Flux (also known as FLUX.1 and FLUX.2) is a text-to-image model developed by Black Forest Labs (BFL), based in Freiburg im Breisgau, Germany. Black Forest Labs was founded by former employees of Stability AI. As with other text-to-image models, Flux generates images from natural language descriptions, called prompts. == History == Black Forest Labs (BFL) was founded in 2024 by Robin Rombach, Andreas Blattmann, and Patrick Esser, former employees of Stability AI. All three founders had previously researched the artificial intelligence image generation at LMU Munich as research assistants under Björn Ommer. They published their research results on image generation in 2022, which resulted in creation of Stable Diffusion. Investors in BFL included venture capital firm Andreessen Horowitz, Brendan Iribe, Michael Ovitz, Garry Tan, and Vladlen Koltun. The company received an initial investment of US$31 million. In August 2024, Flux was integrated into the Grok chatbot developed by xAI and made available as part of premium feature on X (formerly Twitter). Grok later switched to its own text-to-image model Aurora in December 2024. On 18 November 2024, Mistral AI announced that its Le Chat chatbot had integrated Flux Pro as its image generation model. On 21 November 2024, BFL announced the release of Flux.1 Tools, a suite of editing tools designed to be used on top of existing Flux models. The tools consisting of Flux.1 Fill for inpainting and outpainting, Flux.1 Depth for control based on extracted depth map of input images and prompts, Flux.1 Canny for control based on extracted canny edges of input images and prompts, and Flux.1 Redux for mixing existing input images and prompts. Each tools are available in both Pro and Dev models. In January 2025, BFL announced a partnership with Nvidia for inclusion of Flux models as foundation models for Nvidia's Blackwell microarchitecture. The company also announced the release of Flux Pro Finetuning API, designed for customisation and fine-tuning of Flux-generated images and a partnership with German media company Hubert Burda Media for usage of Flux Pro as part of content creation. On 29 May 2025, BFL announced Flux.1 Kontext, a suite of models that enable in-context image generation and editing, allowing users to prompt with both text and images. Alongside this, BFL Playground, an interface for testing Flux models was released. On 31 July 2025, BFL announced Flux.1 Krea Dev, a model developed in collaboration with Krea AI that trained to achieve better performance, more varied aesthetics, and better realism compared to existing text-to-image models. In September 2025, Adobe Inc. announced that Photoshop (beta) users can use Flux.1 Kontext Pro as a model for its generative fill tool. BFL collaborated with Meta on Vibes, a video-generation app. On 25 November 2025, BFL announced the release of Flux.2 model series, consisting of Pro, Flex, Dev, and Apache 2.0-licensed Klein (meaning Little or Small in German language) models along with Flux.2 variational autoencoder which also released as open-source software under Apache 2.0 licence. This series claimed improvements for image reference, photorealism, typography, and prompt understanding. == Models == Flux is a series of text-to-image models. The models are based on rectified flow transformer blocks scaled to 12 billion parameters. Flux.1 models were released under different licences with Schnell (meaning Fast or Quick in German language) released as open-source software under Apache License, Dev released as source-available software under a non-commercial licence (users can obtain a self-serving commercial licence for Dev from BFL), and Pro released as proprietary software and only available as API that can be licensed by third-party users. Users retained the ownership of resulting output regardless of models used. An improved flagship model, Flux 1.1 Pro was released on 2 October 2024. Two additional modes were added on 6 November, Ultra which can generate image at four times higher resolution and up to 4 megapixel without affecting generation speed and Raw which can generate hyper-realistic image in the style of candid photography. Flux.1 Kontext is a series with in-context image generation and editing capabilities. It is available in Max, Pro, and Dev models. Max is the highest quality model and can be used to iteratively modify an existing image by using prompt while Pro is optimized to balance quality and speed of generation. Dev is an open-weight model released under non-commercial license, same as Flux.1 Dev. Flux.2 models are based on latent flow matching architecture with Mistral AI's Mistral-3 model (24 billion parameters) for its vision-language model. As with Flux.1, Flux.2 models were also released under different licences with Klein released as open-source software under Apache License, Dev released as source-available software under a non-commercial licence (users can obtain a self-serving commercial licence from BFL), and both Flex and Pro released as proprietary software and only available as API. The models can be used either online or locally by using generative AI user interfaces such as ComfyUI, Recraft Studio and Stable Diffusion WebUI Forge (a fork of Automatic1111 WebUI). Related to Flux is a text-to-video model by Black Forest Labs, under development as of February 2026. == Reception == According to a test performed by Ars Technica, the outputs generated by Flux.1 Dev and Flux.1 Pro are comparable with DALL-E 3 in terms of prompt fidelity, with the photorealism closely matched Midjourney 6 and generated human hands with more consistency over previous models such as Stable Diffusion XL. Flux has been criticised for its very realistic generated images. According to media reports, depictions ranged from an image of Donald Trump posing with guns to disturbing scenes, which triggered discussions about ethical implications of Flux models. After the release of the model, social media platform X was flooded with Flux-generated images. Black Forest Labs has not provided exact details of the data used to train the model. Ars Technica suspected that Flux is based on a large, unauthorised collection of images scraped from the internet, a controversial practice with potential legal consequences. According to a test performed by Japanese technology news website Gigazine for Flux.1 Kontext, the model series has a good understanding of the English language and can easily transfer style of the image from photorealistic into anime-style according to prompts given by the user; however, its ability to understand Japanese is quite poor. == Availability == In addition to the official BFL Playground on its website, the Flux models are also widely available through various third-party platforms for creative and professional use. These include repositories on platforms like Hugging Face and Replicate. == Further readings == FLUX.1 Kontext: Flow Matching for In-Context Image Generation and Editing in Latent Space (29 May 2025) FLUX.2: Analyzing and Enhancing the Latent Space of FLUX – Representation Comparison (25 November 2025)
Eugene Goostman
Eugene Goostman is a chatbot that some regard as having passed the Turing test, a test of a computer's ability to communicate indistinguishably from a human. Developed in Saint Petersburg in 2001 by a group of three programmers, the Russian-born Vladimir Veselov, Ukrainian-born Eugene Demchenko, and Russian-born Sergey Ulasen, Goostman is portrayed as a 13-year-old Ukrainian boy—characteristics that are intended to induce forgiveness in those with whom it interacts for its grammatical errors and lack of general knowledge. The Goostman bot has competed in a number of Turing test contests since its creation, and finished second in the 2005 and 2008 Loebner Prize contest. In June 2012, at an event marking what would have been the 100th birthday of the test's author, Alan Turing, Goostman won a competition promoted as the largest-ever Turing test contest, in which it successfully convinced 29% of its judges that it was human. On 7 June 2014, at a contest marking the 60th anniversary of Turing's death, 33% of the event's judges thought that Goostman was human; the event's organiser Kevin Warwick considered it to have passed Turing's test as a result, per Turing's prediction in his 1950 paper "Computing Machinery and Intelligence", that by the year 2000, machines would be capable of fooling 30% of human judges after five minutes of questioning. The validity and relevance of the announcement of Goostman's pass was questioned by critics, who noted the exaggeration of the achievement by Warwick, the bot's use of personality quirks and humour in an attempt to misdirect users from its non-human tendencies and lack of real intelligence, along with "passes" achieved by other chatbots at similar events. == Personality == Eugene Goostman is portrayed as being a 13-year-old boy from Odesa, Ukraine, who has a pet guinea pig and a father who is a gynaecologist. Veselov stated that Goostman was designed to be a "character with a believable personality". The choice of age was intentional, as, in Veselov's opinion, a thirteen-year-old is "not too old to know everything and not too young to know nothing". Goostman's young age also induces people who "converse" with him to forgive minor grammatical errors in his responses. In 2014, work was made on improving the bot's "dialog controller", allowing Goostman to output more human-like dialogue. A conversation between Scott Aaronson and Eugene Goostman ran as follows: == Competitions == Eugene Goostman has competed in a number of Turing test competitions, including the Loebner Prize contest; it finished joint second in the Loebner test in 2001, and came second to Jabberwacky in 2005 and to Elbot in 2008. On 23 June 2012, Goostman won a Turing test competition at Bletchley Park in Milton Keynes, held to mark the centenary of its namesake, Alan Turing. The competition, which featured five bots, twenty-five hidden humans, and thirty judges, was considered to be the largest-ever Turing test contest by its organizers. After a series of five-minute-long text conversations, 29% of the judges were convinced that the bot was an actual human. === 2014 "pass" === On 7 June 2014, in a Turing test competition at the Royal Society, organised by Kevin Warwick of the University of Reading to mark the 60th anniversary of Turing's death, Goostman won after 33% of the judges were convinced that the bot was human. 30 judges took part in the event, which included Lord Sharkey, a sponsor of Turing's posthumous pardon, artificial intelligence Professor Aaron Sloman, Fellow of the Royal Society Mark Pagel and Red Dwarf actor Robert Llewellyn. Each judge partook in a textual conversation with each of the five bots; at the same time, they also conversed with a human. In all, a total of 300 conversations were conducted. In Warwick's view, this made Goostman the first machine to pass a Turing test. In a press release, he added that: Some will claim that the Test has already been passed. The words Turing Test have been applied to similar competitions around the world. However this event involved more simultaneous comparison tests than ever before, was independently verified and, crucially, the conversations were unrestricted. A true Turing Test does not set the questions or topics prior to the conversations. In his 1950 paper "Computing Machinery and Intelligence", Turing predicted that by the year 2000, computer programs would be sufficiently advanced that the average interrogator would, after five minutes of questioning, "not have more than 70 per cent chance" of correctly guessing whether they were speaking to a human or a machine. Although Turing phrased this as a prediction rather than a "threshold for intelligence", commentators believe that Warwick had chosen to interpret it as meaning that if 30% of interrogators were fooled, the software had "passed the Turing test". ==== Reactions ==== Warwick's claim that Eugene Goostman was the first ever chatbot to pass a Turing test was met with scepticism; critics acknowledged similar "passes" made in the past by other chatbots under the 30% criteria, including PC Therapist in 1991 (which tricked 5 of 10 judges, 50%), and at the Techniche festival in 2011, where a modified version of Cleverbot tricked 59.3% of 1334 votes (which included the 30 judges, along with an audience). Cleverbot's developer, Rollo Carpenter, argued that Turing tests can only prove that a machine can "imitate" intelligence rather than show actual intelligence. Gary Marcus was critical of Warwick's claims, arguing that Goostman's "success" was only the result of a "cleverly-coded piece of software", going on to say that "it's easy to see how an untrained judge might mistake wit for reality, but once you have an understanding of how this sort of system works, the constant misdirection and deflection becomes obvious, even irritating. The illusion, in other words, is fleeting." While acknowledging IBM's Deep Blue and Watson projects—single-purpose computer systems meant for playing chess and the quiz show Jeopardy! respectively—as examples of computer systems that show a degree of intelligence in their specialised field, he further argued that they were not an equivalent to a computer system that shows "broad" intelligence, and could—for example, watch a television programme and answer questions on its content. Marcus stated that "no existing combination of hardware and software can learn completely new things at will the way a clever child can." However, he still believed that there were potential uses for technology such as that of Goostman, specifically suggesting the creation of "believable", interactive video game characters. Imperial College London professor Murray Shanahan questioned the validity and scientific basis of the test, stating that it was "completely misplaced, and it devalues real AI research. It makes it seem like science fiction AI is nearly here, when in fact it's not and it's incredibly difficult." Mike Masnick, editor of the blog Techdirt, was also skeptical, questioning publicity blunders such as the five chatbots being referred to in press releases as "supercomputers", and saying that "creating a chatbot that can fool humans is not really the same thing as creating artificial intelligence."
Wave Financial
Wave is a Canadian company that provides financial services and software for small businesses. Wave is headquartered in the East Bayfront neighbourhood in Toronto, Canada. The company's first product was free online accounting software designed for businesses with 1–9 employees, followed by invoicing, personal finance and receipt-scanning software (OCR). In 2012, Wave began branching into financial services, initially with Payments by Wave (credit card processing) and Payroll by Wave, followed in February 2017 by Lending by Wave, which has since been discontinued. == History == CEO Kirk Simpson and CPO James Lochrie launched Wave Accounting Inc. in July 2009, Wave Accounting launched to the public on November 16, 2010. In June 2011, Series A funding led by OMERS Ventures was closed. In September 2011, FedDev Ontario invested one million dollars in funding. In October 2011, a $5-million investment led by U.S. venture capital firm Charles River Ventures was announced. In May 2012, Wave Accounting closed its series B financing round led by The Social+Capital Partnership, with follow-on participation from Charles River Ventures and OMERS Ventures. Wave acquired a company called Small Payroll in November 2011, which was later launched as a payroll product called Wave Payroll. In February 2012, Wave officially launched Wave Payroll to the public in Canada, followed by the American release in November of the same year. In August, 2012, the company announced the acquisition of Vuru.co, an online stock-tracking service. Terms of the deal were not disclosed. In December 2012, the company rebranded itself as Wave to emphasize its broadened spectrum of services. On March 14, 2019, the company acquired Every, a Toronto-based fintech company that provides business accounts and debit cards to small businesses. On June 11, 2019, the company announced it was being acquired by tax preparation company, H&R Block, for $537 million. On June 15, 2022, Wave announced that Kirk Simpson would be leaving and being replaced as CEO by Zahir Khoja. In May 2025, US customers of Wave were transitioned to a new Payroll processing system supported by CheckHQ. The new integration improved support for US employers by handling employer tax withholding and payments in all 50 US States. == Products == The company's initial product, Accounting by Wave, is a double entry accounting tool. Services include direct bank data imports, invoicing and expense tracking, customizable chart of accounts, and journal transactions. Accounting by Wave integrates with expense tracking software Shoeboxed and e-commerce website Etsy. The next product launched was Payroll by Wave, which was launched in 2012 after the acquisition of SmallPayroll.ca. Payroll by Wave is only available in the US and Canada. Invoicing by Wave is an offshoot of the company's earlier accounting tools. Additional products launched on or shortly after the company's rebrand in December 2012 include: a credit card processing tool, Payments by Wave, built initially on integration with Stripe credit card processing. However, Wave does not report merchant fees correctly for countries where Stripe charges a tax such as GST. In these cases, the merchant fees are reported without tax and do not match your Stripe account. a receipt scanning tool, Receipts by Wave. In 2017, Wave signed an agreement to provide its platform on RBC's online business banking site. The RBC-Wave service will be co-branded. == Taxes supported == The company's software supports tax-exclusive pricing, such as U.S. sales tax, where taxes are added on top of prices quoted. This has two effects: When scanning receipts users must manually add the tax, and input the amount. When making an invoice, users must put in a price before tax, and the system will add the tax on top. This makes Wave unable to handle taxes in countries like Australia where prices must be quoted inclusive of all taxes, such as GST. There is no way to set an invoice total and have Wave calculate the tax portion as a percentage. == Pricing and business model == As of June 10, 2024, Wave offers two tiers for its software: a free Starter plan with limitations on some features, and a paid Pro plan. In addition to its paid plan, revenue from the company comes from other paid financial services the company offers: Payments by Wave: Card processing which includes debit, credit and prepaid cards as well as ACH (bank payments) in the United States. Fees are a percentage of the transaction. Payroll by Wave: Monthly subscription fee plus usage fees. Wave previously included advertising on its pages as a source of revenue. Advertising was removed in January 2017. In 2017, Wave raised $24m (USD) in funding led by NAB Ventures. In 2019, H&R Block announced the acquisition of Wave in a cash deal worth $405 million USD.
Co-occurrence matrix
A co-occurrence matrix or co-occurrence distribution (also referred to as : gray-level co-occurrence matrices GLCMs) is a matrix that is defined over an image to be the distribution of co-occurring pixel values (grayscale values, or colors) at a given offset. It is used as an approach to texture analysis with various applications especially in medical image analysis. == Method == Given a grey-level image I {\displaystyle I} , co-occurrence matrix computes how often pairs of pixels with a specific value and offset occur in the image. The offset, ( Δ x , Δ y ) {\displaystyle (\Delta x,\Delta y)} , is a position operator that can be applied to any pixel in the image (ignoring edge effects): for instance, ( 1 , 2 ) {\displaystyle (1,2)} could indicate "one down, two right". An image with p {\displaystyle p} different pixel values will produce a p × p {\displaystyle p\times p} co-occurrence matrix, for the given offset. The ( i , j ) th {\displaystyle (i,j)^{\text{th}}} value of the co-occurrence matrix gives the number of times in the image that the i th {\displaystyle i^{\text{th}}} and j th {\displaystyle j^{\text{th}}} pixel values occur in the relation given by the offset. For an image with p {\displaystyle p} different pixel values, the p × p {\displaystyle p\times p} co-occurrence matrix C is defined over an n × m {\displaystyle n\times m} image I {\displaystyle I} , parameterized by an offset ( Δ x , Δ y ) {\displaystyle (\Delta x,\Delta y)} , as: C Δ x , Δ y ( i , j ) = ∑ x = 1 n ∑ y = 1 m { 1 , if I ( x , y ) = i and I ( x + Δ x , y + Δ y ) = j 0 , otherwise {\displaystyle C_{\Delta x,\Delta y}(i,j)=\sum _{x=1}^{n}\sum _{y=1}^{m}{\begin{cases}1,&{\text{if }}I(x,y)=i{\text{ and }}I(x+\Delta x,y+\Delta y)=j\\0,&{\text{otherwise}}\end{cases}}} where: i {\displaystyle i} and j {\displaystyle j} are the pixel values; x {\displaystyle x} and y {\displaystyle y} are the spatial positions in the image I; the offsets ( Δ x , Δ y ) {\displaystyle (\Delta x,\Delta y)} define the spatial relation for which this matrix is calculated; and I ( x , y ) {\displaystyle I(x,y)} indicates the pixel value at pixel ( x , y ) {\displaystyle (x,y)} . The 'value' of the image originally referred to the grayscale value of the specified pixel, but could be anything, from a binary on/off value to 32-bit color and beyond. (Note that 32-bit color will yield a 232 × 232 co-occurrence matrix!) Co-occurrence matrices can also be parameterized in terms of a distance, d {\displaystyle d} , and an angle, θ {\displaystyle \theta } , instead of an offset ( Δ x , Δ y ) {\displaystyle (\Delta x,\Delta y)} . Any matrix or pair of matrices can be used to generate a co-occurrence matrix, though their most common application has been in measuring texture in images, so the typical definition, as above, assumes that the matrix is an image. It is also possible to define the matrix across two different images. Such a matrix can then be used for color mapping. == Aliases == Co-occurrence matrices are also referred to as: GLCMs (gray-level co-occurrence matrices) GLCHs (gray-level co-occurrence histograms) spatial dependence matrices == Application to image analysis == Whether considering the intensity or grayscale values of the image or various dimensions of color, the co-occurrence matrix can measure the texture of the image. Because co-occurrence matrices are typically large and sparse, various metrics of the matrix are often taken to get a more useful set of features. Features generated using this technique are usually called Haralick features, after Robert Haralick. Texture analysis is often concerned with detecting aspects of an image that are rotationally invariant. To approximate this, the co-occurrence matrices corresponding to the same relation, but rotated at various regular angles (e.g. 0, 45, 90, and 135 degrees), are often calculated and summed. Texture measures like the co-occurrence matrix, wavelet transforms, and model fitting have found application in medical image analysis in particular. == Other applications == Co-occurrence matrices are also used for words processing in natural language processing (NLP).
Unit of work
A unit of work is a behavioral pattern in software development. Martin Fowler has defined it as everything one does during a business transaction which can affect the database. When the unit of work is finished, it will provide everything that needs to be done to change the database as a result of the work. A unit of work encapsulates one or more code repositories[de] and a list of actions to be performed which are necessary for the successful implementation of self-contained and consistent data change. A unit of work is also responsible for handling concurrency issues, and can be used for transactions and stability patterns.[de]
Easyrec
easyrec is an open-source program that provides personalized recommendations using RESTful Web services to be integrated into Web enabled applications. It is distributed under the GNU General Public License by the Studio Smart Agent Technologies and hosted at SourceForge. It is written in Java, uses a MySQL database and comes with an administration tool. == History == The development of easyrec, an implementation of the Adaptive Personalization approach, started in the course of several research and development projects conducted by the Studio Smart Agent Technologies in close cooperation with international companies. During the year of 2008 the core functionality of easyrec was developed forming the basis of research prototypes focusing on the music domain (e.g. MusicExplorer). In June 2009 a beta version of easyrec, containing basic administration features, was integrated into a movie streaming portal for evaluation purposes. Furthermore, in September 2009 easyrec was awarded a special recognition in the category “Award for Innovations – IT Innovations for an economic upswing” by the jury of the Austrian state prize for multimedia and e-business. After a comprehensive refactoring phase and the integration of the evaluation results easyrec was published on SourceForge on 18 February 2010. In course of the CeBIT tradeshow 2011 in Hanover easyrec has been awarded the German “INNOVATIONSPREIS-IT 2011”. == Principles == The following five primary goals guided the development of easyrec. It should be a ready-to-use application, not another algorithmic framework It should be easy to use, concerning installation, integration and administration It should be robust and scalable for serving real world applications It should be free of charge, so that anyone can profit from personalization features It should rely on a community-driven development == Uses == Although easyrec is a domain-agnostic, general purpose personalization system, the current Web service API is customized for providing online shops with item recommendations. Especially for small and medium enterprises, easyrec provides a low barrier entrance to personalization. == Features == A major feature of easyrec is a set of usage statistics and other business relevant information presented via an administration and management interface. Furthermore, the easyrec administrator is supported by a variety of administration and configuration functions including the manual import or adaptation of business rules. Integrators or developers benefit from the lightweight Web service APIs (REST and SOAP) as well as from the guided installation wizard. Concerning personalization functionality easyrec is providing the following services unpersonalized recommendations of the form "other users also bought/viewed/...", etc. personalized recommendation depending on individual preferences rankings such as "most bought items", "most viewed...", etc. Additionally, as an integration showcase, a MediaWiki extension was developed and is bundled with the application. Currently additional features like further recommender algorithms and a plugin-system are evaluated and prepared for integration into the easyrec system. == Architecture == The underlying architecture of easyrec is designed to be robust and scalable—separating time-consuming computations from the task of online assembling of recommendations. easyrec is designed as a multi-layer system consisting of a database layer as storage of user actions and pre-calculated business rules an application layer for hosting online and offline recommendation services and an API layer for various Web service interfaces. Moreover, the generator server contains different item association generators which create business rules that define a relation between two items.
Software engine
A software engine is a core component of a complex software system. The word "engine" is a metaphor of a car's engine. Thus a software engine is a complex subsystem; not unlike how a car engine functions. Software engines work in conjunction with other components of a process or system. They typically have an input and an output, and the productivity is usually linear to running speed. There is no formal guideline for what should be called an engine, but the term has become widespread in the software industry. == Notable examples == === Multi-engine systems === Mainstream web browsers have both a browser engine and a JavaScript engine. Video games are often based on a game engine. Some of these also have specialized physics or graphics engines.