AI Chat Hpt

AI Chat Hpt — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

  • Smartphone kill switch

    Smartphone kill switch

    A smartphone kill switch is a software-based security feature that allows a smartphone's owner to remotely render it inoperable if it is lost or stolen, thereby deterring theft. There have been a number of initiatives to legally require kill switches on smartphones. Smartphones have high resale value, and are therefore often the target of theft, with thieves selling them to cartels for resale. A kill switch can deter theft by making devices worthless. == Legal requirements == In the United States, Minnesota was the first state to pass a bill requiring smartphones to have such a feature, and California was the first to require that the feature be turned on by default. The California law requires the kill switch to be resistant to reinstallation of the phone's operating system. The CTIA initially resisted the legislation, fearing that it would make phones easier to hack, but later supported kill switches. There is evidence that this legislation has been effective, with smartphone theft declining by 50% between 2013 and 2017 in San Francisco. Secure Our Smartphones (S.O.S.), a New York State and San Francisco initiative started by New York State Attorney General Eric Schneiderman and San Francisco District Attorney George Gascón. The initiative is co-chaired by Schneiderman, Gascón and Boris Johnson, and has 105 members. == Examples == An Android phone signed into a Google account can be remotely locked and erased via Google's Find My Device service, as long as it is connected to the Internet. To prevent this, a thief must sign the device out of Google before the owner locks or erases it. iPhones have a similar service.

    Read more →
  • Software-defined mobile network

    Software-defined mobile network

    Software-defined mobile networking (SDMN) is an approach to the design of mobile networks where all protocol-specific features are implemented in software, maximizing the use of generic and commodity hardware and software in both the core network and radio access network (RAN). == History == Through the 20th century, telecommunications technology was driven by hardware development, with most functions implemented in special-purpose equipment. In the early 2000s, generally available CPUs became cheap enough to enable commercial software-defined radio (SDR) technology and softswitches. SDMN extends these trends into the design of mobile networks, moving nearly all network functions into software. The term "software-defined mobile network" first appeared in public literature in early 2014, used independently by Lime Microsystems and researchers from University of Oulu, Finland. == Limitations of hardware-based mobile networks == Mobile networks based on special-purpose hardware suffer from the following limitations: They have limited provisions for upgrades and usually must be replaced entirely when new standards are introduced. The individual components are not scalable in terms of performance and capacity, because the capacity of a component is fixed by the hardware implementation. Specialized equipment and its associated specialized software require vendor-specific training for the mobile operator's staff. Specialized hardware systems are usually supported and serviced by a single vendor, resulting in vendor lock-in. == Characteristics of SDMN designs == === Use of software-defined radio === SDR is an important element of SDMN, because it replaces protocol-specific radio hardware with protocol-agnostic digital transceivers. While many earlier digital radio systems used field-programmable gate arrays (FPGAs) or special-purposed digital signal processors (DSPs) for calculations on baseband radio waveforms, the SDMN approach moves all of the baseband processing into general-purpose CPUs. SDMN radio systems also use hardware with publicly-documented interfaces that is designed to be readily reproducible by multiple manufacturers. === Commodity components === SDMN designs avoid the use of components that are specialized as to their functions or that are available from only a single vendor. This is true of both the hardware and software elements of the network. === Software switching and transcoding === The telephony switches of SDMN networks are software-based, including software transcoding for speech codecs. === Centralized, distributed, or hybrid? === A new SDN architecture for wireless distribution systems (WDSs) is explored that eliminates the need for multi-hop flooding of route information and therefore enables WDNs to easily expand. The key idea is to split network control and data forwarding by using two separate frequency bands. The forwarding nodes and the SDN controller exchange link-state information and other network control signaling in one of the bands, while actual data forwarding takes place in the other band. == Advantages of SDMN == The SDMN approach has many advantages over hardware-based mobile network designs. Because SDMN hardware is protocol-agnostic, upgrades are software-only, even across technology generations. In the radio network, these changes can even be made on a site-by-site basis. Because SDMN hardware is designed to be easily sourced and reproduced: SDMN equipment can be serviced by a wider range of vendors, lowering maintenance costs. SDMN equipment can be manufactured anywhere in the world, lowering production costs. Because SDMN software is based on commodity operating systems and development tools: Support staff can be trained more quickly because they are already familiar with the underlying software systems. Many aspects of the SDMN can be monitored and managed with pre-existing tools, because they are already available in the commodity operating systems. Because SDMN network components run on general purpose computers, the network components can be scaled up in capacity by adding more computing power.

    Read more →
  • Mosaik Solutions

    Mosaik Solutions

    Mosaik Solutions (formerly American Roamer) was a company that specializes in wireless coverage data and wireless coverage maps, based in Memphis, Tennessee before being acquired by Ookla. The company collects and crowdsources carrier signal quality from major telecommunications providers or users who have its consumer or enterprise mobile application installed. The data is used to provide insights into places around the world without access to cellular coverage and the development of new coverage patterns, as well as to provide maps showing what provider offers the best service in an area. In 2011, the Federal Communications Commission (FCC), recognized Mosaik Solutions as the "industry standard" for the presence of wireless service at the census-block level. == History == In 2016, Mosaik purchased Sensorly, a free app developed to crowdsource cellular network performance service and provide coverage mapping for wireless networks worldwide. == Products and services == === MapELEMENTS === MapELEMENTS software is a visualization tool that allows users to analyze data from the largest cellular coverage database in the world. === CellMaps === CellMaps is an interactive mapping solution that allows companies to show their network coverage directly on their website through an iframe or API. In 2013 Mosaik launched an android app for CellMaps that provides data directly from carriers so that users can determine what carrier meets their needs in a given area. On the map you can overlay multiple carriers, zoom to street-view level, and drop a pin onto any given spot to get a breakdown of carrier service in that area. === Signal Insights App === Signal Insights is an SaaS platform service available for android users that measures and analyzes the customer's experience in cellular or Wi-Fi networks. Indoor mode allows a user to upload a building floor plan and then map and test specific points in the building for cellular or Wi-Fi connectivity. === Sensorly App === Sensorly is a free app that crowdsources cellular network performance to provide coverage mapping worldwide and mobile speed data to help consumers make informed decisions when choosing a cellular carrier. In February 2017, Sensorly launched Map Trip, a feature that allows users to map their routes and share with others their signal data at a particular point in real time. === TowerSource === TowerSource is a resource for locating cell towers and identifying ownership, availability, fiber routes, type and height. It was acquired by Mosaik Solutions in September 2014. === Network Validator === Network Validator is a SaaS solution designed for users to quickly determine whether global cellular networks exist - by country, operator and wireless technology. === CoverageRight === CoverageRight is composed of licensed GIS file datasets that identify the marketed coverage of wireless operators in the United States and worldwide. It enables users to perform spatial analyses, monitor competitive build-outs, analyze coverage trends and assemble roaming footprints. This data has been utilized by the FCC to analyze wireless coverage nationwide. === Network QoE === Network QoE is an enterprise platform that uses crowdsourced data from cellular devices to detect wireless network issues including 3G, 4G and wifi accessibility, network coverage holes and data performance issues. === Wireless Spectrum Report === In March 2017, Mosaik Solutions launched the Wireless Spectrum Report, a tabular dataset detailing facts about spectrum ownership and availability in the United States.

    Read more →
  • Digital divide

    Digital divide

    Digital divide is inequitable access to and use of digital technology, encompassing four interrelated dimensions: motivational, material, skills, and usage access. The digital divide worsens inequality in access to information and resources. According to 2026 data from the U.S. Census Bureau, a significant 'digital divide' persists, with over 15.7 million Americans lacking access to high-speed broadband. Students from low-income households often face limited access to reliable internet and digital devices, which negatively affects their educational opportunities. In the Information Age, people without access to the Internet and other technology are at a disadvantage, for they are less able to connect with others, find and apply for jobs, shop, and learn. People living in poverty, in insecure housing or who are homeless, elderly people, and those living in rural communities may have limited access to the Internet; in contrast, urban middle class people have easy access to the Internet. Another divide is between producers and consumers of Internet content, which could be a result of educational disparities. While social media use varies across age groups, a US 2010 study reported no racial divide. == History == The historical roots of the digital divide in the United States refer to the increasing gap that occurred during the early modern period between those who could and could not access the real time forms of calculation, decision-making, and visualization offered via written and printed media. "Over time, focus has shifted from binary access to differentiated use, where quality and purpose of engagement vary across socio-economic groups." Within this context, ethical discussions regarding the relationship between education and the free distribution of information were raised by thinkers such as Immanuel Kant, Jean Jacques Rousseau, and Mary Wollstonecraft (1712–1778). The latter advocated that governments should intervene to ensure that any society's economic benefits should be fairly and meaningfully distributed. Amid the Industrial Revolution in Great Britain, Rousseau's idea helped to justify poor laws that created a safety net for those who were harmed by new forms of production. Later, when telegraph and postal systems evolved, many used Rousseau's ideas to argue for full access to those services, even if it meant subsidizing hard-to-serve citizens. Thus, "universal services" referred to innovations in regulation and taxation that would allow phone services such as AT&T in the United States to serve hard-to-serve rural users. In 1996, as telecommunications companies merged with Internet companies, the Federal Communications Commission adopted Telecommunications Act of 1996 to consider regulatory strategies and taxation policies to close the digital divide. Though the term "digital divide" was coined among consumer groups that sought to tax and regulate information and communications technology (ICeT) companies to close the digital divide, the topic soon moved onto a global stage. The focus was the World Trade Organization which passed the Telecommunications Services Act, which resisted regulation of ICT companies so that they would be required to serve hard-to-serve individuals and communities. In 1999, to assuage anti-globalization forces, the WTO hosted the "Financial Solutions to Digital Divide" in Seattle, US, co-organized by Craig Warren Smith of Digital Divide Institute and Bill Gates Sr. the chairman of the Bill and Melinda Gates Foundation. It catalyzed a full-scale global movement to close the digital divide, which quickly spread to all sectors of the global economy. In 2000, US president Bill Clinton mentioned the term in the State of the Union Address. Since the early 2000s, the international community has transitioned from a focus on domestic infrastructure to a global, multi-dimensional framework for digital equity. This shift was formalized through the World Summit on the Information Society (WSIS) in Geneva (2003) and Tunis (2005), where the International Telecommunication Union (ITU) established a roadmap for bridging the Global North-South disparity as part of the Sustainable Development Goals. Academic and policy discourse has since evolved to distinguish between the first-level divide (physical access), the second-level divide (digital literacy), and the third-level divide (the ability to translate technology use into socio-economic capital). By the 2020s, critical reflections on national development emphasized that the divide is fundamentally a socio-institutional gap. Research by Tiwari, Kostenko, and Yekhanurov (2025) identifies four pillars for achieving national digital maturity which are digital governance capacity, institutional design to prevent adverse digital incorporation, infrastructure resilience, and citizen capability. This modern era is characterized by the pursuit of meaningful connectivity, a standard that requires internet access to be not only available but affordable, high-speed, and supportive of active content creation. === During the COVID-19 pandemic === At the outset of the COVID-19 pandemic, governments worldwide issued stay-at-home orders that imposed lockdowns, quarantines, restrictions, and closures. The resulting interruptions to schooling, public services, and business operations drove nearly half of the world's population into seeking alternative methods to live while in isolation. These methods included telemedicine, virtual classrooms, online shopping, technology-based social interactions and working remotely, all of which require access to high-speed or broadband internet access and digital technologies. A Pew Research Centre study reports that 90% of Americans describe the use of the Internet as "essential" during the pandemic. The accelerated use of digital technologies created a landscape where the ability, or lack thereof, to access digital spaces became a crucial factor in everyday life. According to the Pew Research Center, 59% of children from lower-income families were likely to face digital obstacles in completing school assignments. These obstacles included the use of a cellphone to complete homework, having to use public Wi-Fi because of unreliable internet service in the home and lack of access to a computer in the home. This difficulty, titled the homework gap, affects more than 30% of K-12 students living below the poverty threshold, and disproportionally affects American Indian/Alaska Native, Black, and Hispanic students. These types of interruptions or privilege gaps in education exemplify problems in the systemic marginalization of historically oppressed individuals in primary education. The pandemic exposed inequity causing discrepancies in learning. "Large-scale events such as COVID-19 intensify both access and skills gaps, underlining the need for resilient digital inclusion policies. Studies during COVID-19 reveal first-level (access) and second-level (skills) divides, with underserved students struggling with reliable internet, devices, and platform navigation ” A lack of "tech readiness", that is, confident and independent use of devices, was reported among the US elderly population; with more than 50% reporting an inadequate knowledge of devices and more than one-third reporting a lack of confidence. "Older adults often face skills and confidence barriers, illustrating later-stage divides in van Dijk’s model." Moreover, according to a UN research paper, similar results can be found across various Asian countries, with those aged over 74, reporting less confident or inconsistent use of digital devices. This aspect of the digital divide and the elderly occurred during the pandemic as healthcare providers increasingly relied upon telemedicine to manage chronic and acute health conditions. == Aspects == There are various definitions of the digital divide, all with slightly different emphasis, which is evidenced by related concepts like digital inclusion, digital participation, digital skills, media literacy, and digital accessibility.“Van Dijk’s model identifies sequential barriers—motivational, material, skills, and usage—that must be addressed to bridge the divide.” === Infrastructure === The infrastructure by which individuals, households, businesses, and communities connect to the Internet addresses the physical mediums that people use to connect to the Internet such as desktop computers, laptops, basic mobile phones or smartphones, MP3 players, gaming consoles, electronic book readers, and tablets. Traditionally, the nature of the divide has been measured in terms of the existing numbers of subscriptions and digital devices. Given the increasing number of such devices, some have concluded that the digital divide among individuals has increasingly been closing as the result of a natural and almost automatic process. Others point to persistent lower levels of connectivity among women, racial and ethnic minorities, people with lower incomes, rura

    Read more →
  • Luxafor

    Luxafor

    Luxafor () is a brand of office productivity tools designed to improve efficiency and communication in workplaces. The brands main product is LED status indicators for use in office settings. Luxafor is a product line under the company SIA Greynut, based in Riga, Latvia. == History == Luxafor was developed by the technology company SIA Greynut. The brand first gained attention through a Kickstarter campaign in 2015, which aimed to fund its initial product, the Luxafor Flag. Although the campaign was unsuccessful in reaching its funding goal, the product was still brought to market. In 2017, Luxafor launched another Kickstarter campaign for the Luxafor Bluetooth, a wireless version of its LED status indicator. This campaign also did not meet its funding goal, but like its predecessor, the product was still developed and released. Despite initial setbacks, Luxafor Bluetooth has become one of the brand's leading products. == Products == Luxafors main product range is LED status indicators, including: === Luxafor Flag === A USB-powered LED indicator that shows different colors to signal the user's availability. === Luxafor Bluetooth === A wireless LED indicator controlled via Bluetooth, integrating with productivity tools like Slack and Microsoft Teams. === Luxafor Switch === An advanced status indicator designed to manage room and workspace availability. === Other === Other Luxafor products include CO2 Dongle, Smart Button, Mute Button, Pomodoro Timer and others. == Features == Luxafor products are known for their customizable indicators, integration capabilities with IFTTT, Zapier, and remote control features. They are compatible with various operating systems, including Windows and macOS, and can be integrated with numerous communication and productivity platforms, like Microsoft Teams and Cisco Jabber.

    Read more →
  • Algorithmic amplification

    Algorithmic amplification

    Algorithmic amplification is the process by which automated ranking and recommendation systems on digital platforms increase the visibility of certain content beyond its initial audience. Major platforms including Facebook, YouTube, TikTok, and X (formerly Twitter) use such systems to determine what appears in users' feeds and search results. The term is used in research on social media and digital media regulation to describe how platform design choices influence the distribution of online information. Unlike chronological feeds, algorithmic systems evaluate content using signals such as engagement rates, viewing duration, and predicted relevance to individual users. Content that performs strongly on these metrics may be promoted to progressively larger audiences through feeds, search rankings, or autoplay systems. The process is distinct from content moderation, which involves removing, labelling, or restricting content under platform rules, although the two can interact in practice. The concept is closely connected to the attention economy. Research has linked algorithmic amplification to the spread of misinformation and the circulation of political content, as well as to effects on young users' mental health. The scale and direction of those effects remain debated, in part because independent researchers have limited access to the internal workings of platform recommendation systems. Governments in the European Union, United Kingdom, United States, and China have pursued differing regulatory approaches to recommendation algorithms. The EU's Digital Services Act and the UK's Online Safety Act 2023 impose obligations on large platforms related to recommendation system transparency and risk, while China became the first country to enact binding legislation specifically targeting such systems. Internal documents and whistleblower testimony reported by the BBC in 2026 described how competitive pressure between Meta and TikTok led to trade-offs between engagement and user safety in the design of their recommendation systems. == Terminology == The term algorithmic amplification is used in media studies, platform governance scholarship and regulatory literature to describe how automated systems influence the distribution of content beyond what organic user sharing alone would produce. It is distinct from viral spread, which refers primarily to user-driven sharing behaviour, and from algorithmic bias, which describes systematic errors or unfairness in algorithmic outputs. The related term algorithmic curation is used for the broader process of selecting and ordering content, of which amplification is one possible outcome. The phrase also appears in regulatory and legislative discussion of recommendation systems. The European Union's Digital Services Act (DSA) identifies recommendation systems as a potential source of systemic risk, and the term appears frequently in academic and policy commentary on the regulation. In the United States, proposals including the Filter Bubble Transparency Act and the Kids Online Safety Act (KOSA) have used it to frame requirements around recommendation system transparency. In the United Kingdom, the House of Commons Science, Innovation and Technology Committee used the term in a 2025 report on how recommendation algorithms contributed to the spread of misinformation during the 2024 Southport riots. A Joint Declaration on AI and Freedom of Expression adopted in October 2025 by four international freedom of expression mandate holders, including the UN Special Rapporteur on Freedom of Opinion and Expression and the OSCE Representative on Freedom of the Media, stated that recommender systems and other AI-powered curation tools exert "a large hidden influence and gatekeeper role" over what information people access and consume. == Background == Early internet platforms typically displayed content in reverse-chronological order or through keyword-based search systems. Although the term is most often applied to social media, the underlying logic predates social media itself. A 2021 overview traced the origins of modern recommendation systems to the early 1990s, when they were first used experimentally for personal email and information filtering. The 1992 Tapestry mail system and the 1994 GroupLens news filtering system were early milestones before recommendation systems spread into e-commerce and other online services. As user bases and content volumes grew during the 2000s, major platforms including Google, YouTube, and Facebook developed machine-learning systems to personalise content delivery and prioritise material predicted to generate engagement. Facebook introduced its News Feed in 2006, which gradually shifted from chronological presentation towards algorithmically ranked content. YouTube altered its recommendation system in 2012 to prioritise watch time rather than clicks, a change the platform said was prompted by concerns that click-based metrics encouraged misleading thumbnails and low-quality videos. TikTok, launched internationally in 2018, adopted a model in which its primary content surface, the For You feed, is driven almost entirely by algorithmic recommendation rather than by a user's social graph. An internal document obtained by The New York Times in 2021 showed that the platform's algorithm optimised for retention and time spent, using signals such as watch duration, replays, likes, and comments to score and rank videos. Algorithmic recommendation also became central to platforms outside social media. Spotify's personalised features, including Discover Weekly, Release Radar, and Home recommendations, use behavioural signals and inferred "taste profiles" to surface tracks and artists beyond a listener's existing library. An ethnographic study of music curators at streaming platforms described this blend of algorithmic and human editorial selection as an "algo-torial" model of gatekeeping. Amazon adopted item-based collaborative filtering for product recommendations in 1998, and its recommendation engine has been described as one of the earliest large-scale deployments of recommendation technology in e-commerce. The same dynamics operate on adult content platforms. Law professor Amy Adler has argued that from 2007 onwards the pornography industry migrated to algorithm-driven streaming platforms, most of which are controlled by a single near-monopoly company, Aylo (formerly MindGeek). These platforms use algorithmic search engines, suggestions, rigid categorisation of content, and AI-driven search term optimisation in ways that produce the same distorting effects found on mainstream speech platforms, including filter bubbles, feedback loops, and the tendency of algorithmic recommendations to alter individual preferences. == Mechanisms == Recommendation systems commonly combine collaborative filtering, which predicts a user's preferences from the behaviour of similar users, with machine-learning models that predict which content a user is likely to engage with from their prior activity. In a common two-stage design, a platform first generates a set of candidate items from a large content pool and then ranks them using a scoring model with objectives such as predicted engagement or user satisfaction. Small changes in ranking criteria can shift exposure at scale, particularly when applied repeatedly across multiple browsing sessions. These systems typically rely on signals including engagement rates, viewing duration, click-through rates, and network relationships between users. Modern recommendation pipelines continuously update predictions as new behavioural data arrives, allowing platforms to adjust rankings in near real time. Users' revealed preferences, expressed through behaviour such as clicks and viewing time, do not always align with their stated preferences, expressed through explicit feedback such as surveys or content controls. Popularity signals can create feedback dynamics in which early engagement increases the likelihood that content will be shown to additional users. Experimental research on online cultural markets has demonstrated how such feedback processes can produce unequal visibility outcomes even when initial differences in content quality are small. == Beneficial and public-interest uses == Recommendation systems can help users navigate large volumes of content by surfacing material predicted to match their interests or needs, which can improve discoverability on platforms with large content libraries. In public health communication, platforms can help health authorities distribute timely information at scale, though the same recommendation systems also risk amplifying misinformation alongside official guidance. Sociologist Zeynep Tufekci has argued that the shift from independent blogs to large centralised platforms transferred gatekeeping power from traditional media to corporate algorithms. In the case of the Egyptian uprising of 2011, she noted that ordinary users

    Read more →
  • Front-end web development

    Front-end web development

    Front-end web development is the development of the graphical user interface of a website through the use of HTML, CSS, and JavaScript so users can view and interact with that website. == Tools used for front-end development == There are several tools and platforms, such as WordPress, Joomla, and Drupal, available that can be used to develop the front end of a website. === HyperText Markup Language === HyperText Markup Language (HTML) is the modern standard for displaying and structuring web content across the internet. HTML defines what elements will be displayed on a website, and how they will be arranged. All major web browsers are designed to interpret HTML, and most modern websites serve HTML to the user. Hypertext is text displayed on a computer with references to other text, these references (or links,) are termed "hyperlinks." When an internet user interacts with a hyperlinked item, the website serves the user the linked data. This data can be another HTML web-page, JavaScript, or anything else. The latest major release of HTML is HTML5, originally published on October 28, 2014 as a W3C recommendation. A web page may be developed to include many markup tags. For each pair of markup tag normally starts with a Start tag and ends with a matching end tag. The text in between the Start tag and the End tag is called an HTML Element. [1] === Cascading Style Sheets === Cascading Style Sheets (CSS) control the presentation and style of a website. CSS uses a cascading system to resolve style conflicts by applying style rules based on specificity, inheritance, and importance. Media queries allow for adjustments to the site's layout and appearance depending on factors such as screen size and resolution. CSS can be applied in three ways: external stylesheets linked in an HTML file, internal