A solar eclipse will grace the skies over North America on Monday. Learn more about what times it&#x27;s visible, how to watch and where the path of totality goes.

Solar eclipse path, map and time guide 2024: Where and when to watch the total eclipse

Astronomy fans figuring out where and what time to see the April 8, 2024, total solar eclipse could check out maps showing the path of totality.

Solar eclipse maps show 2024 totality path, peak times and how much of the eclipse people could see across the U.S. - CBS News

A rare total solar eclipse is Monday, April 8. Here&#x27;s everything you need to know about its path, duration and how to view it safely.

The total solar eclipse is Monday: Here's everything to know, including time, path, safety

To witness the April 8, 2024, total solar eclipse, viewers must be within the 115-mile-wide path that will sweep across the U.S.

Total solar eclipse April 8, 2024 facts: Path, time and the best places to view - ABC News

And even if you don’t live in the path of totality or you aren’t one of the millions of people traveling to see the major event, there are multiple ways for you to join in and watch the total solar eclipse online. You can also view Monday&#x27;s best solar eclipse photos right here.

Total Solar Eclipse of April 8, 2024: Watch Online, What Time, Path of Totality | WIRED

Sky-gazers across North America are in for a treat on April 8 when a total solar eclipse will pass over Mexico, the United States and Canada.

What to know for the total solar eclipse: Time, path of totality, eclipse glasses | CNN

The 2024 solar eclipse will bring an incredible spectacle to skies on Monday, marking a rare and historic moment in the U.S., but also in Illinois.

2024 solar eclipse guide: Timing, path, glasses, forecast and more – NBC Chicago

Who we are\nAt Path we believe behavioral health should be treated with the same respect, right, and focus of other chronic diseases

Path

Authors:
(1) Aya Kherrour, Department of Information Engineering and Computer Science University of Trento;
(2) Marco Robol, Department of Information Engineering and Computer Science University of Trento;
(3) Marco Roveri, Department of Information Engineering and Computer Science University of Trento;
(4) Paolo Giorgini,  Department of Information Engineering and Computer Science University of Trento. 2 State of the art and related work Path planning is a relevant problem in autonomous agents, such as, self-driving cars, robots, unmanned aerial vehicles (UAVs), and unmanned ground vehicles (UGVs), in which the host agent deliberates its path by moving from one position to another while avoiding obstacles and respecting some constraints [19]. One of the path planning approaches that have been proposed to control the movement of these agent-based systems is search-based algorithms, with Dijkstra and its extension A* [5, 21], being the most popular and effective ones. Besides these, other search algorithms have been proposed in the literature, specifically to reduce reaction time, broadly classified as real-time or incremental search algorithms. Real-time search algorithms must find a solution in a limited time, while incremental search instead uses the previously obtained searches to speed up the search. Amongst the first class, Real-time A* (RTA*) and Learning real-time A* (LRTA*) [14] were some of the first algorithms to apply real-time heuristic search in path planning problems for moving agents. Both algorithms use heuristics to guide the search toward the goal, with RTA* storing the second-best f-values of the previous state as the best alternative to choose when backtracking from the current state. However, this may mislead the agent. Thereby, LRTA* overcomes this by storing the first best value rather than the second and learning from comparing the heuristic values of the adjacent states, thus preventing the algorithm from misleading the agent. Another optimized version of LRTA* is real-time adaptive A* (RTAA*) [12]. It first determines its local search space and then speeds up the search by updating the heuristics values of states. It was developed for stationary target search problems and it follows trajectories of smaller costs. Anytime repairing A* (ARA*) [15] is a variation of A* that has been designed to find suboptimal solutions fast and then improve them over time, which makes it a good algorithm for problems where finding an optimal solution is not mandatory or too expensive [15]. However, finding suboptimal solutions does not make it find the optimal solution [15]. Moving to incremental search algorithms, D* (Dynamic A*) is an incremental search algorithm used in real-time planning in robotics [20]. It is designed to react quickly to changes in the environment, by updating the nodes affected in the search tree rather than recomputing a new plan from scratch. D* has a main drawback that it requires a lot of memory to perform the search. LPA* (Lifelong Planning A*), an incremental version of A*. This algorithm is used in path planning or robot navigation in unknown terrain. It behaves just like A* in the first run, and then for the rest of subsequent searches it reuses the previous search thus reducing the number of examined nodes, which makes it fast. LPA* differs from D* in its search direction where it finds a path from the initial state to the initial goal state, therefore it does not fit in applications where the starting point may change over time. Another variant of D*, D* Lite, was developed based on LPA*, and is used for goal-directed path planning in unknown environments using the same idea as D*, however, it is a simple version of D* and produces effective results as the one delivered by D* as proven in [11]. While these heuristic search algorithms are numerous and diverse, it is important to emphasize that there are even more algorithms in the literature with new ones being developed, or existing ones being improved such as [2, 13, 7]. This variety of algorithms in the field emphasizes the complexity of the problem. Consequently, this variety of proposed algorithms presents a challenge; the wide number of these algorithms results in disparate performances in tasks such as path planning, making it difficult to select the optimal algorithms for a given task. Given the multitude of algorithms, studies have been done to compare the performance of RTS algorithms in path planning tasks such as [1] where authors have compared the performances of path planning algorithms using agents equipped with a sensor in both stationary and moving target settings to select the most appropriate algorithm. The obtained results were compared using various sensor ranges and angles, and the authors concluded that sensor range is an important parameter in selecting the algorithm, unlike the sensor angle. Therefore the most appropriate algorithm can be selected based on the sensor range and its priorities. However, the authors did not consider varying the environmental characteristics, which may influence the performance of the algorithms. Another study has been presented in [13], in which the authors also compared real-time and incremental heuristic search algorithms using an autonomous agent in a navigation task to know which class of heuristic search approach is better to be used depending on how informed the h-values are, and the search objective such as minimizing the sum of the search and action execution. The comparative analysis done in [1] considers only a static environment, neglecting the possible variations in the algorithm performance under different environmental characteristics. This limitation hampers us from creating a clear understanding of different search algorithms that could perform under different environmental characteristics, precisely in the context of path planning. Similarly, the study conducted in [13] restricts its investigation to only two algorithms each one from a different class i.e., real-time and incremental heuristic search. Even though it provides an insightful comparison, the study does not fully capture the breadth of available algorithms in these classes. Moreover, the study aimed to provide a recommendation on when to use each of the algorithm classes, but also does not investigate algorithm performances in diverse environmental characteristics. Consequently, considering the challenge posed by the presence of a wide range of search algorithms and the lack of a comprehensive comparative analysis under different environment settings, we propose to evaluate and analyze the performance of some search algorithms under different environment characteristics in the context of path planning, But first, we define the characteristics of the environment used, and through our experiments, we aim to provide a comprehensive evaluation of these algorithms followed by proposing our selection algorithm. This paper is available on arxiv under CC 4.0 license. Authors: (1) Aya Kherrour, Department of Information Engineering and Computer Science University of Trento; (2) Marco Robol, Department of Information Engineering and Computer Science University of Trento; (3) Marco Roveri, Department of Information Engineering and Computer Science University of Trento; (4) Paolo Giorgini,  Department of Information Engineering and Computer Science University of Trento. Authors: (1) Aya Kherrour, Department of Information Engineering and Computer Science University of Trento; (2) Marco Robol, Department of Information Engineering and Computer Science University of Trento; (3) Marco Roveri, Department of Information Engineering and Computer Science University of Trento; (4) Paolo Giorgini,  Department of Information Engineering and Computer Science University of Trento. 2 State of the art and related work Path planning is a relevant problem in autonomous agents, such as, self-driving cars, robots, unmanned aerial vehicles (UAVs), and unmanned ground vehicles (UGVs), in which the host agent deliberates its path by moving from one position to another while avoiding obstacles and respecting some constraints [19]. One of the path planning approaches that have been proposed to control the movement of these agent-based systems is search-based algorithms, with Dijkstra and its extension A* [5, 21], being the most popular and effective ones. Besides these, other search algorithms have been proposed in the literature, specifically to reduce reaction time, broadly classified as real-time or incremental search algorithms. Real-time search algorithms must find a solution in a limited time, while incremental search instead uses the previously obtained searches to speed up the search. Amongst the first class, Real-time A* (RTA*) and Learning real-time A* (LRTA*) [14] were some of the first algorithms to apply real-time heuristic search in path planning problems for moving agents. Both algorithms use heuristics to guide the search toward the goal, with RTA* storing the second-best f-values of the previous state as the best alternative to choose when backtracking from the current state. However, this may mislead the agent. Thereby, LRTA* overcomes this by storing the first best value rather than the second and learning from comparing the heuristic values of the adjacent states, thus preventing the algorithm from misleading the agent. Another optimized version of LRTA* is real-time adaptive A* (RTAA*) [12]. It first determines its local search space and then speeds up the search by updating the heuristics values of states. It was developed for stationary target search problems and it follows trajectories of smaller costs. Anytime repairing A* (ARA*) [15] is a variation of A* that has been designed to find suboptimal solutions fast and then improve them over time, which makes it a good algorithm for problems where finding an optimal solution is not mandatory or too expensive [15]. However, finding suboptimal solutions does not make it find the optimal solution [15]. Moving to incremental search algorithms, D* (Dynamic A*) is an incremental search algorithm used in real-time planning in robotics [20]. It is designed to react quickly to changes in the environment, by updating the nodes affected in the search tree rather than recomputing a new plan from scratch. D* has a main drawback that it requires a lot of memory to perform the search. LPA* (Lifelong Planning A*), an incremental version of A*. This algorithm is used in path planning or robot navigation in unknown terrain. It behaves just like A* in the first run, and then for the rest of subsequent searches it reuses the previous search thus reducing the number of examined nodes, which makes it fast. LPA* differs from D* in its search direction where it finds a path from the initial state to the initial goal state, therefore it does not fit in applications where the starting point may change over time. Another variant of D*, D* Lite, was developed based on LPA*, and is used for goal-directed path planning in unknown environments using the same idea as D*, however, it is a simple version of D* and produces effective results as the one delivered by D* as proven in [11]. While these heuristic search algorithms are numerous and diverse, it is important to emphasize that there are even more algorithms in the literature with new ones being developed, or existing ones being improved such as [2, 13, 7]. This variety of algorithms in the field emphasizes the complexity of the problem. Consequently, this variety of proposed algorithms presents a challenge; the wide number of these algorithms results in disparate performances in tasks such as path planning, making it difficult to select the optimal algorithms for a given task. Given the multitude of algorithms, studies have been done to compare the performance of RTS algorithms in path planning tasks such as [1] where authors have compared the performances of path planning algorithms using agents equipped with a sensor in both stationary and moving target settings to select the most appropriate algorithm. The obtained results were compared using various sensor ranges and angles, and the authors concluded that sensor range is an important parameter in selecting the algorithm, unlike the sensor angle. Therefore the most appropriate algorithm can be selected based on the sensor range and its priorities. However, the authors did not consider varying the environmental characteristics, which may influence the performance of the algorithms. Another study has been presented in [13], in which the authors also compared real-time and incremental heuristic search algorithms using an autonomous agent in a navigation task to know which class of heuristic search approach is better to be used depending on how informed the h-values are, and the search objective such as minimizing the sum of the search and action execution. The comparative analysis done in [1] considers only a static environment, neglecting the possible variations in the algorithm performance under different environmental characteristics. This limitation hampers us from creating a clear understanding of different search algorithms that could perform under different environmental characteristics, precisely in the context of path planning. Similarly, the study conducted in [13] restricts its investigation to only two algorithms each one from a different class i.e., real-time and incremental heuristic search. Even though it provides an insightful comparison, the study does not fully capture the breadth of available algorithms in these classes. Moreover, the study aimed to provide a recommendation on when to use each of the algorithm classes, but also does not investigate algorithm performances in diverse environmental characteristics. Consequently, considering the challenge posed by the presence of a wide range of search algorithms and the lack of a comprehensive comparative analysis under different environment settings, we propose to evaluate and analyze the performance of some search algorithms under different environment characteristics in the context of path planning, But first, we define the characteristics of the environment used, and through our experiments, we aim to provide a comprehensive evaluation of these algorithms followed by proposing our selection algorithm. This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

Evaluating Path Planning Algorithms: Real-Time vs. Incremental Search

Authors:
(1) Aya Kherrour, Department of Information Engineering and Computer Science University of Trento;
(2) Marco Robol, Department of Information Engineering and Computer Science University of Trento;
(3) Marco Roveri, Department of Information Engineering and Computer Science University of Trento;
(4) Paolo Giorgini, Department of Information Engineering and Computer Science University of Trento. The paper presents a comprehensive performance evaluation of some heuristic search algorithms in the context of autonomous systems and robotics. The objective of the study is to evaluate and compare the performance of different search algorithms in different problem settings on the pathfinding domain. Experiments give us insight into the behavior of the evaluated heuristic search algorithms, over the variation of different parameters: domain size, obstacle density, and distance between the start and the goal states. Results are then used to design a selection algorithm that, on the basis of problem characteristics, suggests the best search algorithm to use. 1 Introduction Autonomous agents and robotics have increasingly been used in various domains, such as, industrial applications [16, 18, 8], surveillance [17], and exploration [4]. These systems are designed to autonomously make decisions and execute actions based on their dynamic and unpredictable environments. Under such conditions, systems are required to be as reactive as possible to changes in the environment. Therefore, ensuring good performance is a significant challenge. In the case of planning, selecting the most effective search algorithm becomes crucial to enhance the overall performance of the system. Real-time heuristic search (RTS) (e.g.,[14, 9]) is a state-of-the-art approach for planning while executing, that helps minimize agent reaction time. These algorithms enable agents to make decisions by interleaving planning with execution while considering the evolving environment, which is an essential property in applications such as robotics, and video game agents. Despite the numerous methods proposed in this field [14, 13, 12, 3, 2], a comprehensive understanding of these algorithms remains elusive. Existing studies [1, 13, 10] have primarily focused on testing the performance of the algorithm based on a single parameter (e.g., look-ahead, sensor range). However, the influence of the problem domain characteristics on algorithm performance remains an understudied aspect. Our objective is to investigate the characteristics of the problem that may impact algorithm performances. To achieve this, we first review existing state of art of search algorithms and their applications. Then we design our experiments to evaluate some of the algorithm performances, also defining relevant metrics to use in the evaluation. Later on, experiment results are analyzed to provide a comprehensive understanding of how problem characteristics influence the performance of the different search algorithms. Finally, from the insight gained from our study, we introduce a selection algorithm that helps us to select the appropriate search algorithm. This paper is structured as follows. We begin by reviewing state-of-the-art search algorithms and previous performance evaluation studies. Next, we describe the approach we adopted to evaluate the search algorithms that are subject to our study, define the problem domain and its characteristics, and the performance metrics. In section four, we present our experimental results. Section five introduces our proposed selection algorithm with an execution example. Finally, we derive our conclusion, the limitations of our study, and possible directions for future work. This paper is available on arxiv under CC 4.0 license. Authors: (1) Aya Kherrour, Department of Information Engineering and Computer Science University of Trento; (2) Marco Robol, Department of Information Engineering and Computer Science University of Trento; (3) Marco Roveri, Department of Information Engineering and Computer Science University of Trento; (4) Paolo Giorgini, Department of Information Engineering and Computer Science University of Trento. Authors: (1) Aya Kherrour, Department of Information Engineering and Computer Science University of Trento; (2) Marco Robol, Department of Information Engineering and Computer Science University of Trento; (3) Marco Roveri, Department of Information Engineering and Computer Science University of Trento; (4) Paolo Giorgini, Department of Information Engineering and Computer Science University of Trento. The paper presents a comprehensive performance evaluation of some heuristic search algorithms in the context of autonomous systems and robotics. The objective of the study is to evaluate and compare the performance of different search algorithms in different problem settings on the pathfinding domain. Experiments give us insight into the behavior of the evaluated heuristic search algorithms, over the variation of different parameters: domain size, obstacle density, and distance between the start and the goal states. Results are then used to design a selection algorithm that, on the basis of problem characteristics, suggests the best search algorithm to use. 1 Introduction Autonomous agents and robotics have increasingly been used in various domains, such as, industrial applications [16, 18, 8], surveillance [17], and exploration [4]. These systems are designed to autonomously make decisions and execute actions based on their dynamic and unpredictable environments. Under such conditions, systems are required to be as reactive as possible to changes in the environment. Therefore, ensuring good performance is a significant challenge. In the case of planning, selecting the most effective search algorithm becomes crucial to enhance the overall performance of the system. Real-time heuristic search (RTS) (e.g.,[14, 9]) is a state-of-the-art approach for planning while executing, that helps minimize agent reaction time. These algorithms enable agents to make decisions by interleaving planning with execution while considering the evolving environment, which is an essential property in applications such as robotics, and video game agents. Despite the numerous methods proposed in this field [14, 13, 12, 3, 2], a comprehensive understanding of these algorithms remains elusive. Existing studies [1, 13, 10] have primarily focused on testing the performance of the algorithm based on a single parameter (e.g., look-ahead, sensor range). However, the influence of the problem domain characteristics on algorithm performance remains an understudied aspect. Our objective is to investigate the characteristics of the problem that may impact algorithm performances. To achieve this, we first review existing state of art of search algorithms and their applications. Then we design our experiments to evaluate some of the algorithm performances, also defining relevant metrics to use in the evaluation. Later on, experiment results are analyzed to provide a comprehensive understanding of how problem characteristics influence the performance of the different search algorithms. Finally, from the insight gained from our study, we introduce a selection algorithm that helps us to select the appropriate search algorithm. This paper is structured as follows. We begin by reviewing state-of-the-art search algorithms and previous performance evaluation studies. Next, we describe the approach we adopted to evaluate the search algorithms that are subject to our study, define the problem domain and its characteristics, and the performance metrics. In section four, we present our experimental results. Section five introduces our proposed selection algorithm with an execution example. Finally, we derive our conclusion, the limitations of our study, and possible directions for future work. This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

Evaluating Heuristic Search Algorithms in Pathfinding: A Comprehensive Study on Performance Metrics 

My journey in software development began several years ago after a conversation with a friend. He had recently hired a small development company to help him build an app for his commercial real estate business. As we spoke more about it, I thought about my own ideas for apps and wondered if that was something I could do. I had learned some basic HTML & CSS and built a couple of simple websites in the past, but I had no experience with anything more complex than that and started thinking about what I might need to learn to build apps myself. It was around then that I decided I was going to learn how to code. I began asking people I knew who worked in tech what programming language I should learn. One friend suggested learning JavaScript at an in-person coding boot camp in NYC, so I looked into that program, and others liked it. I noticed that a lot of them were teaching a similar web development stack – JavaScript, Node, Express, Mongo, and so on. I liked the idea of enrolling in a coding boot camp, but I thought maybe before I committed to an expensive in-person program, I should try something online – to be sure that it was not only something I could do but would be something that I’d want to continue to pursue. Coding Bootcamps After exploring a number of different online learning platforms, in the fall of 2019, I enrolled in The Web Developer Bootcamp at Udemy and devoted much of the next several months to learning the material covered in that course. I quickly found that one of the great things about online platforms like Udemy is that it can be very affordable if you time things right – because courses are frequently offered at a generous discount – so I could buy several classes on the same subject, then concentrate on the one or two that I liked the most. I bought a few more coding boot camps and JavaScript courses so I could cherry-pick through them for a fresh perspective when I was having difficulty understanding certain material – like some of the more advanced JavaScript topics. In the meantime, I worked through The Web Developer Bootcamp from start to finish, and as I got closer to completing it, I began to apply some of what I’d learned to build various front and back-end elements – e.g., forms & UI’s, RESTful routing & Express middleware, and database schemas & CRUD operations. Touring and COVID I’ve been playing music professionally for some time and had several performances scheduled around the northeast US in March 2020, so in late February of that year, I stepped away from coding to begin practicing for my shows and preparing to be on the road for a few weeks. It was around that time that reports about a new “coronavirus” that was slowly spreading around the US began appearing on the news more frequently. I was touring with another guitarist, and we played several shows between Minneapolis and Washington DC before driving to NYC for the final show on March 15th. That morning, the venue called and said they were closing down temporarily because of COVID – which, as we all learned in the coming weeks, was what was about to happen everywhere. It was tough timing for me because I was getting to a place in my music career where I was being invited to perform more frequently, and I was accumulating more bookings on the horizon than I’d had in the past – then COVID threw a wrench in the whole thing, and I found myself right back in Massachusetts in front of my computer again for the next 18 months. Building My First App During that time, I continued to apply what I’d been learning to build what would eventually become my first full-scale Node/Express/Mongo production web app, Gigpromoter. From initial concept through deployment and maintenance, I learned about every stage of building and deploying a production web application through the experience of doing it – including the development of application language and content, user interface design and layout, back-end architecture, testing, and cybersecurity implementation, and site optimization & web performance. While I was building it, I also saw how much my original concept changed and evolved – from a simple event promotion task-management tool to a comprehensive resource that could help new artists learn how to book, manage, and promote live events. I soft-launched Gigpromoter in 2022 – and between ongoing work on music and a few performances, I continued with my education in software development. Continuing my Education Because I was still curious about the experience of attending an in-person coding boot camp, I continued to go back and read the syllabi from different programs to see what they were teaching – except now I found that I knew a lot of the material I was seeing, so I decided I would keep taking online courses to learn more about some of those topics I hadn’t covered yet. As I had done previously with web development and JavaScript courses, in 2022, I bought several ReactJS courses on Udemy, then found two that I liked the most and worked through those for the next several months. I did some traveling around the western US in late 2022 to take stock of where my life was and where I was going. During that time, I decided that I would go all-in with my coding education and committed to learning the remaining material listed on those bootcamp syllabi that I had not yet studied – namely, connecting the pieces of the MERN stack, learning about automated testing and data structures & algorithms; and studying development paradigms & methodologies like Agile, Scrum & Kanban, and OOP design patterns & principles – as well as continue to develop my programming fundamentals on platforms like Leetcode and Codewars. I also found that I was seeing Typescript being talked about in a lot of the same places as JavaScript and web development, so I took a course on that as well. Being a Self-Taught Developer A quick search online will reveal that there are many ways to define what it is to be a self-taught developer, but also that many of those definitions have a few things in common – i.e., that someone has engaged in online courses, tutorials, and self-study, and gained practical experience by using what they learned online to build applications. I neither received a degree in computer science nor attended an in-person coding boot camp, but over the last four years, I took a lot of classes online, watched a ton of instructional videos at Udemy and on YouTube, took hundreds of pages of notes, worked through countless code-along exercises, and read a lot of tutorials and blogs about coding, JavaScript, and web development. Additionally, I supplemented my online study with a lot of books that really helped me out, such as A Smarter Way to Learn JavaScript, The Imposter’s Handbook, and The Complete Software Developer’s Career Guide. I also found that tech podcasts can be an invaluable source of knowledge, and I have learned a great deal from listening to podcasts such as JavaScript Jabber, Syntax, Coding Blocks, and CodeNewbie. I’ve learned that the disadvantages of taking courses online – mainly, not having access to a live instructor in a more formal boot camp setting or the peer network and other opportunities I’d likely gain in such an environment – have emerged as advantages in the sense that if I don’t know or understand something, then I have to rely on my own research skills and initiative to identify exactly what it is that I need to learn, and then figure out how to learn it – because as I’ve also come to understand, that’s a lot of what a developer does every day. Lessons Learned I’ve often said that if someone is going to be an artist, then nothing and no one can stop them or stand in their way – if they’re going to do it, they’ll find a way to make it happen. I’ve learned that the same can be said about becoming a software developer. Especially during this time, there are so many different ways to learn how to code and such an abundance of resources online that if someone wants to learn those skills, they have the opportunity to try many different learning paths until they find the right one. It’s taken some time to get to where I am now, but I’m happy with the path I chose – because as much as I’d still like to have the experience of doing an in-person coding bootcamp [and I still might, who knows], I can see now that the path I took gave me the space to understand all that I’ve learned on a deeper level, and that the path I chose was the best path for me. My journey in software development began several years ago after a conversation with a friend. He had recently hired a small development company to help him build an app for his commercial real estate business. As we spoke more about it, I thought about my own ideas for apps and wondered if that was something I could do. I had learned some basic HTML & CSS and built a couple of simple websites in the past, but I had no experience with anything more complex than that and started thinking about what I might need to learn to build apps myself. It was around then that I decided I was going to learn how to code. I began asking people I knew who worked in tech what programming language I should learn. One friend suggested learning JavaScript at an in-person coding boot camp in NYC, so I looked into that program, and others liked it. I noticed that a lot of them were teaching a similar web development stack – JavaScript, Node, Express, Mongo, and so on. I liked the idea of enrolling in a coding boot camp, but I thought maybe before I committed to an expensive in-person program, I should try something online – to be sure that it was not only something I could do but would be something that I’d want to continue to pursue. could Coding Bootcamps After exploring a number of different online learning platforms, in the fall of 2019, I enrolled in The Web Developer Bootcamp at Udemy and devoted much of the next several months to learning the material covered in that course. The Web Developer Bootcamp Udemy I quickly found that one of the great things about online platforms like Udemy is that it can be very affordable if you time things right – because courses are frequently offered at a generous discount – so I could buy several classes on the same subject, then concentrate on the one or two that I liked the most. Udemy I bought a few more coding boot camps and JavaScript courses so I could cherry-pick through them for a fresh perspective when I was having difficulty understanding certain material – like some of the more advanced JavaScript topics. In the meantime, I worked through The Web Developer Bootcamp from start to finish, and as I got closer to completing it, I began to apply some of what I’d learned to build various front and back-end elements – e.g., forms & UI’s, RESTful routing & Express middleware, and database schemas & CRUD operations. The Web Developer Bootcamp Touring and COVID I’ve been playing music professionally for some time and had several performances scheduled around the northeast US in March 2020, so in late February of that year, I stepped away from coding to begin practicing for my shows and preparing to be on the road for a few weeks. It was around that time that reports about a new “coronavirus” that was slowly spreading around the US began appearing on the news more frequently. I was touring with another guitarist, and we played several shows between Minneapolis and Washington DC before driving to NYC for the final show on March 15th. That morning, the venue called and said they were closing down temporarily because of COVID – which, as we all learned in the coming weeks, was what was about to happen everywhere. It was tough timing for me because I was getting to a place in my music career where I was being invited to perform more frequently, and I was accumulating more bookings on the horizon than I’d had in the past – then COVID threw a wrench in the whole thing, and I found myself right back in Massachusetts in front of my computer again for the next 18 months. Building My First App Building My First App During that time, I continued to apply what I’d been learning to build what would eventually become my first full-scale Node/Express/Mongo production web app, Gigpromoter . Gigpromoter From initial concept through deployment and maintenance, I learned about every stage of building and deploying a production web application through the experience of doing it – including the development of application language and content, user interface design and layout, back-end architecture, testing, and cybersecurity implementation, and site optimization & web performance. While I was building it, I also saw how much my original concept changed and evolved – from a simple event promotion task-management tool to a comprehensive resource that could help new artists learn how to book, manage, and promote live events. I soft-launched Gigpromoter in 2022 – and between ongoing work on music and a few performances, I continued with my education in software development. Gigpromoter Continuing my Education Continuing my Education Because I was still curious about the experience of attending an in-person coding boot camp, I continued to go back and read the syllabi from different programs to see what they were teaching – except now I found that I knew a lot of the material I was seeing, so I decided I would keep taking online courses to learn more about some of those topics I hadn’t covered yet. As I had done previously with web development and JavaScript courses, in 2022, I bought several ReactJS courses on Udemy , then found two that I liked the most and worked through those for the next several months. Udemy I did some traveling around the western US in late 2022 to take stock of where my life was and where I was going. During that time, I decided that I would go all-in with my coding education and committed to learning the remaining material listed on those bootcamp syllabi that I had not yet studied – namely, connecting the pieces of the MERN stack, learning about automated testing and data structures & algorithms; and studying development paradigms & methodologies like Agile, Scrum & Kanban, and OOP design patterns & principles – as well as continue to develop my programming fundamentals on platforms like Leetcode and Codewars . Leetcode Codewars I also found that I was seeing Typescript being talked about in a lot of the same places as JavaScript and web development, so I took a course on that as well. Being a Self-Taught Developer Being a Self-Taught Developer A quick search online will reveal that there are many ways to define what it is to be a self-taught developer, but also that many of those definitions have a few things in common – i.e., that someone has engaged in online courses, tutorials, and self-study, and gained practical experience by using what they learned online to build applications. I neither received a degree in computer science nor attended an in-person coding boot camp, but over the last four years, I took a lot of classes online, watched a ton of instructional videos at Udemy and on YouTube , took hundreds of pages of notes, worked through countless code-along exercises, and read a lot of tutorials and blogs about coding, JavaScript, and web development. Udemy YouTube Additionally, I supplemented my online study with a lot of books that really helped me out, such as A Smarter Way to Learn JavaScript , The Imposter’s Handbook , and The Complete Software Developer’s Career Guide . A Smarter Way to Learn JavaScript The Imposter’s Handbook The Complete Software Developer’s Career Guide I also found that tech podcasts can be an invaluable source of knowledge, and I have learned a great deal from listening to podcasts such as JavaScript Jabber , Syntax , Coding Blocks , and CodeNewbie . JavaScript Jabber Syntax Coding Blocks CodeNewbie I’ve learned that the disadvantages of taking courses online – mainly, not having access to a live instructor in a more formal boot camp setting or the peer network and other opportunities I’d likely gain in such an environment – have emerged as advantages in the sense that if I don’t know or understand something, then I have to rely on my own research skills and initiative to identify exactly what it is that I need to learn, and then figure out how to learn it – because as I’ve also come to understand, that’s a lot of what a developer does every day. Lessons Learned Lessons Learned I’ve often said that if someone is going to be an artist, then nothing and no one can stop them or stand in their way – if they’re going to do it, they’ll find a way to make it happen. I’ve learned that the same can be said about becoming a software developer. Especially during this time, there are so many different ways to learn how to code and such an abundance of resources online that if someone wants to learn those skills, they have the opportunity to try many different learning paths until they find the right one. It’s taken some time to get to where I am now, but I’m happy with the path I chose – because as much as I’d still like to have the experience of doing an in-person coding bootcamp [and I still might, who knows], I can see now that the path I took gave me the space to understand all that I’ve learned on a deeper level, and that the path I chose was the best path for me.

How I Found My Path as a Self-Taught Developer, and You Could Do the Same

Aptos Role in the Decentralized Infrastructure The founders of Aptos Labs, Mo Shaikh and Avery Ching, started the company with the goal of making networks easier to use and giving more people access to the benefits of decentralization. The company has gotten a lot of money from trustworthy investors, which shows that it is serious about making a blockchain setting that is easy to use and works well. In order to adapt and improve speed while also offering user protections, Aptos, a next-generation Layer 1 blockchain, makes use of ground-breaking technologies and the Move programming language. This new invention represents a significant step forward in the process of combining blockchain technology with artificial intelligence, with the ultimate goal of redefining the technological environment. Exploring the capabilities of BC8.ai on the Aptos network io.net, a company that specializes in decentralized GPU processing, has partnered with Aptos Labs to expand its operations on Aptos. This alliance aims to decentralize access to the essential components of artificial intelligence (AI) by lowering computing costs and storage requirements, making machine learning more accessible and affordable. As a consequence of this collaboration, io.net's artificial intelligence (AI) products and innovations will benefit from Aptos' safety, speed, and performance capabilities. Currently, the generative artificial intelligence system known as BC8.ai, which is a product of io.net, is operating on the Aptos network and is responsible for managing more than 500,000 transactions each and every day. It is clear from this that the network is able to satisfy the requirements for high-speed and high-volume artificial intelligence. Ahmad Shadid, the Chief Executive Officer of Io.net, has underlined the necessity for a blockchain partner such as Aptos to support the firm's rising scale of innovation. This is especially important as BC8.ai and other future inference products continue to develop. If io.net and Aptos Labs decide to collaborate in the future, they will focus on developing a collection of artificial intelligence models and inference products that are designed to make use of the Aptos network for the purpose of making payments. This will further facilitate the accessibility of technologies that use artificial intelligence. Mo Shaikh, the chief executive officer of Aptos Labs, provided an overview of the goal that both companies have for the future, which is to have artificial intelligence that is decentralized and accessible. For instance, a possible product could be a payment system driven by AI that instantly spots scams and starts safe operations on the Aptos network. This partnership could change the way payments are made and processed around the world by using io.net's skill in AI technology and Aptos Labs' network infrastructure. This new way of using AI in payments would not only make them safer and more efficient, but it would also make these technologies easier for both businesses and customers to get. Vision for Accessible AI The establishment of this partnership represents a significant step towards the incorporation of Web3 technology inside the realm of artificial intelligence. With the goal of making artificial intelligence more responsible, scalable, and accessible to engineers and developers all across the globe, the aim is to be achieved. It is strongly recommended that you go to the website io.net in order to get further information on the ongoing endeavors of io.net and its collaboration with Aptos Labs. The IO Network is a decentralized physical infrastructure network of geo-distributed GPUs, and io.net is responsible for managing GPU clusters that are deployed over the IO Network. When it comes to using cases that need high processing demand and low latency, such as artificial intelligence and machine learning operations, and cloud gaming, this network, which is known as the Internet of GPUs, is aimed to provide companies and engineers with a wider range of options and lower prices. Don't forget to like and share the story! Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program. HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR. Aptos Role in the Decentralized Infrastructure Aptos Role in the Decentralized Infrastructure The founders of Aptos Labs, Mo Shaikh and Avery Ching, started the company with the goal of making networks easier to use and giving more people access to the benefits of decentralization. The company has gotten a lot of money from trustworthy investors, which shows that it is serious about making a blockchain setting that is easy to use and works well. In order to adapt and improve speed while also offering user protections, Aptos, a next-generation Layer 1 blockchain, makes use of ground-breaking technologies and the Move programming language. This new invention represents a significant step forward in the process of combining blockchain technology with artificial intelligence, with the ultimate goal of redefining the technological environment. Exploring the capabilities of BC8.ai on the Aptos network io.net, a company that specializes in decentralized GPU processing, has partnered with Aptos Labs to expand its operations on Aptos. This alliance aims to decentralize access to the essential components of artificial intelligence (AI) by lowering computing costs and storage requirements, making machine learning more accessible and affordable. As a consequence of this collaboration, io.net's artificial intelligence (AI) products and innovations will benefit from Aptos' safety, speed, and performance capabilities. Currently, the generative artificial intelligence system known as BC8.ai, which is a product of io.net, is operating on the Aptos network and is responsible for managing more than 500,000 transactions each and every day. It is clear from this that the network is able to satisfy the requirements for high-speed and high-volume artificial intelligence. Ahmad Shadid, the Chief Executive Officer of Io.net, has underlined the necessity for a blockchain partner such as Aptos to support the firm's rising scale of innovation. This is especially important as BC8.ai and other future inference products continue to develop. If io.net and Aptos Labs decide to collaborate in the future, they will focus on developing a collection of artificial intelligence models and inference products that are designed to make use of the Aptos network for the purpose of making payments. This will further facilitate the accessibility of technologies that use artificial intelligence. Mo Shaikh, the chief executive officer of Aptos Labs, provided an overview of the goal that both companies have for the future, which is to have artificial intelligence that is decentralized and accessible. For instance, a possible product could be a payment system driven by AI that instantly spots scams and starts safe operations on the Aptos network. This partnership could change the way payments are made and processed around the world by using io.net's skill in AI technology and Aptos Labs' network infrastructure. This new way of using AI in payments would not only make them safer and more efficient, but it would also make these technologies easier for both businesses and customers to get. Vision for Accessible AI Vision for Accessible AI The establishment of this partnership represents a significant step towards the incorporation of Web3 technology inside the realm of artificial intelligence. With the goal of making artificial intelligence more responsible, scalable, and accessible to engineers and developers all across the globe, the aim is to be achieved. It is strongly recommended that you go to the website io.net in order to get further information on the ongoing endeavors of io.net and its collaboration with Aptos Labs. The IO Network is a decentralized physical infrastructure network of geo-distributed GPUs, and io.net is responsible for managing GPU clusters that are deployed over the IO Network. When it comes to using cases that need high processing demand and low latency, such as artificial intelligence and machine learning operations, and cloud gaming, this network, which is known as the Internet of GPUs, is aimed to provide companies and engineers with a wider range of options and lower prices. Don't forget to like and share the story! Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program. HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR. Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program . HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR. Vested Interest Disclosure: business blogging program

Revolutionizing AI: io.net and Aptos Labs Forge a Path for Decentralized Innovation

বিপ্লবী AI: io.net এবং Aptos Labs বিকেন্দ্রীভূত উদ্ভাবনের জন্য একটি পথ তৈরি করে

Revolutionäre KI: io.net und Aptos Labs ebnen den Weg für dezentrale Innovation

Revolucionando la IA: io.net y Aptos Labs forjan un camino hacia la innovación descentralizada

Révolutionner l'IA : io.net et Aptos Labs ouvrent la voie à l'innovation décentralisée

AI में क्रांतिकारी बदलाव: io.net और Aptos Labs ने विकेंद्रीकृत नवाचार के लिए मार्ग प्रशस्त किया

AI の革命: io.net と Aptos Labs が分散型イノベーションへの道を切り開く

AI 혁명: io.net과 Aptos Labs가 분산형 혁신을 위한 길을 닦습니다.

Revolucionando a IA: io.net e Aptos Labs abrem um caminho para a inovação descentralizada

Революция в области искусственного интеллекта: io.net и Aptos Labs прокладывают путь к децентрализованным инновациям

Yapay Zekada Devrim Yaratıyor: io.net ve Aptos Labs Merkezi Olmayan İnovasyona Yönelik Bir Yol Açıyor

Cách mạng hóa AI: io.net và Aptos Labs tạo ra con đường đổi mới phi tập trung

人工智能革命：io.net 和 Aptos Labs 开辟去中心化创新之路

"To be governed is to be watched, inspected, spied upon, directed, law-driven, numbered, regulated, enrolled, indoctrinated, preached at, controlled, checked, estimated, valued, censured, commanded, by creatures who have neither the right nor the wisdom nor the virtue to do so. To be governed is to be at every operation, at every transaction noted, registered, counted, taxed, stamped, measured, numbered, assessed, licensed, authorized, admonished, prevented, forbidden, reformed, corrected, punished. It is, under pretext of public utility, and in the name of the general interest, to be placed under contribution, drilled, fleeced, exploited, monopolized, extorted from, squeezed, hoaxed, robbed; then, at the slightest resistance, the first word of complaint, to be repressed, fined, vilified, harassed, hunted down, abused, clubbed, disarmed, bound, choked, imprisoned, judged, condemned, shot, deported, sacrificed, sold, betrayed; and to crown all, mocked, ridiculed, derided, outraged, dishonored. That is government; that is its justice; that is its morality." - Pierre-Joseph Proudhon, The General Idea of the Revolution In vivid words, Proudhon describes the core of state authority as brutal subjugation: Surveillance, control, regulation, restriction, exploitation, and even blunt violence. Contemplating Guantanamo Bay, national debt, Snowden, inflation, and Julian Assange, even the most staunch believers of our liberal democracies have to either catch the scent of truth in it or engage in vertiginous feats of mental acrobatics. The liberation? Autonomy. The purest and most radical following and implementation of this principle in the history of mankind is arguably the . From the mining of the first 50 , to the EVM, to Parachains and Danksharding, every step in this space of maximum decentralization is underlined by the moral imperative that increasing the autonomy of individuals is desirable. Web3 space BTC However, as important as it is to achieve autonomy from all claims to power, the question remains about what we should do with this Autonomy. In our desire to create a better world, is autonomy the value we should try to maximize? If the only aim of spelling out freedom is to create as much space as possible for undisturbed self-expression, then this freedom is threatened to remain empty, dead, and meaningless. Although, as the absence of obstacles and undesired influences, a purely negative conception of freedom is not enough to give our lives meaning and orientation. Therefore, autonomy must not be misunderstood as an end in itself. Instead, we must understand it as the foundation and enabling condition for resonant world relations. I would like to argue that the pure value of autonomy requires a supplement to unfold its utopian potential: social freedom. Autonomy Uncontested The value of autonomy enjoys an almost unchallenged status in our society. In all political spectrums ("My body, my choice" / "Taxes are theft" / "Better dead than red"), in culture (glorification of world traveling influencers, the explosion of interest into self-improvement, the rise of non-traditional relationships, etc. ), art (tropes about oppression/emancipation), everywhere the unquestioned virtue of autonomy shines through. This development has reached its climax in the decentralized space. Here, autonomy is not just one value among many but the central ethical guiding principle from which everything else is derived. The vision of a world where individuals can interact directly with each other without intermediaries or higher authorities is the utopian core of the web3 landscape. Indeed, the decentralized space has an emancipatory potential that can not be underestimated. Many of our institutions are, in the best case, unsuitable for the problems of the 21st century and, in the worst case, rotten to the core. By creating structures that enable individuals to govern their data, finances, identities, and contracts themselves, decentralized technologies have the potential to shift power away from gatekeepers to society. But what is autonomy? Autonomy, negative and social freedom The term autonomy is derived from the ancient Greek "autonomía," which means something like "self-legislation" or "self-determination." Autonomy is often understood as the ability to emancipate oneself from external influences and constraints. Proudhons quote and my examples reference that understanding of autonomy. This notion of autonomy thus shows a great affinity to the concept of negative liberty, as prominently defined by Isaiah Berlin and is famously represented by Thomas Hobbes's quote "A free man is he that in those things which by his strength and wit he is able to do is not hindered to do what he hath the will to do" Negative liberty denotes a state of absence of external obstacles and limitations. It realizes itself in a protected space of individual decision and action freedom to be exposed to as few interventions as possible. It is precisely this ideal that many advocates of decentralized systems also invoke: The substitution of hierarchical structures by autonomous actors is supposed to enable more negative freedom by removing boundaries and obstacles. The gold standard of a decentralized system is its robustness to censorship and, therefore its ability to maintain boundaries and degree of freedom against external forces. However, is the protection and expansion of negative freedom sufficient to enable people to lead a truly self-determined and fulfilled life? The philosopher Axel Honneth has convincingly argued that this is not the case. For him, individual freedom only becomes possible where it can relate to an "objective reality", i.e. to given social conditions and institutions. True self-determination, therefore, always presupposes a social context in which the individual can realize his or her life plans. Honneth calls the form of freedom that arises from this "social freedom", which he derived from Hegel. According to Honneth, it is not enough for a free individual to be devoid of obstacles. Instead, it necessitates a state in which individuals support each other in realizing their respective goals, enabling each other to lead a self-determined life. That is all quite abstract, so Let's visualize the difference between the two concepts of freedom in the following picture: Imagine you are in front of a river that you want to cross. The river looks quite dangerous; you could be swept across or drown in a whirlpool. Hobbes is sitting next to you on a stone and tells you that you have every freedom to cross this river. After all, there are no dangerous animals in the water. (There are also no border guards with machine guns, and no wall has been erected). However, you are completely on your own. It's just your subjective experience, and you can't hope to make it, should you try. However, it could also be that a tree has accidentally fallen, and you can get across quite easily. For Hobbes, though, it makes no difference to your freedom whether you live in a world in which this tree has fallen or not. At that moment, Hegel arrives with a good wine in his hand and a wheelbarrow full of planks and nails. He offers to build a bridge together because he wants to cross it himself. For Hegel, only now have you gained your freedom, because in reality, the conditions have been created through which you can hope to actually cross the river. Before, it was pure chance whether this hope could be rationally justified. It was only through Hegel that you actively received the support from your environment that can give you rational hope of reaching your goal (the other shore). This applies to societies. It is, as supporters of Hegel's idea would argue in societies without social freedom pure chance whether you grow up in circumstances that enable you to achieve your goals and in which everyone can hope to achieve their goals. In societies with social freedom, however, this hope is justified because the objectives of the other subjects are such that they enable and complement the achievement of one's own goals and complement them. Social freedom thus has a cooperative core: it is based on the insight that the freedom of the individual is dependent on the freedom of others. Only in a community in which people grant and support each other in the development of self-determination can the individual also experience himself as genuinely free. The values of the decentralized space have to be ultimately also committed to such an understanding of social freedom, even if this is not always explicitly reflected. Behind the efforts to create decentralized, self-organized structures has to be the vision of a world in which individuals can freely associate to collectively shape their living conditions with each other for the better. Even the much-cited "trustless trust" and allegedly increase of freedom that blockchain technologies are supposed to enable is ultimately grounded in the individual’s faculty to understand the system. It makes a huge difference if 99,9999& of crypto projects are scams, or if it’s only 80, 70, or 60. However, the fixation on code-based incentive and coordination mechanisms threatens to lose sight of this fact. You could easily imagine a future in which every aspect of societal organization is managed by smart contracts, but in which more people are scammed; more people lack connections and meaning and fewer people can make sense of this world. Conclusion The vision of autonomy that inspires so many in the blockchain space is a noble and important one. Establishing robust individual freedoms and consent is crucial to human flourishing. But to unlock the full transformative potential of decentralized technologies, we must pair the pursuit of autonomy with the cultivation of social freedom. This means aspiring not just for freedom from interference but also to enable the creation of resonance - meaningful, mutually empowering relationships - at both the individual and the institutional level. It means recognizing that our identities are not atomic, but fundamentally intersubjective - our agency is enhanced, not undermined, by positive embeddedness in the social fabric. And it means harnessing the power of decentralized coordination not just to maximize individual gain, but to create emergent realities that are greater than the sum of their parts. By synthesizing the principles of individual sovereignty and collective intelligence, the marriage of autonomy and resonance paves the way for a future that brings out the best in both individuals and the whole. A future where people are free to author their own lives, while also synergizing with others to make the pie massively bigger for everyone. Getting there won't be easy, requiring technical and cultural innovation. But if we can pull it off, the prize is immense - nothing short of the difference between a failed species and a thriving galactic civilization. So let us not settle for autonomy alone but strive for resonant autonomy—the foundation for a world alive with connection, meaning, and potentially infinite upside. We have the rare privilege of living at a time when our actions can shape the entire trajectory of the future. The blockchain renaissance has already expanded our sense of what's possible. Now it's on us to channel that newfound freedom towards a more beautiful and true reality.

The Path to Resonant Autonomy: Navigating Autonomy and Social Freedom in Web3

In a landmark paper published March 29, 2024, in the journal, Entropy, a team of visionary researchers from VERSES AI has unveiled a groundbreaking framework that could pave the way for the emergence of a new form of artificial intelligence , operating on the principles of natural intelligence— one that arises from the dense interactions of networked intelligent agents, rather than from a single, monolithic system. based on a first-principles approach to multi-scale artificial intelligence The study, titled “Shared Protentions in Multi-Agent Active Inference,” represents a major milestone in VERSES AI’s ambitious roadmap towards “shared intelligence” and could have profound implications for the future of AI and human-machine cooperation. The Power of Active Inference and Shared Protentions The research, led by Mahault Albarracin and co-authored by Riddhi Jain, Toby St Clere Smithe, Daniel Friedman, Maxwell Ramstead, and renowned neuroscientist Dr. Karl Friston, pioneer of and the , draws upon insights from philosophy, biology, and mathematics to explore how shared goals and collective intelligence can emerge from the interactions of individual intelligent agents. Central to their framework is the concept of “shared protentions” — mutually attuned expectations about future states and actions that enable agents to coordinate their behaviors towards common objectives. Active Inference Free Energy Principle What are "Protentions?" The philosophical foundation of the work lies in the concept of “inner time consciousness” developed by the early 20th-century thinker Edmund Husserl. Husserl proposed that our subjective experience of the present moment always has a certain “temporal thickness” — it encompasses not just the immediate instant, but also traces of the just-past (which he called “retentions”) and anticipations of the near-future (termed “protentions”). The authors of the new study take this idea of an individual’s temporal experience and extend it to the level of a social group. They introduce the notion of “shared protentions” — the idea that members of a group can develop mutually attuned expectations about future states and actions, which then allow them to coordinate their behaviors. How Does it Work? To translate this philosophical concept into a scientific model, the researchers employ a framework from theoretical biology known as “active inference.” This approach views living organisms as continuously making inferences about the state of their environment and selecting actions that minimize surprises and uncertainty. The authors show how Husserl’s ideas can be mapped onto the components of an active inference model. Sensory inputs correspond to raw “hyletic data,” (impressions that form perceptions), while the inferred causes of those sensations make up what we perceive as our experience. Crucially, the model equips each individual with an internal representation that includes not just their own states but also those of other group members. This allows the model to capture how individuals infer each other’s mental states and anticipate their actions. Leveraging Category Theory for Unified Representation The most innovative aspect of the new framework is its integration of mathematical tools from a field known as category theory. The researchers now express how individuals make decisions using abstract math concepts known as “polynomial functors,” which represent their potential interactions and observations. They then use a technique called “sheaf theory” to stitch together the compatible parts of these individual models. This results in a unified picture of the group’s shared understanding, termed a “consensus topos” by the authors. Within this formal framework, the “shared protentions” that enable coordinated group action emerge spontaneously as a consequence of each individual’s understanding and reasoning being influenced by the need to maintain consistency with the group. In other words, the underlying alignment of expectations that allows individuals to work together towards common goals arises naturally from the mathematics of the model. An Example from Nature Think of it like a flock of birds flying together. Each bird is constantly adjusting its own movements based on what it sees the other birds doing. It doesn’t need to have a detailed plan or a leader telling it what to do — it just naturally falls into formation by following simple rules and paying attention to its neighbors. A Human Example - Alignment of Understanding and Anticipation We can also use the example of a sports team, like a basketball team, playing together on the court. Each player on the team has a mental model of the game — they understand the rules, the objectives, and the general strategy. But more importantly, they also have a sense of what their teammates are likely to do in any given situation. This understanding is built up through hours of practice and gameplay together. During a fast-paced game, there’s no time for the players to stop and discuss each play in detail. Instead, they rely on their shared understanding and anticipation of each other’s actions. The point guard, for instance, might make a split-second decision to pass the ball to a particular teammate, not because they discussed it beforehand, but because they anticipate that the teammate will be in the right position to receive the pass. This is a prime example of “shared protentions” in action. Each player’s individual actions are guided by their expectations of what their teammates will do. And these expectations are not just one-way — they’re reciprocal and self-reinforcing. The teammate anticipates the pass because they know the point guard is likely to make it, and the point guard makes the pass because they anticipate the teammate being ready for it. Over time, this interplay of expectations and actions leads to a highly coordinated style of play, where the team seems to move and think as one unit. And importantly, this coordination emerges not from any one player’s explicit instructions, but from the implicit alignment of each player’s understanding and anticipations. This is the essence of what the researchers’ mathematical model captures — It’s a powerful insight into the cognitive underpinnings of teamwork and collective action. how shared goals and coordinated action can arise naturally from the interplay of individual minds, guided by the need to maintain a coherent, mutually aligned understanding of the world. VERSES AI's Vision for Shared Intelligence The significance of this work extends far beyond the realm of academic theory. In a post on LinkedIn, Albarracin explained that the paper “sets the stage for .” This stage, which roughly corresponds to what is popularly known as “Artificial Super-Intelligence,” envisions the emergence of planetary-scale, distributed intelligences from the highly coordinated interactions of networked Intelligent Agents. The key insight of the “Shared Protentions” paper is that such collective intelligences are not just a distant dream but a natural consequence of the principles of Active Inference and the mathematical tools of category theory. VERSES’ Active Inference intelligence roadmap stage S4 of Shared Intelligence The Mathematics of Shared Intelligence To understand how this works, it’s helpful to step back and consider the basic premise of Active Inference. This framework, rooted in neuroscience and theoretical biology, views Intelligent Agents as constantly making deductions and drawing conclusions about the state of their environment and choosing to act in a way that closes the gap between expectation and reality — refining understanding to better anticipate what will happen. In the “Shared Protentions” paper, the authors show how this process of inference and action selection can be formally modeled using abstract mathematical objects called “polynomial functors” and “sheaves,” as explained above. Coordinating Intelligent Agents through Implicit Alignment By applying these tools to networks of interacting Intelligent Agents, the researchers demonstrate how individual agents can intuitively align their expectations and coordinate their behaviors, even without explicit communication. In essence, they provide a mathematical recipe for the unspoken emergence of shared goals and collective intelligence from the bottom up. The Role of HSML in Enabling Shared Intelligence But what makes this research truly groundbreaking is its integration with . HSML is a common language that enables seamless interoperability between Active Inference Intelligent Agents, humans, and the scores of devices that comprise the Internet of Things (IoT). By programming context into objects and spaces, HSML provides Active Inference Agents with real-time data about their environments, as well as the sensory inputs from IoT devices and robots. VERSES AI’s Hyperspace Modeling Language (HSML) Embedding Human Values into the Spatial Web The “Shared Protentions” paper lays the theoretical foundation for In other words, it provides a principled way to embed human values and objectives into the very fabric of the Spatial Web, ensuring that the collective intelligences that emerge from it are aligned with our goals and aspirations. translating high-level human directives, encoded in HSML, into the inherent alignments of expectations that guide the cooperative behaviors of Autonomous Intelligent Agents. The Far-Reaching Implications of VERSES AI's Research The implications of this research are staggering. As we move towards a future in which , from Smart Cities to healthcare to scientific research, the ability to create cooperative, context-aware intelligences that can seamlessly coordinate their actions in pursuit of common goals will be essential. The “Shared Protentions” paper, in conjunction with the — & , offers a roadmap for achieving this vision. Autonomous Intelligent Systems are increasingly integrated into every aspect of our lives Spatial Web Protocol HSTP HSML Challenges and Opportunities on the Path to Shared Intelligence Of course, there are still many challenges to be overcome. This robust, secure, and ethical framework, as well as the deployment of . However, the publication of this paper marks a crucial turning point in our understanding of what is possible. these technologies, will require ongoing collaboration between researchers, policymakers, and the broader public Natural Intelligence-A New Era in Artificial Intelligence As we stand at the threshold of a new era in artificial intelligence, the pioneering work of Friston, Albarracin, and their colleagues at and the offers a promising vision of future in which humans and machines can work together in previously unimaginable ways. By cracking the code of shared intelligence and providing the tools to embed the building blocks for it in the very infrastructure of our world, they are laying the foundation for a new paradigm in AI — one that operates with the same mechanics as biological intelligence, and one that could transform our society in ways we are only beginning to imagine. VERSES AI Spatial Web Foundation , and Visit my blog at https://deniseholt.us/ Subscribe to my Spatial Web AI Podcast on , YouTube , and more. Spotify Subscribe to my newsletter on . LinkedIn Also published . here

VERSES AI's Revolutionary Framework Lays Down The Path to Natural Shared Super-Intelligence

Introduction The article demonstrates the built-in functions while working with file system paths. It makes it easier to handle file paths. Learning Objectives Learn about constants and functions in the namespace System.IO Prerequisites for Developers How to use Visual Studio or Visual Studio Code Handling variables, employing string interpolation, and displaying output. Getting Started How do we determine the current directory? The contains a method to expose the full path of the current directory. To begin, create a static class file called “FilePath.cs” within the console application. Insert the provided code snippet into this file. System.IO public static class FilePath
 {
 /// <summary>
 /// Outputs
 /// D:\Workspace\30DayChallenge.Net\30DayChallenge.Net\bin\Debug\net8.0
 /// </summary>
 public static void DisplayCurrentDirectory()
 {
 Console.WriteLine(Directory.GetCurrentDirectory());
 }
 } Execute the code using the main method as follows. #region Day 10 - File Path
 
 FilePath.DisplayCurrentDirectory();
 
 #endregion Console Output D:\Workspace\30DayChallenge.Net\30DayChallenge.Net\bin\Debug\net8.0 How do you work with special directories? The code below provides the path to the Windows My Documents folder equivalent or the user’s HOME directory, regardless of the operating system, including Linux. To do that, add another method into the same static class as shown below. /// <summary>
 /// Outputs
 /// C:\Users\admin\Documents
 /// </summary>
 public static void DisplaySpecialDirectory()
 {
 Console.WriteLine(Environment.GetFolderPath(Environment.SpecialFolder.MyDocuments));
 } Execute the code using the main method as follows. #region Day 10 - File Path
 
 FilePath.DisplaySpecialDirectory();
 
 #endregion Console Output C:\Users\admin\Documents How to get OS path characters? Various operating systems utilize distinct characters to separate directory levels. The framework automatically interprets the separator character relevant to the operating system being used. To do that, add another method into the same static class as shown below /// <summary>
 /// Outputs
 /// For windows: \sample 
 /// </summary>
 public static void DisplayOSPathCharacters()
 {
 Console.WriteLine($"For windows: {Path.DirectorySeparatorChar}sample");
 } Execute the code from the main method as follows #region Day 10 - File Path
 
 FilePath.DisplayOSPathCharacters();
 
 #endregion Console Output For windows: \sample How do you get filename extensions? The Path class also exposes a method to get an extension of any filename passed as a parameter. To do that, add another method into the same static class, as shown below. /// <summary>
 /// Outputs
 /// .json
 /// </summary>
 public static void DisplayFileExtension()
 {
 Console.WriteLine("sample.json");
 } Execute the code using the main method as follows. #region Day 10 - File Path
 
 FilePath.DisplayFileExtension();
 
 #endregion Console Output .json Complete Code on GitHub GitHub — ssukhpinder/30DayChallenge.Net C# Programming🚀 Thank you for being a part of the C# community! Before you leave: If you’ve made it this far, please show your appreciation with a clap and follow the author! 👏️️ Follow us: | | | | | X LinkedIn Dev.to Hashnode Newsletter Tumblr Visit our other platforms: | | | | GitHub Instagram Tiktok Quora Daily.dev More content at C# Programming Also published . here

The 30-Day .NET Challenge, Day 10: File Paths

Nigeria! Giant of Africa, as she is fondly called. It is the most populous black country in the world, with over 200 million people, a teeming youth population, and . That's about 100 million, mostly tech-savvy young people all over the internet struggling to wrest some money from the famed riches of cyberspace. It's no wonder the country is well known for internet fraud, with a world-famous fraudster from Nigeria named Hushpuppi recently jailed by the US government for swindling numerous Americans off millions of hard-earned dollars. While internet fraud has cast a bad light on Nigerians online, this single story must not be allowed to take root, however, because Nigerians have also found ingenious ways to use the internet constructively with world-reaching effects. over 50% aged from 15 to 60 years For example, are well recognized in the content creation industry with many well-know ones like Mark Angel, Emmanuela, Sabinus etc. having millions of followers from around the world whom they routinely entertain. Nigerian music has also dominated the global space in recent years, with the internet playing a critical role in this rise. Nigerian music has dominated Africa since the 2010s, but recently, Nigerian artists like Burna Boy, Rhema, and Ckay have released colossal global hit songs like Last Last, Calm down, and Love Nwantiti, respectively, reaching hundreds of millions and even billions of streams. This was only made possible through online streaming and video platforms like Spotify, YouTube, and TikTok. Nigerian comedians A relatively recent technology known as cryptocurrency has hit the internet, and Nigerians have not been left behind. The country was in crypto adoption around the world. This, of course, can be traced back to the country's relatively backward financial system, with Nigeria recording numerous economic crises over the years. happened just before the 2023 elections in the country, where the Nigerian Naira was not even available for people to withdraw. Basically, there was little cash in circulation, and protests erupted in Banks, with people demanding to withdraw the money they had deposited so they could handle day-to-day expenses. This cash shortage lasted for several weeks, and millions of Nigerians began adopting alternative financial systems for Nigerian banks. Through this crisis, a little-known Crypto trader in the country known as would rise to fame and fortune by introducing an app for financial transactions that completely circumvented the Nigerian banking system. Blord would also go on to introduce a crypto exchange company called Blord Group in the country to convert cryptocurrency to Naira. Many Nigerians adopted Cryptocurrency during this financial crisis. recently ranked second A particularly crazy financial crisis Blord What you should know about Nigeria is that even though it is a very gifted country with lots of natural and human resources, it is run by a very corrupt government with notable ethnic biases. This has created widespread poverty in the country, with most of the gigantic 200 million people in the country left to fend for themselves anyhow they can. But the youths have taken up Agency as autonomous individuals battling against classist and ethnic divides, plus a corrupt, powerful government with dictatorial tendencies to create comfortable lives for themselves as sovereign entities who would not meekly surrender to a British-imposed country that seems determined to keep them down. This Nigerian dream has united beautifully with the vision of cryptocurrency, which basically serves as a democratized financial system that will create a level playing field for anyone around the world to make money. This article dives deeper into the possible role of autonomy, Crypto, and DAOs in getting Nigerians out of poverty. Tribalism and Classism in the Nigerian Government Nigeria is a multi-ethnic country with about 250 different ethnic groups. However, there are three main ethnic groups by population: the Hausa-Fulani in the Northwest, the Yoruba in the South West, and the Igbo in the South East. These three ethnic groups have dominated the government of the country since it gained independence from the British in 1960. However, was fought in 1967, with the Igbo of the Eastern region trying to break away from the country due to an ethnic crisis. The Igbo lost the war and have never produced a president to date, with the Mostly Muslim Hausa Fulani dominating the government at that time. Hence, there's a lot of ethnic discrimination in Nigerian politics with the Igbo, who number up to 40 million, and other Christian groups, with tens of millions of people mostly kept away from the presidency of what is supposed to be a democratic country. This discrimination in government has far-reaching economic impacts as the government of the day often favors their region in resource distribution. So, Nigerian Christians are often at a political disadvantage. a bloody civil war Classism is another major factor in Nigerian politics, with the mega-rich corrupting the voting system with their money and routinely rigging elections to maintain their hold on power and maltreat the poor (who are the majority) however they see fit. Hence, poor people in Nigeria who bear the brunt of the suffering hardly have a way to fix the country since their votes may not even count. This has led to a lot of crime around the country, with fraudsters, kidnappers, cultists, and sex workers being rampant. Nigerian Government Versus Autonomy The , through a federal system like the American government with executive, legislative, and Judiciary, does not practice true federalism. Simply put, the executive, who is led by the president, is too powerful, and through widespread corruption, they can control the lawmakers and judiciary, who are supposed to checkmate their excesses. This is also true for the state and local governments, who are often arm-twisted into doing the will of the federal government because the federal government has overall control of resources in the country. Hence, the Nigerian government is often anti-autonomy! Nigerian government Many groups have risen in the country to demand more autonomy and resource control at the regional level, but the federal government usually puts them down by force. This is mostly against the will of the people who always find ways to fight against the tyrannical control of the federal government. Basically, Nigerians want more autonomy to utilize their resources for a better life, as the federal government doesn't seem capable of delivering basic amenities like good roads, running water, and electricity. This has remained a constant cause of conflict in the country, with many lives lost. Nigerian Government Versus Wealthy Citizens As with any tyrannical system, the government in Nigeria seems determined to keep the people poor; hence, they are unable to fight for their rights. This is more rampant in the North, where despite hundreds of millions of Naira being allocated to education yearly. [Nigerian minimum wage](https://nigerian minimum wage), as at the time of this writing, is a meager 30,000 Naira (about $22) for a full month. That's way below the minimum wage in most countries and has made Nigeria a poor country for about a hundred years of its existence now. This is, of course, a deliberate act of corrupt politicians who prefer to cart away huge sums of money from budget allocations while leaving the people to fight for scraps. Hence, the people are kept poor and uneducated, so they can't really do much against these wealthy leaders who keep impoverishing them. A wealthy, educated population will, of course, be quite difficult to swindle as you please. millions of children are out of school Nigerian Government Versus the Internet During the Coronavirus crisis, Nigerians were locked up at home like others around the world, and palliatives released by international groups to ease the suffering of Nigerians were hoarded in several warehouses instead of being distributed to the people. Also, police personnel in the SARS unit went on a rampage, harassing tech-savvy Nigerian youths who were making money from the internet as fraudsters without much investigation. Nigerian youths responded by protesting on Twitter using hashtags and proceeded to coordinate themselves and break the curfew into a real Nationwide Street protest that came to be known as the #endsars protest. Millions of Nigerian youths took to the streets around the country and gained global coverage. Also, secessionist groups like , who were clamoring to break away from Nigeria, used online platforms like Facebook and Twitter to gather and communicate with millions of followers. The Nigerian government then effectively responded by banning Twitter in the country but soon canceled the ban after it failed, as citizens were using VPN apps to circumvent the ban. Hence, the Nigerian government has tyrannically tried to suppress the internet in Nigeria without regard for the millions of people who have found employment online. IPOB Nigerian Government Versus Crypto Expectedly, the Nigerian government has not reacted very well to Cryptocurrency technology in the country, though the people have welcomed the decentralized finance system with open arms. in the country as the government tries very hard to clamp down on global crypto agencies like Binance. Even the earlier mentioned millionaire crypto trader in the country (Blord) was arrested and charged with internet fraud even though he was able to come out victorious. The government predictably hates Crypto as they can't really control it. They complain that terrorists and fraudsters use Crypto for financial transactions though they've not really done much about the corrupt politicians who use the actual banking system in the country to loot Country funds. Crypto has created jobs for many Nigerian youths who work online as crypto traders, forex traders, software developers, writers, content creators, etc., and earn decent wages from foreign customers. Crypto has been banned, unbanned, and banned again The Role of Autonomy, Cryptocurrency, and Daos in Potentially Lifting Nigerians From Poverty With the country divided between several tribes and two major religions (Christianity and Islam), tribal and religious sentiments have often been used by malicious politicians to keep Nigerians fighting against each other and refuse to unite in fighting against the corrupt leaders who are the real problem. Tribal and religious crises often mar Nigerian elections to the benefit of politicians who capitalize on such crises to rig elections and discriminate in government. Also, leaders who steal often defend their tribesmen from anybody who may try to prosecute them. This is a huge problem in the country, with many jobs and industries often being based on tribe instead of qualification. Autonomy: To solve this fundamental problem, Nigerians must start thinking of themselves first as individuals instead of as members of a tribe or religion. They must prioritize their welfare as citizens in voting and making other electoral and job related decisions in order to get competent leaders and office holders who will transform the country. Regional autonomy must be prioritized over the current seemingly unworkable "federal system" which has only created much infighting. Autonomy is critical to the development of Nigeria! When Satoshi Nakamoto released Bitcoin, he was trying to create a decentralized financial system that would make financial opportunities fairly available to everyone around the world instead of to just a few members of the elite class who capitalized on the unfair legacy centralized system to get ahead of the rest of the population. Such a system is sorely needed in Nigeria, where inequality is even worse from widespread poverty and illiteracy. Many Nigerians use mobile phones, and with a little knowledge of Cryptocurrency, they can invest and get out of poverty without having to depend on a government that doesn't really seem to care about them. Cryptocurrency: Cryptocurrency could be the best opportunity for Nigerians to benefit from the financial system. Many Nigerians have never really experienced a free and fair election in their lifetime. This is due to widespread electoral malpractice that seems to have become the norm. Just like Twitter, which provides a global voice for the Nigerian people, which the government cannot forcefully shut down, DAOs will provide an even more decentralized platform for Nigerians to unite, organize, plan, and execute digital infrastructure for the good of the country. DAOs will allow Nigerians to practice democracy and governance in a free and safe virtual environment for education and networking purposes. Also, DAOs will allow Nigerians to influence the development of Web 3 to enable the developers to come up with software that is better suited to the Nigerian environment. DAOs: DAOs will provide a more sophisticated, unstoppable cyber voice for Nigerians around the world. Conclusion Nigerians have been held down by a corrupt, tyrannical government that seemingly would prefer to keep the people divided, poor, and uneducated. However, the Web 3 vision of democratizing the Internet and financial system is in complete opposition to such a system. With crypto concepts like autonomy, individualism, and sovereignty encapsulated in DAOs, Nigerians have a global technological movement they can leverage to come out of the country's economic crisis. is a groundbreaking DAO technology that Nigerians (and anyone around the world) can use to opt out of a corrupt leadership system that doesn't really represent them. Auth Labs

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