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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

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.

What Could the Future of Weather Forecasting Look Like?

Venture capital, or VC (also known as risk capital), is a type of investment typically aimed at early-stage companies that are small to medium-sized. These companies often include startups, which may have little to no revenue yet. Because of this, they require substantial funding to carry out their operations, making this a high-risk investment model. Venture capitalists essentially bet on the potential of these startups, hoping their resources will grow in value as the company succeeds. Venture capital investments can generally be categorized into two types, each depending on the responsibilities, rights, and obligations of the company's founders and investors: General Partner (GP) – All investors share equal authority in deciding the company’s strategies.
Limited Partner (LP) – Not all participants have the same level of involvement; some contribute financially, while others focus on management and operations. When a startup founder seeks venture capital investment, one of the first steps is to determine how much equity each shareholder will hold, how involved investors will be in day-to-day operations, and what level of power and responsibility each party will have. It’s worth noting that venture capital investments are generally aimed at fostering rapid growth within the company. Unlike traditional investments, it's not always necessary to have a fully developed product already on the market. However, investors will evaluate a variety of factors before deciding whether to invest in an idea or product. Some of these factors may be prioritized more than others when it comes to high-risk investments. The Management Team A strong team and solid leadership are critical in determining whether a company has the structure necessary to move forward with its work or ideas. Even if the startup is small, which is common in the early stages, the interaction among team members, their experience, and their skill sets are of utmost importance. One of the key factors is the ability to execute: it's not a dealbreaker if a startup doesn’t yet have a product; the idea can be promising, but what truly differentiates a good investment is the commitment and execution capability of the team. Ideas are plentiful, but not many have the technical skills and drive to make them a reality. For instance, some companies have managed to carve out significant market space, even in competitive industries, thanks to the collaboration and technical expertise of their team. Moreover, a strong leader is essential to harness the strengths of each team member to achieve the company's goals. The Business Model Now, this part can get a bit tricky. Creating a sustainable business model might seem straightforward on paper, but in reality, it can be a real challenge. While a model might look great in theory, it can completely fall apart when put into practice. Today, there are plenty of tools and examples of business models that can be used for inspiration when building your own. It’s important to note that the real appeal of investing in a business model doesn’t necessarily lie in how it functions. Below are a few key factors that are often considered: Scalability


Viability


Competitive


Differentiation
Scalability and viability are crucial indicators of whether a business has the potential to expand, alongside how easily the business model can be replicated. Essentially, a scalable business is one that can increase its production, profitability, and operations without compromising efficiency and returns. If a business is scalable, replicating it becomes significantly easier. Replicability refers to a company’s ability to recreate a successful business model across different locations and markets. Depending on the business context, these factors can serve as significant competitive advantages and are definitely sought after by investors. By highlighting these aspects, both founders and investors can better understand what makes venture capital investments unique, as well as the key components that are analyzed before funding a startup. 3. Product Value A key aspect of venture capital investment is the value of the product being developed. Recently, I came across a book by Tony Fadell, an American software engineer and investor known for his work on the iPod, titled Build. In this book, Fadell provides a practical and clear guide to creating products that have real value. It's a great resource for anyone looking to build a career in product management and development, offering insights on not just how to build a successful career but also how to create something that adds genuine value to users. I first heard about this book through a news article on Google Ventures (now GV), a firm known for its investment in high-value products like GitLab, Stripe, Verve, and others—all widely used across various industries. It’s crucial for any envisioned product to hold value both for its user base and the broader community. Take GitLab, for example—it's a repository tool designed by developers for developers, making a significant impact within its target audience. Even a product that may seem small or niche has a great potential to grow if it can make a difference to its users. However, while creating a product that delivers value may sound simple and obvious, executing this in practice is often far more challenging than it initially appears. Ensuring a product’s value is not just about features and functionality; it’s about understanding user needs and creating something that stands out in a way that matters to its intended audience. In the context of venture capital, a valuable product can significantly increase a company's potential for rapid growth, which is precisely what investors are looking for. Choosing the Right Technology Choosing the right technology is crucial and can determine how valuable a company becomes, both in its early years and in the future. When I talk about technology, I don't just mean software development technologies like programming languages and frameworks, but also the tools that will help scale the business and build an MVP (Minimum Viable Product). Companies like Facebook and Twitter adopted web frameworks that allowed them to build a demonstrable product quickly. The ease provided by programming languages like Ruby and PHP was extremely important not only for constructing the first versions of their products but also for creating something easy to maintain and scale. All of this was only possible with the right management tools and personnel. Nowadays, there are essential technologies available for building an MVP. Below, I’ve listed some that are frequently used for web-focused MVPs: React.js or Vue.js: Popular JavaScript frameworks for creating interactive and responsive user interfaces.


Node.js: A fast and scalable JavaScript backend platform suitable for building RESTful APIs.


MongoDB: A flexible and scalable NoSQL database, suitable for MVPs where the data structure can evolve quickly. Additionally, you can use existing management methodologies and tools, such as Scrum, XP, and Design Sprint. To understand how the choice of technology—and any changes to it during business execution—can impact its value, I recommend reading the following article: ‘On Brex: My Thoughts on Backdrop Powers that Chose Kotlin over Elixir’. This article discusses how changing the backend of a company focused on the financial sector can impact its development and investment potential. Conclusion Securing investors for an idea is no easy task; it's not as simple as having a "lightbulb moment" like we often see in movies or dramatizations. In reality, it requires a lot of effort and planning to build something that can benefit both the people involved and others. The important thing is to stick to the execution and the initial plan, adopting technologies that will allow you to achieve these three key factors: Scalability, Viability, and Replicability. Venture capital, or VC (also known as risk capital), is a type of investment typically aimed at early-stage companies that are small to medium-sized. These companies often include startups, which may have little to no revenue yet. Because of this, they require substantial funding to carry out their operations, making this a high-risk investment model. Venture capitalists essentially bet on the potential of these startups, hoping their resources will grow in value as the company succeeds. Venture capital investments can generally be categorized into two types, each depending on the responsibilities, rights, and obligations of the company's founders and investors: General Partner (GP) – All investors share equal authority in deciding the company’s strategies. Limited Partner (LP) – Not all participants have the same level of involvement; some contribute financially, while others focus on management and operations. General Partner (GP) – All investors share equal authority in deciding the company’s strategies. General Partner (GP) – All investors share equal authority in deciding the company’s strategies. Limited Partner (LP) – Not all participants have the same level of involvement; some contribute financially, while others focus on management and operations. Limited Partner (LP) – Not all participants have the same level of involvement; some contribute financially, while others focus on management and operations. When a startup founder seeks venture capital investment, one of the first steps is to determine how much equity each shareholder will hold, how involved investors will be in day-to-day operations, and what level of power and responsibility each party will have. It’s worth noting that venture capital investments are generally aimed at fostering rapid growth within the company. Unlike traditional investments, it's not always necessary to have a fully developed product already on the market. However, investors will evaluate a variety of factors before deciding whether to invest in an idea or product. Some of these factors may be prioritized more than others when it comes to high-risk investments. The Management Team The Management Team The Management Team A strong team and solid leadership are critical in determining whether a company has the structure necessary to move forward with its work or ideas. Even if the startup is small, which is common in the early stages, the interaction among team members, their experience, and their skill sets are of utmost importance. One of the key factors is the ability to execute: it's not a dealbreaker if a startup doesn’t yet have a product; the idea can be promising, but what truly differentiates a good investment is the commitment and execution capability of the team. Ideas are plentiful, but not many have the technical skills and drive to make them a reality. For instance, some companies have managed to carve out significant market space, even in competitive industries, thanks to the collaboration and technical expertise of their team. Moreover, a strong leader is essential to harness the strengths of each team member to achieve the company's goals. The Business Model The Business Model The Business Model Now, this part can get a bit tricky. Creating a sustainable business model might seem straightforward on paper, but in reality, it can be a real challenge. While a model might look great in theory, it can completely fall apart when put into practice. Today, there are plenty of tools and examples of business models that can be used for inspiration when building your own. It’s important to note that the real appeal of investing in a business model doesn’t necessarily lie in how it functions. Below are a few key factors that are often considered: Scalability Viability Competitive Differentiation
Scalability and viability are crucial indicators of whether a business has the potential to expand, alongside how easily the business model can be replicated. Essentially, a scalable business is one that can increase its production, profitability, and operations without compromising efficiency and returns. If a business is scalable, replicating it becomes significantly easier. Replicability refers to a company’s ability to recreate a successful business model across different locations and markets. Depending on the business context, these factors can serve as significant competitive advantages and are definitely sought after by investors. Scalability Scalability Viability Viability Competitive Competitive Differentiation Scalability and viability are crucial indicators of whether a business has the potential to expand, alongside how easily the business model can be replicated. Essentially, a scalable business is one that can increase its production, profitability, and operations without compromising efficiency and returns. If a business is scalable, replicating it becomes significantly easier. Replicability refers to a company’s ability to recreate a successful business model across different locations and markets. Depending on the business context, these factors can serve as significant competitive advantages and are definitely sought after by investors. Differentiation Scalability and viability are crucial indicators of whether a business has the potential to expand, alongside how easily the business model can be replicated. Essentially, a scalable business is one that can increase its production, profitability, and operations without compromising efficiency and returns. If a business is scalable, replicating it becomes significantly easier. Replicability refers to a company’s ability to recreate a successful business model across different locations and markets. Depending on the business context, these factors can serve as significant competitive advantages and are definitely sought after by investors. Scalability and viability are crucial indicators of whether a business has the potential to expand, alongside how easily the business model can be replicated. Essentially, a scalable business is one that can increase its production, profitability, and operations without compromising efficiency and returns. If a business is scalable, replicating it becomes significantly easier. Replicability refers to a company’s ability to recreate a successful business model across different locations and markets. Depending on the business context, these factors can serve as significant competitive advantages and are definitely sought after by investors. By highlighting these aspects, both founders and investors can better understand what makes venture capital investments unique, as well as the key components that are analyzed before funding a startup. 3. Product Value 3. Product Value A key aspect of venture capital investment is the value of the product being developed. Recently, I came across a book by Tony Fadell, an American software engineer and investor known for his work on the iPod, titled Build . In this book, Fadell provides a practical and clear guide to creating products that have real value. It's a great resource for anyone looking to build a career in product management and development, offering insights on not just how to build a successful career but also how to create something that adds genuine value to users. I first heard about this book through a news article on Google Ventures (now GV), a firm known for its investment in high-value products like GitLab, Stripe, Verve, and others—all widely used across various industries. Build It’s crucial for any envisioned product to hold value both for its user base and the broader community. Take GitLab, for example—it's a repository tool designed by developers for developers, making a significant impact within its target audience. Even a product that may seem small or niche has a great potential to grow if it can make a difference to its users. However, while creating a product that delivers value may sound simple and obvious, executing this in practice is often far more challenging than it initially appears. Ensuring a product’s value is not just about features and functionality; it’s about understanding user needs and creating something that stands out in a way that matters to its intended audience. In the context of venture capital, a valuable product can significantly increase a company's potential for rapid growth, which is precisely what investors are looking for. Choosing the Right Technology Choosing the Right Technology Choosing the right technology is crucial and can determine how valuable a company becomes, both in its early years and in the future. When I talk about technology, I don't just mean software development technologies like programming languages and frameworks, but also the tools that will help scale the business and build an MVP (Minimum Viable Product). Companies like Facebook and Twitter adopted web frameworks that allowed them to build a demonstrable product quickly. The ease provided by programming languages like Ruby and PHP was extremely important not only for constructing the first versions of their products but also for creating something easy to maintain and scale. All of this was only possible with the right management tools and personnel. Nowadays, there are essential technologies available for building an MVP. Below, I’ve listed some that are frequently used for web-focused MVPs: React.js or Vue.js: Popular JavaScript frameworks for creating interactive and responsive user interfaces. Node.js: A fast and scalable JavaScript backend platform suitable for building RESTful APIs. MongoDB: A flexible and scalable NoSQL database, suitable for MVPs where the data structure can evolve quickly. React.js or Vue.js: Popular JavaScript frameworks for creating interactive and responsive user interfaces. React.js or Vue.js : Popular JavaScript frameworks for creating interactive and responsive user interfaces. React.js or Vue.js Node.js: A fast and scalable JavaScript backend platform suitable for building RESTful APIs. Node.js : A fast and scalable JavaScript backend platform suitable for building RESTful APIs. Node.js MongoDB: A flexible and scalable NoSQL database, suitable for MVPs where the data structure can evolve quickly. MongoDB : A flexible and scalable NoSQL database, suitable for MVPs where the data structure can evolve quickly. MongoDB Additionally, you can use existing management methodologies and tools, such as Scrum, XP, and Design Sprint. To understand how the choice of technology—and any changes to it during business execution—can impact its value, I recommend reading the following article: ‘On Brex: My Thoughts on Backdrop Powers that Chose Kotlin over Elixir’ . This article discusses how changing the backend of a company focused on the financial sector can impact its development and investment potential. ‘ On Brex: My Thoughts on Backdrop Powers that Chose Kotlin over Elixir ’ On Brex: My Thoughts on Backdrop Powers that Chose Kotlin over Elixir Conclusion Conclusion Securing investors for an idea is no easy task; it's not as simple as having a "lightbulb moment" like we often see in movies or dramatizations. In reality, it requires a lot of effort and planning to build something that can benefit both the people involved and others. The important thing is to stick to the execution and the initial plan, adopting technologies that will allow you to achieve these three key factors: Scalability, Viability, and Replicability.

Venture Capital Insights: What Investors Look for in Startups

So, I've started playing around with FiveThirtyEight's publicly available polling data. Why? Because I'm kind of just interested in the polling around the 2024 U.S. presidential election and the statistical side of things and was wondering how I would construct my own little prediction from scratch. So, here's what I did for a first try. (Disclaimer: I'm not a professional data scientist. Playing around with the data and exploring some things is really my main focus.) First and foremost, I want to keep it simple for now. As Kahneman explains in his book, Noise: Simple models often get the job done pretty well. ① Therefore, I only consider high-quality pollsters but without weighing their polls differently. I only include data points with a numeric_grade ≥2.5 (best: 3) in 538's dataset. ② What I do weigh is closeness to election day. The closer to November 5, the more relevant the poll. I consider polls from up to 90 days (~3 months) before, weighted linearly. A poll ending on August 7 has a weight of 1/90, and a poll ending on November 4 is one of 90/90. ③ Lastly, I'm not looking at national polls. What I focus on are the 7 swing states that are going to decide the election. With all this, we arrive at the following picture as of September 24. Harris in front in MI, NV, NC, PA, WI; Trump in AZ & GA. Based on these probabilities, we can also calculate the expected electoral votes (EVs) for each swing state, and the race overall: ④ This, however, doesn't take into account polling error, which was significant in both 2016 & 2020. So, I also did ①–③ for those two. I'm not considering elections before that because ever since Trump entered the stage, election dynamics have significantly changed. Old rules don't apply anymore. 2024 will be much more similar to 2020 & 2016 than to any election before that. The polling error for my methodology looks like this: Following the “keep it simple” rule, let's assume the polling error in 2024 will be the average of 2016/20 and apply it to the margins from above, also adding the polling error uncertainty to the uncertainty from the polls: And, again, these are the expected EVs based on the probabilities: Now, one could argue that pollsters might have learned their lesson and polling will get more accurate again this year. This, however, was already an argument in 2020. Plus, at least part of the problem seems to be that certain Trump supporters simply don't want to participate in polls anymore due to trust issues (see, e.g., https://www.cnbc.com/2024/05/04/why-election-polls-were-wrong-in-2016-and-2020-and-whats-changing.html). Obviously, pollsters are trying to learn and adjust, but whether they'll be less off this year than in the two elections before, we'll only see on November 5. For now, I also don't take polling errors from the 2022 elections into account. I have to read more on this first, and I assume that a presidential election is most similar to other presidential elections. So, for this first try, my own little forecast includes one prediction purely based on weighted polls from high-quality pollsters, and one prediction assuming the 2024 polling error will be the average of 2016 & 2020. Please feel free to head over to my Github—where I'll collect and archive everything—and have a look at the complete data and calculations in the latest Excel file: https://github.com/maxspeicher/2024-us-presidential-election/ So, I've started playing around with FiveThirtyEight's publicly available polling data . Why? Because I'm kind of just interested in the polling around the 2024 U.S. presidential election and the statistical side of things and was wondering how I would construct my own little prediction from scratch. So, here's what I did for a first try. FiveThirtyEight's publicly available polling data FiveThirtyEight's publicly available polling data (Disclaimer: I'm not a professional data scientist. Playing around with the data and exploring some things is really my main focus.) First and foremost, I want to keep it simple for now. As Kahneman explains in his book, Noise : Simple models often get the job done pretty well. Noise Noise ① Therefore, I only consider high-quality pollsters but without weighing their polls differently. I only include data points with a numeric_grade ≥2.5 (best: 3) in 538's dataset. ① numeric_grade ② What I do weigh is closeness to election day. The closer to November 5, the more relevant the poll. I consider polls from up to 90 days (~3 months) before, weighted linearly. A poll ending on August 7 has a weight of 1/90, and a poll ending on November 4 is one of 90/90. ② ③ Lastly, I'm not looking at national polls. What I focus on are the 7 swing states that are going to decide the election. ③ With all this, we arrive at the following picture as of September 24. Harris in front in MI, NV, NC, PA, WI; Trump in AZ & GA. Based on these probabilities, we can also calculate the expected electoral votes (EVs) for each swing state, and the race overall: ④ This, however, doesn't take into account polling error, which was significant in both 2016 & 2020. So, I also did ①–③ for those two. I'm not considering elections before that because ever since Trump entered the stage, election dynamics have significantly changed. Old rules don't apply anymore. 2024 will be much more similar to 2020 & 2016 than to any election before that. The polling error for my methodology looks like this: ④ Following the “keep it simple” rule, let's assume the polling error in 2024 will be the average of 2016/20 and apply it to the margins from above, also adding the polling error uncertainty to the uncertainty from the polls: And, again, these are the expected EVs based on the probabilities: Now, one could argue that pollsters might have learned their lesson and polling will get more accurate again this year. This, however, was already an argument in 2020. Plus, at least part of the problem seems to be that certain Trump supporters simply don't want to participate in polls anymore due to trust issues (see, e.g., https://www.cnbc.com/2024/05/04/why-election-polls-were-wrong-in-2016-and-2020-and-whats-changing.html ). https://www.cnbc.com/2024/05/04/why-election-polls-were-wrong-in-2016-and-2020-and-whats-changing.html https://www.cnbc.com/2024/05/04/why-election-polls-were-wrong-in-2016-and-2020-and-whats-changing.html Obviously, pollsters are trying to learn and adjust, but whether they'll be less off this year than in the two elections before, we'll only see on November 5. For now, I also don't take polling errors from the 2022 elections into account. I have to read more on this first, and I assume that a presidential election is most similar to other presidential elections. So, for this first try, my own little forecast includes one prediction purely based on weighted polls from high-quality pollsters, and one prediction assuming the 2024 polling error will be the average of 2016 & 2020. Please feel free to head over to my Github—where I'll collect and archive everything—and have a look at the complete data and calculations in the latest Excel file: https://github.com/maxspeicher/2024-us-presidential-election/ https://github.com/maxspeicher/2024-us-presidential-election/

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