AI Art Legality

AI Art Legality — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

  • Dailyhunt

    Dailyhunt

    Dailyhunt (formerly Newshunt) is an Indian content and news aggregator application based in Bangalore, India that provides local language content in 14 Indian languages from multiple content providers. Viru serves as Founder of Dailyhunt with Co-founder Umang Bedi. == History == Dailyhunt, earlier called Newshunt, was created as a Symbian app in 2009 by two ex-Nokia employees Umesh Kulkarni and Chandrashekhar Sohoni. Later in 2011, Newshunt became available on the Android platform. It was by that time that Virendra Gupta, founder of Verse acquired the application. Virendra Gupta, better known as Viru, had started Verse in 2007 as a value-added service (VAS) company. In 2011, he acquired Newshunt from its owners Umesh and Chandrashekhar. Umesh became the CTO and stayed on to oversee its transition towards the smartphone era. In 2015, Viru renamed Newshunt as Dailyhunt. In early 2018, Viru roped in Umang Bedi, to be the President of Dailyhunt and lead the business with him while focusing on making the benefits of the platform available to a larger audience. Umang was elevated to co-founder in 2020. == Funding == In September 2014, Dailyhunt (then known as Newshunt) closed its Series B funding of INR 1 billion ( or approx $12 million in 2014) from Sequoia Capital India. The Series C funding round was led by Falcon Capital and was closed with $40 million in February 2015. In October 2016, the company received its Series D funding of $25 million from ByteDance and a Series E funding of $6.39 million from Falcon Edge Capital in September 2018. Additionally, Dailyhunt raised $3 Mn (INR 21.75 Cr) in a Series F funding round from Stonebridge Capital in August 2019. Other investors of Dailyhunt include Matrix Partners India, Omidyar Network, Goldman Sachs and Sofina. == Tie-ups and partnerships == In January 2021, Dailyhunt partnered with Twitter to bring ‘Twitter Moments’ to the Indian social app. Dailyhunt app now has a dedicated tab called “Twitter Moments India” to showcase curated tweets pertaining to news and other events. In January 2021, Dailyhunt announced the premiere of Season 2 of the popular show QuoteUnquote with KK (Kapil Khandelwal) on the app. It was the first podcast to have been launched on the Dailyhunt app. In September 2020, Dailyhunt signed up as an Associate Sponsor with Star Sports for Dream 11 IPL 2020. In May 2020, Snapdeal partnered with Dailyhunt to add new content on marketplace. In March 2019, Discovery Communications India, the factual entertainment network, entered into a multi-year partnership with Dailyhunt to showcase short-form content.

    Read more →
  • Tridium

    Tridium

    Tridium Inc. is an American engineering hardware and software company based in Richmond, Virginia, whose products facilitate and integrate the automation of building and other engineering control systems. Since November 2005, the company has operated as an independent business entity of Honeywell International Inc. == History == Tridium Inc. was founded in 1995. In 1999, Tridium launched the Niagara Framework, a software infrastructure that connects all systems and devices to a central console. In 2002, John Petze became president and CEO, replacing Jerry Frank. The company was acquired by Honeywell International Inc in 2005. == Products == Tridium's products facilitate by integrating building automation using open and proprietary communications protocols such as Modbus, Lonworks and BACnet. Tridium is the developer of Niagara Framework. The Niagara Framework is a universal software infrastructure that allows building controls integrators, HVAC and mechanical contractors to build custom, web-enabled applications for accessing, automating and controlling smart devices real-time via local network or over the Internet.

    Read more →
  • Mobile DevOps

    Mobile DevOps

    Mobile DevOps is a set of practices that applies the principles of DevOps specifically to the development of mobile applications. Traditional DevOps focuses on streamlining the software development process in general, but mobile development has its own unique challenges that require a tailored approach. Mobile DevOps is not simply as a branch of DevOps specific to mobile app development, instead an extension and reinterpretation of the DevOps philosophy due to very specific requirements of the mobile world. == Rationale == Traditional DevOps approach has been formed around 2007-2008, close to the dates when iOS and Android mobile operating systems were released to the public. The traditional DevOps approach primarily evolved to meet the changing needs of the software development world with the paradigm shift towards continuous and rapid development and deployment (such as in web development, where interpreted languages are more prevalent than compiled languages). While traditional DevOps embraced agility and flexibility, mobile operating system providers steered towards a walled-garden approach with compiled apps with tight controls over how they can be distributed and installed on a mobile device. This difference in the mobile development mindset compared to what the traditional DevOps approach is advocating, is augmented further with the mobile applications to be deployed on a high number of varying devices and operating systems. Eventually, the concept of Mobile DevOps took off as a trend around 2014-2015, in line with the fast growth of the number of applications in mobile app stores. As individuals and corporations alike are developing and publishing more and more mobile applications, the need for efficiency and shorter release cycles increased, which is addressed by the continuous feedback and continuous development approach within the concept of DevOps, while requiring a significant level of adaptation and extension of the traditional DevOps practices. == Mindset shift from traditional DevOps to mobile DevOps == Mobile DevOps has a unique set of challenges and constraints, which solidifies the fact that it needs to be approached as a separate discipline. These challenges can be outlined as follows: Platform-specific requirements and tight controls of mobile operating system providers, where for instance a macOS device is mandatory for iOS application development and release. The walled-garden approach of distributing mobile apps, specifically applying to iOS applications, which comes with app review and app release delays that would not be needed in web development, for instance. Code signing requirements that come with the walled-garden approach, which introduce additional processes in the mobile application build pipeline along with new security concerns. An entire deployment cycle is re-run even in the slightest code change due to how applications are compiled and delivered to the users. The final product is to be deployed to a wide variety of mobile devices worldwide, which requires extensive testing and user feedback. Monitoring mobile applications require additional tools and approaches to be able to get data from an application running on a mobile device while respecting user privacy. Frequent operating system updates by mobile platforms can require rapid adaptation of apps, introducing further complexity to the development and maintenance cycles. == Benefits of mobile DevOps == Mobile DevOps is not an abstract concept and offers a range of benefits that can help improve the efficiency and effectiveness of the mobile app development process. These benefits can even be quantified by collecting the data within the mobile application development lifecycle. The benefits can be categorized into the following areas: Faster Release Cycles: By automating tasks and streamlining the development process, mobile DevOps enables teams to deliver new features and updates more frequently. Improved Quality: Automated testing and continuous monitoring help to identify and fix bugs earlier in the development cycle, leading to higher quality apps. Optimized Resource Utilization: Mobile DevOps promotes optimized resource utilization by automating tasks and streamlining workflows. Furthermore, mobile DevOps practices like containerization can help to create more efficient and scalable development environments. Increased Agility: Mobile DevOps allows teams to be more responsive to changes in the market and user feedback. == List of Dedicated Mobile DevOps Platforms == Even though it is possible to run a mobile DevOps cycle with most of the CI/CD platforms, they may require significant effort compared to non-mobile CI/CD (e.g. you need to bring your own infrastructure or it may require "reinventing the wheel" for commonly used platforms like Jenkins). To overcome the mobile-specific challenges specified, there are certain platforms that are dedicated to the lifecycle of mobile applications. These platforms exclusively focus on DevOps processes for mobile app development and are also referred as mobile CI/CD platforms. Appcircle (Multiplatform | Cloud-based & On-premise) Visual Studio App Center (Multiplatform | Cloud-based) Xcode Cloud (Apple platforms only | Cloud-based)

    Read more →
  • How Data Happened

    How Data Happened

    How Data Happened: A History from the Age of Reason to the Age of Algorithms is a 2023 non-fiction book written by Columbia University professors Chris Wiggins and Matthew L. Jones. The book explores the history of data and statistics from the end of the 18th century to the present day. == Content == The book starts at the end of the 18th century, when European states began tabulating physical resources, and ends at the present day, when algorithms manipulate our personal information as a commodity. It looks at the rise of data and statistics, and how early statistical methods were used to justify eugenics, quantify supposed racial differences, and develop military and industrial applications. The authors also discuss the impact of the internet and e-commerce on data collection, the rise of data science, and the consequences of government-run surveillance systems collecting vast amounts of personal data for customized, targeted advertising. They emphasize the importance of privacy and democracy and propose remedies to the problems caused by mass data collection, including stronger regulation of the tech industry and collective action by its employees. The book is a historical analysis that provides context for understanding the debates surrounding data and its control. The book has 336 pages and was published in 2023 by W. W. Norton & Company.

    Read more →
  • IMazing

    IMazing

    iMazing is mobile device management software that allows users to transfer files and data between iOS devices (iPhone, iPad and iPod Touch) and macOS or Windows computers, in addition to many other features beyond the scope of what Apple's own tools enable. == History == Developed by DigiDNA, iMazing was initially released in 2008 as DiskAid, enabling users to transfer data and files from the iPhone or iPod Touch to Mac or Windows computers. DiskAid was renamed iMazing in 2014. Version 2.0 was released on September 13, 2016. In August 2021, version 2.14 of iMazing added a spyware detection feature. The feature is based on Amnesty International’s Mobile Verification Toolkit to detect Pegasus Spyware following the publication of Pegasus Project. == Description == With iMazing, an iPhone or iPad can be used similarly to an external hard drive. It performs tasks that iTunes doesn’t offer, including incremental backups of iOS devices, browsing and exporting text and voicemail messages, managing apps, encryption, and migrating data from an old phone to a new one. The menu bar app iMazing Mini enables automatic, wireless and encrypted backups of iPhones. The iMazing HEIC Converter is a free desktop app for Mac and PC that lets users convert photos from HEIC format to JPG or PNG.

    Read more →
  • Test data management

    Test data management

    Test data management (TDM) is a process in software testing concerned with the creation, preparation, and control of data used for testing software systems. It involves supplying datasets required to execute test cases and verifying system behaviour under defined conditions. Test data management is an integral part of the software development lifecycle (SDLC) and is utilized in both manual and automated testing processes. It is applied in environments that use continuous integration and DevOps practices, where test execution requires consistent and repeatable data conditions. == Overview == Test data management includes the generation, selection, and preparation of data for testing purposes, as well as its distribution across test environments. It also involves controlling data versions and ensuring that datasets correspond to specific test scenarios. In many cases, production data is adapted for testing through techniques such as masking or subsetting to reduce size and remove sensitive content. Test data management ensures that test cases are executed with relevant, consistent, and readily available data. This reduces variability in test results and supports reproducibility across test cycles. == Importance == The role of test data management has expanded with the growth of complex, data-driven systems and regulatory requirements governing data usage. Testing often depends on data that reflects real-world conditions, but direct use of production data may introduce security and privacy risks. As a result, organizations apply methods such as data masking and anonymization to meet compliance requirements, including those set by the California Privacy Rights Act (CPRA) and Europe’s General Data Protection Regulation (GDPR). Inadequate control of test data can lead to incomplete test coverage, unreliable test results, or delays in testing processes due to unavailable or inconsistent datasets. == Techniques and tools == Test data management leverages various techniques for preparing and controlling data used in testing. These include the generation of synthetic data, the extraction of subsets from production datasets, and the modification of data to remove or obscure sensitive information. A key technical requirement in these processes is maintaining referential integrity, or ensuring that relationships between data entities remain consistent across different tables and systems after masking or subsetting. Data virtualization is also used to provide access to datasets without full replication. These methods may be implemented using software tools that automate data preparation, masking, and distribution.

    Read more →
  • 15.ai

    15.ai

    15.ai was a free non-commercial web application and research project that uses artificial intelligence to generate text-to-speech voices of fictional characters from popular media. Created by a pseudonymous artificial intelligence researcher known as 15, who began developing the technology as a freshman during their undergraduate research at the Massachusetts Institute of Technology (MIT), the application allows users to make characters from video games, television shows, and movies speak custom text with emotional inflections. The platform is able to generate convincing voice output using minimal training data; the name "15.ai" references the creator's statement that a voice can be cloned with just 15 seconds of audio. It was an early example of an application of generative artificial intelligence during the initial stages of the AI boom. Launched in March 2020, 15.ai became an Internet phenomenon in early 2021 when content utilizing it went viral on social media and quickly gained widespread use among Internet fandoms, such as the My Little Pony: Friendship Is Magic, Team Fortress 2, and SpongeBob SquarePants fandoms. The service featured emotional context through emojis, precise pronunciation control, and multi-speaker capabilities. Critics praised 15.ai's accessibility and emotional control but criticized its technical limitations in prosody options and non-English language support, with mixed results depending on character complexity. 15.ai is credited as the first platform to popularize AI voice cloning in memes and content creation. Voice actors and industry professionals debated 15.ai's implications, raising concerns about employment impacts, voice-related fraud, and potential misuse. In January 2022, it was discovered that a company called Voiceverse had generated voice lines using 15.ai without attribution, promoted them as the byproduct of their own technology, and sold them as non-fungible tokens (NFT) without permission. News publications universally characterized this incident as the company having "stolen" from 15.ai. The service went offline in September 2022 due to legal issues surrounding artificial intelligence and copyright. Its shutdown was followed by the emergence of commercial alternatives whose founders have acknowledged 15.ai's pioneering influence in the field of deep learning speech synthesis. On May 18, 2025, 15 launched 15.dev as the sequel to 15.ai. == History == === Background === The field of speech synthesis underwent a significant transformation with the introduction of deep learning approaches. In 2016, DeepMind's publication of the WaveNet paper marked a shift toward neural network-based speech synthesis, which enabled higher audio quality via causal convolutional neural networks. Previously, concatenative synthesis—which worked by stitching together pre-recorded segments of human speech—was the predominant method for generating artificial speech, but it often produced robotic-sounding results at the boundaries of sentences. In 2018, Google AI's Tacotron 2 showed that neural networks could produce highly natural speech synthesis but required substantial training data (typically tens of hours of audio) to achieve acceptable quality. When trained on two hours of training data, the output quality degraded while still being able to maintain intelligible speech; with 24 minutes of training data, Tacotron 2 failed to produce intelligible speech. The same year saw the emergence of HiFi-GAN, a generative adversarial network (GAN)-based vocoder that improved the efficiency of waveform generation while producing high-fidelity speech, followed by Glow-TTS, which introduced a flow-based approach that allowed for both fast inference and voice style transfer capabilities. Chinese tech companies like Baidu and ByteDance also made contributions to the field by developing breakthroughs that further advanced the technology. === 2016–2020: Conception and development === 15.ai was conceived in 2016 as a research project in deep learning speech synthesis by a developer known as 15 (at the age of 18) during their freshman year at MIT as part of its Undergraduate Research Opportunities Program. 15 was inspired by DeepMind's WaveNet paper, with development continuing through their studies as Google AI released Tacotron 2 the following year. By 2019, they had demonstrated at MIT their ability to replicate WaveNet and Tacotron 2's results using 75% less training data than previously required. The name "15.ai" is a reference to the developer's statement that a voice can be cloned with as little as 15 seconds of data. 15 had originally planned to pursue a PhD based on their undergraduate research, but opted to work in the tech industry instead after their startup was accepted into the Y Combinator accelerator in 2019. After their departure in early 2020, 15 returned to their voice synthesis research and began implementing it as a web application. According to a post on X from 15, instead of using conventional voice datasets like LJSpeech that contained simple, monotone recordings, they sought out more challenging voice samples that could demonstrate the model's ability to handle complex speech patterns and emotional undertones. During this phase, 15 discovered the Pony Preservation Project, a collaborative project started by /mlp/, the My Little Pony board on 4chan. Contributors of the project had manually trimmed, denoised, transcribed, and emotion-tagged thousands of voice lines from My Little Pony: Friendship Is Magic and had compiled them into a dataset that provided ideal training material for 15.ai. === 2020–2022: Release and operation === 15.ai was released on March 2, 2020 as a free and non-commercial web application that did not require user registration to use, but did require the user to accept its terms of service before proceeding. At the time of its launch, the platform had a limited selection of available characters, including those from My Little Pony: Friendship Is Magic and Team Fortress 2. Users were permitted to create any content with the synthesized voices under two conditions: they had to properly credit 15.ai by including "15.ai" in any posts, videos, or projects using the generated audio; and they were prohibited from mixing 15.ai outputs with other text-to-speech outputs in the same work to prevent misrepresentation of the technology's capabilities. On March 8, 2020, Tyler McVicker of Valve News Network uploaded a YouTube video showcasing 15.ai. More voices were added to the website in the following months. In late 2020, 15 implemented a multi-speaker embedding in the deep neural network, which enabled the simultaneous training of multiple voices. Following this, the website's roster expanded from eight to over fifty characters. In addition, this implementation allowed the deep learning model to recognize common emotional patterns across different characters, even when certain emotions were missing from the characters' training data. By May 2020, the site had served over 4.2 million audio files to users. In early 2021, the application gained popularity after skits, memes, and fan content created using 15.ai went viral on Twitter, TikTok, Reddit, Twitch, Facebook, and YouTube. At its peak, the platform incurred operational costs of US$12,000 per month from AWS infrastructure needed to handle millions of daily voice generations; despite receiving offers from companies to acquire 15.ai and its underlying technology, the website remained independent and was funded out of the personal previous startup earnings of the developer. === 2022: Voiceverse NFT controversy === On January 14, 2022, 15 discovered that a blockchain-based company called Voiceverse had generated voice lines using 15.ai, falsely showcased them on Twitter as a demonstration of their own voice technology without permission or attribution, and sold them as NFTs. This came shortly after 15 had stated in December 2021 that they had no interest in incorporating NFTs into their work. A screenshot of the log files posted by 15 showed that Voiceverse had generated audio of characters from My Little Pony: Friendship Is Magic using 15.ai and pitched them up to make them sound unrecognizable, a violation of 15.ai's terms of service, which explicitly prohibited commercial use and required proper attribution. When confronted with evidence, Voiceverse stated that their marketing team had used 15.ai without proper attribution while rushing to create a demo. In response, 15 tweeted "Go fuck yourself," which went viral, amassing hundreds of thousands of retweets and likes on Twitter in support of the developer. The tweets showcasing the stolen voices were subsequently deleted. ==== Aftermath ==== The controversy raised concerns about NFT projects, which, according to critics, were frequently associated with intellectual property theft and questionable business practices. The incident was documented in the AI Incident Database (AIID) and the AI, Alg

    Read more →
  • Rapid PHP Editor

    Rapid PHP Editor

    rapid PHP Editor is a PHP Editor that incorporates many functions such as AutoComplete, Syntax checker, debugger and many other tools for fast PHP development. Rapid PHP Editor also contain other development tools for helping on HTML, CSS, JavaScript and many other languages. Is part of a family of products covering most aspects of modern web development integrating as well many other capabilities used by developers. Some features: (X)HTML to HTML5 CSS to CSS3 Code intelligence Powerful search and replace Support for several frameworks Code beautifier FTP Explorer (FTP/SFTP/FTPS) File explorer Database explorer Code snippets Validators and Debuggers FAST, real fast Many other tools available (many more to describe all here) == History == Rapid PHP Editor was built using the Delphi programming language.

    Read more →
  • Cognitive philology

    Cognitive philology

    Cognitive philology is the science that studies written and oral texts as the product of human mental processes. Studies in cognitive philology compare documentary evidence emerging from textual investigations with results of experimental research, especially in the fields of cognitive and ecological psychology, neurosciences and artificial intelligence. "The point is not the text, but the mind that made it". Cognitive Philology aims to foster communication between literary, textual, philological disciplines on the one hand and researches across the whole range of the cognitive, evolutionary, ecological and human sciences on the other. Cognitive philology: investigates transmission of oral and written text, and categorization processes which lead to classification of knowledge, mostly relying on the information theory; studies how narratives emerge in so called natural conversation and selective process which lead to the rise of literary standards for storytelling, mostly relying on embodied semantics; explores the evolutive and evolutionary role played by rhythm and metre in human ontogenetic and phylogenetic development and the pertinence of the semantic association during processing of cognitive maps; Provides the scientific ground for multimedia critical editions of literary texts. Among the founding thinkers and noteworthy scholars devoted to such investigations are: Alan Richardson: Studies Theory of Mind in early-modern and contemporary literature. Anatole Pierre Fuksas Benoît de Cornulier David Herman: Professor of English at North Carolina State University and an adjunct professor of linguistics at Duke University. He is the author of "Universal Grammar and Narrative Form" and the editor of "Narratologies: New Perspectives on Narrative Analysis". Domenico Fiormonte François Recanati Gilles Fauconnier, a professor in Cognitive science at the University of California, San Diego. He was one of the founders of cognitive linguistics in the 1970s through his work on pragmatic scales and mental spaces. His research explores the areas of conceptual integration and compressions of conceptual mappings in terms of the emergent structure in language. Julián Santano Moreno Luca Nobile Manfred Jahn in Germany Mark Turner Paolo Canettieri

    Read more →
  • Monitoring as a service

    Monitoring as a service

    Monitoring as a service (MaaS) is a cloud-based framework for the deployment of monitoring functionalities for various other services and applications within the cloud. The most common application for MaaS is online state monitoring, which continuously tracks certain states of applications, networks, systems, instances or any element that may be deployable within the cloud.

    Read more →
  • Focus recovery based on the linear canonical transform

    Focus recovery based on the linear canonical transform

    For digital image processing, the Focus recovery from a defocused image is an ill-posed problem since it loses the component of high frequency. Most of the methods for focus recovery are based on depth estimation theory. The Linear canonical transform (LCT) gives a scalable kernel to fit many well-known optical effects. Using LCTs to approximate an optical system for imaging and inverting this system, theoretically permits recovery of a defocused image. == Depth of field and perceptual focus == In photography, depth of field (DOF) means an effective focal length. It is usually used for stressing an object and deemphasizing the background (and/or the foreground). The important measure related to DOF is the lens aperture. Decreasing the diameter of aperture increases focus and lowers resolution and vice versa. == The Huygens–Fresnel principle and DOF == The Huygens–Fresnel principle describes diffraction of wave propagation between two fields. It belongs to Fourier optics rather than geometric optics. The disturbance of diffraction depends on two circumstance parameters, the size of aperture and the interfiled distance. Consider a source field and a destination field, field 1 and field 0, respectively. P1(x1,y1) is the position in the source field, P0(x0,y0) is the position in the destination field. The Huygens–Fresnel principle gives the diffraction formula for two fields U(x0,y0), U(x1,y1) as following: U ( x 0 , y 0 ) = 1 j λ ∫ ∫ U ( x 1 , y 1 ) e j k r 01 r 01 cos ⁡ θ d x 1 d y 1 {\displaystyle \mathbf {U} (x_{0},y_{0})={\frac {1}{j\lambda }}\int \!\int \mathbf {U} (x_{1},y_{1}){\frac {e^{jkr_{01}}}{r_{01}}}\cos \theta dx_{1}dy_{1}} where θ denotes the angle between r 01 {\displaystyle r_{01}} and z {\displaystyle z} . Replace cos θ by r 01 z {\displaystyle {\frac {r_{01}}{z}}} and r 01 {\displaystyle r_{01}} by [ ( x 0 − x 1 ) 2 + ( y 0 − y 1 ) 2 + z 2 ] 1 / 2 {\displaystyle [(x_{0}-x_{1})^{2}+(y_{0}-y_{1})^{2}+z^{2}]^{1/2}} we get U ( x 0 , y 0 ) = 1 j λ z ∫ ∫ U ( x 1 , y 1 ) exp ⁡ ( j k z [ 1 + ( x 0 − x 1 z ) 2 + ( y 0 − y 1 z ) 2 ] 1 / 2 ) 1 + ( x 0 − x 1 z ) 2 + ( y 0 − y 1 z ) 2 d x 1 d y 1 {\displaystyle \mathbf {U} (x_{0},y_{0})={\frac {1}{j\lambda z}}\int \!\int \mathbf {U} (x_{1},y_{1}){\frac {\exp(jkz[1+({\frac {x_{0}-x_{1}}{z}})^{2}+({\frac {y_{0}-y_{1}}{z}})^{2}]^{1/2})}{1+({\frac {x_{0}-x_{1}}{z}})^{2}+({\frac {y_{0}-y_{1}}{z}})^{2}}}dx_{1}dy_{1}} The further distance z or the smaller aperture (x1,y1) causes a greater diffraction. A larger DOF can lead to a more effective focused wave distribution. This seems to be a conflict. Here are the notations: Diffraction In a real imaging environment, the depths of objects comparing to the aperture are usually not enough to lead to serious diffraction. However, a long enough depth of the object can truly blurs the image. Effective Focus Small aperture, small blurring radius, few wave information. Loses details in comparing to a large aperture. In conclusion, diffraction explains a micro behavior whereas DOF shows a macro behavior. Both of them are related to aperture size. == Linear canonical transform == As the meaning of "canonical", the linear canonical transform (LCT) is a scalable transform that connects to many important kernels such as the Fresnel transform, Fraunhofer transform and the fractional Fourier transform. It can be easily controlled by its four parameters, a, b, c, d (3 degrees of freedom). The definition: L M ( f ( u ) ) = ∫ L M ( u , u ′ ) f ( u ′ ) d u ′ {\displaystyle L_{M}(f(u))=\int L_{M}(u,u')f(u')du'} where L M ( u , u ′ ) = { 1 b e − j π / 4 e [ j π ( d b u 2 ) − 2 1 b u u ′ + a b u ′ 2 ] , if b ≠ 0 d e j 2 c d u 2 δ ( u ′ − d u ) , if b = 0 {\displaystyle L_{M}(u,u')={\begin{cases}{\sqrt {\frac {1}{b}}}e^{-j\pi /4}e^{[j\pi ({\frac {d}{b}}u^{2})-2{\frac {1}{b}}uu'+{\frac {a}{b}}u'^{2}]},&{\mbox{if }}b\neq 0\\{\sqrt {d}}e^{{\frac {j}{2}}cdu^{2}}\delta (u'-du),&{\mbox{if }}b=0\end{cases}}} Consider a general imaging system with object distance z0, focal length of the thin lens f and an imaging distance z1. The effect of the propagation in freespace acts as nearly a chirp convolution, that is, the formula of diffraction. Besides, the effect of the propagation in thin lens acts as a chirp multiplication. The parameters are all simplified as paraxial approximations while meeting the freespace propagation. It does not consider aperture size. From the properties of the LCT, it is possible to obtain those 4 parameters for this optical system as: [ 1 − z 1 f λ z 0 − λ z 0 z 1 f + λ z 1 − 1 λ f 1 − z 0 f ] {\displaystyle {\begin{bmatrix}1-{\frac {z_{1}}{f}}\quad &\lambda z_{0}-{\frac {\lambda z_{0}z_{1}}{f}}+\lambda z_{1}\\-{\frac {1}{\lambda f}}\quad &1-{\frac {z_{0}}{f}}\end{bmatrix}}} Once the values of z1, z0 and f are known, the LCT can simulate any optical system.

    Read more →
  • Software development process

    Software development process

    A software development process prescribes a process for developing software. It typically divides an overall effort into smaller steps or sub-processes that are intended to ensure high-quality results. The process may describe specific deliverables – artifacts to be created and completed. Although not strictly limited to it, software development process often refers to the high-level process that governs the development of a software system from its beginning to its end of life – known as a methodology, model or framework. The system development life cycle (SDLC) describes the typical phases that a development effort goes through from the beginning to the end of life for a system – including a software system. A methodology prescribes how engineers go about their work in order to move the system through its life cycle. A methodology is a classification of processes or a blueprint for a process that is devised for the SDLC. For example, many processes can be classified as a spiral model. Software process and software quality are closely interrelated; some unexpected facets and effects have been observed in practice. == Methodology == The SDLC drives the definition of a methodology in that a methodology must address the phases of the SDLC. Generally, a methodology is designed to result in a high-quality system that meets or exceeds expectations (requirements) and is delivered on time and within budget even though computer systems can be complex and integrate disparate components. Various methodologies have been devised, including waterfall, spiral, agile, rapid prototyping, incremental, and synchronize and stabilize. A major difference between methodologies is the degree to which the phases are sequential vs. iterative. Agile methodologies, such as XP and scrum, focus on lightweight processes that allow for rapid changes. Iterative methodologies, such as Rational Unified Process and dynamic systems development method, focus on stabilizing project scope and iteratively expanding or improving products. Sequential or big-design-up-front (BDUF) models, such as waterfall, focus on complete and correct planning to guide larger projects and limit risks to successful and predictable results. Anamorphic development is guided by project scope and adaptive iterations. In scrum, for example, one could say a single user story goes through all the phases of the SDLC within a two-week sprint. By contrast the waterfall methodology, where every business requirement is translated into feature/functional descriptions which are then all implemented typically over a period of months or longer. A project can include both a project life cycle (PLC) and an SDLC, which describe different activities. According to Taylor (2004), "the project life cycle encompasses all the activities of the project, while the systems development life cycle focuses on realizing the product requirements". === History === The term SDLC is often used as an abbreviated version of SDLC methodology. Further, some use SDLC and traditional SDLC to mean the waterfall methodology. According to Elliott (2004), SDLC "originated in the 1960s, to develop large scale functional business systems in an age of large scale business conglomerates. Information systems activities revolved around heavy data processing and number crunching routines". The structured systems analysis and design method (SSADM) was produced for the UK government Office of Government Commerce in the 1980s. Ever since, according to Elliott (2004), "the traditional life cycle approaches to systems development have been increasingly replaced with alternative approaches and frameworks, which attempted to overcome some of the inherent deficiencies of the traditional SDLC". The main idea of the SDLC has been "to pursue the development of information systems in a very deliberate, structured and methodical way, requiring each stage of the life cycle––from the inception of the idea to delivery of the final system––to be carried out rigidly and sequentially" within the context of the framework being applied. Other methodologies were devised later: 1970s Structured programming since 1969 Cap Gemini SDM, originally from PANDATA, the first English translation was published in 1974. SDM stands for System Development Methodology 1980s Structured systems analysis and design method (SSADM) from 1980 onwards Information Requirement Analysis/Soft systems methodology 1990s Object-oriented programming (OOP) developed in the early 1960s and became a dominant programming approach during the mid-1990s Rapid application development (RAD), since 1991 Dynamic systems development method (DSDM), since 1994 Scrum, since 1995 Team software process, since 1998 Rational Unified Process (RUP), maintained by IBM since 1998 Extreme programming, since 1999 2000s Agile Unified Process (AUP) maintained since 2005 by Scott Ambler Disciplined agile delivery (DAD) Supersedes AUP 2010s Scaled Agile Framework (SAFe) Large-Scale Scrum (LeSS) DevOps Since DSDM in 1994, all of the methodologies on the above list except RUP have been agile methodologies - yet many organizations, especially governments, still use pre-agile processes (often waterfall or similar). === Examples === The following are notable methodologies somewhat ordered by popularity. Agile Agile software development refers to a group of frameworks based on iterative development, where requirements and solutions evolve via collaboration between self-organizing cross-functional teams. The term was coined in the year 2001 when the Agile Manifesto was formulated. Waterfall The waterfall model is a sequential development approach, in which development flows one-way (like a waterfall) through the SDLC phases. Spiral In 1988, Barry Boehm published a software system development spiral model, which combines key aspects of the waterfall model and rapid prototyping, in an effort to combine advantages of top-down and bottom-up concepts. It emphases a key area many felt had been neglected by other methodologies: deliberate iterative risk analysis, particularly suited to large-scale complex systems. Incremental Various methods combine linear and iterative methodologies, with the primary objective of reducing inherent project risk by breaking a project into smaller segments and providing more ease-of-change during the development process. Prototyping Software prototyping is about creating prototypes, i.e. incomplete versions of the software program being developed. Rapid Rapid application development (RAD) is a methodology which favors iterative development and the rapid construction of prototypes instead of large amounts of up-front planning. The "planning" of software developed using RAD is interleaved with writing the software itself. The lack of extensive pre-planning generally allows software to be written much faster and makes it easier to change requirements. Shape Up Shape Up is a software development approach introduced by Basecamp in 2018. It is a set of principles and techniques that Basecamp developed internally to overcome the problem of projects dragging on with no clear end. Its primary target audience is remote teams. Shape Up has no estimation and velocity tracking, backlogs, or sprints, unlike waterfall, agile, or scrum. Instead, those concepts are replaced with appetite, betting, and cycles. As of 2022, besides Basecamp, notable organizations that have adopted Shape Up include UserVoice and Block. Chaos Chaos model has one main rule: always resolve the most important issue first. Incremental funding Incremental funding methodology - an iterative approach. Lightweight Lightweight methodology - a general term for methods that only have a few rules and practices. Structured systems analysis and design Structured systems analysis and design method - a specific version of waterfall. Slow programming As part of the larger slow movement, emphasizes careful and gradual work without (or minimal) time pressures. Slow programming aims to avoid bugs and overly quick release schedules. V-Model V-Model (software development) - an extension of the waterfall model. Unified Process Unified Process (UP) is an iterative software development methodology framework, based on Unified Modeling Language (UML). UP organizes the development of software into four phases, each consisting of one or more executable iterations of the software at that stage of development: inception, elaboration, construction, and guidelines. === Comparison === The waterfall model describes the SDLC phases such that each builds on the result of the previous one. Not every project requires that the phases be sequential. For relatively simple projects, phases may be combined or overlapping. Alternative methodologies to waterfall are described and compared below. == Process meta-models == Some process models are abstract descriptions for evaluating, comparing, and improving the specific process adopted by an organization. ISO/IEC 12207 ISO/IEC 12207 i

    Read more →
  • Software design

    Software design

    Software design is the process of conceptualizing how a software system will work before it is implemented or modified. Software design also refers to the direct result of the design process – the concepts of how the software will work which may be formally documented or may be maintained less formally, including via oral tradition. The design process enables a designer to model aspects of a software system before it exists with the intent of making the effort of writing the code more efficiently. Creativity, past experience, a sense of what makes "good" software, and a commitment to quality are success factors for a competent design. A software design can be compared to an architected plan for a house. High-level plans represent the totality of the house (e.g., a three-dimensional rendering of the house). Lower-level plans provide guidance for constructing each detail (e.g., the plumbing lay). Similarly, the software design model provides a variety of views of the proposed software solution. == Part of the overall process == In terms of the waterfall development process, software design is the activity that occurs after requirements analysis and before coding. Requirements analysis determines what the system needs to do without determining how it will do it, and thus, multiple designs can be imagined that satisfy the requirements. The design can be created while coding, without a plan or requirements analysis, but for more complex projects this is less feasible. Completing a design prior to coding allows for multidisciplinary designers and subject-matter experts to collaborate with programmers to produce software that is useful and technically sound. Sometimes, a simulation or prototype is created to model the system in an effort to determine a valid and good design. == Code as design == A common point of confusion with the term design in software is that the process applies at multiple levels of abstraction such as a high-level software architecture and lower-level components, functions and algorithms. A relatively formal process may occur at high levels of abstraction but at lower levels, the design process is almost always less formal where the only artifact of design may be the code itself. To the extent that this is true, software design refers to the design of the design. Edsger W. Dijkstra referred to this layering of semantic levels as the "radical novelty" of computer programming, and Donald Knuth used his experience writing TeX to describe the futility of attempting to design a program prior to implementing it: TEX would have been a complete failure if I had merely specified it and not participated fully in its initial implementation. The process of implementation constantly led me to unanticipated questions and to new insights about how the original specifications could be improved. == Artifacts == A design process may include the production of art Software design documentation such as flow chart, use case, Pseudocode, Unified Modeling Language model and other Fundamental modeling concepts. For user centered software, design may involve user experience design yielding a storyboard to help determine those specifications. Documentation may be reviewed to allow constraints, specifications and even requirements to be adjusted prior to coding. == Iterative design == Software systems inherently deal with uncertainties, and the size of software components can significantly influence a system's outcomes, both positively and negatively. Neal Ford and Mark Richards propose an iterative approach to address the challenge of identifying and right-sizing components. This method emphasizes continuous refinement as teams develop a more nuanced understanding of system behavior and requirements. The approach typically involves a cycle with several stages: A high-level partitioning strategy is established, often categorized as technical or domain-based. Guidelines for the smallest meaningful deployable unit, referred to as "quanta," are defined. While these foundational decisions are made early, they may be revisited later in the cycle if necessary. Initial components are identified based on the established strategy. Requirements are assigned to the identified components. The roles and responsibilities of each component are analyzed to ensure clarity and minimize overlap. Architectural characteristics, such as scalability, fault tolerance, and maintainability, are evaluated. Components may be restructured based on feedback from development teams. This cycle serves as a general framework and can be adapted to different domains. == Design principles == Design principles enable a software engineer to navigate the design process. Davis suggested principles which have been refined over time as: The design process should not suffer from "tunnel vision" A good designer should consider alternative approaches, judging each based on the requirements of the problem, the resources available to do the job. The design should be traceable to the analysis model Because a single element of the design model can often be traced back to multiple requirements, it is necessary to have a means for tracking how requirements have been satisfied by the design model. The design should not reinvent the wheel Systems are constructed using a set of design patterns, many of which have likely been encountered before. These patterns should always be chosen as an alternative to reinvention. Time is short and resources are limited; design time should be invested in representing (truly new) ideas by integrating patterns that already exist (when applicable). The design should "minimize the intellectual distance" between the software and the problem as it exists in the real world That is, the structure of the software design should, whenever possible, mimic the structure of the problem domain. The design should exhibit uniformity and integration A design is uniform if it appears fully coherent. In order to achieve this outcome, rules of style and format should be defined for a design team before design work begins. A design is integrated if care is taken in defining interfaces between design components. The design should be structured to accommodate change The design concepts discussed in the next section enable a design to achieve this principle. The design should be structured to degrade gently, even when aberrant data, events, or operating conditions are encountered Well-designed software should never "bomb"; it should be designed to accommodate unusual circumstances, and if it must terminate processing, it should do so in a graceful manner. Design is not coding, coding is not design Even when detailed procedural designs are created for program components, the level of abstraction of the design model is higher than the source code. The only design decisions made at the coding level should address the small implementation details that enable the procedural design to be coded. The design should be assessed for quality as it is being created, not after the fact A variety of design concepts and design measures are available to assist the designer in assessing quality throughout the development process. The design should be reviewed to minimize conceptual (semantic) errors There is sometimes a tendency to focus on minutiae when the design is reviewed, missing the forest for the trees. A design team should ensure that major conceptual elements of the design (omissions, ambiguity, inconsistency) have been addressed before worrying about the syntax of the design model. == Design concepts == Design concepts provide a designer with a foundation from which more sophisticated methods can be applied. Design concepts include: Abstraction Reducing the information content of a concept or an observable phenomenon, typically to retain only information that is relevant for a particular purpose. It is an act of Representing essential features without including the background details or explanations. Architecture The overall structure of the software and the ways in which that structure provides conceptual integrity for a system. Good software architecture will yield a good return on investment with respect to the desired outcome of the project, e.g. in terms of performance, quality, schedule and cost. Control hierarchy A program structure that represents the organization of a program component and implies a hierarchy of control. Data structure Representing the logical relationship between elements of data. Design pattern A designer may identify a design aspect of the system that has solved in the past. The reuse of such patterns can increase software development velocity. Information hiding Modules should be specified and designed so that information contained within a module is inaccessible to other modules that have no need for such information. Modularity Dividing the solution into parts (modules). Refinement The process of elaboration. A hierarchy is developed by decomposing a macrosco

    Read more →
  • Software development process

    Software development process

    A software development process prescribes a process for developing software. It typically divides an overall effort into smaller steps or sub-processes that are intended to ensure high-quality results. The process may describe specific deliverables – artifacts to be created and completed. Although not strictly limited to it, software development process often refers to the high-level process that governs the development of a software system from its beginning to its end of life – known as a methodology, model or framework. The system development life cycle (SDLC) describes the typical phases that a development effort goes through from the beginning to the end of life for a system – including a software system. A methodology prescribes how engineers go about their work in order to move the system through its life cycle. A methodology is a classification of processes or a blueprint for a process that is devised for the SDLC. For example, many processes can be classified as a spiral model. Software process and software quality are closely interrelated; some unexpected facets and effects have been observed in practice. == Methodology == The SDLC drives the definition of a methodology in that a methodology must address the phases of the SDLC. Generally, a methodology is designed to result in a high-quality system that meets or exceeds expectations (requirements) and is delivered on time and within budget even though computer systems can be complex and integrate disparate components. Various methodologies have been devised, including waterfall, spiral, agile, rapid prototyping, incremental, and synchronize and stabilize. A major difference between methodologies is the degree to which the phases are sequential vs. iterative. Agile methodologies, such as XP and scrum, focus on lightweight processes that allow for rapid changes. Iterative methodologies, such as Rational Unified Process and dynamic systems development method, focus on stabilizing project scope and iteratively expanding or improving products. Sequential or big-design-up-front (BDUF) models, such as waterfall, focus on complete and correct planning to guide larger projects and limit risks to successful and predictable results. Anamorphic development is guided by project scope and adaptive iterations. In scrum, for example, one could say a single user story goes through all the phases of the SDLC within a two-week sprint. By contrast the waterfall methodology, where every business requirement is translated into feature/functional descriptions which are then all implemented typically over a period of months or longer. A project can include both a project life cycle (PLC) and an SDLC, which describe different activities. According to Taylor (2004), "the project life cycle encompasses all the activities of the project, while the systems development life cycle focuses on realizing the product requirements". === History === The term SDLC is often used as an abbreviated version of SDLC methodology. Further, some use SDLC and traditional SDLC to mean the waterfall methodology. According to Elliott (2004), SDLC "originated in the 1960s, to develop large scale functional business systems in an age of large scale business conglomerates. Information systems activities revolved around heavy data processing and number crunching routines". The structured systems analysis and design method (SSADM) was produced for the UK government Office of Government Commerce in the 1980s. Ever since, according to Elliott (2004), "the traditional life cycle approaches to systems development have been increasingly replaced with alternative approaches and frameworks, which attempted to overcome some of the inherent deficiencies of the traditional SDLC". The main idea of the SDLC has been "to pursue the development of information systems in a very deliberate, structured and methodical way, requiring each stage of the life cycle––from the inception of the idea to delivery of the final system––to be carried out rigidly and sequentially" within the context of the framework being applied. Other methodologies were devised later: 1970s Structured programming since 1969 Cap Gemini SDM, originally from PANDATA, the first English translation was published in 1974. SDM stands for System Development Methodology 1980s Structured systems analysis and design method (SSADM) from 1980 onwards Information Requirement Analysis/Soft systems methodology 1990s Object-oriented programming (OOP) developed in the early 1960s and became a dominant programming approach during the mid-1990s Rapid application development (RAD), since 1991 Dynamic systems development method (DSDM), since 1994 Scrum, since 1995 Team software process, since 1998 Rational Unified Process (RUP), maintained by IBM since 1998 Extreme programming, since 1999 2000s Agile Unified Process (AUP) maintained since 2005 by Scott Ambler Disciplined agile delivery (DAD) Supersedes AUP 2010s Scaled Agile Framework (SAFe) Large-Scale Scrum (LeSS) DevOps Since DSDM in 1994, all of the methodologies on the above list except RUP have been agile methodologies - yet many organizations, especially governments, still use pre-agile processes (often waterfall or similar). === Examples === The following are notable methodologies somewhat ordered by popularity. Agile Agile software development refers to a group of frameworks based on iterative development, where requirements and solutions evolve via collaboration between self-organizing cross-functional teams. The term was coined in the year 2001 when the Agile Manifesto was formulated. Waterfall The waterfall model is a sequential development approach, in which development flows one-way (like a waterfall) through the SDLC phases. Spiral In 1988, Barry Boehm published a software system development spiral model, which combines key aspects of the waterfall model and rapid prototyping, in an effort to combine advantages of top-down and bottom-up concepts. It emphases a key area many felt had been neglected by other methodologies: deliberate iterative risk analysis, particularly suited to large-scale complex systems. Incremental Various methods combine linear and iterative methodologies, with the primary objective of reducing inherent project risk by breaking a project into smaller segments and providing more ease-of-change during the development process. Prototyping Software prototyping is about creating prototypes, i.e. incomplete versions of the software program being developed. Rapid Rapid application development (RAD) is a methodology which favors iterative development and the rapid construction of prototypes instead of large amounts of up-front planning. The "planning" of software developed using RAD is interleaved with writing the software itself. The lack of extensive pre-planning generally allows software to be written much faster and makes it easier to change requirements. Shape Up Shape Up is a software development approach introduced by Basecamp in 2018. It is a set of principles and techniques that Basecamp developed internally to overcome the problem of projects dragging on with no clear end. Its primary target audience is remote teams. Shape Up has no estimation and velocity tracking, backlogs, or sprints, unlike waterfall, agile, or scrum. Instead, those concepts are replaced with appetite, betting, and cycles. As of 2022, besides Basecamp, notable organizations that have adopted Shape Up include UserVoice and Block. Chaos Chaos model has one main rule: always resolve the most important issue first. Incremental funding Incremental funding methodology - an iterative approach. Lightweight Lightweight methodology - a general term for methods that only have a few rules and practices. Structured systems analysis and design Structured systems analysis and design method - a specific version of waterfall. Slow programming As part of the larger slow movement, emphasizes careful and gradual work without (or minimal) time pressures. Slow programming aims to avoid bugs and overly quick release schedules. V-Model V-Model (software development) - an extension of the waterfall model. Unified Process Unified Process (UP) is an iterative software development methodology framework, based on Unified Modeling Language (UML). UP organizes the development of software into four phases, each consisting of one or more executable iterations of the software at that stage of development: inception, elaboration, construction, and guidelines. === Comparison === The waterfall model describes the SDLC phases such that each builds on the result of the previous one. Not every project requires that the phases be sequential. For relatively simple projects, phases may be combined or overlapping. Alternative methodologies to waterfall are described and compared below. == Process meta-models == Some process models are abstract descriptions for evaluating, comparing, and improving the specific process adopted by an organization. ISO/IEC 12207 ISO/IEC 12207 i

    Read more →
  • Switch (app)

    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.”

    Read more →