Ashutosh Saxena is an Indian-American computer scientist, researcher, and entrepreneur known for his contributions to the field of artificial intelligence and large-scale robot learning. His interests include building enterprise AI agents and embodied AI. Saxena is the co-founder and CEO of Caspar.AI, where generative AI parses data from ambient 3D radar sensors to predict 20+ health & wellness markers for pro-active patient care. Prior to Caspar.AI, Ashutosh co-founded Cognical Katapult (NSDQ: KPLT), which provides a no credit required alternative to traditional financing for online and omni-channel retail. Before Katapult, Saxena was an assistant professor in the Computer Science Department and faculty director of the RoboBrain Project (a large-scale AI model for robotics) at Cornell University. == Education == In 2009, with artificial intelligence pioneer Andrew Ng as his advisor, Saxena received both his M.S. and Ph.D. in computer science with an emphasis on artificial intelligence from Stanford University. Saxena received his bachelor's degree in electrical engineering from the Indian Institute of Technology, Kanpur in 2004. == Career == Saxena was the chief scientist of New York-based Holopad, where he worked with Steven Spielberg's team to create walkthroughs and 3D experiences for his movie TinTin. His past experiences include building acoustic AI models at Bose Corporation. Once Ashutosh completed his undergraduate degree, he became a researcher at the Commonwealth Scientific and Industrial Research Organization, where he developed AI models for medical devices. Before Caspar, Saxena pursued other entrepreneurial ventures, such as ZunaVision, an artificial intelligence startup he co-founded with Andrew Ng that uses AI to embed advertising space within videos. Ashutosh served as the CTO of ZunaVision from 2008 to 2010. After ZunaVision, Saxena co-founded Cognical Katapult, which provided financing solutions to nonprime and underbanked consumers powered by artificial intelligence. From 2014 to 2016, Saxena served as the faculty director of the RoboBrain project, which was a joint venture that he started between Stanford University, Cornell University, Brown University, and the University of California, Berkeley that made a knowledge engine for robots. Saxena co-founded Brain of Things in 2015 with David Cheriton, who serves as chief scientist, and was listed as the fastest growing private company reaching an annual recurring revenue of $8 million in three years. It has been widely covered in several outlets including Forbes Japan, and MIT Technology Review. Saxena's work on deep learning won test of time award in 2023 by Robotics Science and Systems. Ashutosh has been recognized for his work by receiving the Alfred P. Sloan Fellow in 2011, Google Faculty Research Award in 2012, Microsoft Faculty Fellowship in 2012, NSF Career award in 2013, One of the Eight Innovators to Watch by the Smithsonian Institution in 2015, and received TR35 Innovator Award by MIT Technology Review in 2018. He was named by San Francisco Business Times as a 40 under 40 young business leader. == Research == Saxena has authored over 100 published papers in the areas of large-scale robot learning and artificial intelligence, with 20,000+ citations. His work in the fields of computer vision and deep learning have been featured in press releases and academic journal reviews. Ashutosh's early work includes the Stanford Artificial Intelligence Robot (STAIR), an AI models that enables to perform tasks such as unload items from a dishwasher, which was covered on the front-page of New York Times. His work on Make3D, was the first work that estimated 3D depth from a single still image. At Cornell University, Ashutosh led the Robot Learning Lab, which used a machine learning approach to train robots to perform tasks in human environments such as generalizing manipulation in 3D point-clouds where robots learn to transfer manipulation trajectories to novel objects utilizing a large sample of demonstrations from crowdsourcing.
List of C++ software and tools
This is a list of notable software and programming tools for the C++ programming language, including libraries, web frameworks, programming language implementations, compilers, integrated development environments (IDEs), and other related software development utilities. == Compilers and IDEs == AMD Optimizing C/C++ Compiler — proprietary fork of LLVM + Clang for Linux C++Builder — rapid application development (RAD) environment Clang – compiler front end for C, C++, and Objective-C, part of LLVM CLion — C++ IDE by JetBrains Code::Blocks — open-source cross-platform IDE that supports multiple compilers including GCC, Clang and Visual C++ CodeLite — cross-platform IDE for the C/C++ programming languages using the wxWidgets toolkit CodeSynthesis XSD – XML Data Binding compiler Dev-C++ — MinGW or TDM-GCC 64bit port of the GCC as its compiler GCC – GNU Compiler Collection Intel C++ Compiler – proprietary high-performance compiler by Intel KDevelop — IDE part of the KDE project and is based on KDE Frameworks and Qt, the C/C++ backend uses Clang. Microsoft Visual C++ – proprietary C++ compiler and IDE for Windows Oracle Developer Studio — Solaris, OpenSolaris, RHEL, and Oracle Linux operating systems. Qt Creator — part of the SDK for the Qt GUI application development framework and uses the Qt API SlickEdit — text editor and IDE Turbo C++ – legacy C++ IDE and compiler popular in the 1990s Understand — IDE that enables static code analysis through an array of visuals, documentation, and metric tools. Visual Studio — integrated development environment by Microsoft that supports C++ Visual Studio Code — integrated development environment by Microsoft that supports C++ Xcode — Apple IDE to develop macOS, iOS, iPadOS, watchOS, tvOS, and visionOS that supports C++ source code. == Debuggers == Allinea DDT – a graphical debugger dbx — a proprietary source-level debugger GNU Debugger – portable debugger that runs on many Unix-like systems Modular Debugger — a C/C++ source level debugger for Solaris and derivates Undo LiveRecorder — time travel debugger == Libraries == Active Template Library – template-based C++ classes developed by Microsoft Apache MXNet — deep learning framework Apache Xerces – parsing, validating, and serializing and manipulating XML. Asio — networking and low-level I/O library Bitpit — scientific computing and mesh manipulation library Boost — collection of peer-reviewed libraries Botan — cryptography library C++ AMP – easy way to write programs that compile and execute on data-parallel hardware, such as graphics cards and GPUs C++ Standard Library — standard library for the language C++/WinRT — library for Microsoft's Windows Runtime platform, designed to provide access to modern Windows APIs. C3D Toolkit — geometric modeling kernel Caffe — deep learning framework CAPD — library for rigorous numerics and dynamical systems Cassowary — constraint-solving toolkit that efficiently solves systems of linear equalities and inequalities Cinder — library for creative coding ClanLib — cross-platform game SDK CMU Sphinx — speech recognition system Crypto++ — cryptographic algorithms library Dlib — general-purpose cross-platform library Dune — partial differential equations using grid-based methods fastText — text representation and text classification library FLTK — GUI toolkit Geospatial Data Abstraction Library — geospatial data access library GDCM — image library General Polygon Clipper — polygon clipping library GiNaC — computer algebra system that uses Class Library for Numbers for implementing arbitrary-precision arithmetic GLFW — OpenGL and window management library HarfBuzz — text rendering and typesetting library High Efficiency Image File Format — digital container format for storing individual digital images and image sequences ITK — image analysis library Integrated Performance Primitives — domain-specific functions that are highly optimized for diverse Intel architectures Jackets library — GPU computing library JSBSim — open-source flight dynamics model JUCE — framework for audio applications KDE Frameworks — collection of libraries from the KDE project KFRlib — digital signal processing framework LEMON — library for optimization and graph problems LevelDB — key–value database library Libdash — MPEG-DASH streaming library libLAS — reading and writing geospatial data encoded in the ASPRS laser (LAS) file format libsigc++ — typesafe callbacks LibRaw — free and open-source software library for reading raw files from digital cameras libSBML — application programming interface (API) for the SBML (Systems Biology Markup Language) LIBSVM — sequential minimal optimization (SMO) algorithm for kernelized support vector machines Libx — DirectX .X files graphics library Loki — collection of design patterns LIVE555 — multimedia streaming library Metakit — embedded database library Microsoft Cognitive Toolkit — deep learning toolkit Microsoft Foundation Class Library — object-oriented library for developing desktop applications for Windows Microsoft SEAL — homomorphic encryption library mlpack — machine learning and AI library Mobile Robot Programming Toolkit — robotics research library Object Windows Library — Object Windows Library, superseded by VCL Open Cascade — CAD and 3D modeling library Open Asset Import Library — 3D model import library to provide a common API for different 3D asset file formats OpenCV – computer vision and machine learning library OpenFOAM — computational fluid dynamics toolkit OpenH264 — real-time encoding and decoding video streams in the H.264/MPEG-4 AVC format OpenImageIO — image processing library Open Inventor — higher layer of programming for OpenGL OpenNN — neural networks library OpenVDB — sparse volume data library openFrameworks — creative coding toolkit OpenRTM-aist — robotics middleware library Oracle Template Library — database access that supports IBM Db2 and Open Database Connectivity Orfeo toolbox — remote sensing image processing library OR-Tools — operations research and optimization library Parallel Augmented Maps — ordered sets, ordered maps, and augmented maps. Parallel Patterns Library — Microsoft library that provides features for multicore programming PhysX — physics simulation engine POCO C++ Libraries — general-purpose libraries for software development Poppler — PDF rendering library Protocol Buffers — data serialization library Qt — cross-platform widget toolkit QuantLib — quantitative finance library RocksDB — key–value database library ROOT — data analysis framework from CERN ROS — robotics middleware Scintilla — source code editing component SDL – Simple DirectMedia Layer, cross-platform development library for multimedia applications SFML – Simple and Fast Multimedia Library Shark – open-source machine learning library Shogun — machine learning toolbox Skia — 2D graphics library Snappy — compression library Sound Object Library — music and audio development Standard Template Library — library of containers and algorithms Stapl — parallel computing library SymbolicC++ — symbolic computation library TerraLib — GIS library Tesseract OCR — optical character recognition engine Threading Building Blocks — parallel computing library ThreadWeaver — concurrency framework Tiny-dnn — lightweight deep learning library TinyXML — lightweight XML parser Tkrzw — key–value databases VTD-XML — XML processing library wxWidgets — cross-platform GUI toolkit x265 — video encoding library for HEVC XGBoost — gradient boosting library Windows Template Library — Win32 development === Mathematical and numerical libraries === == Tools == Akonadi — a C++/Qt framework and storage service for personal information management BALL – framework and set of algorithms and data structures for molecular modelling and computational structural bioinformatics Boehm garbage collector – conservative garbage collector CEGUI — C++ GUI library ClanLib – video game SDK CMake — cross-platform build system for C++ projects Confidential Consortium Framework – blockchain infrastructure framework DaviX – WebDAV client Doxygen — documentation generator for C++ and other languages FLTK — Fast Light Toolkit, cross-platform GUI library Fox toolkit — C++ GUI toolkit GDB — GNU Project debugger, often used with C and C++ gtkmm — official C++ interface for the popular GUI library GTK HOOPS Visualize — 3D computer graphics HPX — partitioned global address space Parallel programming Runtime System JUCE — cross-platform C++ audio and GUI framework LessTif — free clone of Motif GUI toolkit MFC — Microsoft Foundation Class library Nana — modern C++ GUI toolkit PTK Toolkit — 2D rendering engine and SDK, and portability options. Qt — cross-platform C++ GUI toolkit Rogue Wave — C++ GUI toolkit TnFOX — C++ GUI toolkit Ultimate++ — cross-platform C++ GUI framework Valgrind — tool suite for debugging and profiling C/C++ programs wxWidgets — cross-platform C++ GUI toolkit x265 — encoder for creating digital video streams in the High Efficiency Vid
TuVox
TuVox is a company that produces VXML-based telephone speech-recognition applications to replace DTMF touch-tone systems for their clients. == History == TuVox was founded in 2001 by Steven S. Pollock and Ashok Khosla, formerly of Apple Computer Corporation and Claris Corporation. Since then, TuVox has grown to over 150 employees and has US offices in Cupertino, California and Boca Raton, Florida as well as international offices in London, Vancouver and Sydney. In 2005, TuVox acquired the customers and hosting facilities of Net-By-Tel. In 2007, the company raised $20m for its speech recognition, and phone menu software. On July 22, 2010, West Interactive — a subsidiary of West Corporation — announced its acquisition of TuVox. == Customers == TuVox clients include: 1-800-Flowers.com, AMC Entertainment, American Airlines, British Airways, M&T Bank, Canon Inc., Gateway, Inc., Motorola, Progress Energy Inc., Telecom New Zealand, Time, Inc., BECU, Virgin America and USAA.
Interim Measures for the Management of Generative AI Services
The Interim Measures for the Management of Generative AI Services (Chinese: 生成式人工智能服务管理暂行办法; pinyin: Shēngchéng shì réngōng zhìnéng fúwù guǎnlǐ zànxíng bànfǎ) are a set of regulations governing public-facing generative artificial intelligence services in China. Issued on 10 July 2023 and effective from 15 August 2023, they were China's first binding regulation specifically targeting generative AI. They have been described as among the earliest such regulations adopted by any country. The measures were jointly issued by the Cyberspace Administration of China (CAC) and six other national bodies: the National Development and Reform Commission, the Ministry of Education, the Ministry of Science and Technology, the Ministry of Industry and Information Technology, the Ministry of Public Security, and the National Radio and Television Administration. Among the measures' most prominent requirements is that generative AI services must uphold Core Socialist Values and must not generate content that could subvert state power, harm national security, or undermine social stability. The measures also require providers of public-facing generative AI services to undergo security assessments and register their algorithms with the CAC. As of December 2025, 748 generative AI services had completed the filing process at the national level. == Background == The Interim Measures build on two earlier sets of regulations targeting specific algorithm applications. The Administrative Provisions on Algorithm Recommendation for Internet Information Services, effective from March 2022, established China's algorithm registry and required providers of recommendation algorithms with "public opinion properties or social mobilization capabilities" to file with the CAC and undergo security assessments. The Administrative Provisions on Deep Synthesis of Internet Information Services, effective from January 2023, extended similar requirements to algorithms used for generating synthetic media such as deepfakes. In April 2023, the CAC released a draft of the generative AI regulation for public comment. The draft included several requirements that attracted attention, including that generated content should "embody Core Socialist Values" and that training data should be "true and accurate". The public consultation period ran until May 2023. The final version, published in July 2023, was substantially revised from the draft. According to an analysis by the Future of Privacy Forum, changes appeared to reflect feedback from industry stakeholders including Baidu, Xiaomi, SenseTime, and others, as well as input from government-affiliated research institutes. The final measures adopted a more permissive tone, with the CAC describing its approach as "inclusive and prudent" (包容审慎) and emphasising "classified and graded" (分类分级) supervision. == Scope == The measures apply to services that use generative AI technology to provide text, images, audio, video, or other content to the public within mainland China (Article 2). They do not apply to organisations that develop or use generative AI internally without offering services to the domestic public, such as industry associations, enterprises, and research institutions. Overseas providers whose services are accessible to users in China are also subject to the measures. == Key provisions == === Content requirements === Article 4 sets out the core content obligations. Providers and users of generative AI services must uphold the Core Socialist Values. The measures prohibit generating content that incites subversion of national sovereignty or the socialist system, endangers national security or the nation's image, incites separatism, promotes terrorism or extremism, promotes ethnic hatred or discrimination, or contains violence, obscenity, or false information prohibited by law. These content prohibitions largely mirror those in Article 12 of the Cybersecurity Law and in prior regulations governing online content. Article 4 also requires that models be designed and trained to avoid discrimination, that services respect intellectual property rights, and that providers take effective measures to improve the transparency and accuracy of generated content. === Training data and labelling === Article 7 requires providers to ensure that training data is of high quality and legitimately sourced, and that it does not infringe upon intellectual property rights. Where personal information is used, consent must be obtained. The final version of this provision removed language from the draft that would have held providers responsible for the "legitimacy" of all pretraining data, replacing it with a requirement to "employ effective measures to improve the quality of training data". Article 8 requires providers to establish labelling rules for training data and to conduct quality assessments of data annotations. Article 12 requires that generated images, videos, and other synthetic content be labelled as AI-generated. === User rights and privacy === Article 11 requires providers to protect user privacy, to minimise the collection and retention of personal data, and to refrain from unlawfully sharing user information. Users have the right to request review, correction, or deletion of their personal information. Article 10 requires providers to take measures to prevent excessive dependence on or addiction to generative AI services by minors. === Security assessment and algorithm filing === Article 17 requires that providers of generative AI services with "public opinion properties or the capacity for social mobilization" (具有舆论属性或者社会动员能力) carry out security assessments and complete algorithm filing procedures in accordance with the Administrative Provisions on Algorithm Recommendation for Internet Information Services. == Implementation == === Algorithm filing process === In practice, the filing requirements under the Interim Measures have developed into a two-tier process. The first tier is the standard algorithm filing (算法备案) under the pre-existing Algorithm Recommendation Provisions, which involves submitting information about an algorithm's design, purpose, and data sources to the CAC. This process is primarily a registration mechanism. For public-facing generative AI products, there is an additional, more rigorous process commonly referred to as the "large model filing" (大模型备案). This involves submitting a security self-assessment report, data annotation rules, a keyword blocking list, and evaluation test question sets. The process includes technical testing at the provincial level, followed by review at the national CAC level. The algorithm filing targets specific algorithms, while the large model filing evaluates the broader system architecture, training data, model parameters, and potential social impact. The CAC publishes lists of generative AI services that have successfully completed the filing process. The first such list was published on 2 April 2024. According to the CAC's year-end announcements, 302 generative AI services had completed national-level filing by the end of 2024 (of which 238 were new that year), alongside 105 applications that completed local-level registration. By the end of 2025, the cumulative total had risen to 748 national-level filings and 435 local-level registrations. === Content compliance and testing === According to the Carnegie Endowment, the CAC has conducted compliance audits of generative AI services with a particular focus on ensuring appropriate responses to queries about politically sensitive topics. The large model filing process requires providers to pass both provincial-level and national-level technical testing before their services can be made available to the public. On 1 March 2024, the National Technical Committee 260 on Cybersecurity (TC260) published TC260-003, the Basic Security Requirements for Generative AI Services (生成式人工智能服务安全基本要求), a technical standard that provides detailed guidance on the security assessments required under the Interim Measures. The standard covers requirements for training data safety, model security, and content safety evaluation, and is used as a reference for the filing process. == Analysis == === Relationship to broader Chinese internet regulation === The content requirements in the Interim Measures extend China's existing framework for online information control to generative AI. Legal scholars have noted that the "Core Socialist Values" provision and the specific content prohibitions are consistent with longstanding requirements imposed on internet platforms under the Cybersecurity Law and related regulations. The Asia Society Policy Institute has described the Chinese government's highest regulatory priority in this area as retaining control of information, noting that content-related obligations receive stricter enforcement than other provisions. === Nature of the filing system === The character of the filing system has been debated by scholars. Angela Huyue Zh
The 100 (TV series)
The 100 (pronounced "The Hundred" ) is an American post-apocalyptic science fiction drama television series that premiered on March 19, 2014, on the CW network, and ended on September 30, 2020. Developed by Jason Rothenberg, the series is based on the young adult novel series The 100 by Kass Morgan. The 100 follows descendants of post-apocalyptic survivors from a space habitat, the Ark, who return to Earth nearly a century after a devastating nuclear apocalypse; the first people sent to Earth are a group of juvenile delinquents who encounter another group of survivors on the ground. The juvenile delinquents include Clarke Griffin (Eliza Taylor), Finn Collins (Thomas McDonell), Bellamy Blake (Bob Morley), Octavia Blake (Marie Avgeropoulos), Jasper Jordan (Devon Bostick), Monty Green (Christopher Larkin), and John Murphy (Richard Harmon). Other lead characters include Clarke's mother Dr. Abby Griffin (Paige Turco), Marcus Kane (Henry Ian Cusick), and Chancellor Thelonious Jaha (Isaiah Washington), all of whom are council members on the Ark, and Raven Reyes (Lindsey Morgan), a mechanic aboard the Ark. == Plot == Ninety-seven years after a devastating nuclear apocalypse wipes out most human life on Earth, thousands of people now live in a space station orbiting Earth, which they call the Ark. Three generations have been born in space, but when life-support systems on the Ark begin to fail, one hundred juvenile detainees are sent to Earth in a last attempt to determine whether it is habitable, or at least save resources for the remaining residents of the Ark. They discover that some humans survived the apocalypse: the Grounders, who live in clans locked in a power struggle; the Reapers, another group of grounders who have been turned into cannibals by the Mountain Men; and the Mountain Men, who live in Mount Weather, descended from those who locked themselves away before the apocalypse. Under the leadership of Clarke and Bellamy, the juveniles attempt to survive the harsh surface conditions, battle hostile grounders and establish communication with the Ark. In the second season, the survivors face a new threat from the Mountain Men, who harvest their bone marrow to survive the radiation. Clarke and the others form a fragile alliance with the grounders to rescue their people. The season ends with Clarke making a devastating choice to save them all. In season three, power struggles erupt between the Arkadians and the grounders after a controversial new leader takes charge. Meanwhile, an AI named A.L.I.E., responsible for the original apocalypse, begins taking control of people’s minds. Clarke destroys A.L.I.E. but learns another disaster is imminent. In the fourth season, nuclear reactors are melting down, threatening to wipe out life again. Clarke and her friends search for ways to survive, including experimenting with radiation-resistant blood and finding an underground bunker. As time runs out, only a select few are able to take shelter. The fifth season picks up six years later, when Earth is left largely uninhabitable except for one green valley, where new enemies arrive. Clarke protects her adopted daughter Madi while former survivors return from space and underground, triggering another war. The battle ends with the valley destroyed and the group entering cryosleep to find a new home. In season six, the group awakens 125 years later on a new planet called Sanctum, ruled by powerful families known as the Primes. Clarke fights to stop body-snatching rituals and protect her people from new threats, including a rebel group and a dangerous AI influence. The season ends with major losses and the destruction of the Primes' rule. In the seventh and final season, the survivors face unrest on Sanctum and clash with a mysterious group called the Disciples, who believe Clarke is key to saving humanity. A wormhole network reveals multiple planets and a final "test" that determines the fate of the species. Most transcend into a higher consciousness, but Clarke and a few others choose to live out their lives on a reborn Earth. == Cast and characters == Eliza Taylor as Clarke Griffin Paige Turco as Abigail "Abby" Griffin (seasons 1–6; guest season 7) Thomas McDonell as Finn Collins (seasons 1–2) Eli Goree as Wells Jaha (season 1; guest season 2) Marie Avgeropoulos as Octavia Blake Bob Morley as Bellamy Blake Kelly Hu as Callie "Cece" Cartwig (season 1) Christopher Larkin as Monty Green (seasons 1–5; guest season 6) Devon Bostick as Jasper Jordan (seasons 1–4) Isaiah Washington as Thelonious Jaha (seasons 1–5) Henry Ian Cusick as Marcus Kane (seasons 1–6) Lindsey Morgan as Raven Reyes (seasons 2–7; recurring season 1) Ricky Whittle as Lincoln (seasons 2–3; recurring season 1) Richard Harmon as John Murphy (seasons 3–7; recurring seasons 1–2) Zach McGowan as Roan (season 4; recurring season 3; guest season 7) Tasya Teles as Echo / Ash (seasons 5–7; guest seasons 2–3; recurring season 4) Shannon Kook as Jordan Green (seasons 6–7; guest season 5) JR Bourne as Russell Lightbourne / Malachi / Sheidheda (season 7; recurring season 6) Chuku Modu as Gabriel Santiago (season 7; recurring season 6) Shelby Flannery as Hope Diyoza (season 7; guest season 6) =
Decision list
Decision lists are a representation for Boolean functions which can be easily learned from examples. Single term decision lists are more expressive than disjunctions and conjunctions; however, 1-term decision lists are less expressive than the general disjunctive normal form and the conjunctive normal form. The language specified by a k-length decision list includes as a subset the language specified by a k-depth decision tree. Learning decision lists can be used for attribute efficient learning, a type of machine learning. == Definition == A decision list (DL) of length r is of the form: if f1 then output b1 else if f2 then output b2 ... else if fr then output br where fi is the ith formula and bi is the ith boolean for i ∈ { 1... r } {\displaystyle i\in \{1...r\}} . The last if-then-else is the default case, which means formula fr is always equal to true. A k-DL is a decision list where all of formulas have at most k terms. Sometimes "decision list" is used to refer to a 1-DL, where all of the formulas are either a variable or its negation.
Dry Drowning
Dry Drowning is a cyberpunk mystery visual novel developed by Studio V and published by VLG Publishing and WhisperGames for Microsoft Windows on August 2, 2019. It was released on the Nintendo Switch on February 22, 2021. == Gameplay == The player takes control of Mordred Foley and has to read through the story, while making decisions at certain points. Depending on the choices, the player can influence the relationship to other characters as well as the course of the game, discovering more than 150 story branches, and eventually reach one out of three different endings with variations. The game also includes passages where the player has to find clues or items on the screen by clicking on them. These can be used in interrogation scenes with certain characters in order to unmask them and discover their lies. Throughout the game, the player has access to an in-game operating system called AquaOS. With that, they can re-read their conversations, look at their found items, and read biographies of the characters encountered. == Plot == The game is set in the fictional and totalitarian city Nova Polemos in Europa in 2066. Mordred Foley and Hera Kairis are private investigators and before the events of the game, they sent two of the most dangerous serial killers ever, Jennifer Kingston and Robert Herrington, to the electric chair. However, after their execution, their agency underwent an investigation for falsifying the evidence presented during the case, which completely destroyed its reputation. Now they want to restart their careers and lives, while dealing with their past traumas. Soon, Mordred is caught up in several cases that all led him to believe that the dreaded serial killer named Pandora has returned. In order to solve these cases, both Mordred and Hera have to face their pasts and fears, all while a racist political party is about to make the lives of refugees in Nova Polemos even worse. == Development == The game was initially conceived by Giacomo Masi and Samuele Zolfanelli, then developed by Studio V and directed and written by Giacomo Masi. It was originally written in Italian and translated into English, Chinese, Japanese, Korean, and German. The soundtrack was composed, written, and performed by Giorgio Maioli. The ending theme and Hera's pieces, performed on piano, were created by Alessandro Masi. The background and character artworks were made by Giulia Carli, other graphic elements such as the UI were created by Samuele Zolfanelli. The developers cited L.A. Noire, Ace Attorney, Blade Runner and Heavy Rain as some of their inspirations for the game. === Releases === Dry Drowning was originally released on Microsoft Windows through Steam, GOG, Itch.io, and Utomik in August 2019. In July 2019, Giacomo Masi announced the game would be released for Xbox One in 2020, though it was not released that year. A Nintendo Switch port was released on February 22, 2021, and a version for PlayStation 4 is set to release in 2021. == Reception == According to review aggregator platform Metacritic, Dry Drowning received "mixed or average reviews" for PC based on 11 reviews and "generally favorable reviews" for Nintendo Switch based on 6 reviews. Fellow review aggregator OpenCritic assessed that the game received fair approval, being recommended by 55% of critics. 4players.de gave a positive rating of 80% and wrote: "Stylish noir thriller with an interesting story, but mechanical limitations – despite a variety of possible interactions." Screen Rant gave a mixed rating of 3 out of 5 stars and wrote, "Dry Drowning may be a fair bit messy, but there's charm here. Players who are willing to embrace the cheesier elements will find some joy in its well-crafted setting and a decent murder mystery plot. The game is constrictive and lacks the genuine shock and engagement of top tier visual novels like Doki Doki Literature Club!, but there are some moments of clever world building and a strong enough mystery propelling it." The Italian review site SpazioGames gave a positive rating of 8.5 out of 10 points and wrote: "Dry Drowning is a very good game with great narrative experience. Every relationship between the characters is layered to increase player involvement, and each choice has different consequences. A thriller game that deserves to be played." === Awards === The game won Best of EGS 2019 and Best of JOIN 2019 awards, an honorable mention at GAMEROME and was nominated as "Best Italian Debut Game" at the Italian Video Game Awards 2020. It was also declared Best Game at Join The Indie 2019.