Loxo

loxo

A Floating City and The Blockade Runners by Jules Verne, is part of the HackerNoon Books Series. You can jump to any chapter in this book . Chapter IX. here CHAPTER IX. It must be confessed the Doctor’s words were not very comforting, the passengers would not have heard them without shuddering. Was he joking, or did he speak seriously? Was it, indeed true, that he went with the “Great Eastern” in all her voyages, to be present at some catastrophe? Every thing is possible for an eccentric, especially when he is English. However, the “Great Eastern” continued her course, tossing like a canoe, and keeping strictly to the loxodromic line of steamers. It is well known, that on a flat surface, the nearest way from one point to another is by a straight line. On a sphere it is the curved line formed by the circumference of great circles. Ships have an interest in following this route, in order to make the shortest passage, but sailing vessels cannot pursue this track against a head-wind, so that steamers alone are able to maintain a direct course, and take the route of the great circles. This is what the “Great Eastern” did, making a little for the north-west. The rolling never ceased, that horrible sea-sickness, at the same time contagious and epidemic, made rapid progress. Several of the passengers, with wan, pallid faces, and sunken cheeks, remained on deck, in order to breathe the fresh air, the greater part of them were furious at the unlucky steam-ship, which was conducting herself like a mere buoy, and at the freighter’s advertisements, which had stated that sea-sickness was “unknown on board.” At nine o’clock in the morning an object three or four miles off was signalled from the larboard quarter. Was it a waif, the carcass of a whale, or the hull of a ship? As yet it was not distinguishable. A group of convalescent passengers stood on the upper deck, at the bows, looking at this waif which was floating three hundred miles from the nearest land. Meanwhile the “Great Eastern” was bearing towards the object signalled; all opera-glasses were promptly raised, and there was no lack of conjecture. Between the Americans, and English, to whom every pretext for a wager is welcome, betting at once commenced. Among the most desperate of the betters I noticed a tall man, whose countenance struck me as one of profound duplicity. His features were stamped with a look of general hatred, which neither a physiognomist, nor physiologist could mistake; his forehead was seamed with a deep furrow, his manner was at the same time audacious and listless, his eyebrows nearly meeting, partly concealed the stony eyes beneath, his shoulders were high and his chin thrust forward, in fact all the indications of insolence and knavery were united in his appearance. He spoke in loud pompous tones, while some of his worthy associates laughed at his coarse jokes. This personage pretended to recognize in the waif the carcass of a whale, and he backed his opinion by heavy stakes, which soon found ready acceptance. These wagers, amounting to several hundred dollars, he lost every one; in fact, the waif was the hull of a ship; the steamer rapidly drew near it, and we could already see the rusty copper of her keel. It was a three-mast ship of about five or six hundred tons, deprived of her masts and rigging, and lying on one side, with broken chains hanging from her davits. “Had this steam-ship been abandoned by her crew?” This was now the prevailing question, however no one appeared on the deck, perhaps the shipwrecked ones had taken refuge inside. I saw an object moving for several moments at the bows, but it turned out to be only the remains of the jib lashed to and fro by the wind. The hull was quite visible at the distance of half a mile; she was a comparatively new ship, and in a perfect state of preservation; her cargo, which had been shifted by the wind, obliged her to lie along on her starboard side. The “Great Eastern” drew nearer, and, passing round, gave notice of her presence by several shrill whistles; but the waif remained silent, and unanimated; nothing was to be seen, not even a shore-boat from the wrecked vessel was visible on the wide expanse of water. The crew had undoubtedly had time to leave her, but could they have reached land, which was three hundred miles off? Could a frail boat live on a sea like that which had rocked the “Great Eastern” so frightfully? And when could this catastrophe have happened? It was evident that the shipwreck had taken place farther west, for the wind and waves must have driven the hull far out of her course. These questions were destined to remain unanswered. When the steam-ship came alongside the stern of the wreck, I could read distinctly the name “Lerida,” but the port she belonged to was not given. A merchant-vessel or a man-of-war would have had no hesitation in manning this hull which, undoubtedly, contained a valuable cargo, but as the “Great Eastern” was on regular service, she could not take this waif in tow for so many hundreds of miles; it was equally impossible to return and take it to the nearest port. Therefore, to the great regret of the sailors, it had to be abandoned, and it was soon a mere speck in the distance. The group of passengers dispersed, some to the saloons, others to their cabins, and even the lunch-bell failed to awaken the slumberers, worn out by sea-sickness. About noon Captain Anderson ordered sail to be hoisted, so that the ship, better supported, did not roll so much. About HackerNoon Book Series: We bring you the most important technical, scientific, and insightful public domain books. This book is part of the public domain. Jules Verne (2022). A Floating City and The Blockade Runners. Urbana, Illinois: Project Gutenberg. Retrieved October 2022 https://www.gutenberg.org/files/67829/67829-h/67829-h.htm This eBook is for the use of anyone anywhere at no cost and with almost no restrictions whatsoever. You may copy it, give it away or re-use it under the terms of the Project Gutenberg License included with this eBook or online at , located at . www.gutenberg.org https://www.gutenberg.org/policy/license.html

It must be confessed the Doctor’s words were not very comforting

PLUTONIC AND METAMORPHIC ROCKS:—CLEAVAGE AND FOLIATION

On average, companies spend days on on-site firmware updates. Meanwhile, over-the-air ones take minutes. Real estate representatives often сhoose the second option, meeting customer needs and increasing revenue. In this article, I share my experience with making a traditional firmware update and how it slowed down business and describe how to design the Internet of Things (IoT) platform. Why We Gave Up Traditional Firmware Update Processes How is the firmware of devices usually carried out in smart buildings? The company appoints an employee who periodically conducts on-site customization. If you are a developer with several properties in one city, it is suitable. But whether it will allow your business to expand - remains a mystery. When a company enters a regional or international market, inconveniences arise. For example: To flash the software on time, developers need to monitor its work. Difficulties in tracking devices worldwide. That's why companies cannot serve smart buildings efficiently. Lack of a single control center for sensors from different manufacturers. Every time an employee has a business trip, the company loses resources. High traveling costs. Usually, developers provide on-site firmware for free. They lose the opportunity to generate additional revenue and improve the customer experience. Work to the detriment. That is why we considered implementing an IoT platform for device management for our client. Why not? Thus the client can control buildings' functioning, timeliness of utility provision, settlement transactions, and many other lovely options. Developers understand the value of this approach and benefit from it and create paid platforms as an additional service to their buildings. Here're a few companies that offer to implement monetization options: provides two IoT platforms for smart house control: Alarm.com and Control4. The company offers keyless entry door lock, garage, utility sensors management. It provides clients with the freedom of action, regulating the situation from its side. It affects the customer's convenience and provides a single point of control. TBI Home Builder promises that your entire home will work as one harmonious system meaning the lighting, heating, blinds and more will all work in tandem together to create a truly intelligent home. Loxone gives you complete control from anywhere. A Vivint smart home system lets you control your lights, check on your porch, or adjust the temperature from anywhere—all from the Vivint app. All Vivint products work together seamlessly giving you the peace of mind that comes with a secure, comfortable home. Vivint helps keep the people and places you care about safe. And works on subscription starting from just $10 per month. XFINITY Home Even well-known tech giants don't stand aside. For example, states they can help you to be relaxed and feel safe in your home by simplifying your routine and having all essential systems like fire, security, water leaks and others at your sight (or screen). ** **brings smart lighting to everyday homes, allowing anyone to create memorable moments and automate with light. promises a totally Connected Life with SmartThings (and they do connect almost everything). BOSH Philips Hue Samsung In turn, by creating a single IoT ecosystem, the developer gets the opportunity to: analyze up-to-date data on device operation in real-time form predictions concerning further devices operation and eliminate all potential risks promptly respond to any issues with connected devices and make over-the-air firmware updates implement a variety of action plans depending on the situation try new business models, expanding revenue streams cut hidden costs improve employee efficiency Win-win. How To Start Designing Digital Service Platform For IoT Devices Management: tips from the practice Define business goals and learn business processes. You should decide with your client, what issues this IoT platform will fix and what goals to achieve. For example, one of our clients, a global developer with 1 million smart buildings around the world, tried to solve the following issues using software: create firmware management platform and launch it as a paid service gain control over all devices at the facilities create a new business model that allows for reselling firmware All of these goals are directly related to current business processes. Therefore, their analysis is vital. You may need to upgrade some of them before building the platform. Otherwise, the last one will not live up to expectations. Decide on a way of platform creation. You can always initiate development from scratch and build your unique product, as the Belgian MCS Solutions did. Management engaged Intellias to design a that fully enables smart buildings within their lifecycle. Thus, the company has provided customers with additional value and boosted their experience. facility IoT platform You can and should choose a route if your business requires it. But get ready to invest more time and resources. An alternative is to use a sort of an open-source low code platform like IBM Watson Studio, XME platform or Bosch IoT Suite. Consequently, you'll also get a custom flexible product, but 2x faster and cheaper. Find a suitable vendor. Sure, the client can deal with it by engaging in-house engineers. But one of the key challenges in building IoT platforms is a lack of expertise in this field. Sometimes you should tell the client that the time needed to train their technicals is much more than designing a digital service platform. On the other hand, vendor-side engineers have experience and probably specialize in creating IoT software. Therefore, everyone benefits more from working with a remote team. Write down requirements for software and estimate the future project from your side. Please pay special attention to requirements, as clarity depends on the entire future development process. You should simulate a vision for the product and convey it to anyone creating it. Besides, if you have a resource, try to estimate the project yourself. You can find an independent expert, use the analogy method, etc. It will help determine the approximate budget for the IoT platform development. Take care of product architecture. A firmware update architecture for the Internet of Things devices is a crucial stage. An involved architect will simulate interactions between the platform, connected devices, and web app. Consider microservice architecture first. It will allow your product to be scalable and handle countless requests simultaneously. Notice the way the architect tries to connect all sensors and the platform. You should know in advance what types of devices you are using in your smart buildings, as the quality of the connection depends on them. Use middleware. Point-to-point integration leads to several risks. For example, one service failure can provoke downtime in a core ​process. By implementing middleware, you reduce the load on the underlying systems and ensure a quality connection. Deal with challenges. Building a platform that allows you to manage devices remotely and make over-the-air firmware updates is not an easy process. You will have to unify lots of devices from different manufacturers using a variety of communication protocols. An indicator of the quality platform here is the secure firmware update. So take care of it, especially if you want to receive additional commercial benefits (for example, the above firmware resale). The Bottom Line Following predictions, there will be about by 2025. Considering that we're using 175 mln smart buildings now, the lion's share of devices will fall on them. It means the sooner we all think about developing an IoT platform for over-the-air firmware, the more benefits we’ll get. 75 bln of connected devices

Why we Gave Up on the Traditional Firmware Update Process

Building for the Web 3 Ecosystem Polkadot is leading and bringing seminars, presentations, and a $15,000 prize pool to ETHDenver  as part of the world's biggest Ethereum hackathon. Polkadot offers a $15,000 prize pool as part of the ETHDenver BUIDLathon (hackathon). Since 2018, ETHDenver has brought blockchain and crypto engineers, designers, enthusiasts, cypher-punks, and the decentralised technology community to Denver, Colorado.  The inquisitive minds who have gathered here for the BUIDLathon "hackathon" are looking for ways to innovate, to create breakthrough ideas for the next phase of the Internet, Web3. Polkadot is a decentralised network that may be used for various purposes. It's a layer-0 blockchain architecture at its core, supporting many self-sovereign blockchains with common security and interoperability services. In a word, this implies that parachains built on Polkadot may be optimised from the start for everything from DeFi to gaming to smart contracts to privacy...the possibilities are endless. These parachains can also host level 2 dapps, such as Solidity/EVM deployments. This opens up an exciting new phase for Web3 with trustless bridges to other networks like Ethereum and Bitcoin and the capacity to interoperate. **Urban Osvald, Developer Relations Partnerships Lead, W3F, said: ** We’re thrilled to be back at ETHDenver. Not only is it a refreshing change of pace to participate in an in-person event, but also to see how the entire Polkadot ecosystem has grown. We have over twenty hours of Polkadot content, from in-depth Substrate workshops led by Parity core engineers to dozens of ecosystem projects showcasing their tech in presentations, workshops, and panel discussions. I’m confident that this year ETHDenver will be a splashing success! With new parachains being introduced to the Polkadot ecosystem regularly, the community is constructing new parachains for a chance to win a piece of the $15,000 prize pool. This sum is divided 50/50 across the hackathon's virtual and in-person components. Polkadot is based on Substrate, a blockchain-building platform that is modular. You may easily develop and deploy your own blockchain using its pre-built components. Substrate allows for forkless updates, making changes as technology and needs evolve easy. Substrate blockchains may function independently or integrate with Polkadot and Kusama, its wild-cousin canary network. The Polkadot ecosystem also has a vibrant community that includes everyone from seasoned developers to those intrigued by the technology and its potential applications. Understanding How Polkadot Parachain Auctions Work A parachain auction is an open, permissionless auction that allows parachains (substrate-based independent blockchains) to connect to the Polkadot or Kusama Relay Chains. The auctions' goal is to distribute parachain slots to projects that desire to use either network. The main goal is to have up to 100 parachains operating simultaneously. These will, however, be opened in phases, with 20-30 parachains functioning within the first year. These parachain slots are limited to stimulate competition and the most efficient use of fixed Relay Chain resources. Because the number of available slots is limited, prospective parachains must outbid one another to get a lease, both for the original launch and renewal after the lease has expired. Leases on Kusama, for example, are limited to 48 weeks, whereas Polkadot allows for a 96-week commitment. Only successful parachains with traction, active use, and a sustainable token economic model may keep a slot while abandoned or failing parachains relinquish their lease to more promising challengers. In addition to the apparent bootstrapping goal, the crowd loan feature provides a few other reasons. For starters, it distinguishes between initiatives and the tokens provided. This is a key contrast from a direct fundraising mechanism such as a public auction since the donated tokens may only be used to earn a slot and must be returned if that objective is not accomplished. Second, projects must develop a passionate community to back their crowd loan campaign. While this is a part of other fundraising efforts outside of Polkadot, the frequent nature of the auctions means that many potential projects are fighting for each individual's KSM or DOT tokens, which can only be used to support one project at a time. The approach is built on a competitive spirit and a feeling of fairness. Catch all the breaking news, and Don’t forget to like the story!

Polkadot is Leading the World's Largest Ethereum Hackathon for Web3

If you’ve been writing Go code for a while, you’ve probably come across range loops. They’re elegant, concise, and incredibly handy for iterating over slices, maps, and channels. But did you know that their behavior can sometimes be counterintuitive, leading to subtle bugs in your code? In this article, I’ll break down the mechanics of range loops, explain why these quirks occur, and share actionable tips to avoid common pitfalls. By the end, you’ll have a deeper understanding of Go and be better equipped to write reliable, bug-free code. 🚀 What Makes Range Loops Tricky? At first glance, range loops in Go appear straightforward. Here’s an example you’ve likely seen: codenumbers := []int{1, 2, 3, 4, 5}  
for index, value := range numbers {  
    fmt.Printf("Index: %d, Value: %d\n", index, value)  
} This prints the index and value for each element in the slice. Simple, right? But here’s where things get interesting (and potentially frustrating). The Underlying Mechanics of Range When you use range, Go creates a copy of the value you’re iterating over. For slices, this means you’re not directly accessing the elements but working with their copies. This subtle detail can lead to unexpected behavior. Take this example: codewords := []string{"Go", "is", "awesome"}  
wordPointers := []*string{}  

for _, word := range words {  
    wordPointers = append(wordPointers, &word)  
}  

for _, pointer := range wordPointers {  
    fmt.Println(*pointer)  
} You might expect this to print: Go  
is  
awesome But instead, you’ll see: awesome  
awesome  
awesome Why? Because word is a single variable reused in every iteration. The loop appends the same variable’s address each time, and by the end of the loop, word holds the value of the last element. Avoiding Common Pitfalls Here are some tips to keep your range loops bug-free: Understand Copying Behavior:If you’re modifying elements, remember thatrange loops work with copies for slices and arrays. Use index-based access if you need to modify the original elements.
for i := range numbers {  
    numbers[i] *= 2  
}  




Be Cautious with Pointers:If you need to capture references to elements, use the index instead of the loop variable.
for i := range words {  
    wordPointers = append(wordPointers, &words[i])  
}  




Debug Complex Loops:Usefmt.Printf to inspect variables and their memory addresses. This helps identify if you’re unintentionally working with shared references. Why This Matters Understanding these quirks isn’t just about avoiding bugs—it’s about mastering Go. As you tackle more complex projects, these small details can have significant impacts on performance, correctness, and maintainability. This article is part of a series I’m writing to explore common mistakes in Go programming. The first topic, range loops, is something many developers encounter but don’t fully understand until they’ve run into a bug. Final Thoughts Range loops are one of Go’s most powerful tools, but like any tool, they come with nuances. By understanding how they evaluate and iterate over elements, you can write cleaner, more reliable code. If you’ve ever been bitten by a range loop bug—or have tips of your own—I’d love to hear from you! Let’s share knowledge and grow as a community. 🔗 Read the full article on my LinkedIn For more insights like this, follow my journey in exploring the intricacies of Go programming. 🚀 If you’ve been writing Go code for a while, you’ve probably come across range loops. They’re elegant, concise, and incredibly handy for iterating over slices, maps, and channels. But did you know that their behavior can sometimes be counterintuitive, leading to subtle bugs in your code? range In this article, I’ll break down the mechanics of range loops, explain why these quirks occur, and share actionable tips to avoid common pitfalls. By the end, you’ll have a deeper understanding of Go and be better equipped to write reliable, bug-free code. 🚀 range What Makes Range Loops Tricky? What Makes Range At first glance, range loops in Go appear straightforward. Here’s an example you’ve likely seen: range codenumbers := []int{1, 2, 3, 4, 5}  
for index, value := range numbers {  
    fmt.Printf("Index: %d, Value: %d\n", index, value)  
} codenumbers := []int{1, 2, 3, 4, 5}  
for index, value := range numbers {  
    fmt.Printf("Index: %d, Value: %d\n", index, value)  
} This prints the index and value for each element in the slice. Simple, right? But here’s where things get interesting (and potentially frustrating). The Underlying Mechanics of Range The Underlying Mechanics of Range When you use range , Go creates a copy of the value you’re iterating over. For slices, this means you’re not directly accessing the elements but working with their copies. This subtle detail can lead to unexpected behavior. range Take this example: codewords := []string{"Go", "is", "awesome"}  
wordPointers := []*string{}  

for _, word := range words {  
    wordPointers = append(wordPointers, &word)  
}  

for _, pointer := range wordPointers {  
    fmt.Println(*pointer)  
} codewords := []string{"Go", "is", "awesome"}  
wordPointers := []*string{}  

for _, word := range words {  
    wordPointers = append(wordPointers, &word)  
}  

for _, pointer := range wordPointers {  
    fmt.Println(*pointer)  
} You might expect this to print: Go  
is  
awesome Go  
is  
awesome But instead, you’ll see: awesome  
awesome  
awesome awesome  
awesome  
awesome Why? Because word is a single variable reused in every iteration. The loop appends the same variable’s address each time, and by the end of the loop, word holds the value of the last element. word word Avoiding Common Pitfalls Avoiding Common Pitfalls Here are some tips to keep your range loops bug-free: range Understand Copying Behavior:If you’re modifying elements, remember thatrange loops work with copies for slices and arrays. Use index-based access if you need to modify the original elements.
for i := range numbers {  
    numbers[i] *= 2  
} Be Cautious with Pointers:If you need to capture references to elements, use the index instead of the loop variable.
for i := range words {  
    wordPointers = append(wordPointers, &words[i])  
} Debug Complex Loops:Usefmt.Printf to inspect variables and their memory addresses. This helps identify if you’re unintentionally working with shared references. Understand Copying Behavior:If you’re modifying elements, remember thatrange loops work with copies for slices and arrays. Use index-based access if you need to modify the original elements. for i := range numbers {  
    numbers[i] *= 2  
} Understand Copying Behavior : If you’re modifying elements, remember that range loops work with copies for slices and arrays. Use index-based access if you need to modify the original elements. Understand Copying Behavior range for i := range numbers {  
    numbers[i] *= 2  
} for i := range numbers {  
    numbers[i] *= 2  
} Be Cautious with Pointers:If you need to capture references to elements, use the index instead of the loop variable. for i := range words {  
    wordPointers = append(wordPointers, &words[i])  
} Be Cautious with Pointers : If you need to capture references to elements, use the index instead of the loop variable. Be Cautious with Pointers for i := range words {  
    wordPointers = append(wordPointers, &words[i])  
} for i := range words {  
    wordPointers = append(wordPointers, &words[i])  
} Debug Complex Loops:Usefmt.Printf to inspect variables and their memory addresses. This helps identify if you’re unintentionally working with shared references. Debug Complex Loops : Use fmt.Printf to inspect variables and their memory addresses. This helps identify if you’re unintentionally working with shared references. Debug Complex Loops fmt.Printf Why This Matters Why This Matters Understanding these quirks isn’t just about avoiding bugs—it’s about mastering Go. As you tackle more complex projects, these small details can have significant impacts on performance, correctness, and maintainability. This article is part of a series I’m writing to explore common mistakes in Go programming. The first topic, range loops, is something many developers encounter but don’t fully understand until they’ve run into a bug. range Final Thoughts Final Thoughts Range loops are one of Go’s most powerful tools, but like any tool, they come with nuances. By understanding how they evaluate and iterate over elements, you can write cleaner, more reliable code. Range If you’ve ever been bitten by a range loop bug—or have tips of your own—I’d love to hear from you! Let’s share knowledge and grow as a community. range 🔗 Read the full article on my LinkedIn Read the full article on my LinkedIn For more insights like this, follow my journey in exploring the intricacies of Go programming. 🚀

Unlock the Secrets of Range Loops: How Element Evaluation Affects Your Go Code

Data science and user experience design have recently become critical to the success of companies' sophisticated internal digital tools for analyzing, processing information, and predicting what-if scenarios. Used by internal teams and operations staff, these tools aim to optimize processes, predict outcomes, and inform decision-making in increasingly complex systems. For those working in data science, a common approach is to view humans as “human-in-the-loop,” meaning users provide inputs to a system or validate outputs, but their role remains secondary to technology and algorithms. It is worth considering that the complexity and nuanced requirements of modeling tools have begun to require a shift to a more human-centered approach to make the tools accessible to a wider audience. This article examines human-centered design and the need for such a focus for those working on data science projects and suggests strategies to facilitate this transition. Determining the Difference To address human-related issues (emotional state, learning, outcome), the concept of “human-in-the-loop” (HITL) was introduced, which involves incorporating human knowledge into the modeling process. The HITL approach is often a common way of thinking among data science teams, where humans are seen as secondary players who help the machine function better or validate the results generated by algorithms. This interdisciplinary approach crosses computer science, cognitive science, and psychology and consists of three components: data preprocessing, data modeling, and process modification to improve performance. However, it is important to consider that the results and performance of ML models can be unpredictable, so it remains unclear which aspect of human-machine interaction yields the best learning results. People are thus viewed as add-ons to the system—sources of labeled data, corrections, or ideas that help fine-tune the models—without taking into account their broader needs, motivations, or limitations. Human involvement depends on the type of solution being implemented: strategic, tactical, or operational. In operational and tactical systems, the human role is usually minimal. In contrast, in strategic solutions, the human role is critical because these decisions require context, explainability, and domain expertise. They often rely on human interpretation to validate assumptions, assess risks, and align outcomes with broader goals. Human-centered design, on the other hand, places the user at the center of the design and development process by aligning technology with human needs, behaviors, motivation, and goals. Understanding the "why" behind user behaviors is a key component of human-centered design, which integrates user context and requirements thoroughly into all phases of product development. For data scientists, embracing human-centered design is more than just incorporating human input into systems; it also entails actively reshaping systems to meet the actual users’ needs and developing tools that empower rather than burden them. Why Human-Centered Design Matters In complex, high-stakes environments, inconsistent forecasts, confusing workflows, or overly complex interfaces can lead to poor decision-making, frustration, and operational inefficiencies. That’s why a human-centered approach is invaluable in such settings, enabling users to receive answers tailored to their needs. Often, users view data science solutions as “black boxes,” making it difficult to trust them or understand the results they produce. Developing tools and solutions that explain the relationships between inputs and outputs and the behavior of models can improve trust and usability, empowering users to confidently make informed decisions. This need for explainability and transparency is directly related to the need for human-centered design and more understandable solutions. When tools are designed with end-users in mind, they are more likely to be adopted and used effectively by users. When data scientists understand the nuances of user tasks, they can create models and interfaces that match users’ real workflows and contexts, rather than idealized or abstract ones. This increases trust in the tool’s output and minimizes the learning curve for users. Moreover, the accuracy and applicability of machine learning models can be highly dependent on the user context. These subtleties are better captured by a human-centered approach, which ultimately produces more accurate and meaningful predictions for users. By providing users with the tools they need to make informed decisions, human-centered design empowers users. Instead of relying on the user to validate a machine’s predictions, tools can allow users to make decisions alongside the machine, fostering collaboration rather than a directive relationship. Practical Steps to Move Toward Human-Centered Design While the benefits of human-centered design may be clear and obvious, shifting from a “human in the loop” requires both structural changes and cultural and team support. Here are some actionable steps teams can take to embrace human-centered design. 1. User Research First Researching users and their context is fundamental to human-centered design. Thus, interviews, ethnographic studies, observations of users’ workflows, and identification of pain points, motivations, and goals help to better understand and study users. Such research produces qualitative insights that can be complemented by quantitative data, providing a deeper understanding of users' context. Additionally, creating user journey maps that detail the workflows and touchpoints users have with various tools and other organizational functions allows for a more accurate assessment of what users are experiencing. This visual tool can help highlight opportunities where these tools can add more value or streamline user processes. 2. Prototype With a Purpose To make efficient use of a team's time by experimenting with different models, assumptions, and data visualizations while focusing not only on achieving technical goals but also on creating features that are clear, relevant, and useful to the user, rapid prototyping can help. It’s a fundamental practice of human-centered design that brings ideas to life and allows users to test the prototype early, for example in Figma, without spending time on development. This ensures that user feedback is obtained before a huge amount of time is spent on product development and helps to quickly get to the point where the user's problem is solved by matching the real needs and defining what tasks the user needs to perform to achieve it. 3. Design for Trust and Transparency A critical component of human-centered design is building trust and transparency so that when users receive forecasts or recommendations, they understand the validity of those results and can make business decisions and act on them with confidence. Trust in data tools can be achieved by helping users understand why the system suggests a certain outcome or path and by visualizing results that are understandable to the user and consistent with their mental model. 4. Development of a Collaborative and Interdisciplinary Team Culture Creating successful human-centered data tools requires collaboration across disciplines. Having a shared team vision based on the human-centered design will help bridge gaps in expertise and perspectives, ensuring that the final digital solution meets all team goals, prioritising user satisfaction, usability, and utility. Additionally, building empathy through shared UX activities helps develop a more empathetic understanding of the end user. 5. Measuring Success With User-Centric Metrics Implementing human-centred design means going beyond technical success metrics and focusing on including metrics that reflect user satisfaction and performance: a) regular feedback from users on their satisfaction with the tool and specific features; b) task success rates and time to complete them; c) feedback loops such as surveys or regular feedback sessions that help identify and resolve issues in real time, rather than waiting for problems to escalate. Conclusion Moving from “human-in-the-loop” to human-centred design is a mindset shift that emphasises designing for users rather than simply using users to validate machine output. In companies where decisions can have a high impact and significant risk, human-centred design helps better align tools with users' real-world needs and context. For data scientists, this mental shift paves the way for building digital tools that are not only functionally efficient but also truly empower users and enable them to engage a broader audience. When data tools are built with a human-centric mindset, they become tools that help users make better, faster, and more confident decisions, ultimately improving both the user experience and operational outcomes. Data science and user experience design have recently become critical to the success of companies' sophisticated internal digital tools for analyzing, processing information, and predicting what-if scenarios. Used by internal teams and operations staff, these tools aim to optimize processes, predict outcomes, and inform decision-making in increasingly complex systems. For those working in data science, a common approach is to view humans as “human-in-the-loop,” meaning users provide inputs to a system or validate outputs, but their role remains secondary to technology and algorithms. It is worth considering that the complexity and nuanced requirements of modeling tools have begun to require a shift to a more human-centered approach to make the tools accessible to a wider audience. This article examines human-centered design and the need for such a focus for those working on data science projects and suggests strategies to facilitate this transition. Determining the Difference To address human-related issues (emotional state, learning, outcome), the concept of “human-in-the-loop” (HITL) was introduced, which involves incorporating human knowledge into the modeling process. The HITL approach is often a common way of thinking among data science teams, where humans are seen as secondary players who help the machine function better or validate the results generated by algorithms. This interdisciplinary approach crosses computer science, cognitive science, and psychology and consists of three components: data preprocessing, data modeling, and process modification to improve performance. However, it is important to consider that the results and performance of ML models can be unpredictable, so it remains unclear which aspect of human-machine interaction yields the best learning results. People are thus viewed as add-ons to the system—sources of labeled data, corrections, or ideas that help fine-tune the models—without taking into account their broader needs, motivations, or limitations. Human involvement depends on the type of solution being implemented: strategic, tactical, or operational. In operational and tactical systems, the human role is usually minimal. In contrast, in strategic solutions, the human role is critical because these decisions require context, explainability, and domain expertise. They often rely on human interpretation to validate assumptions, assess risks, and align outcomes with broader goals. Human-centered design, on the other hand, places the user at the center of the design and development process by aligning technology with human needs, behaviors, motivation, and goals. Understanding the "why" behind user behaviors is a key component of human-centered design, which integrates user context and requirements thoroughly into all phases of product development. For data scientists, embracing human-centered design is more than just incorporating human input into systems; it also entails actively reshaping systems to meet the actual users’ needs and developing tools that empower rather than burden them. Why Human-Centered Design Matters In complex, high-stakes environments, inconsistent forecasts, confusing workflows, or overly complex interfaces can lead to poor decision-making, frustration, and operational inefficiencies. That’s why a human-centered approach is invaluable in such settings, enabling users to receive answers tailored to their needs. Often, users view data science solutions as “black boxes,” making it difficult to trust them or understand the results they produce. Developing tools and solutions that explain the relationships between inputs and outputs and the behavior of models can improve trust and usability, empowering users to confidently make informed decisions. This need for explainability and transparency is directly related to the need for human-centered design and more understandable solutions. When tools are designed with end-users in mind, they are more likely to be adopted and used effectively by users. When data scientists understand the nuances of user tasks, they can create models and interfaces that match users’ real workflows and contexts, rather than idealized or abstract ones. This increases trust in the tool’s output and minimizes the learning curve for users. Moreover, the accuracy and applicability of machine learning models can be highly dependent on the user context. These subtleties are better captured by a human-centered approach, which ultimately produces more accurate and meaningful predictions for users. By providing users with the tools they need to make informed decisions, human-centered design empowers users. Instead of relying on the user to validate a machine’s predictions, tools can allow users to make decisions alongside the machine, fostering collaboration rather than a directive relationship. Practical Steps to Move Toward Human-Centered Design While the benefits of human-centered design may be clear and obvious, shifting from a “human in the loop” requires both structural changes and cultural and team support. Here are some actionable steps teams can take to embrace human-centered design. 1. User Research First Researching users and their context is fundamental to human-centered design. Thus, interviews, ethnographic studies, observations of users’ workflows, and identification of pain points, motivations, and goals help to better understand and study users. Such research produces qualitative insights that can be complemented by quantitative data, providing a deeper understanding of users' context. Additionally, creating user journey maps that detail the workflows and touchpoints users have with various tools and other organizational functions allows for a more accurate assessment of what users are experiencing. This visual tool can help highlight opportunities where these tools can add more value or streamline user processes. 2. Prototype With a Purpose To make efficient use of a team's time by experimenting with different models, assumptions, and data visualizations while focusing not only on achieving technical goals but also on creating features that are clear, relevant, and useful to the user, rapid prototyping can help. It’s a fundamental practice of human-centered design that brings ideas to life and allows users to test the prototype early, for example in Figma, without spending time on development. This ensures that user feedback is obtained before a huge amount of time is spent on product development and helps to quickly get to the point where the user's problem is solved by matching the real needs and defining what tasks the user needs to perform to achieve it. 3. Design for Trust and Transparency A critical component of human-centered design is building trust and transparency so that when users receive forecasts or recommendations, they understand the validity of those results and can make business decisions and act on them with confidence. Trust in data tools can be achieved by helping users understand why the system suggests a certain outcome or path and by visualizing results that are understandable to the user and consistent with their mental model. 4. Development of a Collaborative and Interdisciplinary Team Culture Creating successful human-centered data tools requires collaboration across disciplines. Having a shared team vision based on the human-centered design will help bridge gaps in expertise and perspectives, ensuring that the final digital solution meets all team goals, prioritising user satisfaction, usability, and utility. Additionally, building empathy through shared UX activities helps develop a more empathetic understanding of the end user. 5. Measuring Success With User-Centric Metrics Implementing human-centred design means going beyond technical success metrics and focusing on including metrics that reflect user satisfaction and performance: a) regular feedback from users on their satisfaction with the tool and specific features; b) task success rates and time to complete them; c) feedback loops such as surveys or regular feedback sessions that help identify and resolve issues in real time, rather than waiting for problems to escalate. Conclusion Moving from “human-in-the-loop” to human-centred design is a mindset shift that emphasises designing for users rather than simply using users to validate machine output. In companies where decisions can have a high impact and significant risk, human-centred design helps better align tools with users' real-world needs and context. For data scientists, this mental shift paves the way for building digital tools that are not only functionally efficient but also truly empower users and enable them to engage a broader audience. When data tools are built with a human-centric mindset, they become tools that help users make better, faster, and more confident decisions, ultimately improving both the user experience and operational outcomes.

Design and Data Science: From a Human-in-the-Loop Approach to Human-Centered Design

Over-optimized loops hurt the eyes TL;DR: Don't optimize loops without a clear need and concrete real-world evidence Problems Premature Optimization
Reduced readability
Increased complexity
Difficult to maintain
Slower debugging Solutions Keep it simple
Prioritize clarity
Avoid premature tweaks
Refactor when needed Context You might think optimizing every loop will improve performance, but this approach backfires when you sacrifice clarity for unproven gains. Writing complex code to avoid hypothetical slowdowns often makes it hard for others (and your future self) to understand or debug your code. It would be best if you prioritized readability. Keep loops simple and only optimize when you know a bottleneck exists in real usage scenarios. Sample Code Wrong # Over-optimized and less readable
result = [item.process() for item in items if item.is_valid()] Right # Clearer and easier to understand
result = []
for item in items:
    if item.is_valid():
        result.append(item.process()) Detection [x]Semi-Automatic Look for list comprehensions or complex loop structures that optimize performance without real performance benchmark evidence. Exceptions Concrete evidence on mission-critical algorithms Tags Premature Optimization Level [x]Intermediate AI Generation AI tools often prioritize functional correctness so that they might produce clean, simple loops. if you prompt AI for performance at all costs, it could create over-optimized code even for straightforward tasks. AI Detection With proper instructions to stress readability and maintainability, AI can detect and fix this smell by simplifying loops and choosing clarity over premature optimization. Try Them! Remember: AI Assistants make lots of mistakes Without Proper Instructions

With Specific Instructions



ChatGPT

ChatGPT



Claude

Claude



Perplexity

Perplexity



Copilot

Copilot



Gemini

Gemini Conclusion Don’t sacrifice readability by optimizing too early. You can optimize later if a loop becomes a proven bottleneck. Until then, clear and simple code will save time, reduce bugs, and make it more maintainable. Related Reading https://hackernoon.com/how-to-find-the-stinky-parts-of-your-code-part-iv-7sc3w8n https://hackernoon.com/how-to-find-the-stinky-parts-of-your-code-part-xxvi https://hackernoon.com/how-to-find-the-stinky-parts-of-your-code-part-ii-o96s3wl4 Disclaimer: Code Smells are my opinion. Credit(s): Photo by Tine Ivanič on Unsplash More computing sins are committed in the name of efficiency without necessarily achieving it than for any other single reason. W. A. Wulf https://hackernoon.com/400-thought-provoking-software-engineering-quotes?embedable=true This article is part of the CodeSmell Series: How to Find the Stinky Parts of your Code Over-optimized loops hurt the eyes Over-optimized loops hurt the eyes TL;DR: Don't optimize loops without a clear need and concrete real-world evidence TL;DR: Don't optimize loops without a clear need and concrete real-world evidence Problems Premature Optimization Reduced readability Increased complexity Difficult to maintain Slower debugging Premature Optimization Premature Optimization Reduced readability Increased complexity Difficult to maintain Slower debugging Solutions Keep it simple Prioritize clarity Avoid premature tweaks Refactor when needed Keep it simple Prioritize clarity Avoid premature tweaks Refactor when needed Context You might think optimizing every loop will improve performance, but this approach backfires when you sacrifice clarity for unproven gains. Writing complex code to avoid hypothetical slowdowns often makes it hard for others (and your future self) to understand or debug your code. It would be best if you prioritized readability. Keep loops simple and only optimize when you know a bottleneck exists in real usage scenarios. hypothetical real usage Sample Code Wrong # Over-optimized and less readable
result = [item.process() for item in items if item.is_valid()] # Over-optimized and less readable
result = [item.process() for item in items if item.is_valid()] Right # Clearer and easier to understand
result = []
for item in items:
    if item.is_valid():
        result.append(item.process()) # Clearer and easier to understand
result = []
for item in items:
    if item.is_valid():
        result.append(item.process()) Detection [x]Semi-Automatic [x] Semi-Automatic [x] Look for list comprehensions or complex loop structures that optimize performance without real performance benchmark evidence. Exceptions Concrete evidence on mission-critical algorithms Concrete evidence on mission-critical algorithms Tags Premature Optimization Premature Optimization Level [x]Intermediate [x] Intermediate [x] AI Generation AI tools often prioritize functional correctness so that they might produce clean, simple loops. if you prompt AI for performance at all costs, it could create over-optimized code even for straightforward tasks. AI Detection With proper instructions to stress readability and maintainability, AI can detect and fix this smell by simplifying loops and choosing clarity over premature optimization. Try Them! Try Them! Remember: AI Assistants make lots of mistakes Remember: AI Assistants make lots of mistakes Without Proper Instructions

With Specific Instructions



ChatGPT

ChatGPT



Claude

Claude



Perplexity

Perplexity



Copilot

Copilot



Gemini

Gemini Without Proper Instructions

With Specific Instructions ChatGPT

ChatGPT Claude

Claude Perplexity

Perplexity Copilot

Copilot Gemini

Gemini Without Proper Instructions With Specific Instructions Without Proper Instructions Without Proper Instructions With Specific Instructions With Specific Instructions ChatGPT ChatGPT ChatGPT ChatGPT ChatGPT ChatGPT ChatGPT ChatGPT Claude Claude Claude Claude Claude Claude Claude Claude Perplexity Perplexity Perplexity Perplexity Perplexity Perplexity Perplexity Perplexity Copilot Copilot Copilot Copilot Copilot Copilot Copilot Copilot Gemini Gemini Gemini Gemini Gemini Gemini Gemini Gemini Conclusion Don’t sacrifice readability by optimizing too early. You can optimize later if a loop becomes a proven bottleneck. Until then, clear and simple code will save time, reduce bugs, and make it more maintainable. Related Reading https://hackernoon.com/how-to-find-the-stinky-parts-of-your-code-part-iv-7sc3w8n https://hackernoon.com/how-to-find-the-stinky-parts-of-your-code-part-iv-7sc3w8n https://hackernoon.com/how-to-find-the-stinky-parts-of-your-code-part-xxvi https://hackernoon.com/how-to-find-the-stinky-parts-of-your-code-part-xxvi https://hackernoon.com/how-to-find-the-stinky-parts-of-your-code-part-ii-o96s3wl4 https://hackernoon.com/how-to-find-the-stinky-parts-of-your-code-part-ii-o96s3wl4 Disclaimer: Code Smells are my opinion. Disclaimer: Code Smells are my opinion . opinion Credit(s): Photo by Tine Ivanič on Unsplash Credit(s): Photo by Tine Ivanič on Unsplash Tine Ivanič Unsplash More computing sins are committed in the name of efficiency without necessarily achieving it than for any other single reason. More computing sins are committed in the name of efficiency without necessarily achieving it than for any other single reason. W. A. Wulf W. A. Wulf https://hackernoon.com/400-thought-provoking-software-engineering-quotes?embedable=true https://hackernoon.com/400-thought-provoking-software-engineering-quotes?embedable=true This article is part of the CodeSmell Series: How to Find the Stinky Parts of your Code This article is part of the CodeSmell Series: How to Find the Stinky Parts of your Code How to Find the Stinky Parts of your Code

Code Smell 279 - Loop Premature Optimization

ZeroGPT is one of the most commonly used AI checkers by students, writers, and others looking to scan text without breaking the bank. However, it is also known to present high false positive scores and this can be difficult to counter. Luckily, there are AI humanizers that can help you bypass ZeroGPT. In this guide, we’ll explore PassMe.ai to see if this tool can get the job done. Read on for all the details! How To Bypass ZeroGPT ZeroGPT is an advanced AI detection model developed in Germany and launched back in January 2023. The tool quickly gained popularity online because of the free access it offers and the DeepAnalyse™ technology model it uses to detect AI use. However, it can be too stringent but PassMe.ai is a potential solution that can help you overcome this. PassMe.ai: A Brief Introduction PassMe.ai is an advanced AI stealth writer that is extensively trained to spot the common writing patterns of text used by LLMs like GPT4, Gemini, Claude, ChatGPT, etc. It then reworks and restructures the content to sound more human-like, which enables it to bypass AI detection with excellent readability. The output this tool produces is also guaranteed to be 100% duplicate-free, so you should have no trouble passing any plagiarism scan without any hassle. Additionally, PassMe.ai always retains the original intent behind your message in its rewritten text. Best of all, it supports 50+ native languages including French, Mandarin, Japanese, etc. Having summed up what this tool is all about, let’s find out if it can beat ZeroGPT! How Does PassMe.ai Perform Against ZeroGPT? To determine how these two platforms stack up with one another, we need to conduct a comprehensive test. To start with, we generated a short piece of text using ChatGPT. You can check it out below. We then tested the AI-generated content on ZeroGPT just to confirm its detectability. Here’s the result of the scan. Next up, we went to PassMe.ai to humanize the AI content. All it took was a few simple clicks and it produced a rewritten version in seconds. You can see the output below. The final step was to see if this new version would pass ZeroGPT’s AI detection scan or not. You can see the result below. As you can see, PassMe.ai managed to produce a 100% undetectable result on one of the most stringent AI detectors in the market. With ZeroGPT unable to find any signs of AI involvement in the humanized version, this proves that it can get the job done perfectly. Which Other Tool Can Bypass ZeroGPT? Other alternatives can help you make your AI content undetectable to ZeroGPT, so let’s explore them below. Each of these tools has something unique to offer, so be sure to consider them based on your specific needs and requirements. AIHumanize.com - Best AI Bypass Tool For Maintaining Original Meaning AIHumanize is an effective AI bypass tool that can humanize any type of AI content with contextual accuracy. You can rewrite AI-generated essays, reports, articles, papers, and more, which makes it a versatile tool to rely on. It even works with any AI writer, so you can confidently humanize your AI content no matter which tool generated it. Besides that, it has a unique feature that can mimic your own writing style. This enables you to maintain a consistent brand voice with audiences. Furthermore, AI Humanize is a mobile-optimized site, so you can freely use it to make AI text undetectable via any device and at any time for added convenience. Rewritify AI - Best AI Bypass Tool For Speedy AI Rewriting Rewritify AI offers unmatched speed and precision when it comes to humanizing AI text. The platform can produce a newly rewritten version of your AI content in seconds with grammatical correctness being exceptionally well-maintained. Moreover, it can be relied on to produce plagiarism-free content that is still keyword-rich. As such, it is well-suited for humanizing marketing or advertising content that you want to rank highly on SERPs. Aside from that, Rewritify AI has an AI checker linked to several AI detectors like Sapling, Writer, Crossplag, and more. This enables you to verify your content’s undetectability across all these platforms in one place. Stealthly AI - Best AI Bypass Tool For Native Language Content Stealthly AI is an advanced AI humanizer that is trained on an extensive dataset of native language texts making it capable of rewriting AI content in dozens of languages. This includes Portuguese, Hindi, Spanish, Swahili, and many others. It even ensures that the output is entirely error-free, so there won’t be any need for revisions later. Moreover, Stealthly AI can accommodate bulk requests with the tool consistently producing rewritten outputs that all boast a high rate of undetectability and quality. It even offers high word counts on all premium plans, which makes it a cost-effective solution for those who need to humanize AI text often. Uncheck AI - Best AI Bypass Tool For Varied Content Outputs Uncheck AI provides users with access to multiple AI bypass modes that will produce different content outputs to choose from. This can be useful to those who may need multiple versions of the same content. You can choose between Instant, Advanced, and Precise modes with each one ensuring the output is still true to the original meaning. Additionally, Uncheck AI conveniently lets you scan your content using its AI detector feature via the same interface with accurate results. It also has ChatGPT watermark removal, which works to automatically erase and replace any special phrasing that may be typically flagged by AI detectors. Humanizer.org - Best AI Bypass Tool For Ease of Use Humanizer.org is an AI bypass tool with a user-friendly interface that caters well to any beginner. With no cluttered elements or complex features, it only takes a few seconds to make AI content undetectable on any device. The platform is also highly efficient, so you can expect quality output with zero delays or errors. Another highlight of this tool is its unique Power Model, which is guaranteed to help you bypass advanced AI detectors like Originality and Turnitin without fail. Furthermore, Humanizer.org works well for various use cases. It can be utilized by students, marketers, content creators or business professionals looking to bypass AI content detection. Conclusion To sum it all up, ZeroGPT is a tough AI detector that often makes it difficult for AI content to pass unseen but with PassMe.ai, it becomes an entirely simple process. As we’ve shown, its state-of-the-art AI humanization model can rewrite AI content to be more human with guaranteed results. So, if you no longer want to deal with ZeroGPT AI detection, look no further than PassMe.ai! This story was distributed by Margrowth under HackerNoon’s Brand As An Author Program. Learn more about the program here: https://business.hackernoon.com/brand-as-author ZeroGPT is one of the most commonly used AI checkers by students, writers, and others looking to scan text without breaking the bank. However, it is also known to present high false positive scores and this can be difficult to counter. Luckily, there are AI humanizers that can help you bypass ZeroGPT. In this guide, we’ll explore PassMe.ai to see if this tool can get the job done. Read on for all the details! How To Bypass ZeroGPT ZeroGPT is an advanced AI detection model developed in Germany and launched back in January 2023. The tool quickly gained popularity online because of the free access it offers and the DeepAnalyse™ technology model it uses to detect AI use. However, it can be too stringent but PassMe.ai is a potential solution that can help you overcome this. PassMe.ai : A Brief Introduction PassMe.ai PassMe.ai PassMe.ai is an advanced AI stealth writer that is extensively trained to spot the common writing patterns of text used by LLMs like GPT4, Gemini, Claude, ChatGPT, etc. It then reworks and restructures the content to sound more human-like, which enables it to bypass AI detection with excellent readability. advanced AI stealth writer advanced AI stealth writer The output this tool produces is also guaranteed to be 100% duplicate-free, so you should have no trouble passing any plagiarism scan without any hassle. Additionally, PassMe.ai always retains the original intent behind your message in its rewritten text. Best of all, it supports 50+ native languages including French, Mandarin, Japanese, etc. Having summed up what this tool is all about, let’s find out if it can beat ZeroGPT! How Does PassMe.ai Perform Against ZeroGPT? To determine how these two platforms stack up with one another, we need to conduct a comprehensive test. To start with, we generated a short piece of text using ChatGPT. You can check it out below. We then tested the AI-generated content on ZeroGPT just to confirm its detectability. Here’s the result of the scan. Next up, we went to PassMe.ai to humanize the AI content. All it took was a few simple clicks and it produced a rewritten version in seconds. You can see the output below. The final step was to see if this new version would pass ZeroGPT’s AI detection scan or not. You can see the result below. As you can see, PassMe.ai managed to produce a 100% undetectable result on one of the most stringent AI detectors in the market. With ZeroGPT unable to find any signs of AI involvement in the humanized version, this proves that it can get the job done perfectly. Which Other Tool Can Bypass ZeroGPT? Other alternatives can help you make your AI content undetectable to ZeroGPT, so let’s explore them below. Each of these tools has something unique to offer, so be sure to consider them based on your specific needs and requirements. AIHumanize.com - Best AI Bypass Tool For Maintaining Original Meaning AIHumanize.com AIHumanize.com AIHumanize is an effective AI bypass tool that can humanize any type of AI content with contextual accuracy. You can rewrite AI-generated essays, reports, articles, papers, and more, which makes it a versatile tool to rely on. It even works with any AI writer, so you can confidently humanize your AI content no matter which tool generated it. Besides that, it has a unique feature that can mimic your own writing style. This enables you to maintain a consistent brand voice with audiences. Furthermore, AI Humanize is a mobile-optimized site, so you can freely use it to make AI text undetectable via any device and at any time for added convenience. Rewritify AI - Best AI Bypass Tool For Speedy AI Rewriting Rewritify AI Rewritify AI Rewritify AI offers unmatched speed and precision when it comes to humanizing AI text. The platform can produce a newly rewritten version of your AI content in seconds with grammatical correctness being exceptionally well-maintained. Moreover, it can be relied on to produce plagiarism-free content that is still keyword-rich. As such, it is well-suited for humanizing marketing or advertising content that you want to rank highly on SERPs. Aside from that, Rewritify AI has an AI checker linked to several AI detectors like Sapling, Writer, Crossplag, and more. This enables you to verify your content’s undetectability across all these platforms in one place. Stealthly AI - Best AI Bypass Tool For Native Language Content Stealthly AI Stealthly AI Stealthly AI is an advanced AI humanizer that is trained on an extensive dataset of native language texts making it capable of rewriting AI content in dozens of languages. This includes Portuguese, Hindi, Spanish, Swahili, and many others. It even ensures that the output is entirely error-free, so there won’t be any need for revisions later. Moreover, Stealthly AI can accommodate bulk requests with the tool consistently producing rewritten outputs that all boast a high rate of undetectability and quality. It even offers high word counts on all premium plans, which makes it a cost-effective solution for those who need to humanize AI text often. Uncheck AI - Best AI Bypass Tool For Varied Content Outputs Uncheck AI Uncheck AI Uncheck AI provides users with access to multiple AI bypass modes that will produce different content outputs to choose from. This can be useful to those who may need multiple versions of the same content. You can choose between Instant, Advanced, and Precise modes with each one ensuring the output is still true to the original meaning. Additionally, Uncheck AI conveniently lets you scan your content using its AI detector feature via the same interface with accurate results. It also has ChatGPT watermark removal, which works to automatically erase and replace any special phrasing that may be typically flagged by AI detectors. Humanizer.org - Best AI Bypass Tool For Ease of Use Humanizer.org Humanizer.org Humanizer.org is an AI bypass tool with a user-friendly interface that caters well to any beginner. With no cluttered elements or complex features, it only takes a few seconds to make AI content undetectable on any device. The platform is also highly efficient, so you can expect quality output with zero delays or errors. Another highlight of this tool is its unique Power Model, which is guaranteed to help you bypass advanced AI detectors like Originality and Turnitin without fail. Furthermore, Humanizer.org works well for various use cases. It can be utilized by students, marketers, content creators or business professionals looking to bypass AI content detection. Conclusion To sum it all up, ZeroGPT is a tough AI detector that often makes it difficult for AI content to pass unseen but with PassMe.ai, it becomes an entirely simple process. As we’ve shown, its state-of-the-art AI humanization model can rewrite AI content to be more human with guaranteed results. So, if you no longer want to deal with ZeroGPT AI detection, look no further than PassMe.ai ! look no further than PassMe.ai look no further than PassMe.ai This story was distributed by Margrowth under HackerNoon’s Brand As An Author Program. Learn more about the program here: https://business.hackernoon.com/brand-as-author This story was distributed by Margrowth under HackerNoon’s Brand As An Author Program. Learn more about the program here: https://business.hackernoon.com/brand-as-author This story was distributed by Margrowth under HackerNoon’s Brand As An Author Program. Learn more about the program here: https://business.hackernoon.com/brand-as-author This story was distributed by Margrowth under HackerNoon’s Brand As An Author Program. Learn more about the program here: https://business.hackernoon.com/brand-as-author https://business.hackernoon.com/brand-as-author

How To Bypass ZeroGPT: An In-Depth Look Into PassMe.ai

One key concept central to ensuring that AI remains a tool that benefits humanity rather than harms it is the notion of "Human in the Loop" (HITL). But what exactly is HITL, and why is it so vital in today’s AI landscape? What is "Human in the Loop"? Human in the Loop (HITL) refers to a system design in which humans are actively involved in the decision-making process of an AI system. Unlike fully automated AI systems that operate without any human intervention, HITL systems incorporate human judgment at critical stages—especially when decisions involve high stakes or ethical considerations. This model relies on a combination of machine efficiency and human intuition, ensuring that the final output is aligned with human values and societal norms. The Importance of a "Kill Switch" One of the most crucial aspects of HITL systems is the integration of a "kill switch" or an emergency stop mechanism. This is a literal or metaphorical button that allows human operators to override AI decisions or shut down the system entirely if it starts behaving in unintended or harmful ways. A kill switch is not just a precaution; it is a safeguard that recognizes the inherent unpredictability of AI systems, especially as they grow more complex and autonomous. AI, by design, can learn and adapt in ways that even its creators might not fully anticipate. Without the ability for a human to intervene—whether to halt a runaway algorithm or correct a decision that could have disastrous consequences—we risk losing control over the very technologies we created. This is particularly critical in sectors like healthcare, finance, and law enforcement, where AI-driven decisions can significantly impact human lives. Why HITL is Essential for Ethical and Safe AI As AI continues to infiltrate our daily lives, from facial recognition systems to predictive policing algorithms, ethical concerns have risen to the forefront. HITL is essential to ensuring that AI systems remain fair, transparent, and aligned with human rights. For instance, without human oversight, AI algorithms trained on biased data can perpetuate or even exacerbate discrimination. By keeping humans in the loop, we introduce a level of accountability that purely automated systems lack. Moreover, HITL offers a vital counterbalance to the "black box" nature of many AI systems. These systems often provide outputs without clear explanations of how they reached their conclusions. Human oversight allows us to question, validate, and adjust these decisions, reducing the risks associated with opaque or unexplainable outcomes. The Unseen Influence of AI on Our Lives Even without us fully realizing it, AI is already shaping our behaviors and choices. Social media algorithms decide which content we see, influencing our opinions and emotions. Recommender systems on platforms like YouTube, Netflix, and Amazon subtly guide our consumption habits, reinforcing our preferences while sometimes trapping us in echo chambers. The concerning part is that many of these AI-driven systems operate without meaningful human intervention or oversight. They are optimized for engagement and profit, often at the expense of societal well-being. These systems continuously learn from our interactions, amplifying biases, spreading misinformation, and fueling polarization—all without our conscious input or control. This highlights the urgent need for HITL in AI governance. By incorporating human judgment, we can steer these systems away from harmful outcomes, ensuring that they work for us rather than against us. Conclusion: A Call for Responsible AI Design Human in the Loop is more than a technical framework; it’s a philosophical stance that recognizes the limits of automation. As AI becomes more embedded in the fabric of our society, we must resist the temptation to hand over full control to machines. We must maintain a human presence, not just as operators but as moral arbiters, constantly assessing whether these systems are truly serving the common good. A future where AI operates unchecked and devoid of human oversight is not a distant dystopia; it’s a possibility that’s already creeping into our reality. By embracing HITL and embedding robust kill switches into AI systems, we can ensure that technology remains a tool that empowers humanity rather than endangering it. In an age where machines can learn and adapt faster than ever before, keeping humans in the loop is not just advisable—it’s essential. About Me: 25+ year IT veteran combining data, AI, risk management, strategy, and education. 4x hackathon winner and social impact from data advocate. Currently working to jumpstart the AI workforce in the Philippines. Learn more about me here: https://docligot.com One key concept central to ensuring that AI remains a tool that benefits humanity rather than harms it is the notion of "Human in the Loop" (HITL). But what exactly is HITL, and why is it so vital in today’s AI landscape? What is "Human in the Loop"? Human in the Loop (HITL) refers to a system design in which humans are actively involved in the decision-making process of an AI system. Unlike fully automated AI systems that operate without any human intervention, HITL systems incorporate human judgment at critical stages—especially when decisions involve high stakes or ethical considerations. This model relies on a combination of machine efficiency and human intuition, ensuring that the final output is aligned with human values and societal norms. system design in which humans are actively involved The Importance of a "Kill Switch" One of the most crucial aspects of HITL systems is the integration of a "kill switch" or an emergency stop mechanism . This is a literal or metaphorical button that allows human operators to override AI decisions or shut down the system entirely if it starts behaving in unintended or harmful ways. A kill switch is not just a precaution; it is a safeguard that recognizes the inherent unpredictability of AI systems, especially as they grow more complex and autonomous. integration of a "kill switch" or an emergency stop mechanism AI, by design, can learn and adapt in ways that even its creators might not fully anticipate. Without the ability for a human to intervene—whether to halt a runaway algorithm or correct a decision that could have disastrous consequences—we risk losing control over the very technologies we created. This is particularly critical in sectors like healthcare, finance, and law enforcement, where AI-driven decisions can significantly impact human lives. Why HITL is Essential for Ethical and Safe AI As AI continues to infiltrate our daily lives, from facial recognition systems to predictive policing algorithms, ethical concerns have risen to the forefront. HITL is essential to ensuring that AI systems remain fair, transparent, and aligned with human rights. For instance, without human oversight, AI algorithms trained on biased data can perpetuate or even exacerbate discrimination. By keeping humans in the loop, we introduce a level of accountability that purely automated systems lack. Moreover, HITL offers a vital counterbalance to the "black box" nature of many AI systems . These systems often provide outputs without clear explanations of how they reached their conclusions. Human oversight allows us to question, validate, and adjust these decisions, reducing the risks associated with opaque or unexplainable outcomes. "black box" nature of many AI systems The Unseen Influence of AI on Our Lives Even without us fully realizing it, AI is already shaping our behaviors and choices. Social media algorithms decide which content we see, influencing our opinions and emotions. Recommender systems on platforms like YouTube, Netflix, and Amazon subtly guide our consumption habits, reinforcing our preferences while sometimes trapping us in echo chambers. The concerning part is that many of these AI-driven systems operate without meaningful human intervention or oversight. They are optimized for engagement and profit, often at the expense of societal well-being. These systems continuously learn from our interactions, amplifying biases, spreading misinformation, and fueling polarization—all without our conscious input or control. This highlights the urgent need for HITL in AI governance. By incorporating human judgment, we can steer these systems away from harmful outcomes, ensuring that they work for us rather than against us. Conclusion: A Call for Responsible AI Design Human in the Loop is more than a technical framework; it’s a philosophical stance that recognizes the limits of automation. As AI becomes more embedded in the fabric of our society, we must resist the temptation to hand over full control to machines. We must maintain a human presence, not just as operators but as moral arbiters, constantly assessing whether these systems are truly serving the common good. A future where AI operates unchecked and devoid of human oversight is not a distant dystopia; it’s a possibility that’s already creeping into our reality. By embracing HITL and embedding robust kill switches into AI systems, we can ensure that technology remains a tool that empowers humanity rather than endangering it. In an age where machines can learn and adapt faster than ever before, keeping humans in the loop is not just advisable—it’s essential. About Me: 25+ year IT veteran combining data, AI, risk management, strategy, and education. 4x hackathon winner and social impact from data advocate. Currently working to jumpstart the AI workforce in the Philippines. Learn more about me here: https://docligot.com https://docligot.com

Human in the Loop: A Crucial Safeguard in the Age of AI

Meteorology is rapidly evolving due to the development of new technology. Traditional forecasting methods, which relied heavily on physical instruments and historical data, are now being reshaped by integrating artificial intelligence (AI) and machine learning. This shift is expected to improve the accuracy and granularity of forecasts and fundamentally change how we interact with weather information. Bridging the Gap Between Short-Term and Long-Term Forecasts One of the most intriguing advancements in meteorology is the use of AI to bridge the gap between short-term and long-term forecasting. Traditionally, predicting weather events a few minutes to 24 hours ahead has been a challenge. Machine learning models are now being trained on vast datasets to predict weather patterns with unprecedented precision. These models can integrate real-time data, such as satellite imagery and user-generated reports, to produce hyperlocal forecasts. Being able to predict severe weather, like sudden storms or heavy rain, more quickly and accurately could be a big breakthrough soon. Enhancing User Experience Improvements in user experience must complement technological advancements. Integrating these new technologies in a way that is accessible and useful to the average user is one of the biggest challenges. Weather apps have become a primary source of information for many and must evolve to present complex data in a simple and actionable manner. The key to success lies in user-centric design. People need information quickly and in a format that is easy to understand, whether it’s for a sporting event, a morning commute, or planning a trip. The challenge is to personalize this information without overwhelming the user. Towards a More Adaptive Forecasting System Personalization is the future of weather apps. Imagine a weather app that knows you’re about to go for a run and gives you a tailored forecast for your route and time of day. Or one that alerts you to potential weather disruptions based on your calendar events. This level of personalization, however, raises concerns about data privacy and user consent. It’s crucial for companies to be transparent about how they use data, and users should have control over what they share. With responsible practices, personalized experiences can enhance usability without compromising privacy. The Role of AI in Meteorology The pioneers in using AI assistants in weather apps were Pixel phones (which analyze forecast data and offer summaries). Soon, AI hints appeared in the Rain Viewer weather app. I am sure that this is just the beginning of the trend, and soon, we will see the widespread use of AI in weather apps. Predicting the weather for the next decade is challenging due to the fast-paced technological advancements. AI is playing a crucial role in this evolution. The focus has shifted from hardware advancements, like new sensors, to developing sophisticated software that enhances weather prediction algorithms. By using big data, AI refines both short-term and long-term forecasts. Even though physical instruments might not change much, new software will soon make weather forecasts more accurate and reliable. Current forecasting models cannot process all the available data effectively, especially satellite information. There are five operational meteorological satellites, yet we can’t fully utilize their data. Most of it just gets stored as images without being immediately analyzed. The challenge is in processing all the available massive amounts of data from modern meteorological equipment in real time to provide hyperlocal forecasts. AI and machine learning can help fill this gap by automating data analysis and improving forecast accuracy. The breakthrough will be predicting exactly which houses a tornado will hit so that people can evacuate in time (now the Tornado Warning issues for large territories, and everyone knows that “there will be no tornado” and ignore it due to lack of such hyperlocal predictions). Simplifying User Experience Through Integration The future of weather forecasting is moving towards simplifying how information is delivered and processed. In the near future, you may walk past a digital billboard that shows you personalized weather information—like a forecast for your specific location—without needing to pull out your phone. The goal is to integrate weather data seamlessly into our daily environments. Dedicated weather apps may not be necessary, as the information will be readily accessible on any screen and personalized to our needs, current location, and activity. Weather Apps as Part of a Broader Ecosystem As standalone entities, weather apps may become redundant. People don’t need weather apps just to know the weather. They need them to make decisions, like whether to take an umbrella, call a taxi, or plan a vacation. Weather data is already being incorporated into various ecosystems. Modern cars can replan you driving route because of severe weather events, and smart home thermostats can adjust temperature settings based on forecasts, smart irrigation systems check not only how soil is wet, but also how much precipitation will come in the next 24 hours. Weather information is everywhere—cars, fridges, calendars, and even when buying tickets for a trip. This trend will continue, making weather forecasting an integral part of our everyday decisions rather than a separate service. Back to Basics: The Future of Weather Apps Interestingly, the future of weather apps might involve a return to simplicity. Over time, as features and complexities were added, many apps lost their initial value. The essence of a good weather app lies in its ease of use and clarity of information. The focus should be on creating an intuitive, straightforward experience where users can access relevant information without hassle. A More Integrated, User-Friendly Future As weather forecasting technology advances, it will become more important in our daily decisions. The focus will be on delivering relevant, timely, and personalized information through various digital platforms and devices. The future of weather forecasting is about making predictions a natural part of our daily lives, helping us make better decisions and simplifying our routines. It’s an exciting time for meteorology, and we are only beginning to scratch the surface of what is possible. The power of weather forecasting is in how it helps make our lives safer and easier. As technology advances, we must harness this potential to create a future where we are always ready for the weather, not just reacting to it. Meteorology is rapidly evolving due to the development of new technology. Traditional forecasting methods, which relied heavily on physical instruments and historical data, are now being reshaped by integrating artificial intelligence (AI) and machine learning. This shift is expected to improve the accuracy and granularity of forecasts and fundamentally change how we interact with weather information. Bridging the Gap Between Short-Term and Long-Term Forecasts One of the most intriguing advancements in meteorology is the use of AI to bridge the gap between short-term and long-term forecasting. Traditionally, predicting weather events a few minutes to 24 hours ahead has been a challenge. Machine learning models are now being trained on vast datasets to predict weather patterns with unprecedented precision. These models can integrate real-time data, such as satellite imagery and user-generated reports, to produce hyperlocal forecasts. Being able to predict severe weather, like sudden storms or heavy rain, more quickly and accurately could be a big breakthrough soon. Enhancing User Experience Improvements in user experience must complement technological advancements. Integrating these new technologies in a way that is accessible and useful to the average user is one of the biggest challenges. Weather apps have become a primary source of information for many and must evolve to present complex data in a simple and actionable manner. The key to success lies in user-centric design. People need information quickly and in a format that is easy to understand, whether it’s for a sporting event, a morning commute, or planning a trip. The challenge is to personalize this information without overwhelming the user. Towards a More Adaptive Forecasting System Personalization is the future of weather apps. Imagine a weather app that knows you’re about to go for a run and gives you a tailored forecast for your route and time of day. Or one that alerts you to potential weather disruptions based on your calendar events. This level of personalization, however, raises concerns about data privacy and user consent. It’s crucial for companies to be transparent about how they use data, and users should have control over what they share. With responsible practices, personalized experiences can enhance usability without compromising privacy. The Role of AI in Meteorology The pioneers in using AI assistants in weather apps were Pixel phones (which analyze forecast data and offer summaries). Soon, AI hints appeared in the Rain Viewer weather app. I am sure that this is just the beginning of the trend, and soon, we will see the widespread use of AI in weather apps. Predicting the weather for the next decade is challenging due to the fast-paced technological advancements. AI is playing a crucial role in this evolution. The focus has shifted from hardware advancements, like new sensors, to developing sophisticated software that enhances weather prediction algorithms. By using big data, AI refines both short-term and long-term forecasts. Even though physical instruments might not change much, new software will soon make weather forecasts more accurate and reliable. Current forecasting models cannot process all the available data effectively, especially satellite information. There are five operational meteorological satellites, yet we can’t fully utilize their data. Most of it just gets stored as images without being immediately analyzed. The challenge is in processing all the available massive amounts of data from modern meteorological equipment in real time to provide hyperlocal forecasts. AI and machine learning can help fill this gap by automating data analysis and improving forecast accuracy. The breakthrough will be predicting exactly which houses a tornado will hit so that people can evacuate in time (now the Tornado Warning issues for large territories, and everyone knows that “there will be no tornado” and ignore it due to lack of such hyperlocal predictions). Simplifying User Experience Through Integration The future of weather forecasting is moving towards simplifying how information is delivered and processed. In the near future, you may walk past a digital billboard that shows you personalized weather information—like a forecast for your specific location—without needing to pull out your phone. The goal is to integrate weather data seamlessly into our daily environments. Dedicated weather apps may not be necessary, as the information will be readily accessible on any screen and personalized to our needs, current location, and activity. Weather Apps as Part of a Broader Ecosystem As standalone entities, weather apps may become redundant. People don’t need weather apps just to know the weather. They need them to make decisions, like whether to take an umbrella, call a taxi, or plan a vacation. Weather data is already being incorporated into various ecosystems. Modern cars can replan you driving route because of severe weather events, and smart home thermostats can adjust temperature settings based on forecasts, smart irrigation systems check not only how soil is wet, but also how much precipitation will come in the next 24 hours. Weather information is everywhere—cars, fridges, calendars, and even when buying tickets for a trip. This trend will continue, making weather forecasting an integral part of our everyday decisions rather than a separate service. Back to Basics: The Future of Weather Apps Interestingly, the future of weather apps might involve a return to simplicity. Over time, as features and complexities were added, many apps lost their initial value. The essence of a good weather app lies in its ease of use and clarity of information. The focus should be on creating an intuitive, straightforward experience where users can access relevant information without hassle. A More Integrated, User-Friendly Future As weather forecasting technology advances, it will become more important in our daily decisions. The focus will be on delivering relevant, timely, and personalized information through various digital platforms and devices. The future of weather forecasting is about making predictions a natural part of our daily lives, helping us make better decisions and simplifying our routines. It’s an exciting time for meteorology, and we are only beginning to scratch the surface of what is possible. The power of weather forecasting is in how it helps make our lives safer and easier. As technology advances, we must harness this potential to create a future where we are always ready for the weather, not just reacting to it.

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