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Startups Of The Year 2023: Asia Winners

Author:
(1) Salvatore Iaconesi, ISIA Design Florence and *Corresponding author (salvatore.iaconesi@artisopensource.net). Table of Links Abstract and 1. A Hymn 2. Asymmetry 3. Bubbles, Guinea Pigs 4. Interface and Data Biopolitics 5. Conclusions: Implications for Design and References Abstract This article describes their biopolitical implications for design from psychological, cultural, legal, functional and aesthetic/perceptive ways, in the framework of Hyperconnectivity: the condition according to which person-to-person, person-to-machine and machine-to-machine communication progressively shift to networked and digital means. A definition is given for the terms of "interface biopolitics" and "data biopolitics", as well as evidence supporting these definitions and a description of the technological, theoretical and practice-based innovations bringing them into meaningful existence. Interfaces, algorithms, artificial intelligences of various types, the tendency in quantified self and the concept of "information bubbles" will be examined in terms of interface and data biopolitics, from the point of view of design, and for their implications in terms of freedoms, transparency, justice and accessibility to human rights. A working hypothesis is described for technologically relevant design practices and education processes, in order to confront with these issues in critical, ethical and inclusive ways. Keywords: Hyperconnectivity, Algorithms, Biopolitics, Ethics, Data 1. A Hymn In her “Hymn of Acxiom” folk singer Vienna Teng (2013) starts off with lyrics “Somebody hears you, you know that…”, in what seems to be a church choir. After listening for a bit, the real topic the artist is discussing about becomes clear: Acxiom is not a benevolent divinity somewhere in the cosmosphere caringly waiting to hear the troubles of his beloved human beings, but, rather, a highpowered data broker which has been described as “the Private NSA” (Tom’s Guide, 2013), as the silent, largest consumer data processor in the world (Fortune Magazine, 2004) and as “Big Brother in Arkansas” (NY Times, 2012). The topic of the song is data-surveillance. The idea for the song came while the author was pursuing an MBA at the University of Michigan: a colleague working with Acxiom data was shocked about the amount of information the company had available about herself and her husband. An interesting thing about the song is that the creepy, Orwellian, lyrics also empathize with databases as well as excoriating them. This is, in fact, an interesting point of view. As, on the one hand, we – directly and indirectly – consent tour data to be collected through our behaviors and basically accepting our lifestyle, on the other hand we feel deeply uneasy about that and from its implications. As we benefit from enterprises being able to provide us with products and services which are “more relevant” for us (more on this later in the article), we are simultaneously wary of the fact that a single subject knows so much about us and uses it to “sell us” to the highest bidder, and what this possibility implies for our liberties, freedoms and rights. Even more, it is progressively harder, if not impossible, for us, to know and understand what parts of our online and offline environment are determined from these data collection processes, or about which subjects have this data about us available, or how they are using it and for what purposes (Lafrance, 2014). In fact, the entries we see when we browse search engines, social media websites, and other online services are completely determined by these processes: the operators of these services feed the data they acquired about us to their classification algorithms which, in return, use it to determine what we may be more and less interested about, or where their optimal business opportunities lay in relation to our profile. This is not only true for our online lives: the offline world is quickly catching up. Physical mailings; fidelity cards; algorithmic research and control applied to stores, services and spaces of the city; imaging through security cameras; object and facial recognition on devices and architectures; the Internet of Things (IoT) and its sensors, possibilities for user identification and biometrics. These all combine with a plethora of other options which are turning us into data subjects, which can be recognized and tracked on databases as in the physical world. This paper is available on arxiv under CC BY 4.0 DEED license. Author: (1) Salvatore Iaconesi, ISIA Design Florence and *Corresponding author (salvatore.iaconesi@artisopensource.net). Author: Author: (1) Salvatore Iaconesi, ISIA Design Florence and *Corresponding author (salvatore.iaconesi@artisopensource.net). Table of Links Abstract and 1. A Hymn Abstract and 1. A Hymn 2. Asymmetry 2. Asymmetry 3. Bubbles, Guinea Pigs 3. Bubbles, Guinea Pigs 4. Interface and Data Biopolitics 4. Interface and Data Biopolitics 5. Conclusions: Implications for Design and References 5. Conclusions: Implications for Design and References Abstract This article describes their biopolitical implications for design from psychological, cultural, legal, functional and aesthetic/perceptive ways, in the framework of Hyperconnectivity: the condition according to which person-to-person, person-to-machine and machine-to-machine communication progressively shift to networked and digital means. A definition is given for the terms of "interface biopolitics" and "data biopolitics", as well as evidence supporting these definitions and a description of the technological, theoretical and practice-based innovations bringing them into meaningful existence. Interfaces, algorithms, artificial intelligences of various types, the tendency in quantified self and the concept of "information bubbles" will be examined in terms of interface and data biopolitics, from the point of view of design, and for their implications in terms of freedoms, transparency, justice and accessibility to human rights. A working hypothesis is described for technologically relevant design practices and education processes, in order to confront with these issues in critical, ethical and inclusive ways. Keywords : Hyperconnectivity, Algorithms, Biopolitics, Ethics, Data Keywords 1. A Hymn In her “Hymn of Acxiom” folk singer Vienna Teng (2013) starts off with lyrics “Somebody hears you, you know that…”, in what seems to be a church choir. After listening for a bit, the real topic the artist is discussing about becomes clear: Acxiom is not a benevolent divinity somewhere in the cosmosphere caringly waiting to hear the troubles of his beloved human beings, but, rather, a highpowered data broker which has been described as “the Private NSA” (Tom’s Guide, 2013), as the silent, largest consumer data processor in the world (Fortune Magazine, 2004) and as “Big Brother in Arkansas” (NY Times, 2012). The topic of the song is data-surveillance. The idea for the song came while the author was pursuing an MBA at the University of Michigan: a colleague working with Acxiom data was shocked about the amount of information the company had available about herself and her husband. An interesting thing about the song is that the creepy, Orwellian, lyrics also empathize with databases as well as excoriating them. This is, in fact, an interesting point of view. As, on the one hand, we – directly and indirectly – consent tour data to be collected through our behaviors and basically accepting our lifestyle, on the other hand we feel deeply uneasy about that and from its implications. As we benefit from enterprises being able to provide us with products and services which are “more relevant” for us (more on this later in the article), we are simultaneously wary of the fact that a single subject knows so much about us and uses it to “sell us” to the highest bidder, and what this possibility implies for our liberties, freedoms and rights. Even more, it is progressively harder, if not impossible, for us, to know and understand what parts of our online and offline environment are determined from these data collection processes, or about which subjects have this data about us available, or how they are using it and for what purposes (Lafrance, 2014). In fact, the entries we see when we browse search engines, social media websites, and other online services are completely determined by these processes: the operators of these services feed the data they acquired about us to their classification algorithms which, in return, use it to determine what we may be more and less interested about, or where their optimal business opportunities lay in relation to our profile. This is not only true for our online lives: the offline world is quickly catching up. Physical mailings; fidelity cards; algorithmic research and control applied to stores, services and spaces of the city; imaging through security cameras; object and facial recognition on devices and architectures; the Internet of Things (IoT) and its sensors, possibilities for user identification and biometrics. These all combine with a plethora of other options which are turning us into data subjects, which can be recognized and tracked on databases as in the physical world. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

Interface and Data Biopolitics in the Age of Hyperconnectivity: Abstract and A Hymn

Web3 hiring culture. What can we improve?

ওয়েব 3 নিয়োগের সংস্কৃতি। আমরা কি উন্নতি করতে পারি?

Einstellungskultur bei Web3. Was können wir verbessern?

Cultura de contratación Web3. ¿Qué podemos mejorar?

Culture d'embauche Web3. Que pouvons-nous améliorer ?

वेब3 भर्ती संस्कृति। हम क्या सुधार कर सकते हैं?

Web3 の採用文化。何を改善できるでしょうか?

Web3 채용 문화. 무엇을 개선할 수 있나요?

Cultura de contratação da Web3. O que podemos melhorar?

Культура найма Web3. Что мы можем улучшить?

Web3 işe alma kültürü. Neyi geliştirebiliriz?

Văn hóa tuyển dụng Web3. Chúng ta có thể cải thiện điều gì?

Web3 招聘文化。我们可以改进什么？

The Power of Campaign Management in Driving Business Objectives and Checkout Success

Content Overview A space odyssey Demographic targeting isn’t new Targeting the old way The future of AI in political campaigns and its potential impact How is my local politician using AI to target me? AI’s Theory of Mind HAL’s big brother (Artificial General Intelligence) Should I be scared? A space odyssey The sentient Artificial Intelligence (AI) known as HAL-9000 (Heuristically programmed ALgorithmic computer) in the popular 1969 science fiction book is a chilling story of how a computer, programmed with a simple directive, can provide companionship, guidance, and assistance, not unlike your present-day ‘Hey Google’. 2001: A Space Odyssey In the book, the groundbreaking technology denoted as HAL is capable of natural language processing, facial recognition, and speech synthesis. These are purposeful reasons why AI currently exists, be it for improving healthcare through personalized treatments, supporting natural disaster responses using satellite imagery, or simply as a natural language chatbot to answer candidate questions for a political campaign. All good things. Fans of the book will remember that the AI takes a turn for the worse, challenging the human’s motives to protect its existence. The story continues to show how the relationship between its creators and AI can quickly spiral into despair and desperation after a conflict occurs between humans and technology. The book exposes the ethical implications of creating AI systems that appear self-aware and have autonomy, ultimately leading to humanity’s undoing, a popular narrative amongst many science fiction . AI writers Fast forward 50 years to the present day, and HAL makes Siri look like a chimpanzee and Google Assistant like a glorified ) so for now, we’re seemingly safe, you have nothing to worry about, and the world continues to spin without an ominous red light flashing back at us. Dr. Sbastio, (throwback to anyone from the late ’80s Demographic targeting isn’t new This isn’t a book review on Arthur C. Clarke’s remarkably accurate prediction on the , but a post about how AI may be targeting you and your family right now, influencing your decisions based on your political stance, age, ethnicity, and sexual orientation. Demographic targeting isn’t new; before the internet, exit polls, cold calling & paper-based surveys were used to profile communities. They would subsequently be sent direct mailings and cold-called, persuaded to vote for their candidate. Some of these techniques are still used today, although sparingly, given their high degree of variance and inaccuracy. future of AI This is where AI demographic targeting can significantly enhance accuracy. Using machine learning algorithms to analyze large volumes of user data, AI can find patterns where humans have failed. Generally speaking, the more data the model is trained on, the more accurate the predictions may be. What can we do now, that we couldn’t do before All your posts, LinkedIn articles, AMAs, and even that rant you sent last week can be harvested by these AI models. While this may seem terrifying, it depends on how the data is regulated. On the one hand, AI can do things that were not possible before, that is, enhance voter engagement by providing personalized information, as well as help in resource allocation by providing campaigners optimization techniques on which voter groups are more likely to be swayed, thus allowing them to prioritize groups differently. On the other, AI can also be used nefariously to indoctrinate young minds, take advantage of short attention spans, and . How all these ethical and social issues are handled is still undecided, as most countries do not have regulations in place. Facebook Reddit Twitter deep fake opponents’ images to sway decisions Technological revolution Artificial intelligence is transformative; it will undoubtedly change how we work and interact with technology on a monumental scale. This is where we are so far: Perhaps the iPhone launch wasn’t as revolutionary as the discovery of fire, but you get the picture. Targeting the old way Most political campaigns today take advantage of retargeting using cookies or what is known as a pixel on your machine. They follow you around the web like a leach targeting you with ads for products you already own. This is not Artificial Intelligence; this is just a dumb cookie. Ultimately this cookie tracks and retargets advertisements based on your browsing history. Typically this was done in the 2016 & as ad spending shifted from traditional forms such as TV and radio to more cost-effective digital media. Outside the US, Hong Kong also increased online ad spending to f s just to promote one policy speech. 2020 United States Primaries our times what it did in the previous year Was all this money well spent? Does targeting individuals this way reach their intended group, or does a healthy majority of the ad spend fall into an abyss of bots, fake accounts, and an unintended audience? Research done by the University of Baltimore suggests that at minimum 20% of ad spending goes to bots, ineligible voters, and other attackers using techniques such as . All of these could potentially be solved with AI trained on the right data, although, as I will discuss further, AI is not a panacea. IP masquerading Dumb cookies & AI’s new consent bar It is interesting to note that there are policies against placing trackers and cookies on users’ devices unless consent is given. You’re certainly aware of that pestering bar that keeps popping up all over the web, the one that you often fail to read before hastily whacking “Accept.” You can thank the Europeans for that. Although perhaps regulation is precisely what AI requires, the European Union may be exactly what we need. Their tight regulatory framework could force the rest of the world to mandate an that controls the ethical use and consent for how AI models ingest our data and disseminate information by way of , thus increasing trust and verifiability, more on this later. AI policy Explainable AI Consider this consent bar: If you hadn’t already noticed, that was an attempt to poke fun at the potential regulations that may be required for AI safety, as almost none currently exists. Expect a facsimile of the above to appear at websites near you; brownie points if you spotted the Borg reference. How did we get here? Political parties used to win races with community outreach, door-to-door canvassing, and engagement with local businesses, it took grit to get your message out, and the people these parties represent saw right through the party leaders’ B.S and, most importantly, made up their own minds with data they collected, the campaign message was human-led, with text generated from the candidate’s mouth. The future of AI in political campaigns and its potential impact The future of AI in political campaigns might be one we should be wary of in its current unregulated state. Instead of winning with good traditional economic policy and honest campaigning, the party with the most computing power combined with the biggest ad budget will win the race. In a very recent case, the Republican National Committee (RNC, a major part of the US republican party) used an criticizing Joe Biden in a doomsday-like dystopia video with fake AI-generated images. It was reported that members of the public believed the . AI-generated video videos were real Three years ago, AI was not really being utilised in election campaigns…You don’t have to be a software designer or a video editing expert to create very realistic-looking videos…We’ve gone beyond Photoshopping small parts of an image to basically generating a completely new image out of thin air. — Darrell West, Center for Technology Innovation at the Brookings Institution Going back to answer the question, was AI successful in reaching its demographics? Well, in the RNC case, yes, a lot of attention was drawn to that doomsday video in which a lot of voters, including democrats, believed the video was real, was it ethical? Probably not, exacerbating existing biases and reinforcing stereotypes has negative consequences for fairness and equality in the political process. Was it legal? Well, that is up to interpretation. According to the FCC, , it would be ‘unconstitutional’ otherwise. you’re pretty much allowed to lie How is my local politician using AI to target me? Politicians have been utilizing technology to market their platforms for years. From Obama’s 2008 ” ” victory to Trump’s hourly tweets to the Tories (UK conservative party) 2015 , there has been a massive move toward online demographic targeting, although misappropriating millions to line Mark Zuckerberg’s pocket may not seem like a scalable plan. email election Facebook campaign The future of AI in politics is predicting which group of people will likely sway to your party and what type of content will coax them. This is already being done and will be a significant timesaver for future Democratic elections. Another way AI will help is the ability to quickly find donors at a fraction of what it would typically cost. This can be achieved by using regression techniques, a type of supervised learning that trains on existing donor data. Your local politician will know now if you’re worth their time inviting you to that fancy fundraiser, the likelihood of your donation, and the approximate amount. This doesn’t sound too good, what is the future of AI? AI progress is inevitable and will continue to be used as a force for good as long as research emerges from the field of . Limiting the amount of identifiable data amassed from users’ machines is one regulatory hurdle. As you read this, there is a high chance of telemetry data ( ) being sent from your machine to the cloud. Your data and search queries are crowdsourced, contributing to advancements in natural language processing, deep learning, and large-scale training. Pat yourself on your back, you’re helping humanity, and your data is now part of the collective! AI safety cite Knowledge is power As the saying goes, “Knowledge is power,” thanks to your data, political campaigns are harnessing that power to gain insights into your behavior and preferences. As we have seen, campaigners can now target potential voters with surgical precision, personalize messages with increasing complexity, and have the ability to pinpoint where campaign benefactors live; this is just the tip of the AI iceberg. AI’s Theory of Mind The future of AI is evolving rapidly. In the last 3 years, AI has developed the Theory of Mind, a measurement of cognitive ability and strategic thinking. Scientists use this test to measure if humans or animals have self-consciousness and morality in social interactions. This same test was applied to GPT, and the results were astounding. The last test conducted in Nov 2022 concluded that GPT 3.5 had reached the strategic level of a 9-year-old child. What does this mean for demographic targeting? Well, If the president calls you to say , perhaps it could be AI-Joe Biden. hello HAL’s big brother (Artificial General Intelligence) As AI improves through (RLHF), which is a fancy way of assigning a thumbs up or down to a prompt response, the next iteration of AI could be (AGI). A hypothetical HAL-like agent that theoretically can accomplish any task autonomously without intervention. How this progress to AGI will affect the world is currently unknown, although many, including OpenAI, the company behind the infamous GPT, . Reinforcement Learning with Human Feedback Artificial General Intelligence are urging clear regulation How can this technology be improved? Whether AI is used for demographic targeting, natural language processing for virtual assistants, weather forecasting, or healthcare, there is much improvement to be made. AI is still embryonic, and regulation will need to be in lockstep with the fast pace of iteration. As Satya Nadela puts it, we’re still in the early stages of AI development: We’ve gone from the bicycle to the steam engine with the launch of ChatGPT3 — Satya Nadela, CEO, Microsoft — May 2023 Build Conference Improvements are needed in Long-term safety, a key concern of in its recent build conference. “ ” includes safe testing of user data and monitoring of inconsistencies in NPL to detect violence, self-harm, and hate speech. Microsoft Azure AI Safety Explainable AI Furthermore, trust needs to be reinforced by way of (XAI), which is the process that allows users to trust the output of machine learning algorithms. What ChatGPT believes to be true may not actually be true, and you, as a consumer, should be able to verify how the result of the algorithm came to be. Deep fake political videos could include fine print as to how the video was generated and what data is fact or fiction. A lot needs to be done to create a more transparent and reliable AI system that is safe for the general public to use. Explainable Artificial Intelligence Should I be scared? You need not be scared but educated and vigilant, look out for red flags, read the fine print, and know how the AI results are generated. Protect minors and teach them about the pitfalls and inaccuracies of such technologies. Know that propaganda can come in many forms, including something that may look real, feel real but entirely AI-generated. The last of human-led elections has begun, and you will be targeted. First, they ignore you, then they laugh at you, then they fight you, then you win. – Anonymous, but it’s highly possible it was written by HAL-9000. Originally posted . here

How AI Will Impact Political Campaigns in the Near Future—It All Started With Hal-9000

কীভাবে এআই অদূর ভবিষ্যতে রাজনৈতিক প্রচারাভিযানকে প্রভাবিত করবে—এটি Hal-9000 দিয়ে শুরু হয়েছিল

Wie sich KI in naher Zukunft auf politische Kampagnen auswirken wird – alles begann mit Hal-9000

Cómo afectará la IA a las campañas políticas en un futuro próximo: todo comenzó con Hal-9000

Comment l’IA aura un impact sur les campagnes politiques dans un avenir proche : tout a commencé avec Hal-9000

एआई निकट भविष्य में राजनीतिक अभियानों को कैसे प्रभावित करेगा—यह सब एचएएल-9000 से शुरू हुआ

AI は近い将来の政治キャンペーンにどのような影響を与えるか—すべては Hal-9000 から始まった

AI가 가까운 미래에 정치 캠페인에 어떤 영향을 미칠 것인가? 모든 것은 Hal-9000에서 시작되었습니다

Como a IA impactará as campanhas políticas no futuro próximo – tudo começou com o Hal-9000

Как ИИ повлияет на политические кампании в ближайшем будущем: все началось с Hal-9000

Yapay Zeka Yakın Gelecekte Siyasi Kampanyaları Nasıl Etkileyecek? Her Şey Hal-9000 ile Başladı

AI sẽ tác động đến các chiến dịch chính trị như thế nào trong tương lai gần—Tất cả đều bắt đầu với Hal-9000

人工智能将如何在不久的将来影响政治竞选——一切都从 Hal-9000 开始

Communities are hard. They are hard to build, hard to maintain, and hard to grow. But, building communities has been one of the most rewarding things in my career, because I met so many amazing people and was forced to learn and do things out of my comfort zone. I remember over 10 years ago I attended my first small tech conference - CouchConf in Berlin. Luckily there was a BerlinJS Meetup happening around the same time and I was blown away by the community and the people. They have a GitHub repository set up for the website and talks are submitted as GitHub Issues. I was amazed by the simplicity and transparency of the process, so I forked the repo and founded BarcelonaJS a few weeks later. This was the start of my own organizers' journey. A little anecdote of a regular and very frustrating experience: You spent hours preparing, communicating, finding and inviting speakers, finding and securing a date & venue, talking to sponsors for food and drinks all to put together a great event. And when the time comes you're sitting there alone or with a fraction of the people you thought would come, because Meetup says "150 people RSVPed YES". And in one rare occasion that was just bad luck with the weather, but more often when I talked to people afterward I heard this: This is amazing, I wish I'd been there, but I did not know! This phrase was the motivation for me to start exploring GitHub as a community tool because GitHub is one of the most amazing platforms for automation and that's what you need to do as an organizer: automate the crap out of everything to publish the event on Eventbrite, Meetup, Twitter, Facebook, Telegram Groups, WhatsApp, Email, Calendar, etc. etc. etc. and do that 2 weeks before, 1 week before, 3 days before, 1 day before, 1 hour before the event so nobody can say afterward: "I did not know about this". Because in the end, you're not doing this for yourself but for the community. During my travels, I came across Node.js and JavaScript communities on Meetup with thousands of members, but not a single upcoming or recent event. This can happen for many reasons, but it's often because the organizers do not have time or have moved on to other things, and it's hard to find a successor to keep things running. Meetup and other platforms are great for the discovery of communities and events, but they make it hard for members to reach out to the community in case an organizer is no longer active and to take over / revive the community. As a developer, I spend a lot of time on GitHub and am familiar with the tools and workflows, so I started exploring on how to use GitHub as a community platform. Benefits of using GitHub for Community Building Open Source & Open Community The first and most obvious benefit is that public repositories on GitHub are Open Source. This means all the content, issues, and discussions are public and can be forked, copied, and reused by anyone. This is great for communities because it means that if the community is abandoned, anyone can fork it and continue the work. It also means that if you want to start a new community, you can fork an existing one and adapt it to your needs. GitHub has user/team/organization management built in, so you don't need to build anything yourself. It's easy to invite someone into the organization or to add teams with different permissions of the organization. Most of us know how to use GitHub and visit the site on a daily basis, so there's no need to bookmark additional sites for database administration, content management or other tools. With GitHub Actions, we can run automated tasks on a schedule or on demand, and with GitHub Pages we can host our community website for free. Building in Public & Transparency For me, one of the most important benefits of building a community on GitHub is full transparency and open communication. Everything[1] is public, so there's no need to worry about who has access to what. This means that anyone can contribute to the community, and anyone can see what's going on. This is great for building trust and for building a community that is inclusive and welcoming to everyone. My aim with communities like BarcelonaJS, CDC etc. was/is to create free and open spaces for developers to share and connect. And "free" is where transparency comes in. In the past, I always kept my hands off financial contributions. If a company wanted to sponsor, they could order food or drinks directly to the venue, but I wouldn't facilitate. Thanks to the , this has become much easier and we're now able to accept financial contributions and make them transparent to the community. These are used for example to pay for the domain(s), to get food and drinks, run ad campaigns etc. Open Collective [1] of course, you can also create a private repo for internal organizers discussions etc. Drawbacks & Caveats GitHub is not a Community/Event Platform like Meetup, so there are some caveats. I've grown used to treating Issues as "Events" or "Talk Proposals" and GitHub Issue Forms to structure submissions. But it's not ideal and takes time for newcomers to understand "Create an issue to submit a talk". There are no "comfort features" to pick a date, location on a map, etc. and trigger a simple email notification to hundreds of people. The Open (Developer) Community This entire concept is primarily focusing on developers and engineers, as it evolves around GitHub and IssueOps. To give an idea of my previous experience, here are a few communities and conferences I've helped build: , 2012 - 2018 BarcelonaJS Hackers & Founders Barcelona, 2012 - 2015 Node.js Barcelona Meetup Group, 2012 - 2018 Couchbase Barcelona Meetup, 2012 - 2015 NodeConf Barcelona, 2015, 2016, 2017 MediterráneaJS, 2015 2018 - 2021 CyprusJS 2020 - present Cyprus Developer Community While building these, I've continuously worked on a set of tools, workflows and automation to make the lives of organizers easier, because it's such a tough and often thankless job. So here's my of how I construct a public community on GitHub. Open Community Concept Labels & Structure The first step is to set up a GitHub organization and create two repositories: events talks The names are obvious: in the events repository are , and in the talks repository are templates for submitting . Talks can be linked to events to build up an agenda and, if you manage to get engagement from the community (remember: it's hard!), comments and reactions such as 👍 or ❤️ can be used to vote on talks. templates for creating new events talk proposals You can also use a to manage the event lifecycle through the proposal, scheduling and announcement phases: GitHub Project In Cyprus, we added labels for the different regions on the island, but most importantly an "Approved" label is needed. All new issues are created with the "Event" label, but only someone with "triage" permission (organizers team) can "approve" the event. This is important to avoid spam and to make sure the event is relevant. Now that there's an organization, some repos with issues and some structure in place, it's time to automate. GitEvents / goes and started with the name as a collaboration "hack weekend" between the London Node User Group (LNUG) and the Barccelona Node.js Group. Back then, GitHub Actions didn't exist and we tried to use Webhooks for issues to build up structured data for GitHub Hosted websites based on Meetup.com data. The setup was cumbersome and the project was abandoned for a while. GitEvents GitEvents on GitHub way back (2014) gitup Today, is a simple set of GitHub Actions to automate issue workflows. "Git Ops" for meetups and events. All that's needed is a GitHub Organization and a GitHub App. Some of the features are: GitEvents : Any interaction with GitHub repositories, issues or discussions triggers a workflow to invite the user to the GitHub organization and adds them to the "Community Members" team. The permissions are the same as for the public, but a little "growth & engagement hack" is that you can @-mention all community members in issues. This is great for announcements, but shouldn't be abused, otherwise you quickly lose members. Inclusive Org : I put together a set of simple templates for events, talks, code of conduct replies etc. to quickly get started with GitEvents. Issue Templates : are re-usable GitHub Workflows to automate marketing and common tasks such as emailing/tweeting/blueskying members, creating/updating events on Discord, EventBrite, Meetup etc. Broadcast : is a GitHub Action to generate iCal files for events. is a standard for calendar events. People can just add this as a subscription to their calendar to keep up to date with community events. We created a redirect to the github file, so people can subscribe to the calendar with a simple link: . ICS iCal https://calendar.cdc.cy Everything is "plug and play" so you can pick the Actions you like and it's relatively easy to compose them into a workflow. For examples, check out the . Cyprus Developer Community Workflows If you'd like to get started with GitEvents and need some help, please reach out on our GitEvents is a work in progress and there's always especially with integrations to other platforms etc. If you want to help, please get in touch. Discord Server a lot to do GitHub Issue Forms Issue Forms are still a relatively little-known feature on GitHub. Instead of a regular Markdown templates, a with a form configuration. YAML file can be provided This is pretty cool to get structured input, but once saved as "Issue", the data is semantically useless because it's just Markdown. I've experimented with Milestones, Markdown headers, Labels etc. to add semantic information like dates, locations etc. to an issue, but nothing worked well. So I started building . This helps to parse an issue that was created through a form to extract dates, links, images, lists etc. and convert them into structured JSON data. This can directly be passed on to other platforms like Discord, EventBrite, Meetup, etc. or used in mailings, tweets etc. GitHub Issue Forms Body Parser The as a library to parse any Markdown text. Only recently I realized entire files can be parsed for structuring data for a website: Issue Forms Body Parser is also available on npm README.md query ($organization: String!, $repository: String!) {
  repository(owner: $organization, name: $repository) {
    id
    name
    object(expression: "main:README.md") {
      ... on Blob {
        text
      }
    }
  }
} This is the query to retrieve file content from the branch of a repository, and you can pass that into the "body parser": main const structuredData = await bodyParser(readmeFile()?.repository.object.text) Instead of keeping yet another copy of your community description, you can now fetch the section from the file and use it on your website. About README.md GraphQL API With the latest website I wanted to explore and push the boundaries of what's possible with Issues, files and structured JSON data and I'm fairly pleased with the result: https://cdc.cy README.md most of the website content is fetched from or files on GitHub; everyone can edit, no CMS or account etc. required README.md .json upcoming and past events are based on GitHub issues (approved & event-labelled issues on the repo) events organizers are fetched from GitHub Team Members Events can be annotated/enhanced with proper location data from a "common locations" file Speaker profiles are entirely based on GitHub User Profiles, we can fetch avatars, bio (readme), location, etc. and build pretty speaker profile pages We can display some stats such as "number of members of the organization", "number of past/upcoming events" etc. All of these features were possible through the and by parsing the Markdown files with the body parser. GraphQL API Summary & Outlook I've been working on this concept for a while and it's still changing shape every once in a while. But the basic ideas I've adopted from BerlinJS haven't changed: : everything is public and transparent, and everyone is welcome (as long as they follow the Code of Conduct) Open Community Events and Talks/Proposals are issues in a repository Labels help with structure and automation Building all of this has taken a lot of time and energy and there are still lots of things missing I don't have time for: proper email integration with schedules, reminders, etc. finalize EventBrite integration add Meetup integration add WhatsApp/Telegram/Slack integration improve issue handling, and automatic comments (ie. Code of Conduct reminder, follow-up reminder etc.) If you think this can help you and your community and want to get involved in piecing the puzzle together, please reach out on the , on or directly on one of the . gitevents Discord Server GitHub Discussions GitHub Issues If you're in or , please join and support the local developer groups. Cyprus Barcelona Random Organizers Thoughts, Tips & Tricks from personal experience Venues: picking a company office as a venue is easy, but has lots of drawbacks. It's easy to manipulate people once you have them in the office (look at our cool place, we even have a cocktail bar!). Remember the company has different motives than you (recruiting, marketing, etc.). Sometimes there may even be conflicts, that people aren't allowed to visit the competitors' office. If possible, I prefer "neutral" co-working and event spaces and thanks to Open Collective you can get sponsorships to pay for the venue with fair (recruiting/marketing/etc) rules for everyone. Reminders: we're all busy and it's easy to forget things. Not everyone is as engaged, motivated & psyched about the event as you are, so regular reminders keep people engaged and help to build a habit. Find a good cadence for reminders, and do not overdo it and spam people. Different media work for different people, so I try to cover as many as possible. Scarcity "I've noticed that announcing the event just one week before it's happening and having a limited amount of seats works better then releasing month in advance with 100+ capacity" – Dmitry Zaets, BarcelonaJs Perks: "Giveaways are fun, and giving away tickets to conferences and other stuff can be nice perks for attending" – Dmitry Zaets, BarcelonaJs Consistency: "Establish a realistic schedule from the outset. Whether it's a monthly meetup or a quarterly conference, consistency is key. Create a roadmap, allocate responsibilities, and stick to the plan. This not only keeps your audience engaged but also helps in building a reliable community." – Federico Rampazzo, CDC.cy Finding Speakers: "Don't be afraid to approach experts who might be willing to share their insights. A lot of your members are likely to have interesting stories worth sharing! It can be an enriching experience for them as much as for the community. Taking photos, videos, audio recording of the events is valuable and help the speakers in their speaking career, as well as increasing the reach of the event." – Federico Rampazzo, CDC.cy

GitHub and the Art of Building Open Communities

গিটহাব এবং আর্ট অফ বিল্ডিং ওপেন কমিউনিটি

GitHub und die Kunst, offene Communities aufzubauen

GitHub y el arte de construir comunidades abiertas

GitHub et l'art de créer des communautés ouvertes

GitHub और खुले समुदायों के निर्माण की कला

GitHub とオープン コミュニティを構築する技術

GitHub와 개방형 커뮤니티 구축 기술

GitHub e a arte de construir comunidades abertas

GitHub и искусство создания открытых сообществ

GitHub ve Açık Topluluklar Oluşturma Sanatı

GitHub và nghệ thuật xây dựng cộng đồng mở

GitHub 和构建开放社区的艺术

With a large number of microservices in a project, we have to face the fact that in some of them, we have to duplicate the same code (utility classes, configurations, etc.), and have found a bug in one place, we have to search and fix it everywhere. At the same time, if microservices are supported by different developers, each of them will fix the bug in their own way, and it will be more difficult to bring everything to uniformity in the future. Let's consider several approaches to code sharing in microservices, by example. Let's imagine that we have a pet clinic website where you can register, choose a doctor, and make an appointment. The project may contain many microservices, but we will take the following: - responsible for authorization, registration, and work with users (both doctors and pet owners). user-service - responsible for email distribution (it can be news, appointment reminders, etc.). notification-service - a microservice that will aggregate responses from internal microservices (such as user-service) and prepare a response for the frontend. gateway Categories of microservices In our example, microservices can be divided into several categories: Microservices that use a database and do not provide any API methods. In the list above, this is a notification service that does scheduled mailings or uses some message broker (like Kafka or rabbitMQ). Microservices that do not use the database but communicate only with third-party APIs and provide their API methods for interaction. In our example, this is a gateway that only processes responses from other microservices. Microservices that use both the database and provide their own API methods for operation (like user-service). What if we want all these microservices to follow some internal standards? For example, those that provide API methods for communication should have auto-documentation describing all methods via OpenAPI, and logging in all microservices should be configured so that all logs are sent to Splunk. In this case, we will have to duplicate configurations and some utility classes to handle dates, prices, and other things in each microservice. In order to avoid this, we will need our own library where we will store all the shared code. Let's consider several possible implementations of this library. One library for all microservices We can create a new maven project in which we add all dependencies that can be used in microservices. In this case, each microservice will have a large number of unused dependencies, which will increase the size of the final jar file. In addition, we will have to be strict about not creating new beans unnecessarily (why do we need a database bean if we don't need it in a microservice?). Many different shared libraries We can split our library into many projects. In this case, we will get rid of the problems from the previous example, but we will encounter new ones. If we change several libraries at once, we will have to make many pull requests and publish many versions of these libraries. One common multi-module library In this case, all changes are in one pull request; there are no unnecessary dependencies and beans in microservices, and you can publish separate versions of subprojects as well as release a single version. Let's take a closer look at how to implement such a library using Maven. To give you an example, let's define the submodules we might need and what will be in them: - in it, we will store something basic for all microservices. For example, some of our basic exceptions are PlatformException or ApplicationException. We can also store there some basic data structures like Pair, Timer, and all similar things that can be used in any of our microservices regardless of their purpose. core - in this module, we will store general-purpose utility classes. For example, classes for working with dates, prices, JSON, etc. utils submodule will contain some useful things for working with the database. It can be custom serializers / deserializers of database fields and many other things. In our example, this submodule can be used by user-service and notification-service. database - in this, we will be able to describe the configuration to send all our logs to Splunk. logging - in this submodule, we can store everything that may be needed in microservices that interact with other APIs. It can be used as a gateway. client - here, we will store everything for services that will provide API methods. For example, configuration for autogeneration of documentation and the like. It can be used by user-service and gateway. api Any other submodules you may need. Creating a multi-module project Let's start with the base pom file. We will use the pom file from the as the parent to ensure that dependencies in submodules have the same versions as in all microservices. previous article <?xml version="1.0" encoding="UTF-8"?>
<project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
	xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 https://maven.apache.org/xsd/maven-4.0.0.xsd">
	<modelVersion>4.0.0</modelVersion>
	<parent>
		<groupId>com.example</groupId>
		<artifactId>base-pom</artifactId>
		<version>0.0.1-SNAPSHOT</version>
	</parent>
	<groupId>com.example.common</groupId>
	<artifactId>common-library-parent</artifactId>
	<version>0.0.1-SNAPSHOT</version>
	<packaging>pom</packaging>

	<name>common-library-example-parent</name>
	<description>Demo project</description>

	<modules>
		<module>core</module>
        <module>utils</module>
		<module>database</module>
        <module>logging</module>
        <module>client</module>
        <module>api</module>
	</modules>
</project> Each submodule needs to have its own pom file added. In it, you can add all dependencies required in a particular submodule, including dependencies on other submodules. For example, let's take a pom file from the client submodule, which requires a dependency on feign (I wrote more about how to use it in ) and exceptions from the core submodule. So the pom file will look like this: my article <?xml version="1.0" encoding="UTF-8"?>
<project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
	xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 https://maven.apache.org/xsd/maven-4.0.0.xsd">
	<modelVersion>4.0.0</modelVersion>
	<parent>
		<groupId>com.example</groupId>
		<artifactId>common-library-parent</artifactId>
		<version>0.0.1-SNAPSHOT</version>
	</parent>
	<artifactId>client</artifactId>
	<version>${project.parent.version}</version>
	<name>client</name>
	<description>Demo project</description>

	<dependencies>
		<dependency>
			<groupId>${project.groupId}</groupId>
			<artifactId>core</artifactId>
			<version>${project.version}</version>
		</dependency>
        <dependency>
        	<groupId>io.github.openfeign</groupId>
        	<artifactId>feign-spring4</artifactId>
        </dependency>
	</dependencies>
</project> In a submodule, we can add classes to extend the feign client capabilities. For example, its implementation of logging requests/responses from API and much more. The final structure of the project will be as follows: Adding a multi-module library to microservices In order to use this library, as with parent pom, it must first be published to a local repository (or another repository such as Nexus) with the command. You will see the following: mvn install Next, you can add the necessary submodules to the microservices. Let's add the client submodule to our gateway microservice in order to add user-service integration to it easily: <properties>
	<common-lib.version>0.0.1-SNAPSHOT</common-lib.version>
</properties>

<dependencies>
	<dependency>
		<groupId>com.example</groupId>
		<artifactId>client</artifactId>
		<version>${common-lib.version}</version>
	</dependency>
</dependencies> Of course, the number of submodules can be any number. It all depends on what is required in your project. Useful links Repository with an example library An article on dependency management in microservice architecture An article with an example of a simple GitHub integration using feign OpenFeign repository with documentation

Code Sharing in Microservice Architecture: Creating Your Own Common Library

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