AI For Business Development

AI For Business Development — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

Zé Delivery

Zé Delivery is a startup developed by Brazilian drinks company AmBev which offers an app for delivering drinks. The app is available for Android and iOS. Created in 2016 by AmBev's ZX Ventures hub, the service has an international presence in Argentina, Paraguay, Bolivia, Panama and the Dominican Republic. It is also present in more than 300 Brazilian cities. Because it has an extensive category of alcoholic beverages, the service is only used by people over 18. It also offers soft drinks, juices, energy drinks and other non-alcoholic beverages.
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Excalidraw

Excalidraw is an open-source, web-based virtual whiteboard and diagramming application. It is used to create diagrams, wireframes, and sketches within a web browser without requiring account registration. The software features a characteristic hand-drawn visual style and supports real-time multi-user collaboration using client-side end-to-end encryption. Excalidraw is released under the MIT License and is maintained by Excalidraw s.r.o., a company based in Brno, Czech Republic. == History == Excalidraw was created on 1 January 2020 by Christopher Chedeau, a software engineer at Meta Platforms. Chedeau, who previously co-created React Native and Prettier, initially developed the application as a personal project before registering the domain on 3 January 2020. Within its first months, the project attracted open-source contributors who assisted in expanding its features and rewriting the codebase into TypeScript and React. By early 2021, day-to-day operations moved to Czech developers David Luzar and Milos Vetesnik. In May 2021, the team incorporated Excalidraw s.r.o. in Brno and launched a commercial cloud-based version named Excalidraw+ to fund the open-source project's development. By May 2026, the main open-source repository on GitHub had accumulated over 123,000 stars. == Features and architecture == The application provides an infinite canvas for geometric shapes, lines, arrows, text, and freehand drawing. Its visual presentation relies on Rough.js, a JavaScript graphics library that alters standard vector paths to mimic irregular, hand-drawn lines. Excalidraw operates as a Progressive web application (PWA), allowing local installation and offline usage, saving data natively to local browser storage. Files use a native, JSON-based extension format (.excalidraw), and canvases can be exported to PNG or SVG formats. Real-time collaboration sessions are executed using Socket.IO via a relay server. Data transmission uses the browser's native Web Cryptography API to achieve end-to-end encryption. A symmetric AES key is generated on the client side and appended to the sharing URL as a fragment identifier (following the # character). Because web browsers do not transmit URL fragments to HTTP servers, the data remains unreadable to the distribution server. == Ecosystem == Excalidraw is distributed as an npm package, allowing third-party developers to embed the whiteboard component directly into external React web applications. Community-developed extensions integrate the application's file format into text editors and note-taking systems, including Visual Studio Code and Obsidian. The platform also has native integrations in commercial platforms such as Notion and HackerRank. == Reception == Google's developer relations team published a technical case study on Excalidraw as a reference implementation for Progressive Web Apps. The analysis highlighted the software's adoption of advanced web platform capabilities, specifically its utilization of the File System Access API and native Clipboard API to replicate desktop software behavior within a web browser environment.
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Plant Nanny

Plant Nanny is a water tracker mobile application which reminds users to drink water. It was developed by Taiwanese app maker Fourdesire. The app was first released in 2013 and is available on the Apple App Store for iPhones and the Google Play Store for Android devices. == Description == Play Nanny uses a game method that allows users to turn their virtual selves into plants, which grows and thrives as the user drinks more water. The app sends occasional push notifications to remind users to drink water throughout the day. Users can choose from a wide range of plants, including cacti and carnations, and track their water intake. The app uses two resources, How to calculate how much water you should drink by Jennifer Stone (2018) and Human energy requirements by the Food and Agriculture Organization (2004), to calculate the recommended daily water intake for its users. Upon downloading the app, users are prompted to input basic personal information which is then used to calculate the recommended daily water intake and prompts them to drink the appropriate amount. == Accolades ==
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Open Data Center Alliance

opendatacenteralliance.org appears to have been closed down. The Open Data Center Alliance is an independent organization created in Oct. 2010 with the assistance of Intel to coordinate the development of standards for cloud computing. Approximately 100 companies, which account for more than $50bn of IT spending, have joined the Alliance, including BMW, Royal Dutch Shell and Marriott Hotels. "The Alliance's Cloud 2015 vision is aimed at creating a federated cloud where common standards will be laid down for those in the hardware and software arena." == Usage Model Roadmap == The organization sees a growing need for solutions developed in an open, industry-standard and multivendor fashion, and has thus created a usage model roadmap featuring 19 prioritized usage models. The usage models provide detailed requirements for data center and cloud solutions, and will include detailed technical documentation discussing the requirements for technology deployments. To further its roadmap development, the steering committee established five initial technical workgroups in the areas of infrastructure, management, regulation & ecosystem, security and services. The organization delivered a 0.50 usage model roadmap to Open Data Center Alliance technical workgroups in Oct. 2010, and delivered a full 1.0 roadmap for public use in June 2011. == Membership == The steering committee consists of BMW, Capgemini, China Life, China Unicom Group, Deutsche Bank, JPMorgan Chase, Lockheed Martin, Marriott International, Inc., National Australia Bank, Royal Dutch Shell, Terremark and UBS. Other members include AT&T, CERN, eBay, Logica, Motorola Mobility Inc. and Nokia. "The demands on the IT organisations are coming at such an alarming rate that there are many, many different solutions being developed today that maybe don't work with each other. We need one voice, one road map, so that companies are able to say to manufacturers here is a clear vision of what they should be developing their product to do." says Marvin Wheeler, of Terremark, chairman of the Alliance. "While it's unclear how successful this alliance will be, it is at least shedding the spotlight on cloud interoperability, a big emerging issue," said Larry Dignan of ZDNet.
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Integrated writing environment

An integrated writing environment (IWE) is software that provides comprehensive writing and knowledge management functionality for writers and information workers. IWEs enable writers and information workers to perform a variety of tasks related to the document in the IWE in a single environment. This provides a distraction-free workspace and streamlined writing experience. IWEs provide similar efficiency and functionality benefits to writers and information professionals that integrated development environments (IDEs) provide to software developers. == Overview == IWEs are designed to maximize productivity and help improve the quality of written work by integrating together tools that allow users to work effectively in a single application. The IWE features may include integrated content search, reversion management, outlining, note management, and reference management, as may be suitable for the target field of use. == List of IWEs == Celtx This IWE is intended for screenplay writers and has screenplay writing and management tools. Celtex provides tools for the pre-production work phase, story development, storyboarding, script breakdowns, production scheduling, and reports. Scrivener This IWE targets novel, research paper, and script writing. Scrivener provides tools to organize notes and research documents for easy access and referencing. After completing the writing, Scrivener allows the user to export the document to formats supported by common word processors, such as Microsoft Word. TeXstudio This IWE targets LaTeX documents and provides interactive spelling checker, code folding, and syntax highlighting.
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Color layout descriptor

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

Switch was a mobile-only job-matching app that connected candidates directly to hiring managers. Candidates could upload their resumes and connect their social and professional media profiles, but remain anonymous while searching. Users received a daily set of job recommendations that fit their backgrounds and salary criteria, and swipe right to apply. Employers post many jobs on Switch directly, which eliminates the need for third-party job boards and recruiters, and connects job seekers to hiring managers. Switch reveals a candidate’s identity to one employer at a time, only after the candidate matches with that employer. When candidates and employers match, they can chat within the app. Switch is available for iOS, with an Android version in development. == History == === Founding === Yarden Tadmor founded Switch in New York City in January 2014. For the first 10 months, Tadmor funded the company himself. By December 2014, Switch had raised $1.4 million in funding from venture capitals firms Metamorphic Ventures, SG VC, BAM and Rhodium. Tadmor's inspiration for Switch came after being frustrated by his experience both as a job seeker, and also as a supervisor hiring at numerous technology startup companies. Tadmor has said of Switch, “We operate on the five-second resume principle, which is usually the amount of time a recruiter spends on a resume. They scan through the typical data points and move on.” Switch was designed for passive job seekers to browse openings discreetly and connect quickly. Originally, Switch served only the New York metro area technology sector while in early beta, but Tadmor always intended to expand into national coverage. Soon, the company started including all major metropolitan markets across the U.S. In May 2015, Switch announced it would start sourcing tech and media jobs from all the job boards available online. Later in 2015, Switch began to post jobs in smaller urban areas. The company also expanded industries and jobs to include restaurant staff, retail sales, healthcare, nursing and education. Tadmor subsequently founded Livekick, a one-on-one private fitness and yoga instruction company, based in New York. == Operation == In May 2015, Switch reported generating over 400,000 job applications. The company said that nine of the 50 largest websites in the U.S. were using the service. It had grown its customer base to thousands of companies in a few months from launch including Microsoft, Amazon, Facebook, IBM, Yahoo!, eBay, DropBox, SoundCloud, and Wikipedia. John Cline, software development manager at eBay, told ABC’s Good Morning America that Switch is now his “main way of finding new prospective employees.” Switch uses a double opt-in technique, meaning job seekers and employers must both say yes before moving forward. They also use swiping technology and intelligent matching algorithms to connect job seekers and employers. The user experience is different for each group, but the major attraction for both sides is the speed at which they can be connected. === Features === Swipe is a major aspect of the Switch user experience. Job seekers swipe to apply to jobs, or left to pass on positions. Employers respond and swipe right to reciprocate interest, or left to eliminate the candidate. Direct connection between job seekers and employers allows hiring managers and job seekers to start an immediate conversation. Hiring managers can message with job seekers within the app, and both parties can quickly vet one another and decide whether to move forward. Easy profile creation from social media and in-app profile editing helps job seekers focus on finding a job. === Users === Job Seekers can either load their profile manually or pull in professional credentials from social media. They can post validated photos on their Facebook account. Switch’s matching algorithm analyzes the job seeker’s location, experience, and skills to bring them jobs they may be interested in. Job seekers swipe to apply and, if the employer shows interest too, only then does Switch’s system reveal the job seeker’s identity to the corporate recruiter or hiring manager. The job seeker and hiring manager can then chat through the app. Employers behave similarly to job seekers. Hiring managers or corporate recruiters sign up online, add open positions, then view Switch-recommended candidates or wait for job seekers to swipe right. Employers can select relevant job seekers by swiping right on their profiles, then chat directly in the app. === Subscriptions === The app is currently free for users and employers. == Company overview == === Financials === Switch closed out its seed round in May 2015 with $2 million in seed round funding. Investors include Marker VC, Metamorphic, Rhodium, 500 Startups, BAM, SG VC and Marcel Legrand. In a July 2015 interview with Tadmor, he claimed that Switch had raised $2.4 million to date. == Reception == Thanks to its swipe technology and double opt-in make-up, the media often refers to Switch as the Tinder for jobs. Switch has received features in lists and app reviews as an effective tool to improve your digital job search, particularly on the mobile platform. “It’s minimal effort to connect with relevant matches,” said Good Morning America workplace contributor Tory Johnson. “Which is what everybody wants to find.”
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SciPy

SciPy (pronounced "sigh pie") is a free and open-source Python library used for scientific computing and technical computing. SciPy contains modules for optimization, linear algebra, integration, interpolation, special functions, fast Fourier transform, signal and image processing, ordinary differential equation solvers and other tasks common in science and engineering. SciPy is also a family of conferences for users and developers of these tools: SciPy (in the United States), EuroSciPy (in Europe) and SciPy.in (in India). Enthought originated the SciPy conference in the United States and continues to sponsor many of the international conferences as well as host the SciPy website. The SciPy library is currently distributed under the BSD license, and its development is sponsored and supported by an open community of developers. It is also supported by NumFOCUS, a community foundation for supporting reproducible and accessible science. == Components == The SciPy package is at the core of Python's scientific computing capabilities. Available sub-packages include: cluster: hierarchical clustering, vector quantization, K-means constants: physical constants and conversion factors datasets: various example datasets for demonstrating image and data processing differentiate: numerical differentiation for first and second derivatives fft: Discrete Fourier Transform algorithms fftpack: Legacy interface for Discrete Fourier Transforms integrate: numerical integration routines interpolate: interpolation tools io: data input and output, including support for MATLAB and Matrix Market files linalg: linear algebra routines ndimage: various functions for multi-dimensional image processing odr: orthogonal distance regression classes and algorithms optimize: optimization algorithms including linear programming and a variety of numerical nonlinear programming optimizers signal: signal processing tools sparse: sparse matrices and related algorithms spatial: algorithms for spatial structures such as k-d trees, nearest neighbors, convex hulls, etc. special: special functions stats: statistical functions == Data structures == The basic data structure used by SciPy is a multidimensional array provided by the NumPy module. NumPy provides some functions for linear algebra, Fourier transforms, and random number generation, but not with the generality of the equivalent functions in SciPy. NumPy can also be used as an efficient multidimensional container of data with arbitrary datatypes. This allows NumPy to seamlessly and speedily integrate with a wide variety of databases. Older versions of SciPy used Numeric as an array type, which is now deprecated in favor of the newer NumPy array code. == History == In the 1990s, Python was extended to include an array type for numerical computing called Numeric. (This package was eventually replaced by NumPy, which was written by Travis Oliphant in 2006 as a blending of Numeric and Numarray, with Numarray itself being started in 2001.) As of 2000, there was a growing number of extension modules and increasing interest in creating a complete environment for scientific and technical computing. In 2001, Travis Oliphant, Eric Jones, and Pearu Peterson merged code they had written and called the resulting package SciPy. The newly created package provided a standard collection of common numerical operations on top of the Numeric array data structure. Shortly thereafter, Fernando Pérez released IPython, an enhanced interactive shell widely used in the technical computing community, and John Hunter released the first version of Matplotlib, the 2D plotting library for technical computing. Since then the SciPy environment has continued to grow with more packages and tools for technical computing. == Scientific Python versus ScientificPython == In the scientific literature, SciPy is occasionally referred to as "Scientific Python (SciPy)". This is incorrect: the official name of the project is just "SciPy". Furthermore, expanding "SciPy" as "Scientific Python" may cause confusion with "ScientificPython", a project led by Konrad Hinsen of Orléans University that was active between 1995 and 2014. "Scientific Python" is also used for the related ecosystem of tools.
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Three-factor learning

In neuroscience and machine learning, three-factor learning is the combination of Hebbian plasticity with a third modulatory factor to stabilise and enhance synaptic learning. This third factor can represent various signals such as reward, punishment, error, surprise, or novelty, often implemented through neuromodulators. == Description == Three-factor learning introduces the concept of eligibility traces, which flag synapses for potential modification pending the arrival of the third factor, and helps temporal credit assignement by bridging the gap between rapid neuronal firing and slower behavioral timescales, from which learning can be done. Biological basis for Three-factor learning rules have been supported by experimental evidence. This approach addresses the instability of classical Hebbian learning by minimizing autocorrelation and maximizing cross-correlation between inputs.
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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.
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LiveChat

LiveChat is an AI customer service software with chatbot, online chat, help desk software, and web analytics capabilities. LiveChat is used by over 76,000 companies. It was first launched in 2002 and is offered via a SaaS (software as a service) business model by Text. Organizations use LiveChat as a single point of contact to manage customer service and online sales activities with a single program. == Product == LiveChat is proprietary software. LiveChat's website chat widget can be embedded on customers' websites as a small chat box, often displayed in the bottom right corner of the web browser. It can be used to conduct chats, share files and save transcripts. The agent application is used by company employees to respond to questions asked by the customers. This is available through both web-based application, desktop applications, and mobile apps. Web chat sessions can be initiated by the visiting customer, or by the agent, either manually or automatically by the LiveChat system when the visitor meets the predefined criteria (i.e. searched keyword, time on website, encountered error, etc.). LiveChat's system attempts to identify the best prospects visiting a website based on data gathered from past purchasing decisions. Other features include real-time website traffic monitoring, built-in ticketing system and agents' efficiency analytics. LiveChat is available in 48 languages. == Research and reception == Reviewing LiveChat's usefulness for online learning in 2020, psychologist Jaclyn Broadbent said "LiveChat occurs as a real-time conversation, it can be time-consuming for staff and disruptive to other tasks." However, using it has resulted in reduced communication traffic from other channels, such as the discussion boards or email. As a teacher, the best time to be available on LiveChat is when you are doing other administrative jobs." Since 2014 LiveChat has been publishing Customer Service Report - an annual study of customer satisfaction and analysis of online business communication trends. It includes research of thousands of companies and millions of customer service email and live support interactions.
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Cloud-Based Secure File Transfer

Cloud-Based Secure File Transfer is a managed or hosted file transfer service that provides cloud storage that can be accessed via SSH File Transfer Protocol (SFTP). These services allow secure, reliable file transfers while offering the scalability, redundancy, and high availability of cloud infrastructure. == Technical overview == The evolution of file transfer protocols began with File Transfer Protocol (FTP) and SSH File Transfer Protocol (SFTP). SFTP offered enhanced security through the use of SSH (Secure Shell) encryption, which addressed many of the security concerns associated with traditional FTP. Over time, as businesses increasingly adopted cloud infrastructure, the demand for services that integrate secure file transfer with cloud storage led to the rise of Cloud-Based Secure File Transfer services. These services combine the benefits of secure, encrypted file transfer with the scalability and flexibility of cloud-based storage systems. Traditional on-premises SFTP typically involves setting up and managing physical or virtual servers to handle file transfers. In contrast, Cloud-Based Secure File Transfer utilizes managed cloud infrastructure, such as AWS EC2, Azure VMs, or Google Cloud, to automate scaling, ensure redundancy, and provide high availability. These cloud environments can be configured to automatically scale with demand, enabling businesses to handle large volumes of data transfers without the need for extensive physical hardware. == Features == Scalability and availability: Cloud-Based Secure File Transfer services are inherently scalable, with features like load balancing, multi-region deployments, and auto-scaling groups that adjust resources in response to traffic spikes. This ensures that the system can handle varying workloads and provides continuous availability, even during high-demand periods. Cost-effectiveness: By eliminating the need for physical infrastructure and reducing ongoing server maintenance costs, Cloud-Based Secure File Transfer services offer significant cost savings compared to traditional on-premises services. Cloud providers typically offer pay-as-you-go pricing models, where users only pay for the resources they use, further optimizing costs. Security and compliance: Cloud-Based Secure File Transfer products offer strong security measures, including end-to-end encryption, key management, detailed logging, and auditing. These services are often compliant with industry regulations such as HIPAA (Health Insurance Portability and Accountability Act), GDPR (General Data Protection Regulation), and SOC 2 (System and Organization Controls), ensuring that data transfers meet necessary security and privacy standards. == Cloud-Based Secure File Transfer providers == == Uses == Cloud-Based Secure File Transfer is used across various industries to securely transfer sensitive data and integrate into business workflows. In healthcare, Cloud-Based Secure File Transfer is essential for securely transferring electronic Protected Health Information (ePHI), ensuring compliance with regulations like HIPAA. In financial institutions, it is used to protect sensitive financial data during transfer, maintaining privacy and security. Data analytics also benefits from Cloud-Based Secure File Transfer, offering a secure and efficient method for transferring large datasets between systems or partners. Technically, Cloud-Based Secure File Transfer is often integrated into enterprise workflows through automated file transfers, using scripting or APIs. It also plays a key role in cloud backup and disaster recovery, ensuring that files are securely transferred and stored in cloud environments, which supports business continuity. However, businesses must address certain implementation challenges. Despite its secure design, Cloud-Based Secure File Transfer is not immune to risks such as misconfigured SSH keys, improper access control, or inadequate encryption. Regular security audits and careful configuration management are necessary to minimize the risk of data breaches. Additionally, integrating Cloud-Based Secure File Transfer with legacy systems can present challenges, such as incompatible APIs or outdated authentication methods. == Comparisons with related technologies == Cloud-Based Secure File Transfer differs from traditional SFTP primarily in its deployment and management model. Traditional SFTP services are typically hosted on-premises or on virtual servers, requiring manual configuration, ongoing infrastructure maintenance, and security management by in-house IT teams. In contrast, Cloud-Based Secure File Transfer is offered as a Software-as-a-Service (SaaS) service, reducing infrastructure overhead by eliminating the need for dedicated hardware or virtual machines. This model simplifies management through centralized web-based interfaces, automated updates, and built-in scalability. While Cloud-Based Secure File Transfer is focused on providing secure file transfers over the SFTP protocol, Managed File Transfer (MFT) platforms generally support a broader range of protocols, including FTP, FTPS, HTTP/S, and AS2. MFT services often include advanced features such as end-to-end encryption, extensive automation, compliance reporting, and integration with enterprise systems. Cloud-Based Secure File Transfer services may offer some of these features but are typically more lightweight and streamlined, targeting organizations seeking a secure and scalable alternative to traditional SFTP without the full suite of MFT capabilities. As such, Cloud-Based Secure File Transfer can be seen as a specialized subset within the broader managed file transfer ecosystem.
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Flok (company)

Flok (formerly Loyalblocks) was an American tech startup based in New York City that provides marketing services such as chatbots/AI, customer loyalty programs, mobile apps and CRM services to local businesses. In January 2017, the company was acquired by Wix.com. Around March 2017, Flok ceased regular communication. At some point in 2019 Flok communicated to its customers that it would shut down in March 2020. == Background == Flok was founded in 2011 by Ido Gaver and Eran Kirshenboim and has offices in Tel Aviv, Israel. In May 2013, Flok secured a $9 million Series A Round from General Catalyst Partners with participation from Founder Collective and existing investor Gemini Israel Ventures. In total, Flok has raised over $18 million in venture capital in three rounds. In May 2014, Flok announced a self-service loyalty platform for SMBs to build their own programs with beacon integration. At that time, approximately 40,000 businesses were using the service. In 2016, Flok released a turnkey chatbot service for local businesses, and was featured in AdWeek for developing the first weed bot chatbot for a California cannabis business. == Services == Flok offered an eponymous customer-facing app, that consumers use to receive rewards and deals from partner businesses, and a Flok business app for merchants to manage the platform.
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WikiTok

WikiTok is a web application that shows random Wikipedia articles in an infinite scrolling format similar to TikTok. Created by New York-based civil and software engineer Isaac Gemal in February 2025, the application aims to provide an "anti-algorithmic" alternative to traditional social media platforms while combating doomscrolling. == Development == WikiTok was developed on February 3, 2025, after Gemal saw a viral tweet from developer Tyler Angert proposing "all of wikipedia on a single, scrollable page". Gemal created the initial prototype in approximately two hours, using the Claude LLM and Cursor coding editor, completing it by 2 a.m. that same night. The application was built with React 18, TypeScript, Tailwind CSS, and Vite, consisting of only a few hundred lines of code with no backend infrastructure. Gemal has made the project open-source, with the code publicly available on GitHub. == Functionality == WikiTok serves users completely random Wikipedia article snippets from Wikipedia's nearly 9.5 million entries. Each article preview includes a full-screen image from Wikipedia and a short text excerpt. Users can click "Read More" to access the full Wikipedia article, or continue scrolling to see the next random entry. The application functions as a progressive web app that can be downloaded as a hybrid between an app and a website. It works on both mobile and desktop browsers, and is designed to be responsive whether users scroll with their thumb on mobile devices or use a cursor on desktop computers. As of 2025, WikiTok supports 14 different languages for article translation. == Philosophy == Gemal has explicitly positioned WikiTok as "anti-algorithmic," resisting numerous requests to implement personalized content algorithms. In an interview with Business Insider, Gemal stated: "I have had plenty of people message me and even make issues on my GitHub asking for some insane crazy WikiTok algorithm... we're already ruled by ruthless, opaque algorithms in our everyday life; why can't we just have one little corner in the world without them?" The application aims to combat "doomscrolling" – the practice of mindlessly consuming negative news online that can cause anxiety, helplessness, and anger. Unlike traditional social media platforms, WikiTok deliberately excludes algorithms, advertisements, and user tracking.
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Cloudlet

A cloudlet is a mobility-enhanced small-scale cloud datacenter that is located at the edge of the Internet. The main purpose of the cloudlet is supporting resource-intensive and interactive mobile applications by providing powerful computing resources to mobile devices with lower latency. It is a new architectural element that extends today's cloud computing infrastructure. It represents the middle tier of a 3-tier hierarchy: mobile device - cloudlet - cloud. A cloudlet can be viewed as a data center in a box whose goal is to bring the cloud closer. The cloudlet term was first coined by M. Satyanarayanan, Victor Bahl, Ramón Cáceres, and Nigel Davies, and a prototype implementation is developed by Carnegie Mellon University as a research project. The concept of cloudlet is also known as follow me cloud, and mobile micro-cloud. == Motivation == Many mobile services split the application into a front-end client program and a back-end server program following the traditional client-server model. The front-end mobile application offloads its functionality to the back-end servers for various reasons such as speeding up processing. With the advent of cloud computing, the back-end server is typically hosted at the cloud datacenter. Though the use of a cloud datacenter offers various benefits such as scalability and elasticity, its consolidation and centralization lead to a large separation between a mobile device and its associated datacenter. End-to-end communication then involves many network hops and results in high latencies and low bandwidth. For the reasons of latency, some emerging mobile applications require cloud offload infrastructure to be close to the mobile device to achieve low response time. In the ideal case, it is just one wireless hop away. For example, the offload infrastructure could be located in a cellular base station or it could be LAN-connected to a set of Wi-Fi base stations. The individual elements of this offload infrastructure are referred to as cloudlets. == Applications == Cloudlets aim to support mobile applications that are both resource-intensive and interactive. Augmented reality applications that use head-tracked systems require end-to-end latencies of less than 16 ms. Cloud games with remote rendering also require low latencies and high bandwidth. Wearable cognitive assistance systems combine devices such as Google Glass with cloud-based processing to guide users through complex tasks. This futuristic genre of applications is characterized as “astonishingly transformative” by the report of the 2013 NSF Workshop on Future Directions in Wireless Networking. These applications use cloud resources in the critical path of real-time user interaction. Consequently, they cannot tolerate end-to-end operation latencies of more than a few tens of milliseconds. Apple Siri and Google Now which perform compute-intensive speech recognition in the cloud, are further examples in this emerging space. == Cloudlet vs Cloud == There is significant overlap in the requirements for cloud and cloudlet. At both levels, there is the need for: (a) strong isolation between untrusted user-level computations; (b) mechanisms for authentication, access control, and metering; (c) dynamic resource allocation for user-level computations; and, (d) the ability to support a very wide range of user-level computations, with minimal restrictions on their process structure, programming languages or operating systems. At a cloud datacenter, these requirements are met today using the virtual machine (VM) abstraction. For the same reasons they are used in cloud computing today, VMs are used as an abstraction for cloudlets. Meanwhile, there are a few but important differentiators between cloud and cloudlet. === Rapid provisioning === Different from cloud data centers that are optimized for launching existing VM images in their storage tier, cloudlets need to be much more agile in their provisioning. Their association with mobile devices is highly dynamic, with considerable churn due to user mobility. A user from far away may unexpectedly show up at a cloudlet (e.g., if he just got off an international flight) and try to use it for an application such as a personalized language translator. For that user, the provisioning delay before he is able to use the application impacts usability. === VM handoff across cloudlets === If a mobile device user moves away from the cloudlet he is currently using, the interactive response will degrade as the logical network distance increases. To address this effect of user mobility, the offloaded services on the first cloudlet need to be transferred to the second cloudlet maintaining end-to-end network quality. This resembles live migration in cloud computing but differs considerably in a sense that the VM handoff happens in Wide Area Network (WAN). == OpenStack++ == Since the cloudlet model requires reconfiguration or additional deployment of hardware/software, it is important to provide a systematic way to incentivise the deployment. However, it can face a classic bootstrapping problem. Cloudlets need practical applications to incentivize cloudlet deployment. However, developers cannot heavily rely on cloudlet infrastructure until it is widely deployed. To break this deadlock and bootstrap the cloudlet deployment, researchers at Carnegie Mellon University proposed OpenStack++ that extends OpenStack to leverage its open ecosystem. OpenStack++ provides a set of cloudlet-specific APIs as OpenStack extensions. == Commercial implementations and standardization effort == By 2015 cloudlet based applications were commercially available. In 2017 the National Institute of Standards and Technology published draft standards for fog computing in which cloudlets were defined as nodes on the fog architecture.
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