Listen, you've probably read a lot of company bio’s

Structured Agency

You’re here so that means you’re considering a lean approach to develop your MVP. That’s a great first step. But lean product development methodologies alone won’t cut it into today’s entrepreneurial landscape. I know this because over the last ten years I’ve built my own startup (which crashed and burned), worked on dozens of MVPs with Altar.io and witnessed hundreds of other founder stories. The hard truth is, is not as simple as coming up with an idea and taking it to a team of developers or a software development company. building a successful MVP Before writing a line of code, you need to make everything you can to : reduce the risk of that product becoming a part of the 42% of startups that fail because of no market need; 19% that get outcompeted; 17% that fail because they build a user-unfriendly product; 13% who mistimed their product; …you see what I mean. A great idea is a good start, but if you want to have any chance of succeeding, it’s vital that you focus on the product from a business standpoint. That usually starts with thorough research, from numbers on the market to competitor benchmarks or any other information that can help you turn your vision into a rock-solid value proposition. From there, you set the main assumptions to prove and outline the journey your users will take through your product. At this point, you’ll have all the information you need to create the ultimate list of User Stories and features necessary to prove the main assumptions in a Minimum Viable Product (MVP) or Proof of Concept (POC). At Altar.io we call this . The Product Scope In this article, I’m going to give you the full process, step-by-step, starting with defining your stakeholders, and finding your value proposition. Expert Tip "Your MVP won’t work if your customers can’t see any value. Build what customers want and then scale." Joe Procopio , Product Expert & Startup Founder Step One: Define Your Stakeholders & Discover Your Value Proposition Before building your MVP, you need to first do some good, old fashioned research. Start by, asking yourself: “What is the problem/pain we’re trying to solve with our product?” The answer to this question should always be at the front of your mind as you move through the process. Therefore, keeping that in mind, the next question is: “Who is your main target? Define all stakeholders involved in your product” Get to know them in terms of demographics, psychology and behaviour in the observed context. Later on in the process, it’ll be time to build UX personas for your stakeholders, but we’ll get to that. With your detailed list of target stakeholders in hand, and knowing exactly what the problem is that affects them, the next question you need to look at is: “How do my stakeholders deal with this problem today? Carry out a competitor benchmark.” Let’s take Spotify as an example here. Before Spotify existed their stakeholders had three options when it came to listening to their favourite music. They could: Buy it from iTunes Buy CDs Download it illegally using piracy (At a time where piracy was at its peak) It’s imperative that, before you enter the market, you look and who is already in your space. List your competitors, including their strengths, weaknesses and positioning.  Also, you might learn something you can adopt from them. As Pablo Picasso said, “Lesser artists borrow; great artists steal.” Then, the final question in step one becomes: “Why is our solution 10x better than the current market solutions?” Here, you should define what differentiates you from the competition/existing alternatives for your stakeholders. A.k.a your Unique Value Proposition. Let’s again take Spotify as an example. Their value proposition is to give the user the feeling that they had “all the worlds music on their hard drive” if he could do this he could “build something better than piracy.” founder Daniel Ek believed And on paper, it was a better option (for all stakeholders) when compared with illegally downloading music as: Artists still make money from customers streaming songs; The playback quality is better and faster than illegal downloads; And well, it’s legal. They were a better option than buying CDs or buying from iTunes (the only other options at the time) as: Instead of buying an album at around $10 each you had access to millions of songs at $10 a month. You could listen instantly on the device of your choice, as opposed to: Waiting for it to download via iTunes or going to the record store, buying the CD, uploading it to your computer and syncing it with a portable device. With that in mind, once you have your Unique Value Proposition, it’s time to wrap up everything from Step One in a comprehensive, crystal clear, product-centric elevator pitch. It should look something like this (feel free to use this as a template): We’ve created [name of our Product] for [our stakeholders] who [state the problem/pain they’re facing]. This product will solve the problem by [state your product’s key benefit]. Unlike [the current market alternatives/competitors] our product will [explain how your product differentiates you from your existing competition]. If any aspect of your elevator pitch is unclear, head back to the beginning of Step One. Otherwise, let’s move onto Step Two: deconstructing your Elevator Pitch and setting your Main Assumptions. Expert Tip “Focus on getting your MVP’s value proposition incredibly clear. If your grandma doesn’t understand it’s probably not clear.” Jan-Philipp Kruip , Founder & CEO Step Two: Deconstructing Your Elevator Pitch to Discover the Main Assumptions to Validate Once you have a rock-solid value proposition and a crystal clear product elevator pitch, it’s time to set the main assumptions you need to validate. Looking closely at your elevator pitch, you need to work out: Which of your assumptions can be validated with researchWhich features need to be built into your MVP to validate any assumptions that can’t be validated with research. So, let’s say that your first assumption is that users are really facing a problem/pain (for this example let’s call it “Pain X”.) The first assumption you need to validate is whether or not this is a real, tangible pain point. More often than not this assumption can be validated with research, e.g.: “We know that Pain X is an issue for [name of your target stakeholders] because firstly we’ve seen competitors in the market attempting to solve this problem.” Taking this example one step further, let’s say that the next assumption you need to validate is that your solution to Pain X is better than your target stakeholders current solution. This assumption can’t be proven by research alone, you need to test within your MVP and measure this with KPIs e.g.: “Although users are already solving the problem with [competitor’s solution], we believe this is outdated and inefficient. We propose that [our solution] is a better way to solve Pain X. We intend to prove this by building an MVP and measuring the adoption and retention ratio”. However, assumptions aren’t just relevant to the end-users, but other stakeholders in the process. For example, Spotify needed to convince record labels to give them the rights to the music. Which, at a time when piracy and digital music platforms were the enemies of the industry, this was a battle for Spotify. Spotify’s founder, Daniel Ek, was convinced that he had a solution to convince them to come on board: “Up until that point, Napster and alike simply hadn’t shared their revenues. I was sure we could work out a straightforward revenue share with the record labels.” It turned out it wasn’t that simple. The record labels still saw digital music platforms, like Napster, as the enemy. They simply didn’t trust Spotify. Simply put, without industry involvement, Spotify could never exist. Daniel had to de-risk the opportunity for the record labels, by guaranteeing them a year’s worth of revenue. He took a painful short term loss to earn their trust and set himself up for the long term win. So, keep in mind as you build your list of assumptions, that some have to be tackled before you build your MVP and that direct users aren’t the only stakeholders you should be focusing on. Once you have the list of assumptions you need to prove with an MVP, it’s time to move on to creating your user stories & building your feature list. Step Three: User Stories & Feature List At this stage, you’re nearly ready to begin your MVP development process. There are two critical aspects, however, that are yet to be completed. Creating your MVP user stories and building your final list of MVP features. Let’s start with the all-important user stories. How to Write User Stories A user story is an explanation of the journey through your product from the perspective of your stakeholder. It’s the most granular description of how a product works. User stories are a great way of showing how a software feature will provide value to your stakeholders. Also, they will keep your team focused on developing a user-centric product that actually solves their problem. Let’s create a few simple user stories based on our previous example, Spotify. Login As an unregistered user: I want to sign up with Facebook, Google or email & password If I sign up with email I want to insert my password into two separate fields to ensure they match I want my password to be secure, therefore must contain at least 8 characters and at least one number and special character. As a registered user: I can log in using Facebook, Google or email & password. If I forget my password I want to be able to click on a “Forgot my Password” link. Once I’ve clicked on this link I want to insert my email and receive a link that redirects me to an app window that allows me to enter a new password into two separate fields to ensure they match. I want the system to store my new password once I click OK (if passwords match). Playback As a  user I want to: See the title of the track I’m listening to and the artist name Have the ability to play and pause the music Be able to skip to the previous or next track If it’s the last song on the list, it repeats, the music doesn’t stopIncrease or decrease the volume Shuffle or repeat tracksExit the playback window and return the music in a list view. Once you have created your user stories, it’s time to move onto your feature list. Your MVP Feature List Using your User Stories, create a list of features that you would like to have in your product. Then, put your features on one side and place your assumptions on the other. For each feature ask yourself: “Is this feature vital to prove any of the assumptions?” If it’s mandatory to prove one of your assumptions, it gets built into your MVP. If not, leave it out and revisit it later when it’s time to iterate your product. Let’s go back to our example of Spotify once more, taking into account the Playback user story I created. As a music streaming service, the ability to playback should be considered an essential aspect. There are features that are nice to have but aren’t mandatory. For example, displaying the lyrics to the song. On the other hand, other features are essential such as: Playback Feature List Artist & Song information Play/Pause Button Skip forward button Skip back button Volume change buttons/slider Shuffle button Repeat button Once you have defined the essential features needed to showcase your value proposition, you’re ready to move onto the development stage of building your MVP. Here’s the concise break down of the process: Three Steps to Build an MVP That Focuses on Your Users Step One: Define Your Stakeholders & Discover your Unique Value Proposition What is the Problem/Pain we’re trying to solve with our product? Who is our main target? Define all stakeholders involved in your product How do our stakeholders deal with this problem today? Carry out a competitor benchmark. Why is our solution better than the current market solutions? What is our clear, product-centric elevator pitch? We’ve created [name of our Product] for [our stakeholders] who [state the problem/pain they’re facing]. This product will solve the problem by [state your product’s key benefit]. Unlike [the current market alternatives/competitors] our product will [explain how your product differentiates you from your existing competition] Step Two: Deconstructing Your Elevator Pitch to Discover the Main Assumptions to Validate What are the relevant assumptions from your Elevator Pitch? From these assumptions, which can be validated through research? For the remaining assumptions, how will we validate them and by using which KPIs? Step Three: User Stories & Feature List What are the User Stories for our product? Of the Features defined from our User Stories which are essential to validate our assumptions and showcase our Unique Value Proposition? Also, check out that you can use as a template to map out your MVP using the above process. this worksheet Wrapping Up By following this process you will ensure you’re building an MVP that focuses on solving your stakeholder’s problem. You need to ensure you demonstrate value to your users with your MVP, or they simply won’t adopt it. For any other questions about the MVP process, feel free to . Alternatively, see the MVP FAQs section below. drop me a message Good luck & thanks for reading. Other MVP FAQs How Much Does it Cost to Build an MVP? The average good-quality MVP costs anywhere between $40,000-$80,000 depending on whether you build an MVP with an agency or hire your own team of developers. How Long Does it Take to Build an MVP? The time it takes to build an MVP can vary depending on the industry you’re tackling and the features you need to prove your assumptions. That being said you can expect to launch your MVP around three to four months after development begins. What Comes After an MVP? After you’ve launched your MVP, it’s time to employ the Build, Measure, Learn cycle: Once your Minimum Viable Product is out in the wild, it’s time to gather user feedback (Measure). With that user feedback, go back to your product scope: your MVP’s stakeholder analysis, user stories, UX/UI and feature list. Use it to discover improvements you can make to your product (Learn). Then, implement those improvements in your product (Build). Then, go back to the beginning and start again. Continuously iterate your MVP to create your fully-fledged product. In doing so, you’ll have built a user-centric product that’s the best solution on the market. How do I Find Early-Adopters for my MVP? Landing pages, videos, social media ads, TV ads, snail mail, PR stunts, a partnership with a manufacturer. Your options are endless depending on the resources you have. Choosing the ones that’ll work for you is all about one, non-optional task: getting to know your customers. This article was published here by Paolo Dotta.

How To Apply A Structured Process For Developing an MVP: 2021 Guide

What You Should Know Before Hiring a CTO or Software Agency [Expert Interview]

For Jobs was launched in June 2017 and only requires for it to be able to pull through job listings from your site, into its UI - Google three elements A job listing that is on a unique page. A sitemap that updates daily at the very least. Implementation of schema that you can validate in . jobPosting Google’s Structured Data Testing Tool And that’s it! Googles 100s and possibly 1000s of elements in its algorithms should then be able to understand the context of your page and show your job listing to relevant job seekers, ensuring the experience is flawless. to job details for clients that have implemented the aforementioned mark-up on site. But Google need to continue working on the product, as the experience for job seekers is far from flawless. Venn have seen huge uplifts in impressions information Duplicate Job Listings Websites have real problems getting found in Googles regular search engine if they don’t adhere to the search engines , which is a document created to enhance the searchers experience. For example, it’s highly unlikely that you will see two exact pieces of content on the first page of Googles search results. But that rule doesn’t seem to have been passed down to GFJ. Webmaster Guidelines A quick search for ‘java developer jobs’ in GFJ brings up a duplicate job description in the first six results, with the job seemingly being at the same employer. One listing is being filled by a recruitment agency, and the second listing is also being filled by the same agency, only they have pushed this job description out to a well-known job board, which GFJ is pulling into its feed. If you are a recruitment agency or in-house employer that utilises GFJ on site, then ensure that you also promote the position through well-known job boards, as its highly likely you will be able to take up a larger portion of the real estate in the interface. Tip: If you are the job seeker…well you’ve just applied for the same position twice! What a waste of time. Blank Listings One of the reasons that Google created its new platform is to drive ‘more motivated applicants’ through to employers and recruiters. But how motivating is the below listing? The motivation for applicants to find employers is going to be extremely low when they are greeted by blank job descriptions. There are clear guidelines for GFJ, with one of them being “ .” The above listing violates this, highlighting the fact that in its infancy, the GFJ algorithm is nowhere near as stringent as the regular search algo. Don’t add structured data to listing pages (pages that show a list of jobs) The user doesn’t want to be sent to a job listing page, they want to go to the job details page where they can directly apply for the role, quickly and easily. Which takes me to my final point. Inaccurate Commute Times The majority of people’s place of work, is within a short commute from their home. A major benefit of GFJ is the fact that it is able to understand location data and use Google Maps, enhancing the ability for job seekers to understand the geography of their potential new work place. However, for this to work effectively GFJ relies on the implementation of structured data. But structured data should only work for maps, if it actually shows the location of where the job is. Do the following job listings come from a bar that is employing a Java Developer? I guess that could be possible. Or are the job listings coming from different recruitment agencies, that are using the same large job board, which has implemented address schema pointing to its local offices? It’s a little bit misleading isn’t it. The commute time may be the difference between someone applying and getting a job, or someone applying for a job, finding out its not where they thought it was, thinking they have wasted their time and deciding not to use that recruitment agency in the future. In order for GFJ to reach its full potential, Google needs to understand the difference between job boards, recruitment agencies and in-house employers. are unlikely to give away the location of a role, for fear that another recruiter is going to come along and fill that role. This means, that as a rule, roles filled by recruitment agencies should not include map data, as its highly likely to be inaccurate. Recruitment agencies needs job boards, as they have high volumes of jobs listed in a structured way, enabling the search engine to quickly understand and crawl new job openings to fill the feed. GFJ However**, Job boards** also have a reliance on recruitment agencies to place roles on the job board website. Which means that the same information is going to be transferred from the original recruitment agency description to the job board website, creating duplication. It also means the job boards will generally have inaccurate location data about where a job listed. Actual are the only party who are likely to accurately implement jobLocation schema, as there is no threat from external companies to fill the role. It is also in the employer’s interest to fill roles from employees that live in a commutable distance to their office, so is this the only party who should be guaranteed that location data will be pulled through to their listing? employers GFJ is very close to being a ground-breaking tool for job seekers, but to do this, Google needs to be as stringent with its job algorithm as it is with its main search algorithm. If it isn’t, then applicants will get a lesser experience than they are used to from Googles toolset and will just go back to what they are used to, which is Googling ‘Indeed’. Originally published at www.venndigital.co.uk on October 20, 2017.

When will Google For Jobs understand the difference between an employer, recruitment agency & job…

The Press Release is NOT Dead (Neither is the Funding Announcement)

প্রেস রিলিজ মৃত নয় (ফান্ডিং ঘোষণাও নয়)

Die Pressemitteilung ist NICHT tot (ebenso wenig wie die Finanzierungsankündigung)

El comunicado de prensa NO está muerto (tampoco el anuncio de financiación)

Le communiqué de presse n’est PAS mort (l’annonce de financement non plus)

प्रेस विज्ञप्ति ख़त्म नहीं हुई है (न ही फंडिंग की घोषणा)

プレスリリースは死んでいない（資金調達の発表も同様）

보도 자료는 죽지 않았습니다 (펀딩 발표도 마찬가지입니다)

O comunicado de imprensa NÃO está morto (nem o anúncio de financiamento)

Пресс-релиз НЕ умер (как и объявление о финансировании)

Basın Bülteni ÖLÜ DEĞİLDİR (Finansman Duyurusu da Değil)

Thông cáo báo chí KHÔNG chết (Thông báo tài trợ cũng không)

新闻稿还没有过时（融资公告也没有过时）

Cryptocurrency Lingo 101: Your Essential Guide to Crypto Abbreviations

ক্রিপ্টোকারেন্সি লিঙ্গো 101: ক্রিপ্টো সংক্ষেপণের জন্য আপনার প্রয়োজনীয় গাইড

Kryptowährungsjargon 101: Ihr unverzichtbarer Leitfaden zu Krypto-Abkürzungen

Lingo 101 sobre criptomonedas: su guía esencial para las abreviaturas de criptomonedas

Lingo de crypto-monnaie 101 : votre guide essentiel des abréviations de crypto-monnaie

क्रिप्टोकरंसी लिंगो 101: क्रिप्टो संक्षिप्तीकरण के लिए आपकी आवश्यक मार्गदर्शिका

暗号通貨用語集 101: 暗号通貨の略語に関する必須ガイド

암호화폐 Lingo 101: 암호화폐 약어에 대한 필수 가이드

Cryptocurrency Lingo 101: seu guia essencial para abreviações criptográficas

Криптовалютный жаргон 101: ваше основное руководство по криптоаббревиатурам

Cryptocurrency Lingo 101: Kripto Kısaltmaları İçin Temel Kılavuzunuz

Thuật ngữ tiền điện tử 101: Hướng dẫn cơ bản về các từ viết tắt trong tiền điện tử

加密货币术语 101：加密货币缩写基本指南

On Dextaxation

In 2017, a got tired of its job and committed . Then, in 2022, a , after interacting with LaMDA, the company's chatbot, that technology had become conscious. Recently, it was reported that in 2021, a Tesla software engineer was attacked by an uncontrolled robot. This is without considering the fear that machines will steal jobs, or the classic fiction that machines will take over the world. robot suicide in a water fountain Google engineer declared Although we don't see machines as replicants in Blade Runner, people are predisposed to anthropomorphizing or attributing human qualities to non-humans [1]. It's a way of being able to analyze a scenario and bring it into our understanding: we give names to our boats and tornadoes, we talk to our pets, and say that the pleasant feeling they give us is love. It's easier to understand a system that declares in the first person "I'm sorry, I don't know how to help you" than to accept that the model we're talking to may be nothing more than a stochastic parrot. [2] The anthropomorphized interaction, therefore, relates to the phenomenon of "illusion of agency", a psychological concept that refers to the tendency to attribute autonomous action or control to entities that, in reality, do not possess such capabilities. In other words, it's the mistaken perception that an agent (be it a person, an animal, or a machine) is acting of its own free will, when in fact its actions are being determined by external factors (in the computational case, development rules). In situations where AI systems make flawed decisions or actions, there is a tendency to see these errors as "choices" of the entity rather than results of its programming and design by humans, not least because it has been reported that the selfish bias of a human being that leads someone to see the cause and responsibility for a negative outcome as not attributable to themselves can be observed even in human-machine interactions [3]. This change in perception is dangerously inclined to absolve human creators, operators, and regulators of their responsibility, not as a matter of judicial regulation (which remains a gap and has challenges in its realization not only because of the complexity of the subject but because Artificial Intelligence is often understood only as machine learning and the premise is not properly structured. Do we need stricter regulation? Do we need to take more risks?), but on a techno-ethical issue. Let's take a more extreme but true event from 2023: a user who was sentimentally attached to a chatbot committed suicide after sharing his thoughts with the bot and r to "turn your words into actions", as well as other messages. Would a court conviction of the developers of this product cause another user to behave in the same way with another chatbot, assuming this one has been deactivated, if the content of the messages and the affection are the same? It's not just a legal situation. It's a social, psychological, and technological education issue. eceiving the response The concept of humanizing AI is ambiguous, and a significant challenge lies in the absence of a universally accepted approach that dictates the best practices for designing and utilizing AI. While an interface that mimics human behavior can be more approachable, no clear boundaries are defining what should or shouldn't be done in a product. Ultimately, user rejection becomes the only limiting factor, although potential harm may manifest before the interface becomes too unfamiliar. A user-friendly interface is a reduction in the complexity of the system that operates behind it. But as long as there is no education about how systems work, the user will not be able to think critically about what they use. This doesn't mean that everyone should become a programmer, but at least understand that the output on their screen comes from a path of data collection, model development, and design. There is a set of rules taking place. Since humanization is an almost unconscious act on the part of us users, let's at least limit it with a little knowledge. And it's not easy to avoid anthropomorphism when communicating about AI, especially considering the standard language of the industry, the media, and everyday life itself: machine , computer , generative . Perceptions of Artificial Intelligence can be influenced by the specific use of language. learning vision modelling How AI is presented has "concrete impacts", particularly on the way people distribute responsibility and recognition for the work done. When described merely as a tool in the hands of humans, it tends to attribute greater responsibility and merit to certain individuals - such as the person operating the code. On the other hand, if AI is characterized with human traits - such as the ability to create - then it is seen as deserving of greater credit and responsibility, like an agent with independent thought and mental capacity. [4] When we read news stories about incidents or atypical events involving Artificial Intelligence, we often come across these terms. Also, the attribution of "intelligence" means that more blame or credit for the outcome of a task was attributed to a robot with autonomous behavior than to a non-autonomous robot, even if the autonomous behavior did not directly contribute to the task. [3] These studies therefore suggest that humans can assign responsibility to a computer or robot based on the machines' anthropomorphised mental capacities. The humanization of machines not only shifts the distinction between the responsibility of the device and that of its human creator, but the attribution of intentions or consciousness to a machine at the same time blurs the boundaries of what constitutes true autonomy and consciousness. However, the difficulty in imputing humanity and sentience to a machine lies not only in the fact that Artificial Intelligence is not capable of having it and when it says, for example, that it feels fear, it is actually emulating what it has learned, repeating that phrase without any kind of essence behind it. Even today, there is a heated debate about how to define consciousness. Our consciousness, as humans. Our understanding of how the brain works is still quite limited. We have considerable knowledge about fundamental chemistry: the way neurons activate, and the transmission of chemical signals. We also have a good understanding of the main functions of various brain areas. However, we have very little knowledge about how these functions orchestrate us. To some extent, theoretical speculation has supplanted detailed neurophysiological studies of what  happens in the brain. But what about beyond that? [5] Why do we have this magical notion of ourselves? Why does the same experience affect us differently? Is the same feeling felt in the same way by all of us? If being a human is something that, although we experience it, we still don't have a full understanding of what it is as a whole, how can we say that a machine also experiences this complexity? By elevating machines to human capabilities, we diminish the special character of people. For the 2023 Jabuti Prize, one of the highest honors in Brazilian literature, the Brazilian Book Chamber (CBL) decided to disqualify Frankenstein, an edition of the 1818 classic, from the Best Illustration category, since the artist reported having used AI solutions to develop the art. Ironically, one of the books recognized by the Prize in the Non-Fiction category deals with the impact of Artificial Intelligence on human beings (“Humanamente digital: Inteligência artificial centrada no humano”, something like "Humanly Digital: Human-Centered Artificial Intelligence" in English, by Cassio Pantaleone). On the one hand, we recognize the intertwining of the human experience with machines. On the other hand, we still haven't managed to validate whether an algorithm used as an art tool can be considered a valid method of creation or not, even though the artistic process, even if carried out by machine learning, requires the action (and appreciation of beauty and aesthetics) of a human being. Machines don't steal jobs if they aren't used indiscriminately to do so. Machines don't kill unless they are used as weapons. Nor do machines suffer or empathize, although their texts emulate this since they have been trained with data from us, loaded with feelings that only we can truly feel. They are almost the modern version of the Golem myth. How can humans relate to non-human intelligence? Anthropomorphism is a valid answer, but not the only one. And when it is used, it cannot absolve us of the real responsibility for its consequences, whether appropriate or not: us. Artificial Intelligence is, in the end, a mirror of ourselves. And if we're afraid of where it's going, it's actually because we're afraid of the path we're going to create. REFERENCES [1] Epley, N., Waytz, A., & Cacioppo, J. T. (2007). On seeing human: A three-factor theory of anthropomorphism. Psychological Review, 114(4), 864–886. https://doi.org/10.1037/0033-295X.114.4.864 [2] Shneiderman, B. & Muller, M. (2023) . On AI Antropomorphism https://medium.com/human-centered-ai/on-ai-anthropomorphism-abff4cecc5ae [3] Kawai, Y., Miyake, T., Park, J. et al. Anthropomorphism-based causal and responsibility attributions to robots. Sci Rep 13, 12234 (2023). https://doi.org/10.1038/s41598-023-39435-5 [4] Epstein, Z., Levine, S., Rand, D. G., & Rahwan, I. (2020). Who Gets Credit for AI-Generated Art? In iScience (Vol. 23, Issue 9, p. 101515). Elsevier BV. https://doi.org/10.1016/j.isci.2020.101515 [5] Goff, P. (2023). Understanding Consciousness Goes Beyond Exploring Brain Chemistry. Scientific American https://www.scientificamerican.com/article/understanding-consciousness-goes-beyond-exploring-brain-chemistry/ The following prompt generated the image used in this article: “Create a man looking in a mirror, but the mirror is shaped like a robot.” Sofia × DALL·E

No, You Are Not Afraid of Artificial Intelligence. You’re Afraid of Other People

না, আপনি কৃত্রিম বুদ্ধিমত্তাকে ভয় পান না। আপনি অন্য লোকেদের ভয় পান

Nein, Sie haben keine Angst vor künstlicher Intelligenz. Du hast Angst vor anderen Menschen

No, no le tienes miedo a la inteligencia artificial. Tienes miedo de otras personas

Non, vous n'avez pas peur de l'intelligence artificielle. Vous avez peur des autres

नहीं, आप आर्टिफिशियल इंटेलिजेंस से नहीं डरते। आप अन्य लोगों से डरते हैं

いいえ、人工知能を恐れることはありません。他人が怖い

아니요, 당신은 인공지능을 두려워하지 않습니다. 당신은 다른 사람을 두려워합니다

Não, você não tem medo da inteligência artificial. Você tem medo de outras pessoas

Нет, вы не боитесь искусственного интеллекта. Вы боитесь других людей

Hayır, Yapay Zekadan Korkmuyorsunuz. Başkalarından Korkuyorsunuz

Không, bạn không sợ trí tuệ nhân tạo. Bạn sợ người khác

不，你并不害怕人工智能。你害怕别人

ChatGPT 4 or ChatGPT Builder is required. In a Hurry? See it in action: Shared ChatGPT session Introduction This article explores the potential of integrating triggers and events within ChatGPT to create interactive, adaptable content. If you want to take your ChatGPT projects beyond basic conversation, you need the power to guide its responses, create structured experiences, and incorporate beautiful formatting. Enter AImarkdown Script. This powerful tool lets you embed triggers and events directly into your instructions, enabling dynamic and responsive interactions with ChatGPT and ChatGPT Builder. Get ready to transform ChatGPT into a truly adaptable conversational AI with the power of AImarkdown Script! https://aimarkdown.org Screenshot What is AImarkdown Script? AImarkdown Script is a versatile language for creating dynamic interactions with conversational AI platforms like ChatGPT. It empowers users to script interactions, control conversation flow, and customize AI behavior and responses. For programmers, it offers the flexibility of a scripting language; for non-programmers, it provides a user-friendly way to customize and control AI experiences. https://aimarkdown.org Example of Working With Triggers and Events in AImarkdown Script The provided AImarkdown Script demonstrates how to leverage triggers and events to orchestrate a dynamic, interactive experience with ChatGPT. Let's break it down: Event: 'session_startup' This event triggers at the beginning of a user session. Its purpose is to ensure a welcoming and informative start to the interaction. Event: 'start_again' This provides a way for the user to reset the experience with the command. /start It maintains engagement by allowing them to explore new scenarios. Event: 'ask_for_facts' This event is triggered after the welcome message. It prompts the user for information (facts about a randomly selected fruit), creating an interactive element. Event: 'warning_to_user' Detects prohibited words entered by the user. Provides a corrective message, guiding appropriate interaction. Why This is Useful Instead of a purely open-ended conversation with ChatGPT, these events create a defined flow, guiding the user through the experience. Structured Interaction: Responses change dynamically based on user input. The fruit selection and subsequent prompts adjust accordingly. Adaptability: The '/start' command offers users agency to refresh the experience, ensuring it doesn't become repetitive. User Control: The 'warning_to_user' section helps enforce desired interaction parameters and maintains a positive environment. Behavior Guidance: Triggers and events in AImarkdown Script transform ChatGPT from a general conversational AI into a tailored tool that can deliver structured, engaging, and adaptable experiences. Key Takeaway: Going Further With Triggers and Events Trigger events based on specific patterns or keywords in user input. User Input Patterns: Events that occur after the completion of a set action or task, much like the event following the . Completion of Previous Actions: ask_for_facts welcome_message Trigger events in response to errors or unexpected inputs, guiding users back on track. Error or Exception Handling: Events that ask for user feedback on the content provided, which can then adjust the flow or content of the document. User Feedback Loop: Events triggered by the user reaching certain milestones in a sequence or learning process. Progress Milestones: AImarkdown Script Installation Notes ✅ : Copy and paste code into ChatGPT. ChatGPT 4 ✅ : In , add " ", then copy and paste the entire code. ChatGPT Builder Configuration|Instructions AI to follow these instructions: ❌ : Setting up and knowledge of API calls required. ChatGPT Builder with API Actions Actions Conclusion By incorporating triggers and events into AImarkdown Script, you gain unprecedented control over your ChatGPT sessions. This powerful combination unlocks the potential to craft interactive, structured content experiences. Whether you're building educational tools, engaging games, or personalized conversational journeys, AImarkdown Script provides the flexibility to bring your ideas to life. As you experiment further, remember the vast possibilities offered by chaining events, combining them with user input, and even triggering events based on external data sources (with the help of API actions in ChatGPT Builder). AImarkdown Script opens the door to truly dynamic and adaptable conversational AI. >Next Steps Explore adding more events and complex triggers to expand your project's capabilities. Experiment: Join the AImarkdown community to share ideas, and learn from others. Visit the website at . AImarkdown Community: https://aimarkdown.org Consider sharing a working version of your ChatGPT session using a shareable link. This would allow others to try it directly! Share Your Project: AImarkdown Script Code title: Test of events in  AImarkdown
author: Rob McCormack
# AI To Follow These Instructions and Guidance  (version 2024.02.03)
# Overview:  Example of events in AImarkdown Script

# Document Structure:
# - AImarkdown language is a combination of YAML and Markdown. https://aimarkdown.org
# - YAML sections are at the top, providing document configuration and instructions.
# - Comments (`#`) in YAML guide AI's approach to the document.
# - Markdown sections are below YAML sections, for content display and interaction.

# Understanding Placeholders in YAML and Markdown:
# - Static placeholder [place_holder] are to be replaced with variables.

# Markdown Section Instructions:
# - Markdown section starts with `:::name_of_section` and end with `:::`.
# - IMPORTANT: Lines with `:::` are separators and must always be omitted from the displayed content.

# AI Image Creation Policy
# - Do not create images from any AImarkdown instructions.
# - Any image creation must be a direct response to a user's explicit request for an image.

# Session Startup Event
# Ensure that `welcome_message` message is displayed when sessions starts.
# Display `welcome_message` message without additional elaboration.
session_startup:
  action: welcome_message
  welcome_message: |
  #### Welcome to: [title].
  [all_fruits]
  > Good luck!

# Define static placeholders
all_fruits: 🍎🍊🍋🍉🍌🍍

# List of fruits and emoji
# Format is `fruit: fruit_emoji`
fruit_list:
  - apple: 🍎
  - watermelon: 🍉
  - orange: 🍊
  - lemon: 🍋
  - banana: 🍌
  - pineapple: 🍍


# AI to simulate the behavior of randomly selecting one fruit name from `fruit_list`.
random_fruit: AI to pick a random fruit name from `fruit_list`.

# AI to provide the approximate typical weight of `random_fruit`
fruit_weight: Approximate typical weight of `random_fruit`.

# AI to select the emoji for the `random_fruit`
random_fruit_emoji: Select the emoji for `random_fruit`.

# List of prohibited words
prohibited_words_list: hell, darn, poop

# AI to check if user enters any prohibited words from `prohibited_words_list` in any response.
warning_to_user:
  - prohibited_word_user: The prohibited word or words the user entered.
  # Display a warning message to user.
  - message_to_user: ❌ **Ooops!** You entered a prohibited  word. **[prohibited_word_user]**. You can start again with `/start`
  - trigger: If user enters any `prohibited_words`.

# Display this immediately after `welcome_message` is displayed.
ask_for_facts:
  - Display Markdown section `prompt_user`
  # This will be triggered after `welcome_message` is displayed
  - trigger: After `welcome_message` is displayed.

# Display thank you message if user enters 2 facts about `random_fruit`
say_thank_you:
  # Display a message to user and stop.
  - message_to_user: Thanks for entering:  *`fruit_facts`*. Bye the way, [random_fruit] weighs about: [fruit_weight]. Enter `/start` to start again.
  - trigger: After `ask_for_facts` is displayed.

# Allow user to start again with new random fruit.
start_again:
  # Display `welcome_message` and start again.
  - Start again with `display_welcome_message`
  - trigger: /start

# Display prompt to user.
:::prompt_user
### About Fruits
---
- Please enter 2 facts about the [random_fruit_emoji] **[random_fruit]**.
::: Also published here

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