And make the ECS cluster a container (rectangle) with the service inside. Anyway, it’s pretty decent. client im_out Rover Next up is Rover , another Terraform visualizer. There are several options for how to run Rover. Unfortunately, I wasn’t able to make Rover run terraform under the hood. Image generation with the specified plan didn’t work for me either. So I had to run Rover in standalone mode and explicitly specify previously generated TF plan: Rover terraform plan -out=plan.out
terraform show -json plan.out > plan.json

docker run --rm -it -p 9000:9000 -v $(pwd):/src im2nguyen/rover:latest -planJSONPath=/src/plan.json terraform plan -out=plan.out
terraform show -json plan.out > plan.json

docker run --rm -it -p 9000:9000 -v $(pwd):/src im2nguyen/rover:latest -planJSONPath=/src/plan.json The UI on http://localhost:9000/ appeared to be pretty nice. It might be helpful to click around to get more details on what’s going on. Again, mostly for PRs and a general understanding of resources. http://localhost:9000/ This is how generated diagram looks like I'm pleased with the color legend and grouping. The information seems to be excessive for the needs of an architecture diagram. Again, it’s a terraform visualizer, it shows everything defined in the plan, with no surprises. Alright, you’ve got the idea of terraform visualizers. They try to represent the state of resources defined in .tf files. Is there anything else left overboard? Right, the applications. The whole point of infrastructure is to serve a set of applications. Without showing the apps it all doesn’t make a lot of sense. Inframap did a great job with its app-centric approach. Are Terraform files not enough for the big picture? Let’s fall down the rabbit hole. NoReDraw This tool takes a step further. Instead of dealing with Terraform, NoReDraw focuses on… everything. Well, currently Gradle, Docker, GitHub Action, and Terraform AWS resources are supported. And ‘supported’ is a bit of a strong word as the project is still in beta. NoReDraw NoReDraw takes a source folder or git repo URL as an input. Then parses and cross-matches everything it can. Want to add more? Implement your own provider as the tool is highly customizable. Don’t like how components are linked together? Create your own LinkStrategy . Another cool benefit is that you can generate a diagram in Plant UML or Mermaid . This gives you an option to embed the drawing directly into your docs as text. LinkStrategy Plant UML Mermaid wget https://github.com/uladzimirFilipchanka/noredraw/raw/main/noredraw.sh && chmod +x noredraw.sh

./noredraw.sh \
--source . \
--export PNG \
--output noredraw.png wget https://github.com/uladzimirFilipchanka/noredraw/raw/main/noredraw.sh && chmod +x noredraw.sh

./noredraw.sh \
--source . \
--export PNG \
--output noredraw.png Let’s see, from bottom to top. Gradle builds a Jar named arch-diagram-sample . Then it gets containerized by Docker and ends up being a Docker image. The image is assigned to the task which is deployed into Amazon ECS via a GitHub Actions workflow. All correct! arch-diagram-sample The diagram definitely lacks in style, but if you plan to embed it you’ll have to suffer enjoy it anyway. At least some extra coloring wouldn’t hurt. The attributes in blocks nicely describe each artifact and are very handy. I really hope NoReDraw gets more traction in the near future. More providers and features can make it production-ready. suffer Others Finally, let's look at some other tools and services. They didn’t make it to the shortlist but are worth mentioning. Pluralith Pluralith This service helps to visualize and manage cloud infrastructure by parsing Terraform code. Pluralith goes beyond 'regular' visualizers and offers real-time updates. With nicely done CI/CD integrations (GitHub and GitLab as of now). Lucidscale Lucidscale The older sibling of Lucidchart. It’s a super-powerful tool for managing cloud infrastructure. Lucidscale offers automated and detailed visualizations but works another way. It connects to your cloud account and generates diagrams of the architecture. Currently, AWS, Azure, and GCP are supported. In this case, you don’t even need to adhere to infrastructure-as-a-code. Lucidscale provides all that you may or may not need. Including live sync, filtering, customization, and collaboration. Cloudcraft Cloudcraft Like Lucidscale it connects to your cloud account. It offers deep integration with AWS (and now Azure), but focuses on simplicity.
Cloudcraft has a minimalistic and user-friendly interface. It allows you to quickly create cloud architecture diagrams. You might miss some minor features in Cloudcraft, but the beauty of diagrams compensates for it. Outro Outro Yes, there are definitely more tools available on the market (let me know about your favorite ones in the comments). Maintaining up-to-date infrastructure diagrams can be a challenge. But with the right tools, it becomes much more manageable. The key is to find the one that best fits your needs and workflow.

Say Goodbye to Outdated Diagrams: Automate Your Infrastructure Visualization

Evolution of Data Sharding Towards Automation and Flexibility in Apache Doris

অ্যাপাচি ডরিসে অটোমেশন এবং নমনীয়তার দিকে ডেটা ভাগ করার বিবর্তন

Entwicklung des Data Sharding hin zu Automatisierung und Flexibilität in Apache Doris

Evolución de la fragmentación de datos hacia la automatización y la flexibilidad en Apache Doris

Evolution du partage de données vers l'automatisation et la flexibilité dans Apache Doris

अपाचे डोरिस में स्वचालन और लचीलेपन की ओर डेटा शार्डिंग का विकास

Apache Doris における自動化と柔軟性に向けたデータ シャーディングの進化

Apache Doris의 자동화 및 유연성을 향한 데이터 샤딩의 진화

Evolução da fragmentação de dados em direção à automação e flexibilidade no Apache Doris

Эволюция сегментирования данных в сторону автоматизации и гибкости в Apache Doris

Apache Doris'te Veri Parçalamanın Otomasyona ve Esnekliğe Doğru Evrimi

Sự phát triển của việc phân chia dữ liệu theo hướng tự động hóa và linh hoạt trong Apache Doris

Apache Doris 中数据分片向自动化和灵活性的演变

Authors:
(1) Sérgio M. Rebelo, University of Coimbra, Centre for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, Coimbra, Portugal and a Corresponding author;
(2) Tiago Martins, University of Coimbra, Centre for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, Coimbra, Portugal and a Corresponding author;
(3) Diogo Ferreira, University of Coimbra, Centre for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, Coimbra, Portugal and a Corresponding author;
(4) Artur Rebelo, University of Coimbra, Centre for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, Coimbra, Portugal. Table of Links Abstract and 1. Introduction
Computational Approaches in Editorial Design
System
Experimentation and Discussion
Conclusion
Acknowledgments and References Abstract This paper proposes a generative approach for the automatic typesetting of books in desktop publishing. The presented system consists in a computer script that operates inside a widely used design software tool and implements a generative process based on several typographic rules, styles and principles which have been identified in the literature. The performance of the proposed system is tested through an experiment which included the evaluation of its outputs with people. The results reveal the ability of the system to consistently create varied book designs from the same input content as well as visually coherent book designs with different contents while complying with fundamental typographic principles. Keywords Design tools; Data-driven Design; Generative Design; Graphic design; Typography. 1. Introduction Typography is the art of giving our language a visual form. Thus, it is through typography that we materialise and store our knowledge and information [1]. Since the publication of the first typographic book, in the mid-fifteenth century, society has been looking for the most appropriate way to convey a message typographically. In the field of editorial design, this effort has focused on the search for the best principles for designing typographic compositions, such as books [2]. Advances in digital technologies have been changing the work process of graphic designers. The emergence of computational approaches in the design domain enabled designers to explore new perspectives, new conceptual and visual possibilities, and achieve new types of solutions. Furthermore, the emergence of computational approaches comes with a paradigm shift in the role of the designer, who begins to create processes that enable the creation of designs, instead of designing the final solution. In other words, the design concept is translated into a computer program that systematically explores various design possibilities from the original concept. That said, in the particular case of layout design, we consider that the potential of computational approaches is not yet being fully explored. In this work, we explore a computational generative approach for the automatic design of book layouts. The result is a computer system, which operates inside the Adobe InDesign environment, that automatically generates book designs from input content. Figure 1 shows different books created with the presented system. It starts by receiving the input content, namely text and images. Before generating compositions for the input content, the designer can specify restrictions on some of the visual characteristics of the output compositions, e.g. format, size, grid and font. Then, the system automatically typesets the content based on a set of typography rules and principles found in fundamental literature in the field. In the end, the system presents the generated composition to the user as an editable Adobe InDesign document. Overall, the system is capable of creating layout compositions that comply with specific fundamental typographic principles while matching the graphic preferences of the user. In addition, experiments conducted with the system demonstrate its ability to autonomously generate varied compositions with the same input content and also generate visually coherent compositions for different input contents. In addition, it creates functional layout designs in an almost unpredictable manner. This reveals the great potential of this approach to layout design, both in generating outputs that the designer uses as final solutions and in using the outputs as starting points for further explorations. This work is aligned with the experiments previously developed by Ferreira et al. [3] The remaining of the article is organised as follows: Section 2 overviews related works; Section 3 describes the system, namely the interaction process, the engine that runs the system, as well as its inputs and outputs; Section 4 presents an experiment conducted to validate the designs created with the system and discusses the results obtained; Lastly, Section 5 summarises the main contributions of this work and identifies future work. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. Authors: (1) Sérgio M. Rebelo, University of Coimbra, Centre for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, Coimbra, Portugal and a Corresponding author; (2) Tiago Martins, University of Coimbra, Centre for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, Coimbra, Portugal and a Corresponding author; (3) Diogo Ferreira, University of Coimbra, Centre for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, Coimbra, Portugal and a Corresponding author; (4) Artur Rebelo, University of Coimbra, Centre for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, Coimbra, Portugal. Authors: Authors: (1) Sérgio M. Rebelo, University of Coimbra, Centre for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, Coimbra, Portugal and a Corresponding author; (2) Tiago Martins, University of Coimbra, Centre for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, Coimbra, Portugal and a Corresponding author; (3) Diogo Ferreira, University of Coimbra, Centre for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, Coimbra, Portugal and a Corresponding author; (4) Artur Rebelo, University of Coimbra, Centre for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, Coimbra, Portugal. Table of Links Abstract and 1. Introduction Computational Approaches in Editorial Design System Experimentation and Discussion Conclusion Acknowledgments and References Abstract and 1. Introduction Abstract and 1. Introduction Computational Approaches in Editorial Design Computational Approaches in Editorial Design System System Experimentation and Discussion Experimentation and Discussion Conclusion Conclusion Acknowledgments and References Acknowledgments and References Abstract This paper proposes a generative approach for the automatic typesetting of books in desktop publishing. The presented system consists in a computer script that operates inside a widely used design software tool and implements a generative process based on several typographic rules, styles and principles which have been identified in the literature. The performance of the proposed system is tested through an experiment which included the evaluation of its outputs with people. The results reveal the ability of the system to consistently create varied book designs from the same input content as well as visually coherent book designs with different contents while complying with fundamental typographic principles. Keywords Keywords Design tools; Data-driven Design; Generative Design; Graphic design; Typography. 1. Introduction Typography is the art of giving our language a visual form. Thus, it is through typography that we materialise and store our knowledge and information [1]. Since the publication of the first typographic book, in the mid-fifteenth century, society has been looking for the most appropriate way to convey a message typographically. In the field of editorial design, this effort has focused on the search for the best principles for designing typographic compositions, such as books [2]. Advances in digital technologies have been changing the work process of graphic designers. The emergence of computational approaches in the design domain enabled designers to explore new perspectives, new conceptual and visual possibilities, and achieve new types of solutions. Furthermore, the emergence of computational approaches comes with a paradigm shift in the role of the designer, who begins to create processes that enable the creation of designs, instead of designing the final solution. In other words, the design concept is translated into a computer program that systematically explores various design possibilities from the original concept. That said, in the particular case of layout design, we consider that the potential of computational approaches is not yet being fully explored. In this work, we explore a computational generative approach for the automatic design of book layouts. The result is a computer system, which operates inside the Adobe InDesign environment, that automatically generates book designs from input content. Figure 1 shows different books created with the presented system. It starts by receiving the input content, namely text and images. Before generating compositions for the input content, the designer can specify restrictions on some of the visual characteristics of the output compositions, e.g. format, size, grid and font. Then, the system automatically typesets the content based on a set of typography rules and principles found in fundamental literature in the field. In the end, the system presents the generated composition to the user as an editable Adobe InDesign document. Overall, the system is capable of creating layout compositions that comply with specific fundamental typographic principles while matching the graphic preferences of the user. In addition, experiments conducted with the system demonstrate its ability to autonomously generate varied compositions with the same input content and also generate visually coherent compositions for different input contents. In addition, it creates functional layout designs in an almost unpredictable manner. This reveals the great potential of this approach to layout design, both in generating outputs that the designer uses as final solutions and in using the outputs as starting points for further explorations. This work is aligned with the experiments previously developed by Ferreira et al. [3] The remaining of the article is organised as follows: Section 2 overviews related works; Section 3 describes the system, namely the interaction process, the engine that runs the system, as well as its inputs and outputs; Section 4 presents an experiment conducted to validate the designs created with the system and discusses the results obtained; Lastly, Section 5 summarises the main contributions of this work and identifies future work. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. available on arxiv

Towards the Automation of Book Typesetting: Abstract and Introduction

Authors:
(1) Sérgio M. Rebelo, University of Coimbra, Centre for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, Coimbra, Portugal and a Corresponding author;
(2) Tiago Martins, University of Coimbra, Centre for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, Coimbra, Portugal and a Corresponding author;
(3) Diogo Ferreira, University of Coimbra, Centre for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, Coimbra, Portugal and a Corresponding author;
(4) Artur Rebelo, University of Coimbra, Centre for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, Coimbra, Portugal. Table of Links Abstract and 1. Introduction
Computational Approaches in Editorial Design
System
Experimentation and Discussion
Conclusion
Acknowledgments and References 5. Conclusion We have presented a novel approach to computationally design books. The presented system implements a generative design process which takes advantage of the scripting capabilities of Adobe InDesign to procedurally typeset books from content provided by the user. We have shown the ability of the system to (i) create book designs that consistently comply with a series of typographic rules, styles and principles identified in the literature; (ii) produce visually diversified books from the same input content; and (iii) produce visually coherent books with different contents. The work presented in the paper may challenge the typical roles of both the tool and the designer. First, by automatically creating and suggesting design alternatives, the tool ends up playing a more active role in the design process. Then, by modifying and developing custom tools, the designer is no longer a mere tool user and becomes the author of tools tailored to specific needs. We believe this shift can be fruitful since it enables the exploration and discovery of new technical and creative possibilities. This work can hopefully provide directions to further research on generative processes for supporting design exploration and finding unique designs. In the particular case of typography, generative approaches such as the one presented in the paper can be useful and reveal great potential, especially in the current print-on-demand market and digital publishing, where each publication may be unique. Our future work will move in the direction of employing Artificial Intelligence techniques, such as Evolutionary Computation and Machine Learning, to enable a deeper exploration of the vast space of book designs that can be achieved with the system and also to automatically suggest settings to designers according to their needs or goals. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. Authors: (1) Sérgio M. Rebelo, University of Coimbra, Centre for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, Coimbra, Portugal and a Corresponding author; (2) Tiago Martins, University of Coimbra, Centre for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, Coimbra, Portugal and a Corresponding author; (3) Diogo Ferreira, University of Coimbra, Centre for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, Coimbra, Portugal and a Corresponding author; (4) Artur Rebelo, University of Coimbra, Centre for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, Coimbra, Portugal. Authors: Authors: (1) Sérgio M. Rebelo, University of Coimbra, Centre for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, Coimbra, Portugal and a Corresponding author; (2) Tiago Martins, University of Coimbra, Centre for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, Coimbra, Portugal and a Corresponding author; (3) Diogo Ferreira, University of Coimbra, Centre for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, Coimbra, Portugal and a Corresponding author; (4) Artur Rebelo, University of Coimbra, Centre for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, Coimbra, Portugal. Table of Links Abstract and 1. Introduction Computational Approaches in Editorial Design System Experimentation and Discussion Conclusion Acknowledgments and References Abstract and 1. Introduction Abstract and 1. Introduction Computational Approaches in Editorial Design Computational Approaches in Editorial Design System System Experimentation and Discussion Experimentation and Discussion Conclusion Conclusion Acknowledgments and References Acknowledgments and References 5. Conclusion We have presented a novel approach to computationally design books. The presented system implements a generative design process which takes advantage of the scripting capabilities of Adobe InDesign to procedurally typeset books from content provided by the user. We have shown the ability of the system to (i) create book designs that consistently comply with a series of typographic rules, styles and principles identified in the literature; (ii) produce visually diversified books from the same input content; and (iii) produce visually coherent books with different contents. The work presented in the paper may challenge the typical roles of both the tool and the designer. First, by automatically creating and suggesting design alternatives, the tool ends up playing a more active role in the design process. Then, by modifying and developing custom tools, the designer is no longer a mere tool user and becomes the author of tools tailored to specific needs. We believe this shift can be fruitful since it enables the exploration and discovery of new technical and creative possibilities. This work can hopefully provide directions to further research on generative processes for supporting design exploration and finding unique designs. In the particular case of typography, generative approaches such as the one presented in the paper can be useful and reveal great potential, especially in the current print-on-demand market and digital publishing, where each publication may be unique. Our future work will move in the direction of employing Artificial Intelligence techniques, such as Evolutionary Computation and Machine Learning, to enable a deeper exploration of the vast space of book designs that can be achieved with the system and also to automatically suggest settings to designers according to their needs or goals. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. available on arxiv

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