WestJet is resuming <strong>Swoop</strong>&#x27;s St. John&#x27;s to Hamilton route and brings back the Tampa, Florida route.

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WestJet is adding a new, non-stop service from St. John&#x27;s to Hamilton, Ontario to replace a similar route prev...

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Swoop

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It’s November, which means the holiday season has started — and with it, an influx of online orders. Most consumers who have ordered something online have experienced delays, failed delivery attempts or worse — lost packages. Frankly, tracking numbers and text message updates aren’t cutting it. Could artificial intelligence finally provide a solution to this problem? Packages Are Going Missing and It’s a Problem A package is supposed to arrive today. The person who placed the order watches their front door like a hawk after their app’s tracker changes from “shipping” to “out for delivery.” The estimated delivery time comes and goes — and nothing gets dropped off. Their phone pings with a “Sorry we missed you” message. What happened? Situations like these are common. Whether the seller reschedules, a delivery driver skips part of their route to save time or a porch pirate swoops in at the last second, parcels go missing all the time. In 2023, almost 40% of people in the United States had at least one package stolen. This problem is challenging to remedy simply because the delivery network is massive. Accurately tracking missing orders is possible, but it is a tall order with today’s technical limitations. Companies must monitor warehouse workers, drivers and vehicles. Not to mention, they must handle fraud cases where customers claim to have received nothing when, in reality, they did. Even in ideal circumstances, accidents happen — a small envelope could fall behind the sorting machine or get stuck under a larger box. Even when shipments eventually reach the right address, they stay in the delivery network too long. According to the United States Postal Service (USPS), 17.3% of first-class mail was not delivered on time in the second quarter of the fiscal year 2024, up 3.2 percentage points from the previous quarter. Rates for marketing mail and periodicals also worsened. The number of parcels going missing in the reverse logistics process is often overlooked. Around 30% of all online orders are sent back, compared to just 9% in brick-and-mortar stores. A disappearing parcel is particularly frustrating here, as the person attempting the return may be denied a refund or end up on the hook for the full price of the item. Why Use AI to Track People’s Missing Packages? AI is a powerful technology. A single advanced algorithm can process massive datasets, make data-driven decisions and understand language, regardless of whether it is a chatbot, generative algorithm or deep learning model. Training determines its purpose and behavior, so it can be utilized in virtually any use case. Versatility is useful when analyzing data since processing centers can produce billions of drop-off images or location data points in short periods. There is even more to go through if they use Internet of Things (IoT) tools or telematics systems in their fleets. AI can respond to prompts with voice, image, text or audio output, streamlining information retrieval and summary. Only a technology like AI can handle all the data created by the delivery network’s massive, interconnected system of sellers, fleets, customers and warehouses. Its rapid analysis isn’t its only boon — it also has automation capabilities. An advanced model can operate around the clock without stopping for breaks, food or sleep. Is AI Already Being Used to Track Packages? Several delivery companies are already using AI to track shipments in transit. For instance, the United Parcel Service uses an AI-enabled supply chain. It uses historical and real-time data to calculate the fastest, safest way to boost the on-time delivery rate. Its algorithm can also anticipate disruptions and provide a comprehensive view of the fleet’s network. If other firms deploy AI at the edge — at the network’s boundary where data is created — they wouldn’t have to build data centers to process or analyze the massive amount of logistics information they receive. On top of using fewer computing resources, they’d enable real-time results with little to no delay, improving output accuracy. In a different approach, USPS uses an AI-enabled edge computing program powered by NVIDIA technology. Its model analyzes billions of images that the processing center generates. Before, finding a missing order took a team of up to 10 people multiple days. With this platform, a single individual can track one down in around two hours. 4 Ways AI Can Track Stolen and Lost Packages There are several ways AI can help people track their missing parcels. 1. Provide Real-Time Updates Even with a tracking number, updates are often too vague, inaccurate or outdated to be helpful. According to one survey, nine in 10 people want to be able to track their order, with 47% wanting to know exactly where their item is at all times. AI can fulfill this desire, providing real-time updates as shipments travel. 2. Calculate Delivery Estimate Since as many as 20% of deliveries fail on the first attempt, reschedules are common. While most people wait a few extra days, some don’t receive their items for weeks or months. With AI, they can see how long it will take for a driver to reattempt delivery. The algorithm can factor in variables like weather, consumer demand, route closures and delays. 3. Analyze the Drop-Off Photos An AI could leverage image recognition technology to analyze millions of drop-off photos. It can flag the delivery as incorrect or suspicious if it notices any discrepancies. If there are enough clues in the photo’s visual details or metadata, the model could help workers track down the missing parcel. 4. Answer Peoples’ Questions Globally, 46% of online shoppers expect to receive their orders within two to three days. However, delays, reschedules and cancellations are common. When these situations arise, an AI-powered chatbot can answer consumers’ questions to help them understand what happened. Depending on its design, it could discuss the item’s location or estimate delivery dates. How Integration Could Enhance AI’s Capabilities While AI is powerful on its own, companies could see significant improvements after integration. For example, embedding a machine learning model into a surveillance system or camera doorbell could help recipients identify porch pirates or misbehaving delivery drivers. This way, they could potentially narrow down the location of their missing item. An AI-powered computer vision system — an AI-powered device that can interpret visual information — could do something similar, monitoring production lines and automatic sorting machines for misplaced parcels. If something falls off of a belt, gets stuck under another container or slips between a crack in the machine, it could alert a human worker. Integrations can even help in transit. Combining AI and IoT sensors enables real-time updates on a product’s condition and location. Recipients could receive alerts if their item is tampered with, dropped off at the wrong location or stolen. The algorithm would filter out false positives and concisely explain what happened. Could AI Prevent Packages From Getting Lost? While finding missing parcels is an excellent use of a machine learning model, stopping this problem at the source is a more effective solution. Companies could prevent products from getting lost in the first place by leveraging chatbots for communication. The higher the success rate for the first delivery attempt, the less likely the item will disappear from the system. Decision-makers could also optimize routes to reduce workers’ stress. Demand has far outpaced the workforce’s capabilities — experts predict e-commerce sales will reach over $8 trillion in sales by 2027, a 237% increase in one decade — which puts pressure on drivers to haul more boxes and complete routes faster. Companies increasingly hire freelance and gig workers to compensate for their labor shortage. These workers are often paid by the number of packages they deliver, incentivizing them to race through their routes. Using AI to optimize their trip saves them time, lowering the chance they will make mistakes in transit. The Bottom Line of Using AI to Track Orders Although AI is not a miracle technology, it is one of the best solutions on the market for this problem. Processing centers and delivery drivers need something that can process information rapidly, output responses in natural language and interact with multiple people simultaneously. It’s November, which means the holiday season has started — and with it, an influx of online orders. It’s November, which means the holiday season has started — and with it, an influx of online orders. Most consumers who have ordered something online have experienced delays, failed delivery attempts or worse — lost packages. Frankly, tracking numbers and text message updates aren’t cutting it. Could artificial intelligence finally provide a solution to this problem? Most consumers who have ordered something online have experienced delays, failed delivery attempts or worse — lost packages. Frankly, tracking numbers and text message updates aren’t cutting it. Could artificial intelligence finally provide a solution to this problem? Packages Are Going Missing and It’s a Problem Packages Are Going Missing and It’s a Problem A package is supposed to arrive today. The person who placed the order watches their front door like a hawk after their app’s tracker changes from “shipping” to “out for delivery.” The estimated delivery time comes and goes — and nothing gets dropped off. Their phone pings with a “Sorry we missed you” message. What happened? Situations like these are common. Whether the seller reschedules, a delivery driver skips part of their route to save time or a porch pirate swoops in at the last second, parcels go missing all the time. In 2023, almost 40% of people in the United States had at least one package stolen. This problem is challenging to remedy simply because the delivery network is massive. almost 40% of people almost 40% of people Accurately tracking missing orders is possible, but it is a tall order with today’s technical limitations. Companies must monitor warehouse workers, drivers and vehicles. Not to mention, they must handle fraud cases where customers claim to have received nothing when, in reality, they did. Even in ideal circumstances, accidents happen — a small envelope could fall behind the sorting machine or get stuck under a larger box. Even when shipments eventually reach the right address, they stay in the delivery network too long. According to the United States Postal Service (USPS), 17.3% of first-class mail was not delivered on time in the second quarter of the fiscal year 2024, up 3.2 percentage points from the previous quarter. Rates for marketing mail and periodicals also worsened. 17.3% of first-class mail 17.3% of first-class mail The number of parcels going missing in the reverse logistics process is often overlooked. Around 30% of all online orders are sent back, compared to just 9% in brick-and-mortar stores. A disappearing parcel is particularly frustrating here, as the person attempting the return may be denied a refund or end up on the hook for the full price of the item. 30% of all online orders 30% of all online orders Why Use AI to Track People’s Missing Packages? AI is a powerful technology. A single advanced algorithm can process massive datasets, make data-driven decisions and understand language, regardless of whether it is a chatbot, generative algorithm or deep learning model. Training determines its purpose and behavior, so it can be utilized in virtually any use case. Versatility is useful when analyzing data since processing centers can produce billions of drop-off images or location data points in short periods. There is even more to go through if they use Internet of Things (IoT) tools or telematics systems in their fleets. AI can respond to prompts with voice, image, text or audio output, streamlining information retrieval and summary. Only a technology like AI can handle all the data created by the delivery network’s massive, interconnected system of sellers, fleets, customers and warehouses. Its rapid analysis isn’t its only boon — it also has automation capabilities. An advanced model can operate around the clock without stopping for breaks, food or sleep. Is AI Already Being Used to Track Packages? Several delivery companies are already using AI to track shipments in transit. For instance, the United Parcel Service uses an AI-enabled supply chain. It uses historical and real-time data to calculate the fastest, safest way to boost the on-time delivery rate. Its algorithm can also anticipate disruptions and provide a comprehensive view of the fleet’s network. uses historical and real-time data uses historical and real-time data If other firms deploy AI at the edge — at the network’s boundary where data is created — they wouldn’t have to build data centers to process or analyze the massive amount of logistics information they receive. On top of using fewer computing resources, they’d enable real-time results with little to no delay, improving output accuracy. In a different approach, USPS uses an AI-enabled edge computing program powered by NVIDIA technology. Its model analyzes billions of images that the processing center generates. Before, finding a missing order took a team of up to 10 people multiple days. With this platform, a single individual can track one down in around two hours. team of up to 10 people team of up to 10 people 4 Ways AI Can Track Stolen and Lost Packages There are several ways AI can help people track their missing parcels. 1. Provide Real-Time Updates Even with a tracking number, updates are often too vague, inaccurate or outdated to be helpful. According to one survey, nine in 10 people want to be able to track their order, with 47% wanting to know exactly where their item is at all times. AI can fulfill this desire, providing real-time updates as shipments travel. nine in 10 people nine in 10 people 2. Calculate Delivery Estimate Since as many as 20% of deliveries fail on the first attempt, reschedules are common. While most people wait a few extra days, some don’t receive their items for weeks or months. With AI, they can see how long it will take for a driver to reattempt delivery. The algorithm can factor in variables like weather, consumer demand, route closures and delays. as many as 20% of deliveries as many as 20% of deliveries 3. Analyze the Drop-Off Photos An AI could leverage image recognition technology to analyze millions of drop-off photos. It can flag the delivery as incorrect or suspicious if it notices any discrepancies. If there are enough clues in the photo’s visual details or metadata, the model could help workers track down the missing parcel. 4. Answer Peoples’ Questions Globally, 46% of online shoppers expect to receive their orders within two to three days. However, delays, reschedules and cancellations are common. When these situations arise, an AI-powered chatbot can answer consumers’ questions to help them understand what happened. Depending on its design, it could discuss the item’s location or estimate delivery dates. 46% of online shoppers 46% of online shoppers How Integration Could Enhance AI’s Capabilities While AI is powerful on its own, companies could see significant improvements after integration. For example, embedding a machine learning model into a surveillance system or camera doorbell could help recipients identify porch pirates or misbehaving delivery drivers. This way, they could potentially narrow down the location of their missing item. An AI-powered computer vision system — an AI-powered device that can interpret visual information — could do something similar, monitoring production lines and automatic sorting machines for misplaced parcels. If something falls off of a belt, gets stuck under another container or slips between a crack in the machine, it could alert a human worker. Integrations can even help in transit. Combining AI and IoT sensors enables real-time updates on a product’s condition and location. Recipients could receive alerts if their item is tampered with, dropped off at the wrong location or stolen. The algorithm would filter out false positives and concisely explain what happened. Could AI Prevent Packages From Getting Lost? While finding missing parcels is an excellent use of a machine learning model, stopping this problem at the source is a more effective solution. Companies could prevent products from getting lost in the first place by leveraging chatbots for communication. The higher the success rate for the first delivery attempt, the less likely the item will disappear from the system. Decision-makers could also optimize routes to reduce workers’ stress. Demand has far outpaced the workforce’s capabilities — experts predict e-commerce sales will reach over $8 trillion in sales by 2027, a 237% increase in one decade — which puts pressure on drivers to haul more boxes and complete routes faster. reach over $8 trillion in sales reach over $8 trillion in sales Companies increasingly hire freelance and gig workers to compensate for their labor shortage. These workers are often paid by the number of packages they deliver, incentivizing them to race through their routes. Using AI to optimize their trip saves them time, lowering the chance they will make mistakes in transit. The Bottom Line of Using AI to Track Orders Although AI is not a miracle technology, it is one of the best solutions on the market for this problem. Processing centers and delivery drivers need something that can process information rapidly, output responses in natural language and interact with multiple people simultaneously.

AI Can Help Track Your Missing Packages

Emotion As A Service: AI’s Next Market Is Your Heart

العاطفة كخدمة: السوق القادمة للذكاء الاصطناعي هي قلبك

Емоцията като услуга: Следващият пазар на AI е вашето сърце

পরিষেবা হিসাবে আবেগ: AI এর পরবর্তী বাজার আপনার হৃদয়

La emoción como servicio: el próximo mercado de la IA es tu corazón

Emosyon kòm yon sèvis: pwochen mache AI a se kè ou

感情をサービスとして: AIの次の市場はあなたの心

អារម្មណ៍ជាសេវាកម្ម៖ ទីផ្សារបន្ទាប់របស់ AI គឺជាបេះដូងរបស់អ្នក។

Кызмат катары эмоция: AIнын кийинки рыногу - бул сиздин жүрөгүңүз

Emocje jako usługa: następnym rynkiem dla sztucznej inteligencji jest Twoje serce

Emotion seSevhisi: AI Inotevera Musika Mwoyo Wako

ስምዒት ከም ኣገልግሎት፡ ቀጻሊ ዕዳጋ AI ልብኻ’ዩ።

Cảm xúc như một dịch vụ: Thị trường tiếp theo của AI là trái tim của bạn

In the wild world of Web3, there's constant chatter about memecoins—are they distracting us from “serious” projects, or are they the heart and soul of this decentralized revolution? Well, buckle up because I’ve got some thoughts. Spoiler alert: this isn’t going to be your average TED Talk—it’s going to be a ride, a long one. So grab your moon boots because we’re diving into the world of memecoins. What is a Coin or Token, Anyway? Before we get to the spicy stuff, let’s clear up some jargon. Back in the day (and by "day," I mean 2013, which in crypto years feels like a century), a “coin” referred to the native currency of a blockchain. You know, like ETH for Ethereum or SOL for Solana. Tokens, on the other hand, were created on top of blockchains—like $UNI on Ethereum. But in today’s multi-chain, cross-chain, and just-about-every-chain world, that line between coins and tokens has gotten a little blurry. Native coins can moonwalk as tokens on other chains, so the technical difference isn’t what it used to be. Coins or tokens, at the end of the day, are digital assets. And whether you’re talking about a coin or a token, both represent value in their respective projects. The magic really happens when they start getting traded. In crypto, much like the stock market, value often boils down to how much people believe in it Aping into Digital Assets From a trading perspective, digital assets are like the new baseball cards—except with more charts, volatility, and late-night price checks. When you buy a coin or token, you're not just investing in some tech—you’re buying into a project’s story, its vision, and sometimes, let’s be real, pure hype. It’s like owning a little slice of whatever the project promises to be. Whether it’s solving world-changing problems with decentralized finance or just giving your portfolio a pump, coins and tokens both represent potential value. Now, here's where it gets fun: in the chaotic world of trading, the tech behind an asset often matters less than the market's mood. Is everyone buzzing on Crypto Twitter? Did an influencer mention it in a livestream? Boom—your asset might just moon today. In crypto, much like the stock market, value often boils down to how much people believe in it. Memecoins take this principle and turn it into pure, glorious chaos. 2011: The Start of Crypto Culture When Satoshi Nakamoto dropped the Bitcoin white paper in 2011, it was more than a new digital currency—it was the start of a revolution. The internet needed its own money: decentralized, censorship-resistant, and designed to bypass traditional systems like banks. And just like that, the decentralized world was born, powered by cryptography and offering real ownership for the first time. Back in those early days, crypto was all about counterculture. Meetups weren’t held in shiny tech offices—they were in back alleys and obscure forums, where anarchists, geeks, and visionaries came together. This was the beginning of crypto culture, where community and decentralization took center stage. And let’s face it, that spirit of rebellion still runs deep today. The real issue is that big money often swoops in, distorts things, and leaves retail holding the bag Crypto Trading and Cycles As the crypto space grew, trading cycles emerged—booms and busts driven by innovation and the hunt for profit. But here’s the best part: in crypto, retail traders finally have the power. Unlike traditional markets, where Wall Street calls the shots, in crypto anyone can participate. No middlemen, no gatekeepers, just you and your wallet. Retail investors aren’t just adding fuel to the fire—they’re seizing the opportunity. Cycles are a given in any market. Prices rise, prices fall. But the real issue is that big money often swoops in, distorts things, and leaves retail holding the bag. In crypto, though, retail has a fighting chance. They’re not just chasing profits—they’re taking control of their financial future. And every cycle, new projects gain momentum—just look at Bitcoin, Solana, and the rise of L2 ecosystems. Bitcoin: The Mother of All Memecoins Here’s a fun fact: Bitcoin is the OG memecoin. That’s right, before Shiba Inu or Doge, Bitcoin was just a narrative. It wasn’t backed by anything except belief, community, and a wild dream of decentralized money. People experimented with it (remember the legendary Pizza Day?), and eventually, Bitcoin became something much bigger. But it all started with a narrative and a community, and that’s the essence of a memecoin. The Rise and Rise of Memecoins Let’s face it—crypto thrives on counterculture, and nothing embodies that better than memecoins. We love to make fun of ourselves, our projects, and the entire space through memes. Memecoins take that spirit and crank it up to eleven. Dogecoin started as a joke and “a fork of a fork of a fork” in 2013, but guess what? The joke became real. With a strong, fun-loving community behind it, Dogecoin grew into a legitimate force in the crypto world. It’s proof that community and narrative matter just as much—if not more—than technology. Now, memecoins are having their biggest moment yet. While big money has wormed its way into the rest of crypto, memecoins have remained refreshingly honest (at least the gud ones 😉). They’re driven by community, not corporate greed. They offer retail traders the chance to take control, to claim a piece of the upside. Memecoins bring back that wild, unfiltered trading spirit. Memecoins represent a chance for retail to reclaim control, for community to shine, and for crypto to stay weird—in the best way possible Memecoins and the Cycle That’s Brewing The days of simply slapping a meme on a token and watching it moon are fading. A new breed of memecoins is emerging—coins like Watercoin, SPX6900, and HarryPotterObamaSonic10Inu—built on transparency, fairness, impact, movement, and with goals. These tokens are powered by culture and community. They’re about more than a quick pump—they’re about building something real. From my own journey as co-initiator of Lambo Llama, I can tell you how liberating and fun it is to start such a movement. We are building cross-chain community tokens and L2, built on the memecoin DNA but designed to be more. It’s about inclusivity, learning, and connecting communities in our fragmented and tribalized space. And yes, we don’t take ourselves too seriously, fun is a door-opener for learning, connecting and experimenting. Our community has set the goals and is growing. Memecoins bring back the fun, the chaos, and, most importantly, the community that makes crypto great. They’re reclaiming the space from big money, reminding us what crypto was always meant to be about: empowerment, transparency, and people-driven movements. As we enter a new cycle, memecoins are set to play a huge role. They’re not just surviving—they’re thriving. Memecoins are here to stay because they represent something much deeper than memes. They represent a chance for retail to reclaim control, for the community to shine, and for crypto to stay weird—in the best way possible. So, buckle up because the next ride is going to be wild—and memecoins are leading the charge. Disclaimer: I am a co-initiator of Lambo Llama, so I have my bias 😆 In the wild world of Web3, there's constant chatter about memecoins—are they distracting us from “serious” projects, or are they the heart and soul of this decentralized revolution? Well, buckle up because I’ve got some thoughts. Spoiler alert: this isn’t going to be your average TED Talk—it’s going to be a ride, a long one. So grab your moon boots because we’re diving into the world of memecoins. What is a Coin or Token, Anyway? What is a Coin or Token, Anyway? Before we get to the spicy stuff, let’s clear up some jargon. Back in the day (and by "day," I mean 2013, which in crypto years feels like a century), a “coin” referred to the native currency of a blockchain. You know, like ETH for Ethereum or SOL for Solana. Tokens, on the other hand, were created on top of blockchains—like $UNI on Ethereum. But in today’s multi-chain, cross-chain, and just-about-every-chain world, that line between coins and tokens has gotten a little blurry. Native coins can moonwalk as tokens on other chains, so the technical difference isn’t what it used to be. Coins or tokens, at the end of the day, are digital assets. And whether you’re talking about a coin or a token, both represent value in their respective projects. The magic really happens when they start getting traded. In crypto, much like the stock market, value often boils down to how much people believe in it In crypto, much like the stock market, value often boils down to how much people believe in it Aping into Digital Assets Aping into Digital Assets From a trading perspective, digital assets are like the new baseball cards—except with more charts, volatility, and late-night price checks. When you buy a coin or token, you're not just investing in some tech—you’re buying into a project’s story, its vision, and sometimes, let’s be real, pure hype. It’s like owning a little slice of whatever the project promises to be. Whether it’s solving world-changing problems with decentralized finance or just giving your portfolio a pump, coins and tokens both represent potential value. Now, here's where it gets fun: in the chaotic world of trading, the tech behind an asset often matters less than the market's mood. Is everyone buzzing on Crypto Twitter? Did an influencer mention it in a livestream? Boom—your asset might just moon today. In crypto, much like the stock market, value often boils down to how much people believe in it. Memecoins take this principle and turn it into pure, glorious chaos. 2011: The Start of Crypto Culture 2011: The Start of Crypto Culture When Satoshi Nakamoto dropped the Bitcoin white paper in 2011, it was more than a new digital currency—it was the start of a revolution. The internet needed its own money: decentralized, censorship-resistant, and designed to bypass traditional systems like banks. And just like that, the decentralized world was born, powered by cryptography and offering real ownership for the first time. Back in those early days, crypto was all about counterculture. Meetups weren’t held in shiny tech offices—they were in back alleys and obscure forums, where anarchists, geeks, and visionaries came together. This was the beginning of crypto culture, where community and decentralization took center stage. And let’s face it, that spirit of rebellion still runs deep today. The real issue is that big money often swoops in, distorts things, and leaves retail holding the bag The real issue is that big money often swoops in, distorts things, and leaves retail holding the bag Crypto Trading and Cycles Crypto Trading and Cycles As the crypto space grew, trading cycles emerged—booms and busts driven by innovation and the hunt for profit. But here’s the best part: in crypto, retail traders finally have the power. Unlike traditional markets, where Wall Street calls the shots, in crypto anyone can participate. No middlemen, no gatekeepers, just you and your wallet. Retail investors aren’t just adding fuel to the fire—they’re seizing the opportunity. Cycles are a given in any market. Prices rise, prices fall. But the real issue is that big money often swoops in, distorts things, and leaves retail holding the bag. In crypto, though, retail has a fighting chance. They’re not just chasing profits—they’re taking control of their financial future. And every cycle, new projects gain momentum—just look at Bitcoin, Solana, and the rise of L2 ecosystems. Bitcoin: The Mother of All Memecoins Bitcoin: The Mother of All Memecoins Here’s a fun fact: Bitcoin is the OG memecoin. That’s right, before Shiba Inu or Doge, Bitcoin was just a narrative. It wasn’t backed by anything except belief, community, and a wild dream of decentralized money. People experimented with it (remember the legendary Pizza Day?), and eventually, Bitcoin became something much bigger. But it all started with a narrative and a community, and that’s the essence of a memecoin. The Rise and Rise of Memecoins The Rise and Rise of Memecoins Let’s face it—crypto thrives on counterculture, and nothing embodies that better than memecoins. We love to make fun of ourselves, our projects, and the entire space through memes. Memecoins take that spirit and crank it up to eleven. Dogecoin started as a joke and “a fork of a fork of a fork” in 2013, but guess what? The joke became real. With a strong, fun-loving community behind it, Dogecoin grew into a legitimate force in the crypto world. It’s proof that community and narrative matter just as much—if not more—than technology. Now, memecoins are having their biggest moment yet. While big money has wormed its way into the rest of crypto, memecoins have remained refreshingly honest (at least the gud ones 😉). They’re driven by community, not corporate greed . They offer retail traders the chance to take control, to claim a piece of the upside. Memecoins bring back that wild, unfiltered trading spirit. honest community, not corporate greed Memecoins represent a chance for retail to reclaim control, for community to shine, and for crypto to stay weird—in the best way possible Memecoins represent a chance for retail to reclaim control, for community to shine, and for crypto to stay weird—in the best way possible Memecoins and the Cycle That’s Brewing Memecoins and the Cycle That’s Brewing The days of simply slapping a meme on a token and watching it moon are fading. A new breed of memecoins is emerging—coins like Watercoin, SPX6900, and HarryPotterObamaSonic10Inu—built on transparency, fairness, impact, movement, and with goals. These tokens are powered by culture and community . They’re about more than a quick pump—they’re about building something real. culture community From my own journey as co-initiator of Lambo Llama , I can tell you how liberating and fun it is to start such a movement. We are building cross-chain community tokens and L2, built on the memecoin DNA but designed to be more. It’s about inclusivity, learning, and connecting communities in our fragmented and tribalized space. And yes, we don’t take ourselves too seriously, fun is a door-opener for learning, connecting and experimenting. Our community has set the goals and is growing. Lambo Llama Memecoins bring back the fun, the chaos, and, most importantly, the community that makes crypto great. They’re reclaiming the space from big money, reminding us what crypto was always meant to be about: empowerment, transparency, and people-driven movements. community As we enter a new cycle, memecoins are set to play a huge role. They’re not just surviving—they’re thriving. Memecoins are here to stay because they represent something much deeper than memes. They represent a chance for retail to reclaim control, for the community to shine, and for crypto to stay weird—in the best way possible. So, buckle up because the next ride is going to be wild—and memecoins are leading the charge. Disclaimer: I am a co-initiator of Lambo Llama, so I have my bias 😆 Disclaimer: I am a co-initiator of Lambo Llama, so I have my bias 😆

Memecoins Are Essential and Awesome

Lessons in Development, Hard Work, and Critical Thinking Life before AI, being a developer, was a different world — a harder world in the sense that everything you do requires tremendous focus and incredible persistence. Challenges were real, and so were the triumphs that followed. Honest to God, I sometimes miss parts of that time. I was reminded of those early days vividly. There were no AI-powered tools back in those days that could swoop in and solve your problems for you. Every problem was a mountain which you had to climb alone. If you had bugs in your code, no AI could have run a simulation or suggested a fix. You had to work out by yourself. Countless late nights staring at my screen, combing through lines of code trying to figure out where I did it wrong. Debugging was like detective work. Sometimes, finally, it would come out what I was hunting for, and the relief was almost overwhelming — like just unlocking this huge mystery that had been gnawing at me for hours maybe even days. You’d learn so much in those moments. Not only about the code you’re writing but about the system itself — the inner workings of the technology. I only learned when I needed to know how to do something; it wasn’t because some tool just pointed me in the right direction, otherwise. It was slow and often frustrating, but this slow burn of learning made me a better developer. Without the safety net of AI, I needed to know what I was doing at each step. I think one of the reasons why developers nowadays miss this depth of understanding is that they have these incredibly powerful tools right there at their fingertips. And, of course, I cannot forget the level of “manuals” of everything: everything required a tremendous amount of effort, including writing test cases, for example. Nowadays, AI can write tests for you or suggest improvements, and everything is ready for you; back then, it was all up to me. I had to think of all the ways that my code might break and code for each one. And these weren’t glamorous pieces of work, but they were pretty darn important. Every test that I wrote taught me something about how fragile my code could be, and how I could make my code robust. The same applied to optimizing code. I had no fancy AI tools to analyze my work and suggest ways of efficiency. Instead, I had to spend hours iterating over different versions of a function, trying to shave off milliseconds from execution times or reduce memory consumption. It was boring, but it sharpened my understanding of how systems work under the hood. It also meant knowing about memory management, algorithm efficiency, and how the hardware was going to interpret my code. There’s no autopilot to do it for you. It was different too, as far as collaboration with my team was concerned. We had no AI to turn to quickly for debugging or optimization, so we relied on each other nearly for everything. We brainstormed, troubleshoot, and sometimes argued over the best ways to solve a problem. It was never easy, but there was a camaraderie in just knowing we were all in the same boat, all struggling with the same challenges. The beauty of it was that we were learning together every step of the way. It was tough getting through acquiring new skills back then. There were no shortcuts. So if you wanted to learn a new programming language or technology, you had to dive in deep. There wasn’t an AI assistant to fill in the gaps or give real-time feedback. If you didn’t know how something worked, you figured it out by yourself or through your mentors and peers. That’s how I learned C# — through trial and error and countless mistakes. I shall never forget the headache of trying to get a hold of things like multithreading, exception handling, or memory leaks. There wasn’t a silver bullet-just long hours of frustration followed by satisfaction when things worked. Another was the errors. Every time I hit a bug, it felt like hitting a wall. But with each of those walls lay an opportunity. To debug did not mean that you solved the problem; it was instead, learning why the problem existed in the first place. It is what those experiences shaped me: how to approach problems methodically and patiently. In that world before AI, what mattered above all else were your skills. Knowing how to write code was certainly not sufficient; it required creativity when solving problems and critical thinking. I still remember starting from scratch so many times because I realized my original thought was inefficient or wrong in general. But that was what made me improve. And each time I re-wrote a function or refactored a class, I was improving too, I was getting to know how to make performance and readability decisions, and how to trade-off between speed and stability. Even crafting something as simple as a SQL query was a chore in precision. There was no such thing as AI-crafted databases that could optimize queries on the fly. I spent hours futzing with them, trying to make sure that they ran as well as possible, especially when I was dealing with larger datasets. All these performance tweaks had been hard-won through trial and painful error. Then quietly began AI’s influence in the world of development. At first, it was just little things auto-complete in my IDE getting smarter, code editors catching errors before I even ran the program. I did not realize it at that time, but changes like these were game-changers. Things that took me hours to get done could be completed in a few minutes. Debugging, optimizing-even generating some boilerplate code became easier. It is like having an assistant who could see farther than my tiny horizon, an assistant who could point out all the problems that I had never considered at all. But as easy as it was, so was it strange. I was no longer asked to dig deep into the problems anymore. The AI was already there before I could understand the issue- presenting the solutions for me. It made me much more efficient, yes, but part of me misses the depth of understanding that came with doing things the hard way. There is satisfaction in cracking on your own a tough problem that AI, for all its brilliance, can’t reenact. Learning has changed these days and in this AI-driven era. The hard work isn’t gone; it’s just different. Today, developers do not bother memorizing syntax or spending hours debugging. It is all about how to work with AI. How to use these tools to be more efficient at building faster, more scalable solutions. However, it is still the same problem-solving essence. It can be described as AI suggesting a potential fix, but then, the decision still lies on the side of the developer whether it is right and whether it fits into the overall context of the project. This new chapter has been exciting and challenging for me. The transition to technological leadership allowed me to free up more energy to focus on bigger problems, building complex systems. I will always look back at the days before AI with a certain sort of nostalgia. It was long nights and tough-won victories that made me the developer that I am today. And for that, I appreciate this efficiency brought on by AI, but I will never forget the lessons I learned when it was just me, my code, and that quiet determination to figure things out. Follow on Medium: The AI Guy Lessons in Development, Hard Work, and Critical Thinking Life before AI, being a developer, was a different world — a harder world in the sense that everything you do requires tremendous focus and incredible persistence. Challenges were real, and so were the triumphs that followed. Honest to God, I sometimes miss parts of that time. I was reminded of those early days vividly. There were no AI-powered tools back in those days that could swoop in and solve your problems for you. Every problem was a mountain which you had to climb alone. If you had bugs in your code, no AI could have run a simulation or suggested a fix. You had to work out by yourself. Countless late nights staring at my screen, combing through lines of code trying to figure out where I did it wrong. Debugging was like detective work. Sometimes, finally, it would come out what I was hunting for, and the relief was almost overwhelming — like just unlocking this huge mystery that had been gnawing at me for hours maybe even days. You’d learn so much in those moments. Not only about the code you’re writing but about the system itself — the inner workings of the technology. I only learned when I needed to know how to do something; it wasn’t because some tool just pointed me in the right direction, otherwise. It was slow and often frustrating, but this slow burn of learning made me a better developer. Without the safety net of AI, I needed to know what I was doing at each step. I think one of the reasons why developers nowadays miss this depth of understanding is that they have these incredibly powerful tools right there at their fingertips. And, of course, I cannot forget the level of “manuals” of everything: everything required a tremendous amount of effort, including writing test cases, for example. Nowadays, AI can write tests for you or suggest improvements, and everything is ready for you; back then, it was all up to me. I had to think of all the ways that my code might break and code for each one. And these weren’t glamorous pieces of work, but they were pretty darn important. Every test that I wrote taught me something about how fragile my code could be, and how I could make my code robust. The same applied to optimizing code. I had no fancy AI tools to analyze my work and suggest ways of efficiency. Instead, I had to spend hours iterating over different versions of a function, trying to shave off milliseconds from execution times or reduce memory consumption. It was boring, but it sharpened my understanding of how systems work under the hood. It also meant knowing about memory management, algorithm efficiency, and how the hardware was going to interpret my code. There’s no autopilot to do it for you. It was different too, as far as collaboration with my team was concerned. We had no AI to turn to quickly for debugging or optimization, so we relied on each other nearly for everything. We brainstormed, troubleshoot, and sometimes argued over the best ways to solve a problem. It was never easy, but there was a camaraderie in just knowing we were all in the same boat, all struggling with the same challenges. The beauty of it was that we were learning together every step of the way. It was tough getting through acquiring new skills back then. There were no shortcuts. So if you wanted to learn a new programming language or technology, you had to dive in deep. There wasn’t an AI assistant to fill in the gaps or give real-time feedback. If you didn’t know how something worked, you figured it out by yourself or through your mentors and peers. That’s how I learned C# — through trial and error and countless mistakes. I shall never forget the headache of trying to get a hold of things like multithreading, exception handling, or memory leaks. There wasn’t a silver bullet-just long hours of frustration followed by satisfaction when things worked. Another was the errors. Every time I hit a bug, it felt like hitting a wall. But with each of those walls lay an opportunity. To debug did not mean that you solved the problem; it was instead, learning why the problem existed in the first place. It is what those experiences shaped me: how to approach problems methodically and patiently. In that world before AI, what mattered above all else were your skills. Knowing how to write code was certainly not sufficient; it required creativity when solving problems and critical thinking. I still remember starting from scratch so many times because I realized my original thought was inefficient or wrong in general. But that was what made me improve. And each time I re-wrote a function or refactored a class, I was improving too, I was getting to know how to make performance and readability decisions, and how to trade-off between speed and stability. Even crafting something as simple as a SQL query was a chore in precision. There was no such thing as AI-crafted databases that could optimize queries on the fly. I spent hours futzing with them, trying to make sure that they ran as well as possible, especially when I was dealing with larger datasets. All these performance tweaks had been hard-won through trial and painful error. Then quietly began AI’s influence in the world of development. At first, it was just little things auto-complete in my IDE getting smarter, code editors catching errors before I even ran the program. I did not realize it at that time, but changes like these were game-changers. Things that took me hours to get done could be completed in a few minutes. Debugging, optimizing-even generating some boilerplate code became easier. It is like having an assistant who could see farther than my tiny horizon, an assistant who could point out all the problems that I had never considered at all. But as easy as it was, so was it strange. I was no longer asked to dig deep into the problems anymore. The AI was already there before I could understand the issue- presenting the solutions for me. It made me much more efficient, yes, but part of me misses the depth of understanding that came with doing things the hard way. There is satisfaction in cracking on your own a tough problem that AI, for all its brilliance, can’t reenact. Learning has changed these days and in this AI-driven era. The hard work isn’t gone; it’s just different. Today, developers do not bother memorizing syntax or spending hours debugging. It is all about how to work with AI. How to use these tools to be more efficient at building faster, more scalable solutions. However, it is still the same problem-solving essence. It can be described as AI suggesting a potential fix, but then, the decision still lies on the side of the developer whether it is right and whether it fits into the overall context of the project. This new chapter has been exciting and challenging for me. The transition to technological leadership allowed me to free up more energy to focus on bigger problems, building complex systems. I will always look back at the days before AI with a certain sort of nostalgia. It was long nights and tough-won victories that made me the developer that I am today. And for that, I appreciate this efficiency brought on by AI, but I will never forget the lessons I learned when it was just me, my code, and that quiet determination to figure things out. Follow on Medium: The AI Guy The AI Guy

The World Before AI

How HeyFood Grew From a Small Campus Tool to 100k Users in 5 Cities

HeyFood কিভাবে একটি ছোট ক্যাম্পাস টুল থেকে 5টি শহরে 100,000 ব্যবহারকারীদের কাছে বৃদ্ধি পেয়েছে

Wie HeyFood von einem kleinen Campus-Tool zu 100.000 Benutzern in 5 Städten wuchs

Cómo HeyFood pasó de ser una pequeña herramienta en un campus a contar con 100.000 usuarios en 5 ciudades

Comment HeyFood est passé d'un petit outil de campus à 100 000 utilisateurs dans 5 villes

कैसे HeyFood एक छोटे कैंपस टूल से 5 शहरों में 100k उपयोगकर्ताओं तक पहुंच गया

HeyFood が小さなキャンパス ツールから 5 都市で 10 万人のユーザーを獲得するまでに成長した経緯

HeyFood가 소규모 캠퍼스 도구에서 5개 도시의 10만 명의 사용자로 성장한 과정

Como o HeyFood cresceu de uma pequena ferramenta de campus para 100 mil usuários em 5 cidades

Как HeyFood вырос из небольшого кампусного инструмента до 100 тыс. пользователей в 5 городах

HeyFood Küçük Bir Kampüs Aracından 5 Şehirde 100.000 Kullanıcıya Nasıl Ulaştı?

HeyFood đã phát triển như thế nào từ một công cụ nhỏ trong khuôn viên trường học thành 100.000 người dùng tại 5 thành phố

HeyFood 如何从校园小工具发展成为覆盖 5 个城市的 10 万名用户

The Internet is becoming a breeding ground for spam. Not the tinned variety—no, we’re talking about the relentless, soul-crushing onslaught of automated form spam that has made running a website feel like an endless game of whack-a-mole with bots Every time you think you've dealt with it, a new flood arrives, turning your contact forms, comment sections, and customer feedback pages into the equivalent of a dumpster fire. And where are the hosting providers in all of this chaos? Sitting on the sidelines, waiting for you to report the damage. Why are the companies we entrust with our websites seemingly more reactive than proactive when it comes to combating the spam plague? According to CleanTalk, a popular and highly effective WordPress plugin provider, their service now protects most web forms from unwanted spam and it does amazing job mind you, However, in an alarming twist, the evolution of sneaky spam bots is beginning to outwit these protections, slipping past even the most advanced filters. The Proactive Approach: Still a Fantasy Let’s be real. Hosting providers have the resources. They have the infrastructure. They can see the patterns. But instead of stopping spam attacks before they cripple your site, they wait—oh, they wait until it’s far too late, and your inbox is flooded with a thousand messages about miracle weight loss pills, sketchy loan offers, and suspicious ‘business opportunities.’ Then, and only then, do they swoop in, offering "support" (if you can even call it that). It begs the question: If they know spam bots are such a rampant issue, why aren’t they doing more? It’s like having a security guard at the front door of a building, but instead of stopping intruders, they wait until the intruder has raided the entire place before calling for backup. Where's the sense in that? They Have the Tools, But They're Not Using Them We’ve all heard the excuse, "Oh, but you can install third-party plugins." Yeah, no thanks. Why should customers be forced to play tech support for their own websites when the people hosting them should already be deploying the latest anti-spam and anti-malware systems? We’re not asking for a miracle, just for you to use the tools you’ve got in a way that makes sense! Cloudflare’s out there doing it, why can’t they? Hosting providers are quick to offer flashy deals—“99% uptime!” and “24/7 customer support!”—but where’s the promise of “We’ll proactively block malicious bots and won’t allow our servers to be used in attacks on other websites. Enough is Enough It’s 2024, and automated form spam is still an unresolved problem because hosting providers are playing a dangerous game of catch-up. They prefer to wait until the bot tsunami hits, responding only when the damage has been done. The result? Countless hours wasted cleaning up spam, frustrated business owners, and customer trust eroded by the lack of effective spam protection. The Internet is becoming a breeding ground for spam. Not the tinned variety—no, we’re talking about the relentless, soul-crushing onslaught of automated form spam that has made running a website feel like an endless game of whack-a-mole with bots The Internet is becoming a breeding ground for spam . Not the tinned variety—no, we’re talking about the relentless, soul-crushing onslaught of automated form spam that has made running a website feel like an endless game of whack-a-mole with bots spam Every time you think you've dealt with it, a new flood arrives, turning your contact forms, comment sections, and customer feedback pages into the equivalent of a dumpster fire. And where are the hosting providers in all of this chaos? Sitting on the sidelines, waiting for you to report the damage. Why are the companies we entrust with our websites seemingly more reactive than proactive when it comes to combating the spam plague? you According to CleanTalk , a popular and highly effective WordPress plugin provider, their service now protects most web forms from unwanted spam and it does amazing job mind you, However, in an alarming twist, the evolution of sneaky spam bots is beginning to outwit these protections, slipping past even the most advanced filters. CleanTalk The Proactive Approach: Still a Fantasy The Proactive Approach: Still a Fantasy Let’s be real. Hosting providers have the resources. They have the infrastructure. They can see the patterns. But instead of stopping spam attacks before they cripple your site, they wait—oh, they wait until it’s far too late, and your inbox is flooded with a thousand messages about miracle weight loss pills, sketchy loan offers, and suspicious ‘business opportunities.’ Then, and only then, do they swoop in, offering "support" (if you can even call it that). only It begs the question: If they know spam bots are such a rampant issue, why aren’t they doing more? It’s like having a security guard at the front door of a building, but instead of stopping intruders, they wait until the intruder has raided the entire place before calling for backup. Where's the sense in that? They Have the Tools, But They're Not Using Them They Have the Tools, But They're Not Using Them We’ve all heard the excuse, "Oh, but you can install third-party plugins." Yeah, no thanks. Why should customers be forced to play tech support for their own websites when the people hosting them should already be deploying the latest anti-spam and anti-malware systems? We’re not asking for a miracle, just for you to use the tools you’ve got in a way that makes sense! Cloudflare’s out there doing it, why can’t they? Hosting providers are quick to offer flashy deals—“99% uptime!” and “24/7 customer support!”—but where’s the promise of “We’ll proactively block malicious bots and won’t allow our servers to be used in attacks on other websites. Enough is Enough Enough is Enough It’s 2024, and automated form spam is still an unresolved problem because hosting providers are playing a dangerous game of catch-up. They prefer to wait until the bot tsunami hits, responding only when the damage has been done. The result? Countless hours wasted cleaning up spam, frustrated business owners, and customer trust eroded by the lack of effective spam protection.

The Automated Spam War: Why Hosting Providers Only Fight Back After the Damage Is Done?

Breaking the Procrastination Cycle: Stop Paying the Hidden Costs of Saying "Tomorrow" 

Role of AI in Immunization: The Shots that Your Body Needs

টিকাদানে এআই-এর ভূমিকা: আপনার শরীরের প্রয়োজনীয় শট

Rolle der KI bei der Immunisierung: Die Impfungen, die Ihr Körper braucht

El papel de la IA en la inmunización: las vacunas que tu cuerpo necesita

Le rôle de l’IA dans la vaccination : les vaccins dont votre corps a besoin

टीकाकरण में एआई की भूमिका: आपके शरीर को किन टीकों की ज़रूरत है

予防接種におけるAIの役割: 体に必要な注射

면역 접종에서 AI의 역할: 신체에 필요한 주사

Papel da IA na imunização: as vacinas que seu corpo precisa

Роль ИИ в иммунизации: прививки, необходимые вашему организму

Yapay Zekanın Aşılamadaki Rolü: Vücudunuzun İhtiyaç Duyduğu Aşılar

Vai trò của AI trong tiêm chủng: Các mũi tiêm mà cơ thể bạn cần

人工智能在免疫中的作用：你的身体需要注射哪些疫苗

Stories, News & Mentions about SWOOP

Swoop’s Stories on HackerNoon

Swoop's News & Mentions