AI Chatbot Free Unlimited

AI Chatbot Free Unlimited — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

Instance-based learning

In machine learning, instance-based learning (sometimes called memory-based learning) is a family of learning algorithms that, instead of performing explicit generalization, compare new problem instances with instances seen in training, which have been stored in memory. Because computation is postponed until a new instance is observed, these algorithms are sometimes referred to as "lazy." It is called instance-based because it constructs hypotheses directly from the training instances themselves. This means that the hypothesis complexity can grow with the data: in the worst case, a hypothesis is a list of n training items and the computational complexity of classifying a single new instance is O(n). One advantage that instance-based learning has over other methods of machine learning is its ability to adapt its model to previously unseen data. Instance-based learners may simply store a new instance or throw an old instance away. Examples of instance-based learning algorithms are the k-nearest neighbors algorithm, kernel machines and RBF networks. These store (a subset of) their training set; when predicting a value/class for a new instance, they compute distances or similarities between this instance and the training instances to make a decision. To battle the memory complexity of storing all training instances, as well as the risk of overfitting to noise in the training set, instance reduction algorithms have been proposed.
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ALL-IN-1

ALL-IN-1 was an office automation product developed and sold by Digital Equipment Corporation in the 1980s. It was one of the first purchasable off the shelf electronic mail products. It was later known as Office Server V3.2 for OpenVMS Alpha and OpenVMS VAX systems before being discontinued. == Overview == ALL-IN-1 was advertised as an office automation system including functionality in Electronic Messaging, Word Processing and Time Management. It offered an application development platform and customization capabilities that ranged from scripting to code-level integration. ALL-IN-1 was designed and developed by Skip Walter, John Churin and Marty Skinner from Digital Equipment Corporation who began work in 1977. Sheila Chance was hired as the software engineering manager in 1981. The first version of the software, called CP/OSS, the Charlotte Package of Office System Services, named after the location of the developers, was released in May 1982. In 1983, the product was renamed ALL-IN-1 and the Charlotte group continued to develop versions 1.1 through 1.3. Digital then made the decision to move most of the development activity to its central engineering facility in Reading, United Kingdom, where a group there took responsibility for the product from version 2.0 (released in field test in 1984 and to customers in 1985) onward. The Charlotte group continued to work on the Time Management subsystem until version 2.3 and other contributions were made from groups based in Sophia Antipolis, France (System for Customization Management and the integration with VAX Notes), Reading (Message Router and MAILbus), and Nashua, New Hampshire (FMS). ALL-IN-1 V3.0 introduced shared file cabinets and the File Cabinet Server (FCS) to lay the foundation for an eventual integration with TeamLinks, Digital's PC office client. Previous integrations with PCs included PC ALL-IN-1, a DOS-based product introduced in 1989 that never proved popular with customers. Bob Wyman was the first product manager. He oversaw the growth of the product culminating in over $2 billion per year in revenue and market leadership in the proprietary office automation sector. Other consultants from Digital Equipment Corporation involved include Frank Nicodem, Donald Vickers and Tony Redmond.
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Captions (app)

Mirage (formerly known as Captions) is a video-generating, video-editing and AI research company headquartered in New York City. Their first app, Captions, is available on iOS, Android, and Web and offers a suite of tools aimed at streamlining the creation and editing of videos. Their enterprise platform, Mirage Studio, generates AI actors and videos for marketing assets and video campaigns. == History == Mirage was co-founded by Gaurav Misra and Dwight Churchill. During Misra's time leading design engineering at Snap Inc., he followed the rise of a new category of video, the "talking video." In 2021, Misra left Snap to found Mirage with his former colleague Churchill. Later that year, the Captions app launched with early backing from venture capital firms Sequoia Capital and Andreessen Horowitz as well as individual investors. In 2023, the company released Lipdub, an Al dubbing app which translates any video with spoken audio into 28 languages. In October 2023, Captions shared that it maintained over 100,000 daily active users with "about a million" videos being created monthly. In November 2024, Captions acquired AlpacaML, a generative AI company that focused on art and other images. In June 2025, Captions launched Mirage Studio, for marketers and advertising agencies. In September 2025, Captions rebranded their company to Mirage. This change reflects the company's focus on developing their proprietary foundation model and future video products. == Products == The Captions app offers features to automate common production tasks including captioning, editing, dubbing, script creation, and music integration. Mirage Studio allows users to generate AI avatars and create short-form videos from prompts or audio. == Awards == In 2023, the company was recognized as part of Fast Company's "Next Big Things In Tech" series. In 2024, the company won 2 Webby Awards for Best Use of AI & Machine Learning and Creative Production.
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Oculus Quill

Quill is a painting and animation software for virtual reality. It runs on Microsoft Windows with Oculus Rift headsets. It is used to create 3D paintings and animated cartoons. Quill was released on November 29, 2016, on the Oculus Store. Theater Elsewhere(formerly Quill Theater), an application for viewing creations made in Quill, was later made available following the release of the Oculus Quest. In September 2021, Facebook, now known as Meta Platforms, and the owner of Oculus, sold Quill to its original creator, who continues to develop and support the app. == Development == Quill was originally developed by Oculus Story Studio as an internal tool for the creative needs of the studio's project Dear Angelica directed by Saschka Unseld along with its art-director Wesley Allsbrook. == Controls == The software works on Oculus Rift utilizing its 6DoF motion controllers. Users can paint in 3D space using their hands naturally, and animate those paintings with keyframes. They can also capture videos and photos of their creations. == Reception == Dear Angelica, a VR story fully painted in Quill, was nominated for an Emmy Award in 2017.
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ZeroPC

ZeroPC was a commercial webtop developed by ZeroDesktop, Inc. located in San Mateo, California. ZeroPC has been called a personal cloud OS. It mimicked the look, feel and functionality of the desktop environment of a real operating system. The software was launched in September 2011 through Disrupt SF 2011 event and recently selected to the finalist of SXSW 2012 in Innovative Web Technology category. ZeroPC is web-based and required a Java applet to operate bundled productivity tool Thinkfree. The web applications found on ZeroPC are built on Java in the back end. Features included drag-and-drop functionality, cloud dashboard and personal cloud storage meta services. ZeroPC 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. ZeroPC aggregates content so users can easily access, transfer and share whatever content they want, using a web browser from any device. Its meta-cloud layer supports Dropbox, Box, SugarSync, OneDrive, 4Shared, Google Drive, Evernote, Picasa, Flickr, Instagram, Facebook, Twitter, and Photobucket. ZeroPC Cloud OS platform also provides extensive APIs for iOS and Android App developers. Some of the features found on ZeroPC are: File sharing, Webmail, Cloud Content Navigator, Instant messenger, Sticky Note, Audio/Video Player and Office productivity applications. ZeroPC 2.0 platform ran on AWS for free and paid users. Its platform is licensable to Telco and ISV for commercial purpose. Their clients are SFR, SK Telecom, Hancom and others. As of June 1, 2017, ZeroPC's servers were switched off completely, and ZeroPC is no longer in service since its parent company, NComputing, had launched Virtual Desktop Service in the cloud (AWS) to public. == Browser and Platform Compatibility == The ZeroPC web desktop was compatible with Mac OS X and Microsoft Windows platforms. It is certified to operate on Safari 6.0, Firefox 15.0.1, Google Chrome 22.0.1229.79 m and Internet Explorer 8 and 9. The ZeroPC front end user interface executes entirely within a web browser (see above) and uses HTML, some features of HTML5, JavaScript, AJAX and an optional Java plug-in. == Security == All communication between the ZeroPC front end user interface and the ZeroPC back end servers is encrypted using SSL (HTTPS) protocol. Furthermore, any content stored in the ZeroPC server-side repository is also encrypted using 256-bit Advanced Encryption Standard (AES-256) by Amazon S3 on AWS. ZeroPC users could connect their ZeroPC profile to other storage services such as Dropbox and Box. This connection allows the ZeroPC user to fully manage their content stored in these other storage services. To establish the connection ZeroPC rigorously adhered to the Oauth implementation provided by the target storage service. Upon completion of the Oauth process, ZeroPC stores the relevant access token in the user's profile. This token, along with all other sensitive password related data was encrypted using AES 256-bit key size. == Implementations == As noted above, the ZeroPC platform was hosted on Amazon Web Services infrastructure and is available to the general consumer. A user was allowed to sign up by selecting one of three account plans including a no-cost option. The ZeroPC could also be white-labeled for organizations wishing to provide this functionality to their own users. The white-label options include managed hosting on Amazon Web Services infrastructure and also installation within the organization's IT infrastructure. == User Access Points == The ZeroPC infrastructure provided user access to content and features in several different ways. As described in this article the user can access their information by signing into the ZeroPC web desktop. Additionally, ZeroPC offers native applications designed to run on popular mobile devices including smartphones and tablets. == Leadership == ZeroPC was founded by Chief Executive Officer, Young Song, an entrepreneur who previously founded NComputing, a $60 million venture-backed company. He also co-founded eMachines, Inc., a low-cost computer brand (later acquired by Gateway).
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Cheekd

Cheekd is a dating app based in New York City. It was founded in 2010 by Lori Cheek. == History == The service debuted with the name "Cheek'd". Founder Lori Cheek appeared on the television program, Shark Tank in February 2014, but did not succeed in obtaining funding from any of the five judges. She said Cheek’d only had 1000 subscribers at that time. === Business card model === Cheek'd offered two plans, paid and free. For $25, subscribers got a set of 50 business cards that could be given out once someone caught their eye. Each card had a phrase, an online code, and a URL to the subscriber's account. Recipients could look up the giver's profile. In addition to purchasing cards, there was a $9.95 monthly membership fee. === Smartphone app === In 2015, the service's name changed from "Cheek'd" to "Cheekd". The new app used Bluetooth technology to alert users whenever a compatible user was within a 30-foot radius, instead of using cards. == Patent lawsuit == The original business card-based model for Cheekd had been claimed as a patented process by Lori Cheek, as U.S. patent 8,543,465. In September 2017, a complaint was filed, alleging that the idea was not original to Lori Cheek. Cheek responded, stating that the complaint was baseless, and a complete fabrication. The lawsuit Pirri v. Cheek was dismissed in a pre-trial conference in New York's Federal Court on April 5, 2018.
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Landweber iteration

The Landweber iteration or Landweber algorithm is an algorithm to solve ill-posed linear inverse problems, and it has been extended to solve non-linear problems that involve constraints. The method was first proposed in the 1950s by Louis Landweber, and it can be now viewed as a special case of many other more general methods. == Basic algorithm == The original Landweber algorithm attempts to recover a signal x from (noisy) measurements y. The linear version assumes that y = A x {\displaystyle y=Ax} for a linear operator A. When the problem is in finite dimensions, A is just a matrix. When A is nonsingular, then an explicit solution is x = A − 1 y {\displaystyle x=A^{-1}y} . However, if A is ill-conditioned, the explicit solution is a poor choice since it is sensitive to any noise in the data y. If A is singular, this explicit solution doesn't even exist. The Landweber algorithm is an attempt to regularize the problem, and is one of the alternatives to Tikhonov regularization. We may view the Landweber algorithm as solving: min x ‖ A x − y ‖ 2 2 / 2 {\displaystyle \min _{x}\|Ax-y\|_{2}^{2}/2} using an iterative method. The algorithm is given by the update x k + 1 = x k − ω A ∗ ( A x k − y ) . {\displaystyle x_{k+1}=x_{k}-\omega A^{}(Ax_{k}-y).} where the relaxation factor ω {\displaystyle \omega } satisfies 0 < ω < 2 / σ 1 2 {\displaystyle 0<\omega <2/\sigma _{1}^{2}} . Here σ 1 {\displaystyle \sigma _{1}} is the largest singular value of A {\displaystyle A} . If we write f ( x ) = ‖ A x − y ‖ 2 2 / 2 {\displaystyle f(x)=\|Ax-y\|_{2}^{2}/2} , then the update can be written in terms of the gradient x k + 1 = x k − ω ∇ f ( x k ) {\displaystyle x_{k+1}=x_{k}-\omega \nabla f(x_{k})} and hence the algorithm is a special case of gradient descent. For ill-posed problems, the iterative method needs to be stopped at a suitable iteration index, because it semi-converges. This means that the iterates approach a regularized solution during the first iterations, but become unstable in further iterations. The reciprocal of the iteration index 1 / k {\displaystyle 1/k} acts as a regularization parameter. A suitable parameter is found, when the mismatch ‖ A x k − y ‖ 2 2 {\displaystyle \|Ax_{k}-y\|_{2}^{2}} approaches the noise level. Using the Landweber iteration as a regularization algorithm has been discussed in the literature. == Nonlinear extension == In general, the updates generated by x k + 1 = x k − τ ∇ f ( x k ) {\displaystyle x_{k+1}=x_{k}-\tau \nabla f(x_{k})} will generate a sequence f ( x k ) {\displaystyle f(x_{k})} that converges to a minimizer of f whenever f is convex and the stepsize τ {\displaystyle \tau } is chosen such that 0 < τ < 2 / ( ‖ ∇ f ‖ 2 ) {\displaystyle 0<\tau <2/(\|\nabla f\|^{2})} where ‖ ⋅ ‖ {\displaystyle \|\cdot \|} is the spectral norm. Since this is special type of gradient descent, there currently is not much benefit to analyzing it on its own as the nonlinear Landweber, but such analysis was performed historically by many communities not aware of unifying frameworks. The nonlinear Landweber problem has been studied in many papers in many communities; see, for example. == Extension to constrained problems == If f is a convex function and C is a convex set, then the problem min x ∈ C f ( x ) {\displaystyle \min _{x\in C}f(x)} can be solved by the constrained, nonlinear Landweber iteration, given by: x k + 1 = P C ( x k − τ ∇ f ( x k ) ) {\displaystyle x_{k+1}={\mathcal {P}}_{C}(x_{k}-\tau \nabla f(x_{k}))} where P {\displaystyle {\mathcal {P}}} is the projection onto the set C. Convergence is guaranteed when 0 < τ < 2 / ( ‖ A ‖ 2 ) {\displaystyle 0<\tau <2/(\|A\|^{2})} . This is again a special case of projected gradient descent (which is a special case of the forward–backward algorithm) as discussed in. == Applications == Since the method has been around since the 1950s, it has been adopted and rediscovered by many scientific communities, especially those studying ill-posed problems. In X-ray computed tomography it is called simultaneous iterative reconstruction technique (SIRT). It has also been used in the computer vision community and the signal restoration community. It is also used in image processing, since many image problems, such as deconvolution, are ill-posed. Variants of this method have been used also in sparse approximation problems and compressed sensing settings.
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Couch to 5K

Couch to 5K, abbreviated C25K, is an exercise plan that gradually progresses from beginner running toward a 5 kilometre (3.1 mile) run over nine weeks. == Operations == The Couch to 5K running plan, also known as C25K, created by Josh Clark in 1996, was developed with the expectation of creating a plan for new runners to start running. The plan is aimed to have users work out for 20 to 30 minutes, three days a week. Within the program, users can be expected to perform different tasks such as intervals of running with period of short walks in between to help build endurance in the weeks up to the final goal of a 5K run. During the nine weeks leading up to the race, the runner will learn to set their own pace and where their strengths and weaknesses are within running. Often, the daily workouts start with a five-minute warm-up walk and works up to running five kilometres without a walking break within nine weeks. Users are not expected to have any experience in running and can be some of the first running that they ever do. The main goal is to turn that unexperienced runner into someone who can run a 5K. Clark started the website Kick and featured C25K on the site. In 2001, Kick merged with Cool Running, a New England–based running site. Clark later sold his stake in Cool Running and the Couch to 5K program. Cool Running was absorbed into Active.com, operated by Active Network, LLC. Active Network provides mobile apps for Couch to 5K, as well as 5K to 10K, a follow-up program. The NHS in the UK provides downloadable podcasts and a smartphone app (Android and iOS) for the plan. A mobile app, created by Zen Labs, has training plans that are based on the Couch to 5K running plan from CoolRunning.com. It is one of the highest-rated health and fitness apps available on Android and iOS. As of 2016, the C25K app has been used by over 5 million people.
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Poop Map

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

Customer support is a range of services to assist customers in making cost effective and correct use of a product. It includes assistance in planning, installation, training, troubleshooting, maintenance, upgrading, and disposal of a product. Regarding technology products such as mobile phones, televisions, computers, software products or other electronic or mechanical goods, it is termed technical support. It aims to ensure users can effectively operate the product and resolve any issues that may arise throughout its lifecycle. Support is delivered through various channels, including telephone, email, live chat, self-service knowledge bases, and social media. Research indicates that most customers attempt to resolve issues through self-service before contacting a representative. For products sold across multiple regions, support may be provided in several languages, as consumers tend to prefer assistance in their native language. Requirements for customer contact centres are defined in international standards such as ISO 18295.
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VistaCreate

VistaCreate (formerly Crello) is an online graphic design platform for non-designers, launched in 2016. As of 2022, it has more than 10 million users in 192 countries. == Overview == VistaCreate (then known as Crello) was launched in 2016 as a part of Depositphotos. In 2019, the product hit a milestone of 1 million registered users and also launched mobile apps. In 2020, the library of templates and objects became free. A music library and a background remover tool were added to the platform. In May 2021, Moufflons Basketball, in collaboration with VistaCreate, organized a poster design competition in support of gender equality in sports. In October 2021, Vistaprint acquired Crello and its parent company, Depositphotos, for a total price of $85 million. After the acquisition, Crello was rebranded to VistaCreate. Along with Vistaprint and 99designs, it became part of the new Vista parent brand. After Russia started a full-scale war on the territory of Ukraine in February 2022, VistaCreate suspended all business in Russia and Belarus. VistaCreate's team and Depositphotos gathered collections of images and templates dedicated to the war in Ukraine.
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LENA Foundation

The LENA Foundation is an American nonprofit organisation which provides tools for measuring children's language acquisition and exposure. Specifically, the LENA system consists of a digital language processor which is worn by a child and records and analyses their auditory environment, using propriety software. It then presents a summary of child-adult conversation, such as conversation turns and word counts. The purpose of the LENA system is to encourage interactive talk between children (between the age of two to forty-eight months) and their caretakers. The LENA system is also used for research; while useful for researchers who wish to save transcription costs or observe the child in its natural state, the accuracy of this system, while often quite high, varies between contexts, for example notably in the case of hard of hearing children. Because of this, several researchers recommend caution in using only the LENA system on its own for the purposes of scientific research. == History == The LENA Foundation was established in 2009 by Terrance and Judith Paul, founders of Renaissance Learning, Inc., with the purpose of aiding children with disabilities and assisting with early learning. They were inspired by the book "Meaningful Differences in the Everyday Experience of American Children" by Dr. Betty Hart and Dr. Todd Risley. A pilot version of the LENA system was launched in February 2006. The LENA Research Foundation was registered as a tax-exempt 501(c)(3) nonprofit in September 2010. The organisation was renamed simply LENA in 2018 and adopted the tagline "Building brains through early talk." LENA has been used for parental feedback, linguistics or paediatrics research, and for specific clinical cases. == Scientific background == In 2018, research using the LENA system showed that there was a link between children's conversational turns and activation of Broca's area (a part of the brain responsible, although not necessarily essential, for language processing). The LENA foundation cites research by its own employees as evidence for the scientific basis of its technology. Said research claims that verbal interaction with young children has an effect on language acquisition, including verbal comprehension skills during adolescence. == LENA System == The LENA software analyses a child's natural language environment, such as verbal exposure, and provides several metrics, such as adult and child speech time, television/recorded audio time, word count, or conversation turn count. The LENA hardware is a recorder that is usually placed into a child's specially-designed vest. The software was trained on over 65,000 hours of manually annotated American English audio recordings. It splits the audio into segments which are categorised as "key child", "other child", "male adult", "noise", etc. The advantages of LENA as opposed to manual transcription are its speed and ease of use; the disadvantages are its potential inaccuracies and lack of transcription capability (which LENA does not profess to attempt). The LENA system has also been criticised for prioritising quantity of speaking over quality (i.e., mastery of the language, as opposed to babble). == Product lines == === LENA Start === LENA Start is a program for parents that utilises feedback from the LENA System in conjunction with weekly group sessions in order to address the home language environment. It was introduced in 2015 and implemented across several U.S. states. In October 2020, during the restrictions of the COVID-19 pandemic, Read Aloud Delaware began a virtual LENA Start program with families statewide, where parents received feedback and participated in one-hour Zoom workshops each week during the 10-week program. === LENA Grow === LENA Grow is a professional development program for teachers in early childhood classrooms. Before launching at sites around the country, the program was first piloted in Escambia County, Florida. === LENA Home === LENA Home is a supplement to existing parent coaching curricula. Typically, home visitors facilitate the use of the LENA System to help parents track their progress towards increasing interactive talk in their homes. === Developmental Snapshot === The LENA Developmental Snapshot, based on a 52-question parent survey, assesses both expressive and receptive language skills and provides an estimate of a child's developmental age from 2 months to 36 months.
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Memory color effect

The memory color effect is the phenomenon that the canonical hue of a type of object acquired through experience (e.g. the sky, a leaf, or a strawberry) can directly modulate the appearance of the actual colors of objects. Human observers acquire memory colors through their experiences with instances of that type. For example, most human observers know that an apple typically has a reddish hue; this knowledge about the canonical color which is represented in memory constitutes a memory color. As an example of the effect, normal human trichromats, when presented with a gray banana, often perceive the gray banana as being yellow - the banana's memory color. In light of this, subjects typically adjust the color of the banana towards the color blue - the opponent color of yellow - when asked to adjust its surface to gray to cancel the subtle activation of banana's memory color. Subsequent empirical studies have also shown the memory color effect on man-made objects (e.g. smurfs, German mailboxes), the effect being especially pronounced for blue and yellow objects. To explain this, researchers have argued that because natural daylight shifts from short wavelengths of light (i.e., bluish hues) towards light of longer wavelengths (i.e., yellowish-orange hues) during the day, the memory colors for blue and yellow objects are recruited by the visual system to a higher degree to compensate for this fluctuation in illumination, thereby providing a stronger memory color effect. == Form identification == Memory color plays a role when detecting an object. In a study where participants were given objects, such as an apple, with two alternate forms for each, a crooked apple and a circular apple, researchers changed the colors of the alternate forms and asked if they could identify them. Most of the participants answered "unsure," suggesting that we use memory color when identifying an object. The research redefined memory color as a phenomenon when "a form's identity affects the phenomenal hue of that form." == Color effect on memorization == Memory color effect can be derived from the human instinct to memorize objects better. Comparing the effect of recognizing gray-scaled images and colored images, results showed that people were able to recall colored images 5% higher compared to gray-scaled images. An important factor was that higher level of contrast between the object and background color influences memory. In a specific study related to this, participants reported that colors were 5% to 10% easier to recognize compared to black and white. == Color constancy and memory color effect == Color constancy is the phenomenon where a surface to appear to be of the same color under a wide rage of illumination. A study tested two hypotheses with regards to color memory; the photoreceptor hypothesis and the surface reflectance hypothesis. The test color was surround either by various color patches forming a complex pattern or a uniform “grey” field at the same chromaticity as that of the illuminant. The test color was presented on a dark background for the control group. It was observed that complex surround results where in line with the surface-reflectance hypothesis and not the photoreceptor hypothesis, showing that the accuracy and precision of color memory are fundamentals to understanding the phenomenon of color constancy. == Significance to the evolution of trichromacy == While objects that possess canonical hues make up a small percentage of the objects which populate humans’ visual experience, the human visual system evolved in an environment populated with objects that possess canonical hues. This suggests that the memory color effect is related to the emergence of trichromacy because it has been argued that trichromacy evolved to optimize the ability to detect ripe fruits—objects that appear in canonical hues. == In perception research == In perception research, the memory color effect is cited as evidence for the opponent color theory, which states that four basic colors can be paired with its opponent color: red—green, blue—yellow. This explains why participants adjust the ripe banana color to a blueish tone to make its memory color yellow as gray. Researchers have also found empirical evidence that suggests memory color is recruited by the visual system to achieve color constancy. For example, participants had a lower percentage of color constancy when looking at a color incongruent scene, such as a purple banana, compared to a color diagnostical scene, a yellow banana. This suggests that color constancy is influenced by the color of objects that we are familiar with, which the memory color effect takes part.
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VGACAD

VGACAD was the parent of a suite of shareware graphic utilities made for the MS-DOS operating system used in the IBM PC and clones. It was popular for editing and capturing images using BSAVE (graphics image format) and provided an early graphic editing suite compatible with multiple graphic cards and resolutions, used on the IBM PC. == Usage == Written by Lawrence Gozum in 1987, it was the genesis of multiple versions and improvements over 10 years. Ran with his brother, Marvin initially helped with design ideas, strategic focus, technical support calls, and managing the early shareware business. The growth of the VGACAD suite grew quickly to preoccupy most of their time. Lawrence then focused more of his efforts on software and formed Applied Insights, to manage VGACAD and its offspring, VidFun, and Ai Picture Explorer. At its peak, its users ranged from individuals, Federal government offices, museums and major newspapers. == Features == VGACAD was a misnomer, and meant VGA-Computer Assisted Drawing, rather than computer-aided design, as CAD is commonly referred to today. Its longevity was due to its color accuracy, speed, small size, and that its suite of small utilities often worked stand-alone. One called VGACAP, for 'capture', dumped video memory into a file that could later be converted to popular graphic image formats, later made commonplace when Microsoft Windows programmed the print screen key to dump graphics into the clipboard. However, VGACAP ran insulated apart from early versions of Windows, and thus could capture screens were applications prohibited such function.
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Clue (mobile app)

Clue is a menstrual health app developed by the Berlin-based technology company BioWink GmbH. The app has over 15 million users from 180 countries. The startup has raised over $17 million from backers that include Union Square Ventures and Mosaic Ventures. == History == Clue was co-founded by Ida Tin, Hans Raffauf, Mike LaVigne and Moritz von Buttlar in 2012. BioWink GmbH launched the app in 2013. Ida Tin's stated goal was to take female reproductive health “out of taboo land” and to start “a reproductive health revolution.” Tin previously led motorbike tours around the world and wrote a book about her experience. By July 2017, the Clue app had more than 8 million active users on both Android and iOS. Users were representative of more than 180 countries. In 2015, BioWink GmbH closed a $7 million Series A funding round led by Union Square Ventures and Mosaic Ventures, bringing the company's total funding to $10 million. The company was listed as one of Europe's Hottest Startups in 2015 by Wired UK, with Clue being named one of the best apps in 2015 by both Apple and Google. In March 2018, the company launched an editorial site to serve as a resource for accessible and scientific menstrual health information. == Mobile app == The Clue mobile application calculates and predicts a user's period, fertile window, and premenstrual syndrome. It also informs users the most or least likely time for becoming pregnant and allows them to track more than 30 health categories, including sex, sleep, pain, exercise, hair, skin, digestion, emotions and energy. The app can also explain how pill dosages impact fertility and includes an alarm system to allow for reminders for taking pills. In 2015, the company closed a Series A funding round and announced plans to use the proceeds to expand features of the mobile app and hire more staff. Clue also partnered with universities such as Stanford University, Columbia University, University of Washington, and University of Oxford to advance female health research. Clue integrated with Apple Inc.'s HealthKit for iOS 9 in September 2015, allowing data such as body temperature, cervical mucus quality, menstruation, ovulation test results, sexual activity, and spotting directly to the app. In 2016, Clue was available in 15 languages on both iOS and Android. That same year, Clue introduced a cycle-sharing feature and in 2017 a pill-tracking option. In February 2018, Clue made its app available on the Fitbit Ionic smartwatch. In 2026, Clue partnered with UK-based digital healthcare platform Evaro, an NHS-licensed provider, to offer embedded prescription services within the app.
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