Messaging Layer Security (MLS) is a security layer for end-to-end encrypted messages. It is maintained by the MLS working group of the Internet Engineering Task Force (IETF), and is designed to provide an efficient and practical security mechanism for groups as large as 50,000 and for those who access chat systems from multiple devices. == Security properties == Security properties of MLS include message confidentiality, message integrity and authentication, membership authentication, asynchronicity, forward secrecy, post-compromise security, and scalability. == History == The idea was born in 2016 and first discussed in an unofficial meeting during IETF 96 in Berlin with attendees from Wire, Mozilla and Cisco. Initial ideas were based on pairwise encryption for secure 1:1 and group communication. In 2017, an academic paper introducing Asynchronous Ratcheting Trees was published by the University of Oxford and Facebook setting the focus on more efficient encryption schemes. The first BoF took place in February 2018 at IETF 101 in London. The founding members are Mozilla, Facebook, Wire, Google, Twitter, University of Oxford, and INRIA. On March 29, 2023, the IETF approved publication of Messaging Layer Security (MLS) as a new standard. It was officially published on July 19, 2023. At that time, Google announced it intended to add MLS to the end to end encryption used by Google Messages over Rich Communication Services (RCS). In March 2025, the GSMA announced the Universal Profile 3.0 standard of RCS would support MLS and Apple announced it would support this RCS standard on Apple Messages. Both Google Messages and Apple Messages began the rollout of MLS E2EE over RCS in May 2026. Matrix is one of the protocols declaring migration to MLS. In 2026, Discord rolled out end-to-end encryption on voice and video calls, using MLS for scalable group key exchanges. Research on adding post-quantum cryptography (PQC) to MLS is ongoing. The IETF has prepared an Internet-Draft using PQC algorithms in MLS. == Implementations ==
Suno (platform)
Suno is a generative artificial intelligence music creation platform. It is designed to generate music that can include vocals and instrumentation. The platform was initially developed by Suno, Inc., of Cambridge, Massachusetts. Suno has been widely available since December 20, 2023, after the launch of a web application and a partnership with Microsoft, which included Suno as a plugin in Microsoft Copilot. The program operates by producing songs based on text or audio prompts provided by its users. Suno does not disclose the dataset used to train its artificial intelligence. == History == Suno, Inc., was founded by four people: Michael Shulman, Georg Kucsko, Martin Camacho, and Keenan Freyberg. They all worked for Kensho, an AI startup, before starting their own company in Cambridge, Massachusetts. In April 2023, Suno released their open-source text-to-speech and audio model called "Bark" on GitHub. On March 21, 2024, Suno released its V3 version for all users. The new version allowed users to create a limited number of four-minute songs using a free account. Users can pay for more features. In April 2024, a sentimental ballad was generated with Suno based on the text of the MIT License. In June 2024, a lawsuit, led by the Recording Industry Association of America, was filed against Suno and Udio alleging widespread infringement of copyrighted sound recordings. The lawsuit sought to bar the companies from training on copyrighted music, as well as damages of up to $150,000 per work from infringements that have already taken place. On July 1, 2024, a mobile app for Suno was released. On November 19, 2024, Suno upgraded its AI song model program to v4. In January 2025, Michael Shulman remarked on a podcast, "I think the majority of people don't enjoy the majority of the time they spend making music." In March 2025, one day after thousands of musicians including Thom Yorke and ABBA's Björn Ulvaeus signed a letter calling for Suno to stop training its model on copyrighted music, Timbaland endorsed Suno in a video on the company's website. In July 2025, Suno user imoliver signed a record deal with Hallwood Media, which became the first instance of a traditional music label signing an AI-based creator. Hallwood later signed with AI-artist Xania Monet for US$3 million. Monet's songs were generated by Suno AI by poet Telisha Jones. In November 2025, Suno agreed to a $500 million dollar lawsuit settlement, in which Suno would be allowed to train its models on Warner Music Group's music catalog, and WMG would control aspects of AI likeness, music, audio, software, copyrights, AI tools and music created by users on Suno. As part of the settlement, Suno also acquired the concert discovery platform Songkick from WMG. == Controversy == Suno, Inc., has been sued by the Recording Industry Association of America for copyright infringement, and thousands of musicians have signed a letter demanding that the company cease using copyrighted music in their training data. Suno does not disclose the dataset used to train its artificial intelligence.
Snapshot isolation
In databases, and transaction processing (transaction management), snapshot isolation is a guarantee that all reads made in a transaction will see a consistent snapshot of the database (in practice it reads the last committed values that existed at the time it started), and the transaction itself will successfully commit only if no updates it has made conflict with any concurrent updates made since that snapshot. Snapshot isolation has been adopted by several major database management systems, such as InterBase, Firebird, Oracle, MySQL, PostgreSQL, SQL Anywhere, MongoDB and Microsoft SQL Server (2005 and later). The main reason for its adoption is that it allows better performance than serializability, yet still avoids most of the concurrency anomalies that serializability avoids (but not all). In practice snapshot isolation is implemented within multiversion concurrency control (MVCC), where generational values of each data item (versions) are maintained: MVCC is a common way to increase concurrency and performance by generating a new version of a database object each time the object is written, and allowing transactions' read operations of several last relevant versions (of each object). Snapshot isolation has been used to criticize the ANSI SQL-92 standard's definition of isolation levels, as it exhibits none of the "anomalies" that the SQL standard prohibited, yet is not serializable (the anomaly-free isolation level defined by ANSI). In spite of its distinction from serializability, snapshot isolation is sometimes referred to as serializable by Oracle. == Definition == A transaction executing under snapshot isolation appears to operate on a personal snapshot of the database, taken at the start of the transaction. When the transaction concludes, it will successfully commit only if the values updated by the transaction have not been changed externally since the snapshot was taken. Such a write–write conflict will cause the transaction to abort. In a write skew anomaly, two transactions (T1 and T2) concurrently read an overlapping data set (e.g. values V1 and V2), concurrently make disjoint updates (e.g. T1 updates V1, T2 updates V2), and finally concurrently commit, neither having seen the update performed by the other. Were the system serializable, such an anomaly would be impossible, as either T1 or T2 would have to occur "first", and be visible to the other. In contrast, snapshot isolation permits write skew anomalies. As a concrete example, imagine V1 and V2 are two balances held by a single person, Phil. The bank will allow either V1 or V2 to run a deficit, provided the total held in both is never negative (i.e. V1 + V2 ≥ 0). Both balances are currently $100. Phil initiates two transactions concurrently, T1 withdrawing $200 from V1, and T2 withdrawing $200 from V2. If the database guaranteed serializable transactions, the simplest way of coding T1 is to deduct $200 from V1, and then verify that V1 + V2 ≥ 0 still holds, aborting if not. T2 similarly deducts $200 from V2 and then verifies V1 + V2 ≥ 0. Since the transactions must serialize, either T1 happens first, leaving V1 = −$100, V2 = $100, and preventing T2 from succeeding (since V1 + (V2 − $200) is now −$200), or T2 happens first and similarly prevents T1 from committing. If the database is under snapshot isolation(MVCC), however, T1 and T2 operate on private snapshots of the database: each deducts $200 from an account, and then verifies that the new total is zero, using the other account value that held when the snapshot was taken. Since neither update conflicts, both commit successfully, leaving V1 = V2 = −$100, and V1 + V2 = −$200. Some systems built using multiversion concurrency control (MVCC) may support (only) snapshot isolation to allow transactions to proceed without worrying about concurrent operations, and more importantly without needing to re-verify all read operations when the transaction finally commits. This is convenient because MVCC maintains a series of recent history consistent states. The only information that must be stored during the transaction is a list of updates made, which can be scanned for conflicts fairly easily before being committed. However, MVCC systems (such as MarkLogic) will use locks to serialize writes together with MVCC to obtain some of the performance gains and still support the stronger "serializability" level of isolation. == Workarounds == Potential inconsistency problems arising from write skew anomalies can be fixed by adding (otherwise unnecessary) updates to the transactions in order to enforce the serializability property. Materialize the conflict Add a special conflict table, which both transactions update in order to create a direct write–write conflict. Promotion Have one transaction "update" a read-only location (replacing a value with the same value) in order to create a direct write–write conflict (or use an equivalent promotion, e.g. Oracle's SELECT FOR UPDATE). In the example above, we can materialize the conflict by adding a new table which makes the hidden constraint explicit, mapping each person to their total balance. Phil would start off with a total balance of $200, and each transaction would attempt to subtract $200 from this, creating a write–write conflict that would prevent the two from succeeding concurrently. However, this approach violates the normal form. Alternatively, we can promote one of the transaction's reads to a write. For instance, T2 could set V1 = V1, creating an artificial write–write conflict with T1 and, again, preventing the two from succeeding concurrently. This solution may not always be possible. In general, therefore, snapshot isolation puts some of the problem of maintaining non-trivial constraints onto the user, who may not appreciate either the potential pitfalls or the possible solutions. The upside to this transfer is better performance. == Terminology == Snapshot isolation is called "serializable" mode in Oracle and PostgreSQL versions prior to 9.1, which may cause confusion with the "real serializability" mode. There are arguments both for and against this decision; what is clear is that users must be aware of the distinction to avoid possible undesired anomalous behavior in their database system logic. == History == Snapshot isolation arose from work on multiversion concurrency control databases, where multiple versions of the database are maintained concurrently to allow readers to execute without colliding with writers. Such a system allows a natural definition and implementation of such an isolation level. InterBase, later owned by Borland, was acknowledged to provide SI rather than full serializability in version 4, and likely permitted write-skew anomalies since its first release in 1985. Unfortunately, the ANSI SQL-92 standard was written with a lock-based database in mind, and hence is rather vague when applied to MVCC systems. Berenson et al. wrote a paper in 1995 critiquing the SQL standard, and cited snapshot isolation as an example of an isolation level that did not exhibit the standard anomalies described in the ANSI SQL-92 standard, yet still had anomalous behaviour when compared with serializable transactions. In 2008, Cahill et al. showed that write-skew anomalies could be prevented by detecting and aborting "dangerous" triplets of concurrent transactions. This implementation of serializability is well-suited to multiversion concurrency control databases, and has been adopted in PostgreSQL 9.1, where it is known as Serializable Snapshot Isolation (SSI). When used consistently, this eliminates the need for the above workarounds. The downside over snapshot isolation is an increase in aborted transactions. This can perform better or worse than snapshot isolation with the above workarounds, depending on workload.
Hedgeable
Hedgeable, Inc. was a U.S. based financial services company and digital wealth management platform headquartered in New York City. Hedgeable was known for not following set allocations, and instead actively managing accounts in response to market movements. On August 9, 2018, Hedgeable closed its doors to new investors, with existing investors required to transfer out of the company. The company claimed that it was not shutting down but simply removing its SEC registration. == History == Hedgeable was founded in 2009 by twin brothers Michael and Matthew Kane, who previously worked at high-net worth investment managers such as Bridgewater Associates and Spruce Private Investors. Both Michael and Matthew graduated from Penn State University with degrees in finance. Hedgeable is a Registered Investment Advisor with the U.S. Securities and Exchange Commission. The company has received funding from SixThirty and Route 66 Ventures as well as various other angel investors. On August 9, 2018, Hedgeable closed its doors to new investors. == Investing Strategies == Hedgeable did not follow a buy-and-hold approach, but instead actively manages accounts in response to market movements focusing on downside protection in bear markets. Their strategy was different from other robo-advisors, which use Modern Portfolio Theory. Hedgeable offered investment options including Exchange Traded Funds (ETFs) to individual stocks, master limited partnerships, private equity and bitcoin. Mutual funds were not used in portfolios. Although the firm's focus was to provide a direct-to-consumer service, Hedgeable's investment strategies were available to financial advisors and institutions as well through a variety of platforms. == Product Features == When it was open to external clients, Hedgeable aimed to gamify their personal finance experience. Clients could open a new account or transfer an existing account. Hedgeable accepted retirement accounts, taxable accounts, business accounts and various other account types. Hedgeable offered the following features: Downside protection Account aggregation Alternative investments Alpha rewards API Mobile app It was awarded 4/5 for client transparency by Paladin Research. Hedgeable was the winner of the Finovate Fall 2015 Best of Show Award and the GREAT 2015 Tech Award (FinTech Category). In 2016, Hedgeable launched its first iOS mobile app in order to expand their product offerings.
Data access layer
A data access layer (DAL) is a software architectural layer that provides access to data from one or more sources, such as a relational database, NoSQL database, SQL query engine, file system, or other persistent storage. It separates client code from the details of storage systems, query execution, connection handling, and data retrieval. Data access layers are commonly used to centralize data access logic, reduce coupling between applications and data sources, and provide a consistent interface for retrieving, writing, or querying data. Depending on the system, a data access layer may be implemented as application code, a shared library, an intermediary service, or part of a broader database abstraction layer. == In application architecture == In application software, a data access layer provides a boundary between business logic or application code and the systems used to store or retrieve data. For example, a data access layer may expose methods or interfaces for retrieving, writing, or querying data while hiding details such as connection management, SQL statements, storage APIs, error handling, and result conversion. Depending on the application, the layer may return objects, records, tabular results, documents, streams, or other representations of data. A common implementation is a set of classes, functions, or methods that directly reference database queries, stored procedures, storage APIs, or other data sources. For example, instead of using commands such as insert, delete, and update throughout an application to access a specific table, methods such as registerUser or loginUser may be implemented inside the data access layer. Business logic methods from an application can also be mapped to the data access layer. Instead of making several database queries directly, an application can call a single DAL method that abstracts those database calls. Applications using a data access layer may be either dependent on or independent from a particular database server. If the data access layer supports multiple database systems, the application can use any database system that the DAL can access. In either case, the data access layer provides a centralized location for calls into the underlying data store, which can make it easier to maintain, test, or port the application to other storage systems. == Implementation patterns == A data access layer can be implemented using several patterns and technologies, including data access objects, repositories, stored procedures, query builders, database drivers, or object–relational mapping tools. These mechanisms may implement part or all of a data access layer, but are not always equivalent to the layer itself. Object–relational mapping tools are commonly used in data access layers for object-oriented applications that map records in a relational database to objects in a programming language. Other data access layers may expose lower-level database interfaces, tabular results, document-oriented data, files, streams, or protocol-level interfaces. == Use with multiple underlying data systems == A data access layer may be used to abstract differences between multiple underlying data systems, allowing applications to access them through a more consistent interface. In such designs, applications call the DAL rather than interacting directly with each database or storage system. The layer may then handle connection management, query generation, result mapping, error handling, and other implementation details. A data access layer may be implemented as a shared library or as an intermediary service, such as a proxy or gateway. In this configuration, client applications or services connect to the data access layer, which then communicates with one or more underlying databases or query engines. This can provide a common location for authentication, authorization, logging, routing, and translation between different database interfaces. == Interfaces and protocols == Data access layers may expose or use standardized interfaces and protocols for database access. Examples include Open Database Connectivity (ODBC), Java Database Connectivity (JDBC), database-native wire protocols, and newer interfaces such as Apache Arrow Database Connectivity (ADBC) and Arrow Flight SQL. In systems that support multiple data stores, a data access layer may provide a consistent interface while using different drivers, protocols, or query mechanisms internally. == Distinction from related patterns == A data access layer is related to, but broader than, a data access object, which is usually an object-oriented design pattern for encapsulating access to a persistence mechanism. It is also related to a database abstraction layer, which focuses on hiding differences between database systems. In practice, the terms may overlap.
Cozi
Cozi is a family organization website and mobile app designed to streamline household management. It offers shared calendars, to-do lists, shopping lists, and messaging tools, allowing multiple users to coordinate under one account. Founded in 2005 by former Microsoft employees, Cozi has evolved through acquisitions and now operates under OurFamilyWizard. The app is available in both free and premium versions on iOS, Android, and desktop platforms. == History == Cozi was founded in 2005 by Robbie Cape and Jan Miksovsky, two former Microsoft employees who sought to simplify family logistics with technology. The company's first product, Cozi Central, was released on September 25, 2006, and included a family calendar, shopping lists, family messaging and a photo collage screensaver. The company is based in Seattle, Washington. Cozi has both a freemium version, and a paid version called Cozi Gold. Cozi Gold's additional features include Cozi Contacts, a birthday tracker, more reminders, mobile month view, and change notifications. The software can be used on desktop or mobile applications for iOS and Android. On June 5, 2011, Cozi set a Guinness World Record for the longest line of ducks in a row. The line stretched for one mile and was made up of 17,782 rubber ducks. Cozi was acquired by Time Inc. in 2014. After the Meredith Corporation acquired Time in 2018, Cozi was moved into the Parents Network division. On May 4, 2022, Cozi was acquired by OurFamilyWizard of Minneapolis, Minnesota, reporting more than 20 million registered users.
Clipmap
In computer graphics, clipmapping is a method of clipping a mipmap to a subset of data pertinent to the geometry being displayed. This is useful for loading as little data as possible when memory is limited, such as on a graphics processing unit. The technique is used for LODing in NVIDIA’s implementation of voxel cone tracing. The high-resolution levels of the mipmapped scene representation are clipped to a region near the camera, while lower resolution levels are clipped further away. == MegaTexture == MegaTexture is a clipmap implementation developed by id Software. It was introduced in their id Tech 4 engine and also appeared in id Tech 5 and id Tech 6 before being removed in id Tech 7. MegaTexture is a texture allocation technique that uses a single, extremely large texture rather than repeating multiple smaller textures. It is also featured in Splash Damage's game Enemy Territory: Quake Wars, and was developed by id Software former technical director John Carmack. MegaTexture employs a single large texture space for static terrain. The texture is stored on removable media or a computer's hard drive and streamed as needed, allowing large amounts of detail and variation over a large area with comparatively little RAM usage. Depending on the pixel resolution per square meter, covering a large area could require several gigabytes of memory. However, RAM is also filled by the rest of the game and the underlying operating system, limiting the amount available for texturing. As the player moves around the game, different sections of the MegaTexture are loaded into memory. They are then scaled to the correct size and applied to the 3D models of the terrain. Id has presented a more advanced technique that builds upon the MegaTexture idea and virtualizes both the geometry and the textures to obtain unique geometry down to the equivalent of the texel: the sparse voxel octree (SVO). It works by raycasting the geometry represented by voxels (instead of triangles) stored in an octree. The goal is to stream parts of the octree into video memory, going further down along the tree for nearby objects to give them more details, and to use higher level, larger voxels for farther objects, which give an automatic level of detail (LOD) system for both geometry and textures at the same time. The geometric detail that can be obtained using this method is nearly infinite, which removes the need for faking 3-dimensional details with techniques such as normal mapping. Despite that most voxel rendering tests use very large amounts of memory (up to several GB), Jon Olick of id Software claimed the technology is able to compress such SVO to 1.15 bits per voxel of position data. == Virtual texturing == Unlike clipmaps, which clip each mip level around a viewpoint-dependent clipcenter and therefore work best for terrain, virtual texturing preprocesses texture data into equally sized tiles that can be streamed for arbitrary textured geometry. Rage, powered by the id Tech 5 engine, uses a more advanced technique called virtual texturing. Textures can measure up to 128000×128000 pixels and are also used for in-game models and sprites, etc. and not just the terrain. Wolfenstein: The New Order and the 2016 version of Doom also use these. Carmageddon: Reincarnation also uses virtual texturing, though unlike id's virtual texturing system, which is designed for unique texture-mapping everywhere, their system is designed to use storage space sparingly while still offering good blend of texture variation and resolution.