Looking for the best AI bug finder? An AI bug finder is software that uses machine learning to help you get more done — it can save you hours every week by automating repetitive work. Most options offer a generous free tier, with paid plans unlocking higher limits, faster processing, and team features. Whether you are a beginner or a pro, the right AI bug finder slots into your workflow and pays for itself fast. This guide breaks down the top picks, their pros and cons, and who each one is best for.
Immediate mode (computer graphics)
Immediate mode is an API design pattern in computer graphics libraries, in which the client calls directly cause rendering of graphics objects to the display, or in which the data to describe rendering primitives is inserted frame by frame directly from the client into a command list (in the case of immediate mode primitive rendering), without the use of extensive indirection – thus immediate – to retained resources. It does not preclude the use of double-buffering. Retained mode is an alternative approach. Historically, retained mode has been the dominant style in GUI libraries; however, both can coexist in the same library and are not necessarily exclusive in practice. == Overview == In immediate mode, the scene (complete object model of the rendering primitives) is retained in the memory space of the client, instead of the graphics library. This implies that in an immediate mode application, the lists of graphical objects to be rendered are kept by the client and are not saved by the graphics library API. The application must re-issue all drawing commands required to describe the entire scene each time a new frame is required, regardless of actual changes. This method provides on the one hand a maximum of control and flexibility to the application program, but on the other hand it also generates continuous work load on the CPU. Examples of immediate mode rendering systems include Direct2D, OpenGL and Quartz. There are some immediate mode GUIs that are particularly suitable when used in conjunction with immediate mode rendering systems. == Immediate mode primitive rendering == Primitive vertex attribute data may be inserted frame by frame into a command buffer by a rendering API. This involves significant bandwidth and processor time (especially if the graphics processing unit is on a separate bus), but may be advantageous for data generated dynamically by the CPU. It is less common since the advent of increasingly versatile shaders, with which a graphics processing unit may generate increasingly complex effects without the need for CPU intervention. == Immediate mode rendering with vertex buffers == Although drawing commands have to be re-issued for each new frame, modern systems using this method are generally able to avoid the unnecessary duplication of more memory-intensive display data by referring to that unchanging data (via indirection) (e.g. textures and vertex buffers) in the drawing commands. == Immediate mode GUI == Graphical user interfaces traditionally use retained mode-style API design, but immediate mode GUIs instead use an immediate mode-style API design, in which user code directly specifies the GUI elements to draw in the user input loop. For example, rather than having a CreateButton() function that a user would call once to instantiate a button, an immediate-mode GUI API may have a DoButton() function which should be called whenever the button should be on screen. The technique was developed by Casey Muratori in 2002. Prominent implementations include Omar Cornut's Dear ImGui in C++, Nic Barker's Clay in C and Micha Mettke's Nuklear in C.
IAmAnas
#IAmAnas (I Am Anas) is a Twitter hashtag and social media campaign that started in 2015. Users tweeted to express support for the undercover investigative works of Ghanaian journalist Anas Aremeyaw Anas. The campaign restarted in 2018 when the Ghanaian MP and financier of the New Patriotic Party, Kennedy Agyapong, announced his intention to reveal the identity of Anas following the journalist's exposé of corruption at the Ghana Football Association. Anas maintains that "being anonymous has always been his secret weapon." Pictures purported to be of Anas were first released by a TV station owned by Agyapong, and were quickly picked up by other media houses. At least one person, a Dutch-Brazilian model, has claimed ownership of one picture that was released, and has threatened legal action against Agyapong for possibly putting his life in danger. In response to Agyapong, social media users retweeted photos of themselves, random people, or even comic images of entities that resemble the trademark covered face of Anas. When the hashtag first began in 2015, along with other popular uses of the journalist's name, Elizabeth Ohene wrote an article about Ghanaians use of humour in response to dealing with the expose of government corruption. "I do not know when these words will make it into Wikipedia or the Oxford English Dictionary but for the moment you can take it from me that: To go undercover is to anas, to make secret recordings is to anas-anas, to wear disguises is to do an anas, to be caught in the act is to be anased. To have someone exposed taking bribes is to have that person being given the full Anas Aremeyaw Anas."
Homeboyz Interactive
Homeboyz Interactive (HBI) was a faith-based recruitment, training and job placement non-profit business in Milwaukee, Wisconsin, United States, founded by a Jesuit brother in 1996 to transform gang members into productive workers. == History == James Holub, a former Jesuit brother affiliated with Wheeling Jesuit University, asked gang members in the Southside of Milwaukee, WI how they could be helped, to break the cycle of poverty and violence. The youth suggested that they be trained for work they found exciting. To attract interest, the training must lead to jobs that paid at least a living wage, and computer skills seemed the most attractive. The non-profit Homeboyz Interactive was established to prepare professionals in web design, application development, and PC/network support. This non-profit outfit spawned the for-profit web design firm HBI Consulting, which provided trainees with work experience. It turned out more than 20 teachers yearly for computer and computer network programs for high schools and other clients, as well as for computer service providers. Some graduates of the program continued their education, some founded their own business, and others continued working at HBI. The Economist described this effort as "turning thugs into programmers" on Milwaukee's South Side, which has proportionally twice as many murders as New York. Holub had "buried his 28th gang member" before he implemented the Homeboyz plan, with the understanding that "nothing stops a bullet like a job." The programs would pass through about 80 prospects a year who successfully completed training and provide them with a job while studying for their high school equivalency test, before they were asked to decide in which direction to go. Most accepted a job or went on to community college but about 25 entered the Homeboyz training for computer programmers. Of first 150 graduates of this program none lost their job; their average pay after two years was US$63,000. Some preferred to return to full-time work at HBI. By 2002, a total of 142 people had graduated from HBI training and moved into full-time IT careers. The training curriculum as of 2000 included JavaScript and Photoshop, among other web-development tools. In 2000, HBI received a 14% ownership stake in reEmploy.com, a payrolling company, in exchange for the development of an electronic time sheet created by the organization. As of 2001, HBI Consulting, the for profit web design firm, had 72 clients. Among those clients were GE Medical, Toyota Forklift, Northwestern Mutual Life, Verizon Wireless, BP; and Marquette University. Companies that graduates of HBI's training programs secured positions have included Northwestern Mutual and Manpower Inc., United Community Center in Milwaukee and EKI Consulting. A pair of graduates also started their own company in 2002, Innovative Source, a web design firm, which itself has had clients such as the University of Wisconsin-Milwaukee and the Milwaukee Women's Center. This was a common path forward, graduates starting their own consulting firms. In 2004, HBI received a grant for General Support from the Vine and Branches Foundation in the amount of US$120,000. The product Project Foundry found its start in the difficulty of managing project-based learning across dozens of students with widely varying levels of skill, a problem encountered by Shane Krukowski, who developed the software while teaching at HBI. Krukowski subsequently an eponymous company to commercialize the software through a subscription-based business model. Some came to Homeboyz through the criminal courts or Department of Corrections. A Jesuit Volunteer (JV) was assigned to work with the program, and to add a spiritual dimension through regular reflection together. Gradually the market began prioritizing graphic design and flash images more than site construction. After 2006 Homeboyz HBI morphed into several spinoffs and ceased to exist as a separate entity.
Gnowit
Gnowit (pronounced "know it") is a Canadian software company that provides automated, near-real-time monitoring of legislative, regulatory, and political activity across Canada. Its platform aggregates and analyzes information from government publications, parliamentary debates, committee, and proceedings to provide searchable alerts and reports for organizations monitoring public policy and regulatory developments. The system uses natural-language processing and machine learning techniques to organize and filter large volumes of public information.; the company reports that new publication documents are captured and millions of items are added to its repository daily. == History, Founders and Leadership == Gnowit was co-founded in Ottawa in 2010 by Shahzad Khan and Mohammad Al-Azzouni; Khan serves as chief executive officer. Khan holds a PhD in Computer Science from the University of Cambridge, has more than two decades of experience in AI/ML and computational linguistics, and has authored or co-authored 37 peer-reviewed publications and five patents. Traditionally, companies performed this analysis manually; Gnowit has delivered efficiencies achieved through AI innovations. The company has participated in several Canadian startup and accelerator programs, including Carleton University's Lead To Win initiative, the University of Ottawa's Startup Garage, the Invest Ottawa incubator, and the League of Innovators' BOOST program. === Kubernetes validation (2019–2020) === As part of a Canada's Centre of Excellence in Next Generation Networks (CENGN) project, Gnowit validated a containerized version of its web-intelligence software on Kubernetes. Between 2019 and 2020, Gnowit participated in a project with Canada’s Centre of Excellence in Next Generation Networks (CENGN) to test and scale its platform using containerized infrastructure based on Kubernetes. The initiative focused on improving scalability and supporting the company’s transition from a monolithic software architecture to a cloud-native deployment model. == Products and services == Gnowit markets several modules for public-affairs, compliance, and market-intelligence teams. Legislative & Regulatory Monitoring (vAnalyst). vAnalyst is a monitoring platform that tracks legislative and regulatory activity across Canadian federal, provincial, and territorial jurisdictions. The system aggregates parliamentary debates, bills, committee proceedings, and regulatory publications and provides searchable alerts and reporting tools. The product monitors more than two million web sources to surface relevant items quickly. Parliamentary Live (vAnalyst). Monitors live video feeds from parliamentary sessions and committees with same-day transcripts, AI-generated summaries, witness summaries, and motion detection; municipal coverage is offered as an option. Gnowit can avail transcripts up to two weeks before official releases. These transcripts enable users to navigate and review lengthy parliamentary sittings and committee discussions through searchable text. Municipal Monitoring (vAnalyst). The platform also tracks council meetings, agendas, bylaws, and other municipal government publications from hundreds of Canadian municipalities. The platform aggregates these sources into a single searchable interface for reviewing local government decisions. Curation Edge (analyst service). Curation Edge is an add-on service in which expert analysts work and collaborate with clients to develop a tailored curation guide and deliver daily newsletters or briefs on legislation and media. These reports provide concise summaries, relevant links, and optional metadata, prioritizing key updates with additional context and analysis. The service is customizable, including branding and formatting for executive audiences, and is intended to reduce information overload, support decision-making, and streamline the synthesis and distribution of information. === Coverage and sources === Gnowit monitors sources span Canadian government materials across federal, provincial, and territorial jurisdictions Hansard transcripts (All Jurisdictions, including committees), order papers, committee transcripts, gazettes, bills, acts and regulations, consultations, regulatory-agency publications, and global news media as well as press releases and council-meeting materials from hundreds of municipalities. == Partnerships and support == Gnowit reports collaborations with Canadian academic and ecosystem partners, including: Algonquin College Carleton University McGill University University of Ottawa Université du Québec en Outaouais (UQO) Queen's University The company also participated in the accelerator program at Invest Ottawa and has received support from Canadian research and innovation programs, including: NRC Industrial Research Assistance Program (NRC-IRAP) Mitacs Ontario Centre of Innovation (OCI) (formerly OCE) Gnowit has also referenced membership in the Southern Ontario Smart Computing Innovation Platform (Government of Canada profile: FedDev Ontario – SOSCIP overview). == Technology == Gnowit develops technology intended to support timely decision-making by delivering updates from monitored web sources as they are published. The platform applies artificial intelligence (AI) and machine learning (ML) techniques to monitor, capture, clean, analyze, filter, and organize text, and to generate concise briefs. Its technical approach combines Boolean queries, shallow language processing techniques, and machine learning classifiers within a self-service interface. The company has described its longer-term development framework in relation to a belief–desire–intention (BDI) model of intelligent agents on the web. Gnowit and its founder are listed as inventors/assignees on patents concerning multi-document clustering, salient-content extraction, and sentiment analysis methods that are consistent with these features: US 9,600,470 – Method and system relating to re-labelling multi-document clusters (assignee: Whyz Technologies Ltd.). US 9,336,202 – Method and system relating to salient content extraction for information retrieval (assignee: Whyz Technologies Ltd.). CA 2,865,184 C – Method and system relating to re-labelling multi-document clusters. CA 2,865,186 C – Procédé et système concernant l'analyse de sentiment d'un contenu (sentiment analysis; French record). CA 2,865,187 C – Method and system relating to salient content extraction for information retrieval. == Research and community == In January 2025, Gnowit personnel contributed to regulatory NLP by co-authoring a peer-reviewed paper at the 1st Regulatory NLP Workshop (RegNLP 2025), co-located with COLING in Abu Dhabi. Titled Unifying Large Language Models and Knowledge Graphs for Efficient Regulatory Information Retrieval and Answer Generation, the work introduces PolicyInsight, a framework that joins a dynamic policy data model and knowledge graph with LLMs to monitor policy texts, detect changes, and support retrieval and answer generation; the author list includes Shahzad Khan (CEO, Gnowit Inc.). (ACL Anthology, aclweb.org). Similar information-retrieval technologies are widely used for competitive intelligence, policy monitoring, and media analysis. == White paper == Gnowit has published a practical guide, Automated Government Information Monitoring, which outlines how GR and regulatory teams can design a monitoring and briefing workflow and describes Gnowit's automation features and export options (PDF, email, dashboards, CSV/JSON/XML/API).
Pooling layer
In neural networks, a pooling layer is a kind of network layer that downsamples and aggregates information that is dispersed among many vectors into fewer vectors. It has several uses. It removes redundant information, thus reducing the amount of computation and memory required, which makes the model more robust to small variations in the input; and it increases the receptive field of neurons in later layers in the network. == Convolutional neural network pooling == Pooling is most commonly used in convolutional neural networks (CNN). Below is a description of pooling in 2-dimensional CNNs. The generalization to n-dimensions is immediate. As notation, we consider a tensor x ∈ R H × W × C {\displaystyle x\in \mathbb {R} ^{H\times W\times C}} , where H {\displaystyle H} is height, W {\displaystyle W} is width, and C {\displaystyle C} is the number of channels. A pooling layer outputs a tensor y ∈ R H ′ × W ′ × C ′ {\displaystyle y\in \mathbb {R} ^{H'\times W'\times C'}} . We define two variables f , s {\displaystyle f,s} called "filter size" (aka "kernel size") and "stride". Sometimes, it is necessary to use a different filter size and stride for horizontal and vertical directions. In such cases, we define 4 variables: f H , f W , s H , s W {\displaystyle f_{H},f_{W},s_{H},s_{W}} . The receptive field of an entry in the output tensor, y {\displaystyle y} , are all the entries in x {\displaystyle x} that can affect that entry. === Max pooling === Max Pooling (MaxPool) is commonly used in CNNs to reduce the spatial dimensions of feature maps. Define M a x P o o l ( x | f , s ) 0 , 0 , 0 = max ( x 0 : f − 1 , 0 : f − 1 , 0 ) {\displaystyle \mathrm {MaxPool} (x|f,s)_{0,0,0}=\max(x_{0:f-1,0:f-1,0})} where 0 : f − 1 {\displaystyle 0:f-1} means the range 0 , 1 , … , f − 1 {\displaystyle 0,1,\dots ,f-1} . Note that we need to avoid the off-by-one error. The next input is M a x P o o l ( x | f , s ) 1 , 0 , 0 = max ( x s : s + f − 1 , 0 : f − 1 , 0 ) {\displaystyle \mathrm {MaxPool} (x|f,s)_{1,0,0}=\max(x_{s:s+f-1,0:f-1,0})} and so on. The receptive field of y i , j , c {\displaystyle y_{i,j,c}} is x i s + f − 1 , j s + f − 1 , c {\displaystyle x_{is+f-1,js+f-1,c}} , so in general, M a x P o o l ( x | f , s ) i , j , c = m a x ( x i s : i s + f − 1 , j s : j s + f − 1 , c ) {\displaystyle \mathrm {MaxPool} (x|f,s)_{i,j,c}=\mathrm {max} (x_{is:is+f-1,js:js+f-1,c})} If the horizontal and vertical filter size and strides differ, then in general, M a x P o o l ( x | f , s ) i , j , c = m a x ( x i s H : i s H + f H − 1 , j s W : j s W + f W − 1 , c ) {\displaystyle \mathrm {MaxPool} (x|f,s)_{i,j,c}=\mathrm {max} (x_{is_{H}:is_{H}+f_{H}-1,js_{W}:js_{W}+f_{W}-1,c})} More succinctly, we can write y k = max ( { x k ′ | k ′ in the receptive field of k } ) {\displaystyle y_{k}=\max(\{x_{k'}|k'{\text{ in the receptive field of }}k\})} . If H {\displaystyle H} is not expressible as k s + f {\displaystyle ks+f} where k {\displaystyle k} is an integer, then for computing the entries of the output tensor on the boundaries, max pooling would attempt to take as inputs variables off the tensor. In this case, how those non-existent variables are handled depends on the padding conditions, illustrated on the right. Global Max Pooling (GMP) is a specific kind of max pooling where the output tensor has shape R C {\displaystyle \mathbb {R} ^{C}} and the receptive field of y c {\displaystyle y_{c}} is all of x 0 : H , 0 : W , c {\displaystyle x_{0:H,0:W,c}} . That is, it takes the maximum over each entire channel. It is often used just before the final fully connected layers in a CNN classification head. === Average pooling === Average pooling (AvgPool) is similarly defined A v g P o o l ( x | f , s ) i , j , c = a v e r a g e ( x i s : i s + f − 1 , j s : j s + f − 1 , c ) = 1 f 2 ∑ k ∈ i s : i s + f − 1 ∑ l ∈ j s : j s + f − 1 x k , l , c {\displaystyle \mathrm {AvgPool} (x|f,s)_{i,j,c}=\mathrm {average} (x_{is:is+f-1,js:js+f-1,c})={\frac {1}{f^{2}}}\sum _{k\in is:is+f-1}\sum _{l\in js:js+f-1}x_{k,l,c}} Global Average Pooling (GAP) is defined similarly to GMP. It was first proposed in Network-in-Network. Similarly to GMP, it is often used just before the final fully connected layers in a CNN classification head. === Interpolations === There are some interpolations of max pooling and average pooling. Mixed Pooling is a linear sum of max pooling and average pooling. That is, M i x e d P o o l ( x | f , s , w ) = w M a x P o o l ( x | f , s ) + ( 1 − w ) A v g P o o l ( x | f , s ) {\displaystyle \mathrm {MixedPool} (x|f,s,w)=w\mathrm {MaxPool} (x|f,s)+(1-w)\mathrm {AvgPool} (x|f,s)} where w ∈ [ 0 , 1 ] {\displaystyle w\in [0,1]} is either a hyperparameter, a learnable parameter, or randomly sampled anew every time. Lp Pooling is similar to average pooling, but uses Lp norm average instead of average: y k = ( 1 N ∑ k ′ in the receptive field of k | x k ′ | p ) 1 / p {\displaystyle y_{k}=\left({\frac {1}{N}}\sum _{k'{\text{ in the receptive field of }}k}|x_{k'}|^{p}\right)^{1/p}} where N {\displaystyle N} is the size of receptive field, and p ≥ 1 {\displaystyle p\geq 1} is a hyperparameter. If all activations are non-negative, then average pooling is the case of p = 1 {\displaystyle p=1} , and max pooling is the case of p → ∞ {\displaystyle p\to \infty } . Square-root pooling is the case of p = 2 {\displaystyle p=2} . Stochastic pooling samples a random activation x k ′ {\displaystyle x_{k'}} from the receptive field with probability x k ′ ∑ k ″ x k ″ {\displaystyle {\frac {x_{k'}}{\sum _{k''}x_{k''}}}} . It is the same as average pooling in expectation. Softmax pooling is like max pooling, but uses softmax, i.e. ∑ k ′ e β x k ′ x k ′ ∑ k ″ e β x k ″ {\displaystyle {\frac {\sum _{k'}e^{\beta x_{k'}}x_{k'}}{\sum _{k''}e^{\beta x_{k''}}}}} where β > 0 {\displaystyle \beta >0} . Average pooling is the case of β ↓ 0 {\displaystyle \beta \downarrow 0} , and max pooling is the case of β ↑ ∞ {\displaystyle \beta \uparrow \infty } Local Importance-based Pooling generalizes softmax pooling by ∑ k ′ e g ( x k ′ ) x k ′ ∑ k ″ e g ( x k ″ ) {\displaystyle {\frac {\sum _{k'}e^{g(x_{k'})}x_{k'}}{\sum _{k''}e^{g(x_{k''})}}}} where g {\displaystyle g} is a learnable function. === Other poolings === Spatial pyramidal pooling applies max pooling (or any other form of pooling) in a pyramid structure. That is, it applies global max pooling, then applies max pooling to the image divided into 4 equal parts, then 16, etc. The results are then concatenated. It is a hierarchical form of global pooling, and similar to global pooling, it is often used just before a classification head. Region of Interest Pooling (also known as RoI pooling) is a variant of max pooling used in R-CNNs for object detection. It is designed to take an arbitrarily-sized input matrix, and output a fixed-sized output matrix. Covariance pooling computes the covariance matrix of the vectors { x k , l , 0 : C − 1 } k ∈ i s : i s + f − 1 , l ∈ j s : j s + f − 1 {\displaystyle \{x_{k,l,0:C-1}\}_{k\in is:is+f-1,l\in js:js+f-1}} which is then flattened to a C 2 {\displaystyle C^{2}} -dimensional vector y i , j , 0 : C 2 − 1 {\displaystyle y_{i,j,0:C^{2}-1}} . Global covariance pooling is used similarly to global max pooling. As average pooling computes the average, which is a first-degree statistic, and covariance is a second-degree statistic, covariance pooling is also called "second-order pooling". It can be generalized to higher-order poolings. Blur Pooling means applying a blurring method before downsampling. For example, the Rect-2 blur pooling means taking an average pooling at f = 2 , s = 1 {\displaystyle f=2,s=1} , then taking every second pixel (identity with s = 2 {\displaystyle s=2} ). == Vision Transformer pooling == In Vision Transformers (ViT), there are the following common kinds of poolings. BERT-like pooling uses a dummy [CLS] token, "classification". For classification, the output at [CLS] is the classification token, which is then processed by a LayerNorm-feedforward-softmax module into a probability distribution, which is the network's prediction of class probability distribution. This is the one used by the original ViT and Masked Autoencoder. Global average pooling (GAP) does not use the dummy token, but simply takes the average of all output tokens as the classification token. It was mentioned in the original ViT as being equally good. Multihead attention pooling (MAP) applies a multi headed attention block to pooling. Specifically, it takes as input a list of vectors x 1 , x 2 , … , x n {\displaystyle x_{1},x_{2},\dots ,x_{n}} , which might be thought of as the output vectors of a layer of a ViT. It then applies a feedforward layer F F N {\displaystyle \mathrm {FFN} } on each vector, resulting in a matrix V = [ F F N ( v 1 ) , … , F F N ( v n ) ] {\displaystyle V=[\mathrm {FFN} (v_{1}),\dots ,\mathrm {FFN} (v_{n})]} . This is then sent to a multi-headed attention, resulting in M u l t i h e a d e d A
Digital history
Digital history is the use of digital media to further historical analysis, presentation, and research. It is a branch of the digital humanities and an extension of quantitative history, cliometrics, and computing. Digital history is commonly known as digital public history, concerned primarily with engaging online audiences with historical content, or digital research methods, that further academic research. Digital history outputs include: digital archives, online presentations, data and information visualizations, interactive maps, timelines, audio files, and virtual worlds. These outputs are designed to enhance accessibility to users, facilitating engagement with historical content. Recent digital history projects focus on creativity, collaboration, and technical innovation, text mining, corpus linguistics, network analysis, 3D modeling, and big data analysis. By utilizing these resources, the user can rapidly develop new analyses that can link to, extend, and bring to life existing histories. == History == Rooted in earlier social science history work, particularly around the history of enslavement in the United States, early digital history in the 1960s and 70s focused on using computers to conduct quantitative analyses, primarily of demographic and social history data - censuses, election returns, city directories, and other tabular or countable data. - with the aim of producing defensible research findings These early computers could be programmed to conduct statistical analyses of these records, creating tallies, or seeking trends across records. This research into historical demography was rooted in the rise of social history as a field of historical interest. The historians involved in this work sought to quantify past societies, to come to new conclusions about communities and population. Computers proved capable tools for that type of work. By the late 1970s younger historians turned to cultural studies, most of these studies involved online databases that were checked by Professionals in Great Britain about once a year. The outpouring of quantitative studies by established scholars continued. Since then, quantitative history and cliometrics have been used primarily by historically minded economists and political scientists. In the late 1980s quantifiers founded the Association for History and Computing. This movement provided some of the impetus for the rise of digital history in the 1990s. The more recent roots of digital history were in software rather than online networks. In 1982, the Library of Congress embarked on its Optical Disk Pilot Project, which placed text and images from its collection on to laserdiscs and CD-ROMs. The library started offering online exhibits in 1992 when it launched Selected Civil War Photographs. In 1993, Roy Rosenzweig, along with Steve Brier and Josh Brown, produced their award-winning CD-ROM Who Built America? From the Centennial Exposition of 1876 to the Great War of 1914, designed for Apple, Inc. that integrated images, text, film and sound clips, displayed in a visual interface that supported a text narrative. Among the earliest online digital history projects were The Heritage Project of the University of Kansas, and medieval historian Dr. Lynn Nelson's World History Index and History Central Catalogue. Another was The Valley of the Shadow, conceived in 1991 by current University of Richmond professor of humanities and president emeritus, Edward L. Ayers, who was then at the University of Virginia. The Institute for Advanced Technology in the Humanities (IATH) at the University of Virginia adopted the Valley Project and partnered with IBM to collect and transcribe historical sources into digital files. The project collected data related to Augusta County in Virginia and Franklin County in Pennsylvania during the American Civil War. In 1996, William G. Thomas III joined Ayers on the Valley Project. Together, they produced an online article entitled "The Differences Slavery Made: A Close Analysis of Two American Communities," which also appeared in The American Historical Review in 2003. A CD-ROM also accompanied the Valley Project, published by W. W. Norton and Company in 2000. Rosenzweig, who died October 11, 2007, founded the Center for History and New Media (CHNM) at George Mason University in 1994. Today, CHNM boasts several digital tools available to historians, such as Zotero, Omeka or Tropy. In 1997, Ayers and Thomas used the term "digital history" when they proposed and founded the Virginia Center for Digital History (VCDH) at the University of Virginia, the earliest center devoted exclusively to history. Several other institutions promoting digital history include the Center for Humane Arts, Letters, and Social Sciences Online (MATRIX) at Michigan State University, Maryland's Institute for Technology in the Humanities, and the Center for Digital Research in the Humanities at the University of Nebraska. In 2004, Emory University launched Southern Spaces, a "peer-reviewed Internet journal and scholarly forum" examining the history of the South. == Applications == There are many potential benefits to the use of digital history when combined with traditional historical methods. Some of these applications include: Combining traditional historical methods and new research methods in order to come to new conclusions. Using different tools to extract and analyse larger amounts of data that would not be manageable otherwise. Create models and maps of data extracted to create a visualisation of the data. Data extracted and analysed can be placed alongside existing historiography to increase combined historical knowledge. By adding new research methods to existing historical method, historians can benefit greatly from the ability to work with larger amounts of data and develop new interpretations from this. == Notable Projects == The collaborative nature of most digital history endeavors has meant that the discipline has developed primarily at institutions with the resources to sponsor content research and technical innovation. Two of the first centers, George Mason University's Center for History and New Media and the Virginia Center for Digital History at the University of Virginia have been among the leaders in the development of digital history projects and the education of digital historians. Some of the noteworthy projects emerging from these pioneering centers are The Geography of Slavery, The Texas Slavery Project, and The Countryside Transformed at VCDH and Liberty, Equality, Fraternity: Exploring the French Revolution and The Lost Museum at the CHNM. In each of these projects, mediated archives holding multiple types of sources are combined with digital tools to analyze and illuminate an historical question to a varying degree; this integration of content and tools with analysis is one of the hallmarks of digital history—projects move beyond archives or collections and into scholarly analysis and the use of digital tools to develop that analysis. The differences between the ways projects incorporate these integrations are a measure of the development of the field and point to the ongoing debates over what digital history can and should be. While many of the projects at VCDH, CHNM, and other university's centers have been geared towards academics and post-secondary education, the University of Victoria (British Columbia), in conjunction with the Université de Sherbrooke and the Ontario Institute for Studies in Education at the University of Toronto, has created as series of projects for all ages, "Great Unsolved Mysteries in Canadian History." Laden with instructional aids, this site asks teachers to introduce students to historical research methods to help them develop analytical skills and a sense of the complexities of their national history. Issues of race, religion, and gender are addressed in carefully constructed modules that cover incidents in Canadian history from Viking exploration through the 1920s. One of the original co-creators of the project, John Lutz has also developed Victoria's Victoria with the University of Victoria and Malaspina University-College. In addition to Ayers, Thomas, Lutz, and Rosenzweig, numerous other individual scholars work with digital history techniques and have made and/or continue to make important contributions to the field. Robert Darnton's 2000 article, "An Early Information Society: News and the Media in Eighteenth-Century Paris" was supplemented with electronic resources and is an early model of the discussions around digital history and its future in the humanities. One of the first major digital projects to be reviewed by the American Historical Review (AHR) was Philip Ethington's "Los Angeles and the Problem of Urban Historical Knowledge"—a multimedia exploration of changes to Los Angeles' physical profile over the course of several decades. In this essay, he also expresses his beliefs that historians have major power in