AI Detector No Character Limit

AI Detector No Character Limit — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

AI warfare

AI warfare refers to the use of artificial intelligence technologies to automate military operation and enhance or bypass human decision-making in armed conflicts. AI is used to rapidly analyze large volumes of military intelligence data, including making recommendations or decisions on who and what to target. Abdul-Rahman al-Rawi, a 20-year-old student, was the first acknowledged civilian killed by AI-assisted airstrike in a U.S. strike in Iraq in 2024. In 2026, the U.S. declared it would become an 'AI-first' warfighting force. Husain et al (2018) coined the term hyperwar to refer to warfare which is algorithmic or controlled by artificial intelligence, with little to no human decision-making. == 2026 Iran war == The 2026 Iran war has been described as the "first AI war", although the Untied States and Israel have previously used AI to identify targets during the Gaza war. The U.S. has used AI tools to attack Iran. These tools have been used for military intelligence, targeting, and damage assessment in the war in Iran. Using the Maven smart system, the U.S. attacked 1,000 targets in the first 24 hours of the war and 5,000 targets over the course of 10 days. While the U.S. had used Maven in 2022 to share targeting information with Ukraine and strike against Iraq, Syria, and against the Houthis in 2024, Iran's attacks are its biggest. Authorities are looking into whether artificial intelligence was involved in the airstrike on an Iranian girls' school that killed 170 civilians, the majority of whom were female students. The United States Central Command emphasized that humans were making final targeting decisions. Per a White House tally released on April 8, the U.S. military hit over 13,000 targets in Iran during the war's first 38 days, including more than 2,000 command-and-control sites, 1,500 air defense targets, and 1,450 industrial infrastructure targets. == Gaza war == As part of the Gaza war, the Israel Defense Forces (IDF) have used artificial intelligence to rapidly and automatically perform much of the process of determining what to bomb. IDF's Unit 8200 developed AI systems, dubbed the Gospel and Lavender, to find targets for the Israeli Air Force to bomb. The Gospel automatically provides targeting recommendations to human analysts, who decide whether to approve strikes. Lavender identified 37,000 Hamas-linked individuals early in the war, and was used alongside the Gospel, which chooses buildings or structures as targets. According to a report by +972 Magazine and Local Call, strikes assisted by Lavender were routinely permitted to kill 5–20 civilians for each suspected Hamas militant, who were often bombed at home with their families. The IDF denies these claims, maintaining that every strike is assessed to minimize collateral damage, and that there is no policy "to kill tens of thousands of people in their homes." Israel deployed AI technologies during the Gaza war for audio analysis, facial recognition, and airstrike targeting. One such system was used to help identify the location of Hamas commander Ibrahim Biari through phone call analysis, leading to strikes that killed him as well as more than 125 civilians. == 2022 Russian Ukraine war == Kyiv launched a project with Palantir called Brave1 Dataroom to build AI systems using the extensive combat data Ukraine has gathered since Russia’s full-scale invasion in 2022. The country has also created tools for in-depth airstrike analysis, introduced AI to process large volumes of intelligence, and incorporated these technologies into the planning of long-range strike operations. == Involved companies == Maven Smart System is developed by Palantir. It integrates Anthropic's Claude as its large language model, and uses Amazon's AWS servers as its cloud infrastructure. Since Anthropic's refusal to support autonomous weapons development and domestic surveillance efforts. In its place, other AI firms, including OpenAI, have been brought in to take over that role. == Involved state actors == In 2024, the United States Department of Defense had 800-plus active AI-related projects and requested $1.8 billion in AI funding, with Project Maven and Project Artemis (AI-resistant drones developed together with Ukraine) being the main ones. The technology has been used in Iran, Iraq, Syria and Yemen to identify targets. China is pursuing intelligentized warfare, integrating AI across all combat domains—land, sea, air, space, and cyber—with military AI spending exceeding $1.6 billion annually. == International regulation == Since 2014, states meeting within the framework of the Convention on Certain Conventional Weapons have discussed lethal autonomous weapon systems. In 2016, the treaty's states parties established an open-ended Group of Governmental Experts on Lethal Autonomous Weapons Systems to continue those discussions. The discussions have addressed international humanitarian law, accountability, possible prohibitions and regulations, and the extent of human control required over AI-enabled weapons.
Read more →
Product-family engineering

Product-family engineering (PFE), also known as product-line engineering (PLE), is based on the ideas of "domain engineering" created by the Software Engineering Institute, a term coined by James Neighbors in his 1980 dissertation at University of California, Irvine. Software product lines are quite common in our daily lives, but before a product family can be successfully established, an extensive process has to be followed. This process is known as product-family engineering. Product-family engineering can be defined as a method that creates an underlying architecture of an organization's product platform. It provides an architecture that is based on commonality as well as planned variabilities. The various product variants can be derived from the basic product family, which creates the opportunity to reuse and differentiate on products in the family. Product-family engineering is conceptually similar to the widespread use of vehicle platforms in the automotive industry. Product-family engineering is a relatively new approach to the creation of new products, recently evolving to Model-Based Product Line Engineering (MBPLE), emphasizing the centrality of a model-centric approach in PLE. It focuses on the process of engineering new products in such a way that it is possible to reuse product components and apply variability with decreased costs and time. Product-family engineering is all about reusing components and structures as much as possible, according to the ISO/IEC 26550/2015 and the latest ISO/IEC 26580/2021 that introduced the concept of feature-based Product Line Engineering. Several studies have proven that using a product-family engineering approach for product development can have several benefits. Here is a list of some of them: Higher productivity Higher quality Faster time-to-market Lower labor needs The Nokia case mentioned below also illustrates these benefits. In 2025 the publishing of the book Model-Based Product Line Engineering (MBPLE): The feature-based path to product lines success by Marco Forlingieri, Tim Weilkiens and Hugo Guillermo Chalé-Gongora formalized the foundation of the discipline, including best practices and new industrial cases. == Overall process == The product family engineering process consists of several phases. The three main phases are: Phase 1: Product management Phase 2: Domain engineering Phase 3: Product engineering The process has been modeled on a higher abstraction level. This has the advantage that it can be applied to all kinds of product lines and families, not only software. The model can be applied to any product family. Figure 1 (below) shows a model of the entire process. Below, the process is described in detail. The process description contains elaborations of the activities and the important concepts being used. All concepts printed in italic are explained in Table 1. === Phase 1: product management === The first phase is the starting up of the whole process. In this phase some important aspects are defined especially with regard to economic aspects. This phase is responsible for outlining market strategies and defining a scope, which tells what should and should not be inside the product family. ==== Evaluate business visioning ==== During this first activity all context information relevant for defining the scope of the product line is collected and evaluated. It is important to define a clear market strategy and take external market information into account, such as consumer demands. The activity should deliver a context document that contains guidelines, constraints and the product strategy. ==== Define product line scope ==== Scoping techniques are applied to define which aspects are within the scope. This is based upon the previous step in the process, where external factors have been taken into account. The output is a product portfolio description, which includes a list of current and future products and also a product roadmap. It can be argued whether phase 1, product management, is part of the product-family-engineering process, because it could be seen as an individual business process that is more focused on the management aspects instead of the product aspect. However phase 2 needs some important input from this phase, as a large piece of the scope is defined in this phase. So from this point of view it is important to include the product-management phase (phase 1) into the entire process as a base for the domain-engineering process. === Phase 2: domain engineering === During the domain-engineering phases, the variable and common requirements are gathered for the whole product line. The goal is to establish a reusable platform. The output of this phase is a set of common and variable requirements for all products in the product line. ==== Analyze domain requirements ==== This activity includes all activities for analyzing the domain with regard to concept requirements. The requirements are categorized and split up into two new activities. The output is a document with the domain analysis. As can be seen in Figure 1 the process of defining common requirements is a parallel process with defining variable requirements. Both activities take place at the same time. ==== Define common requirements ==== Includes all activities for eliciting and documenting the common requirements of the product line, resulting in a document with reusable common requirements. ==== Define variable requirements ==== Includes all activities for eliciting and documenting the variable requirements of the product line, resulting in a document with variable requirements. ==== Design domain ==== This process step consists of activities for defining the reference architecture of the product line. This generates an abstract structure for all products in the product line. ==== Implement domain ==== During this step a detailed design of the reusable components and the implementation of these components are created. ==== Test domain ==== Validates and verifies the reusability of components. Components are tested against their specifications. After successful testing of all components in different use cases and scenarios, the domain engineering phase has been completed. === Phase 3: product engineering === In the final phase a product X is being engineered. This product X uses the commonalities and variability from the domain engineering phase, so product X is being derived from the platform established in the domain engineering phase. It basically takes all common requirements and similarities from the preceding phase plus its own variable requirements. Using the base from the domain engineering phase and the individual requirements of the product engineering phase a complete and new product can be built. After the product has been fully tested and approved, the product X can be delivered. ==== Define product requirements ==== Developing the product requirements specification for the individual product and reuse the requirements from the preceding phase. ==== Design product ==== All activities for producing the product architecture. Makes use of the reference architecture from the step "design domain", it selects and configures the required parts of the reference architecture and incorporates product specific adaptations. ==== Build product ==== During this process the product is built, using selections and configurations of the reusable components. ==== Test product ==== During this step the product is verified and validated against its specifications. A test report gives information about all tests that were carried out, this gives an overview of possible errors in the product. If the product in the next step is not accepted, the process will loop back to "build product", in Figure 1 this is indicated as "[unsatisfied]". ==== Deliver and support product ==== The final step is the acceptance of the final product. If it has been successfully tested and approved to be complete, it can be delivered. If the product does not satisfy to the specifications, it has to be rebuilt and tested again. The next figure shows the overall process of product-family engineering as described above. It is a full process overview with all concepts attached to the different steps. == Process data diagram == On the left side the entire process from the top to bottom has been drawn. All activities on the left side are linked to the concepts on the right side through dotted lines. Every concept has a number, which reflects the association with other concepts. == List of concepts == Below the list with concepts will be explained. Most concept definitions are extracted from Pohl, Bockle, & Linden (2005) and also some new definitions have been added. Table 1: List of concepts == Example == There are some good examples of the use of product family engineering, which were quite successful. The abstract model of product family engineering allows different kinds of uses, most of them are related to the consumer electronics m
Read more →
PerfKitBenchmarker

PerfKit Benchmarker is an open source benchmarking tool used to measure and compare cloud offerings. PerfKit Benchmarker is licensed under the Apache 2 license terms. PerfKit Benchmarker is a community effort involving over 500 participants including researchers, academic institutions and companies together with the originator, Google. == General == PerfKit Benchmarker (PKB) is a community effort to deliver a repeatable, consistent, and open way of measuring Cloud Performance. It supports a growing list of cloud providers including: Alibaba Cloud, Amazon Web Services, CloudStack, DigitalOcean, Google Cloud Platform, Kubernetes, Microsoft Azure, OpenStack, Rackspace, IBM Bluemix (Softlayer). In addition to Cloud Providers to supports container orchestration including Kubernetes [1] and Mesos [2] and local "static" workstations and clusters of computers [3]. The goal is to create an open source living benchmark [framework] that represents how Cloud developers are building applications, evaluating Cloud alternatives, learning how to architect applications for each cloud. Living because it will change and morph quickly as developers change. PerfKit Benchmarker measures the end to end time to provision resources in the cloud, in addition to reporting on the most standard metrics of peak performance, e.g.: latency, throughput, time-to-complete, IOPS. PerfKit Benchmarker reduces the complexity in running benchmarks on supported cloud providers by unified and simple commands. It's designed to operate via vendor provided command line tools. PerfKit Benchmarker contains a canonical set of public benchmarks. All benchmarks are running with default/initial state and configuration (Not tuned to in favor of any providers). This provides a way to benchmark across cloud platforms, while getting a transparent view of application throughput, latency, variance, and overhead. == History == PerfKit Benchmarker (PKB) was started by Anthony F. Voellm, Alain Hamel, and Eric Hankland at Google in 2014. Once an initial "alpha" was in place Anthony F. Voellm and Ivan Santa Maria Filho built a community including ARM, Broadcom, Canonical, CenturyLink, Cisco, CloudHarmony, CloudSpectator, EcoCloud@EPFL, Intel, Mellanox, Microsoft, Qualcomm Technologies, Inc., Rackspace, Red Hat, Tradeworx Inc., and Thesys Technologies LLC. This community worked together behind the scenes in a private GitHub project to create an open way to measure cloud performance. This community released the first public "beta" was released on February 11, 2015, and announced in a blog post at which point the GitHub project was open to everyone. After almost a year and with large adaption (600+ participants on GitHub) the V1.0.0 was released along with a detailed architectural design on December 10, 2015. == Benchmarks == A list of available benchmarks from PerfKitBenchmarker: (The latest set of benchmarks can be found at GitHub readme file.) == Industry participants == Since Google open sourced the PerfKitBenchmarker, it became a community effort from over 30 leading researchers, academic schools and industry companies. Those organizations include: ARM, Broadcom, Canonical, CenturyLink, Cisco, CloudHarmony, Cloud Spectator, EcoCloud@EPFL, Intel, Mellanox, Microsoft, Qualcomm Technologies, Rackspace, Red Hat, and Thesys Technologies. In addition, Stanford and MIT are leading quarterly discussions on default benchmarks and settings proposed by the community. EcoCloud@EPFL is integrating CloudSuite into PerfKit Benchmarker. == Example runs == On Google Cloud Platform On AWS On Azure On Rackspace On a local machine
Read more →
Human visual system model

A human visual system model (HVS model) is used by image processing, video processing and computer vision experts to deal with biological and psychological processes that are not yet fully understood. Such a model is used to simplify the behaviors of what is a very complex system. As our knowledge of the true visual system improves, the model is updated. Psychovisual study is the study of the psychology of vision. The human visual system model can produce desired effects in perception and vision. Examples of using an HVS model include color television, lossy compression, and Cathode-ray tube (CRT) television. Originally, it was thought that color television required too high a bandwidth for the then available technology. Then it was noticed that the color resolution of the HVS was much lower than the brightness resolution; this allowed color to be squeezed into the signal by chroma subsampling. Another example is lossy image compression, like JPEG. Our HVS model says we cannot see high frequency detail, so in JPEG we can quantize these components without a perceptible loss of quality. Similar concepts are applied in audio compression, where sound frequencies inaudible to humans are band-stop filtered. Several HVS features are derived from evolution when we needed to defend ourselves or hunt for food. We often see demonstrations of HVS features when we are looking at optical illusions. == Block diagram of HVS == == Assumptions about the HVS == Low-pass filter characteristic (limited number of rods in human eye): see Mach bands Lack of color resolution (fewer cones in human eye than rods) Motion sensitivity More sensitive in peripheral vision Stronger than texture sensitivity, e.g. viewing a camouflaged animal Texture stronger than disparity – 3D depth resolution does not need to be so accurate Integral Face recognition (babies smile at faces) Depth inverted face looks normal (facial features overrule depth information) Upside down face with inverted mouth and eyes looks normal == Examples of taking advantage of an HVS model == Flicker frequency of film and television using persistence of vision to fool viewer into seeing a continuous image Interlaced television painting half images to give the impression of a higher flicker frequency Color television (chrominance at half resolution of luminance corresponding to proportions of rods and cones in eye) Image compression (difficult to see higher frequencies more harshly quantized) Motion estimation (use luminance and ignore color) Watermarking and Steganography
Read more →
Deep Instinct

Deep Instinct is a cybersecurity company that applies deep learning to cybersecurity. The company implements artificial intelligence to the task of preventing and detecting malware. The company was the recipient of the Technology Pioneer by The World Economic Forum in 2017. Lane Bess has been CEO of the company since 2022. == Overview == In 2015, Deep Instinct was founded by Guy Caspi, Dr. Eli David, and Nadav Maman. The headquarters of the company is located in New York City. In July 2017, NVIDIA became an investor. According to Tom's Hardware, NVIDIA’s investment enabled access to a GPU-based neural network and CUDA platform, which they were using to achieve maximum vulnerability detection rates. As of February 2020, the company had raised $43 million in Series C funding round. In April 2021, Deep Instinct raised $100 million in Series D funding to accelerate growth. == Partnerships == In April 2019, Deep Instinct partnered with Chinese artist, Guo O. Dong on an art project titled, The Persistence of Chaos, consisting of a laptop infected with 6 pieces of malware that represented $95 billion in damages. The art was auctioned with a final bid of $1,345,000. In the same year, Globes reported that, HP Inc partnered with Deep Instinct to launch their security solution HP SureSense, which has been applied to the EliteBook and Zbook devices.
Read more →
Python (programming language)

Python is a high-level, general-purpose programming language that emphasizes code readability, simplicity, and ease-of-writing with the use of significant indentation, "plain English" naming, an extensive ("batteries-included") standard library, and garbage collection. Python supports multiple programming paradigms but with an emphasis on object-oriented programming and dynamic typing. Guido van Rossum began working on Python in the late 1980s as a successor to the ABC programming language. Python 3.0, released in 2008, was a major revision and not completely backward-compatible with earlier versions. Beginning with Python 3.5, capabilities and keywords for typing were added to the language, allowing optional static typing. As of 2026, the Python Software Foundation supports Python 3.10, 3.11, 3.12, 3.13, and 3.14, following the project's annual release cycle and five-year support policy. Python 3.15 is currently in the alpha development phase, and the stable release is expected to launch in October 2026. Earlier versions in the 3.x series have reached end-of-life and no longer receive security updates. Python has gained extensive use in the machine learning community. It is widely taught as an introductory programming language. Since 2003, Python has consistently ranked among the top ten most popular programming languages in the TIOBE Programming Community Index, which ranks programming languages based on searches across 24 platforms. == History == Python was conceived in the late 1980s by Guido van Rossum at Centrum Wiskunde & Informatica (CWI) in the Netherlands. It was designed as a successor to the ABC programming language, which was inspired by SETL, capable of exception handling and interfacing with the Amoeba operating system. Python implementation began in December 1989. Van Rossum first released it in 1991 as Python 0.9.0. Van Rossum assumed sole responsibility for the project, as the lead developer, until 12 July 2018, when he announced his "permanent vacation" from responsibilities as Python's "benevolent dictator for life" (BDFL); this title was bestowed on him by the Python community to reflect his long-term commitment as the project's chief decision-maker. (He has since come out of retirement and is self-titled "BDFL-emeritus".) In January 2019, active Python core developers elected a five-member Steering Council to lead the project. The name Python derives from the British comedy series Monty Python's Flying Circus. (See § Naming.) Python 2.0 was released on 16 October 2000, featuring many new features such as list comprehensions, cycle-detecting garbage collection, reference counting, and Unicode support. Python 2.7's end-of-life was initially set for 2015, and then postponed to 2020 out of concern that a large body of existing code could not easily be forward-ported to Python 3. It no longer receives security patches or updates. While Python 2.7 and older versions are officially unsupported, a different unofficial Python implementation, PyPy, continues to support Python 2, i.e., "2.7.18+" (plus 3.11), with the plus signifying (at least some) "backported security updates". Python 3.0 was released on 3 December 2008, and was a major revision and not completely backward-compatible with earlier versions, with some new semantics and changed syntax. Python 2.7.18, released in 2020, was the last release of Python 2. Several releases in the Python 3.x series have added new syntax to the language, and made a few (considered very minor) backward-incompatible changes. As of May 2026, Python 3.14.5 is the latest stable release. All older 3.x versions had a security update down to Python 3.9.24 then again with 3.9.25, the final version in 3.9 series. Python 3.10 is, since November 2025, the oldest supported branch. Python 3.15 has an alpha released, and Android has an official downloadable executable available for Python 3.14. Releases receive two years of full support followed by three years of security support. == Design philosophy and features == Python is a multi-paradigm programming language. Object-oriented programming and structured programming are fully supported, and many of their features support functional programming and aspect-oriented programming – including metaprogramming and metaobjects. Many other paradigms are supported via extensions, including design by contract and logic programming. Python is often referred to as a 'glue language' because it is purposely designed to be able to integrate components written in other languages. Python uses dynamic typing and a combination of reference counting and a cycle-detecting garbage collector for memory management. It uses dynamic name resolution (late binding), which binds method and variable names during program execution. Python's design offers some support for functional programming in the "Lisp tradition". It has filter, map, and reduce functions; list comprehensions, dictionaries, sets, and generator expressions. The standard library has two modules (itertools and functools) that implement functional tools borrowed from Haskell and Standard ML. Python's core philosophy is summarized in the Zen of Python (PEP 20) written by Tim Peters, which includes aphorisms such as these: Explicit is better than implicit. Simple is better than complex. Readability counts. Special cases aren't special enough to break the rules. Although practicality beats purity, errors should never pass silently, unless explicitly silenced. There should be one-- and preferably only one --obvious way to do it. However, Python has received criticism for violating these principles and adding unnecessary language bloat. Responses to these criticisms note that the Zen of Python is a guideline rather than a rule. The addition of some new features had been controversial: Guido van Rossum resigned as Benevolent Dictator for Life after conflict about adding the assignment expression operator in Python 3.8. Nevertheless, rather than building all functionality into its core, Python was designed to be highly extensible through modules. This compact modularity has made it particularly popular as a means of adding programmable interfaces to existing applications. Van Rossum's vision of a small core language with a large standard library and an easily extensible interpreter stemmed from his frustrations with ABC, which represented the opposite approach. Python claims to strive for a simpler, less-cluttered syntax and grammar, while giving developers a choice in their coding methodology. Python lacks do .. while loops, which Rossum considered harmful. In contrast to Perl's motto "there is more than one way to do it", Python advocates an approach where "there should be one – and preferably only one – obvious way to do it". In practice, however, Python provides many ways to achieve a given goal. There are at least three ways to format a string literal, with no certainty as to which one a programmer should use. Alex Martelli is a Fellow at the Python Software Foundation and Python book author; he wrote that "To describe something as 'clever' is not considered a compliment in the Python culture." Python's developers typically prioritize readability over performance. For example, they reject patches to non-critical parts of the CPython reference implementation that would offer increases in speed that do not justify the cost of clarity and readability. Execution speed can be improved by moving speed-critical functions to extension modules written in languages such as C, or by using a just-in-time compiler like PyPy. Also, it is possible to transpile to other languages. However, this approach either fails to achieve the expected speed-up, since Python is a very dynamic language, or only a restricted subset of Python is compiled (with potential minor semantic changes). Python is meant to be a fun language to use. This goal is reflected in the name – a tribute to the British comedy group Monty Python – and in playful approaches to some tutorials and reference materials. For instance, some code examples use the terms "spam" and "eggs" (in reference to a Monty Python sketch), rather than the typical terms "foo" and "bar". A common neologism in the Python community is pythonic, which has a broad range of meanings related to program style: Pythonic code may use Python idioms well; be natural or show fluency in the language; or conform with Python's minimalist philosophy and emphasis on readability. === Enhancement Proposals === Python Enhancement Proposals are a design document for either providing information to the Python community, or proposal for new feature in Python. PEPs are intented to explain new processes in Python, provide naming conventions or document the processes in the language. PEPs are overseen by Python Steering Council. There are 3 kinds of PEPs, with those are being standards track PEP, Informational PEP and Process PEPs which has their own unique meanings. They were firstly introduced in 2000, in
Read more →
Gitter

Gitter is an open-source instant messaging and chat room system for developers and users of GitLab and GitHub repositories. Gitter is provided as software as a service, with a free option providing all basic features and the ability to create a single private chat room, and paid subscription options for individuals and organisations, which allows them to create arbitrary numbers of private chat rooms. Individual chat rooms can be created for individual Git repositories on GitHub. Chatroom privacy follows the privacy settings of the associated GitHub repository: thus, a chatroom for a private (i.e. members-only) GitHub repository is also private to those with access to the repository. A graphical badge linking to the chat room can then be placed in the git repository's README file, bringing it to the attention of all users and developers of the project. Users can chat in the chat rooms, or access private chat rooms for repositories they have access to, by logging into Gitter via GitHub. Gitter is similar to Slack. Like Slack, it automatically logs all messages in the cloud. In late 2020, New Vector Limited acquired Gitter from GitLab, and announced Gitter's features would eventually be moved to New Vector's flagship product, Element, thereby replacing Gitter entirely. On February 13, 2023, Gitter migrated their service to a custom-branded Matrix instance that uses Element for its web interface. == Features prior to Migration to Matrix == Gitter supports: Notifications, which are batched up on mobile devices to avoid annoyance Inline media files Viewing and subscribing to ("starring") multiple chat rooms in one web browser tab Linking to individual files in the linked git repository Linking to GitHub issues (by typing # and then the issue number) in the linked Git repository, with hovercards showing the details of the issue GitHub-flavored Markdown in chat messages Online status for users User hovercards, based on their GitHub profiles and statistics (number of GitHub followers, etc.) Browsable and searchable message archives, grouped by month Connection from IRC clients Gitter on iOS support authentication using GitHub or Twitter === Integrations with non-GitHub sites and applications === Gitter integrates with Trello, Jenkins, Travis CI, Drone (software), Heroku, and Bitbucket, among others. === Apps === Official Gitter apps for Windows, Mac, Linux, iOS and Android are available. === Account registration === Like other chat technologies, Gitter allows clients to instant message each other. It allows people to authenticate using a GitHub account and join a chatroom from a web browser, thus not requiring one to install any software, or create additional online accounts. == History == Gitter was created by some developers who were initially trying to create a generic web-based chat product, but then wrote extra code to hook their chat application up to GitHub to meet their own needs, and realised that they could turn the combined product into a viable specialist product in its own right. Gitter came out of beta in 2014. During the beta period, Gitter delivered 1.8 million chat messages. On March 15, 2017, GitLab announced the acquisition of Gitter. Included in the announcement was the stated intent that Gitter would continue as a standalone project. It was published as open source under an MIT License as of June 2017. On September 30, 2020, New Vector Limited acquired Gitter from GitLab, and announced upcoming support for the Matrix protocol in Gitter, which went live by the end of the year. Gitter's features would eventually be moved to New Vector's flagship product, Element, thereby replacing Gitter entirely. On February 13, 2023, Gitter migrated their service to a custom-branded Matrix instance that uses Element for its web interface. == Implementation prior to Migration to Matrix == The Gitter web application is implemented entirely in JavaScript, with the back end being implemented on Node.js. The source code to the web application was formerly proprietary (it was open-sourced in June 2017), although Gitter had made numerous auxiliary projects available as open-source software, such as an IRC bridge for IRC users who prefer using IRC client applications (and their extra features) to converse in the Gitter chat rooms.
Read more →
Mentimeter

Mentimeter (or Menti for short) is a Swedish company based in Stockholm that develops and maintains an eponymous app used to create presentations with real-time feedback. == Foundation and background == Based in Stockholm, Sweden, the Mentimeter app was started by Swedish entrepreneur Johnny Warström and Niklas Ingvar as a response to unproductive meetings. The initial start-up budget was $500,000 raised by a group of prominent investors, including Per Appelgren in 2014, following the market's tendency to invest in Scandinavia. The app also focuses on online collaboration for the education sector, allowing students or public members to answer questions anonymously. The app enables users to share knowledge and real-time feedback on mobile devices with presentations, polls or brainstorming sessions in classes, meetings, gatherings, conferences and other group activities. == Achievements == By 2021, Mentimeter had over 270 million users and was one of Sweden's fastest-growing startups. The company also ranked #10 on 20 Fastest Growing 500 Startups Batch 16 Companies. It was ranked Stockholm's fastest growing company of the 2018 edition of the DI Gasell Award. Mentimeter has a freemium business model.
Read more →
Telligent Community

Telligent Community is a community and collaboration software platform developed by Telligent Systems and was first released in 2004. Telligent Community is built on the Telligent Evolution platform, with a variety of core applications running on top of it such as blogs, forums, media galleries, and wikis. Additional applications from third parties using the API's and REST stack can be installed or integrated with the platform. Telligent Community is built with ASP.NET, C#, and Microsoft SQL Server. It is available as downloadable software that can be installed on a web server or via hosting providers. The current version is Verint Community 12.0 which was released February 2012. The product used to be named Community Server before being rebranded as part of the 5.0 release. == History == Telligent Systems was founded by Rob Howard in 2004, who was previously part of Microsoft's ASP.NET team. Telligent introduced its first product, Community Server, in the fall of 2004. Community Server was one of the first integrated community platforms that brought together blogs, photo galleries, wikis, forums, user profiles and more. Community Server was based on the merger of three then-widely used open source ASP.NET projects: the ASP.NET Forums, nGallery photo gallery, and .Text blog engine. The people behind those projects (Scott Watermasysk, Jason Alexander, and Rob Howard) joined together as Telligent Systems and along with several other software developers created Community Server 1.0. Between 2004 and 2009 Community Server steadily grew in scope, features, and capabilities. In 2008 Telligent Systems released a second version of Community Server that targeted as an Enterprise Social Software platform used to create and manage internal employee communities and intranets. Originally branded as Community Server Evolution this was later renamed Telligent Enterprise. Telligent also announced a new Enterprise Reporting platform at its first Community Server Developers Conference in 2008, which was later renamed Harvest. It was one of the first analytics suites for enterprise collaboration software, and provides social analytics including sentiment analysis, social fingerprints, and buzz analysis on social networking sites such as Twitter. Telligent rebranded all of its products on June 23, 2009 at the Enterprise 2.0 conference when it launched its new Evolution platform product suite. Community Server became known as Telligent Community, Community Server Evolution became known as Telligent Enterprise and the underlying platform that both run on is now referred to as Telligent Evolution. The Social Analytics suite was renamed Telligent Analytics.
Read more →
Seed (programming)

Seed is a JavaScript interpreter and a library of the GNOME project to create standalone applications in JavaScript. It uses the JavaScript engine JavaScriptCore of the WebKit project. It is possible to easily create modules in C. Seed is integrated in GNOME since the 2.28 version and is used by two games in the GNOME Games package. It is also used by the Web web browser for the design of its extensions. The module is also officially supported by the GTK+ project. == Hello world in Seed == This example uses the standard output to output the string "Hello, World". == A program using GTK+ == This code shows an empty window named "Example". == Modules == To use a module, just instantiate a class having for name imports. followed by the name of the module respecting the case sensitivity. The modules using GObject Introspection, who starts by imports.gi. : Gtk Gst GObject Gio Clutter GLib Gdk WebKit GdkPixbuf, GdkPixbuf Libxml Cairo DBus MPFR Os (system library) Canvas (using Cairo) multiprocessing readline Archived 2009-11-09 at the Wayback Machine ffi sqlite sandbox Archived 2009-11-09 at the Wayback Machine == List of the Seed versions == The names of the versions of Seed are albums of famous rock bands.
Read more →
StatCrunch

StatCrunch is a web-based statistical software application from Pearson Education. StatCrunch was originally created for use in college statistics courses. As a full-featured statistics package, it is now also used for research and for other statistical analysis purposes. == History == American statistics professor Webster West created StatCrunch in 1997. Over the next 19 years West assisted by others added many more statistical procedures and graphing capabilities, and made user interface improvements. In 2005, West received two awards for StatCrunch: the CAUSEweb Resource of the Year Award and the MERLOT Classics Award. In 2013, the StatCrunch Java code was rewritten in JavaScript in order to avoid Java browser security problems, and so that it would run on iOS and Android. In 2015, new ways of importing data were added, including importing multi-page data directly from Wikipedia tables and other Web sources, and also importing with drag-and-drop for various data formats. In 2016, StatCrunch was acquired by Pearson Education, which had already been serving as the primary distributor of StatCrunch for several years. == Software == A StatCrunch license is included with many of Pearson's statistical textbooks. Because StatCrunch is a web application, it works on multiple platforms, including Windows, macOS, iOS, and Android. Data in StatCrunch is represented in a "data table" view, which is similar to a spreadsheet view, but unlike spreadsheets, the cells in a data table can only contain numbers or text. Formulas cannot be stored in these cells. There are many ways to import data into StatCrunch. Data can be typed directly into cells in the data table. Entire blocks of data may be cut-and-pasted into the data table. Text files (.csv, .txt, etc.) and Microsoft Excel files (.xls and .xlsx) can be drag-and-dropped into the data table. Data can be pulled into StatCrunch directly from Wikipedia tables or other Web tables, including multi-page tables. Data can be loaded directly from Google Drive and Dropbox. Shared data sets saved by other StatCrunch community users can be searched for by title or keyword and opened in a data table. Graphs, results, and reports created by StatCrunch can be shared with other users, in addition to the sharing of data sets. StatCrunch has a library of data transformation functions. StatCrunch can also recode and reorganize data. All data is stored in memory, and all processing happens on the client, so response is fast, even with large data sets. StatCrunch can interact with multiple graphs simultaneously. If a user selects a data point on one graph, then that same data point is highlighted on all other displayed graphs. In addition to standard statistical and graphing procedures, StatCrunch has a collection of about forty "applets" which illustrate statistical concepts interactively.
Read more →
Springpad

Springpad was a free online application and web service that allowed its registered users to save, organize and share collected ideas and information. As users added content to their Springpad accounts, the application automatically identified and categorized it, then generated additional snippets based on the types of objects added—for example, listing price comparisons for products and showtimes for movies. Springpad was also available as apps on the iPad, iPhone and Android that synchronized with the Web interface. Springpad was bundled on new Toshiba notebook computers through a Web application subscription service. On May 23, 2014, Springpad announced that it would cease operations on June 25, 2014. The company then allowed users to export their data (as JSON and read-only HTML formats), or to automatically migrate it to Evernote accounts before the expiration date. == Features == Springpad users could use the main site interface which uses HTML5 from most browsers or use the smartphone app to capture notes, tasks, or lists which were then added to the user's "My Stuff", the user's personal database or collection. Additionally Springpad let users look up items of interest which were then automatically categorized based on type or manually categorized by the user. Category types included recipes, movies, products, restaurants and wine. Events could also be added to Springpad, and if the user used Google Calendar, they could opt to sync the event to it. In addition to the smartphone app and site, Springpad could be used via browser extension for Google Chrome, or the Springpad Clipper, a bookmarklet to analyze webpages and clip relevant information from them—for example, the ingredients needed for a recipe—or to add the site as a normal bookmark. Another way users could add content to their Springpad "My Stuff" was by emailing entries to an email address specified on Springpad registration. Springpad's smartphone apps could be used to scan barcodes to identify products, save them to the user's "My Stuff", and automatically generate additional product information and links. The mobile app could also save images taken with the phone's camera, and locate nearby businesses. With most of the content added to a user's "My Stuff", relevant news, useful links and other helpful information could be viewed. Users could also attach additional notes and images to content they had already saved, and could add reminders and alerts which could be emailed to the user or texted to their phone. Springpad also added alerts to its own Alerts section for relevant news, deals or coupons for specific products users added. For additional organization, anything added to Springpad could also be tagged. Users could also add entries to "Notebooks" to separate content by projects, or any other way they wished. Each Notebook included a section called a "Board", which acted as a pin board where users could "pin" content they'd added to the Notebook, allowing them to visually lay out items. If the user added a map to the Board and had entries that included an address, Springpad could automatically point out entries on the map. By default, everything added to Springpad was private. However users could change the privacy settings for each of the types of items added, decide to make specific items public and shareable on Facebook and Twitter, add them to their public page, or keep them private but links to them with specific people.
Read more →
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
Read more →
Cloud testing

Cloud testing is a form of software testing in which web applications use cloud computing environments (a "cloud") to simulate real-world user traffic. == Steps == Companies simulate real world Web users by using cloud testing services that are provided by cloud service vendors such as Advaltis, Compuware, HP, Keynote Systems, Neotys, RadView and SOASTA. Once user scenarios are developed and the test is designed, these service providers leverage cloud servers (provided by cloud platform vendors such as Amazon.com, Google, Rackspace, Microsoft, etc.) to generate web traffic that originates from around the world. Once the test is complete, the cloud service providers deliver results and analytics back to corporate IT professionals through real-time dashboards for a complete analysis of how their applications and the internet will perform during peak volumes. == Applications == Cloud testing is often seen as only performance or load tests, however, as discussed earlier it covers many other types of testing. Cloud computing itself is often referred to as the marriage of software as a service (SaaS) and utility computing. In regard to test execution, the software offered as a service may be a transaction generator and the cloud provider's infrastructure software, or may just be the latter. Distributed Systems and Parallel Systems mainly use this approach for testing, because of their inherent complex nature. D-Cloud is an example of such a software testing environment. == Tools == Leading cloud computing service providers include, among others, Amazon, Microsoft, Google, RadView, Skytap, HP and SOASTA. == Benefits == The ability and cost to simulate web traffic for software testing purposes has been an inhibitor to overall web reliability. The low cost and accessibility of the cloud's extremely large computing resources provides the ability to replicate real world usage of these systems by geographically distributed users, executing wide varieties of user scenarios, at scales previously unattainable in traditional testing environments. Minimal start-up time along with quality assurance can be achieved by cloud testing. Following are some of the key benefits: Reduction in capital expenditure Highly scalable
Read more →
Lingua Libre

Lingua Libre is an online collaborative project and tool by the Wikimédia France association, which aims to build a collaborative, multilingual, audiovisual speech corpus under a free license. It mostly consists of a rapid recording online service which allows the user to chain hundreds of recordings. Contributors have produced content in 310+ languages. == Description == Lingua Libre enables the recording of words, phrases or sentences of any language, oral (audio recording) or signed (video recording). Words are presented to the speaker in the form of a list, created on the spot, in advance, or by reusing an existing Wikimedia category. The speaker simply reads the word displayed on the screen, and the software moves on to the next word when it detects a silence after the read word. This principle, borrowed from the open source software Shtooka recorder with the help of its creator, Nicolas Vion, makes it possible to record several hundreds of words per hour. The recordings are then uploaded automatically from the web client to the Wikimedia Commons media library. In spring 2021, Lingua Libre was offline due to a fire in Strasbourg, but no audio recordings were lost. === Use of the recordings === The recordings can be consulted either on Lingua Libre or on Commons. They are mainly used on other Wikimedia projects, for example to illustrate entries on Wiktionaries or proper nouns in Wikipedia articles. The re-use of the recordings in a language teaching context is envisaged. Language learners can freely download pronunciations and use them on GoldenDict, a popular dictionary software. Thus, audio recordings can be used as “Pronunciation Dictionaries” on GoldenDict without needing internet connection. The recordings are also reused in Natural Language Processing projects, for example to drive Mozilla's DeepSpeech speech recognition engines. == Versions == Lingua Libre was initiated on January 23, 2015 and has had three successive versions: === Lingua Libre v.1 (2016) === As part of the Languages of France project, which aims to document and promote the regional languages of France on Wikimedia and Internet projects in general, the conception of Lingua Libre started in November 2015, partly funded by the DGLFLF (General Delegation for the French language and the languages of France). The first version of the project was launched in August 2016. Only suitable for audio recording, Lingua Libre was shown during a workshop on Occitan language in December 2016, and then presented to the online Wikimedia community and at international events in 2017. === Lingua Libre v.2 (2018) === A complete rebuilding was launched at the end of 2017. The new version of Lingua Libre is based on MediaWiki, uses Wikibase and OAuth to better integrate into the Wikimedia environment. The interface is translated via Translatewiki.net so that the project can be used by a large number of communities. The new version of the site was ready in June 2018 and opened to the public in August 2018. === Lingua Libre v.2.2 (2020) === In 2020, important changes were made to the platform; a new look was developed especially for the site, the .org domain replaced the .fr domain used until then, and added support for sign languages through video recording. == Statistics == In the first two years of the project's launch, approximately 10,000 recordings were made. The transition to v.2 was accompanied by a sharp increase in the contributions. The number of recordings multiplied by 10 in less than a year, exceeding the 100,000 threshold in May 2019. These recordings were made by 127 speakers in almost 50 languages. By September 2020, the platform had more than 300,000 recordings in 90 languages with more than 350 speakers. The 500,000 recordings milestone was reached in June 2021, thanks to 540 speakers of 120 languages.
Read more →