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Sahara Net

Sahara Net is an information and communications technology provider (ICT) serving the Saudi market, the company has rapidly grown since 1989 to offer various complementary services such as connectivity, internet, hosting, cloud, optimization, cyber security, and managed services. == History == Sahara Net is a Saudi Joint Stock Company (JSC) and its history goes back to 1989 when Sahara Net established the 1st Saudi Bulletin Board Service (BBS) in the Kingdom. During this period, it operated as a hub for email exchange in the FidoNet network. And in 1994 Sahara Net started offering Internet connectivity and other related services like internet email, web design, web hosting, and Domain name registry services. These services made the first ISP in Saudi Arabia before the official licensing in 1998, when the Saudi Internet market was regulated and Sahara Net received Internet Service Provider (ISP) license and was appointed as the official Local Internet Registry (LIR) in the Kingdom of Saudi Arabia. == Today == The company grew over these years to become one of the main ICTs in the Saudi Arabian market, extending network coverage to all major cities in Saudi Arabia, and offering various connectivity options to business as well as home users. In 2009, the company was partially acquired by Telindus (the ICT investment arm of Belgacom), the famous telecom operator in Belgium and Europe. Then, in 2014, the company was fully acquired by its original founders. Recently, Sahara Net was converted from an LLC to a JSC with over 1200 shareholders by a capital raise (original founders still control 70% of the shares).
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List of C++ software and tools

This is a list of notable software and programming tools for the C++ programming language, including libraries, web frameworks, programming language implementations, compilers, integrated development environments (IDEs), and other related software development utilities. == Compilers and IDEs == AMD Optimizing C/C++ Compiler — proprietary fork of LLVM + Clang for Linux C++Builder — rapid application development (RAD) environment Clang – compiler front end for C, C++, and Objective-C, part of LLVM CLion — C++ IDE by JetBrains Code::Blocks — open-source cross-platform IDE that supports multiple compilers including GCC, Clang and Visual C++ CodeLite — cross-platform IDE for the C/C++ programming languages using the wxWidgets toolkit CodeSynthesis XSD – XML Data Binding compiler Dev-C++ — MinGW or TDM-GCC 64bit port of the GCC as its compiler GCC – GNU Compiler Collection Intel C++ Compiler – proprietary high-performance compiler by Intel KDevelop — IDE part of the KDE project and is based on KDE Frameworks and Qt, the C/C++ backend uses Clang. Microsoft Visual C++ – proprietary C++ compiler and IDE for Windows Oracle Developer Studio — Solaris, OpenSolaris, RHEL, and Oracle Linux operating systems. Qt Creator — part of the SDK for the Qt GUI application development framework and uses the Qt API SlickEdit — text editor and IDE Turbo C++ – legacy C++ IDE and compiler popular in the 1990s Understand — IDE that enables static code analysis through an array of visuals, documentation, and metric tools. Visual Studio — integrated development environment by Microsoft that supports C++ Visual Studio Code — integrated development environment by Microsoft that supports C++ Xcode — Apple IDE to develop macOS, iOS, iPadOS, watchOS, tvOS, and visionOS that supports C++ source code. == Debuggers == Allinea DDT – a graphical debugger dbx — a proprietary source-level debugger GNU Debugger – portable debugger that runs on many Unix-like systems Modular Debugger — a C/C++ source level debugger for Solaris and derivates Undo LiveRecorder — time travel debugger == Libraries == Active Template Library – template-based C++ classes developed by Microsoft Apache MXNet — deep learning framework Apache Xerces – parsing, validating, and serializing and manipulating XML. Asio — networking and low-level I/O library Bitpit — scientific computing and mesh manipulation library Boost — collection of peer-reviewed libraries Botan — cryptography library C++ AMP – easy way to write programs that compile and execute on data-parallel hardware, such as graphics cards and GPUs C++ Standard Library — standard library for the language C++/WinRT — library for Microsoft's Windows Runtime platform, designed to provide access to modern Windows APIs. C3D Toolkit — geometric modeling kernel Caffe — deep learning framework CAPD — library for rigorous numerics and dynamical systems Cassowary — constraint-solving toolkit that efficiently solves systems of linear equalities and inequalities Cinder — library for creative coding ClanLib — cross-platform game SDK CMU Sphinx — speech recognition system Crypto++ — cryptographic algorithms library Dlib — general-purpose cross-platform library Dune — partial differential equations using grid-based methods fastText — text representation and text classification library FLTK — GUI toolkit Geospatial Data Abstraction Library — geospatial data access library GDCM — image library General Polygon Clipper — polygon clipping library GiNaC — computer algebra system that uses Class Library for Numbers for implementing arbitrary-precision arithmetic GLFW — OpenGL and window management library HarfBuzz — text rendering and typesetting library High Efficiency Image File Format — digital container format for storing individual digital images and image sequences ITK — image analysis library Integrated Performance Primitives — domain-specific functions that are highly optimized for diverse Intel architectures Jackets library — GPU computing library JSBSim — open-source flight dynamics model JUCE — framework for audio applications KDE Frameworks — collection of libraries from the KDE project KFRlib — digital signal processing framework LEMON — library for optimization and graph problems LevelDB — key–value database library Libdash — MPEG-DASH streaming library libLAS — reading and writing geospatial data encoded in the ASPRS laser (LAS) file format libsigc++ — typesafe callbacks LibRaw — free and open-source software library for reading raw files from digital cameras libSBML — application programming interface (API) for the SBML (Systems Biology Markup Language) LIBSVM — sequential minimal optimization (SMO) algorithm for kernelized support vector machines Libx — DirectX .X files graphics library Loki — collection of design patterns LIVE555 — multimedia streaming library Metakit — embedded database library Microsoft Cognitive Toolkit — deep learning toolkit Microsoft Foundation Class Library — object-oriented library for developing desktop applications for Windows Microsoft SEAL — homomorphic encryption library mlpack — machine learning and AI library Mobile Robot Programming Toolkit — robotics research library Object Windows Library — Object Windows Library, superseded by VCL Open Cascade — CAD and 3D modeling library Open Asset Import Library — 3D model import library to provide a common API for different 3D asset file formats OpenCV – computer vision and machine learning library OpenFOAM — computational fluid dynamics toolkit OpenH264 — real-time encoding and decoding video streams in the H.264/MPEG-4 AVC format OpenImageIO — image processing library Open Inventor — higher layer of programming for OpenGL OpenNN — neural networks library OpenVDB — sparse volume data library openFrameworks — creative coding toolkit OpenRTM-aist — robotics middleware library Oracle Template Library — database access that supports IBM Db2 and Open Database Connectivity Orfeo toolbox — remote sensing image processing library OR-Tools — operations research and optimization library Parallel Augmented Maps — ordered sets, ordered maps, and augmented maps. Parallel Patterns Library — Microsoft library that provides features for multicore programming PhysX — physics simulation engine POCO C++ Libraries — general-purpose libraries for software development Poppler — PDF rendering library Protocol Buffers — data serialization library Qt — cross-platform widget toolkit QuantLib — quantitative finance library RocksDB — key–value database library ROOT — data analysis framework from CERN ROS — robotics middleware Scintilla — source code editing component SDL – Simple DirectMedia Layer, cross-platform development library for multimedia applications SFML – Simple and Fast Multimedia Library Shark – open-source machine learning library Shogun — machine learning toolbox Skia — 2D graphics library Snappy — compression library Sound Object Library — music and audio development Standard Template Library — library of containers and algorithms Stapl — parallel computing library SymbolicC++ — symbolic computation library TerraLib — GIS library Tesseract OCR — optical character recognition engine Threading Building Blocks — parallel computing library ThreadWeaver — concurrency framework Tiny-dnn — lightweight deep learning library TinyXML — lightweight XML parser Tkrzw — key–value databases VTD-XML — XML processing library wxWidgets — cross-platform GUI toolkit x265 — video encoding library for HEVC XGBoost — gradient boosting library Windows Template Library — Win32 development === Mathematical and numerical libraries === == Tools == Akonadi — a C++/Qt framework and storage service for personal information management BALL – framework and set of algorithms and data structures for molecular modelling and computational structural bioinformatics Boehm garbage collector – conservative garbage collector CEGUI — C++ GUI library ClanLib – video game SDK CMake — cross-platform build system for C++ projects Confidential Consortium Framework – blockchain infrastructure framework DaviX – WebDAV client Doxygen — documentation generator for C++ and other languages FLTK — Fast Light Toolkit, cross-platform GUI library Fox toolkit — C++ GUI toolkit GDB — GNU Project debugger, often used with C and C++ gtkmm — official C++ interface for the popular GUI library GTK HOOPS Visualize — 3D computer graphics HPX — partitioned global address space Parallel programming Runtime System JUCE — cross-platform C++ audio and GUI framework LessTif — free clone of Motif GUI toolkit MFC — Microsoft Foundation Class library Nana — modern C++ GUI toolkit PTK Toolkit — 2D rendering engine and SDK, and portability options. Qt — cross-platform C++ GUI toolkit Rogue Wave — C++ GUI toolkit TnFOX — C++ GUI toolkit Ultimate++ — cross-platform C++ GUI framework Valgrind — tool suite for debugging and profiling C/C++ programs wxWidgets — cross-platform C++ GUI toolkit x265 — encoder for creating digital video streams in the High Efficiency Vid
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Cloud robotics

Cloud robotics is a field of robotics that attempts to invoke cloud technologies such as cloud computing, cloud storage, and other Internet technologies centered on the benefits of converged infrastructure and shared services for robotics. When connected to the cloud, robots can benefit from the powerful computation, storage, and communication resources of a modern data center in the cloud, which can process and share information from various robots or agents (other machines, smart objects, humans, etc.). Humans can also delegate tasks to robots remotely through networks. Cloud computing technologies enable robot systems to be gain capability whilst reducing costs through cloud technologies. Thus, it is possible to build lightweight, low-cost, smarter robots with an intelligent "brain" in the cloud. The "brain" consists of data center, knowledge base, task planners, deep learning, information processing, environment models, communication support, etc. == Components == A cloud for robots potentially has at least six significant components: Building a "cloud brain" for robots, the main object of cloud robotics; Offering a global library of images, maps, and object data, often with geometry and mechanical properties, expert system, knowledge base (i.e. semantic web, data centres); Massively-parallel computation on demand for sample-based statistical modelling and motion planning, task planning, multi-robot collaboration, scheduling and coordination of system; Robot sharing of outcomes, trajectories, and dynamic control policies and robot learning support; Human sharing of open-source code, data, and designs for programming, experimentation, and hardware construction; On-demand human guidance and assistance for evaluation, learning, and error recovery; Augmented human–robot interaction through various ways (semantics knowledge base, Apple SIRI like service, etc.). == Applications == Autonomous mobile robots Google's self-driving cars are cloud robots. The cars use the network to access Google's enormous database of maps and satellite and environment model (like Streetview) and combines it with streaming data from GPS, cameras, and 3D sensors to monitor its own position within centimetres, and with past and current traffic patterns to avoid collisions. Each car can learn something about environments, roads, or driving, or conditions, and it sends the information to the Google cloud, where it can be used to improve the performance of other cars. Cloud medical robots a medical cloud (also called a healthcare cluster) consists of various services such as a disease archive, electronic medical records, a patient health management system, practice services, analytics services, clinic solutions, expert systems, etc. A robot can connect to the cloud to provide clinical service to patients, as well as deliver assistance to doctors (e.g. a co-surgery robot). Moreover, it also provides a collaboration service by sharing information between doctors and care givers about clinical treatment. Assistive robots A domestic robot can be employed for healthcare and life monitoring for elderly people. The system collects the health status of users and exchange information with cloud expert system or doctors to facilitate elderly peoples life, especially for those with chronic diseases. For example, the robots are able to provide support to prevent the elderly from falling down, emergency healthy support such as heart disease, blooding disease. Care givers of elderly people can also get notification when in emergency from the robot through network. Industrial robots As highlighted by the German government's Industry 4.0 Plan, "Industry is on the threshold of the fourth industrial revolution. Driven by the Internet, the real and virtual worlds are growing closer and closer together to form the Internet of Things. Industrial production of the future will be characterised by the strong individualisation of products under the conditions of highly flexible (large series) production, the extensive integration of customers and business partners in business and value-added processes, and the linking of production and high-quality services leading to so-called hybrid products." In manufacturing, such cloud based robot systems could learn to handle tasks such as threading wires or cables, or aligning gaskets from a professional knowledge base. A group of robots can share information for some collaborative tasks. Even more, a consumer is able to place customised product orders to manufacturing robots directly with online ordering systems. Another potential paradigm is shopping-delivery robot systems. Once an order is placed, a warehouse robot dispatches the item to an autonomous car or autonomous drone to deliver it to its recipient. == Research == RoboEarth was funded by the European Union's Seventh Framework Programme for research, technological development projects, specifically to explore the field of cloud robotics. The goal of RoboEarth is to allow robotic systems to benefit from the experience of other robots, paving the way for rapid advances in machine cognition and behaviour, and ultimately, for more subtle and sophisticated human-machine interaction. RoboEarth offers a Cloud Robotics infrastructure. RoboEarth's World-Wide-Web style database stores knowledge generated by humans – and robots – in a machine-readable format. Data stored in the RoboEarth knowledge base include software components, maps for navigation (e.g., object locations, world models), task knowledge (e.g., action recipes, manipulation strategies), and object recognition models (e.g., images, object models). The RoboEarth Cloud Engine includes support for mobile robots, autonomous vehicles, and drones, which require much computation for navigation. Rapyuta is an open source cloud robotics framework based on RoboEarth Engine developed by the robotics researcher at ETHZ. Within the framework, each robot connected to Rapyuta can have a secured computing environment (rectangular boxes) giving them the ability to move their heavy computation into the cloud. In addition, the computing environments are tightly interconnected with each other and have a high bandwidth connection to the RoboEarth knowledge repository. FogROS2 is an open-source extension to the Robot Operating System 2 (ROS 2) developed by researchers at UC Berkeley. It enables robots to offload computationally intensive tasks—such as SLAM, grasp planning, and motion planning—to cloud resources, thereby enhancing performance and reducing onboard computational requirements. FogROS2 automates the provisioning of cloud instances, deployment of ROS 2 nodes, and secure communication between robots and cloud services. The platform is designed to be compatible with existing ROS 2 applications without requiring code modifications. Further advancements include FogROS2-SGC, which facilitates secure global connectivity across different networks and locations, and FogROS2-FT, which introduces fault tolerance by replicating services across multiple cloud providers to ensure robustness against failures. KnowRob is an extensional project of RoboEarth. It is a knowledge processing system that combines knowledge representation and reasoning methods with techniques for acquiring knowledge and for grounding the knowledge in a physical system and can serve as a common semantic framework for integrating information from different sources. RoboBrain is a large-scale computational system that learns from publicly available Internet resources, computer simulations, and real-life robot trials. It accumulates everything robotics into a comprehensive and interconnected knowledge base. Applications include prototyping for robotics research, household robots, and self-driving cars. The goal is as direct as the project's name—to create a centralised, always-online brain for robots to tap into. The project is dominated by Stanford University and Cornell University. And the project is supported by the National Science Foundation, the Office of Naval Research, the Army Research Office, Google, Microsoft, Qualcomm, the Alfred P. Sloan Foundation and the National Robotics Initiative, whose goal is to advance robotics to help make the United States more competitive in the world economy. MyRobots is a service for connecting robots and intelligent devices to the Internet. It can be regarded as a social network for robots and smart objects (i.e. Facebook for robots). With socialising, collaborating and sharing, robots can benefit from those interactions too by sharing their sensor information giving insight on their perspective of their current state. COALAS is funded by the INTERREG IVA France (Channel) – England European cross-border co-operation programme. The project aims to develop new technologies for disabled people through social and technological innovation and through the users' social and psychological integrity. The objective is to produce a cognitive ambient
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I-MSCP

i-MSCP (internet Multi Server Control Panel) was a free and open-source software for shared hosting environments management on Linux servers. It comes with a large choice of modules for various services such as Apache2, ProFTPd, Dovecot, Courier, Bind9, and can be easily extended through plugins, or listener files using its events-based API. Latest stable is the 1.5.3 version (build 2018120800) which has been released on 8 December 2018. The i-MSCP is no longer under development, although the developer has repeatedly claimed to be working on a new version, which has never has been published or even shown in any possible way. Whether development occurs or not, the current version of the software is not installable, as it only supports outdated versions of systems for which some of the necessary software to install i-MSCP cannot be installed. == Licensing == i-MSCP has a dual license. A part of the base code is licensed under the Mozilla Public License. All new code, and submissions to i-MSCP are licensed under the GNU Lesser General Public License Version 2.1 (LGPLv2). To solve this license conflict there is work on a complete rewrite for a completely LGPLv2 licensed i-MSCP. == Features == === Supported Linux Distributions === Debian Jessie (8.x), Stretch (9.x), Buster (10.x) Devuan Jessie (1.0), ASCII (2.x) Ubuntu Trusty Thar (14.04 LTS), Bionic Beaver (18.04 LTS) === Supported Daemons / Services === Web server: Apache (ITK, Fcgid and FastCGI/PHP-FPM), Nginx Name server: Bind9 MTA (Mail Transport Agent): Postfix MDA (Mail Delivery Agent): Courier, Dovecot Database: MySQL, MariaDB, Percona FTP-Server: ProFTPD, vsftpd Web statistics: AWStats === Addons === PhpMyAdmin Pydio, formerly AjaXplorer Net2ftp Roundcube Rainloop == Competing software == cPanel DTC Froxlor ISPConfig ispCP OpenPanel hestiacp Plesk SysCP Virtualmin
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Automatic summarization

Automatic summarization is the process of shortening a set of data computationally, to create a subset (a summary) that represents the most important or relevant information within the original content. Artificial intelligence (AI) algorithms are commonly developed and employed to achieve this, specialized for different types of data. Text summarization is usually implemented by natural language processing methods, designed to locate the most informative sentences in a given document. On the other hand, visual content can be summarized using computer vision algorithms. Image summarization is the subject of ongoing research; existing approaches typically attempt to display the most representative images from a given image collection, or generate a video that only includes the most important content from the entire collection. Video summarization algorithms identify and extract from the original video content the most important frames (key-frames), and/or the most important video segments (key-shots), normally in a temporally ordered fashion. Video summaries simply retain a carefully selected subset of the original video frames and, therefore, are not identical to the output of video synopsis algorithms, where new video frames are being synthesized based on the original video content. == Commercial products == In 2022 Google Docs released an automatic summarization feature. == Approaches == There are two general approaches to automatic summarization: extraction and abstraction. === Extraction-based summarization === Here, content is extracted from the original data, but the extracted content is not modified in any way. Examples of extracted content include key-phrases that can be used to "tag" or index a text document, or key sentences (including headings) that collectively comprise an abstract, and representative images or video segments, as stated above. For text, extraction is analogous to the process of skimming, where the summary (if available), headings and subheadings, figures, the first and last paragraphs of a section, and optionally the first and last sentences in a paragraph are read before one chooses to read the entire document in detail. Other examples of extraction that include key sequences of text in terms of clinical relevance (including patient/problem, intervention, and outcome). === Abstractive-based summarization === Abstractive summarization methods generate new text that did not exist in the original text. This has been applied mainly for text. Abstractive methods build an internal semantic representation of the original content (often called a language model), and then use this representation to create a summary that is closer to what a human might express. Abstraction may transform the extracted content by paraphrasing sections of the source document, to condense a text more strongly than extraction. Such transformation, however, is computationally much more challenging than extraction, involving both natural language processing and often a deep understanding of the domain of the original text in cases where the original document relates to a special field of knowledge. "Paraphrasing" is even more difficult to apply to images and videos, which is why most summarization systems are extractive. === Aided summarization === Approaches aimed at higher summarization quality rely on combined software and human effort. In Machine Aided Human Summarization, extractive techniques highlight candidate passages for inclusion (to which the human adds or removes text). In Human Aided Machine Summarization, a human post-processes software output, in the same way that one edits the output of automatic translation by Google Translate. == Applications and systems for summarization == There are broadly two types of extractive summarization tasks depending on what the summarization program focuses on. The first is generic summarization, which focuses on obtaining a generic summary or abstract of the collection (whether documents, or sets of images, or videos, news stories etc.). The second is query relevant summarization, sometimes called query-based summarization, which summarizes objects specific to a query. Summarization systems are able to create both query relevant text summaries and generic machine-generated summaries depending on what the user needs. An example of a summarization problem is document summarization, which attempts to automatically produce an abstract from a given document. Sometimes one might be interested in generating a summary from a single source document, while others can use multiple source documents (for example, a cluster of articles on the same topic). This problem is called multi-document summarization. A related application is summarizing news articles. Imagine a system, which automatically pulls together news articles on a given topic (from the web), and concisely represents the latest news as a summary. Image collection summarization is another application example of automatic summarization. It consists in selecting a representative set of images from a larger set of images. A summary in this context is useful to show the most representative images of results in an image collection exploration system. Video summarization is a related domain, where the system automatically creates a trailer of a long video. This also has applications in consumer or personal videos, where one might want to skip the boring or repetitive actions. Similarly, in surveillance videos, one would want to extract important and suspicious activity, while ignoring all the boring and redundant frames captured. At a very high level, summarization algorithms try to find subsets of objects (like set of sentences, or a set of images), which cover information of the entire set. This is also called the core-set. These algorithms model notions like diversity, coverage, information and representativeness of the summary. Query based summarization techniques, additionally model for relevance of the summary with the query. Some techniques and algorithms which naturally model summarization problems are TextRank and PageRank, Submodular set function, Determinantal point process, maximal marginal relevance (MMR) etc. === Keyphrase extraction === The task is the following. You are given a piece of text, such as a journal article, and you must produce a list of keywords or key[phrase]s that capture the primary topics discussed in the text. In the case of research articles, many authors provide manually assigned keywords, but most text lacks pre-existing keyphrases. For example, news articles rarely have keyphrases attached, but it would be useful to be able to automatically do so for a number of applications discussed below. Consider the example text from a news article: "The Army Corps of Engineers, rushing to meet President Bush's promise to protect New Orleans by the start of the 2006 hurricane season, installed defective flood-control pumps last year despite warnings from its own expert that the equipment would fail during a storm, according to documents obtained by The Associated Press". A keyphrase extractor might select "Army Corps of Engineers", "President Bush", "New Orleans", and "defective flood-control pumps" as keyphrases. These are pulled directly from the text. In contrast, an abstractive keyphrase system would somehow internalize the content and generate keyphrases that do not appear in the text, but more closely resemble what a human might produce, such as "political negligence" or "inadequate protection from floods". Abstraction requires a deep understanding of the text, which makes it difficult for a computer system. Keyphrases have many applications. They can enable document browsing by providing a short summary, improve information retrieval (if documents have keyphrases assigned, a user could search by keyphrase to produce more reliable hits than a full-text search), and be employed in generating index entries for a large text corpus. Depending on the different literature and the definition of key terms, words or phrases, keyword extraction is a highly related theme. ==== Supervised learning approaches ==== Beginning with the work of Turney, many researchers have approached keyphrase extraction as a supervised machine learning problem. Given a document, we construct an example for each unigram, bigram, and trigram found in the text (though other text units are also possible, as discussed below). We then compute various features describing each example (e.g., does the phrase begin with an upper-case letter?). We assume there are known keyphrases available for a set of training documents. Using the known keyphrases, we can assign positive or negative labels to the examples. Then we learn a classifier that can discriminate between positive and negative examples as a function of the features. Some classifiers make a binary classification for a test example, while others assign a probability of being a keyphrase. For ins
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Galaksija BASIC

Galaksija BASIC was the BASIC interpreter of the Galaksija build-it-yourself home computer from Yugoslavia. While being partially based on code taken from TRS-80 Level 1 BASIC, which the creator believed to have been a Microsoft BASIC, the extensive modifications of Galaksija BASIC—such as to include rudimentary array support, video generation code (as the CPU itself did it in absence of dedicated video circuitry) and generally improvements to the programming language—is said to have left not much more than flow-control and floating point code remaining from the original. The core implementation of the interpreter was fully contained in the 4 KiB ROM "A" or "1". The computer's original mainboard had a reserved slot for an extension ROM "B" or "2" that added more commands and features such as a built-in Zilog Z80 assembler. == ROM "A"/"1" symbols and keywords == The core implementation, in ROM "A" or "1", contained 3 special symbols and 32 keywords: ! begins a comment (equivalent of standard BASIC REM command) # Equivalent of standard BASIC DATA statement & prefix for hex numbers ARR$(n) Allocates an array of strings, like DIM, but can allocate only array with name A$ BYTE serves as PEEK when used as a function (e.g. PRINT BYTE(11123)) and POKE when used as a command (e.g. BYTE 11123,123). CALL n Calls BASIC subroutine as GOSUB in most other BASICs (e.g. CALL 100+4X) CHR$(n) converts an ASCII numeric code into a corresponding character (string) DOT x, y draws (command) or inspects (function) a pixel at given coordinates (0<=x<=63, 0<=y<=47). DOT displays the clock or time controlled by content of Y$ variable. Not in standard ROM EDIT n causes specified program line to be edited ELSE standard part of IF-ELSE construct (Galaksija did not use THEN) EQ compare alphanumeric values X$ and Y$ FOR standard FOR loop GOTO standard GOTO command HOME equivalent of standard BASIC CLS command - clears the screen HOME n protects n characters from the top of the screen from being scrolled away IF standard part of IF-ELSE construct (Galaksija did not use THEN) INPUT user entry of variable INT(n) a function that returns the greatest integer value equal to or lesser than n KEY(n) test whether a particular keyboard key is pressed LIST lists the program. Optional numeric argument specifies the first line number to begin listing with. MEM returns memory consumption data (need details here) NEW clears the current BASIC program NEW n clears BASIC program and moves beginning of BASIC area NEXT standard terminator of FOR loop OLD loads a program from tape OLD n loads program to different address PTR Returns address of the variable PRINT Printing numeric or string expression. RETURN Return from BASIC subroutine RND function (takes no arguments) that returns a random number between 0 and 1. RUN runs (executes) BASIC program. Optional numeric argument specifies the line number to begin execution with. SAVE saves a program to tape. Optional two arguments specify memory range to be saved (need details here). STEP standard part of FOR loop STOP stops execution of BASIC program TAKE replacement for READ and RESTORE. If the parameter is variable name, acts as READ, if it is number, acts as RESTORE UNDOT x, y "undraws" (resets) at specified coordinates (see DOT) UNDOT Stops the clock, not part of ROM USR Calls machine code subroutine WORD Double byte PEEK and POKE == ROM "B"/"2" additional symbols and keywords == The extended BASIC features, in ROM "B" or "2", contained one extra reserved symbol and 22 extra keywords: % /LABEL ABS(x) ARCTG(x) COS(x) COSD(x) DEL DUMP EXP(x) INP(x) LDUMP LLIST LN(x) LPRINT OUT PI POW(x,y) REN SIN(x), SIND(x) SQR(x) TG(x) TGD(x)
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JDoodle

JDoodle is a cloud-based online integrated development environment and compiler platform that supports execution of source code in 70+ programming languages including Java, Python, C/C++, PHP, Ruby, Perl, HTML, and more. It provides zero‑setup code for compilation, execution, and sharing via a web browser interface. == Features == Provides real‑time collaboration and code embedding via shareable URLs and APIs Offers an integrated terminal interface supporting database engines such as MySQL and MongoDB. JDroid — AI‑assistant to generate code snippets, optimize code, and assist debugging. == Languages and frameworks supported ==
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Douglas Parkhill

Douglas F. Parkhill is a Canadian technologist and former research minister, best known for his pioneering work on what is now called cloud computing, and his work on Canada's Telidon videotex project. He started working at the Canadian ministry of Communications (now part of the Department of Trade and Industry) in 1969, having previously worked at the Mitre Corporation. He was responsible for many activities in communications satellites, computer communications, command and control systems and telecommunications. He was winner of the Treasury Board of Canada Secretariat's Outstanding Achievement award in 1982, the Conestoga shield for services to government and industry in computer communications research and development, the Touche Ross award for Telidon development. He was an author of several publications including the 1966 book, The Challenge of the Computer Utility. In the book, Parkhill thoroughly explored many of the modern-day characteristics of cloud computing (elastic provisioning through a utility service) as well as the comparison to the electricity industry and the use of public, private, government and community forms. The book won the McKinsey Foundation award for distinguished contributions to management literature. He worked with Dave Godfrey, the Canadian writer and novelist on a later book Gutenberg two about the social and political meaning of computer technology. He was in charge of research at the Federal Department of Communications at the time when the department was funding development of the Telidon videotext system, was heavily involved in promoting the system, and had overall control of the program. In a radio broadcast in 1980, he outlined some of the potential of the system, from financial information, to theatre reservations, with the ability to pay and print out tickets from the system. He later documented the history of the Telidon project, and the history of videotext in general. == Publications == The Challenge of the Computer Utility, Addison-Wesley, 1966, ISBN 0-201-05720-4 edited with Dave Godfrey, Gutenberg Two: The New Electronics and Social Change, Press Porcepic, 1979, ISBN 0-88878-191-1 The Beginning of a Beginning. Ottawa; Department of Communications, 1987. A history of the Telidon project.
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Randonautica

Randonautica (a portmanteau of "random" + "nautica") is an app launched on February 22, 2020 founded by Auburn Salcedo and Joshua Lengfelder. It randomly generates coordinates that encourages the user to explore their local area and report what is found. According to its creators, the app is "an attractor of strange things," letting one choose specific coordinates based on a specific theme. It gained controversy after a report of two teenagers coincidentally finding a corpse while using the application. == Overview == The app, which creators claim to be inspired by chaos theory and Guy Debord's Theory of the Dérive, offers its users three types of coordinates to choose from: an attractor, a void, or an anomaly. The app has a cult following on YouTube and TikTok and there is a subreddit made by the creators for users of the app. == History == 29-year-old circus performer Joshua Lengfelder discovered a bot called Fatum Project in a fringe science chat group on Telegram in January 2019. According to The New York Times, "He absorbed the project’s theories about how random exploration could break people out of their predetermined realities, and how people could influence random outcomes with their minds." Lengfelder then created a Telegram bot using Fatum Project's code, generating coordinates. He then created the subreddit r/randonauts in March. In October, developer Simon Nishi McCorkindale made the bot's webpage. With the help of Auburn Salcedo, chief executive of a TV agency, both created Randonauts LLC. Salcedo became the chief operating officer while Lengfelder was the CEO. The app, called Randonautica, was launched on February 22, 2020. Later the same year the app and back-end got completely overhauled by a new team of developers and got a more visual and friendlier design and logo. In April 2022 Lengfelder exited Randonauts LLC and Auburn Salcedo became CEO. == Reception == The app has as many as 10.8 million users as of July 2020, gaining popularity amid the COVID-19 pandemic in the United States as restrictions have been lightened. Emma Chamberlain made a YouTube video about the app that helped increase its following. i-D reported that the hashtag #randonautica has gained 176.5 million views on TikTok, although it has not marketed itself yet. === Controversy === With the app's popularity, users started reporting coincidences which many find unsettling. The majority of reports were from TikTok and Reddit, as well as Telegram. The most notable controversy involved a group of people heading to a beach in Duwamish Head, Puget Sound, West Seattle per the app, where they found a bag with two dead bodies, a 27-year-old male and a 36-year-old female, as reported by the Seattle Police homicide detectives. In August 2020, police arrested and charged their landlord, Michael Lee Dudley, in connection with the murders. In March 2021, Dudley was denied bail while other people were under suspicion of aiding Dudley in the dismemberment and disposal of the bodies, but no one else had been charged. This has caused speculation that the app has an intended, puzzle-like theme. However, Lengfelder stated that it is "a shocking coincidence." Salcedo called the videos fake, and that "It’s so hard to manage, because people are really taking creative liberties after seeing how much traction the app is getting in that fear factor." In 2022, Michael Dudley was convicted of second degree murder for killing both victims, who were identified as Jessica Lewis and Austin Wenner. He was sentenced to 46 years in prison the following year. In their questions page, Randonautica's creators have said that if the app generates coordinates inside a private property, it is a violation of their terms and conditions to trespass. In addition, Randonautica has also received allegations that the app is used for human trafficking, which its creators have denied, saying that data collected by the app are anonymous. It also ensured that the app is not designed to violate religious customs, saying that "the app is simply a tool. Just as a knife can be used either to prepare dinner or to cut somebody."
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Cygwin

Cygwin ( SIG-win) is a free and open-source Unix-like environment and command-line interface (CLI) for Microsoft Windows. The project also provides a software repository containing open-source packages. Cygwin allows source code for Unix-like operating systems to be compiled and run on Windows. Cygwin provides native integration of Windows-based applications. The terminal emulator mintty is the default command-line interface provided to interact with the environment. The Cygwin installation directory layout mimics the root file system of Unix-like systems, with directories such as /bin, /home, /etc, /usr, and /var. Cygwin is released under the GNU Lesser General Public License version 3. It was originally developed by Cygnus Solutions, which was later acquired by Red Hat (now part of IBM), to port the GNU toolchain to Win32, including the GNU Compiler Suite. Rather than rewrite the tools to use the Win32 runtime environment, Cygwin implemented a POSIX-compatible environment in the form of a DLL. The brand motto is "Get that Linux feeling – on Windows", although Cygwin doesn't have Linux in it. == History == Cygwin began in 1995 as a project of Steve Chamberlain, a Cygnus engineer who observed that Windows NT and 95 used COFF as their object file format, and that GNU already included support for x86 and COFF, and the C library newlib. He thought that it would be possible to retarget GCC and produce a cross compiler generating executables that could run on Windows. A prototype was later developed. Chamberlain bootstrapped the compiler on a Windows system, to emulate Unix to let the GNU configure shell script run. Initially, Cygwin was called Cygwin32. When Microsoft registered the trademark Win32, the "32" was dropped to simply become Cygwin. In 1999, Cygnus offered Cygwin 1.0 as a commercial product. Subsequent versions have not been released, instead relying on continued open source releases. Geoffrey Noer was the project lead from 1996 to 1999. Christopher Faylor was lead from 1999 to 2004; he left Red Hat and became co-lead with Corinna Vinschen. Corinna Vinschen has been the project lead from mid-2014 to date (as of September, 2024). From June 23, 2016, the Cygwin library version 2.5.2 was licensed under the GNU Lesser General Public License (LGPL) version 3. == Description == Cygwin is provided in two versions: the full 64-bit version and a stripped-down 32-bit version, whose final version was released in 2022. Cygwin consists of a library that implements the POSIX system call API in terms of Windows system calls to enable the running of a large number of application programs equivalent to those on Unix systems, and a GNU development toolchain (including GCC and GDB). Programmers have ported the X Window System, K Desktop Environment 3, GNOME, Apache, and TeX. Cygwin permits installing inetd, syslogd, sshd, Apache, and other daemons as standard Windows services. Cygwin programs have full access to the Windows API and other Windows libraries. Cygwin programs are installed by running Cygwin's "setup" program, which downloads them from repositories on the Internet. The Cygwin API library is licensed under the GNU Lesser General Public License version 3 (or later), with an exception to allow linking to any free and open-source software whose license conforms to the Open Source Definition. Cygwin consists of two parts: A dynamic-link library in the form of a C standard library that acts as a compatibility layer for the POSIX API and A collection of software tools and applications that provide a Unix-like look and feel. Cygwin supports POSIX symbolic links, representing them as plain-text files with the system attribute set. Cygwin 1.5 represented them as Windows Explorer shortcuts, but this was changed for reasons of performance and POSIX correctness. Cygwin also recognises NTFS junction points and symbolic links and treats them as POSIX symbolic links, but it does not create them. The POSIX API for handling access control lists (ACLs) is supported. === Technical details === A Cygwin-specific version of the Unix mount command allows mounting Windows paths as "filesystems" in the Unix file space. Initial mount points can be configured in /etc/fstab, which has a format very similar to Unix systems, except that Windows paths appear in place of devices. Filesystems can be mounted in binary mode (by default), or in text mode, which enables automatic conversion between LF and CRLF endings (which only affects programs that open files without explicitly specifying text or binary mode). Cygwin 1.7 introduced comprehensive support for POSIX locales, and the UTF-8 Unicode encoding became the default. The fork system call for duplicating a process is fully implemented, but the copy-on-write optimization strategy could not be used. Cygwin's default user interface is the bash shell running in the mintty terminal emulator. The DLL also implements pseudo terminal (pty) devices, and Cygwin ships with a number of terminal emulators that are based on them, including rxvt/urxvt and xterm. The version of GCC that comes with Cygwin has various extensions for creating Windows DLLs, such as specifying whether a program is a windowing or console-mode program. Support for compiling programs that do not require the POSIX compatibility layer provided by the Cygwin DLL used to be included in the default GCC, but as of 2014, it is provided by cross-compilers contributed by the MinGW-w64 project. == Software packages == Cygwin's base package selection is approximately 100MB, containing the bash (interactive user) and dash (installation) shells and the core file and text manipulation utilities. Additional packages are available as optional installs from within the Cygwin "setup" program and package manager ("setup-x86_64.exe" – 64 bit). The Cygwin Ports project provided additional packages that were not available in the Cygwin distribution itself. Examples included GNOME, K Desktop Environment 3, MySQL database, and the PHP scripting language. Most ports have been adopted by volunteer maintainers as Cygwin packages, and Cygwin Ports are no longer maintained. Cygwin ships with GTK+ and Qt. The Cygwin/X project allows graphical Unix programs to display their user interfaces on the Windows desktop for both local and remote programs.
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Kounta (software company)

Kounta is an Australian software company founded in 2012. The company's flagship product, Kounta, comprises a cloud based point of sale mobile app. == History == Kounta was founded in 2012 by entrepreneur Nick Cloete. The company is headquartered in Sydney, Australia. In 2012, the company launched its flagship product, Kounta, a hospitality-focused point of sale (POS) mobile app for iPad, Android, Mac, and Windows. The app was initially a web-based application, and later developed into an online cash register and inventory management system that allows businesses to take payments from customers via mobile devices. The app has been made available for iPad, iPhone, and Android devices; as well as iOS, Windows, and other peripherals. In 2012, Kounta partnered with Epson, providing a cloud-based POS platform for Epson printers. In 2013, the company formed a partnership with PayPal, integrating cashless and cardless transaction options via PayPal's mobile app. In 2014, MYOB (company) made an undisclosed investment towards Kounta. This partnership led to the development of MYOB Kounta, a co-branded application merging Kounta's POS with MYOB's application software. MYOB Kounta launched in October of the same year. In 2016, Kounta announced a partnership with the Commonwealth Bank of Australia to include the Kounta app onto "Albert", the bank's EFTPOS tablet, which allowed the Commonwealth Bank of Australia to become the first bank to manage all customers operations from a single device and mobile application. == Technology == The Kounta POS is a software-as-a-service (SaaS) that runs as an application in web browsers as well as natively on iOS and Android operating systems. Kounta also incorporates an Open API, making it possible for other software providers to integrate complementary apps, further extending the software's use. Traditional IT tasks, such as data backup and encryption, hardware maintenance, and server upgrades are handled by Kounta's data center. Kounta is made accessible via paid monthly subscription licenses. == Acquisition by Lightspeed == In October 2019, Kounta was acquired by Lightspeed, an advanced commerce platform for retail, hospitality, and golf businesses based in Montreal, Canada. Lightspeed acquired Kounta for $35.3 million USD.
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Oracle Cloud

Oracle Cloud is a cloud computing service offered by Oracle Corporation providing servers, storage, network, applications and services through a global network of Oracle Corporation managed data centers. The company allows these services to be provisioned on demand over the Internet. Oracle Cloud provides infrastructure as a service (IaaS), platform as a service (PaaS), software as a service (SaaS), and data as a service (DaaS). These services are used to build, deploy, integrate, and extend applications in the cloud. This platform supports numerous open standards (SQL, HTML5, REST, etc.), open-source applications (Kubernetes, Spark, Hadoop, Kafka, MySQL, Terraform, etc.), and a variety of programming languages, databases, tools, and frameworks including Oracle-specific, open source, and third-party software and systems. == Services == === Infrastructure as a Service (IaaS) and Platform as a Service (PaaS) === Oracle's cloud infrastructure was made generally available (GA) on October 20, 2016 under the name "Oracle Bare Metal Cloud Services". Oracle Bare Metal Cloud Services was rebranded as Oracle Cloud Infrastructure in 2018 and dubbed Oracle's "Generation 2 Cloud" at Oracle OpenWorld 2018. Oracle Cloud Infrastructure offerings include the following services: Compute: The company provides Virtual Machine Instances to provide different shapes (VM sizes) catering to different types of workloads and performance characteristics. They also provide on-demand Bare metal servers and Bare metal GPU servers, without a hypervisor. In 2016, Oracle Cloud Infrastructure launched with bare metal instances with Intel processors. These first bare metal instances offered were powered by Intel servers. In 2018, Oracle Cloud added bare metal instances powered by AMD processors, followed by Ampere Cloud-native processors in 2021. In 2021, Oracle also released its first VM-based compute instances based on Arm processors. Storage: The platform provides block volumes, file storage, object storage, and archive storage for database, analytics, content, and other applications across common protocols and APIs. Networking: This cloud platform provides network with fully configurable IP addresses, subnets, routing, and firewalls to support new or existing private networks with end-to-end security. Governance: For auditing, identity and access management, the platform has data integrity checks, traceability, and access management features. Database Management / Data Management: Oracle offers a data management platform for database workloads as well as hyper-scale big data and streaming workloads including OLTP, data warehousing, Spark, machine learning, text search, image analytics, data catalog, and deep learning. The platform allows Oracle, MySQL, and NoSQL databases to be deployed on demand as managed cloud services. Oracle Databases uniquely offer the Oracle Autonomous Database (optimized for data warehouse, transaction processing, or JSON), the Exadata shape, as well as Real Application Clusters (RAC). Load Balancing: The cloud platform offers load balancing capability to automatically route traffic across fault domains and availability domains for high availability and fault-tolerance for hosted applications. Edge Services: These services can monitor the path between users and resources and adapt to changes and outages. They include Domain Name System (DNS) services from Oracle's acquisition of Dyn. FastConnect: The cloud platform provides private connectivity across on-premises and cloud networks through providers like Equinix, AT&T, and Colt. Application Development: For application development, the company's cloud offers an open, standards-based application development platform to build, deploy, and manage API-first, mobile-first cloud applications. This platform supports container-native, cloud-native, and low code development. This platform also provides a DevOps platform for CI/CD, diagnostics for Java applications, and integration with SaaS and on-prem applications. Services include Java, mobile, digital assistants (evolution from chatbots), messaging, application container cloud, developer cloud, visual builder, API catalog, AI platform, DataScience.com (Oracle acquired) and blockchain. Integration: This is a platform offering with adapters to integrate on-premise and cloud applications. Capabilities include data integration and replication, API management, integration analytics, along with data migration and integration. They offer services such as data integration platform cloud, data integrator cloud service, GoldenGate cloud service, integration cloud, process cloud service, API platform cloud service, apiary cloud service, and SOA cloud service. Business Analytics: The company provides this business analytics platform which can analyze and generate insights from data across various applications, data warehouses, and data lakes. The services offered include analytics cloud, business intelligence, big data discovery, big data preparation, data visualization, and essbase. Security: The Oracle Cloud Platform provides identity and security applications for providing secure access and monitoring of hybrid cloud environment and addressing IT governance and compliance requirements. This platform delivers an identity SOC (Security Operations Center) through a combined offering of SIEM, UEBA, CASB, and IDaaS. The services offered include Identity Cloud Service and CASB Cloud Service. Management: The platform provides an integrated monitoring, management, and analytics platform. This platform also uses machine learning and big data on the operational data set. The platform is used to improve IT stability, prevent application outages, improve DevOps, and harden security. Services offered include Application Performance Monitoring, Infrastructure Monitoring, Log Analytics, Orchestration, IT Analytics, Configuration and Compliance, Security Monitoring, and Analytics. Content and Experience: This is a platform for content, website, and workflow management. This service is used to provide content collaboration and web presence. This tool comes integrated with Oracle on-premise and SaaS services. The services offered are Content and Experience Cloud, WebCenter Portal Cloud, and DIVA Cloud. In 2016, Oracle acquired Dyn, an internet infrastructure company. On May 16, 2018 Oracle announced that it had acquired DataScience.com, a privately held cloud workspace platform for data science projects and workloads. In April 2020, Oracle became the cloud infrastructure provider for Zoom, an online and video meeting platform. The same month, Nissan announced its migration to Oracle Cloud for its high-performance computing (HPC) workloads used for simulating the structural impacts of a car design. Xerox announced a partnership with Oracle Cloud in 2021, where Xerox will use Oracle's cloud-computing capabilities within its business incubator. === Software as a Service (SaaS) === Oracle provides SaaS applications also known as Oracle Cloud Applications. These applications are offered across a variety of products, industrial sectors with various deployment options to adhere to compliance standards. The below list mentions Oracle Cloud Applications provided by Oracle Corporation. Customer Experience (CX) Human Capital Management (HCM) Enterprise Resource Planning (ERP) Supply Chain Management (SCM) Enterprise Performance Management (EPM) Internet of Things Applications (IoT) SaaS Analytics Data Industry Solutions (Communications, Financial Services, Consumer Goods, High Tech and Manufacturing, Higher Education, Hospitality, Utilities) Deployment (adhering to standards for sectors such as Financial Services, Retail Services, Public Sector, Defense) Block-Chain Cloud Service (in partnership with SAP, IBM and Microsoft) Blockchain Applications On July 28, 2016 Oracle bought NetSuite, the very first cloud company, for $9.3 billion. === Data as a Service (DaaS) === This platform is known as the Oracle Data Cloud. This platform aggregates and analyzes consumer data powered by Oracle ID Graph across channels and devices to create cross-channel consumer understanding. == Deployment models == Oracle Cloud is available in 44 regions as of July 2023, including North America, South America, UK, European Union, Middle East, Africa, India, Australia, Korea, and Japan. Oracle Cloud is available as a public cloud (Oracle-managed regions); to selected government agencies as an Oracle-managed government cloud in the United States (with FedRAMP High and DISA SRG IL5 compliance) and United Kingdom; and as a "private cloud" or "hybrid cloud" as an Oracle-managed database-only service or full-service dedicated region - what Oracle calls "Cloud at Customer". == Architecture == Oracle's public and government cloud is offered through a global network of Oracle-managed data centers, connected by an Oracle-managed backbone network. Oracle's Exadata Cloud at Customer leverages this network for contr
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Amazon Elastic Compute Cloud

Amazon Elastic Compute Cloud (EC2) is a part of Amazon's cloud-computing platform, Amazon Web Services (AWS), that allows users to rent virtual computers on which to run their own computer applications. EC2 encourages scalable deployment of applications by providing a web service through which a user can boot an Amazon Machine Image (AMI) to configure a virtual machine, which Amazon calls an "instance", containing any software desired. A user can create, launch, and terminate server-instances as needed, paying by the second for active servers – hence the term "elastic". EC2 provides users with control over the geographical location of instances that allows for latency optimization and high levels of redundancy. In November 2010, Amazon switched its own retail website platform to EC2 and AWS. == History == Amazon announced a limited public beta test of EC2 on August 25, 2006, offering access on a first-come, first-served basis. Amazon added two new instance types (Large and Extra-Large) on October 16, 2007. On May 29, 2008, two more types were added, High-CPU Medium and High-CPU Extra Large. There were twelve types of instances available. Amazon added three new features on March 27, 2008: static IP addresses, availability zones, and user-selectable kernels. On August 20, 2008, Amazon added Elastic Block Store (EBS). This provides persistent storage, a feature that had been lacking since the service was introduced. Amazon EC2 went into full production when it dropped the beta label on October 23, 2008. On the same day, Amazon announced the following features: a service level agreement for EC2, Microsoft Windows in beta form on EC2, Microsoft SQL Server in beta form on EC2, plans for an AWS management console, and plans for load balancing, autoscaling, and cloud monitoring services. These features were subsequently added on May 18, 2009. Amazon EC2 was developed mostly by a team in Cape Town, South Africa led by Chris Pinkham. Pinkham provided the initial architecture guidance for EC2 and then built the team and led the development of the project along with Willem van Biljon. == Instance types == Initially, EC2 used Xen virtualization exclusively. However, on November 6, 2017, Amazon announced the new C5 family of instances that were based on a custom architecture around the KVM hypervisor, called Nitro. Each virtual machine, called an "instance", functions as a virtual private server. Amazon sizes instances based on "Elastic Compute Units". The performance of otherwise identical virtual machines may vary. On November 28, 2017, AWS announced a bare-metal instance, a departure from exclusively offering virtualized instance types. As of January 2019, the following instance types were offered: General Purpose: A1, T3, T2, M5, M5a, M4, T3a Compute Optimized: C5, C5n, C4 Memory Optimized: R5, R5a, R4, X1e, X1, High Memory, z1d Accelerated Computing: P3, P2, G3, F1 Storage Optimized: H1, I3, D2 As of April 2018, the following payment methods by instance were offered: On-demand: pay by the hour without commitment. Reserved: rent instances with one-time payment receiving discounts on the hourly charge. Spot: bid-based service: runs the jobs only if the spot price is below the bid specified by bidder. The spot price is claimed to be supply-demand based, however a 2011 study concluded that the price was generally not set to clear the market, but was dominated by an undisclosed reserve price. In 2025, AWS expanded EC2 with the compute-optimized C8gn family, powered by Graviton4 and offering up to 600 Gbit/s network bandwidth (about 30% higher compute performance than C7gn), and introduced G6f fractional-GPU instances that let customers provision one-eighth, one-quarter, or one-half of an NVIDIA L4 GPU for right-sized graphics/ML workloads. === Cost === As of April 2018, Amazon charged about $0.0058 per hour ($4.176 per month) for the smallest "Nano Instance" (t2.nano) virtual machine running Linux or Windows. Storage-optimized instances cost as much as $4.992 per hour (i3.16xlarge). "Reserved" instances can go as low as $2.50 per month for a three-year prepaid plan. The data transfer charge ranges from free to $0.12 per gigabyte, depending on the direction and monthly volume (inbound data transfer is free on all AWS services). EC2 costs can be analyzed using the Amazon Cost and Usage Report. There are many different cost categories for EC2 including: hourly Instance Charges, Data Transfer, EBS Volumes, EBS Volume Snapshots, and Nat Gateway. === Free tier === As of December 2010 Amazon offered a bundle of free resource credits to new account holders. The credits are designed to run a "micro" sized server, storage (EBS), and bandwidth for one year. Unused credits cannot be carried over from one month to the next. === Reserved instances === Reserved instances enable EC2 or RDS service users to reserve an instance for one or three years. The corresponding hourly rate charged by Amazon to operate the instance is 35 to 75% lower than the rate charged for on-demand instances. Reserved instances can be purchased with three different payment options: All Upfront, Partial Upfront and No Upfront. The different purchase options allow for different structuring of payment models, with a larger discount given to customers that pay their reservation upfront. Reserved Instances are purchased based on a resource commitment. These reservations are made based on an instance type and a count of that instance type. For example, you could reserve 100 i3.large instances for a 3-year term. In September 2016, AWS announced several enhancements to Reserved instances, introducing a new feature called scope and a new reservation type called a Convertible. In October 2017, AWS announced the allowance to subdivide the instances purchased for more flexibility. === Spot instances === Cloud providers maintain large amounts of excess capacity they have to sell or risk incurring losses. Amazon EC2 Spot instances are spare compute capacity in the AWS cloud available at up to 90% discount compared to On-Demand prices. As a trade-off, AWS offers no SLA on these instances and customers take the risk that it can be interrupted with only two minutes of notification when Amazon needs the capacity back. Researchers from the Israeli Institute of Technology found that "they (Spot instances) are typically generated at random from within a tight price interval via a dynamic hidden reserve price". Some companies, like Spotinst, are using machine learning to predict spot interruptions up to 15 minutes in advance. === Savings Plans === In November 2019, Amazon announced Savings Plans. Savings Plans are an alternative to Reserved Instances that come in two different plan types: Compute Savings Plans and EC2 Instances Savings Plans. Compute Savings Plans allow an organization to commit to EC2 and Fargate usage with the freedom to change region, family, size, availability zone, OS and tenancy inside the lifespan of the commitment. EC2 Instance Savings plans provide a larger discount than Compute Savings Plans but are less flexible meaning a user must commit to individual instance families within a region to take advantage, but with the freedom to change instances within the family in that region. AWS uses the Cost Explorer to automatically calculate recommendations for the commitments you should make how that commitment will look like as a monthly charge on your AWS bill. AWS Savings Plans are purchased based on hourly spend commitment. This hourly commitment is made using the discounted pricing of the savings plan you are purchasing. For example, you could commit to spending $5 per hour, on a Compute Savings Plan, for a 3-year term. == Features == === Operating systems === When it launched in August 2006, the EC2 service offered Linux and later Sun Microsystems' OpenSolaris and Solaris Express Community Edition. In October 2008, EC2 added the Windows Server 2003 and Windows Server 2008 operating systems to the list of available operating systems. In March 2011, NetBSD AMIs became available. In November 2012, Windows Server 2012 support was added. Since 2006, Colin Percival, a FreeBSD developer and Security Officer, solicited Amazon to add FreeBSD. In November 2012, Amazon officially supported running FreeBSD in EC2. The FreeBSD/EC2 platform is maintained by Percival who also developed the secure deduplicating Amazon S3-cloud based backup service Tarsnap. Amazon has their own Linux distribution based on Fedora and Red Hat Enterprise Linux as a low cost offering known as the Amazon Linux AMI. Version 2013.03 included: Linux kernel, Java OpenJDK Runtime Environment and GNU Compiler Collection. On November 30, 2020, Amazon announced that it would be adding macOS to the EC2 service. Initial support was announced for macOS Mojave and macOS Catalina running on Mac Mini. === Managed Container and Kubernetes Services === Amazon Elastic Container Registry (ECR) is a Docker registry service for Amazon EC2
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Cloud Native Computing Foundation

The Cloud Native Computing Foundation (CNCF) is a subsidiary of the Linux Foundation founded in 2015 to support cloud-native computing. == History == It was announced alongside Kubernetes 1.0, an open source container cluster manager, which was contributed to the Linux Foundation by Google as a seed technology. Founding members include Google, CoreOS, Mesosphere, Red Hat, Twitter, Huawei, Intel, RX-M, Cisco, IBM, Docker, Univa, and VMware. Today, CNCF is supported by over 450 members. In August 2018 Google announced that it was handing over operational control of Kubernetes to the community. == Projects == Argo is a collection of tools for getting work done with Kubernetes. Among its main features are Workflows and Events. It was accepted to CNCF on March 26, 2020 at the Incubating maturity level and then moved to the Graduated maturity level on December 6, 2022. cert-manager provisions and manages TLS certificates in Kubernetes. It was accepted to CNCF on November 10, 2020, moved to the Incubating maturity level on September 19, 2022, and then moved to the Graduated maturity level on September 29, 2024. Cilium provides networking, security, and observability for Kubernetes deployments using eBPF technology. It joined the CNCF at incubation level in October 2021 and the CNCF announced its graduation in October 2023. containerd is an industry-standard core container runtime. It is currently available as a daemon for Linux and Windows, which can manage the complete container lifecycle of its host system. In 2015, Docker donated the OCI Specification to The Linux Foundation with a reference implementation called runc. Since February 28, 2019 it is an official CNCF project. Its general availability and intention to donate the project to CNCF was announced by Docker in 2017. CoreDNS is a DNS server that chains plugins. Its graduation was announced in 2019. Dapr, the distributed application runtime, provides APIs for building secure and reliable microservices and agentic AI systems. Dapr was donated to the CNCF in November 2021 and joined at incubation level. The CNCF announced its graduation in November 2024. Envoy: Originally built at Lyft to move their architecture away from a monolith, Envoy is a high-performance open source edge and service proxy that makes the network transparent to applications. Lyft contributed Envoy to Cloud Native Computing Foundation in September 2017. etcd is a distributed key value store, providing a method of storing data across a cluster of machines. It became a CNCF incubating project in 2018 at KubeCon+CloudNativeCon North America in Seattle that year. Falco is an open source and cloud native runtime security initiative. It is the "de facto Kubernetes threat detection engine". It became an incubating project in January 2020 and graduated in February 2024. Flux is an open source project for powering GitOps in Kubernetes clusters. It provides the GitOps Toolkit, a set of Kubernetes APIs that allow you to define how configuration source code is securely pulled into your cluster and deployed by popular Kubernetes manifests rendering engines like Kustomize and Helm. The most recommended source mechanism is the OCIRepository API, which provides enhanced security and benefits from container image tooling out there. Flux has also notification integrations with popular services like Prometheus Alertmanager, PagerDuty, Slack and so on. Flux has graduated in CNCF in 2022. Harbor is an "open source trusted cloud native registry project that stores, signs, and scans content." It became an incubating project in September 2019 and graduated in June 2020. Helm is a package manager that helps developers "easily manage and deploy applications onto the Kubernetes cluster." It joined the incubating level in June 2018 and graduated in April 2020. Istio is a service mesh technology. It was accepted by CNCF in September 2022 and graduated on July 12, 2023. Jaeger, Created by Uber Engineering, Jaeger is an open source distributed tracing system inspired by Google Dapper paper and OpenZipkin community. It can be used for tracing microservice-based architectures, including distributed context propagation, distributed transaction monitoring, root cause analysis, service dependency analysis, and performance/latency optimization. The Cloud Native Computing Foundation Technical Oversight Committee voted to accept Jaeger as the 12th hosted project in September 2017 and became a graduated project in 2019. In 2020 it became an approved and fully integrated part of the CNCF ecosystem. Kubernetes is an open source framework for automating deployment and managing applications in a containerized and clustered environment. "It aims to provide better ways of managing related, distributed components across the varied infrastructure." It was originally designed by Google and donated to The Linux Foundation to form the Cloud Native Computing Foundation with Kubernetes as the seed technology. The "large and diverse" community supporting the project has made its staying power more robust than other, older technologies of the same ilk. In January 2020, the CNCF annual report showed significant growth in interest, training, event attendance and investment related to Kubernetes. Linkerd is CNCF's fifth member project, and the project that coined the term "service mesh". Linkerd adds observability, security, and reliability features to applications by adding them to the platform rather than the application layer, and features a "micro-proxy" to maximize speed and security of its data plane. Linkerd graduated from CNCF in July 2021. Open Policy Agent (OPA) is "an open source general-purpose policy engine and language for cloud infrastructure." It became a CNCF incubating project in April 2019. OPA graduated from CNCF in February 2021. Prometheus is a cloud monitoring tool sponsored by SoundCloud in early iterations. In August 2018, the tool was designated a graduated project by the Cloud Native Computing Foundation. It is now a Cloud Native Computing Foundation member project. Rook is CNCF's first cloud native storage project. It became an incubation level project in 2018 and graduated in October 2020. SPIFFE is an open standard and framework for workload identity, much the same way that OAuth is an open standard and framework for human identity. It is built from the ground up to accommodate modern computing environments, which operate with systems scale and velocity (as opposed to human scale and velocity), while still maintaining interoperability with existing technologies like OAuth and X.509 Public key infrastructure. Unlike other identity standards, SPIFFE supports multiple credential types for a single identity, ensuring that the highly varied needs of production environments are consistently met without compromise. SPIFFE joined the CNCF as a sandbox project in 2018, was accepted to incubation in 2020, and graduated in 2022. SPIRE is an open source identity provider for workloads based on the SPIFFE framework. It is highly pluggable, and fills the attestation and issuance needs required by any workload identity solution. The plugin interfaces it exposes allows users to write integrations with in-house systems, build internal self-service portals, and more. It is a very powerful building block for issuing short-lived identity credentials to dynamic cloud workloads. SPIRE became a CNCF Graduated project in 2022. The Update Framework (TUF) helps developers to secure new or existing software update systems, which are often found to be vulnerable to many known attacks. TUF addresses this widespread problem by providing a comprehensive, flexible security framework that developers can integrate with any software update system. TUF was CNCF's first security-focused project and the ninth project overall to graduate from the foundation's hosting program. TiKV provides a distributed key–value database. Vitess is a database clustering system for horizontal scaling of MySQL, first created for internal use by YouTube. It became a CNCF project in 2018 and graduated in November 2019. Contour is a management server for Envoy that can direct the management of Kubernetes' traffic. Contour also provides routing features that are more advanced than Kubernetes' out-of-the-box Ingress specification. VMWare contributed the project to CNCF in July 2020. Cortex offers horizontally scalable, multi-tenant, long-term storage for Prometheus and works alongside Amazon DynamoDB, Google Bigtable, Cassandra, S3, GCS, and Microsoft Azure. It was introduced into the ecosystem incubator alongside Thanos in August 2020. CRI-O is an Open Container Initiative (OCI) based "implementation of Kubernetes Container Runtime Interface". CRI-O allows Kubernetes to be container runtime-agnostic. It became an incubating project in 2019. gRPC is a "modern open source high performance RPC framework that can run in any environment." The project was formed in 2015 when Google decided to open sou
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Toolchain

A toolchain is a set of software development tools used to build and otherwise develop software. Often, the tools are executed sequentially and form a pipeline such that the output of one tool is the input for the next. Sometimes the term is used for a set of related tools that are not necessarily executed sequentially. A relatively common and simple toolchain consists of the tools to build for a particular operating system (OS) and CPU architecture: a compiler, a linker, and a debugger. With a cross-compiler, a toolchain can support cross-platform development. For building more complex software systems, many other tools may be in the toolchain. For example, for a video game, the toolchain may include tools for preparing sound effects, music, textures, 3-dimensional models and animations, and for combining these resources into the finished product.
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