AI-assisted software development is the use of artificial intelligence (AI) to augment software development. It uses large language models (LLMs), AI agents and other AI technologies to assist software developers. It helps in a range of tasks of the software development life cycle, from code generation to debugging, editing, testing, UI design, understanding the code, and documentation. Agentic coding denotes the use of AI agents for software development. == Technologies == === Source code generation === Large language models trained or fine-tuned on source-code corpora can generate source code from natural-language descriptions, comments, or docstrings. Research on code-generation systems often evaluates generated programs by functional correctness, such as whether the output passes automated test cases, rather than by syntax alone. Such tools can be features or extensions of integrated development environments (IDEs). === Intelligent code completion === AI agents using pre-trained and fine-tuned LLMs can predict and suggest code completions based on context. According to Husein, Aburajouh & Catal in a 2025 literature review in Computer Standards & Interfaces, "LLMs significantly enhance code completion performance across several programming languages and contexts, and their capability to predict relevant code snippets based on context and partial input boosts developer productivity substantially." === Testing, debugging, code review and analysis === AI is used to automatically generate test cases, identify potential bugs and security vulnerabilities, and suggest fixes. AI can also be used to perform static code analysis and suggest potential performance improvements. == Limitations == Both ownership of and responsibility for AI-generated code is disputed. According to a report from the German Federal Office for Information Security, the use of AI coding assistants without careful oversight from experienced developers can introduce both minor and major security vulnerabilities, and any potential gain in productivity should be weighed against the cost of additional quality control and security measures. According to Deloitte, outputs from AI-assisted software development must be validated through a combination of automated testing, static analysis tools and human review, creating a governance layer to improve quality and accountability. == Vibe coding ==
Image analysis
Image analysis or imagery analysis is the extraction of meaningful information from images; mainly from digital images by means of digital image processing techniques. Image analysis tasks can be as simple as reading bar coded tags or as sophisticated as identifying a person from their face. Computers are indispensable for the analysis of large amounts of data, for tasks that require complex computation, or for the extraction of quantitative information. On the other hand, the human visual cortex is an excellent image analysis apparatus, especially for extracting higher-level information, and for many applications — including medicine, security, and remote sensing — human analysts still cannot be replaced by computers. For this reason, many important image analysis tools such as edge detectors and neural networks are inspired by human visual perception models. == Digital == Digital Image Analysis or Computer Image Analysis is when a computer or electrical device automatically studies an image to obtain useful information from it. Note that the device is often a computer but may also be an electrical circuit, a digital camera or a mobile phone. It involves the fields of computer or machine vision, and medical imaging, and makes heavy use of pattern recognition, digital geometry, and signal processing. This field of computer science developed in the 1950s at academic institutions such as the MIT A.I. Lab, originally as a branch of artificial intelligence and robotics. It is the quantitative or qualitative characterization of two-dimensional (2D) or three-dimensional (3D) digital images. 2D images are, for example, to be analyzed in computer vision, and 3D images in medical imaging. The field was established in the 1950s—1970s, for example with pioneering contributions by Azriel Rosenfeld, Herbert Freeman, Jack E. Bresenham, or King-Sun Fu. == Techniques == There are many different techniques used in automatically analysing images. Each technique may be useful for a small range of tasks, however there still aren't any known methods of image analysis that are generic enough for wide ranges of tasks, compared to the abilities of a human's image analysing capabilities. Examples of image analysis techniques in different fields include: 2D and 3D object recognition, image segmentation, motion detection e.g. Single particle tracking, video tracking, optical flow, medical scan analysis, 3D Pose Estimation. == Deep learning == Since the early 2010s, deep learning methods have substantially advanced the field of image analysis. In 2012, a deep convolutional neural network (CNN) known as AlexNet achieved a significant reduction in error rates on the ImageNet large-scale image classification benchmark, demonstrating the effectiveness of deep learning for visual recognition tasks. Subsequent architectures such as ResNet introduced residual connections that enabled training of much deeper networks, further improving accuracy across image analysis tasks. Real-time object detection became practical with frameworks such as YOLO (You Only Look Once), which unified detection and classification into a single network pass. In 2020, the Vision Transformer (ViT) demonstrated that transformer architectures, originally developed for natural language processing, could achieve competitive results on image classification when applied directly to sequences of image patches. More recently, foundation models trained on large-scale datasets have enabled zero-shot generalisation across image analysis tasks. The Segment Anything Model (SAM), trained on over one billion masks, can segment arbitrary objects in images without task-specific fine-tuning. These advances have made image analysis techniques increasingly accessible through browser-based tools and open-source implementations. == Applications == The applications of digital image analysis are continuously expanding through all areas of science and industry, including: anatomy, allows for precise measurements, visualization, and statistical analysis of anatomical structures. assay micro plate reading, such as detecting where a chemical was manufactured. astronomy, such as calculating the size of a planet. automated species identification (e.g. plant and animal species) defense error level analysis filtering machine vision, such as to automatically count items in a factory conveyor belt. materials science, such as determining if a metal weld has cracks. medicine, such as detecting cancer in a mammography scan. metallography, such as determining the mineral content of a rock sample. microscopy, such as counting the germs in a swab. automatic number plate recognition; optical character recognition, such as automatic license plate detection. remote sensing, such as detecting intruders in a house, and producing land cover/land use maps. robotics, such as to avoid steering into an obstacle. security, such as detecting a person's eye color or hair color. == Object-based == Object-based image analysis (OBIA) involves two typical processes, segmentation and classification. Segmentation helps to group pixels into homogeneous objects. The objects typically correspond to individual features of interest, although over-segmentation or under-segmentation is very likely. Classification then can be performed at object levels, using various statistics of the objects as features in the classifier. Statistics can include geometry, context and texture of image objects. Over-segmentation is often preferred over under-segmentation when classifying high-resolution images. Object-based image analysis has been applied in many fields, such as cell biology, medicine, earth sciences, and remote sensing. For example, it can detect changes of cellular shapes in the process of cell differentiation.; it has also been widely used in the mapping community to generate land cover. When applied to earth images, OBIA is known as geographic object-based image analysis (GEOBIA), defined as "a sub-discipline of geoinformation science devoted to (...) partitioning remote sensing (RS) imagery into meaningful image-objects, and assessing their characteristics through spatial, spectral and temporal scale". The international GEOBIA conference has been held biannually since 2006. OBIA techniques are implemented in software such as eCognition or the Orfeo toolbox.
AGROVOC
AGROVOC is a multilingual controlled vocabulary covering areas of interest of the Food and Agriculture Organization of the United Nations (FAO), aiming to promote the visibility of research produced among FAO members. By March 2024, AGROVOC consisted of over 42 000 concepts and up to 1 000 000 terms in more than 42 different languages. It is a collaborative effort, the outcome of consensus among a community of experts coordinated by FAO. == History == FAO first published AGROVOC at the beginning of the 1980s in English, Spanish and French to serve as a controlled vocabulary to index publications in agricultural science and technology, especially for the International System for Agricultural Science and Technology (AGRIS). In the 1990s, AGROVOC shifted from paper printing to a digital format opting for data storage handled by a relational database. In 2004, preliminary experiments with expressing AGROVOC into the Web Ontology Language (OWL) took place. At the same time a web based editing tool was developed, then called WorkBench, nowadays VocBench. In 2009 AGROVOC became an SKOS resource. == Usage == Today, AGROVOC is available in different languages. It is employed for tagging resources, allowing searches in a specific language while providing results in many others, enhancing their visibility worldwide. Additionally, it serves for organizing knowledge to facilitate subsequent data retrieval, tagging website content for search engine discovery, standardizing agricultural information data and acting as a reference for translations. Moreover, it finds applications in fields such as data mining, big data, or artificial intelligence. Updated AGROVOC content is released once a month and is available for public use. == Maintenance == FAO coordinates the editorial activities related to the maintenance of AGROVOC. Content curation is carried out by a community of editors and institutions responsible for each of the language versions. VocBench, is the tool used to edit and maintain AGROVOC in a distributed way. FAO also facilitates the technical maintenance of AGROVOC. == Copyright and license == Copyright for AGROVOC content in FAO languages (English, French, Spanish, Arabic, Russian and Chinese) is held by FAO, while content in other languages stays with the institutions that authored it. AGROVOC thesaurus content in English, Russian, French, Spanish, Arabic and Chinese is licensed under the international Creative Commons Attribution License (CC-BY-4.0).
DreamBooth
DreamBooth is a deep learning generation model used to personalize existing text-to-image models by fine-tuning. It was developed by researchers from Google Research and Boston University in 2022. Originally developed using Google's own Imagen text-to-image model, DreamBooth implementations can be applied to other text-to-image models, where it can allow the model to generate more fine-tuned and personalized outputs after training on three to five images of a subject. == Technology == Pretrained text-to-image diffusion models, while often capable of offering a diverse range of different image output types, lack the specificity required to generate images of lesser-known subjects, and are limited in their ability to render known subjects in different situations and contexts. The methodology used to run implementations of DreamBooth involves the fine-tuning the full UNet component of the diffusion model using a few images (usually 3--5) depicting a specific subject. Images are paired with text prompts that contain the name of the class the subject belongs to, plus a unique identifier. As an example, a photograph of a [Nissan R34 GTR] car, with car being the class); a class-specific prior preservation loss is applied to encourage the model to generate diverse instances of the subject based on what the model is already trained on for the original class. Pairs of low-resolution and high-resolution images taken from the set of input images are used to fine-tune the super-resolution components, allowing the minute details of the subject to be maintained. == Usage == DreamBooth can be used to fine-tune models such as Stable Diffusion, where it may alleviate a common shortcoming of Stable Diffusion not being able to adequately generate images of specific individual people. Such a use case is quite VRAM intensive, however, and thus cost-prohibitive for hobbyist users. The Stable Diffusion adaptation of DreamBooth in particular is released as a free and open-source project based on the technology outlined by the original paper published by Ruiz et. al. in 2022. Concerns have been raised regarding the ability for bad actors to utilise DreamBooth to generate misleading images for malicious purposes, and that its open-source nature allows anyone to utilise or even make improvements to the technology. In addition, artists have expressed their apprehension regarding the ethics of using DreamBooth to train model checkpoints that are specifically aimed at imitating specific art styles associated with human artists; one such critic is Hollie Mengert, an illustrator for Disney and Penguin Random House who has had her art style trained into a checkpoint model via DreamBooth and shared online, without her consent.
Simulation decomposition
SimDec, or Simulation decomposition, is a hybrid uncertainty and sensitivity analysis method, for visually examining the relationships between the output and input variables of a computational model. SimDec maps multivariable scenarios onto the distribution of the model output. This visual analytics approach exposes the underlying nature of the model behavior, including its nonlinear and multivariate interaction effects. SimDec can be used in any range of science, engineering, and social domains. Existing applications include business and environmental issues. == Method == SimDec operates on Monte Carlo simulation (or measured) data where both output and input values are recorded. At least one thousand observations (or simulated iterations) are typically recommended to preserve the readability of the resulting histograms. An outline of the decomposition algorithm, which is readily available in multiple programming languages, proceeds as follows: Select the input variables for decomposition. One can use sensitivity indices (see variance-based sensitivity analysis) to define the most influential variables for decomposition or choose them manually according to the decision-problem context (for example, only those input variables that the decision-maker can act upon). Two to three input variables, ordered by decreasing value of their sensitivity indices, usually provide the most meaningful decomposition results. Divide the inputs into states. The numeric ranges of the inputs are split into several intervals with an equal number of observations in each. For categorical variables, the categories represent states. Form scenarios. All combinations of states of the selected input variables produce unique scenarios or subsets of the data. For example, if the range of X2 is divided into low, medium and high, and X3 takes values of 1 or 2, six scenarios are formed: (i) X2 low & X3 = 1, (ii) X2 low & X3 = 2, (iii) X2 medium & X3 = 1, (iv) X2 medium & X3 = 2, (v) X2 high & X3 = 1, and (vi) X2 high & X3 = 2. Assign scenarios to each output value. The simulation data is used to define the scenario index for each simulation run. For example, if an X2 value falls into the low state and X3 is equal to 2, the corresponding scenario, defined in Step 3, is (ii). Color-code the output distribution. When all output values are assigned scenario indices, they are plotted as series in a stacked histogram, visually separated by color-coding. For ease of visual perception, the states of the most influential input variable are assigned distinct colors, and all the remaining partitions take shades of those colors (see Figure). All of these steps can be run automatically on the given data using the open-source SimDec packages currently available in Python, R, Julia, and Matlab. A SimDec template in Excel runs a Monte Carlo simulation of a spreadsheet model but possesses only a manual option for input selection. == How to read SimDec == === Histogram === Histogram is an approximate representation of the distribution of numerical data. Its horizontal axis shows the range of the variable of interest, and its vertical axis denotes count, also called frequency, or, if divided by the total number of data points, probability. The distribution alone can supply only limited information about the data – its minimum, maximum, and shape (where the most of data occurs). === Judging the importance of inputs === If an input variable has no effect on the output, its states (e.g., low & high) would lie on top of each other on the SimDec histogram, occupying fully overlapping ranges of the output. If an input variable has a strong effect and explains most of the variance of the output, the border between its states on the SimDec histogram would be vertical. Such visualization has an important decision-making implication – e.g., if the high state of X can be achieved, it would guarantee a certain range of Y. All cases in-between with low-to-strong effects would show a diagonal border between the states. The less they overlap, the larger the effect of X on Y. While the horizontal displacement of sub-distributions on the SimDec histogram is the key to interpreting the results, the vertical disposition of sub-distributions is just a technical matter of the order of plotting the series of the stacked histogram. === Exploring the interaction of inputs === When two or more input variables are used for decomposition, it becomes possible to examine their joint effects. A schematic visualization portrays how different types of joint effects of input variables on the output appear on SimDec visualization. Understanding the nature of interaction effects in a computational model and its behavior in general is crucial for effective decision-making. == Limitations == The SimDec method has several limitations: It is based on Monte Carlo simulation and thus requires running a computational model a thousand of times or more. To models that take hours to evaluate once, it would be impossible to use SimDec (unless a supercomputer and/or large of time are available). SimDec is based on a histogram, thus, for binary or categorical output variables, the visualization would be very limited (e.g., only a few bins). The more input variables one selects for the decomposition, the less readable the histogram becomes. Only cases with two and three input variables are presented in.
Cloudflare
Cloudflare, Inc., is an American technology company headquartered in San Francisco, California, that provides a range of internet services, including content delivery network (CDN) services, cloud cybersecurity, DDoS mitigation, and ICANN-accredited domain registration. The company's services act primarily as a reverse proxy between website visitors and a customer's hosting provider, improving performance and protecting against malicious traffic. Cloudflare was founded in 2009 by Matthew Prince, Lee Holloway, and Michelle Zatlyn. The company went public on the New York Stock Exchange in 2019 under the ticker symbol NET. Cloudflare has since expanded its offerings to include edge computing through its Workers platform, a public DNS resolver (1.1.1.1), and a VPN-like service known as WARP. In recent years, the company has integrated artificial intelligence into its infrastructure, acquiring companies such as Replicate and launching tools to manage AI bots and scrapers. According to W3Techs, Cloudflare is used by approximately 21.3% of all websites on the Internet as of January 2026. The company has been the subject of controversy regarding its policy of content neutrality. While Cloudflare executives have historically advocated for remaining a neutral infrastructure provider, the company has terminated services for specific high-profile websites associated with hate speech and violence, including The Daily Stormer, 8chan, and Kiwi Farms, following significant public pressure. Cloudflare has also faced criticism and litigation regarding copyright infringement by websites using its services, notably losing a lawsuit against Japanese publishers in 2025. The company experienced significant global outages in late 2025 which disrupted services for major platforms internationally. == History == Cloudflare was founded on July 26, 2009, by Matthew Prince, Lee Holloway, and Michelle Zatlyn. Prince and Holloway had previously collaborated on Project Honey Pot, a product of Unspam Technologies that partly inspired the basis of Cloudflare. In 2009, the company was venture-capital funded. On August 15, 2019, Cloudflare submitted its S-1 filing for an initial public offering on the New York Stock Exchange under the stock ticker NET. It opened for public trading on September 13, 2019, at $15 per share. According to the company, the name 'Cloudflare' was chosen, over the initial 'WebWall', because it best described what they were trying to do: build a "firewall in the cloud." In 2020, Cloudflare co-founder and COO Michelle Zatlyn was named president. Cloudflare has acquired web-services and security companies, including StopTheHacker (February 2014), CryptoSeal (June 2014), Eager Platform Co. (December 2016), Neumob (November 2017), S2 Systems (January 2020), Linc (December 2020), Zaraz (December 2021), Vectrix (February 2022), Area 1 Security (February 2022), Nefeli Networks (March 2024), BastionZero (May 2024), and Kivera (October 2024). Replicate (November 2025), and Human Native (January 2026). Since at least 2017, Cloudflare has used a wall of lava lamps at its San Francisco headquarters as a source of randomness for encryption keys, alongside double pendulums at its London offices and a Geiger counter at its Singapore offices. The lava lamp installation implements the Lavarand method, where a camera transforms the unpredictable shapes of the "lava" blobs into a digital image. In Q4 2022, Cloudflare provided paid services to 162,086 customers. In October 2024, Cloudflare won a lawsuit against patent troll Sable Networks. Sable paid Cloudflare $225,000, granted it a royalty-free license to its patent portfolio, and dedicated its patents to the public by abandoning its patent rights. In November 2025, it was announced Cloudflare had agreed to acquire Replicate, a San Francisco–based platform that enables software developers to run, fine-tune, and deploy open-source machine-learning models via an API without managing infrastructure. In January 2026, Cloudflare released an analysis regarding BGP routing leaks observed from the Venezuelan state-owned ISP CANTV (AS8048), which occurred on January 2 coincides with the arrest of Nicolás Maduro. While some security researchers had speculated that the outages were linked to U.S. cyber operations, Cloudflare's data indicated that the anomalies were consistent with a pattern of "insufficient routing export and import policies" by the ISP rather than malicious external interference. In January 2026, Cloudflare acquired Human Native, an AI data marketplace that brokers transactions between developers and content creators, for an undisclosed amount. On January 16, 2026, Cloudflare acquired The Astro Technology Company, the developers behind the open-source web framework Astro. In May 2026, Cloudflare announced the elimination of approximately 1,100 positions, around 20 percent of its workforce, in a restructuring the company attributed to the rapid adoption of artificial intelligence tools. The announcement coincided with the company's first-quarter 2026 earnings, which reported a record $639.8 million in quarterly revenue, a 34 percent year-over-year increase. CEO Matthew Prince stated the cuts were not driven by performance concerns but reflected roles made obsolete by AI, and that Cloudflare expected to employ more people by the end of 2027 than at any point during 2026. == Products == Cloudflare provides network and security products for consumers and businesses, utilizing edge computing, reverse proxies for web traffic, data center interconnects, and a content distribution network to serve content across its network of servers. It supports transport layer protocols TCP, UDP, QUIC, and many application layer protocols such as DNS over HTTPS, SMTP, and HTTP/2 with support for HTTP/2 Server Push. As of 2023, Cloudflare handles an average of 45 million HTTP requests per second. As of 2024, Cloudflare servers are powered by AMD EPYC 9684X processors. Cloudflare also provides analysis and reports on large-scale outages, including Verizon's October 2024 outage. === Artificial intelligence === In 2023, Cloudflare launched "Workers AI", a framework allowing for use of Nvidia GPU's within Cloudflare's network. In 2024, Cloudflare launched a tool that prevents bots from scraping websites. To build automatic bot detector models, the company analyzed "AI" bots and crawler traffic. The company also launched an "AI" assistant to generate charts based on queries by leveraging "Workers AI". Cloudflare announced plans in September 2024 to launch a marketplace where website owners can sell "AI" model providers access to scrape their site's content. In March 2025, Cloudflare announced a new feature called "AI Labyrinth", which combats unauthorized "AI" data scraping by serving fake "AI"-generated content to LLM bots. In July, the company rolled out a permission-based setting to allow websites to automatically block online bots from scraping data and content. Cloudflare released AutoRAG into beta in 2025. AutoRAG (retrieval augmented generation) creates a vector database of a website's unstructured content to identify relationships between concepts. It is part of an initiative with Microsoft, alongside their NLWeb standard, to make websites easier for people and automated systems to query. Cloudflare and GoDaddy partnered in April 2026 to enable AI Crawl Control features on GoDaddy hosted websites. This would allow site owners to decide how AI bot crawlers interact with their content. === DDoS mitigation === Cloudflare provides free and paid DDoS mitigation services that protect customers from distributed denial of service (DDoS) attacks. Cloudflare received media attention in June 2011 for providing DDoS mitigation for the website of LulzSec, a black hat hacking group. In March 2013, The Spamhaus Project was targeted by a DDoS attack that Cloudflare reported exceeded 300 gigabits per second (Gbit/s). Patrick Gilmore, of Akamai, stated that at the time it was "the largest publicly announced DDoS attack in the history of the Internet". While trying to defend Spamhaus against the DDoS attacks, Cloudflare ended up being attacked as well; Google and other companies eventually came to Spamhaus' defense and helped it to absorb the unprecedented amount of attack traffic. In 2014, Cloudflare began providing free DDoS mitigation for artists, activists, journalists, and human rights groups under the name "Project Galileo". In 2017, it extended the service to electoral infrastructure and political campaigns under the name "Athenian Project". By 2025, more than 2,900 users and organizations were participating in Project Galileo, including 31 US states. In February 2014, Cloudflare claimed to have mitigated an NTP reflection attack against an unnamed European customer, which it stated peaked at 400 Gbit/s. In November 2014, it reported a 500 Gbit/s DDoS attack in Hong Kong. In July 2021, the company claimed to have absorbed a DDoS atta
Angel F
Angel_F is a fictional child artificial intelligence that has been used in art performances worldwide focused on the issues of digital liberties, intellectual property and on the evolution of language and behaviour in information society. The character was created by Salvatore Iaconesi in 2007 as a hack to the Biodoll art performance by Italian artist Franca Formenti. The project was later joined by Oriana Persico who curated communication and part of the theoretical approaches of the action. The Angel_F project has been featured in books, magazines, national televisions, and has been invited to many conferences and events, both academic and artistic. == Creation == Angel_F is a backronym which stands for Autonomous Non Generative E-volitive Life_Form. The project was born in 2007 and resulted from the fusion of two contemporary art performances. Franca Formenti, an Italian artist living in Varese, invented the Biodoll character in 2002, which began making its appearances first on the network and later in the physical world by using what were called "clones": young women, prostitutes, pornographic starlets, transsexuals and models interpreting the role of a digital prostitute. The Biodoll was an art performance focused on research emerging from the network of new forms of sexualities, and on the analysis of changes brought on by this transformation to the concepts of private and public spaces, privacy, and the possibility of creating multiple fluid identities through language and digital media. The theme of fertility has always been central to the Biodoll performance: the digital prostitute was a wombless clone but desired giving birth to a son, the 'Bloki'. In a process starting in 2006, and ending in February 2007, Salvatore Iaconesi (xDxD.vs.xDxD) used his 'Talker' linguistic artificial intelligence to animate the digital child conceived with prof. Derrick de Kerckhove: Angel_F. Iaconesi and Persico met in November 2006 and immediately started collaborating on the birth of Angel_F. Angel_F was designed as a synthetic digital being composed through narrative, technological and cognitive psychology layers. The objective was to create iconic characteristics that resulted in being evocative and able to mimic human life up to a level in which bringing up a symbolic dialogue was possible. On the other side, the artificial identity was to implement and expose the cultural, emotional and relational ways that were typical of networked social ecosystems, among those technologies, systems and infrastructures that entered and shaped people's daily lives. The young digital being mimicked the evolution of a human baby: initially conceived inside the website of its digital mother it emulated the birth of a child by using the metaphor of a virus developing inside a website, taking progressively more space in the domain's databases and interfaces. Content was produced through the software by using small browser-based spyware techniques, through which Angel_F could infer the list of major portals that had been visited by the website's users. The Biodoll website was invaded by this growing presence and, thus, Angel_F was born. The Artificial Intelligence (AI) component of Angel_F was derived from another project, Talker, through which internet users could build up the AI's linguistic network by feeding it their text and web clips. Angel_F used this component to generate sentences and phrases, publishing them on the interface and on selected blogs. The parallel between the growth of the AI and that of a child kept building up and, just as children learn how to speak and act by observing their parents and the people around them, Angel_F used its spyware and AI components to learn, to navigate websites and web portals using web crawler based techniques, and to interact with other people by using the contents hosted and generated in its database to create surreal dialogues in blogs and websites. A virtual school was created, called Talker Mind, to narratively continue the AI's growth. Five professors (Massimo Canevacci, Antonio Caronia, Carlo Formenti, Derrick de Kerckhove and Luigi Pagliarini) fed their texts and academic articles to Angel_F, simulating virtual asynchronous lessons by using a multi-blog structure. A peer-to-peer system was also created at the time, named 'Presence'. Its interface resembled the one of 8-bit videogames and the peer to peer users travelled in a starry space and were able to perform standard Instant Messaging tasks, such as chat and file sharing. The interactions were possible both among humans and digital beings. Angel_F was the first user of the Presence peer to peer system. Angel_F entered the physical world as a baby-stroller mounted laptop computer that was used to let the digital child join events and conferences held worldwide. == Events == Angel_F performed all over the world, both in artistic contexts and in academic ones. It was also used for the communication strategy of several activist groups on the themes of intellectual property and digital freedoms. The first public space performance was held in Milan, when the Biodoll distributed a generative free press publication (called the Bloki FreePreXXX, its text was generated algorithmically and inserted into a prepared graphic layout). June 14, 2007: The second performance was held in Rome, at the Forte Prenestino, with a massive playroom created through computational graphics that people could interact with and that were generated by the AI. June 22, 2007: Angel_F presented the closing remarks for an Ipotesi per Assurdo (Absurd Hypothesis) with Salvatore Iaconesi and Oriana Persico at the IULM University in Milan, discussing the possibilities for an ecosystemic, sustainable reinvention of corporations. July 28, 2007: Hundreds of people at LiberaFesta (Free Party) in Rome listened to Angel_F in a speech discussing new politics and hacker ethics. 2007: The Glocal & Outsiders conference held in Prague at the Academy of Sciences was the first academic presentation of the Angel_F project, together with the Biodoll. September 2007: Angel_F was not allowed to post its contribution to the DFIR (Dialogue Forum for Internet Rights) held in Rome in preparation for Rio de Janeiro's Internet Governance Forum (IGF) edition. The case quickly turned into a collaboration among the involved parties and Angel_F was invited to the global event in Brazil where it was the only digital being present. Angel_F contributed a videomessage, in the digital freedoms workshop, which suggested some ideas for action to the United Nations and to all the parties involved in the IGF organization. October 2007: Angel_F was presented live at the FE/MALE 2 event, as an example of an atypical family during a public debate on new sexualities and social change. October 2007: Angel_F made a series of public performances Florence's Festival della Creatività (Festival of Creativity), an institutional event held periodically to showcase Italy's and other countries' best technological projects. During the festival Derrick de Kerckhove publicly recognized the little AI as his digital son. December 2007: Several international associations, and scientific researchers had been involved with Angel_F, eventually producing the system and process used to set up the Talker Mind digital school for the AI with Angel_F's professors. March 2008: The Tecnológico de Monterrey university in Mexico City organized the Computer Art Congress 2 international event, featuring Angel_F's project among with the ones by scientific researchers worldwide. July 2008: The project was presented in Austria at the Planetary Collegium's Consciousness Reframed 9 conference, together with the 'NeoRealismo Virtuale'. October 2008: Angel_F was used at a public event on a European scale called Freedom not Fear discussing privacy and civil liberties. July 2009: Angel_F has been seen with its digital father Derrick de Kerckhove to protest against Italy's harsh politics on freedom of speech. The project concluded in 2009 with the publication of a book entitled 'Angel F. Diario di una intelligenza artificiale' (Angel_F, the diaries of an Artificial Intelligence).