As per Govt of India, there are currently about 57 providers of over-the-top media services (OTT) in India, which distribute streaming media or video on demand over the Internet. == History and growth == The first dependent Indian OTT platform was BIGFlix, launched by Reliance Entertainment in 2008. In 2010 Digivive launched India's first OTT mobile app called nexGTv, which provides access to both live TV and on–demand content. nexGTV was the first app to live–stream Indian Premier League matches on smart phones and did so during 2013 and 2014. The livestream of the IPL since 2015, when rights were won, played an important role in the growth of another OTT platform, Hotstar (now JioHotstar) in India. OTT Platforms gained significant momentum in India when both DittoTV (Zee) and Sony Liv were launched in the Indian market around 2013. Following the initial push of Regional OTT platforms like Aha, Hoichoi, Sun NXT, Planet Marathi, Chaupal & MX Player. The Indian OTT industry saw rapid transformation with the entry of global OTT companies such as Netflix and Amazon Prime Video into the Indian market in 2016. Replacement of this competition with global enterprises caused local rivals to innovate in both region and hyper-regional content. === Hotstar === Hotstar (now JioHotstar) is the most subscribed–to OTT platform in India, owned by JioStar as of February 2025, with around 500 million active users and over 650 million downloads. According to Hotstar's India Watch Report 2018, 96% of watch time on Hotstar comes from videos longer than 20 minutes, while one–third of Hotstar subscribers watch television shows. In 2019, Hotstar began investing ₹120 crore in generating original content such as "Hotstar Specials." 80% of the viewership on Hotstar comes from drama, movies and sports programs. Hotstar has the exclusive streaming rights of IPL in India. === Netflix === American streaming service Netflix entered India in January 2016. In April 2017, it was registered as a limited liability partnership (LLP) and started commissioning content. It earned a net profit of ₹2020,000 (₹2.02 million) for fiscal year 2017. In fiscal year 2018, Netflix earned revenues of ₹580 million. According to Morgan Stanley Research, Netflix had the highest average watch time of more than 120 minutes but viewer counts of around 20 million in July 2018. As of 2018, Netflix has six million subscribers, of which 5–6% are paid members. India was not affected by Netflix's July 2018 increase in subscription rates for the US and Latin America. Netflix has stated its intent to invest ₹600 crore in the production of Indian original programming. In late 2018, Netflix bought 150,000 square feet (14,000 m2) of office space in Bandra–Kurla Complex (BKC) in Mumbai as their head office. As of December 2018, Netflix has more than 40 employees in India. === Other OTT providers === Sun NXT is an Indian video on demand service run by Sun TV Network. It was launched in June 2017, streaming in the Tamil language and six other languages. The platform has more than 4,000 Tamil movies and 200 Tamil shows, as well as regional movies and shows. Sun NXT also streams a large library of its own Sun TV shows and movies. Amazon Prime Video was launched in 2016. The platform has 2,300 titles available including 2,000 movies and about 400 shows. It has announced that it will invest ₹20 billion in creating original content in India. Besides English, Prime Video is available in six Indian languages as of December 2018. Amazon India launched Amazon Prime Music in February 2018. Eros Now, an OTT platform launched by Eros International, has the most content among the OTT providers in India, including over 12,000 films, 100,000 music tracks and albums, and 100 TV shows. Eros Now was named the Best OTT Platform of the Year 2019 at the British Asian Media Awards. It has 211.5 million registered users and 36.2 million paying subscribers as of September 2020. In February 2020, Aha OTT platform was launched, broadcasting exclusively Telugu content. In 2021, Planet Marathi became the first OTT platform dedicated to Marathi content in India, including web-series, films, music, theater, fiction and non-fiction reality shows. It is available for both Android and iOS mobile devices along with Android TV and Amazon Fire TV devices. Bollywood actress Madhuri Dixit helped launch the platform. With rising interest for Korean dramas, Rakuten Viki saw its biggest jump of web traffic from India in 2020 due to the COVID-19 lockdown, which led to ad localization on the platform. The OTT market in fiscal year 2020 was estimated to be worth $1.7 billion. === SonyLIV and ZEE5 === In December 2021, Sony and Zee announced their merger, and announced plans to merge their OTT platforms. The merger was called off. === OTT services launched as Amazon Prime video channels === The list is by alphabetical order, not by rank or popularity. == Content regulation == Due to the absence of any rules and regulation regarding OTT content, many OTT providers were accused of showing nudity, vulgarity and obscenity and hurting Hindu religious sentiments in their shows. Series which were the focus of controversy include Four More Shots Please!, Tandav, Paatal Lok, Sacred Games, Mirzapur Lust stories franchise, Rana Naidu. Thank You for Coming, and Annapoorani (2023). According to media reports, between 2018 and 2024, some OTT platforms emerged which started showing porn in the form of web series. Both the Supreme Court and Delhi High Court say that OTT regulation is necessary. === OTT regulation === On 25 Feb 2021, Indian govt introduced self-regulation rules for OTT platforms to stop obscene content and abusive language. On 19 March 2023, I&B minister Anurag Thakur said that self regulation does not mean that OTT should show obscenity and nudity. On 15 April 2023, I&B Secretary Apurva Chandra has said because of the government's soft-touch regulations on OTT industry have led to the creation of content that is undesirable and vulgar. On 26 April 2023, MIB India said that if nudity and obscenity is seen on any OTT platform, strict action will be taken against it. On 16 May 2023, Don't show obscene content, parliamentary panel told to Netflix and Amazon Prime Video. On 20 June 2023, the government told Netflix, Disney+ Hotstar and all other streaming services that their content should be independently reviewed for obscenity and violence before being shown online. On 27 June 2023, DPCGC took punitive action against Ullu for streaming obscene content and asked them to remove all their explicit shows or remove all adult scenes within 15 days. On 18 July 2023, Anarug Thakur said in a meeting with all OTT stakeholders that demeaning Indian culture will not be tolerated. OTT can't show vulgarity and nudity in the garb of 'creative expression'.The cited sources do not mention vulgarity - they say this was about demeaning Indian culture/society. On 22 August 2023, Indian government assured that it will bring rules and regulation to regulate vulgar and obscene content on social media and OTT platforms. On 10 November 2023, MIB India introduces the 'Broadcasting Service Regulation Bill', which included Programme code with Content Evaluation Committee(CEC) for every OTT platforms. Currently public consultation is ongoing till 15 January 2024. The draft bill mandates that all OTT streaming platforms can only broadcast those web series or content, which will be duly certified by Content Evaluation Committee(CEC). On 14 March 2024, the Ministry of Information and Broadcasting banned over 18 OTT apps from Google play store and suspended all of their 57 social media accounts, as well as closed nineteen streaming websites. The banned platforms were MoodX, Prime Play, Hunters, Besharams, Rabbit movies, Voovi, Fugi, Mojflix, Chikooflix, Nuefliks, Xtramood, NeonX VIP, X Prime, Tri Flicks, Uncut Adda, Dreams Films, Hot Shots VIP, and Yessma. On 25 July 2025, the Ministry of Information and Broadcasting banned from 25 OTT apps from Google play store and suspended all of their 40 social media accounts, as well as 26 closed streaming websites. The banned platforms were include ALTT, Ullu, Big Shots App, Desiflix, Boomex, NeonX VIP, Navarasa Lite, Gulab App, Kangan App, Bull App, ShowHit, Jalva App, Wow Entertainment, Look Entertainment, Hitprime, Fugi, Feneo, ShowX, Sol Talkies, Adda TV, HotX VIP, Hulchul App, MoodX, Triflicks, and Mojflix. On 24 February 2026, the Ministry of Information and Broadcasting banned from 5 OTT apps from Google play store and suspended all of their 5 social media accounts, as well as 5 closed streaming websites. The banned platforms were include Feel App, Digi Movieplex, Jugnu App, MoodX VIP, and Koyal Playpro. === Legal action === Currently OTT is regulated under the IT Rules 2021, which clearly stated that 'No content that is prohibited by law at the time being force can be Publishing or transmitted'. MIB has continuously taking action
Couchbase Server
Couchbase Server, originally known as Membase, is a source-available, distributed (shared-nothing architecture) multi-model NoSQL document-oriented database software package optimized for interactive applications. These applications may serve many concurrent users by creating, storing, retrieving, aggregating, manipulating and presenting data. In support of these kinds of application needs, Couchbase Server is designed to provide easy-to-scale key-value, or JSON document access, with low latency and high sustainability throughput. It is designed to be clustered from a single machine to very large-scale deployments spanning many machines. Couchbase Server provided client protocol compatibility with memcached, but added disk persistence, data replication, live cluster reconfiguration, rebalancing and multitenancy with data partitioning. == Product history == Membase was developed by several leaders of the memcached project, who had founded a company, NorthScale, to develop a key-value store with the simplicity, speed, and scalability of memcached, but also the storage, persistence and querying capabilities of a database. The original membase source code was contributed by NorthScale, and project co-sponsors Zynga and Naver Corporation (then known as NHN) to a new project on membase.org in June 2010. On February 8, 2011, the Membase project founders and Membase, Inc. announced a merger with CouchOne (a company with many of the principal players behind CouchDB) with an associated project merger. The merged company was called Couchbase, Inc. In January 2012, Couchbase released Couchbase Server 1.8. In September of 2012, Orbitz said it had changed some of its systems to use Couchbase. In December of 2012, Couchbase Server 2.0 (announced in July 2011) was released and included a new JSON document store, indexing and querying, incremental MapReduce and replication across data centers. == Architecture == Every Couchbase node consists of a data service, index service, query service, and cluster manager component. Starting with the 4.0 release, the three services can be distributed to run on separate nodes of the cluster if needed. In the parlance of Eric Brewer's CAP theorem, Couchbase is normally a CP type system meaning it provides consistency and partition tolerance, or it can be set up as an AP system with multiple clusters. === Cluster manager === The cluster manager supervises the configuration and behavior of all the servers in a Couchbase cluster. It configures and supervises inter-node behavior like managing replication streams and re-balancing operations. It also provides metric aggregation and consensus functions for the cluster, and a RESTful cluster management interface. The cluster manager uses the Erlang programming language and the Open Telecom Platform. ==== Replication and fail-over ==== Data replication within the nodes of a cluster can be controlled with several parameters. In December of 2012, support was added for replication between different data centers. === Data manager === The data manager stores and retrieves documents in response to data operations from applications. It asynchronously writes data to disk after acknowledging to the client. In version 1.7 and later, applications can optionally ensure data is written to more than one server or to disk before acknowledging a write to the client. Parameters define item ages that affect when data is persisted, and how max memory and migration from main-memory to disk is handled. It supports working sets greater than a memory quota per "node" or "bucket". External systems can subscribe to filtered data streams, supporting, for example, full text search indexing, data analytics or archiving. ==== Data format ==== A document is the most basic unit of data manipulation in Couchbase Server. Documents are stored in JSON document format with no predefined schemas. Non-JSON documents can also be stored in Couchbase Server (binary, serialized values, XML, etc.) ==== Object-managed cache ==== Couchbase Server includes a built-in multi-threaded object-managed cache that implements memcached compatible APIs such as get, set, delete, append, prepend etc. ==== Storage engine ==== Couchbase Server has a tail-append storage design that is immune to data corruption, OOM killers or sudden loss of power. Data is written to the data file in an append-only manner, which enables Couchbase to do mostly sequential writes for update, and provide an optimized access patterns for disk I/O. === Performance === A performance benchmark done by Altoros in 2012, compared Couchbase Server with other technologies. Cisco Systems published a benchmark that measured the latency and throughput of Couchbase Server with a mixed workload in 2012. == Licensing and support == Couchbase Server is a packaged version of Couchbase's open source software technology and is available in a community edition without recent bug fixes with an Apache 2.0 license and an edition for commercial use. Couchbase Server builds are available for Ubuntu, Debian, Red Hat, SUSE, Oracle Linux, Microsoft Windows and macOS operating systems. Couchbase has supported software developers' kits for the programming languages .NET, PHP, Ruby, Python, C, Node.js, Java, Go, and Scala. == SQL++ == A query language called SQL++ (formerly called N1QL), is used for manipulating the JSON data in Couchbase, just like SQL manipulates data in RDBMS. It has SELECT, INSERT, UPDATE, DELETE, MERGE statements to operate on JSON data. It was initially announced in March 2015 as "SQL for documents". The SQL++ data model is non-first normal form (N1NF) with support for nested attributes and domain-oriented normalization. The SQL++ data model is also a proper superset and generalization of the relational model. === Example === Like query SELECT FROM `bucket` WHERE email LIKE "%@example.org"; Array query SELECT FROM `bucket` WHERE ANY x IN friends SATISFIES x.name = "Pavan" END; == Couchbase Mobile == Couchbase Mobile / Couchbase Lite is a mobile database providing data replication. Couchbase Lite (originally TouchDB) provides native libraries for offline-first NoSQL databases with built-in peer-to-peer or client-server replication mechanisms. Sync Gateway manages secure access and synchronization of data between Couchbase Lite and Couchbase Server. Couchbase Lite added support for Vector Search in version 3.2, allowing cloud to edge support for vector search in mobile applications. == Uses == Couchbase began as an evolution of Memcached, a high-speed data cache, and can be used as a drop-in replacement for Memcached, providing high availability for memcached application without code changes. Couchbase is used to support applications where a flexible data model, easy scalability, and consistent high performance are required, such as tracking real-time user activity or providing a store of user preferences or online applications. Couchbase Mobile, which stores data locally on devices (usually mobile devices) is used to create “offline-first” applications that can operate when a device is not connected to a network and synchronize with Couchbase Server once a network connection is re-established. The Catalyst Lab at Northwestern University uses Couchbase Mobile to support the Evo application, a healthy lifestyle research program where data is used to help participants improve dietary quality, physical activity, stress, or sleep. Amadeus uses Couchbase with Apache Kafka to support their “open, simple, and agile” strategy to consume and integrate data on loyalty programs for airline and other travel partners. High scalability is needed when disruptive travel events create a need to recognize and compensate high value customers. Starting in 2012, it played a role in LinkedIn's caching systems, including backend caching for recruiter and jobs products, counters for security defense mechanisms, for internal applications. == Alternatives == For caching, Couchbase competes with Memcached and Redis. For document databases, Couchbase competes with other document-oriented database systems. It is commonly compared with MongoDB, Amazon DynamoDB, Oracle RDBMS, DataStax, Google Bigtable, MariaDB, IBM Cloudant, Redis Enterprise, SingleStore, and MarkLogic.
Chaos Communication Congress
The Chaos Communication Congress is an annual hacker conference organized by the Chaos Computer Club. The congress features a variety of lectures and workshops on technical and political issues related to security, cryptography, privacy and online freedom of speech. It has taken place regularly at the end of the year since 1984, with the current date and duration (27–30 December) established in 2005. It is considered one of the largest events of its kind, alongside DEF CON in Las Vegas. == History == The congress is held in Germany. It started in 1984 in Hamburg, moved to Berlin in 1998, and back to Hamburg in 2012, having exceeded the capacity of the Berlin venue with more than 4500 attendees. Since then, it attracts an increasing number of people: around 6600 attendees in 2012, over 13000 in 2015, and more than 15000 in 2017. From 2017 to 2019, it took place at the Trade Fair Grounds in Leipzig, since the Hamburg venue (CCH) was closed for renovation in 2017 and the existing space was not enough for the growing congress. The congress moved back to Hamburg in 2023, after the renovation of CCH was finished. A large range of speakers are featured. The event is organized by volunteers called Chaos Angels. The non-members entry fee for four days was €100 in 2016, and was raised to €120 in 2018 to include a public transport ticket for the Leipzig area. An important part of the congress are the assemblies, semi-open spaces with clusters of tables and internet connections for groups and individuals to collaborate and socialize in projects, workshops and hands-on talks. These assembly spaces, introduced at the 2012 meeting, combine the hack center project space and distributed group spaces of former years. From 1997 to 2004 the congress also hosted the annual German Lockpicking Championships. 2005 was the first year the Congress lasted four days instead of three and lacked the German Lockpicking Championships. 2020 was the first year where the Congress did not take place at a physical location due to the COVID-19 pandemic, giving way to the first Remote Chaos Experience (rC3). The Chaos Computer Club announced to return to the now newly renovated Congress Center Hamburg for the 37th edition of the Chaos Communication Congress. The announcement confirms the usual date of 27-30 December, notably omitting the year it will be held. On 18 October 2022, they confirmed that the congress will indeed not be held in 2022. On 6 October 2023, the CCC announced that 37C3 will take place again on the usual dates in 2023. === Timeline ===
Data
Data ( DAY-tə, US also DAT-ə, India: DEE-tə) is a collection of discrete or continuous values that conveys information, describing the quantity, quality, fact, statistics, other basic units of meaning, or simply sequences of symbols that may be further interpreted formally. A data point or datum is an individual value in a collection of data. Data is usually organized into structures such as tables that provide additional context and meaning, and may itself be used as data in larger structures. Data may be used as variables in a computational process. Data may represent abstract ideas or concrete measurements. Data is commonly used in scientific research, economics, and virtually every other form of human organizational activity. Examples of data sets include price indices (such as the consumer price index), unemployment rates, literacy rates, and census data. In this context, data represents the raw facts and figures from which useful information can be extracted. Data is collected using techniques such as measurement, observation, query, or analysis, and is typically represented as numbers or characters that may be further processed. Field data is data that is collected in an uncontrolled, in-situ environment. Experimental data is data that is generated in the course of a controlled scientific experiment. Data is analyzed using techniques such as calculation, reasoning, discussion, presentation, visualization, or other forms of post-analysis. Prior to analysis, raw data (or unprocessed data) is typically cleaned: Outliers are removed, and obvious instrument or data entry errors are corrected. Data can be seen as the smallest unit of factual information that can be used as a basis for calculation, reasoning, or discussion. Data can range from abstract ideas to concrete measurements, including, but not limited to, statistics. Thematically connected data presented in some relevant context can be viewed as information. Contextually connected pieces of information can then be described as data insights or intelligence. The stock of insights and intelligence that accumulate over time, resulting from the synthesis of data into information, can then be described as knowledge. Data has been described as "the new oil of the digital economy". Data, as a general concept, refers to the fact that some existing information or knowledge is represented or coded in some form suitable for better usage or processing. Advances in computing technologies have led to the advent of big data, which generally refers to very large quantities of data, typically at the petabyte scale. If restricted to traditional data analysis methods and computing, working with such large (and growing) datasets is difficult, even impossible. In response, the relatively new field of data science uses machine learning (and other artificial intelligence) methods that allow for efficient applications of analytic methods to big data. == Etymology and terminology == The Latin word data is the plural of datum, "(thing) given," and the neuter past participle of dare, "to give". The first English use of the word "data" is from the 1640s. The word "data" was first used to mean "transmissible and storable computer information" in 1946. The expression "data processing" was first used in 1954. When "data" is used more generally as a synonym for "information", it is treated as a mass noun in singular form. This usage is common in everyday language and in technical and scientific fields such as software development and computer science. One example of this usage is the term "big data". When used more specifically to refer to the processing and analysis of sets of data, the term retains its plural form. This usage is common in the natural sciences, life sciences, social sciences, software development and computer science, and grew in popularity in the 20th and 21st centuries. Some style guides do not recognize the different meanings of the term and simply recommend the form that best suits the target audience of the guide. For example, APA style as of the 7th edition requires "data" to be treated as a plural form. == Meaning == Data, information, knowledge, and wisdom are closely related concepts, but each has its role concerning the other, and each term has its meaning. According to a common view, data is collected and analyzed; data only becomes information suitable for making decisions once it has been analyzed in some fashion. One can say that the extent to which a set of data is informative to someone depends on the extent to which it is unexpected by that person. The amount of information contained in a data stream may be characterized by its Shannon entropy. Knowledge is the awareness of its environment that some entity possesses, whereas data merely communicates that knowledge. For example, the entry in a database specifying the height of Mount Everest is a datum that communicates a precisely measured value. This measurement may be included in a book along with other data on Mount Everest to describe the mountain in a manner useful for those who wish to decide on the best method to climb it. Awareness of the characteristics represented by this data is knowledge. Data are often assumed to be the least abstract concept, information the next least, and knowledge the most abstract. In this view, data becomes information by interpretation; e.g., the height of Mount Everest is generally considered "data", a book on Mount Everest geological characteristics may be considered "information", and a climber's guidebook containing practical information on the best way to reach Mount Everest's peak may be considered "knowledge". "Information" bears a diversity of meanings that range from everyday usage to technical use. This view, however, has also been argued to reverse how data emerges from information, and information from knowledge. Generally speaking, the concept of information is closely related to notions of constraint, communication, control, data, form, instruction, knowledge, meaning, mental stimulus, pattern, perception, and representation. Beynon-Davies uses the concept of a sign to differentiate between data and information; data is a series of symbols, while information occurs when the symbols are used to refer to something. Before the development of computing devices and machines, people had to manually collect data and impose patterns on it. With the development of computing devices and machines, these devices can also collect data. In the 2010s, computers were widely used in many fields to collect data and sort or process it, in disciplines ranging from marketing, analysis of social service usage by citizens to scientific research. These patterns in the data are seen as information that can be used to enhance knowledge. These patterns may be interpreted as "truth" (though "truth" can be a subjective concept) and may be authorized as aesthetic and ethical criteria in some disciplines or cultures. Events that leave behind perceivable physical or virtual remains can be traced back through data. Marks are no longer considered data once the link between the mark and observation is broken. Mechanical computing devices are classified according to how they represent data. An analog computer represents a datum as a voltage, distance, position, or other physical quantity. A digital computer represents a piece of data as a sequence of symbols drawn from a fixed alphabet. The most common digital computers use a binary alphabet, that is, an alphabet of two characters typically denoted "0" and "1". More familiar representations, such as numbers or letters, are then constructed from the binary alphabet. Some special forms of data are distinguished. A computer program is a collection of data, that can be interpreted as instructions. Most computer languages make a distinction between programs and the other data on which programs operate, but in some languages, notably Lisp and similar languages, programs are essentially indistinguishable from other data. It is also useful to distinguish metadata, that is, a description of other data. A similar yet earlier term for metadata is "ancillary data." The prototypical example of metadata is the library catalog, which is a description of the contents of books. == Data sources == With respect to ownership of data collected in the course of marketing or other corporate collection, data has been characterized according to party depending on how close the data is to the source or if it has been generated through additional processing. "Zero-party data" refers to data that customers "intentionally and proactively shares". This kind of data can come from a variety of sources, including: subscriptions, preference centers, quizzes, surveys, pop-up forms, and interactive digital experiences. "First-party data" may be collected by a company directly from its customers. The secure exchange of first-party data among companies can be done using data clean rooms. "S
Private message
In computer networking, a private message (PM), or direct message (DM), refers to a private communication, often text-based, sent or received by a user of a private communication channel on any given platform. Unlike public posts, PMs are only viewable by the participants. Long a function present on IRCs and Internet forums, private channels for PMs have also been prevalent features on instant messaging (IM) and on social media networks. It may be either synchronous (e.g. on an IM) or asynchronous (e.g. on an Internet forum). The term private message (PM) originated as a feature on internet forums, while the term direct message (DM) originated as a feature on Twitter. Due to the popularity of the latter service, DM has since been appropriated by other platforms, such as Instagram, and is often genericized in popular usage. == Overview == There are two main types of private messages, and one obscure type: One type includes those found on IRCs and Internet forums, as well as on social media services like Twitter, Facebook, and Instagram, where the focus is public posting, PMs allow users to communicate privately without leaving the platform. The second type are those relayed through instant messaging platforms such as WhatsApp and Snapchat, where users join the networks primarily to exchange PMs. A third type, peer-to-peer messaging, occurs when users create and own the infrastructure used to transmit and store the messages; while features vary depending on application, they give the user full control over the data they transmit. An example of software that enables this kind of messaging is Classified-ads. Besides serving as a tool to connect privately with friends and family, PMs have gained momentum in the workplace. Working professionals use PMs to reach coworkers in other spaces and increase efficiency during meetings. Although useful, using PMs in the workplace may blur the boundary between work and private lives. Some common forms of private messaging today include Facebook messaging (sometimes referred to as "inboxing"), Twitter direct messaging, and Instagram direct messaging. These forms of private messaging provide a private space on a usually public site. For instance, most activity on Twitter is public, but Twitter DMs provide a private space for communication between two users. This differs from mediums like email, texting, and Snapchat, where most or all activity is always private. Modern forms of private messaging may include multimedia messages, such as pictures or videos. == History == Email was first developed to send messages between different computers on ARPANET in 1971. Access to ARPANET was primarily limited to universities and other research institutions. Starting in 1983 or 1984, FidoNet allowed home computer users to send and receive email via bulletin board systems. Information services such as CompuServe, America Online, and Prodigy also helped to popularizes online messaging. The advent of the public World Wide Web in 1993 increased access to email via internet service providers, and later via webmail. Instant messaging systems became popular in the mid 1990s, as Internet access improved and personal computers became more common. The introduction of Skype in 2003 popularized Internet-based voice and video messaging. Direct messaging is now a feature of all major social networking services. == Privacy concerns == In January 2014, Matthew Campbell and Michael Hurley filed a class-action lawsuit against Facebook for breaching the Electronic Communications Privacy Act. They alleged that private messages which contained URLs were being read and used to generate profit, through data mining and user profiling, and that it was misleading for Facebook to refer to the functionality as "private" with the implication that the communication was "free from surveillance". In 2012, some Facebook users misinterpreted a redesign of the Facebook wall as publicly sharing private messages from 2008–2009. These were found to be public wall posts from those years, made at a time when it was not possible to like or comment on a wall post, making the notes look like private messages.
Accelerated Linear Algebra
XLA (Accelerated Linear Algebra) is an open-source compiler for machine learning developed by the OpenXLA project. XLA is designed to improve the performance of machine learning models by optimizing the computation graphs at a lower level, making it particularly useful for large-scale computations and high-performance machine learning models. Key features of XLA include: Compilation of Computation Graphs: Compiles computation graphs into efficient machine code. Optimization Techniques: Applies operation fusion, memory optimization, and other techniques. Hardware Support: Optimizes models for various hardware, including CPUs, GPUs, and NPUs. Improved Model Execution Time: Aims to reduce machine learning models' execution time for both training and inference. Seamless Integration: Can be used with existing machine learning code with minimal changes. XLA represents a significant step in optimizing machine learning models, providing developers with tools to enhance computational efficiency and performance. == OpenXLA Project == OpenXLA Project is an open-source machine learning compiler and infrastructure initiative intended to provide a common set of tools for compiling and deploying machine learning models across different frameworks and hardware platforms. It provides a modular compilation stack that can be used by major deep learning frameworks like JAX, PyTorch, and TensorFlow. The project focuses on supplying shared components for optimization, portability, and execution across CPUs, GPUs, and specialized accelerators. Its design emphasizes interoperability between frameworks and a standardized set of representations for model computation. == Components == The OpenXLA ecosystem includes several core components: XLA – A deep learning compiler that optimizes computational graphs for multiple hardware targets. PJRT – A runtime interface that allows different back-ends to connect to XLA through a consistent API. StableHLO – A high-level operator set intended to serve as a stable, portable representation for ML models across compilers and frameworks. Shardy – An MLIR-based system for describing and transforming models that run in distributed or multi-device environments. Additional profiling, testing, and integration tools maintained under the OpenXLA organization. == Users and adopters == Several machine learning frameworks can use or interoperate with OpenXLA components, including JAX, TensorFlow, and parts of the PyTorch ecosystem. The project is developed with participation from multiple hardware and software organizations that contribute back-end integrations, testing, or specifications for their devices. This includes Alibaba, Amazon Web Services, AMD, Anyscale, Apple, Arm, Cerebras, Google, Graphcore, Hugging Face, Intel, Meta, NVIDIA and SiFive. == Supported target devices == x86-64 ARM64 NVIDIA GPU AMD GPU Intel GPU Apple GPU Google TPU AWS Trainium, Inferentia Cerebras Graphcore IPU == Governance == OpenXLA is developed as a community project with its work carried out in public repositories, discussion forums, and design meetings. Some components, such as StableHLO, began with stewardship from specific organizations and have outlined plans for more formal and distributed governance models as the project matures. == History == The project was announced in 2022 as an effort to coordinate development of ML compiler technologies across major AI companies, notably: Alibaba, Amazon Web Services, AMD, Anyscale, Apple, Arm, Cerebras, Google, Graphcore, Hugging Face, Intel, Meta, NVIDIA and SiFive.. It consolidated the XLA compiler, introduced StableHLO as a portable operator set, and created a unified structure for additional tools. Development continues within multiple repositories under the OpenXLA umbrella. It was founded by Eugene Burmako, James Rubin, Magnus Hyttsten, Mehdi Amini, Navid Khajouei, and Thea Lamkin from Google's Machine Learning organization.
Menu hack
A menu hack is a non-standard method of ordering food, usually at fast-food or fast casual restaurants, that offers a different result than what is explicitly stated on a menu. Menu hacks may range from a simple alternate flavor to "gaming the system" in order to obtain more food than normal. They are often spread on social media platforms such as TikTok, and are more popular with Generation Z, which has been known to customize their orders more than previous generations. Hacks are sometimes officially added to the menu after their popularity grows. However, in some cases, they have been criticized for overburdening fast food employees with outlandish requests, sparking debate as to whether certain menu hacks are unethical. The list of all possible menu hacks is called a secret menu. == History == The term "menu hack" stems from hacker culture and its tradition of overcoming previously imposed limitations. However, the tradition of ordering from a secret menu dates back to the early days of fast food. "Animal style" fries, a word of mouth menu item ordered from In-N-Out since the 1960s, was rumored to have been created by local surfers. In the Information Age, the rise of social media gave influencers the ability to communicate unique food combinations to their followers, which proved to go viral easily. Design mistakes in food ordering apps also proved to be easily exploitable. In some cases, these hacks boosted the profile of brands on social media, while in others, they caused financial harm when the company was unprepared to handle the sudden influx of unusual orders. One restaurant chain notable for the phenomenon is Chipotle Mexican Grill. A viral hack from Alexis Frost, suggesting a quesadilla with fajita vegetables inside, dipped in Chipotle vinaigrette mixed with sour cream, obtained 1.9 million views on TikTok, overloading the chain's workers, who had to work harder to prepare more vegetables and vinaigrette. Some restaurants began to deny the dish to customers, forcing them to only order meat and cheese on quesadillas. The company ultimately left the dish on the menu, but urged customers to stop ordering it via social media. When it later officially added the Fajita Quesadilla to the menu, digital sales nearly doubled. A method to order nachos, which are not officially on the menu, was also noted by customers. Starbucks is also famous for menu hacks, including the Pink Drink, a "Barbiecore" beverage in which coconut milk replaced the water in the strawberry açaí refresher. After it went viral, the company made it a permanent menu item and distributed it bottled in grocery stores. == Controversy == Menu hacks have been subject to a growing backlash, with employees stating that they "dread" younger customers due to the proliferation of unusual orders. Service industry workers, already overworked and underpaid, have called the rise of menu hacks and their difficulty to make an additional reason to unionize and demand higher wages.