RIDE!

ride

In the US system, when investors purchase convertible notes from a startup, the startup issues the shares to all investors simultaneously and pledges them as collateral for each c.n. transaction. This allows each investor to claim a fixed stake and, importantly, a % of the shares or a fixed amount of the return on the debt instrument. Let's consider an example of a Seed investment in startup that has released an MVP and attracted some initial clients:The convertible notes are backed by a 12% equity stake for $3m. The terms of the notes allow the holder to return the principal and/or convert them into shares at the option of the note holder upon the liquidity event(sale, liquidation, IPO). Please note that noteholders don't participate in the mgmt of the company and have voting rights only in relation to the sale or any other form of full monetisation of the investment. Notes are not subject to any trading restrictions in the secondary market. A monetisation event could be the sale of the company for $25m, in which case the investor would receive their $3m. If the company is sold for $50m, it becomes a more attractive investment as the stock will be worth $6m. Conversely, if the company is sold for $10m, it may be beneficial to take on debt, with the investor receiving their $3m.If an investor takes equity, the entire value of the company is equity. Conversely, if they take debt, the value of the company is divided into debt and equity, with the remainder being capital to be distributed among the shareholders. A notes are senior to the Seed notes in the event of a liquidity event - this means that the A notes holders will first make the conversion decision, and then the Seed holders. Therefore, the LIFO applies to all rounds.In each round, the investors' shares remain unchanged, but the sale proceeds result in a dilution of the founders' & team's shares. Founders often have the right to veto all matters except the vote on a liquidity event.What if the investors were to exert pressure and only agree to a new round at a 2x liquidation preference? This implies that at the liquidity event, the option is between redeeming the bond at 200% of par value or a block of shares in the security. Consequently, it's possible that all the notes from rounds B-C-D will be presented as debt instruments, leaving only the backers', Seed and A investments in the shares. The implication is that the startup game has a deliberately negative probability of a positive outcome with a positive math expectation. Consequently, it's the median practice that is most worthy of analysis, rather than the "unicorn in 3 years" cases. Focus on the business, not the lottery. It’s also advisable to exercise caution with regard to growth, in order to avoid the necessity of having to select 2-3 preferences. By assigning X's to the note, you're effectively creating a loan at a nominal interest rate! In the US system, when investors purchase convertible notes from a startup, the startup issues the shares to all investors simultaneously and pledges them as collateral for each c.n. transaction. This allows each investor to claim a fixed stake and, importantly, a % of the shares or a fixed amount of the return on the debt instrument. Let's consider an example of a Seed investment in startup that has released an MVP and attracted some initial clients: The convertible notes are backed by a 12% equity stake for $3m. The terms of the notes allow the holder to return the principal and/or convert them into shares at the option of the note holder upon the liquidity event(sale, liquidation, IPO). Please note that noteholders don't participate in the mgmt of the company and have voting rights only in relation to the sale or any other form of full monetisation of the investment. Notes are not subject to any trading restrictions in the secondary market. A monetisation event could be the sale of the company for $25m, in which case the investor would receive their $3m. If the company is sold for $50m, it becomes a more attractive investment as the stock will be worth $6m. Conversely, if the company is sold for $10m, it may be beneficial to take on debt, with the investor receiving their $3m. If an investor takes equity, the entire value of the company is equity. Conversely, if they take debt, the value of the company is divided into debt and equity, with the remainder being capital to be distributed among the shareholders. A notes are senior to the Seed notes in the event of a liquidity event - this means that the A notes holders will first make the conversion decision, and then the Seed holders. Therefore, the LIFO applies to all rounds. In each round, the investors' shares remain unchanged, but the sale proceeds result in a dilution of the founders' & team's shares. Founders often have the right to veto all matters except the vote on a liquidity event. What if the investors were to exert pressure and only agree to a new round at a 2x liquidation preference? This implies that at the liquidity event, the option is between redeeming the bond at 200% of par value or a block of shares in the security. Consequently, it's possible that all the notes from rounds B-C-D will be presented as debt instruments, leaving only the backers', Seed and A investments in the shares. The implication is that the startup game has a deliberately negative probability of a positive outcome with a positive math expectation. Consequently, it's the median practice that is most worthy of analysis, rather than the "unicorn in 3 years" cases. Focus on the business, not the lottery. It’s also advisable to exercise caution with regard to growth, in order to avoid the necessity of having to select 2-3 preferences. By assigning X's to the note, you're effectively creating a loan at a nominal interest rate!

How Can a Startup Avoid Being Taken for a Ride by an Investor?

Creating a Ride-a-request Application Using TomTom Maps API

Damn Near Every Digital Service is Ripping You Off to Float Free-riders

Damn Near Every Digital Service is You Ripping off to Float Free-riders

Fast jeder digitale Dienst betrügt Sie, um Trittbrettfahrer zu gewinnen

Casi todos los servicios digitales te están estafando para convertirte en usuarios libres

Presque tous les services numériques vous arnaquent pour faire flotter les free-riders

लगभग हर डिजिटल सेवा आपको मुफ्त में लाभ कमाने के लिए लूट रही है

ほぼすべてのデジタルサービスが、ただ乗りする人をだますためにあなたを騙している

빌어먹을 거의 모든 디지털 서비스가 당신을 떠다니는 무임승차로 끌어들이고 있습니다.

Quase todos os serviços digitais estão enganando você para flutuar como free-riders

Черт возьми, почти каждый цифровой сервис обманывает вас, чтобы пускать в ход безбилетников

Lanet olsun, Neredeyse Her Dijital Hizmet Sizi Serbest Sürüşçülere Yöneltiyor

Chết tiệt, gần như mọi dịch vụ kỹ thuật số đều đang lừa bạn để thả nổi những người đi xe miễn phí

几乎每一项数字服务都在欺骗你，让你搭便车

The rideshare and delivery markets are undergoing an upheaval driven by rapid technological advancements and shifting consumer behaviors. Companies like Wave are surfing this change with bespoke artificial intelligence (AI) technology, carving out a competitive edge in a burgeoning industry. As the market expands, opportunities abound for those willing to adapt. Wave’s own personal brand of AI-driven safety and community involvement has positioned it quite advantageously, taking the market by storm in U.S. cities like Atlanta and Chicago. The Expanding U.S. Sector The global rideshare market is projected to reach $335 billion by 2025, growing at a compound annual growth rate (CAGR) of 18.2% from 2020 to 2025. This growth is fueled by increasing urbanization, the rising preference for shared mobility, and the convenience of on-demand services. Similarly, the online food delivery market is expected to surpass $213 billion by 2030. This explosive expansion is primarily driven by the proliferation of platform-to-consumer services and the lasting impact of the COVID-19 pandemic. In the United States, DoorDash leads the food delivery market with a 67% market share, followed by Uber Eats with 23%. The intense competition among these giants has focused on efficiency and customer experience, with companies investing heavily in AI and automation to streamline operations and reduce costs. "AI has changed the rules of the game. Not only in transportation or food delivery but every other aspect of the industry as well," says Diondre Lewis, CEO of Wave. "At Wave, AI has enabled us to optimize routes, predict demand, enhance safety, and provide better customer support." Room for Growth in the Markets The rideshare and delivery markets present numerous opportunities for savvy businesses looking to capitalize on recent technological developments. One among these trends is the increasing efficiency of last-mile delivery, which now represents 53% of the total cost of transportation. Companies are exploring various technologies, including AI, autonomous vehicles, and crowdsourcing, to reduce these costs and improve delivery times. Autonomous vehicles, including drones, are also becoming a viable solution for last-mile deliveries, offering the potential to cut costs and enhance efficiency significantly. This trend is expected to gain momentum as technology advances and regulatory frameworks evolve, addressing labor shortages and improving service efficiency. Another opportunity lies in the growing demand for personalized and efficient delivery services. AI-powered platforms can analyze vast amounts of data to provide personalized recommendations, optimize delivery routes, and predict peak demand times, much like Wave itself does. "The name of the game is customer satisfaction. There is no better way to meet the varied demands of our clients than with machine learning. We offer services from food delivery to corporate rideshares and non-emergency medical transport," Lewis explains. The Role of AI in Shaping the Future AI technology is credited with most of the transformation in the rideshare and delivery markets. Companies increasingly rely on AI to optimize their operations, improve safety, and provide personalized services. Wave's AI-powered platform is the very epitome of the potential AI presents. The company's proprietary AI algorithms, such as dynamic micro-zone demand prediction and real-time optimal route adjustment, aid drivers with picking the most efficient paths, reducing fuel consumption and wait times. These artificial intelligence algorithms are also utilized in Wave’s steadfast focus on safety. The algorithm analyzes vehicular operation to suggest the best time to perform routine maintenance and through automated disturbance or vehicular accident detection. Wave's Technological Potential Wave has carefully positioned itself to capitalize on these market opportunities. Since its inception, the company has focused on underserved markets, such as non-emergency medical transport and exclusive event transit. This allows it to tap into niche segments that larger competitors often overlook. "Beyond profit, Wave stands for serving those groups often forgotten by bigger companies. We even offer services for transportation of young children, with the requisite safety measures and monitoring in place," Lewis says. As consumer preferences shift toward shared mobility and on-demand services, Wave readies itself for further expansion. The rideshare and delivery markets are ripe with opportunities for companies willing to evolve. Wave's drive to further build upon its bespoke AI platform positions it as a formidable contender in the market. This piece was published as part of HackerNoon’s Business Blogging Program. For more details, please click here. The rideshare and delivery markets are undergoing an upheaval driven by rapid technological advancements and shifting consumer behaviors. Companies like Wave are surfing this change with bespoke artificial intelligence (AI) technology, carving out a competitive edge in a burgeoning industry. Wave Wave As the market expands, opportunities abound for those willing to adapt. Wave’s own personal brand of AI-driven safety and community involvement has positioned it quite advantageously, taking the market by storm in U.S. cities like Atlanta and Chicago. The Expanding U.S. Sector The Expanding U.S. Sector The global rideshare market is projected to reach $335 billion by 2025, growing at a compound annual growth rate (CAGR) of 18.2% from 2020 to 2025. This growth is fueled by increasing urbanization, the rising preference for shared mobility, and the convenience of on-demand services. Similarly, the online food delivery market is expected to surpass $213 billion by 2030. This explosive expansion is primarily driven by the proliferation of platform-to-consumer services and the lasting impact of the COVID-19 pandemic. In the United States, DoorDash leads the food delivery market with a 67% market share, followed by Uber Eats with 23%. The intense competition among these giants has focused on efficiency and customer experience, with companies investing heavily in AI and automation to streamline operations and reduce costs. " AI has changed the rules of the game. Not only in transportation or food delivery but every other aspect of the industry as well, " says Diondre Lewis, CEO of Wave. " At Wave, AI has enabled us to optimize routes, predict demand, enhance safety, and provide better customer support. " AI has changed the rules of the game. Not only in transportation or food delivery but every other aspect of the industry as well, At Wave, AI has enabled us to optimize routes, predict demand, enhance safety, and provide better customer support. Room for Growth in the Markets Room for Growth in the Markets The rideshare and delivery markets present numerous opportunities for savvy businesses looking to capitalize on recent technological developments. One among these trends is the increasing efficiency of last-mile delivery, which now represents 53% of the total cost of transportation. Companies are exploring various technologies, including AI, autonomous vehicles, and crowdsourcing, to reduce these costs and improve delivery times. Autonomous vehicles, including drones, are also becoming a viable solution for last-mile deliveries, offering the potential to cut costs and enhance efficiency significantly. This trend is expected to gain momentum as technology advances and regulatory frameworks evolve, addressing labor shortages and improving service efficiency. Another opportunity lies in the growing demand for personalized and efficient delivery services. AI-powered platforms can analyze vast amounts of data to provide personalized recommendations, optimize delivery routes, and predict peak demand times, much like Wave itself does. " The name of the game is customer satisfaction. There is no better way to meet the varied demands of our clients than with machine learning. We offer services from food delivery to corporate rideshares and non-emergency medical transport ," Lewis explains. The name of the game is customer satisfaction. There is no better way to meet the varied demands of our clients than with machine learning. We offer services from food delivery to corporate rideshares and non-emergency medical transport The Role of AI in Shaping the Future The Role of AI in Shaping the Future AI technology is credited with most of the transformation in the rideshare and delivery markets. Companies increasingly rely on AI to optimize their operations, improve safety, and provide personalized services. Wave's AI-powered platform is the very epitome of the potential AI presents. The company's proprietary AI algorithms, such as dynamic micro-zone demand prediction and real-time optimal route adjustment, aid drivers with picking the most efficient paths, reducing fuel consumption and wait times. These artificial intelligence algorithms are also utilized in Wave’s steadfast focus on safety. The algorithm analyzes vehicular operation to suggest the best time to perform routine maintenance and through automated disturbance or vehicular accident detection. Wave's Technological Potential Wave's Technological Potential Wave has carefully positioned itself to capitalize on these market opportunities. Since its inception, the company has focused on underserved markets, such as non-emergency medical transport and exclusive event transit. This allows it to tap into niche segments that larger competitors often overlook. " Beyond profit, Wave stands for serving those groups often forgotten by bigger companies. We even offer services for transportation of young children, with the requisite safety measures and monitoring in place ," Lewis says. Beyond profit, Wave stands for serving those groups often forgotten by bigger companies. We even offer services for transportation of young children, with the requisite safety measures and monitoring in place As consumer preferences shift toward shared mobility and on-demand services, Wave readies itself for further expansion. The rideshare and delivery markets are ripe with opportunities for companies willing to evolve. Wave's drive to further build upon its bespoke AI platform positions it as a formidable contender in the market. This piece was published as part of HackerNoon’s Business Blogging Program. For more details, please click here. This piece was published as part of HackerNoon’s Business Blogging Program. For more details, please click here . here here

Wave's AI-Based Innovations: The Key to Dominating the Rideshare and Delivery Market

ওয়েভের এআই-ভিত্তিক উদ্ভাবন: রাইডশেয়ার এবং ডেলিভারি বাজারে আধিপত্য বিস্তারের চাবিকাঠি

KI-basierte Innovationen von Wave: Der Schlüssel zur Beherrschung des Rideshare- und Liefermarkts

Innovaciones basadas en inteligencia artificial de Wave: la clave para dominar el mercado de viajes compartidos y entregas

Les innovations basées sur l'IA de Wave : la clé pour dominer le marché du covoiturage et de la livraison

वेव के एआई-आधारित नवाचार: राइडशेयर और डिलीवरी बाजार पर हावी होने की कुंजी

Wave の AI ベースのイノベーション: ライドシェアと配送市場を支配する鍵

Wave의 AI 기반 혁신: 차량 공유 및 배달 시장을 지배하는 열쇠

Inovações baseadas em IA da Wave: a chave para dominar o mercado de transporte compartilhado e entrega

Инновации Wave на основе искусственного интеллекта: ключ к доминированию на рынке совместных поездок и доставки

Wave'in Yapay Zeka Tabanlı Yenilikleri: Araç Paylaşımı ve Teslimat Pazarına Hakim Olmanın Anahtarı

Những đổi mới dựa trên AI của Wave: Chìa khóa để thống trị thị trường giao hàng và chia sẻ xe

Wave 基于人工智能的创新：主宰拼车和配送市场的关键

The TechBeat: Optimizing Ride-Hailing with Machine Learning: A Product Manager's Journey (4/9/2024)

The TechBeat: Optimizing Ride-Hailing with Machine Learning: A Product Manager's Journey (4/7/2024)

The TechBeat: Optimizing Ride-Hailing with Machine Learning: A Product Manager's Journey (4/5/2024)

Annually, worldwide turn to ride-hailing apps for transportation, with Russia alone accounting for more than 2.5 billion rides per year. The responsibility of matching each passenger with the ideal driver for every trip falls squarely on the shoulders of the ride-hailing service. over 1.4 billion people So, how do market leaders tackle this complex task? By processing gigabytes of real-time data and deploying algorithms, including those based on machine learning, they ensure the passenger is paired with the most suitable driver. At a high level, the process involves several critical steps: But how exactly are all these data points synthesized? Gathering all available drivers in the vicinity. Filtering out those who are not currently available, for instance, drivers who are in an 'unavailable' status or those who do not meet the class criteria (such as Economy drivers for a Comfort ride). Ranking the remaining drivers based on known factors, such as their proximity to the pickup location, their intended direction, and their tendency to accept rides with a similar profile. aggregating nearby orders for a period and then intelligently redistributing drivers among these orders to ensure each one receives the best match. This strategy enables companies to allocate drivers in under half a minute, significantly reducing the wait time for clients. The next step involves batching: However, this level of efficiency is only achievable when you have: Drivers, Data about them, An effective algorithm. During my tenure at one of the world's leading taxi services, I served as a Product Manager in the Marketplace domain. My primary responsibilities revolved around ensuring client reliability and maximizing company profits, which were the key objectives. In this capacity, I oversaw partner integrations as a product and optimized supply allocation logic using machine learning tools. Let's go! Optimizing Reliability and Efficiency in the UK Taxi Market with ML The platform in the UK was launched in 2019. Each time we launched, we had to configure the setup in one way or another. Overall, it worked out for us: we achieved acceptable reliability figures (in the ride-hailing market, the main reliability metric usually represents the ratio of successfully completed rides to all customer orders). However, in many cases, our decisions were more "expertise-based" than data-driven: we relied on the knowledge of mobility platform staff to advise on the strengths of each partner. It was wonderful to see our unique value proposition actually working, being used by customers, and generating revenue. Nevertheless, the company had already gained experience in driver matching, and we knew we could optimize the ride exchange market. So, our ML engineers were put to work, and the results were encouraging: every key metric, on average, increased by 5%. It was also surprising to see how incorrectly some of our configurations had been set up before. Even though we were operating in two entirely different markets (more on the Russian experience below), the pattern repeated. There was still room for improvement by gradually fine-tuning the weights of features, introducing new features, or discarding some. However, some areas (remote cities or just places where the immobility platform does not have a strong customer base) had to remain under manual control. Yet, the "Ride Exchange platform" became smarter, more reliable, and cost-efficient. Not to mention, we became the first in the world to create a market of fully integrated transportation services for customers in one app, and also the first to power it with ML. The main metric for us is the percentage of satisfied customers who booked a ride and eventually took a ride to their desired destination (GC/GCR – Gross Completion Rate). Since the launch, I have experienced a lot with the platform and learned many lessons. Some of them include: Finding the right balance in the historical depth of data to account for both recent data about the supplier (from the last 24 hours) and the averages over the last three months. Understanding which features will be useful, and which will not? There are two ways to answer this: by conducting a simulation and/or a real experiment. How do you convince stakeholders that it's worth it if you only have an idea so far? Consider showing them a prototype or a clear concept of your solution. While this might not be the best move for consumer-focused products, it works wonders in B2B and B2B2C setups. In these environments, where feedback cycles can drag on, waiting for weeks just for another demo isn't ideal. A quick, tangible demo can speed up decision-making and keep the collaboration moving smoothly. Here’s How I Approached the Russian Market How it Started As we entered the 2020s, mostly private entities. The proliferation of commercial enterprises was attributed to a reduction in state restrictions on the issuance of permits and regulatory measures on tariff control. This led to a surge in small businesses and intense competition in the market. At that time, my mobility platform operated as a standard taxi service aggregator, competing with other market players like , , and Citymobil for both customers and drivers. The taxi market was facing losses due to escalating competition, while shifts in consumer demand further complicated the landscape. taxi services in Russia were provided by around 4,300 organizations, Uber Yandex Mission aimed to unite all possible transportation players worldwide on a single platform, creating a fundamentally new level of service for corporate clients that prioritizes the speed of car arrival and optimized trip costs. The mobility platform secured the largest driver base in Russia through strategic agreements and partnerships, particularly with Citymobil in 2020 and another major player in 2021. The mobility platform Our product team's objective After these partnership agreements, I had to synchronize numerous processes. One of the key tasks was to optimize the distribution of customer orders on the platform among providers to enhance reliability and reduce costs for each trip. However, a critical market question arose: How do we decide whom to use and the best pathways for the customer? Here are the steps we took: Started with manual rules for distributing orders among providers. Observed the results, but they were not satisfactory. Conceptualized an that could make decisions on the distribution of each specific trip based on its unique criteria: ML model Order profile: location, duration, time, etc. Market metrics of each provider. Real-time data on our partners: dynamic prices, driver locations. Historical market knowledge in each location and an assessment of the current situation at a given time. Two factors to consider: major expenses are unrelated to the trip—acquisition and operational costs. The following data points illustrate how ML-driven decisions surpass manually-made ones Top Metrics: Increased the percentage of successful orders by 1.7% Reduced driver search time by 12% Increased trip revenue by 9% Control - manual driver search configuration Test - using the ML model. GCR – gross completion rate (rides taken out of all booked), higher is better % Cancelled – how many rides get cancelled after a driver is found, lower is better GP per ride – gross profit per ride, higher is better Margin – revenue per ride, less driver/partner payments, higher is better AVG AT – time it takes to find a driver, lower is better Take these recommendations with you to make ML deployment processes more efficient: While optimizing for efficiency and cost-effectiveness is crucial, you should maintain a strong focus on user experience. Balancing the algorithmic decision-making process with user preferences and expectations is essential to ensure sustained customer loyalty. Regular feedback mechanisms and user surveys can provide valuable insights into evolving customer needs. User-focused optimization: Recognizing the differences between markets, especially the on-demand nature of the Russian taxi market, is vital. You should tailor its strategies to suit the specific demands and behaviors of each market it operates in. This may involve adjusting algorithms, promotional strategies, or service offerings to align with the unique characteristics of each region. Market-specific strategies: The success metrics showcased in the case study are positive indicators, but you should establish a continuous evaluation process. Regularly assessing the impact of ML-driven decisions on key performance indicators, market share, and customer satisfaction will allow for prompt adjustments and enhancements. Sustain investment in machine learning technologies across various operational facets, ensuring ongoing refinement and adaptation to market changes. This should extend beyond supply allocation logic to other critical operations. Regular evaluation and adjustment:

Optimizing Ride-Hailing with Machine Learning: A Product Manager's Journey

মেশিন লার্নিং সহ রাইড-হেইলিং অপ্টিমাইজ করা: একটি পণ্য পরিচালকের যাত্রা

Mitfahrdienste mit maschinellem Lernen optimieren: Die Reise eines Produktmanagers

Optimización del transporte compartido con aprendizaje automático: el viaje de un gerente de producto

Optimiser les services de covoiturage avec l'apprentissage automatique : le parcours d'un chef de produit

मशीन लर्निंग के साथ राइड-हेलिंग को अनुकूलित करना: एक उत्पाद प्रबंधक की यात्रा

機械学習による配車サービスの最適化: プロダクトマネージャーの旅

머신 러닝을 통한 차량 공유 최적화: 제품 관리자의 여정

Otimizando Ride-Hailing com aprendizado de máquina: a jornada de um gerente de produto

Оптимизация заказов пассажиров с помощью машинного обучения: путь менеджера по продукту

Araç Aramayı Makine Öğrenimiyle Optimize Etme: Bir Ürün Yöneticisinin Yolculuğu

Tối ưu hóa dịch vụ gọi xe bằng Machine Learning: Hành trình của Giám đốc sản phẩm

利用机器学习优化叫车服务：产品经理的旅程

Curiosity A flurry of excitement flowed through me like an espresso shot. Solana, the ETH-killer cryptocurrency, was on a tear. 30...35... the numbers on my screen kept climbing, each tick a tiny explosion in my gut. It was like watching a gravity-defying rocket, with the downward flames setting the sky ablaze. Instinctively, I reached for Twitter / X , the digital city street where the rhythms of the crypto world pulsate. " " a headline screamed in bold, accompanied by a flurry of hashtags. 100k views, and it was still climbing.  Was this the beginning of the long-awaited Solana moon mission? Solana is trending like hot cake, Excitement Solana wasn't alone in its ascent. Other coins, once slumbering in the shadows, were erupting like volcanoes, spewing forth returns of 2x, 3x, even 5x in a matter of days, sometimes hours. The market, once a lethargic bear, was now a raging bull, and I, a wide-eyed matador, felt the irresistible urge to step into the arena. Gone was the caution of the bear market, replaced by a heady cocktail of excitement and greed. Stories of Bitcoin halving,  moon missions fueled by small cap gaming coins such as NAKA etc, offer whispered promises of unimaginable wealth. My once hesitant fingers were dancing across the keyboard, now searching for the next rocket launch and the next ticket to the crypto moon. Research But before I plunged into the frenzy, I knew I had to tread carefully. The market, like a beautiful but treacherous mistress, demanded respect, not blind faith. I armed myself with research, Twitter / X becoming my war room, top gurus like @ardizor and @ZssBecker my generals. I devoured narratives, dissecting L1s, RWAs, AI, L2s, DeFi, and GameFi, searching for hidden gems, potential moonshots waiting to explode. Charts became my battle maps, my goto tool is TradingView. I have subscribed as premium member. Each technical indicator is like a soldier reporting from the front lines. I tracked breakouts from ascending wedges, rejoiced at bullish signals from MACDs, and felt my heart quicken as price action danced past the resistance levels, leaving ample room for the rocket to soar. Piling in Armed with right knowledge and a healthy dose of caution, I took my first tentative steps into the market. Small nibbles, cautious entries, each move a calculated risk. But as my confidence grew, so did my appetite. The FOMO, a fearsome beast, began to whisper in my ear, its voice growing louder with each green candle. News of the Chainlink broke out with the announcement of CCIP.  Its partnership with SWIFT sent shivers of excitement down my spine. The thrill of the hunt, the dance with risk, it was intoxicating. Then I started to shift my attention to Crypto bubbles, targeting the hottest coins of the hour, catching those that are on fires and riding the profit waves. Maximizing gain But I wasn't content with mere gains. I craved the adrenaline rush, the wind in my digital hair as I soared through the stratosphere. The 3x leverage token, a forbidden fruit dangling before my eyes, promised to amplify my returns, to turn my modest capital into a king's ransom. With a calculated click, and the potential rewards outweighing the inherent risk, I leveraged up. LINK, the loyal horse, galloped from 7.5 to 10.8, painting the charts green in its wake. My 50% gain, amplified by the leverage, transformed into a staggering 150%. I was on top of the world, a crypto king surveying my digital dominion. FCON, a small cap gaming coin, caught the frenzy fire, doubled in price. Dips My monitor, a glowing oracle, displayed Bitcoin's sudden nosedive, profit taking from short term traders. And it was dragging the altcoins down, including my precious LINK and speculating FCON, into its treacherous depths. "Darn," I muttered, a playful twang in my voice. But beneath the lightheartedness, a steely resolve simmered. Days of meticulous planning had paid off. My trailing-stop orders, those digital guards, had anticipated this very moment. Lessons learned from  the past boom and bust crypto cycle. "Crypto is too yo-yo," I sighed, my mouth acting out. It was true. This digital playground was a rollercoaster of emotions, a constant test of nerves and reflexes. But it was also what made it exhilarating, a chess match against an unpredictable opponent. The storm hit hard, but I held my ground. The beauty of discipline, the sweet reward of foresight, washed over me. I had weathered the short term storm, a testament to my research, my caution, and my improved execution. So, I leaned back in my chair, with the monitor's glow reflecting in my eyes. The crypto yo-yo may have been spinning, but I was ready to ride it, eyes wide open, heart steady, and a smile playing on my lips. The Wrap up As the day unfolded, the market came alive. Green candles danced with red ones, a tango of hope and fear. I executed my plan, buying low, selling higher, riding the waves of volatility with the grace of a seasoned surfer. Each successful trade, each point of profit, felt like a small victory, a brick laid in the foundation of my digital fortress. By nightfall, the screen in my crypto cave reflected a different landscape. My portfolio, though not bursting with riches, bore the marks of a day well-played. The dip, a mere bump in the road, had been navigated with skill and resilience. And as I closed my laptop, a sense of quiet satisfaction came over me. Tomorrow and Beyond Tomorrow, a new adventure awaited. The market, a living, breathing entity, would present new challenges, new opportunities. And I, the crypto explorer, would be ready, my mind sharp, my spirit restless, my finger hovering over the buy button, eager to write the next chapter in my digital odyssey. For in the ever-shifting world of crypto, the true adventure wasn't just about the gains, but about the constant learning, the adapting, the thrill of the unknown. And I, for one, wouldn't trade it for anything in the world.

How I Ride the Madness: Dabbling in Crypto Trading

The United States v Meta Platforms Court Filing October 24, 2023 is part of . You can jump to any part in this filing . This is part 47 of 98. HackerNoon’s Legal PDF Series here COUNT II: VIOLATIONS OF THE ARIZONA CONSUMER FRAUD ACT, ARIZ. REV. STAT. §§ 44-1521 to -1534 860. The State of Arizona incorporates and realleges each of the paragraphs 1 through 850 as if fully set forth herein. 861. The conduct described in the preceding paragraphs of this Complaint constitutes deception, deceptive or unfair acts or practices, fraud, false pretenses, false promises, misrepresentations, or concealment, suppression or omission of material facts with intent that others rely on such concealment, suppression or omission, in connection with the sale or advertisement of merchandise in violation of Ariz. Rev. Stat. §§ 44-1521 to -1534. 862. While engaging in the acts and practices alleged in this Complaint, Meta knew or should have known that its conduct was of the nature prohibited by Ariz. Rev. Stat. § 44-1522, subjecting it to enforcement and penalties as provided in Ariz. Rev. Stat. § 44-1531(A). 863. With respect to the concealments, suppressions, or omissions of material fact described above, Meta did so with intent that others rely on such concealments, suppressions, or omissions. 864. With respect to the unfair acts and practices described above, these acts and practices caused or were likely to cause substantial injuries to consumers that were not reasonably avoidable by consumers and were not outweighed by countervailing benefits to consumers or to competition. Continue Reading . Here About HackerNoon Legal PDF Series: We bring you the most important technical and insightful public domain court case filings. This court case 4:23-cv-05448 retrieved on October 25, 2023, from is part of the public domain. The court-created documents are works of the federal government, and under copyright law, are automatically placed in the public domain and may be shared without legal restriction. Washingtonpost.com

Meta's Wild West: Arizona Rides In with Consumer Fraud Allegations

Stories, News & Mentions about RIDE

Ride’s Stories on HackerNoon