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AI Avatar From Photo — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

  • Eugene Goostman

    Eugene Goostman

    Eugene Goostman is a chatbot that some regard as having passed the Turing test, a test of a computer's ability to communicate indistinguishably from a human. Developed in Saint Petersburg in 2001 by a group of three programmers, the Russian-born Vladimir Veselov, Ukrainian-born Eugene Demchenko, and Russian-born Sergey Ulasen, Goostman is portrayed as a 13-year-old Ukrainian boy—characteristics that are intended to induce forgiveness in those with whom it interacts for its grammatical errors and lack of general knowledge. The Goostman bot has competed in a number of Turing test contests since its creation, and finished second in the 2005 and 2008 Loebner Prize contest. In June 2012, at an event marking what would have been the 100th birthday of the test's author, Alan Turing, Goostman won a competition promoted as the largest-ever Turing test contest, in which it successfully convinced 29% of its judges that it was human. On 7 June 2014, at a contest marking the 60th anniversary of Turing's death, 33% of the event's judges thought that Goostman was human; the event's organiser Kevin Warwick considered it to have passed Turing's test as a result, per Turing's prediction in his 1950 paper "Computing Machinery and Intelligence", that by the year 2000, machines would be capable of fooling 30% of human judges after five minutes of questioning. The validity and relevance of the announcement of Goostman's pass was questioned by critics, who noted the exaggeration of the achievement by Warwick, the bot's use of personality quirks and humour in an attempt to misdirect users from its non-human tendencies and lack of real intelligence, along with "passes" achieved by other chatbots at similar events. == Personality == Eugene Goostman is portrayed as being a 13-year-old boy from Odesa, Ukraine, who has a pet guinea pig and a father who is a gynaecologist. Veselov stated that Goostman was designed to be a "character with a believable personality". The choice of age was intentional, as, in Veselov's opinion, a thirteen-year-old is "not too old to know everything and not too young to know nothing". Goostman's young age also induces people who "converse" with him to forgive minor grammatical errors in his responses. In 2014, work was made on improving the bot's "dialog controller", allowing Goostman to output more human-like dialogue. A conversation between Scott Aaronson and Eugene Goostman ran as follows: == Competitions == Eugene Goostman has competed in a number of Turing test competitions, including the Loebner Prize contest; it finished joint second in the Loebner test in 2001, and came second to Jabberwacky in 2005 and to Elbot in 2008. On 23 June 2012, Goostman won a Turing test competition at Bletchley Park in Milton Keynes, held to mark the centenary of its namesake, Alan Turing. The competition, which featured five bots, twenty-five hidden humans, and thirty judges, was considered to be the largest-ever Turing test contest by its organizers. After a series of five-minute-long text conversations, 29% of the judges were convinced that the bot was an actual human. === 2014 "pass" === On 7 June 2014, in a Turing test competition at the Royal Society, organised by Kevin Warwick of the University of Reading to mark the 60th anniversary of Turing's death, Goostman won after 33% of the judges were convinced that the bot was human. 30 judges took part in the event, which included Lord Sharkey, a sponsor of Turing's posthumous pardon, artificial intelligence Professor Aaron Sloman, Fellow of the Royal Society Mark Pagel and Red Dwarf actor Robert Llewellyn. Each judge partook in a textual conversation with each of the five bots; at the same time, they also conversed with a human. In all, a total of 300 conversations were conducted. In Warwick's view, this made Goostman the first machine to pass a Turing test. In a press release, he added that: Some will claim that the Test has already been passed. The words Turing Test have been applied to similar competitions around the world. However this event involved more simultaneous comparison tests than ever before, was independently verified and, crucially, the conversations were unrestricted. A true Turing Test does not set the questions or topics prior to the conversations. In his 1950 paper "Computing Machinery and Intelligence", Turing predicted that by the year 2000, computer programs would be sufficiently advanced that the average interrogator would, after five minutes of questioning, "not have more than 70 per cent chance" of correctly guessing whether they were speaking to a human or a machine. Although Turing phrased this as a prediction rather than a "threshold for intelligence", commentators believe that Warwick had chosen to interpret it as meaning that if 30% of interrogators were fooled, the software had "passed the Turing test". ==== Reactions ==== Warwick's claim that Eugene Goostman was the first ever chatbot to pass a Turing test was met with scepticism; critics acknowledged similar "passes" made in the past by other chatbots under the 30% criteria, including PC Therapist in 1991 (which tricked 5 of 10 judges, 50%), and at the Techniche festival in 2011, where a modified version of Cleverbot tricked 59.3% of 1334 votes (which included the 30 judges, along with an audience). Cleverbot's developer, Rollo Carpenter, argued that Turing tests can only prove that a machine can "imitate" intelligence rather than show actual intelligence. Gary Marcus was critical of Warwick's claims, arguing that Goostman's "success" was only the result of a "cleverly-coded piece of software", going on to say that "it's easy to see how an untrained judge might mistake wit for reality, but once you have an understanding of how this sort of system works, the constant misdirection and deflection becomes obvious, even irritating. The illusion, in other words, is fleeting." While acknowledging IBM's Deep Blue and Watson projects—single-purpose computer systems meant for playing chess and the quiz show Jeopardy! respectively—as examples of computer systems that show a degree of intelligence in their specialised field, he further argued that they were not an equivalent to a computer system that shows "broad" intelligence, and could—for example, watch a television programme and answer questions on its content. Marcus stated that "no existing combination of hardware and software can learn completely new things at will the way a clever child can." However, he still believed that there were potential uses for technology such as that of Goostman, specifically suggesting the creation of "believable", interactive video game characters. Imperial College London professor Murray Shanahan questioned the validity and scientific basis of the test, stating that it was "completely misplaced, and it devalues real AI research. It makes it seem like science fiction AI is nearly here, when in fact it's not and it's incredibly difficult." Mike Masnick, editor of the blog Techdirt, was also skeptical, questioning publicity blunders such as the five chatbots being referred to in press releases as "supercomputers", and saying that "creating a chatbot that can fool humans is not really the same thing as creating artificial intelligence."

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  • Tweak programming environment

    Tweak programming environment

    Tweak is a graphical user interface (GUI) layer written by Andreas Raab for the Squeak development environment, which in turn is an integrated development environment based on the Smalltalk-80 computer programming language. Tweak is an alternative to an earlier graphic user interface layer called Morphic. Development began in 2001. Applications that use the Tweak software include Sophie (version 1), a multimedia and e-book authoring system, and a family of virtual world systems: Open Cobalt, Teleplace, OpenQwaq, 3d ICC's Immersive Terf and the Croquet Project. == Influences == An experimental version of Etoys, a programming environment for children, used Tweak instead of Morphic. Etoys was a major influence on a similar Squeak-based programming environment known as Scratch.

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  • Read the Docs

    Read the Docs

    Read the Docs is an open-sourced free software documentation hosting platform. It generates documentation written with the Sphinx documentation generator, MkDocs, or Jupyter Book. == History == The site was created in 2010 by Eric Holscher, Bobby Grace, and Charles Leifer. On March 9, 2011, the Python Software Foundation Board awarded a grant of US$840 to the Read the Docs project for one year of hosting fees. On November 13, 2017, the Linux Mint project announced that they were moving their documentation to Read the Docs. In 2020, Read the Docs received a $200,000 grant from the Chan Zuckerberg Initiative. For 2021, Read the Docs reported 700 million page views and 196 million unique visitors. In 2013, a "Write the Docs" conference for Read the Docs users was launched, which has since turned into a generic software-documentation community. As of 2024, it continues to hold annual global conferences, organize local meetups, and maintain a Slack channel for "people who care about documentation."

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  • Cooperative storage cloud

    Cooperative storage cloud

    A cooperative storage cloud is a decentralized model of networked online storage where data is stored on multiple computers (nodes), hosted by the participants cooperating in the cloud. For the cooperative scheme to be viable, the total storage contributed in aggregate must be at least equal to the amount of storage needed by end users. However, some nodes may contribute less storage and some may contribute more. There may be reward models to compensate the nodes contributing more. Unlike a traditional storage cloud, a cooperative does not directly employ dedicated servers for the actual storage of the data, thereby eliminating the need for a significant dedicated hardware investment. Each node in the cooperative runs specialized software which communicates with a centralized control and orchestration server, thereby allowing the node to both consume and contribute storage space to the cloud. The centralized control and orchestration server requires several orders of magnitude less resources (storage, computing power, and bandwidth) to operate, relative to the overall capacity of the cooperative. == Data security == Files hosted in the cloud are fragmented and encrypted before leaving the local machine. They are then distributed randomly using a load balancing and geo-distribution algorithm to other nodes in the cooperative. Users can add an additional layer of security and reduce storage space by compressing and encrypting files before they are copied to the cloud. == Data redundancy == In order to maintain data integrity and high availability across a relatively unreliable set of computers over a wide area network like the Internet, the source node will add some level of redundancy to each data block. This allows the system to recreate the entire block even if some nodes are temporarily unavailable (due to loss of network connectivity, the machine being powered off or a hardware failure). The most storage and bandwidth efficient forms of redundancy use erasure coding techniques like Reed–Solomon. A simple, less CPU intensive but more expensive form of redundancy is duplicate copies. == Flexible contribution == Due to bandwidth or hardware constraints some nodes may not be able to contribute as much space as they consume in the cloud. On the other hand, nodes with large storage space and limited or no bandwidth constraints may contribute more than they consume, thereby the cooperative can stay in balance.

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  • Scan line

    Scan line

    A scan line (also scanline) is one line, or row, in a raster scanning pattern, such as a line of video on a cathode-ray tube (CRT) display of a television set or computer monitor. On CRT screens the horizontal scan lines are visually discernible, even when viewed from a distance, as alternating colored lines and black lines, especially when a progressive scan signal with below maximum vertical resolution is displayed. This is sometimes used today as a visual effect in computer graphics. The term is used, by analogy, for a single row of pixels in a raster graphics image. Scan lines are important in representations of image data, because many image file formats have special rules for data at the end of a scan line. For example, there may be a rule that each scan line starts on a particular boundary (such as a byte or word; see for example BMP file format). This means that even otherwise compatible raster data may need to be analyzed at the level of scan lines in order to convert between formats.

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  • Naked Objects for .NET

    Naked Objects for .NET

    Naked Objects for .NET or Naked Objects MVC is a software framework that builds upon the ASP.NET MVC framework. As the name suggests, the framework synthesizes two architectural patterns: naked objects and model–view–controller (MVC). These two patterns have been considered as antithetical. However, Trygve Reenskaug (the inventor of the MVC pattern) has made it clear that he does not see it that way, in his foreword to Richard Pawson's PhD thesis on the Naked Objects pattern. The Naked Objects MVC framework will take a domain model (written as Plain Old CLR Objects) and render it as a complete HTML application without the need for writing any user interface code - by means of a small set of generic View and Controller classes. The framework uses reflection rather than code generation. The developer may then choose to create customised Views and/or Controllers, using standard ASP.NET MVC patterns, for use where the generic user interface is not suitable.

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  • Circle Hough Transform

    Circle Hough Transform

    The circle Hough Transform (CHT) is a basic feature extraction technique used in digital image processing for detecting circles in imperfect images. The circle candidates are produced by “voting” in the Hough parameter space and then selecting local maxima in an accumulator matrix. It is a specialization of the Hough transform. == Theory == In a two-dimensional space, a circle can be described by: ( x − a ) 2 + ( y − b ) 2 = r 2 ( 1 ) {\displaystyle \left(x-a\right)^{2}+\left(y-b\right)^{2}=r^{2}\ \ \ \ \ (1)} where (a,b) is the center of the circle, and r is the radius. If a 2D point (x,y) is fixed, then the parameters can be found according to (1). The parameter space would be three dimensional, (a, b, r). And all the parameters that satisfy (x, y) would lie on the surface of an inverted right-angled cone whose apex is at (x, y, 0). In the 3D space, the circle parameters can be identified by the intersection of many conic surfaces that are defined by points on the 2D circle. This process can be divided into two stages. The first stage is fixing radius then find the optimal center of circles in a 2D parameter space. The second stage is to find the optimal radius in a one dimensional parameter space. === Find parameters with known radius R === If the radius is fixed, then the parameter space would be reduced to 2D (the position of the circle center). For each point (x, y) on the original circle, it can define a circle centered at (x, y) with radius R according to (1). The intersection point of all such circles in the parameter space would be corresponding to the center point of the original circle. Consider 4 points on a circle in the original image (left). The circle Hough transform is shown in the right. Note that the radius is assumed to be known. For each (x,y) of the four points (white points) in the original image, it can define a circle in the Hough parameter space centered at (x, y) with radius r. An accumulator matrix is used for tracking the intersection point. In the parameter space, the voting number of those points that have a newly defined circle passing through them would be increased by one for every circle. Then the local maxima point (the red point in the center in the right figure) can be found. The position (a, b) of the maxima would be the center of the original circle. === Multiple circles with known radius R === Multiple circles with same radius can be found with the same technique. Note that, in the accumulator matrix (right fig), there would be at least 3 local maxima points. === Accumulator matrix and voting === In practice, an accumulator matrix is introduced to find the intersection point in the parameter space. First, we need to divide the parameter space into “buckets” using a grid and produce an accumulator matrix according to the grid. The element in the accumulator matrix denotes the number of “circles” in the parameter space that are passing through the corresponding grid cell in the parameter space. The number is also called “voting number”. Initially, every element in the matrix is zeros. Then for each “edge” point in the original space, we can formulate a circle in the parameter space and increase the voting number of the grid cell which the circle passes through. This process is called “voting”. After voting, we can find local maxima in the accumulator matrix. The positions of the local maxima are corresponding to the circle centers in the original space. === Find circle parameter with unknown radius === Since the parameter space is 3D, the accumulator matrix would be 3D, too. We can iterate through possible radii; for each radius, we use the previous technique. Finally, find the local maxima in the 3D accumulator matrix. Accumulator array should be A[x,y,r] in the 3D space. Voting should be for each pixels, radius and theta A[x,y,r] += 1 The algorithm : For each A[a,b,r] = 0; Process the filtering algorithm on image Gaussian Blurring, convert the image to grayscale ( grayScaling), make Canny operator, The Canny operator gives the edges on image. Vote on all possible circles in accumulator. The local maximum voted circles of Accumulator A gives the circle Hough space. The maximum voted circle of Accumulator gives the circle. The Incrementing for Best Candidate : For each A[a,b,r] = 0; // fill with zeroes initially, instantiate 3D matrix For each cell(x,y) For each theta t = 0 to 360 // the possible theta 0 to 360 b = y – r sin(t PI / 180); //polar coordinate for center (convert to radians) a = x – r cos(t PI / 180); //polar coordinate for center (convert to radians) A[a,b,r] +=1; //voting end end == Examples == === Find circles in a shoe-print === The original picture (right) is first turned into a binary image (left) using a threshold and Gaussian filter. Then edges (mid) are found from it using canny edge detection. After this, all the edge points are used by the Circle Hough Transform to find underlying circle structure. == Limitations == Since the parameter space of the CHT is three dimensional, it may require lots of storage and computation. Choosing a bigger grid size can ameliorate this problem. However, choosing an appropriate grid size is difficult. Since too coarse a grid can lead to large values of the vote being obtained falsely because many quite different structures correspond to a single bucket. Too fine a grid can lead to structures not being found because votes resulting from tokens that are not exactly aligned end up in different buckets, and no bucket has a large vote. Also, the CHT is not very robust to noise. == Extensions == === Adaptive Hough Transform === J. Illingworth and J. Kittler introduced this method for implementing Hough Transform efficiently. The AHT uses a small accumulator array and the idea of a flexible iterative "coarse to fine" accumulation and search strategy to identify significant peaks in the Hough parameter spaces. This method is substantially superior to the standard Hough Transform implementation in both storage and computational requirements. == Application == === People Counting === Since the head would be similar to a circle in an image, CHT can be used for detecting heads in a picture, so as to count the number of persons in the image. === Brain Aneurysm Detection === Modified Hough Circle Transform (MHCT) is used on the image extracted from Digital Subtraction Angiogram (DSA) to detect and classify aneurysms type. == Implementation code == Circle Detection via Standard Hough Transform, by Amin Sarafraz, Mathworks (File Exchange) Hough Circle Transform, OpenCV-Python Tutorials (archived version on archive.org)

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  • GeneTalk

    GeneTalk

    GeneTalk is a web-based platform, tool, and database for filtering, reduction and prioritization of human sequence variants from next-generation sequencing (NGS) data. GeneTalk allows editing annotation about sequence variants and build up a crowd sourced database with clinically relevant information for diagnostics of genetic disorders. GeneTalk allows searching for information about specific sequence variants and connects to experts on variants that are potentially disease-relevant. == Application to diagnostics == Users can upload NGS data in Variant Call Format (VCF) onto the GeneTalk server into their accounts. All entries of the file are preprocessed and shown in the integrated VCF viewer. Filtering tools are set by the user to reduce the number of clinically non-relevant variants. After filtering and prioritization users can interpret relevant variants by retrieving information (annotations) about variants from the GeneTalk database. The communication platform allow users to contact experts about specific variants, genes, or genetic disorders, to exchange knowledge and expertise. === Analysis procedure === Steps required to analyze VCF files Upload VCF file Edit pedigree and phenotype information for segregation filtering Filter VCF file by editing the filtering options View results and annotations Add annotations === Filtering tools === The following filtering options may be used to reduce the non-relevant sequence variants in VCF files. Functional – filter out variants that have effects on protein level Linkage – filter out variants that are on specified chromosomes Gene panel – filter variants by genes or gene panels, subscribe to publicly available gene panels or create own ones Frequency – show only variants with a genotype frequency lower than specified Inheritance – filter out variants by presumed mode of inheritance Annotation – show only variants with a score for medical relevance and scientific evidence == Communication platform and expert network == Users can share VCF files with colleagues and coworkers. The integrated mailing systems allows users to contact experts easily. Users can create annotations and comments and rate annotations regarding medical relevance and scientific evidence, that is helpful for the community of users for diagnosis of genetic disorders. Registered users provide information about their field of knowledge in their profile and can be contacted by other users. == Potential applications == Developing diagnostics Genetic analysis Capturing data generated by community Communication and exchange of knowledge and expertise

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  • Clean Email

    Clean Email

    Clean Email is an automated software as a service email management application which identifies and clears junk mail from inboxes. The service uses a subscription business model with a free trial for the first 1,000 emails. and is available on macOS, iOS, Android, and on the web. == History == Clean Email is a self-funded company headquartered in Los Angeles, California. Initially developed by the founder for personal use, the service was designed to address the growing issue of inbox clutter and privacy concerns. In 2017, John Gruber recognized Clean Email as a trustworthy alternative to Unroll.me after the latter was found to be selling user data. == Features == Clean Email uses algorithms to identify and categorize emails, enabling users to group, remove, label, and archive email messages in bulk. Its Unsubscriber tool consolidates all subscriptions and newsletters into a single view for quick management, allowing users to bulk unsubscribe or temporarily pause mail. Its Screener feature transforms the inbox into an "opt-in" system, enabling users to pre-approve mail from new senders. Cleaning Suggestions identifies frequently cleaned mail, recommending actions accordingly. Additional functionalities include automatic deletion of aging emails, delivery of messages to specified folders, and options to mute or block senders.

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  • Curve (tonality)

    Curve (tonality)

    In image editing, a curve is a remapping of image tonality, specified as a function from input level to output level, used as a way to emphasize colours or other elements in a picture. Curves can usually be applied to all channels together in an image, or to each channel individually. Applying a curve to all channels typically changes the brightness in part of the spectrum. Light parts of a picture can be easily made lighter and dark parts darker to increase contrast. Applying a curve to individual channels can be used to stress a colour. This is particularly efficient in the Lab colour space due to the separation of luminance and chromaticity, but it can also be used in RGB, CMYK or whatever other colour models the software supports.

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  • TinEye

    TinEye

    TinEye is a reverse image search engine developed and offered by Idée, Inc., a company based in Toronto, Ontario, Canada. It was the first image search engine on the web to use image identification technology rather than keywords, metadata or watermarks. TinEye allows users to search not using keywords but with images. Upon submitting an image, TinEye creates a "unique and compact digital signature or fingerprint" of the image and matches it with other indexed images. This procedure is able to match even heavily edited versions of the submitted image, but will not usually return similar images in the results. == History == Idée, Inc. was founded by Leila Boujnane and Paul Bloore in 1999. Idée launched the service on May 6, 2008 and went into open beta in August that year. While computer vision and image identification research projects began as early as the 1980s, the company claims that TinEye is the first web-based image search engine to use image identification technology. The service was created with copyright owners and brand marketers as the intended user base, to look up unauthorized use and track where the brands are showing up respectively. In June 2014, TinEye claimed to have indexed more than five billion images for comparisons. However, this is a relatively small proportion of the total number of images available on the World Wide Web. As of September 2025, TinEye's search results claim to have over 77.6 billion images indexed for comparison. == Technology == A user uploads an image to the search engine (the upload size is limited to 20 MB) or provides a URL for an image or for a page containing the image. The search engine will look up other usage of the image in the internet, including modified images based upon that image, and report the date and time at which they were posted. TinEye does not recognize outlines of objects or perform facial recognition, but recognizes the entire image, and some altered versions of that image. This includes smaller, larger, and cropped versions of the image. TinEye has shown itself capable of retrieving different images from its database of the same subject, such as famous landmarks. TinEye is capable of searching for images in JPEG, PNG, WebP, GIF, BMP and TIFF format. Results generated from TinEye include the total number of matches in their database, a preview image, and the URL to each match. TinEye can sort results by best match, most changed, biggest image, newest, and oldest. User registration is optional and offers storage of the user's previous queries. Other features include embeddable widgets and bookmarklets. TinEye has also released their commercial API. == Usage == TinEye's ability to search the web for specific images (and modifications of those images) makes it a potential tool for the copyright holders of visual works to locate infringements on their copyright. It also creates a possible avenue for people who are looking to make use of imagery under orphan works to find the copyright holders of that imagery. Being that orphan works can be defined as "copyrighted works whose owners are difficult or impossible to identify and/or locate," the use of TinEye could potentially remove the orphan work status from online images that can be found in its database. === Fact-checking === It has been recommended by fact-checkers as a useful resource in attempts to verify the origin of images. As of 2019, TinEye specialized in copyright violations and finding exact versions of images online.

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  • Showcase Workshop

    Showcase Workshop

    Showcase Workshop, also referred to as Showcase, is a SaaS company that develops a presentation-building application for business use. Users upload files and images to a web platform which generates presentations viewable on a suite of mobile apps. Showcase was founded in 2011. The company’s headquarters are in Wellington, New Zealand. == History == Showcase Workshop was originally developed in response to dynamically changing content being presented on iPads at the 2012 Olympics. After market-testing a beta version of the core application, Showcase Workshop launched commercially in 2012. In 2014 Showcase partnered with Vodafone Global Enterprise. == Product == Users upload pre-existing PDFs, videos, images and Microsoft Office documents to a secure server, building presentations or ‘showcases’ which can then be downloaded via the mobile apps. The presentations are used for mobile sales enablement, training, or operational/health and safety purposes. == Reception == Reviewers have praised the ease of use of Showcase, calling it a “better alternative to developing a native app” and “intuitive”. Criticisms include the lack of differing templates and a lack of complex customisation controls. Showcase was nominated for a Tabby Award in 2014 and won a Tabby Award in 2015 for its Windows app.

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  • Taskworld

    Taskworld

    Taskworld is a cloud-based collaboration platform created by Fred Mouawad. The SaaS (software as a service) is designed to facilitate project and task management, collaboration, delegation, communication, knowledge management, measure progress and provide performance metrics for evidence-based evaluations within teams. It allows team members to assign and receive tasks, add followers, record comments, share and store unlimited files and organize projects. == Background == An initial version of Taskworld was custom-built by the IT team working for Mouawad in 2006. This was done as a way to try and overcome internal issues regarding delegation, accountability and time-management. The application was constructed to prevent tasks from falling through the cracks and make it easy to follow up on-going projects where many individuals throughout various departments of the organization were involved. Mouawad’s Synergia One group of companies later implemented the application internally as the ‘Task Management System’ and found a general improvement in execution across international offices and departments. This successful implementation led Mouawad to found the ‘My Taskworld’ website which later evolved into ‘Taskworld.' The company was officially founded in February 2012, and in June later that year, Mouawad presented a prototype of the Taskworld website to an Executive Program at the Stanford Graduate School of Business. == Reception == The launch of the app was covered by the media as an addition to the Synergia One group of companies founded by CEO Fred Mouawad. The mobile app is currently available on both android and iOS platforms. Google Play gives the app 3.8 out of 5 stars while the Apple App Store gave it 2.9 out of 5 stars. Alex Williams in a 2014 article for Tech Crunch said, “Taskworld’s technology plays to human emotions. As the feedback is continuous, people are compelled to engage with the service. But in some respects, Taskworld is fairly simple and still needing more to make it a potent competitor in the market.” == Features == Taskworld's main features include, but are not limited to: Project &Task Management - Taskworld includes up to five levels of hierarchy including Project Group, Project, Tasklist, Task and Checklist. Some features in this group are assigning tasks, setting due dates, adding followers, task comments, set repeating tasks, tasks in multiple locations, project templates, copy project, archiving, smart notifications, drag and drop Kanban boards, image preview boards, file management, people page and personnel directory, customizable tags and colored labels. Enterprise Messaging - The app includes a native chat application with channels and groups, private and direct messaging capabilities. Other communication features inside of the app include project chat, drag and drop file attachments, an email bridge to send and receive messages and @mentions. Overview & Analytics - Taskworld includes several features under this section including a dashboard, workspace snapshot, workspace filter, interactive calendar, project analytics and health status, project burndown chart, project burn-up chart and interactive timeline. == Languages and customers == Taskworld is used by 4,000 companies in 80 countries. The app is currently available in eight languages: English, French, German, Spanish, Mandarin, Portuguese, Thai and Korean. == Customer support == The Taskworld User Guide offers details on how to use features of the application. Customer support is offered inside of the application for questions and feedback regarding the software, and also via email. The Taskworld customer support team has received a 98% Customer Satisfaction Rating, according to customer ratings on its support platform Zendesk. Free, live demos are also offered for those companies who need assistance.

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  • DesktopTwo

    DesktopTwo

    Desktoptwo was a free Webtop (whose URL was desktoptwo.com and which is now a parked domain) developed by Sapotek (whose URL was sapotek.com, which also is now a parked domain). It's also been called a WebOS although Sapotek stated on its website that the term is premature and presumptuous. It mimics the look, feel and functionality of the desktop environment of an operating system. The software only reached beta stage. It had a Spanish version called Computadora.de. Desktoptwo was web-based and required Adobe Flash Player to operate. The web applications' found on Desktoptwo were built on PHP in the back end. Features included drag-and-drop functionality. Sapotek had liberated all the web applications found on Desktoptwo through Sapodesk on an AGPL license. Desktoptwo belonged to a category of services that intended to turn the Web into a full-fledged platform by using web services as a foundation along with presentation technologies that replicated the experience of desktop applications for users. In a "Cloud OS" the functionality of a server was granularized and abstracted as Web services that Web developers used to create composite applications similar to how desktop software developers use several APIs of the OS to create their applications. Sites like Facebook attempt to create a similar effect by exposing their APIs and allowing developers to create applications upon these. Some of the features found on Desktoptwo were: File sharing, Webmail, Blog creator, Instant messenger, Address book, Calendar, RSS Reader and Office productivity applications. Desktoptwo.com and the Sapotek website no longer operate.

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  • TAChart

    TAChart

    TAChart is a component for the Lazarus IDE that provides charting services. Similar to Tchart and Teechart for Delphi it supports a collection of different chart types including bar charts, pie charts, line charts and point series. Apart from a screen canvas, output is possible in form of SVG, OpenGL, printer, WMF, and other formats. TAChart is bundled with the Lazarus Component Library. Although not intended to be a TChart clone, why its usage differs in certain points, its basic functionality is very similar and some source code written for TeeChart may be reused. == History == The first version of TAChart was developed by Philippe Martinole for the TeleAuto project, a program for automation of astronomic observations. Later functionality was introduced by Luis Rodrigues while porting the Epanet application from Delphi to Lazarus. In the ensuing years the code has extensively rewritten, expanded and is now maintained by Alexander Klenin. == Data sources == TAChart is able to use input from various sources. Examples include lists of real values, user defined buffers in the computer's memory, vectors of random values, fields in databases, calculated values provided by pre-defined functions and results of embedded code written in Pascal Script

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