AI Code Ui

AI Code Ui — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

  • Resisting AI

    Resisting AI

    Resisting AI: An Anti-fascist Approach to Artificial Intelligence is a book on artificial intelligence (AI) by Dan McQuillan, published in 2022 by Bristol University Press. == Content == Resisting AI takes the form of an extended essay, which contrasts optimistic visions about AI's potential by arguing that AI may best be seen as a continuation and reinforcement of bureaucratic forms of discrimination and violence, ultimately fostering authoritarian outcomes. For McQuillan, AI's promise of objective calculability is antithetical to an egalitarian and just society. McQuillan uses the expression "AI violence" to describe how – based on opaque algorithms – various actors can discriminate against categories of people in accessing jobs, loans, medical care, and other benefits. The book suggests that AI has a political resonance with soft eugenic approaches to the valuation of life by modern welfare states, and that AI exhibits eugenic features in its underlying logic, as well as in its technical operations. The parallel is with historical eugenicists achieving saving to the state by sterilizing defectives so the state would not have to care for their offspring. The analysis of McQuillan goes beyond the known critique of AI systems fostering precarious labour markets, addressing "necropolitics", the politics of who is entitled to live, and who to die. Although McQuillan offers a brief history of machine learning at the beginning of the book – with its need for "hidden and undercompensated labour", he is concerned more with the social impacts of AI rather than with its technical aspects. McQuillan sees AI as the continuation of existing bureaucratic systems that already marginalize vulnerable groups – aggravated by the fact that AI systems trained on existing data are likely to reinforce existing discriminations, e.g. in attempting to optimize welfare distribution based on existing data patterns, ultimately creating a system of "self-reinforcing social profiling". In elaborating on the continuation between existing bureaucratic violence and AI, McQuillan connects to Hannah Arendt's concept of the thoughtless bureaucrat in Eichmann in Jerusalem: A Report on the Banality of Evil, which now becomes the algorithm that, lacking intent, cannot be accountable, and is thus endowed with an "algorithmic thoughtlessness". McQuillan defends the "fascist" in the title of the work by arguing that while not all AI is fascist, this emerging technology of control may end up being deployed by fascist or authoritarian regimes. For McQuillan, AI can support the diffusion of states of exception, as a technology impossible to properly regulate and a mechanism for multiplying exceptions more widely. An example of a scenario where AI systems of surveillance could bring discrimination to a new high is the initiative to create LGBT-free zones in Poland. Skeptical of ethical regulations to control the technology, McQuillan suggests people's councils and workers' councils, and other forms of citizens' agency to resist AI. A chapter titled "Post-Machine Learning" makes an appeal for resistance via currents of thought from feminist science (standpoint theory), post-normal science (extended peer communities), and new materialism; McQuillan encourages the reader to question the meaning of "objectivity" and calls for the necessity of alternative ways of knowing. Among the virtuous examples of resistance – possibly to be adopted by the AI workers themselves – McQuillan notes the Lucas Plan of the workers of Lucas Aerospace Corporation, in which a workforce declared redundant took control, reorienting the enterprise toward useful products. McQuillan advocates for what he calls decomputing, an opposition to the sweeping application and expansion of artificial intelligence. Similar to degrowth, the approach criticizes AI as an outgrowth of the systemic issues within capitalist systems. McQuillan argues that a different future is possible, in which distance between people is reduced rather than increased through AI intermediaries. The work of McQuillan warns against "watered-down forms of engagement" with AI, such as citizen juries, which superficially look like democratic deliberation but may actually obscure important decisions about AI that are outside the purview of the engagement situation (McQuillan 2022, 128). In an interview about the book, McQuillan describes himself as an "AI abolitionist". == Reception == The book has been praised for how it "masterfully disassembles AI as an epistemological, social, and political paradigm". On the critical side, a review in the academic journal Justice, Power and Resistance took exception to the "nightmarish visions of Big Brother" offered by McQuillan, and argued that while many elements of AI may pose concern, a critique should not be based on a caricature of what AI is, concluding that McQuillan's work is "less of a theory and more of a Manifesto". Another review notes "a disconnect between the technical aspects of AI and the socio-political analysis McQuillan provides." Although the book was published before the ChatGPT and large language model debate heated up, the book has not lost relevance to the AI discussion. It is noted for suggesting a link between beliefs in artificial intelligence and beliefs in a racialised and gendered visions of intelligence overall, whereby a certain type of rational, measurable intelligence is privileged, leading to "historical notions of hierarchies of being". The blog Reboot praised McQuillan for offering a theory of harm of AI (why AI could end up hurting people and society) that does not just encourage tackling in isolation specific predicted problems with AI-centric systems: bias, non-inclusiveness, exploitativeness, environmental destructiveness, opacity, and non-contestability. For educational policies could also look at AI following the reading of McQuillan: In his book Resisting AI, Dan McQuillan argues that "When we're thinking about the actuality of AI, we can't separate the calculations in the code from the social context of its application" .... McQuillan's particular concern is how many contemporary applications of AI are amplifying existing inequalities and injustices as well as deepening social divisions and instabilities. His book makes a powerful case for anticipating these effects and actively resisting them for the good of societies. Videos and podcasts with an interest in AI and emerging technology have discussed the book.

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  • Microsoft Azure

    Microsoft Azure

    Microsoft Azure, sometimes stylized Azure, and formerly Windows Azure, is the cloud computing platform developed by Microsoft. It offers management, access and development of applications and services to individuals, companies, and governments through its global infrastructure. Microsoft Azure supports many programming languages, tools, and frameworks, including Microsoft-specific and third-party software and systems. Azure was first introduced at the Professional Developers Conference (PDC) in October 2008 under the codename "Project Red Dog". It was officially launched as Windows Azure in February 2010 and later renamed to Microsoft Azure on March 25, 2014. == Services == Microsoft Azure uses large-scale virtualization at Microsoft data centers worldwide and offers more than 600 services. Microsoft Azure offers a service level agreement (SLA) that guarantees 99.9% availability for applications and data hosted on its platform, subject to specific terms and conditions outlined in the SLA documentation. === Computer services === Virtual machines, infrastructure as a service (IaaS), allowing users to launch general-purpose Microsoft Windows and Linux virtual machines, software as a service (SaaS), as well as preconfigured machine images for popular software packages. Starting in 2022, these virtual machines are now powered by Ampere Cloud-native processors. Most users run Linux on Azure, some of the many Linux distributions offered, including Microsoft's own Linux-based Azure Sphere. App services, platform as a service (PaaS) environment, letting developers easily publish and manage websites. Azure Web Sites allows developers to build sites using ASP.NET, PHP, Node.js, Java, or Python, which can be deployed using FTP, Git, Mercurial, Azure DevOps, or uploaded through the user portal. This feature was announced in preview form in June 2012 at the Meet Microsoft Azure event. Customers can create websites in PHP, ASP.NET, Node.js, or Python, or select from several open-source applications from a gallery to deploy. This comprises one aspect of the platform as a service (PaaS) offerings for the Microsoft Azure Platform. It was renamed Web Apps in April 2015. Web Jobs are applications that can be deployed to an App Service environment to implement background processing that can be invoked on a schedule, on-demand, or run continuously. The Blob, Table, and Queue services can be used to communicate between Web Apps and Web Jobs and to provide state. Azure Kubernetes Service (AKS) provides the capability to deploy production-ready Kubernetes clusters in Azure. In July 2023, watermarking support on Azure Virtual Desktop was announced as an optional feature of Screen Capture to provide additional security against data leakage. === Identity === Entra ID connect is used to synchronize on-premises directories and enable SSO (Single Sign On). Entra ID B2C allows the use of consumer identity and access management in the cloud. Entra Domain Services is used to join Azure virtual machines to a domain without domain controllers. Azure information protection can be used to protect sensitive information. Entra ID External Identities is a set of capabilities that allow organizations to collaborate with external users, including customers and partners. On July 11, 2023, Microsoft announced the renaming of Azure AD to Microsoft Entra ID. The name change took place four days later. === Mobile services === Mobile Engagement collects real-time analytics that highlight users' behavior. It also provides push notifications to mobile devices. HockeyApp can be used to develop, distribute, and beta-test mobile apps. === Storage services === Storage Services provides REST and SDK APIs for storing and accessing data on the cloud. Table Service lets programs store structured text in partitioned collections of entities that are accessed by the partition key and primary key. Azure Table Service is a NoSQL non-relational database. Blob Service allows programs to store unstructured text and binary data as object storage blobs that can be accessed by an HTTP(S) path. Blob service also provides security mechanisms to control access to data. Queue Service lets programs communicate asynchronously by message using queues. File Service allows storing and access of data on the cloud using the REST APIs or the SMB protocol. === Communication services === Azure Communication Services offers an SDK for creating web and mobile communications applications that include SMS, video calling, VOIP and PSTN calling, and web-based chat. === Data management === Azure Data Explorer provides big data analytics and data-exploration capabilities. Azure Search provides text search and a subset of OData's structured filters using REST or SDK APIs. Cosmos DB is a NoSQL database service that implements a subset of the SQL SELECT statement on JSON documents. Azure Cache for Redis is a managed implementation of Redis. StorSimple manages storage tasks between on-premises devices and cloud storage. Azure SQL Database works to create, scale, and extend applications into the cloud using Microsoft SQL Server technology. It also integrates with Active Directory, Microsoft System Center, and Hadoop. Azure Synapse Analytics is a fully managed cloud data warehouse. Azure Data Factory is a data integration service that allows creation of data-driven workflows in the cloud for orchestrating and automating data movement and data transformation. Azure Data Lake is a scalable data storage and analytic service for big data analytics workloads that require developers to run massively parallel queries. Azure HDInsight is a big data-relevant service that deploys Hortonworks Hadoop on Microsoft Azure and supports the creation of Hadoop clusters using Linux with Ubuntu. Azure Stream Analytics is a Serverless scalable event-processing engine that enables users to develop and run real-time analytics on multiple streams of data from sources such as devices, sensors, websites, social media, and other applications. === Messaging === The Microsoft Azure Service Bus allows applications running on Azure premises or off-premises devices to communicate with Azure. This helps to build scalable and reliable applications in a service-oriented architecture (SOA). The Azure service bus supports four different types of communication mechanisms: Event Hubs, which provides event and telemetry ingress to the cloud at a massive scale, with low latency and high reliability. For example, an event hub can be used to track data from cell phones such as coordinating with a GPS in real time. Queues, which allows one-directional communication. A sender application would send the message to the service bus queue and a receiver would read from the queue. Though there can be multiple readers for the queue, only one would process a single message. Topics, which provides one-directional communication using a subscriber pattern. It is similar to a queue; however, each subscriber will receive a copy of the message sent to a Topic. Optionally, the subscriber can filter out messages based on specific criteria defined by the subscriber. Relays, which provides bi-directional communication. Unlike queues and topics, a relay does not store in-flight messages in its memory; instead, it just passes them on to the destination application. === Media services === A PaaS offering that can be used for encoding, content protection, streaming, or analytics. === CDN === Azure has a worldwide content delivery network (CDN) designed to efficiently deliver audio, video, applications, images, and other static files. It improves the performance of websites by caching static files closer to users, based on their geographic location. Users can manage the network using a REST-based HTTP API. Azure has 118 point-of-presence locations across 100 cities worldwide (also known as Edge locations) as of January 2023. === Developer === Application Insights Azure DevOps === Management === With Azure Automation, users can easily automate repetitive and time-consuming tasks, often prone to cloud or enterprise setting errors. They can accomplish it using runbooks or desired state configurations for process automation. Microsoft SMA === Azure AI === Microsoft Azure Machine Learning (Azure ML) provides tools and frameworks for developers to create their own machine learning and artificial intelligence (AI) services. Azure AI Services by Microsoft comprises prebuilt APIs, SDKs, and services developers can customize. These services encompass perceptual and cognitive intelligence features such as speech recognition, speaker recognition, neural speech synthesis, face recognition, computer vision, OCR/form understanding, natural language processing, machine translation, and business decision services. Many AI characteristics in Microsoft's products and services, namely Bing, Office, Teams, Xbox, and Windows, are driven by Azure AI Services. Microsoft Foundry (formerly known as Azure AI Studio)

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  • Bright Computing

    Bright Computing

    Bright Computing, Inc. was a developer of software for deploying and managing high-performance (HPC) clusters, Kubernetes clusters, and OpenStack private clouds in on-premises data centers as well as in the public cloud. In 2022, it was acquired by Nvidia. == History == Bright Computing was founded by Matthijs van Leeuwen in 2009, who spun the company out of ClusterVision, which he had co-founded with Alex Ninaber and Arijan Sauer. Alex and Matthijs had worked together at UK’s Compusys, which was one of the first companies to commercially build HPC clusters. They left Compusys in 2002 to start ClusterVision in the Netherlands, after determining there was a growing market for building and managing supercomputer clusters using off-the-shelf hardware components and open source software, tied together with their own customized scripts. ClusterVision also provided delivery and installation support services for HPC clusters at universities and government entities. In 2004, Martijn de Vries joined ClusterVision and began development of cluster management software. The software was made available to customers in 2008, under the name ClusterVisionOS v4. In 2009, Bright Computing was spun out of ClusterVision. ClusterVisionOS was renamed Bright Cluster Manager, and van Leeuwen was named Bright Computing’s CEO. In February 2016, Bright appointed Bill Wagner as chief executive officer. Matthijs van Leeuwen became chief strategy officer, and then left the company and board of directors in 2018. In January 2022 Bright was acquired by Nvidia. Nvidia cited using Bright's Amsterdam facility as a development center. The acquisition occurred after several layoffs under Bill Wagner. == Customers == Early customers included Boeing, Sandia National Laboratories, Virginia Tech, Hewlett Packard, NSA, and Drexel University. Many early customers were introduced through resellers, including SICORP, Cray, Dell, and Advanced HPC. As of 2019, the company had more than 700 customers, including more than fifty Fortune 500 Companies. == Products and services == Bright Cluster Manager for HPC lets customers deploy and manage complete clusters. It provides management for the hardware, the operating system, the HPC software, and users. In 2014, the company announced Bright OpenStack, software to deploy, provision, and manage OpenStack-based private cloud infrastructures. In 2016, Bright started bundling several machine learning frameworks and associated tools and libraries with the product, to make it very easy to get machine learning workload up and running on a Bright cluster. In December 2018, version 8.2 was released, which introduced support for the ARM64 architecture, edge capabilities to build clusters spread out over many different geographical locations, improved workload accounting & reporting features, as well as many improvements to Bright's integration with Kubernetes. Bright Cluster Manager software was frequently sold through original equipment manufacturer (OEM) resellers, including Dell and HPE. In version 10, Bright Cluster Manager was merged into the NVIDIA Base Command Manager. Bright Computing was covered by Software Magazine and Yahoo! Finance, among other publications. == Awards == In 2016, Bright Computing was awarded a €1.5M Horizon 2020 SME Instrument grant from the European Commission. Bright Computing was one of only 33 grant recipients from 960 submitted proposals. In its category only 5 out of 260 grants were awarded. 2015 HPCwire Editor’s Choice Award for “Best HPC Cluster Solution or Technology" Main Software 50 “Highest Growth” award winner, 2013 Deloitte Technology Fast50 “Rising Star 2013” award winner Bio-IT World Conference & Expo ‘13, Boston, MA, winner of “IT Hardware & Infrastructure” category of the “Best of Show Award” program Red Herring Top 100 Global Award, 2013

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  • C3D Toolkit

    C3D Toolkit

    C3D Toolkit is a proprietary cross-platform geometric modeling kit software developed by Russian C3D Labs (previously part of ASCON Group). It's written in C++ . It can be licensed by other companies for use in their 3D computer graphics software products. The most widely known software in which C3D Toolkit is typically used are computer aided design (CAD), computer-aided manufacturing (CAM), and computer-aided engineering (CAE) systems. C3D Toolkit provides routines for 3D modeling, 3D constraint solving, polygonal mesh-to-B-rep conversion, 3D visualization, and 3D file conversions etc. == History == Nikolai Golovanov is a graduate of the Mechanical Engineering department of Bauman Moscow State Technical University as a designer of space launch vehicles. Upon his graduation, he began with the Kolomna Engineering Design bureau, which at the time employed the future founders of ASCON, Alexander Golikov and Tatiana Yankina. While at the bureau, Dr Golovanov developed software for analyzing the strength and stability of shell structures. In 1989, Alexander Golikov and Tatiana Yankina left Kolomna to start up ASCON as a private company. Although they began with just an electronic drawing board, even then they were already conceiving the idea of three-dimensional parametric modeling. This radical concept eventually changed flat drawings into three-dimensional models. The ASCON founders shared their ideas with Nikolai Golovanov, and in 1996 he moved to take up his current position with ASCON. As of 2012 he was involved in developing algorithms for C3D Toolkit. In 2012 the earliest version of the C3D Modeller kernel was extracted from KOMPAS-3D CAD. It was later adopted to a range of different platforms and advertised as a separate product. == Overview == It incorporates five modules: C3D Modeler constructs geometric models, generates flat projections of models, performs triangulations, calculates the inertial characteristics of models, and determines whether collisions occur between the elements of models; C3D Modeler for ODA enables advanced 3D modeling operations through the ODA's standard "OdDb3DSolid" API from the Open Design Alliance; C3D Solver makes connections between the elements of geometric models, and considers the geometric constraints of models being edited; C3D B-Shaper converts polygonal models to boundary representation (B-rep) bodies; C3D Vision controls the quality of rendering for 3D models using mathematical apparatus and software, and the workstation hardware; C3D Converter reads and writes geometric models in a variety of standard exchange formats. == Features == == Development == == Applications == Since 2013 - the date the company started issuing a license for the toolkit -, several companies have adopted C3D software components for their products, users include: Recently, C3D Modeler has been adapted to ODA Platform. In April 2017, C3D Viewer was launched for end users. The application allows to read 3D models in common formats and write it to the C3D file format. Free version is available.

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  • Eager learning

    Eager learning

    In artificial intelligence, eager learning is a learning method in which the system tries to construct a general, input-independent target function during training of the system, as opposed to lazy learning, where generalization beyond the training data is delayed until a query is made to the system. The main advantage gained in employing an eager learning method, such as an artificial neural network, is that the target function will be approximated globally during training, thus requiring much less space than using a lazy learning system. Eager learning systems also deal much better with noise in the training data. Eager learning is an example of offline learning, in which post-training queries to the system have no effect on the system itself, and thus the same query to the system will always produce the same result. The main disadvantage with eager learning is that it is generally unable to provide good local approximations in the target function.

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  • Containerization (computing)

    Containerization (computing)

    In software engineering, containerization is operating-system-level virtualization or application-level virtualization over multiple resources so that software applications can run in isolated user spaces called containers in any cloud or non-cloud environment, regardless of type or vendor. The term "container" has different meanings in different contexts, and it is important to ensure that the intended definition aligns with the audience's understanding. == Usage == Each container is basically a fully functional and portable cloud or non-cloud computing environment surrounding the application and keeping it independent of other environments running in parallel. Individually, each container simulates a different software application and runs isolated processes by bundling related configuration files, libraries and dependencies. But, collectively, multiple containers share a common operating system kernel (OS). In recent times, containerization technology has been widely adopted by cloud computing platforms like Amazon Web Services, Microsoft Azure, Google Cloud Platform, and IBM Cloud. Containerization has also been pursued by the U.S. Department of Defense as a way of more rapidly developing and fielding software updates, with first application in its F-22 air superiority fighter. == History == The concept of containerization in computing originated from early operating system–level isolation mechanisms. One of the earliest implementations was the chroot system call introduced in Version 7 Unix in 1979, which changed the apparent root directory for a process and its children, providing a basic form of filesystem isolation. In the early 2000s, more advanced forms of operating system–level virtualization were developed. FreeBSD introduced "jails" in 2000, which extended isolation by restricting processes to a subset of system resources. Around the same time, Solaris introduced "zones" (also known as Solaris Containers), providing similar capabilities with resource management and isolation features. Linux later incorporated comparable functionality through kernel features such as namespaces and control groups (cgroups), which enabled isolation of process IDs, network stacks, filesystems, and resource allocation. These features formed the foundation for Linux Containers (LXC), which provided a userspace interface for managing containers. The widespread adoption of containerization accelerated with the release of Docker in 2013, which introduced a standardized format for packaging applications and their dependencies, along with tooling for image distribution and container management. == Types of containers == OS containers Application containers == Security issues == Because of the shared OS, security threats can affect the whole containerized system. In containerized environments, security scanners generally protect the OS, but not the application containers, which adds unwanted vulnerability. == Container management, orchestration, clustering == Container orchestration or container management is mostly used in the context of application containers. Implementations providing such orchestration include Kubernetes and Docker swarm. == Container cluster management == Container clusters need to be managed. This includes functionality to create a cluster, to upgrade the software or repair it, balance the load between existing instances, scale by starting or stopping instances to adapt to the number of users, to log activities and monitor produced logs or the application itself by querying sensors. Open-source implementations of such software include OKD and Rancher. Quite a number of companies provide container cluster management as a managed service, like Alibaba, Amazon, Google, and Microsoft.

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  • Poop Map

    Poop Map

    Poop Map is a social app where users can track on a map where and when they defecate. In addition to logging location and time of each bowel movement, users can also add a photo, "like" other users' logs, and rate each account. The social elements of the app allow for groups of users to create a competitive league. Certain behaviors unlock achievements in-app. == Development == The app was created by app developer Nino Uzelac. It was launched in July 2013. == Popularity == The app charted at number one on the Apple App Store charts in 2021 after going viral on TikTok. As of September 2024, the app has a 4.8 rating on the App Store and more than 58,000 ratings. It also has more than one million downloads on the Google Play Store. Poop Map is notably popular among hikers, and has been written about in the outdoors magazine Outside.

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  • Radek Maneuver

    Radek Maneuver

    The Radek Maneuver is a scale-up-then-scale-down tactic used in the administration of web services, specifically those deployed under a cloud computing paradigm (by a provider e.g. Amazon Elastic Compute Cloud or Microsoft Azure). == History == Developed by Olivier "Radek" Dabrowski in the mid-2010s, the Radek Maneuver was originally conceived of in using and maintaining applications running on a PaaS system. == Execution == The Radek Maneuver consists of a series of steps, usually executed via the PaaS or web portal interface. The tactic should be used when a service is misbehaving or otherwise experiencing errors, and the suspected cause is the underlying cloud layer, rather than the application layer. This includes networking issues and other "bad box" problems. The steps are as follows: Identify the application or service which is misbehaving. Increase the compute resource (number of CPU cores, amount of ram) for the instance on which the application is running. This is also known as scaling up. Wait for the application to re-deploy and stabilize. Scale back down to the original instance size. == Principle of action == This scale-up-scale-down method is understood to shift the application to a different physical machine underlying the PaaS service or application virtual machine. While this layer of the cloud computing stack is generally out of the access of an application developer (instead in the hands of the cloud provider), the maneuver allows troubleshooting and dodging errors in that layer.

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  • Google Gadgets

    Google Gadgets

    Google Gadgets are dynamic web content that can be embedded on a web page. They can be added to and interact strongly with Google's iGoogle personalized home page (discontinued in November 2013, although iGoogle Gadgets still work on other websites) and the Google Desktop (discontinued in September 2011) application, as well as Google Wave (also no longer supported by Google) and Google Sites. Webmasters can add and customize a gadget to their own business or personal web site, a process called "syndication". Gadgets are developed by Google and third-party developers using the Google Gadgets API, using basic web technologies such as XML and JavaScript. == Multi-user persistent - Wave Gadgets == With the advent of Google Wave (now Apache Wave), gadgets became able to have persistent storage and multi-user capabilities and better state management. Gadgets using Google Wave in this way were simply known as 'Wave Gadgets'. For instance, a game written using a Google Gadget could use Google Wave technology to record a list of users and high scores without having to worry about how to permanently store the scores on a hosted server. The use of Google Wave would give the gadget multi-user and permanent storage capabilities. For example, scores could be stored in a Google Wave hosted permanently by Google at no cost to the user. As of early 2013, Google Gadgets were deprecated in Google Spreadsheets. Shortly after, they were removed from all spreadsheets. == Technology == Google Gadgets are written in XML and can have HTML and JavaScript components, and were able to use Google Wave. Here is an example of a Hello World program written using Google Gadget technology. Google Gadgets API is a Google API which allows developers to create Google Gadgets easily.

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  • Direct Graphics Access

    Direct Graphics Access

    Direct Graphics Access is a plug-in for the X display servers that allows client programs direct access to the frame buffer. Graphics hardware communicates via a chunk of memory called a frame buffer. This is an array of values that represent pixel color values on the screen. Writing the appropriate values into the frame buffer therefore allows a program to paint areas of the screen. However, as with any shared resource, problems occur when multiple programs attempt to access the same resource, as they tend to write over each other's work. In the X Window System, this is solved by having a central display server that mediates between programs that want to draw on the screen. The display server also used to perform a lot of the drawing work, allowing programs to say Draw me a circle of this radius filled with this pattern or draw this text in this font. The X server does all this work, freeing programmers from having to write their own drawing code. Another advantage of the X architecture is that it works over a network, allowing programs on one machine to display output on the screen of another. Direct Graphics Access allows direct access to the frame buffer and the X-server hands over control of the frame buffer to the client program and waits for the client to hand it back. This means that the client program has control of the whole screen, and so it is mostly used for full-screen video/games.

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  • Language Server Protocol

    Language Server Protocol

    The Language Server Protocol (LSP) is an open, JSON-RPC-based protocol for use between source-code editors or integrated development environments (IDEs) and servers that provide "language intelligence tools": programming language-specific features like code completion, syntax highlighting and marking of warnings and errors, as well as refactoring routines. The goal of the protocol is to allow programming language support to be implemented and distributed independently of any given editor or IDE. In the early 2020s, LSP quickly became a "norm" for language intelligence tools providers. == History == LSP was originally developed for Microsoft Visual Studio Code and is now an open standard. On June 27, 2016, Microsoft announced a collaboration with Red Hat and Codenvy to standardize the protocol's specification. Its specification is hosted and developed on GitHub. == Background == Modern IDEs provide programmers with sophisticated features like code completion, refactoring, navigating to a symbol's definition, syntax highlighting, and error and warning markers. For example, in a text-based programming language, a programmer might want to rename a method read. The programmer could either manually edit the respective source code files and change the appropriate occurrences of the old method name into the new name, or instead use an IDE's refactoring capabilities to make all the necessary changes automatically. To be able to support this style of refactoring, an IDE needs a sophisticated understanding of the programming language that the program's source is written in. A programming tool without such an understanding—for example, one that performs a naive search-and-replace instead—could introduce errors. When renaming a read method, for example, the tool should not replace the partial match in a variable that might be called readyState, nor should it replace the portion of a code comment containing the word "already". Neither should renaming a local variable read, for example, end up altering identically-named variables in other scopes. Conventional compilers or interpreters for a specific programming language are typically unable to provide these language services, because they are written with the goal of either transforming the source code into object code or immediately executing the code. Additionally, language services must be able to handle source code that is not well-formed, e.g. because the programmer is in the middle of editing and has not yet finished typing a statement, procedure, or other construct. Additionally, small changes to a source code file which are done during typing usually change the semantics of the program. In order to provide instant feedback to the user, the editing tool must be able to very quickly evaluate the syntactical and semantical consequences of a specific modification. Compilers and interpreters therefore provide a poor candidate for producing the information needed for an editing tool to consume. Prior to the design and implementation of the Language Server Protocol for the development of Visual Studio Code, most language services were generally tied to a given IDE or other editor. In the absence of the Language Server Protocol, language services are typically implemented by using a tool-specific extension API. Providing the same language service to another editing tool requires effort to adapt the existing code so that the service may target the second editor's extension interfaces. The Language Server Protocol allows for decoupling language services from the editor so that the services may be contained within a general-purpose language server. Any editor can inherit sophisticated support for many different languages by making use of existing language servers. Similarly, a programmer involved with the development of a new programming language can make services for that language available to existing editing tools. Making use of language servers via the Language Server Protocol thus also reduces the burden on vendors of editing tools, because vendors do not need to develop language services of their own for the languages the vendor intends to support, as long as the language servers have already been implemented. The Language Server Protocol also enables the distribution and development of servers contributed by an interested third party, such as end users, without additional involvement by either the vendor of the compiler for the programming language in use or the vendor of the editor to which the language support is being added. LSP is not restricted to programming languages. It can be used for any kind of text-based language, like specifications or domain-specific languages (DSL). == Technical overview == When a user edits one or more source code files using a language server protocol-enabled tool, the tool acts as a client that consumes the language services provided by a language server. The tool may be a text editor or IDE and the language services could be refactoring, code completion, etc. The client informs the server about what the user is doing, e.g., opening a file or inserting a character at a specific text position. The client can also request the server to perform a language service, e.g. to format a specified range in the text document. The server answers a client's request with an appropriate response. For example, the formatting request is answered either by a response that transfers the formatted text to the client or by an error response containing details about the error. The Language Server Protocol defines the messages to be exchanged between client and language server. They are JSON-RPC preceded by headers similar to HTTP. Messages may originate from the server or client. The protocol does not make any provisions about how requests, responses and notifications are transferred between client and server. For example, client and server could be components within the same process exchanging JSON strings via method calls. They could also be different processes on the same or on different machines communicating via network sockets. == Registry == There are lists of LSP-compatible implementations, maintained by the community-driven Langserver.org or Microsoft.

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  • Tinybop

    Tinybop

    Tinybop is a Brooklyn based publisher of apps for children. == History == Tinybop is a Brooklyn-based children's media company established in 2011 by Raul Gutierrez. App titles are released in two series: the Explorer's Library - a series of science apps and Digital Toys - series of open-ended construction apps. == Published apps == Explorer's Library Titles: The Human Body – An anatomy app for children. Released 2013. The company's first app was illustrated by Kelli Anderson and has been downloaded millions of times. Selected for the American Library Association's Notable Children's Media List in 2022. Named Apple App Store's Best of 2013. Winner of the Digital Ehon Yuichi Kimura Prize for Children's Digital Media. Plants – An app about biomes around the world. Homes – An app about houses around with world. Illustrated by Tuesday Bassen. Winner of the Parents Gold Choice Award for children's apps. Simple Machines – A children's physics app about simple machines. The Earth – An app for children about the geologic Earth illustrated by Sarah Jacoby. Weather – A children's weather app. Skyscrapers – A children's app about building tall buildings. Space – An interactive solar system. Mammals – A children's app about mammals illustrated by Wenjia Tang. Winner of the Digital Ehon Award for Children's Educational media. Coral Reef – An app about marine ecosystems. Winner of an Excellence in Early Learning Digital Media Honor from the American Library Association. State of Matter – An app covering solids, liquids, and gases. Winner of Excellence in Early Learning Digital Media Honor from the American Library Association. Light and Color – An app about light and color. Selected for The American Library Association's Notable Children's Media List 2023. Winner of the 2022 Yoichi Sakakihara Prize for Children's Media. Digital Toys Titles: The Robot Factory – A robot building app for children illustrated by Owen Davey. Apple named The Robot Factory as iPad App of the Year in 2015. The Everything Machine – A visual coding app for children. The Everything Machine was named Apple's Best of 2015. Monsters – A monster creation app illustrated by Tianhua Mao. The Infinite Arcade – An arcade game building app. Me: A Kids Diary – A digital journal for children. Selected for The American Library Association's Notable Children's Media List 2020. The Creature Garden – An app that allows children to create fantastical animals illustrated by Natasha Durley. Selected for The American Library Association's Notable Children's Media List 2021. Things that Go Bump – A multiplayer game set in an enchanted Japanese house, released on Apple Arcade in 2018.

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  • System appreciation

    System appreciation

    System appreciation is an activity often included in the maintenance phase of software engineering projects. Key deliverables from this phase include documentation that describes what the system does in terms of its functional features, and how it achieves those features in terms of its architecture and design. Software architecture recovery is often the first step within System appreciation.

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  • Real-Time UML

    Real-Time UML

    Real-Time UML (RTUML) refers to the application of the Unified Modelling Language (UML) for the analysis, design, and implementation of real-time and embedded systems, where timing constraints, concurrency, and resource management are critical. It extends standard UML with profiles, notations, and semantics to handle hard and soft real-time requirements, such as modelling predictable response times and fault tolerance. RTUML is not a separate language but a methodology leveraging UML diagrams (e.g., statecharts, sequence diagrams) for time-sensitive applications like automotive controls, avionics, and medical devices. The term is closely associated with Bruce Powel Douglass, who popularised it through his books and the Harmony process for embedded software development. As of 2025, RTUML remains relevant in industries requiring certified systems, though its adoption varies with agile methodologies and model-driven engineering tools. == Background == Real-Time UML emerged in the late 1990s as UML was standardized by the Object Management Group (OMG) in 1997, addressing the need for object-oriented modeling in real-time systems previously dominated by procedural languages like C. Traditional real-time development relied on "bare metal" programming or theoretical models, but RTUML introduced visual notations for object structure, behaviour, and timing. Bruce Powel Douglass’s 1999 book, Real-Time UML: Developing Efficient Objects for Embedded Systems, formalised the approach, emphasising statecharts for concurrency and timing constraints. Later editions (2004, 2006) incorporated UML 2.0 features like activity and timing diagrams, aligning with OMG’s Real-Time Profile (now part of MARTE—Modelling and Analysis of Real-Time and Embedded Systems). The Harmony process integrates RTUML with executable models for simulation and code generation. RTUML addresses hard real-time systems (e.g., strict deadlines in avionics) versus soft real-time (e.g., media streaming), using UML extensions for schedulability analysis. == Key concepts == RTUML adapts UML diagrams and techniques for real-time needs: Statecharts and Behaviour Modelling: Extended state machines model reactive behaviour, using and-states for concurrency, pseudostates for transitions, and timing constraints (e.g., {duration < 10ms}). Examples include cardiac pacemaker models. Sequence and Interaction Diagrams: Capture message timing, priorities, and resource allocation in multi-threaded systems. Architectural Patterns: Define logical and physical architectures with active objects for concurrency and patterns like observer or publisher-subscriber. Timing and Constraints: Use Object Constraint Language (OCL) for specifying deadlines and priorities. Profiles and Extensions: OMG’s UML Profile for Schedulability, Performance, and Time (SPT) and MARTE add stereotypes like RT::ActiveObject. These support iterative development, from requirements to deployment, often with tools like IBM Rhapsody or Enterprise Architect. == Applications == RTUML is used in: Embedded Systems: Modelling automotive ECUs or UAV controls. Avionics and Defence: DO-178C-compliant designs for fault tolerance. Medical Devices: Pacemakers or ventilators with precise timing. Industrial Automation: RTOS task visualisation via sequence diagrams. Tools like IBM Rhapsody support RTUML for model-based development and code generation in C/C++. == Criticism and adoption == RTUML’s complexity can overwhelm simple systems, and its use in agile environments is limited, where lightweight diagrams are preferred. Surveys indicate UML (including RTUML) is used in 30–50% of embedded projects, often for documentation rather than full model-driven engineering. It remains standard in academia and certified industries like aerospace.

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  • Color layout descriptor

    Color layout descriptor

    In digital image and video processing, a color layout descriptor (CLD) is designed to capture the spatial distribution of color in an image. The feature extraction process consists of two parts: grid based representative color selection and discrete cosine transform with quantization. Color is the most basic quality of the visual contents, therefore it is possible to use colors to describe and represent an image. The MPEG-7 standard has tested the most efficient procedure to describe the color and has selected those that have provided more satisfactory results. This standard proposes different methods to obtain these descriptors, and one tool defined to describe the color is the CLD, that permits describing the color relation between sequences or group of images. The CLD captures the spatial layout of the representative colors on a grid superimposed on a region or image. Representation is based on coefficients of the discrete cosine transform (DCT). This is a very compact descriptor being highly efficient in fast browsing and search applications. It can be applied to still images as well as to video segments. == Definition == The CLD is a very compact and resolution-invariant representation of color for high-speed image retrieval and it has been designed to efficiently represent the spatial distribution of colors. This feature can be used for a wide variety of similarity-based retrieval, content filtering and visualization. It is especially useful for spatial structure-based retrieval applications. This descriptor is obtained by applying the DCT transformation on a 2-D array of local representative colors in Y or Cb or Cr color space. The functionalities of the CLD are basically the matching: – Image-to-image matching – Video clip-to-video clip matching Remark that the CLD is one of the most precise and fast color descriptor. == Extraction == The extraction process of this color descriptor consists of four stages: Image partitioning Representative color selection DCT transformation Zigzag scanning The standard MPEG-7 recommends using the YCbCr color space for the CLD. === Image partitioning === In the image partitioning stage, the input picture (on RGB color space) is divided into 64 blocks to guarantee the invariance to resolution or scale. The inputs and outputs of this step are summarized in the following table: === Representative color selection === After the image partitioning stage, a single representative color is selected from each block. Any method to select the representative color can be applied, but the standard recommends the use of the average of the pixel colors in a block as the corresponding representative color, since it is simpler and the description accuracy is sufficient in general. The selection results in a tiny image icon of size 8x8. The next figure shows this process. Note that in the image of the figure, the resolution of the original image has been maintained only in order to facilitate its representation. The inputs and outputs of this stage are summarized in the next table: Once the tiny image icon is obtained, the color space conversion between RGB and YCbCr is applied. === DCT transformation === In the fourth stage, the luminance (Y) and the blue and red chrominance (Cb and Cr) are transformed by 8x8 DCT, so three sets of 64 DCT coefficients are obtained. To calculate the DCT in a 2D array, the formulas below are used. B p q = α p α q ∑ m = 0 M − 1 ∑ n = 0 N − 1 A m n cos ⁡ π ( 2 m + 1 ) p 2 M cos ⁡ π ( 2 n + 1 ) q 2 N , 0 ≤ p ≤ M − 1 , 0 ≤ q ≤ N − 1 {\displaystyle B_{pq}=\alpha _{p}\alpha _{q}\sum _{m=0}^{M-1}\sum _{n=0}^{N-1}A_{mn}\cos {\frac {\pi (2m+1)p}{2M}}\cos {\frac {\pi (2n+1)q}{2N}},\qquad 0\leq p\leq M-1,\;0\leq q\leq N-1} α p = { 1 M , p = 0 2 M , 1 ≤ p ≤ M − 1 α q = { 1 N , q = 0 2 N , 1 ≤ q ≤ N − 1 {\displaystyle \alpha _{p}={\begin{cases}{\frac {1}{\sqrt {M}}},&p=0\\{\sqrt {\frac {2}{M}}},&1\leq p\leq M-1\end{cases}}\qquad \alpha _{q}={\begin{cases}{\frac {1}{\sqrt {N}}},&q=0\\{\sqrt {\frac {2}{N}}},&1\leq q\leq N-1\end{cases}}} The inputs and outputs of this stage are summarized in the next table: === Zigzag scanning === A zigzag scanning is performed with these three sets of 64 DCT coefficients, following the schema presented in the figure. The purpose of the zigzag scan is to group the low frequency coefficients of the 8x8 matrix into a vector. The inputs and outputs of this stage are summarized in the next table: Finally, these three set of matrices correspond to the CLD of the input image. == Matching == The matching process helps to evaluate if two elements are equal comparing both elements and calculating the distance between them. In the case of color descriptors the matching process helps to evaluate if two images are similar. Its procedure is the following: – Given an image as an input, the application attempts to find an image with a similar descriptor in a data base of images. If we consider two CLDs: {DY, DCb, DCr} { DY‟, DCb‟, DCr‟ }, The distance between the two descriptors can be computed as: D = ∑ i w y i ( D Y i − D Y i ′ ) 2 + ∑ i w b i ( D C b i − D C b i ′ ) 2 + ∑ i w r i ( D C r i − D C r i ′ ) 2 {\displaystyle D={\sqrt {\sum _{i}w_{yi}(DY_{i}-DY_{i}')^{2}}}+{\sqrt {\sum _{i}w_{bi}(DCb_{i}-DCb_{i}')^{2}}}+{\sqrt {\sum _{i}w_{ri}(DCr_{i}-DCr_{i}')^{2}}}} The subscript i represents the zigzag-scanning order of the coefficients. Furthermore, notice that is possible to weight the coefficients (w) in order to adjust the performance of the matching process. These weights let us give to some components of the descriptor more importance than others. Observing the formula, it can be extracted that: – 2 images are the same if the distance is 0 – 2 images are similar if the distance is near to 0 Therefore, this matching process will let to identify images with similar color descriptors. Since the complexity of the similarity matching process shown above is low, high-speed image matching can be achieved.

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