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Global markets, led by US equities, experienced a slight decline this week, wiping out the gains from the previous week. This drop came after Federal Reserve Chair Jerome Powell's recent semi-annual policy appearance at Capitol Hill, where he that the Fed remains hawkish. While Powell acknowledged that inflation has cooled down, he emphasized that it still remains above the 2% target. indicated Consequently, there is a likelihood that the market will witness further rate hikes this year, totaling 0.5%. This would likely translate into two additional 0.25% hikes. These sentiments have dampened the optimism that emerged after the Fed's decision to interest rate hikes earlier this month. Evidently, we are not yet fully out of the woods, and it may take some time before the Fed changes its stance. pause However, it is interesting to note that not all risk-on assets have plummeted in response to this news. Bitcoin, along with the broader cryptocurrency market, has surged this week, and ’s price is now hovering above $30K. BTC Bitcoin Strongly Rebounds on Blackrock ETF Application The crypto market has bounced back to life after several weeks of choppy price action, pushing the total market capitalization past $1.2 trillion. Notably, Bitcoin performed exceptionally well, hitting a market dominance of 50% for the first time in two years, according to data from . TradingView This growing interest in can be attributed to Blackrock's recent Bitcoin ETF application, which has sparked a lot of excitement within the crypto community. BTC Already, two more investment firms, Invesco and WisdomTree, have new Bitcoin ETF filings, signaling renewed optimism about the chances of a Bitcoin ETF being approved in the US in the near future. lodged While this recent rally doesn't necessarily indicate a complete reversal to a bullish trend in the crypto market, it certainly provides a much-needed boost. What's interesting is that it is largely driven by institutional investors and goes against the recent sentiments expressed by US regulators, who have been cracking down on the crypto industry. Exciting times lie ahead for those who have managed to weather the storm! Noteworthy Mentions The Current State of Central Bank Digital Currencies (CBDCS) The debate surrounding Central Bank Digital Currencies (CBDCs) is heating up, with over 114 countries exploring their implementation. CBDCs offer increased transparency and traceability to combat illicit activities, but privacy concerns arise as a trade-off. Striking the right balance is crucial for a smooth transition to this new form of digital currency. Will CBDCs see widespread adoption? Read our latest article . here Industry Shakers Binance troubles far from over Binance, the world's largest cryptocurrency exchange, faced more troubles this week. It deregistered its UK subsidiary and exited the Cyprus market. Additionally, French authorities are investigating Binance for potential anti-money laundering violations. This investigation follows Binance's departure from the Netherlands after failing to obtain a license from the Dutch central bank. Tether FUD and depegging concerns On June 15, the Tether stablecoin (USDT) slightly deviated from its peg to the United States dollar. This occurred due to an imbalance in Curve's 3pool, causing the price of USDT to decrease by 0.3% to approximately 0.997. Tether's Chief Technology Officer, Paolo Ardoino, however, reassured the crypto community via that the depegging incident should not be a cause for concern and affirmed Tether's readiness to redeem any amount. Twitter Deutsche bank applies for a digital asset custody license in Germany Deutsche Bank AG has applied for regulatory permission to operate as a cryptocurrency custodian in Germany. This move follows BlackRock's recent filing with the SEC to establish a spot bitcoin ETF. Deutsche Bank had previously announced its exploration of cryptocurrency custody in February 2021, aiming to provide an institutional-grade storage solution with insurance-grade protection. UK Set to Pass Crypto and Stablecoin Regulations The House of Lords, the second chamber of the UK Parliament, has given its approval to the Financial Services and Markets Bill (FSMB). This bill includes regulations for stablecoins and cryptocurrencies, along with provisions for overseeing crypto promotions. Having already been approved by the House of Commons, the bill will now move on to the final stages, which involve the Consideration of Amendments and obtaining Royal Assent. Also published here.

Crypto Rally Fueled by Blackrock ETF Application

How This Man Fueled Election Fraud Conspiracy Theories in Wisconsin

They say Rome wasn't built in a day, but no one mentioned that developing a mobile app could feel like climbing Mount Everest blindfolded. Welcome to the wild, unpredictable world of mobile , where thrills, chills, and bugs await at every turn. Buckle up and get ready for a roller coaster ride like no other! app debugging The Setup: Innocence Before the Storm In the beginning, the world is filled with promise. You're full of enthusiasm as you craft the blueprint of your app, envisioning the awe and wonder it will bring to users. You code with fervor, fueled by the adrenaline rush of creation. But just when you thought everything was smooth sailing, the first signs of trouble emerge. The Rabbit Hole: You dive deeper into the code, tumbling down a rabbit hole of complexities. The thrill of problem-solving intertwines with frustration. Unexpected Twists and Turns: Each breakthrough is followed by a new challenge. Quirky bugs add laughter amidst the chaos, but persistence keeps you going. The Triumph: After a series of highs and lows, the moment of triumph arrives. You've conquered the bugs and emerged victorious. The Ascent: Struggling Upward As developers, our coding journeys are filled with unexpected twists and turns. From encountering "Null Pointer Exceptions" to chasing elusive bugs, the path to creating flawless mobile apps is anything but smooth. Imagine the initial excitement fading into frustration as you dive into the dark abyss of stack traces and debugging tools. But just when you think you've figured it out, your app crashes without warning, leaving you bewildered. The roller coaster of debugging continues with bugs multiplying like gremlins and the infamous "Heisenbug" playing hide-and-seek. Amidst the chaos, breakthroughs emerge. With determination, you implement clever workarounds and taste the sweet victory of conquering bugs. Each battle becomes a badge of honor, shaping you into a resilient problem solver. The Plunge: Adrenaline Rush of Problem-Solving Diving into the depths of code, I embarked on a thrilling adventure—the adrenaline rush of mobile app problem-solving. Bugs eluded me, but I was ready for the roller coaster ride ahead. With determination, I tackled the labyrinth of code. Each line was a twist, bugs were hairpin curves. The rush of problem-solving fueled my relentless pursuit. The adrenaline coursed as I unraveled hidden glitches, code by code. Bugs taunted me, challenging me to find solutions. It was addictive, an exhilarating dance with the unknown. I dove deeper, armed with creativity and powerful tools. The adrenaline surged, using breakpoints, logs, and trial and error. Problem-solving became a symphony of determination and ingenuity. Navigating treacherous turns, doubt lingered. But conquering bugs drove me on. Victory tasted sweeter, each squashed bug and optimized line celebrated. Emerging triumphant, bugs tamed, I relished accomplishment. Lessons were learned, and problem-solving allure was awakened. A wild ride, but the rewards are immeasurable. The Loop-the-Loop: Ups and Downs of Progress At first, it's trial and error. You twist knobs, and pull levers, hoping for a breakthrough. Yet, the elusive bug remains. Head-scratching and pondering the mysteries of the universe become the norm. Is the bug an extraterrestrial test? Just as defeat looms, a glimmer of hope emerges—a clue! Like a hidden treasure map, it guides you through the bug-infested jungle. Renewed determination propels you forward. But beware, the path to victory isn't linear. Progress comes with fresh challenges, a double loop-the-loop of code intricacies. You'll experience triumph, feeling invincible, only for a new bug to surface, humbling your ego. Yet, with each twist and turn, you gain valuable lessons. Embrace the unexpected, and find creative solutions. You become an expert navigator of the unknown, armed with knowledge and caffeine. Finally, after countless ups and downs, you conquer the last loop-the-loop. The bug is crushed, and triumph fills your soul. You emerge stronger and wiser, a few more grey hairs as proof. The Victory: Conquering the Mobile App Bugs In this heart-pounding tale, I found myself in an intense battle against mischievous mobile app bugs. It was a roller coaster ride that tested my patience and sanity. But little did I know, sweet victory awaited me at the end. The ascent began innocently enough. I embarked on developing a promising mobile app, but bugs emerged like mischievous creatures. Each attempt to squash them felt like grasping at smoke, with bugs slipping through my fingers. Determined not to let the bugs win, I delved deeper into debugging. Armed with tools and techniques, I pressed on, fueled by the desire to triumph. As the battle raged, I stumbled upon a breakthrough—a clue that led me closer to victory. I employed clever strategies, crafted elegant code, and deployed debugging tactics. Small victories fueled my determination. But the victory wasn't a smooth ride. Bugs fought back with unexpected twists. Yet, I refused to surrender. I laughed in the face of adversity and pressed on. And then, it happened—the grand finale. The bugs trembled as I unveiled my final solution. The code aligned, glitches vanished, and the app thrived flawlessly. I had conquered the bugs, emerging victorious. As I reflect, I realize the journey was as valuable as the victory. It sharpened my skills, deepened my understanding, and taught me perseverance. Conclusion In the end, after countless loops, twists, and heart-pounding moments, I emerged victorious from the roller coaster ride of mobile app . The bugs were conquered, and a sense of triumph washed over me. debugging This wild journey taught me invaluable lessons. I learned perseverance and creativity, honing my problem-solving skills and expanding my technical prowess. Amidst the frustrations, I found humor in the chaos. Laughter became my secret weapon, keeping me sane and infusing the process with levity. So, fellow adventurers, embrace the thrills, chills, and mobile app bugs. Embrace the frustrations, for they make the victories sweeter. And most importantly, embrace the ride itself, for it is in this wild journey that we truly grow. As I disembark, carrying scars and memories, I can't help but smile. The next debugging roller coaster awaits, ready for another unforgettable journey. So buckle up and enjoy the ride! Remember, when debugging becomes a roller coaster ride, hold on tight, scream with delight, and savor every moment. Happy debugging, brave developers!

Navigating the Debugging Abyss: Conquering Mobile App Bugs in an Adrenaline-Fueled Adventure

We have been living through the drastic changes caused by COVID-19 for over a year now, and the results impact our as well as physical health. Due to quarantine, it's hard to stay productive, work normally, and make plans for the future. No wonder that the number of people experiencing symptoms of anxiety and depression for the last year, according to a survey by the US Census Bureau. mental wellbeing has increased from 11% to 42% Much of that has been tied to the stress caused by the unpredictability of the world in the last year. People have been compelled to , where they can cheer themselves up (or at least reload when they need to). At this time, when most activities outside the home haven’t been available, connected TV has taken a step forward by offering tons of entertaining content that everyone can access anytime on demand. seek solace in the virtual world CTV as relief during quarantine With CTV, users decide what content they want to watch, when and in what way they want to do so. They control their viewing experience from beginning to end. There is no chance of missing something important because CTV content is loaded, paused, and resumed on demand. Traditional linear TV forces viewers to be in front of their screens at a particular scheduled time, and there is no opportunity to watch the broadcasted content in any other way. Though staying at home during quarantine is usually not difficult, planning a schedule around the shows one wants to watch can still be a hassle. With connected TV, viewers can relax knowing that they can access any content they choose on multiple devices at any time. CTV also offers cost savings. With CTV, you pay only for the content and services you want, instead of paying for a big cable or satellite package which includes all kinds of programming that aren’t relevant to you. This is important during the pandemic when people are trying to tighten their budgets, and high-value, low-cost CTV offers exactly that. But convenience isn’t the only reason why CTV is blossoming these days. People are also attracted to CTV by original content which is available only on certain platforms; it’s so important that companies are on it. If there are TV shows that can be viewed only on one streaming service, or games that can be played only on one gaming platform, people have a very strong reason to adopt that platform to get access to that exclusive entertainment. ready to spend billions of dollars Unique experiences have always been important, but in times when digital media may be the solely available form of entertainment, they are absolutely critical. For example, are motivated by the original content they are able to watch with their subscriptions. And more broad research shows that subscribe to channels and platforms because they want access to videos and games that they wouldn’t be able to otherwise. more than 45% of Disney+ subscribers 47% of viewers Video gaming as a new CTV trend Whereas movies and shows offer a passive viewing experience, games offer interactive entertainment that puts players in control. The participatory experience of gaming is appealing during these stressful times. All of this has led to a in the popularity of gaming among all kinds of people over the first year of the COVID-19 pandemic. noticeable increase The vast and growing variety of available games can satisfy the need for new experiences. Game developers keep on inventing new game ideas, developing new storylines, and building new material for their previous creations. Moreover, video games can be played across a wide variety of platforms, including CTVs. Even before the pandemic, the video gaming industry was on the rise. Beginning in 2019, the number of gamers in the US has grown and is expected to reach more than 177 million in 2021. The gaming industry’s expansion has reached the CTV environment as well. According to our research, from all the apps published on Roku in 2020, 1.4% account for games — twice as many as in 2019, — and this number only promises to grow. an average of 5.7% a month Thanks to that, there are emerging companies who try to saddle a trend, but because of the sheer newness of the market, not a lot of them are doing so professionally. And that is what we at Playcent Games are trying to change: to broaden the OTT market from mostly TV shows to more divergent content. By capitalizing on our experience in the mobile market, we want to bring to the CTV gaming market a lot more professionally created content. We have every reason to believe that interest in OTT content and games on streaming platforms will rise even more in the new post-COVID world. The shows that, during the crisis, a third of consumers have subscribed to a video gaming service or used a cloud gaming service for the first time. Although 55% of Americans 18 and older believe they could this year, they won’t give up new forms of entertainment: predicts that time spent with CTV will not only maintain its levels but continue to go up. This would mean combining the new habits created during quarantine with old ones, creating an altogether new way of interacting with the world. Deloitte digital media trends survey get back to their normal routines Emarketer analysis And we at see big potential in that. Playcent Games

COVID and CTV: How the Pandemic Fueled a Boom in VOD

I’ve been suffering from stress. Stress that has been fueled by imposter syndrome. It’s not something I’ve dealt with before, because I have worked on teams where I’m the only analyst. Now that I’m on a team of 20+ analysts and developers I suddenly feel out of my depth. I used to live in ‘I don’t know what I don’t know’ world. Now I can see quite clearly what I don’t know and it’s overwhelming. Here’s how I’m facing up to how I’m feeling, so I don’t one day run screaming from the building. I don’t have to know everything, about everything. Overthinking small mistakes, feeling totally lost in meetings because I don’t know half the technologies everyone else seems to be completely fine with, always having to reluctantly ask for help after spending an hour googling, lurking through databases and knowing that it must be some simple thing I’m missing. And to make it worse, constantly feeling like I’m falling farther and farther behind the next, new technology or AWS offering. There is so much to learn about how things work now, and things are constantly changing. Instead of comparing myself to the seniors on my team I’m trying to use the sense of panic as motivation to learn more from those around me. Not every request is an emergency Our team has an inbound ticket queue, something new I’ve had to work with this year. While we don’t have SLAs on how quickly these are filled, the feeling they are piling up and not answered instantly is stressful. Mainly because I know I can’t whizz through and get everything ticked off in a day because I don’t have all the answers. They come in waves and it’s a seemingly impossible task to balance project work and ticket demands without falling prey to multitasking. After talking to my manager I’m going to balance tickets with project work by blocking out time for both and remembering that even though the ticket is assigned to me I don’t necessarily need to be the one to answer. There are plenty of subject matter experts on our team. I can’t expect to be an expert in all these areas too. I don’t expect tickets I raise with other teams to be answered instantly, so why do I put pressure on myself this way? Leave work at work I didn’t realise how much I was obsessing myself over my job until I had this conversation with my manager. On my walk to work, I was thinking about work. When I was walking home, I was thinking about work, even if nothing bad had really happened. I would sit on the couch, working or reading about work, then fall into bed dreaming about work. I was causing a lot of my own suffering, so have promised myself I don't think about work when I’m not at work. Remember the good stuff It’s a little braggy, but given I’m the only one who reads it, it doesn’t matter. I’ve started a list of all the new things I’ve learned and technologies I’ve had to start using. Now that I’ve put it on paper it reminds me that I am capable of picking up new things and that they are worth learning. Our team celebrates success with a Monthly Awesome Award. Everyone can nominate someone who has gone above and beyond with some nice words and a funny gif on our team Confluence page. This puts things in perspective and lets us all reflect on the good work we do, and everyone loves a funny gif. It never ends Talking to my manager has been a huge help. Getting feedback that I am on the right track, setting goals and taking some actions to block out time for projects, tickets and training have made the workload seem more manageable. When I feel overwhelmed and think that I’m not meeting expectations I know I can talk to my manager and my team. There’s a lot I don’t know yet, the same as everyone else. Originally published at dev.to .

Managing Stress Fueled by Imposter Syndrome

, shorthand for “educational ,” has a presence that extends beyond home computers and top universities. This shouldn’t come as a shock. Computers, tablets, and other advanced technology has become widely accepted as a part of daily life, and are now being incorporated into high school and middle school programs nationwide. of teachers incorporate technology into their daily curriculum. is changing the lives of teachers and students in more ways than you would think, but the widespread adoption of tech faces many challenges and uphill battles. EdTech technology 75 percent EdTech What does a classroom look like in 2017? We looked at four classrooms from Minnesota to Manhattan, and in every single one EdTech is making an impact at all levels of education. Interestingly enough, they are doing so with similar hardware limitations. In Jason Blanshan’s classes of 7th-9th graders in the small rural town of Ortonville, MN they have both a one-to-one program and access to iPads via an “iPad cart”. Taha Adib teaches 10th grade ELA at Democracy Prep High School in Harlem and uses a similar cart system for MacBooks. Sharing EdTech between classrooms is not uncommon. Both Adib and Blanshan’s schools are implementing one-to-one initiatives. This means one school-provided device per student. Over 50 percent of teachers say they now have a 1:1 student-to-device ratio according to a . This usually means a , in the case of Blanshan and another Minnesota school, Rodgers High School in the suburbs of Minneapolis. At Rodgers, students sign a four-year contract to rent and take the laptops home. 2017 survey Chromebook Most classrooms use a , an interactive whiteboard. The SMART Board plays the same role as a whiteboard but the tech makes it easy to erase, switch between tabs, and organize information. For students, SMART Boards provide a source of tactile learning through interactivity. Live note taking on readings, solving equations, and filling out interactive worksheets as a class is just a handful of the ways SMART boards boost student engagement. SMART Board Readings can be found online, but mostly for non-textbook readings as textbooks are still used in many cases. Teachers assign homework and do all grade book work in a or equivalent grade keeping program. PowerSchool Ryan Olson, a high school social studies teacher in Rodgers, MN, loves , a Learning Management System integrated with Google Docs to make resources more readily accessible.“I can create a Google doc and upload it to Schoology and it creates a copy for each student,” Olson said. “I also put all our learning materials I assigned [on Schoology].” Given how popular Google Suites, especially Docs, is in high school and middle school classrooms, this integration is crucial. Schoology Programs like PowerSchool and Dean’s List bring teaching into the 21st century, and these are just a drop in the bucket of available EdTech. Adib uses , a tool that turns PDFs into digital worksheets, to keep digital work organized and reflective of a physical worksheet. He also uses , which allows him to view and freeze students’ screens in order to focus attention towards the front of the room and to eliminate distractions. According to Adib, most students don’t find this invasive and “actually really like it,” specifically the function where he can control the student’s screen in order to explain a problem they may be having difficulties with. GoFormative GoGuardian Sarah Nordgren teaches fourth grade at a Manhattan elementary school for students with language-based learning disabilities. She uses , a document video camera that acts as a stand-in for the projector transparencies of old. With Elmo, any piece of paper you put on the screen (RIP transparent sheets), you can write on. This allows the students to follow Nordgren’s train of thought and hand as she writes, while allowing for an adaptable lesson plan. Elmo The Changing Role of the Teacher “The district expects us to be at a point where if school is cancelled, school should still go on,” Olson said. “Snow days are online learning days.” At Rogers High School, Schoology and the presence of online assignments and resources are transforming the traditional role the classroom plays in the learning experience. The elimination of snow days in Olson’s district may be alarming if you’re a student (no more impatiently waiting for that announcement the day after a storm), but as a teacher, it seems indicative of the change that must come moving forward. “It’s more important to be curating content so that students can absorb it, rather than getting up and talking about definitions of terms.“ Olson added, specifically citing his role as a social studies teacher. “It’s no longer important to know the capital of Egypt when you can Google that from anywhere you are in the world.” When everything is Google-able, the role of social studies and science is no longer in just teaching the facts, but teaching research skills, media literacy, and identifying bias. Since computer labs have existed in schools, students have been ‘taught’ research skills by being directed towards academic resources and away from Wikipedia. Carefully curating what students read and watch in the classroom is a great way to show them examples of bias while still showing appropriate content. This goes beyond simply teaching keyword skills in a database. Some more specialized methods of teaching could not exist without EdTech. Sarah Nordgren’s students fall into this category. Dyslexia and ADHD are the most well-known of these type of language based learning disabilities. Nordgren uses the Orton Gillingham approach by following the program “ which emphasizes multisensory learning. Multisensory learning involves the use of visual, auditory, and tactile methods simultaneously to enhance learning. The key to this program, and teaching her group of students, is taking each day at the student’s pace, ensuring total comprehension before moving on to a different subject. Preventing Academic Failure” “Tech is essential, because everyday we are changing our lessons. The degree to which they understood it informs what I teach the next day.” Nordgren said. “Teachers used to have to plan so far ahead and it wasn’t changeable. I can be brainstorming different ideas for compositions and I can be typing it into the notebook as the students talk to me, it can be efficient and quick and malleable.” Social Citizenship Social media is perhaps the biggest influence on today’s students that didn’t exist 15 years ago. School is a place to not just learn in the classroom, but to learn how to be a part of a community. Teaching citizenship through social media is a difficult subject for educators. “Ways we reinforce social norms in elementary school is a way of teaching people how to act towards other people, “ Olson said. “Schools are in a position where they are behind on how to address those things. We don’t think about it in a public health way.” Olson addresses how bullying, cyberbullying and other forms of online abuse have not gone away. Educators have always addressed the problem and never the symptom when it comes to cases of . He informed me of the #FreeReid controversy. fighting online abuse In 2014 at Rogers High School a student, Senior Reid Sagehorn, responded to an anonymous tweet about kissing a female gym teacher, with a sarcastic “actually, yes”. This resulted in his suspension, and an investigation into the teacher in question that could have potentially led to her release. It also sparked the #FreeReid campaign on Twitter, where students protested the school’s choice of suspension for the honor roll student and football team captain. While the campaign itself seemed ill conceived, and may have been interpreted much differently in 2017, the result is still that by your senior year of high school, there should be programs in place to teach what can and cannot be said on social media. It’s not a free speech issue; bad social media citizenship can affect the lives and careers of both students and teachers involved. Some schools are beginning to implement programs that teach this at a young age. “We use a program called ‘Responsive Classroom’, Nordgren said of teaching social skills to her fourth graders. “We talk about social media and how to use it appropriately and how to stay safe and report to adults. Honestly, a lot of adults could use it.” The Problem: Tech is built for consumers, not schools Many of these schools received initial shipments of devices through donors or grants. But even a few years down the road, the true problem is coming to light. Tech gets outdated way too fast. These devices are made to keep consumers replacing their gadgets every other year — something more difficult for school districts. “Our school just went through a referendum. We went to ask for money. It passed last Tuesday.” Blanshan said. “If that had not passed, we would not have been able to sustain [our one to one program]. We’ve been running in the red, dipping into the emergency balance.” This is what happens when a small school district cannot afford getting new iPads every few years. Blanshan describes that their iPads are “from the stone age.” The software stops working, the devices begin crashing and the conveniences of EdTech begin to get outweighed by the inconvenience of dying, unsustainable tech. Even Democracy Prep — a private institution with a reputation and financial backing — operates on the same cart system as Ortonville’s public high school. The rate at which technology needs to be replaced is increasing exponentially. Regardless of budget, this is a factor school boards ten years ago didn’t have to worry about. And, especially for public schools, our government focusing on funding educational programs. isn’t exactly Is The EdTech Future Here? While many things look different from when I was in high school five years ago, we are still grappling with understanding the role of technology in the classroom. Not all technology is considered equal in the eyes of educators. Smartphones are universally frowned upon, some schools requiring students to turn them in at the beginning of the day. At the very least most teachers punish their use, especially in classrooms where students are using laptops for educational purposes — phones are an unnecessary distraction. And yes, traditionalists, many classrooms still do use pencil and paper for many tasks, for a variety of reasons that enforce the ways technology cannot easily replace these staples of education. For Sarah Nordgren’s students, and learning disabled students across the country, reading physical texts are easier than reading off a screen. She still uses workbooks for math. “For the most part we use pen and paper,” said Taha Adib, citing the cumbersome nature of unpacking, logging in, and booting up laptops as a barrier to using them for every single exercise within the limitations of a class session. Right now there is no safe and secure way to streamline this process. It is the convenience that drives the appeal of EdTech. Taking notes in a notebook is more convenient for students, while online gradebooks are more convenient for teachers. Classrooms are not using technology for the novelty of it, but because it makes classroom processes more efficient. Both teachers and students must adjust to this, but it is in service of creating a future where students have access to better resources and learning strategies. By Bryn Gelbart | Content Writer at Fueled, a leading app development agency in NYC. Read the original story here .

How EdTech is Changing the Classroom Experience

As we’ve learned from The Social , many new tech innovations are born with a completely different function than what we use them for today. But when it comes to purpose, many pieces of tech, be it old, discarded, or simply in transit are brought to life anew via repurposing. It’s the most clever echelon of users who make this innovation possible. Network Almost universally we see these user trends indicate where designers should focus, and how to adapt for a user base of the future. From marketing stunts to apps that changed an industry, these are the most outrageous uses of tech that the designers never saw coming. technology KFC VR Training The best use of the Oculus Rift is not a video game. It’s an employee training video. Well, it’s a game-like training escape room Colonel Sanders and co. are beginning to use as a supplemental activity to the more traditional employee training process. A demonic disembodied Colonel instructs (and taunts) the prospective KFC worker as they master the five-step cooking process in order to escape the room (or kitchen). Nightmare chicken cooking sims will be the new MOBA in only a matter of years, mark my words. “The Hard Way” is maybe not the ideal name Burger King Google Home Stunt Burger King coordinated the marketing stunt of the century. Well, that’s a simplified guess at what may have led to the Burger King Google Home stunt this April. An ad launched with the intention to trick viewers’ phones and homes to search Wikipedia for the Whopper. Using the line “O.K. Google, what is the Whopper burger?” the 15-second TV and online video promo triggered Google Home devices to search the phrase on Wikipedia and state the ingredients. The goal was along the same lines as a certain . Why anyone thought this stunt would drive traffic to Burger King or educate the viewer is a complete mystery. It was a great and terrible idea (Google sure wasn’t happy) leading to many Wikipedia hijinks that caused the ad to be pulled, but it was one of the most ingenious marketing fails ever pulled. This use of voice recognition technology is innovative and clever, even if frowned upon by most involved. McDonalds campaign from the 70s Turn Your Wii Balance Board into a Smart Scale Anyone remember Wii Fit? The casual fitness game for the Wii? It wasn’t a huge hit, maybe due to the fact that you had to spring for the required Balance Board bringing the base price up to $90, $40 above the typical Wii game. If your balance board is collecting dust under a pile of Guitar Hero controllers, fear not, there is a cool, useful workaround that could turn your ancient peripheral into a scale of the future. Using the open-source Android app , it is possible to connect your Balance Board to the internet, something Nintendo designers surely never imagined. FitScales can sync exercise data and results to RunKeeper or Fitbit, making it easy to track your weight alongside other fitness activities. These apps are free so all you need is your Balance Board and an Android device. FitScales iPad Cutting Board , a cutting board app from Japanese designer Yuta Sugiura, turns your iPad into a smart cutting board. There’s probably a reason this never made it to the app store, but this silly idea is pretty ingenious. This app goes above and beyond by giving perfect measurements that allow you to make even cuts like if you say need to divide a pie eight ways, removing all estimation from the process. iCut makes cutting bread into an exact science. PSA: You probably shouldn’t use your tablet as a culinary tool (unless you’re looking up recipes), but maybe this app is paving the way for smart cutting boards to come. iCut Yum, Challah! Microprocessors as a Heat Source When your laptop starts to overheat you probably get worried and close some tabs and make sure isn’t running. But I bet you never thought of harnessing that power to create a new type of heat. That’s what Qarnot computing did with . This new age heater embeds microprocessors as the source of heat. It’s super safe, eco-friendly, and free energy. With a little bit of marketing and funding to make it a consumer product, Q.rad seems like the ultimate appliance for smart homes of the future. Steam Q.rad The Q.Rad is V Rad Stroke Recovery with Microsoft Kinect In stark contrast to the rest of this list, Microsoft designers are thrilled their product is being used at all, that they decided to sponsor this idea. That a flop such as the Kinect could be used to help stroke victims recover is nothing short of a miracle. There are a PC audience co-opted the Xbox accessory, but this is by far the most significant. The system provides users with exercises that help restore and improve motor function, all the while measuring and monitoring recovery levels. In collaboration with Seoul National University, this innovative project aims to provide treatment from the comfort of your own home. plenty of ways Microsoft sponsored project By Bryn Gelbart | Content Writer at Fueled Read the original story here .

Innovative Uses of Tech Designers Never Anticipated

Let’s unpack this. The past few weeks saw three disparate but related tech developments that will impact the everyday customer. The first is the announcement of Apple “Town Squares” — by far the standout moment in the Apple Keynote, bar the moment the Face ID demo failed for Craig Federighi. As Apple envisions it, we will one day stroll, connect, , and interface with our communities — as well as, I assume, charge our phones — in the Scandi-style courtyards of Apple Town Squares. This strategy was pioneered by Starbucks, and has recently been adopted by Disney, which is transforming its stores into “experiences,” Refinery29 events, Urban Outfitters’s Space Ninety 8 bar, Goop’s , and Barnes & Noble, which is testing concept stores stocked with wine. shop conference A future in which a diversity of people and eucalyptus congregate in a corporate square to connect over laptops. The second notable development was the rollout of Bodega, a startup designed to displace the corner store in favor of a smart vending machine stocking tampon loosies, sodas, and shampoo. The concept, which has two Google alums behind it, seemed to stand for everything wrong with tech, beginning with the appropriative branding. widely scorned The third piece of the puzzle is the dazzling IKEA ARKit app, which lets you plop a KARLSTAD sofa onto a subway platform or into the middle of the street, to see how it looks. IKEA already offered a neat layout tool, but the use of AR is so practical, so tangible and delightful for users, it seemed to answer for consumers not immersed in tech what AR could actually be used for. All of these news items took place against a background of hand-wringing about the state of digital advertising. Revenue is tumbling. Google Chrome is among apps that now make it easy for users to block or pop-ups and autoplay videos. The solution, according to Rob Tarkoff, president and CEO of Lithium Technologies, is to create a more interactive ad experience for consumers. “They want it to be a conversation, they want to engage,” he Quartz. mute told Much thought has been poured into imagining what an immersive ad experience — particularly an experience — but I’m here to tell you this has been around for decades already. augmented reality Every year, tourists flock to Radio City to take in the ’ Christmas Spectacular. I visited in 2005 with my husband (then-boyfriend), who insisted it was a New York rite of passage. I was vaguely aware of the Rockette chorus line, but totally unprepared for what followed. In the opening moments of the show, we were instructed to put on our 3D glasses as we flew with Santa’s sleigh from the North Pole to New York, then swooped down over the rooftops of Manhattan. There, we whooshed up Fifth Avenue, past a CHASE billboard, then past a Coca-Cola sign, and around through Times Square, where sponsorships lit up our eyes in 3D. By the time Santa’s sleigh pulled into Radio City, I had earned 1/20th of an MBA through this augmented tour of Fortune 500 companies with a vested interest in the Christmas Spectacular. Rockettes The Radio City Christmas Spectacular, starring Radio City, a New York Sightseeing(TM) bus, and some dancers. The Rockettes were talented, and the nutcracker was as good as you imagine, but the show takes a left-hand turn late in the two hours, when dancers trot live animals out on stage and form a nativity. Having had Coca-Cola imprinted on my retinas just 80 minutes earlier, this tack toward religion was jarring, baffling, hilarious. But the show barreled on with complete solemnity. You can keep your advertisements, the producers had decided, and have your religion too. Perhaps this best encapsulates the challenge facing companies today: a directive to make things meaningful, to demonstrate the embrace of some cultural set of ~values~, and also to pay the bills. At the end, we took home our Playbills, also emblazoned with CHASE Bank, and called it a net positive. Then again, I wasn’t a child, nor did I have a child with me to experience these digital advertisements in gleaming closeup, halfway between our seats and the stage. For a child, ads are much more insidious, not properly grasped, not as easily seen through. To be sure, AR is already a tool that consumers love. Snapchat filters, Pokemon GO, and the IKEA ARKit app have proven our appetite for an interactive experience. But the sensitivity of users to autoplay videos, and the muscle memory they’ve developed “X”ing out of pop-ups in web browsers suggests that companies will have to be very careful in how they build out these experiences. Who, they need to ask, is this fun for? The biggest question I have about an enterprise like the Rockettes is if I’m paying for a ticket, why don’t I get to have an ad-free experience? The original augmented advertising experience. Janet Manley is head of content for Fueled . Her writing has appeared in the New Yorker, ELLE, Buzzfeed, and CNN.

The Immersive Advertising Experience of the Future Has Existed for Years

Data & AI ROI: 2 Key Metrics for Executives

At the Potomac, Where DC, the Analog Political National Capital, and VC, the Digital Capital, Meet

Authors:
(1) Xiao-Yang Liu, Hongyang Yang, Columbia University (xl2427,hy2500@columbia.edu);
(2) Jiechao Gao, University of Virginia (jg5ycn@virginia.edu);
(3) Christina Dan Wang (Corresponding Author), New York University Shanghai (christina.wang@nyu.edu). Table of Links Abstract and 1 Introduction 2 Related Works and 2.1 Deep Reinforcement Learning Algorithms 2.2 Deep Reinforcement Learning Libraries and 2.3 Deep Reinforcement Learning in Finance 3 The Proposed FinRL Framework and 3.1 Overview of FinRL Framework 3.2 Application Layer 3.3 Agent Layer 3.4 Environment Layer 3.5 Training-Testing-Trading Pipeline 4 Hands-on Tutorials and Benchmark Performance and 4.1 Backtesting Module 4.2 Baseline Strategies and Trading Metrics 4.3 Hands-on Tutorials 4.4 Use Case I: Stock Trading 4.5 Use Case II: Portfolio Allocation and 4.6 Use Case III: Cryptocurrencies Trading 5 Ecosystem of FinRL and Conclusions, and References ABSTRACT Deep reinforcement learning (DRL) has been envisioned to have a competitive edge in quantitative finance. However, there is a steep development curve for quantitative traders to obtain an agent that automatically positions to win in the market, namely to decide where to trade, at what price and what quantity, due to the error-prone programming and arduous debugging. In this paper, we present the first open-source framework FinRL as a full pipeline to help quantitative traders overcome the steep learning curve. FinRL is featured with simplicity, applicability and extensibility under the key principles, full-stack framework, customization, reproducibility and hands-on tutoring Embodied as a three-layer architecture with modular structures, FinRL implements fine-tuned state-of-the-art DRL algorithms and common reward functions, while alleviating the debugging workloads. Thus, we help users pipeline the strategy design at a high turnover rate. At multiple levels of time granularity, FinRL simulates various markets as training environments using historical data and live trading APIs. Being highly extensible, FinRL reserves a set of user-import interfaces and incorporates trading constraints such as market friction, market liquidity and investor’s risk-aversion. Moreover, serving as practitioners’ stepping stones, typical trading tasks are provided as step-by-step tutorials, e.g., stock trading, portfolio allocation, cryptocurrency trading, etc. 1 INTRODUCTION Deep reinforcement learning (DRL), that balances exploration (of uncharted territory) and exploitation (of current knowledge), is a promising approach to automate trading in quantitative finance [50][51][47][54][21][13]. DRL algorithms are powerful in solving dynamic decision-making problems by learning through interactions with an unknown environment and offer two major advantages of portfolio scalability and market model independence [6]. In quantitative finance, algorithmic trading is essentially making dynamic decisions, namely, to decide where to trade, at what price and what quantity, in a highly stochastic and complex financial market. Incorporating many financial factors, as shown in Fig. 1, a DRL trading agent builds a multi-factor model to trade automatically, which is difficult for human traders to accomplish [4, 53]. Therefore, DRL has been envisioned to have a competitive edge in quantitative finance. Many existing works have applied DRL in quantitative financial tasks. Both researchers and industry practitioners are actively designing trading strategies fueled by DRL, since deep neural networks are significantly powerful at estimating the expected return of taking a certain action at a state. Moody and Saffell [33] utilized a policy search for stock trading; Deng et al. [9] showed that DRL can obtain more profits than conventional methods. More applications include stock trading [35, 47, 51, 54], futures contracts [54], alternative data (news sentiments) [22, 35], high frequency trading [15], liquidation strategy analysis [3], and hedging [6]. DRL is also being actively explored in the cryptocurrency market, e.g., automated trading, portfolio allocation, and market making. However, designing a DRL trading strategy is not easy. The programming is error-prone with tedious debugging. The development pipeline includes preprocessing market data, building a training environment, managing trading states, and backtesting trading performance. These steps are standard for implementation but yet time consuming especially for beginners. Therefore, there is an urgent demand for an open-source library to help researchers and quantitative traders to overcome the steep learning curve. In this paper, we present a FinRL framework that automatically streamlines the development of trading strategies, so as to help researchers and quantitative traders to iterate their strategies at a high turnover rate. Users specify the configurations, such as picking data APIs and DRL algorithms, and analyze the performance of trading results. To achieve this, FinRL introduces a three-layer framework. At the bottom is an environment layer that simulates financial markets using actual historical data, such as closing price, shares, trading volume, and technical indicators. In the middle is the agent layer that implements fine-tuned DRL algorithms and common reward functions. The agent interacts with the environment through properly defined reward functions on the state space and action space. The top layer includes applications in automated trading, where we demonstrate several use cases, namely stock trading, portfolio allocation, cryptocurrency trading, etc. We provide baseline trading strategies to alleviate debugging workloads. Under the three-layer framework, FinRL is developed with three primary principles: • Full-stack framework. To provide a full-stack DRL framework with finance-oriented optimizations, including market data APIs, data preprocessing, DRL algorithms, and automated backtesting. Users can transparently make use of such a development pipeline. • Customization. To maintain modularity and extensibility in development by including state-of-the-art DRL algorithms and supporting design of new algorithms. The DRL algorithms can be used to construct trading strategies by simple configurations. • Reproducibility and hands-on tutoring. To provide tutorials such as step-by-step Jupyter notebooks and user guide to help users walk through the pipeline and reproduce the use cases. This leads to a unified framework where developers are able to efficiently explore ideas through high-level configurations and specifications, and to customize their own strategies at request. Our contributions are summarized as follows: • FinRL is the first open-source framework that demonstrates the great potential of applying DRL algorithms in quantitative finance. We build an ecosystem around the FinRL framework, which seeds the rapidly growing AI4Finance community. • The application layer provides interfaces for users to customize FinRL to their own trading tasks. Automated backtesting module and performance metrics are provided to help quantitative traders iterate trading strategies at a high turnover rate. Profitable trading strategies are reproducible and hands-on tutorials are provided in a beginner-friendly fashion. Adjusting the trained models to the rapid changing markets is also possible. • The agent layer provides state-of-the-art DRL algorithms that are adapted to finance with fine-tuned hyperparameters. Users can add new DRL algorithms. • The environment layer includes not only a collection of historical data APIs, but also live trading APIs. They are reconfigured into standard OpenAI gym-style environments [5]. Moreover, it incorporates market frictions and allows users to customize the trading time granularity. The remainder of this paper is organized as follows. Section 2 reviews related works. Section 3 presents the FinRL framework. Section 4 demonstrates benchmark trading tasks using FinRL. We conclude this paper in Section 5. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Xiao-Yang Liu, Hongyang Yang, Columbia University (xl2427,hy2500@columbia.edu); (2) Jiechao Gao, University of Virginia (jg5ycn@virginia.edu); (3) Christina Dan Wang (Corresponding Author), New York University Shanghai (christina.wang@nyu.edu). Authors: Authors: (1) Xiao-Yang Liu, Hongyang Yang, Columbia University (xl2427,hy2500@columbia.edu); (2) Jiechao Gao, University of Virginia (jg5ycn@virginia.edu); (3) Christina Dan Wang ( Corresponding Author ), New York University Shanghai (christina.wang@nyu.edu). Corresponding Author Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 Related Works and 2.1 Deep Reinforcement Learning Algorithms 2 Related Works and 2.1 Deep Reinforcement Learning Algorithms 2.2 Deep Reinforcement Learning Libraries and 2.3 Deep Reinforcement Learning in Finance 2.2 Deep Reinforcement Learning Libraries and 2.3 Deep Reinforcement Learning in Finance 3 The Proposed FinRL Framework and 3.1 Overview of FinRL Framework 3 The Proposed FinRL Framework and 3.1 Overview of FinRL Framework 3.2 Application Layer 3.2 Application Layer 3.3 Agent Layer 3.3 Agent Layer 3.4 Environment Layer 3.4 Environment Layer 3.5 Training-Testing-Trading Pipeline 3.5 Training-Testing-Trading Pipeline 4 Hands-on Tutorials and Benchmark Performance and 4.1 Backtesting Module 4 Hands-on Tutorials and Benchmark Performance and 4.1 Backtesting Module 4.2 Baseline Strategies and Trading Metrics 4.2 Baseline Strategies and Trading Metrics 4.3 Hands-on Tutorials 4.3 Hands-on Tutorials 4.4 Use Case I: Stock Trading 4.4 Use Case I: Stock Trading 4.5 Use Case II: Portfolio Allocation and 4.6 Use Case III: Cryptocurrencies Trading 4.5 Use Case II: Portfolio Allocation and 4.6 Use Case III: Cryptocurrencies Trading 5 Ecosystem of FinRL and Conclusions, and References 5 Ecosystem of FinRL and Conclusions, and References ABSTRACT Deep reinforcement learning (DRL) has been envisioned to have a competitive edge in quantitative finance. However, there is a steep development curve for quantitative traders to obtain an agent that automatically positions to win in the market, namely to decide where to trade, at what price and what quantity, due to the error-prone programming and arduous debugging. In this paper, we present the first open-source framework FinRL as a full pipeline to help quantitative traders overcome the steep learning curve. FinRL is featured with simplicity, applicability and extensibility under the key principles, full-stack framework, customization, reproducibility and hands-on tutoring Embodied as a three-layer architecture with modular structures, FinRL implements fine-tuned state-of-the-art DRL algorithms and common reward functions, while alleviating the debugging workloads. Thus, we help users pipeline the strategy design at a high turnover rate. At multiple levels of time granularity, FinRL simulates various markets as training environments using historical data and live trading APIs. Being highly extensible, FinRL reserves a set of user-import interfaces and incorporates trading constraints such as market friction, market liquidity and investor’s risk-aversion. Moreover, serving as practitioners’ stepping stones, typical trading tasks are provided as step-by-step tutorials, e.g., stock trading, portfolio allocation, cryptocurrency trading, etc. 1 INTRODUCTION Deep reinforcement learning (DRL), that balances exploration (of uncharted territory) and exploitation (of current knowledge), is a promising approach to automate trading in quantitative finance [50][51][47][54][21][13]. DRL algorithms are powerful in solving dynamic decision-making problems by learning through interactions with an unknown environment and offer two major advantages of portfolio scalability and market model independence [6]. In quantitative finance, algorithmic trading is essentially making dynamic decisions, namely, to decide where to trade, at what price and what quantity, in a highly stochastic and complex financial market. Incorporating many financial factors, as shown in Fig. 1, a DRL trading agent builds a multi-factor model to trade automatically, which is difficult for human traders to accomplish [4, 53]. Therefore, DRL has been envisioned to have a competitive edge in quantitative finance. Many existing works have applied DRL in quantitative financial tasks. Both researchers and industry practitioners are actively designing trading strategies fueled by DRL, since deep neural networks are significantly powerful at estimating the expected return of taking a certain action at a state. Moody and Saffell [33] utilized a policy search for stock trading; Deng et al. [9] showed that DRL can obtain more profits than conventional methods. More applications include stock trading [35, 47, 51, 54], futures contracts [54], alternative data (news sentiments) [22, 35], high frequency trading [15], liquidation strategy analysis [3], and hedging [6]. DRL is also being actively explored in the cryptocurrency market, e.g., automated trading, portfolio allocation, and market making. However, designing a DRL trading strategy is not easy. The programming is error-prone with tedious debugging. The development pipeline includes preprocessing market data, building a training environment, managing trading states, and backtesting trading performance. These steps are standard for implementation but yet time consuming especially for beginners. Therefore, there is an urgent demand for an open-source library to help researchers and quantitative traders to overcome the steep learning curve. In this paper, we present a FinRL framework that automatically streamlines the development of trading strategies, so as to help researchers and quantitative traders to iterate their strategies at a high turnover rate. Users specify the configurations, such as picking data APIs and DRL algorithms, and analyze the performance of trading results. To achieve this, FinRL introduces a three-layer framework. At the bottom is an environment layer that simulates financial markets using actual historical data, such as closing price, shares, trading volume, and technical indicators. In the middle is the agent layer that implements fine-tuned DRL algorithms and common reward functions. The agent interacts with the environment through properly defined reward functions on the state space and action space. The top layer includes applications in automated trading, where we demonstrate several use cases, namely stock trading, portfolio allocation, cryptocurrency trading, etc. We provide baseline trading strategies to alleviate debugging workloads. Under the three-layer framework, FinRL is developed with three primary principles: • Full-stack framework. To provide a full-stack DRL framework with finance-oriented optimizations, including market data APIs, data preprocessing, DRL algorithms, and automated backtesting. Users can transparently make use of such a development pipeline. • Full-stack framework. • Customization. To maintain modularity and extensibility in development by including state-of-the-art DRL algorithms and supporting design of new algorithms. The DRL algorithms can be used to construct trading strategies by simple configurations. • Customization. • Reproducibility and hands-on tutoring. To provide tutorials such as step-by-step Jupyter notebooks and user guide to help users walk through the pipeline and reproduce the use cases. • Reproducibility and hands-on tutoring. This leads to a unified framework where developers are able to efficiently explore ideas through high-level configurations and specifications, and to customize their own strategies at request. Our contributions are summarized as follows: • FinRL is the first open-source framework that demonstrates the great potential of applying DRL algorithms in quantitative finance. We build an ecosystem around the FinRL framework, which seeds the rapidly growing AI4Finance community. • The application layer provides interfaces for users to customize FinRL to their own trading tasks. Automated backtesting module and performance metrics are provided to help quantitative traders iterate trading strategies at a high turnover rate. Profitable trading strategies are reproducible and hands-on tutorials are provided in a beginner-friendly fashion. Adjusting the trained models to the rapid changing markets is also possible. • The agent layer provides state-of-the-art DRL algorithms that are adapted to finance with fine-tuned hyperparameters. Users can add new DRL algorithms. • The environment layer includes not only a collection of historical data APIs, but also live trading APIs. They are reconfigured into standard OpenAI gym-style environments [5]. Moreover, it incorporates market frictions and allows users to customize the trading time granularity. The remainder of this paper is organized as follows. Section 2 reviews related works. Section 3 presents the FinRL framework. Section 4 demonstrates benchmark trading tasks using FinRL. We conclude this paper in Section 5. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

Deep Reinforcement Learning Framework to Automate Trading in Quantitative Finance

পরিমাণগত অর্থায়নে ট্রেডিং স্বয়ংক্রিয় করার জন্য গভীর শক্তিবৃদ্ধি শেখার কাঠামো

Deep Reinforcement Learning Framework zur Automatisierung des Handels im quantitativen Finanzwesen

Marco de aprendizaje por refuerzo profundo para automatizar el comercio en finanzas cuantitativas

Cadre d'apprentissage par renforcement profond pour automatiser le trading en finance quantitative

क्वांटिटेटिव फाइनेंस में ट्रेडिंग को स्वचालित करने के लिए डीप रीइनफोर्समेंट लर्निंग फ्रेमवर्क

定量金融における取引を自動化する深層強化学習フレームワーク

양적 금융 거래를 자동화하기 위한 심층 강화 학습 프레임워크

Estrutura de aprendizagem por reforço profundo para automatizar negociações em finanças quantitativas

Структура глубокого обучения с подкреплением для автоматизации торговли в области количественных финансов

Kantitatif Finansta Ticareti Otomatikleştirmek için Derin Takviyeli Öğrenme Çerçevesi

Khung học tập củng cố sâu để tự động hóa giao dịch trong tài chính định lượng

深度强化学习框架实现量化金融交易自动化

Engage! Exploring Technologies of the Final Frontier Cutting-edge technologies are shaping our world, yet a lot has been lost to the complexities of their development. So, I decided to write a trilogy that would simplify the understanding of these emerging technologies that are shaping our future. 1 - The Collective loves Data: How Big Data is Shaping, and predicting, our Future 2 - Warp Core of Confidence: How Blockchain Creates Trust in the Digital Frontier 3 - Holodeck Heroes: Building AI Companions for the Final Frontier First, A Little About Me. I am Manoj Boopathi Raj a Software Engineer at Google. I’ve worked on Google products used by hundreds of millions of users, perhaps even you. If you’ve ever used Google AI Assistant in your car, I made sure it actually understands you past all the noise of the road and highway. I made sure that when you say ‘take a selfie,’ your Android phone does exactly that. I’ve also kept spam out of YouTube so your search results are exactly what you’re looking for and made sure your e-sim-enabled Android phone is always connected to the strongest network so you are never stuck with a loading screen. And yes, I believe humanity should ‘boldly go where no man has gone before’. What Is Big Data? Big data isn't your typical data. It's a massive and ever-growing collection of information that comes in all shapes and sizes: structured (think spreadsheets), unstructured (like social media posts), and somewhere in between. Traditional data tools struggle to handle this data flood. But within this chaos lies a hidden treasure! By analyzing big data, organizations can unlock valuable insights through machine learning, predictive modeling, and other advanced techniques. Here's what makes big data unique: Volume: We're talking terabytes, petabytes, even exabytes! The sheer amount of data is mind-boggling. Variety: Forget neat rows and columns. Big data can be text, numbers, images, videos – a wild mix of everything. Velocity: This data keeps flowing in, constantly generated and collected at an ever-increasing speed. These three characteristics, often referred to as the "3 Vs of Big Data" (Volume, Variety, Velocity), were first identified by Doug Laney in 2001. While these are the core Vs, some add others like Veracity (accuracy), Value, and Variability. Big data isn't defined by a specific size but by the challenges it presents and the opportunities it holds. By harnessing its power, organizations can make smarter decisions and unlock a whole new world of possibilities. Big Data Examples Big data isn't just company records. It's a giant melting pot of information from all corners of the digital world: Our Digital Footprint: Every click, search, and purchase we make online generates data. Imagine all that activity adding up! Behind the Scenes: Businesses have mountains of data from transactions, emails, and customer interactions. The Machine World: Sensors in factories, power grids, and even our homes are constantly creating data about how things work. Social Pulse: Social media is a huge source of big data, with every post, like, and share adding to the information pool. External Insights: Big data can also include external sources like weather data, traffic patterns, and scientific research to give a broader picture. This isn't an exhaustive list, but it shows how big data truly comes from everywhere. And it's not just text – images, videos, and audio files are all big data too. Some big data applications even deal with information that's constantly flowing in, like live traffic updates. Storage and Processing Big data can't be squeezed into a traditional filing cabinet. Instead, it's often stored in a vast digital reservoir called a data lake. Unlike data warehouses that hold only structured data in neat rows and columns, data lakes can handle any type of information – structured, unstructured (like social media posts), or something in between. They typically rely on powerful platforms like Hadoop clusters, cloud storage services, or NoSQL databases. Big data systems are like complex ecosystems. Many use a distributed architecture, where a central data lake interacts with other systems like relational databases or data warehouses. This allows for flexibility in how data is stored and accessed. Sometimes, data is kept raw in the lake and processed on-demand for specific needs, like business intelligence. In other cases, it's prepped beforehand using data mining and preparation tools for regular analysis. Processing all this data requires serious muscle. Clustered systems, often powered by technologies like Hadoop and Spark, distribute the workload across numerous servers to handle the heavy lifting. This kind of power can be expensive to maintain, which is why the cloud has become a popular option. Organizations can set up their own cloud-based systems or leverage managed big-data-as-a-service offerings. The beauty of the cloud? You can scale resources up for big data projects and then down when they're finished. This way, you only pay for what you use, keeping costs under control. Big Data's Secret: Analytics Big data is a treasure trove of information, but to extract valuable insights, we need the right tools and techniques. Here's how big data analytics works: Data Preparation: Laying the Foundation Before diving in, data scientists need to understand the data they have and what they're hoping to find. This crucial first step involves data preparation, which includes cleaning, organizing, and transforming the data into a usable format. Unleashing the Power of Analytics Once the data is prepped, it's time to unleash the power of analytics! Data scientists use various techniques, like machine learning, deep learning, and statistical analysis, to run different applications. Here are some examples using customer data: Customer 360 View: By comparing customer behavior and engagement with competitor data, businesses can gain valuable insights and tailor strategies accordingly. Social Listening: Analyzing social media conversations about your brand helps identify potential issues and target the right audiences for marketing campaigns. Marketing Insights: Data empowers you to optimize marketing campaigns and promotions for better results. Sentiment Analysis: Uncover customer sentiment by analyzing their experiences. This helps improve customer service and satisfaction levels. Big Data's Future The future of big data is brimming with exciting possibilities fueled by cutting-edge technology. Here are some key trends shaping the landscape: AI and Machine Learning: As data sets balloon in size, human analysis becomes less efficient. AI and machine learning algorithms are stepping in to handle large-scale analysis and even preliminary tasks like data cleaning and preparation. Automated machine learning tools will play a significant role in streamlining these processes. Storage on Steroids: Cloud storage capabilities are constantly evolving, offering ever-increasing capacity. Data lakes and warehouses, both on-premises and cloud-based, are attractive options for storing this ever-growing data deluge. Data Governance Takes Priority: As the volume of data we use explodes, data governance and regulations will become more comprehensive and commonplace. Safeguarding and regulating this vast resource will be paramount. Quantum Leap for Big Data: While still in its early stages, quantum computing holds immense potential for big data analysis. Its superior processing power could significantly speed up data analysis, but for now, it remains accessible only to large enterprises with substantial resources. These advancements promise to unlock the full potential of big data, leading to even deeper insights and groundbreaking discoveries across various industries. With the right tools and regulations in place, the future of big data looks bright and full of possibilities. Engage! Exploring Technologies of the Final Frontier Engage! Exploring Technologies of the Final Frontier Cutting-edge technologies are shaping our world, yet a lot has been lost to the complexities of their development. So, I decided to write a trilogy that would simplify the understanding of these emerging technologies that are shaping our future. 1 - The Collective loves Data: How Big Data is Shaping, and predicting, our Future 2 - Warp Core of Confidence: How Blockchain Creates Trust in the Digital Frontier 3 - Holodeck Heroes: Building AI Companions for the Final Frontier First, A Little About Me. First, A Little About Me. I am Manoj Boopathi Raj a Software Engineer at Google. I’ve worked on Google products used by hundreds of millions of users, perhaps even you. If you’ve ever used Google AI Assistant in your car, I made sure it actually understands you past all the noise of the road and highway. Manoj Boopathi Raj Manoj Boopathi Raj I made sure that when you say ‘take a selfie,’ your Android phone does exactly that. I’ve also kept spam out of YouTube so your search results are exactly what you’re looking for and made sure your e-sim-enabled Android phone is always connected to the strongest network so you are never stuck with a loading screen. And yes, I believe humanity should ‘boldly go where no man has gone before’ . ‘boldly go where no man has gone before’ ‘boldly go where no man has gone before’ What Is Big Data? What Is Big Data? Big data isn't your typical data. It's a massive and ever-growing collection of information that comes in all shapes and sizes: structured (think spreadsheets), unstructured (like social media posts), and somewhere in between. Traditional data tools struggle to handle this data flood. But within this chaos lies a hidden treasure! By analyzing big data, organizations can unlock valuable insights through machine learning, predictive modeling, and other advanced techniques. Here's what makes big data unique: Volume: We're talking terabytes, petabytes, even exabytes! The sheer amount of data is mind-boggling. Volume: We're talking terabytes, petabytes, even exabytes! The sheer amount of data is mind-boggling. Volume: Variety: Forget neat rows and columns. Big data can be text, numbers, images, videos – a wild mix of everything. Variety: Forget neat rows and columns. Big data can be text, numbers, images, videos – a wild mix of everything. Variety: Velocity: This data keeps flowing in, constantly generated and collected at an ever-increasing speed. Velocity: This data keeps flowing in, constantly generated and collected at an ever-increasing speed. Velocity: These three characteristics, often referred to as the "3 Vs of Big Data" (Volume, Variety, Velocity), were first identified by Doug Laney in 2001. While these are the core Vs, some add others like Veracity (accuracy), Value, and Variability. Big data isn't defined by a specific size but by the challenges it presents and the opportunities it holds. By harnessing its power, organizations can make smarter decisions and unlock a whole new world of possibilities. Big Data Examples Big Data Examples Big data isn't just company records. It's a giant melting pot of information from all corners of the digital world: Our Digital Footprint: Every click, search, and purchase we make online generates data. Imagine all that activity adding up! Our Digital Footprint: Every click, search, and purchase we make online generates data. Imagine all that activity adding up! Our Digital Footprint: Behind the Scenes: Businesses have mountains of data from transactions, emails, and customer interactions. Behind the Scenes: Businesses have mountains of data from transactions, emails, and customer interactions. Behind the Scenes: The Machine World: Sensors in factories, power grids, and even our homes are constantly creating data about how things work. The Machine World: Sensors in factories, power grids, and even our homes are constantly creating data about how things work. The Machine World: Social Pulse: Social media is a huge source of big data, with every post, like, and share adding to the information pool. Social Pulse: Social media is a huge source of big data, with every post, like, and share adding to the information pool. Social Pulse: External Insights: Big data can also include external sources like weather data, traffic patterns, and scientific research to give a broader picture. External Insights: Big data can also include external sources like weather data, traffic patterns, and scientific research to give a broader picture. External Insights: Big data can also include external sources like weather data, traffic patterns, and scientific research to give a broader picture. External Insights: This isn't an exhaustive list, but it shows how big data truly comes from everywhere. And it's not just text – images, videos, and audio files are all big data too. Some big data applications even deal with information that's constantly flowing in, like live traffic updates. Storage and Processing Storage and Processing Big data can't be squeezed into a traditional filing cabinet. Instead, it's often stored in a vast digital reservoir called a data lake. Unlike data warehouses that hold only structured data in neat rows and columns, data lakes can handle any type of information – structured, unstructured (like social media posts), or something in between. They typically rely on powerful platforms like Hadoop clusters, cloud storage services, or NoSQL databases. Big data systems are like complex ecosystems. Many use a distributed architecture, where a central data lake interacts with other systems like relational databases or data warehouses. This allows for flexibility in how data is stored and accessed. Sometimes, data is kept raw in the lake and processed on-demand for specific needs, like business intelligence. In other cases, it's prepped beforehand using data mining and preparation tools for regular analysis. Processing all this data requires serious muscle. Clustered systems, often powered by technologies like Hadoop and Spark, distribute the workload across numerous servers to handle the heavy lifting. This kind of power can be expensive to maintain, which is why the cloud has become a popular option. Organizations can set up their own cloud-based systems or leverage managed big-data-as-a-service offerings. The beauty of the cloud? You can scale resources up for big data projects and then down when they're finished. This way, you only pay for what you use, keeping costs under control. Big Data's Secret: Analytics Big Data's Secret: Analytics Big data is a treasure trove of information, but to extract valuable insights, we need the right tools and techniques. Here's how big data analytics works: Data Preparation: Laying the Foundation Before diving in, data scientists need to understand the data they have and what they're hoping to find. This crucial first step involves data preparation, which includes cleaning, organizing, and transforming the data into a usable format. Unleashing the Power of Analytics Once the data is prepped, it's time to unleash the power of analytics! Data scientists use various techniques, like machine learning, deep learning, and statistical analysis, to run different applications. Here are some examples using customer data: Customer 360 View: By comparing customer behavior and engagement with competitor data, businesses can gain valuable insights and tailor strategies accordingly. Customer 360 View: By comparing customer behavior and engagement with competitor data, businesses can gain valuable insights and tailor strategies accordingly. Customer 360 View: Social Listening: Analyzing social media conversations about your brand helps identify potential issues and target the right audiences for marketing campaigns. Social Listening: Analyzing social media conversations about your brand helps identify potential issues and target the right audiences for marketing campaigns. Social Listening: Marketing Insights: Data empowers you to optimize marketing campaigns and promotions for better results. Marketing Insights: Data empowers you to optimize marketing campaigns and promotions for better results. Marketing Insights: Sentiment Analysis: Uncover customer sentiment by analyzing their experiences. This helps improve customer service and satisfaction levels. Sentiment Analysis: Uncover customer sentiment by analyzing their experiences. This helps improve customer service and satisfaction levels. Sentiment Analysis: Big Data's Future Big Data's Future The future of big data is brimming with exciting possibilities fueled by cutting-edge technology. Here are some key trends shaping the landscape: AI and Machine Learning: As data sets balloon in size, human analysis becomes less efficient. AI and machine learning algorithms are stepping in to handle large-scale analysis and even preliminary tasks like data cleaning and preparation. Automated machine learning tools will play a significant role in streamlining these processes. AI and Machine Learning: As data sets balloon in size, human analysis becomes less efficient. AI and machine learning algorithms are stepping in to handle large-scale analysis and even preliminary tasks like data cleaning and preparation. Automated machine learning tools will play a significant role in streamlining these processes. AI and Machine Learning: Storage on Steroids: Cloud storage capabilities are constantly evolving, offering ever-increasing capacity. Data lakes and warehouses, both on-premises and cloud-based, are attractive options for storing this ever-growing data deluge. Storage on Steroids: Cloud storage capabilities are constantly evolving, offering ever-increasing capacity. Data lakes and warehouses, both on-premises and cloud-based, are attractive options for storing this ever-growing data deluge. Storage on Steroids: Data Governance Takes Priority: As the volume of data we use explodes, data governance and regulations will become more comprehensive and commonplace. Safeguarding and regulating this vast resource will be paramount. Data Governance Takes Priority: As the volume of data we use explodes, data governance and regulations will become more comprehensive and commonplace. Safeguarding and regulating this vast resource will be paramount. Data Governance Takes Priority: Quantum Leap for Big Data: While still in its early stages, quantum computing holds immense potential for big data analysis. Its superior processing power could significantly speed up data analysis, but for now, it remains accessible only to large enterprises with substantial resources. Quantum Leap for Big Data: While still in its early stages, quantum computing holds immense potential for big data analysis. Its superior processing power could significantly speed up data analysis, but for now, it remains accessible only to large enterprises with substantial resources. Quantum Leap for Big Data: These advancements promise to unlock the full potential of big data, leading to even deeper insights and groundbreaking discoveries across various industries. With the right tools and regulations in place, the future of big data looks bright and full of possibilities.

The Collective Loves Data: How Big Data Is Shaping and Predicting Our Future

Four Thousand Years of Blood, Snot, and Tears

চার হাজার বছরের রক্ত, স্নোট এবং অশ্রু

Viertausend Jahre Blut, Rotz und Tränen

Cuatro mil años de sangre, mocos y lágrimas

Quatre mille ans de sang, de morve et de larmes

चार हज़ार साल का खून, बलगम और आँसू

4000年にわたる血と鼻水と涙

4천년의 피와 콧물과 눈물

Quatro mil anos de sangue, ranho e lágrimas

Четыре тысячи лет крови, соплей и слез

Dört Bin Yıllık Kan, Sümük ve Gözyaşı

Bốn ngàn năm máu, nước mũi và nước mắt

四千年的血、鼻、泪

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Since April 1, the crypto market has been hit with a tsunami of new tokens. We’re talking more than one million in less than sixty days. Over 370,000 new tokens sprouted on Ethereum. But Solana stole the show. A whopping 640,000+ new tokens, most of them meme-tastic creations were launched on the platform. What does this surge in new coins mean for developers, project founders, and CEOs in the space? What You Should Know This isn't your grandma's crypto market. This landscape is a meme-fueled rollercoaster. Of the total 643,000 new tokens launched on Solana, 466,914 were memecoins. Pump.fun, Solana's memecoin factory, is churning them out faster than a Shiba Inu chasing its tail. Some of these freshly minted tokens are straight rug pulls. They only want to survive long enough to cash out and ghost investors who were too lazy to do their own research. Don’t let bad actors dash off carrying your hopes, dreams, and hard-earned cash. A Dune Analytics dashboard tracks the number of new tokens launched on the Solana-based memecoin platform pump.fun. But Ethereum isn't sitting on the sidelines. Its layer-2 blockchain, Base, has become a playground for degens creating memecoins at warp speed. With numbers doubling since April, the massive volume of new tokens means devs, founders, and CEOs must prioritize transparency, security, and building trust with their communities. We’ve been here before. Back in 2017 when the ICO craze was really popping I reviewed hundreds of projects for a top review site. Most of them were trash. From January to December 2017, the value of BTC rose 1500% – the largest one-year increase in history. As bitcoin gained value and legitimacy, trading volumes increased and hundreds of new crypto coins were issued. The following year 751 crypto coins became defunct – the most casualties of any year. Of those, 390 failed due to lack of trade volume and 237 due to issues with fraud. Don’t be the next victim. I’ve grown stealthy at spotting crypto winners and losers based on their publicly available information. You don’t have to be Nostradamus to know most meme projects will crash and burn. A handful will emerge stronger than ever. These will have a community that is vibrant and engaged. The token will tap into humor or pop culture effectively and spark virality. Those with long-term sustainability will move beyond the hype, trends, and shilling. They’ll boast active development, innovative features, or clear utility within a decentralized application (dApp) or ecosystem. These are the projects that soar above the sea of mostly shitcoins riding FOMO and FUD. Keep reading for some additional tea on winning strategies in crypto’s Wild West. Standing Out Is More Difficult Than Ever Project founders and CEOs must be aware of the increased saturation. With one million new tokens flooding the market, it's safe to say that competition has never been more intense. Every project wants more attention and investors. Standing out in this crowded market will require exceptional innovation, marketing, and community-building strategies. Now, get this. The influx of tokens, particularly meme coins, suggests a shift in user expectations. Many users are now seeking entertainment, community, and quick gains alongside traditional investment opportunities. Projects that can cater to these diverse needs may find greater success. Strategies for Success If you’re a project founder, dev, or CEO you need to be prepared to handle intense competition for attention, funding, and user adoption. For me, this demands exceptional innovation, marketing, and community-building strategies. Remember high levels of competition in the space also signals a thriving and dynamic market packed with opportunities. To strike success in this crowded field requires identifying niche markets, catering to specific user needs, and offering unique value propositions can lead to success even in a crowded field. The Gold Rush was a period of intense competition, where thousands flocked to California in hopes of striking it rich. Like the crypto space, it was a crowded field, with everyone seeking the same precious resource. But, not everyone found gold by digging in the same spot. Some prospectors found success by venturing into less explored areas, using different techniques, or focusing on providing services to other miners (like selling tools or supplies). One pro tip is to focus on differentiation. Whether it's through unique features, innovative technology, or a compelling value proposition, finding ways to stand out is crucial in a crowded market. Building a Strong Community An effective strategy for project founders to differentiate themselves and achieve long-term success is to build a strong community. This includes tactics for engaging with investors, fostering trust, and creating a loyal following. This community-driven approach not only strengthens a project's position but also fosters a sense of shared ownership and purpose among its members. I’ve seen the spillover buzz from retail investors make a project more appealing to institutional investors. It’s a powerful force that can propel any project to new heights. The Bored Ape Yacht Club (BAYC), a collection of 10,000 unique Bored Ape NFTs is an example of successfully building a strong community. This project executed several strategies including exclusive benefits, celebrity endorsement, and an active social media presence. They continue to share updates and build a sense of togetherness among members. Creative and Effective Marketing Strategies Fostering community-driven initiatives aligns with another essential tool in the arsenal of project founders. Creative and effective marketing strategies are crucial for projects to stand out from the crowd. Leveraging the power of social media trends, forming strategic partnerships with influencers, and publicity campaigns on crypto news sites help founders stay afloat and emerge stronger than ever. By harnessing these tactics, projects can gain much-needed visibility and traction in an increasingly saturated market. Shiba Inu (SHIB) is a project that started as a meme coin and managed to create a massive following and achieve significant market success. I’ve followed some of their effective marketing approaches. Right out of the gate, SHIB leveraged Reddit, Twitter, and Telegram to engage directly with their audience. They focused on community engagement, viral marketing, strategic partnerships, and transparency. Navigating Regulatory Challenges Project founders and CEOs must stay informed about the latest regulations and compliance measures to ensure their token offerings are above board and legally sound. Regulatory challenges are becoming increasingly complex and can land you in jail. Get expert legal counsel, and implement strong compliance protocols. These moves are essential steps for projects to thrive in the constantly changing regulatory environment. A proactive approach reduces legal risks and increases trust and credibility with investors and users. Ripple (XRP) is a prime example of a crypto project that effectively navigates regulatory challenges. Despite facing significant hurdles, their comprehensive approach to regulation is a model of resilience and adaptability. They continue to apply a combination of proactive legal defense, strategic partnerships, and industry advocacy. This is why they’re positioned as a leader in the blockchain and crypto space. Collaborating and Networking Strategic partnerships and collaborations can be a game-changer for project founders and CEOs. Teaming up with other industry players, networking with investors, and connecting with experts are necessary tools to strengthen your position in the market. This interconnected approach stirs innovation. It also creates a supportive ecosystem. Founders are able to build authority and generate organic traffic to their projects. This is a great way to overcome challenges and discover new opportunities in the face of a million new tokens flooding the market. A prime example of a crypto project successfully collaborating and networking in the current environment is Polygon (MATIC). This layer-2 (L2) scaling solution focuses on enhancing scalability and user experience for Ethereum and other blockchain ecosystems. It also collaborates heavily with DeFi, NFT, and gaming projects. Conclusion With this recent explosion of tokens, the crypto world is more chaotic (and potentially profitable) than ever. Founders, developers, and CEOs must adapt. Stay ready to pivot your strategies and tactics to gain a competitive advantage. How can you embrace new technologies like AI or ML to stay ahead of the curve? The crypto market is evolving with this token tsunami as investors fall into two camps. Will FOMO (fear of missing out) cause your audience to dive headfirst into the memecoin madness? Or does FUD (fear, uncertainty, and doubt) make your prospects hodl Bitcoin and laugh at the memecoin chaos from the sidelines? Do your research and experiment. It’s simple. The future of your project is in your hands. Make it count. Those who can navigate this dynamic, memecoin-filled landscape with innovation, integrity, and adaptability are poised to thrive. The world of crypto is continuously changing. Stay afloat, stay innovative, and stay connected. Since April 1, the crypto market has been hit with a tsunami of new tokens. We’re talking more than one million in less than sixty days. Over 370,000 new tokens sprouted on Ethereum. But Solana stole the show. A whopping 640,000+ new tokens, most of them meme-tastic creations were launched on the platform. What does this surge in new coins mean for developers, project founders, and CEOs in the space? What You Should Know This isn't your grandma's crypto market. This landscape is a meme-fueled rollercoaster. Of the total 643,000 new tokens launched on Solana, 466,914 were memecoins. Pump.fun, Solana's memecoin factory, is churning them out faster than a Shiba Inu chasing its tail. Some of these freshly minted tokens are straight rug pulls. They only want to survive long enough to cash out and ghost investors who were too lazy to do their own research. Don’t let bad actors dash off carrying your hopes, dreams, and hard-earned cash. A Dune Analytics dashboard tracks the number of new tokens launched on the Solana-based memecoin platform pump.fun. But Ethereum isn't sitting on the sidelines. Its layer-2 blockchain, Base, has become a playground for degens creating memecoins at warp speed. With numbers doubling since April, the massive volume of new tokens means devs, founders, and CEOs must prioritize transparency, security, and building trust with their communities. We’ve been here before. Back in 2017 when the ICO craze was really popping I reviewed hundreds of projects for a top review site. Most of them were trash. From January to December 2017, the value of BTC rose 1500% – the largest one-year increase in history. As bitcoin gained value and legitimacy, trading volumes increased and hundreds of new crypto coins were issued. The following year 751 crypto coins became defunct – the most casualties of any year. Of those, 390 failed due to lack of trade volume and 237 due to issues with fraud. Don’t be the next victim. 751 crypto coins became defunct I’ve grown stealthy at spotting crypto winners and losers based on their publicly available information. You don’t have to be Nostradamus to know most meme projects will crash and burn. A handful will emerge stronger than ever. These will have a community that is vibrant and engaged. The token will tap into humor or pop culture effectively and spark virality. Those with long-term sustainability will move beyond the hype, trends, and shilling. They’ll boast active development, innovative features, or clear utility within a decentralized application (dApp) or ecosystem. These are the projects that soar above the sea of mostly shitcoins riding FOMO and FUD. Keep reading for some additional tea on winning strategies in crypto’s Wild West. Standing Out Is More Difficult Than Ever Project founders and CEOs must be aware of the increased saturation. With one million new tokens flooding the market, it's safe to say that competition has never been more intense. Every project wants more attention and investors. Standing out in this crowded market will require exceptional innovation, marketing, and community-building strategies. Now, get this. The influx of tokens, particularly meme coins, suggests a shift in user expectations. Many users are now seeking entertainment, community, and quick gains alongside traditional investment opportunities. Projects that can cater to these diverse needs may find greater success. Strategies for Success If you’re a project founder, dev, or CEO you need to be prepared to handle intense competition for attention, funding, and user adoption. For me, this demands exceptional innovation, marketing, and community-building strategies. Remember high levels of competition in the space also signals a thriving and dynamic market packed with opportunities. To strike success in this crowded field requires identifying niche markets, catering to specific user needs, and offering unique value propositions can lead to success even in a crowded field. The Gold Rush was a period of intense competition, where thousands flocked to California in hopes of striking it rich. Like the crypto space, it was a crowded field, with everyone seeking the same precious resource. But, not everyone found gold by digging in the same spot. Some prospectors found success by venturing into less explored areas, using different techniques, or focusing on providing services to other miners (like selling tools or supplies). One pro tip is to focus on differentiation . Whether it's through unique features, innovative technology, or a compelling value proposition, finding ways to stand out is crucial in a crowded market. focus on differentiation focus on differentiation Building a Strong Community An effective strategy for project founders to differentiate themselves and achieve long-term success is to build a strong community. This includes tactics for engaging with investors, fostering trust, and creating a loyal following. This community-driven approach not only strengthens a project's position but also fosters a sense of shared ownership and purpose among its members. I’ve seen the spillover buzz from retail investors make a project more appealing to institutional investors. It’s a powerful force that can propel any project to new heights. The Bored Ape Yacht Club (BAYC), a collection of 10,000 unique Bored Ape NFTs is an example of successfully building a strong community. This project executed several strategies including exclusive benefits, celebrity endorsement, and an active social media presence. They continue to share updates and build a sense of togetherness among members. Creative and Effective Marketing Strategies Fostering community-driven initiatives aligns with another essential tool in the arsenal of project founders. Creative and effective marketing strategies are crucial for projects to stand out from the crowd. Leveraging the power of social media trends, forming strategic partnerships with influencers, and publicity campaigns on crypto news sites help founders stay afloat and emerge stronger than ever. By harnessing these tactics, projects can gain much-needed visibility and traction in an increasingly saturated market. Shiba Inu (SHIB) is a project that started as a meme coin and managed to create a massive following and achieve significant market success. I’ve followed some of their effective marketing approaches. Right out of the gate, SHIB leveraged Reddit, Twitter, and Telegram to engage directly with their audience. They focused on community engagement, viral marketing, strategic partnerships, and transparency. Navigating Regulatory Challenges Navigating Regulatory Challenges Project founders and CEOs must stay informed about the latest regulations and compliance measures to ensure their token offerings are above board and legally sound. Regulatory challenges are becoming increasingly complex and can land you in jail. Get expert legal counsel, and implement strong compliance protocols. These moves are essential steps for projects to thrive in the constantly changing regulatory environment. A proactive approach reduces legal risks and increases trust and credibility with investors and users. Ripple (XRP) is a prime example of a crypto project that effectively navigates regulatory challenges. Despite facing significant hurdles, their comprehensive approach to regulation is a model of resilience and adaptability. They continue to apply a combination of proactive legal defense, strategic partnerships, and industry advocacy. This is why they’re positioned as a leader in the blockchain and crypto space. Collaborating and Networking Collaborating and Networking Strategic partnerships and collaborations can be a game-changer for project founders and CEOs. Teaming up with other industry players, networking with investors, and connecting with experts are necessary tools to strengthen your position in the market. This interconnected approach stirs innovation. It also creates a supportive ecosystem. Founders are able to build authority and generate organic traffic to their projects. This is a great way to overcome challenges and discover new opportunities in the face of a million new tokens flooding the market. A prime example of a crypto project successfully collaborating and networking in the current environment is Polygon (MATIC). This layer-2 (L2) scaling solution focuses on enhancing scalability and user experience for Ethereum and other blockchain ecosystems. It also collaborates heavily with DeFi, NFT, and gaming projects. Conclusion With this recent explosion of tokens, the crypto world is more chaotic (and potentially profitable) than ever. Founders, developers, and CEOs must adapt. Stay ready to pivot your strategies and tactics to gain a competitive advantage. How can you embrace new technologies like AI or ML to stay ahead of the curve? The crypto market is evolving with this token tsunami as investors fall into two camps. Will FOMO (fear of missing out) cause your audience to dive headfirst into the memecoin madness? Or does FUD (fear, uncertainty, and doubt) make your prospects hodl Bitcoin and laugh at the memecoin chaos from the sidelines? Do your research and experiment. It’s simple. The future of your project is in your hands. Make it count. Those who can navigate this dynamic, memecoin-filled landscape with innovation, integrity, and adaptability are poised to thrive. The world of crypto is continuously changing. Stay afloat, stay innovative, and stay connected.

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