Rep. Jim Jordan speaks briefly to reporters as he departs a House Republican Caucus at the U.S. Capitol on October 16, 2023, in Washington, D.C. Drew Angerer / Getty Images A memo crafted by the campaign arm of Democrats in the House of Representatives ...

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Follow Pitchfork on Facebook Follow Pitchfork on Twitter Follow Pitchfork on Instagram Follow Pitchfork on Tumblr Follow Pitchfork on Snapchat Do Not Sell My Personal Information.

Floating Points

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Get the most recent info and news about Pocket Points on HackerNoon, where 10k+ technologists publish stories for 4M+ monthly readers.

Pocket Points is a mobile app that rewards students to not use their phones during class.

Pocket Points

Even a product that solves world hunger needs marketing. No matter how grand or revolutionary your business idea is, burning out is a real possibility if you don’t have the necessary understanding of how your product will fit, survive and grow in its market. In other words, having that great product is only part of the equation to entrepreneurial success. There’s lots of vital research and understanding that must also be a factor. The product-market fit is an essential dimension of this research. It helps a business understand how healthy the market it is and to what degree their product or service will satisfy the needs of that market. Unfortunately, as necessary as this element is, particularly for lean startups, it often goes undefined. This can cause businesses to fall flat for two primary reasons: The market just wasn’t ready for the product The market didn’t understand how the product would satisfy their need(s) To ensure that you don’t fall into one of these devastating pitfalls for startup businesses, here are 5 steps to define your product-market fit and gain a deeper understanding of how to scale and market your products. Step #1: Know Your Target Audience Your very first step is to know your target audience. Who is the type of person that will benefit most from this product and be easily enticed to buy it? You likely already have a type of customer in mind. However, this idea isn’t always aligned with reality, at least not fully. The market for your product may be entirely different than expected. Or, it may be larger than you thought, which means there are more potential buyers to learn and understand. There are a few different ways you can determine your target audience: ● Look at your existing customers; what types of markets do they represent? ● Study your competitors and the audiences they are targeting ● Conduct market research (surveys, one-on-one interviews) with consumers regarding your product ● Pure guesswork and hypothesizing and then testing that theory at market It’s okay if you don’t get your target audience defined immediately. It can be adjusted in later steps, especially when you have more accurate insights on the competitive landscape. Step #2: Recognize Unsatisfied Customer Needs Once you’ve established (or at least formed a hypothesis regarding) your target audience, you can begin thinking about their needs and how your products can satisfy them. When you can offer a product or service that meets an unsatisfied need, then you have a vibrant market opportunity. All products will face competition in the market. The level of that competition will impact how difficult it is to find underserved customer needs. Your target audience will ultimately compare and judge your offerings based on what the competition brings to the table. The best way to identify these unsatisfied needs in the market is to ask. Engage with individuals in your target audience and ask what their points of pain or dissatisfaction are with similar products. How are the other products or companies in the industry performing in terms of meeting customer needs? What can be improved? These conversations are sure to yield some of the best insights into how your products can deliver the necessary degree of value needed in the market. Remember, there may be instances where customers are satisfied with their existing solutions to consider yours. Or, the market may simply not be matured enough for the value that your solution brings. too Step #3: Develop A Value Proposition Now that you have a clearer understanding of what is missing from the market, you can begin strategizing how you’ll meet these customer needs better than the alternative solutions. Depending on the competition in your unique space or industry, your target customers may have multiple needs that can be incorporated into your value proposition. This is the essence of a product-market fit: finding the gap(s) that your company can fill. This ensures that your products have a place in the market to attract the attention of customers. You’ll be able to differentiate yourself from the competition and gain that essential market share that your business needs to survive and grow. That said, you should focus on only a manageable number of value proposition. There may be lots of gaps in your industry, but trying to fill them all at once will cause you to underdeliver on satisfying those needs. This is the old adage of, “anything worth doing is worth doing right.” At the end of the day, your customers are going to be much more satisfied if you fill a single need completely, then not meeting expectations on three or four different value propositions. This is especially true for lean startups that need to carve out a reputation quickly. Be known for doing one thing exceptionally well, and the customers will come. stands out by stating they’re “a new way to truly understand your developers”. stands out by stating you can “offer and earn rewards for phone-free time in class and while driving”. As your business grows, you can scale up to capitalize on these other gaps. Wavdev Pocket Points Step #4: Create And Manage Buyer Personas Once you have a fully developed value proposition, you also gain a market-ready product. So, it’s time to bring that to buyers. This means readdressing your target audience from step #1, double-checking that it is still the right market for your product (and newly recognized value prop) and then beginning to learn how to pitch to these individuals effectively. There’s a good chance that there are multiple different types of buyers, industries or uses for your product. Thus, you have to create different buyer personas for each of these unique types of customers because they may each require a different marketing approach. There’s no guarantee that what’s worked with one buyer persona will be as effective with the other audience segments. Similar to your value proposition, having a narrow focus is also vital with creating and managing your buyer personas. Yes, your product may appeal to a lot of different industries and have different uses, but trying to sell to all of them at once can be extremely difficult, especially for budding startups with limited resources. Instead, go very deep into a single vertical; become the go-to industry product in that realm. Once you have this established reputation with one industry or buyer persona, it will be easier to move to other spaces. You’ll not only have the word-of-mouth reputation working for you, but you’ll also have a documented, successful sales and marketing process that you can quickly adapt to new domains. Step #5: Test, Test, Test Your product-market fit will always be changing because the market is still moving: customer behaviors, needs and attitudes shift, new products enter the space, competition grows and shrinks, etc. To ensure that you’re always able to adapt to these changes in the landscape, you need to recognize that your product-market fit is dynamic, not static. There’s a lot of different components that make up the product-market mix, and they can all be tested and improved upon. Trying out various price points or marketing/sales tactics, or even shifting to a new target audience\, can all potentially benefit (or hurt) your business. But, you never know until you do some safe test. It’s also important to continuously stay engaged with your customers. They are easily your biggest asset when it comes to strengthening your product-market fit and defining your value proposition(s). To test your market-product fit, you need to have the right metrics. This puts quantitative data behind your success or lack thereof. Data, such as customer churn rates, conversion rates or sales cycle info, can help replace some of the guesswork of your product-market fit with accurate, data-born insights. Therefore, you should be continuously tracking, testing and measuring the success of your product-market fit, while simultaneously exploring new possibilities to capitalize on. Conclusions There’s a lot of moving parts involving your product-market strategy, which means a lot can go wrong if you aren’t careful. Following these steps and continuously working to improve your products to align with the current needs and attitudes of the market will always lead to greater success. As you continue to test and improve your product-market fit, you may find yourself repeating some of these steps over and over. This is normal and will help fuel your understanding of how to create better product-market fits in the future. That said, there may be times when your product just isn’t a fit for the market. This can be difficult to stomach, but the sooner you can spot those early warning signs of a lousy product-market fit, the less damaging it will be to get out of that space and on to other, profitable ventures. Hopefully, these five steps will empower you and your business to find and capitalize on the strategic gaps in your markets.

5 Steps Towards Finding Your Product-Market Fit

Managing school and keeping your sanity is already hard enough. You’re studying something you’re fairly certain won’t get you a job right away, you’re crawling deeper into a debt hole that’s too scary to imagine, and you’re contemplating how much simpler life would be selling hats on a Mexican beach. But it doesn’t have to be that way. There are a ton of apps and websites out there save money, stay fit, work smarter, manage your time, and learn more. Why not take advantage of them? And even if you’re not a , a lot of these are useful for just about anyone. designed to help you — student →For saving money — See all of your transactions and manage your budget in one place Mint — Earn coupons for spending time off of your phone when you’re on campus Pocketpoints — Make free or crazy cheap domestic and international phone calls over the web Google Hangouts — Free student discounts on pretty much anything StudentRate.com — Sell your old textbooks to make a little extra cash Chegg →For cheaper books and textbooks — The Netflix for books and reading Scribd — Buy and rent textbooks at a lower price than your campus store Chegg — Buy used or older versions of expensive textbooks Amazon →For a healthier brain and body — Track your sleep and wake up more well-rested Sleep Cycle — Guided meditation for managing anxiety and stress Calm — At-home yoga for any mood or fitness goal Yoga with Adriene — 4 minute bodyweight workouts that you can do at home Tabata Timer + this link →For being more productive with your time — Block yourself from spending too much time on your phone Flipd — Set daily or weekly goals and turn them into habits Habitbull — Track how your time is spent online RescueTime — Offline reading for reducing web-based distractions Pocket — Block yourself from visiting certain websites during a set time frame SelfControl →For organizing your study-work life — Intuitive to-do list app that syncs with all of your devices Todoist — Academic planner for keeping track of due dates and exams MyStudyLife →For improving your creativity and writing skills — Learn and remember 1000’s of new words VocabularyBuilder — Get the right citations for more than 60 types of sources EasyBib — Get inspired about any subject and learn something new TED Talks →For working better in groups — Collaborate every component of your project in one place and track each group member’s participation Google Drive — Snap a photo of your notes & turn it into a PDF document CamScanner — Create group chats to get organized, share documents, & know who has seen your message (now a separate app for less distracting chats) Facebook Messenger If you liked this post, please share and recommend!

25 of the best apps and websites to get you through school (and life)

Scientific American Supplement, No. 365, December 30, 1882 by Various, is part of the HackerNoon Books Series. You can jump to any chapter in this book . A NEW POCKET OPERA GLASS. here A NEW POCKET OPERA GLASS. Inasmuch as high power combined with small size is usually required in an opera glass, manufacturers have always striven to unite these two features in their instruments, and have succeeded in producing glasses which, although sufficiently small to be carried in the waistcoat pocket, are nevertheless powerful enough to allow quite distant objects to be clearly distinguished. Recently, a Parisian optician has succeeded in constructing an instrument of this kind that is somewhat of a novelty in its way, since its mechanism allows it to be closed in such a manner as to take up no more space than a package of cigarettes (Fig. 1.) It is constructed as follows: AB and CD (Fig. 1) are two metallic tubes, in which slide with slight friction two other tubes. Into the upper part of the latter are inserted two hollow elliptical eye-pieces, which move therein with slight friction, and which are united by the two supports tor the wheel, (Fig. 4), and endless screw that serve for focusing the instrument. The eyepieces, TT, are held in the tube by means of two screws, (Figs. 2 and 4), in such a way that they can revolve around the latter as axes. The lenses of the eye-piece are fixed therein by means of a copper ring. The object glasses are placed in the ends of the tubes, AB and CD, at . bb vv oo When the instrument is closed, it forms a cylinder 35 millimeters in diameter by 11 centimeters in length. To open it, it is grasped by the extremities and drawn apart horizontally so as to bring it into the position shown in Fig. 2. Then it is turned over so that the screw, V, points upward, while at the same time the two tubes are pressed gently downward. This causes the eye-pieces to revolve around their axes, , and brings the two tubes parallel with each other.— vv La Nature. About HackerNoon Book Series: We bring you the most important technical, scientific, and insightful public domain books. This book is part of the public domain. Various (2006). Scientific American Supplement, No. 365, December 30, 1882. Urbana, Illinois: Project Gutenberg. Retrieved https://www.gutenberg.org/cache/epub/18763/pg18763-images.html This eBook is for the use of anyone anywhere at no cost and with almost no restrictions whatsoever. You may copy it, give it away or re-use it under the terms of the Project Gutenberg License included with this eBook or online at , located at . www.gutenberg.org https://www.gutenberg.org/policy/license.html

A NEW POCKET OPERA GLASS

Smartphones have become integral to modern life since the launch of Apple’s first iPhone in 2007. They hold much of our personal information and give us access to services and apps like email, mobile banking, and social media. Years later, arrived in our smartphones. The technology, which refers to computer systems or machines that perform tasks that usually require human intelligence, has since advanced. AI is now powered by a variety of applications, including: artificial intelligence (AI) Machine learning (ML) algorithms detect patterns in data to make predictions and improve over time based on new inputs. Natural language processing (NLP) analyses and generates human language. Computer vision identifies and interprets visual inputs like images and video. Robotics integrates AI to make automated machines that can move and interact with the physical world. It’s no wonder that technology companies across the globe are heavily investing in AI, some to specifically improve a consumer’s smartphone experience through virtual assistants and other services. Here are : some numbers Google has invested $30.7 billion in AI to improve its search engine and virtual assistant, Google Assistant. Facebook has invested $22.1 billion to improve the social media platform’s user experience (UX). Amazon has invested $10 billion to improve its delivery services and voice assistant, Alexa. Microsoft has invested $10 billion to improve its products and services, including voice recognition. Let’s look at how our smartphones already integrate AI. Virtual assistants Apple pioneered mobile virtual assistants when it launched Siri in 2011. Afterward, Google Assistant, Huawei’s Celia, Samsung’s Bixby, and others joined the competition. They rely on natural language processing and machine learning, two subsets of AI, to improve performance over time. A virtual assistant, also called an AI assistant or digital assistant, can understand human voice commands, engage in two-way conversations with a user, and complete day-to-day tasks, including making a phone call, adding an event to your calendar, or checking the weather. Virtual assistants are primarily available on smartphones and home automation devices (such as Amazon’s Alexa). However, Samsung’s Bixby is also incorporated into some smart refrigerators. You can search the entire internet by making a voice command to your virtual assistant, and search engines offer better results because they understand natural language, not just keywords. Facial Recognition Facial recognition, also called facial detection and Face ID, uses computer vision to find and identify human faces in digital images and video. It is now commonly used to unlock your smartphone, access locked apps such as mobile banking, and make digital wallet payments. Facial recognition first became available on Samsung phones as early as 2011. However, it was not very secure as it used 2D images, so a printed photo of your face or even one displayed on another phone could trick the technology. Later, Apple took over the technology with the launch of the iPhone X in 2017. Face ID works by illuminating your face with infrared light and projecting 30,000 infrared points onto your face to build a detailed 3D map of your features. Apple said: “The probability that a random person in the population could look at your iPhone or iPad Pro and unlock it using Face ID is approximately 1 in 1,000,000 with a single enrolled appearance.” By 2024, biometric facial recognition hardware will be deployed on over mobile devices, accounting for 90% of smartphones. 800 million Recommendations and Suggestions AI consistently analyses your smartphone usage, browsing history, and other data and insights to predict content that might be of interest to you or to recommend apps, products, and more. App suggestions are another advancement but aren’t as widely used yet. Both Apple and Google continue investigating how their operating systems can guess why you’ve unlocked your smartphone from factors including the time of day and location. For example, if you’ve plugged in or connected your earphones/headphones, your smartphone might suggest opening Spotify, Apple Music, or Audible. Another instance is if it’s 8 a.m. on a weekday and you’re at home, your smartphone might suggest Google Maps or Apple Maps, predicting that you want to know the traffic on your route to work. Augmented Reality Augmented reality (AR) is software that combines a real-world environment with digital overlays to offer an interactive experience. AR powers several mobile apps, including games such as Pokemon Go - where an avatar is placed on a map of the player’s real-world surroundings - and furniture shopping apps such as Wayfair - which lets you visualize new furniture in a physical room with their ‘View in Room’ feature. Photography Many recent smartphones, such as the and Pixel 6 Pro, now have AI cameras that use tools to combine the capabilities of a professional camera with features that were previously only available in editing software. Here are some examples: Google Pixel 6 Optical flow technology rapidly snaps multiple pictures and then uses AI software to combine them into a single image without blurring. AI powers Pixel’s Night Sight feature, where photos shot in low light can still look bright and clear. AI software in Pixel phones can also fix the exposure of high-contrast photos, where shadows make one part of the image look dark, and bright light makes another part look washed out. Image Search Computer vision identifies and interprets visual inputs like images and video and can, therefore, offer image search. You can use the ASOS app to search for an item of clothing or pair of shoes you want or the Amazon app to search for a product using an image from your camera or camera roll of a similar product. Whilst Image Search on the Google app can be used the same (searching a wide range of online clothing retailers), it can also be used to find the original artist of an image or where a household product is from. Predictive Text A smartphone keyboard can use natural language processing to learn from your writing style over time and offer next-word suggestions, making typing faster and easier. Compared to other aspects of AI, predictive text is straightforward because the rules of a language are universal. If you type “Are” into a message or search box, the chances are the next word will be “you” or “there’ - and then there are only a limited number of words that suitably follow. Wrapping Up AI technology and applications make our smartphones more responsive and human-like. Virtual assistants, predictive text, facial recognition, personalized recommendations and suggestions, and other AI-powered features enhance how we interact with our devices. , an author and internationally recognized AI expert, said: “ ” Bernard Marr The future of the smartphone is all about AI. AI is allowing our phones to become more personalized and more helpful. At the same time, AI is helping to solve some of the biggest problems with smartphones today – like battery life.

AI in Your Pocket: How Do Our Smartphones Already Integrate AI?

“ " - Edward Snowden Arguing that you don't care about the right to privacy because you have nothing to hide is no different than saying you don't care about free speech because you have nothing to say. “Alexa, tell me a joke” sends a shudder through the intergalactic transmission web. Amazon servers bank the speech-to-text snippet. The algorithm belches a response after feasting on a data buffet where the Library of Congress is hors d’oeuvres and 90 million dinner conversations is the Rib-eye. The chronic, bone-deep privacy invasion by always-on location tracking, always-listening smart speakers, and always-watching surveillance cameras is a cliche of modern life: Who cares? Knowledge is power, and a data shared is a joke or a health tip earned. “Privacy,” the term, needs a makeover. Privacy is not a smoke-screen to protect pedophiles who dredge the internet’s most despicable depths. It’s a shield for the anorexic teenager assailed with ads for weight-loss pills that dump gasoline on her burning anxieties. Privacy is managing boundaries through control of personal data, handing you the keys to your mind’s gate—stormed by social media, online news, and increasingly, physical devices like Amazon’s Alexa, and . smartwatches that are doubling as palm-sized shrinks If you accept that our every step or click is propelled by infinite inscrutable forces, then you know how easily humans are manipulated. When your cell-phone buzzes, does your hand plunge into your pocket before you register what’s happening? Embracing privacy is an act of humility; it’s the acknowledgment that targeted ads and notifications affect you in subtle ways that you may not understand or control. Privacy means self-control; it means setting mental boundaries. Let’s talk a little bit more about what that means in practice. Brittany Kaiser is the Cambridge Analytica whistleblower, the star of the Netflix documentary The Great Hack, and the Own Your Data foundation founder. IoTeX partnered with Own Your Data earlier this year detailing practical steps everyday users can take to control their data. for a webinar Brittany Kaiser’s Own Your Data cuts to the entrails of privacy: Describing personal data as personal property as intimate and proprietary as a bedroom, and as we’ve seen with , sometimes the data is literally a video feed of your child’s bedroom. hacks of indoor cameras including Amazon’s Ring We should have the power to opt-in to data sharing. There are plenty of instances when sharing is beneficial—movie recommendations, directions, and targeted ads for helpful products. But the current model of bottomless extraction and secret manipulation needs to be flipped, handing data back to users who set the rules of engagement. The idea that data should be personal property is not a fringe one. Andrew Yang’s 2020 campaign stating: “ Data generated by each individual needs to be owned by them, with certain rights conveyed that will allow them to know how it’s used and protect it.” laid out a proposal Legislators in New York State called the NYPA that goes far beyond Europe’s GDPR and California’s CCPA: “Unlike the CCPA, which gives consumers the right to “opt-out” from the sale of their personal data, the NYPA requires consumers to “opt-in” for the use of their personal data.” proposed a piece of legislation The modern internet has been pillaged by digital imperialists who took our personal property without asking. Most people do not view their data in the same way they view their homes or physical property. But they should. Think about how much of your life is spent interfacing with a digital intermediary: Your laptop, your smartphone, and your smart-home devices, the latter of which serve as omnipresent eyes and ears inside your home. Amid this pandemic, it might be the majority. It’s time for a mindset shift. Some companies embrace a Privacy-By-Design philosophy, putting the user in charge of their personal data and fulfilling the goal of the legislation proposed by Andrew Yang and New York state. Any device Powered-by-IoTeX belongs in this category, the first of which is a home security camera, called . Ucam But this is not about any single company. It’s about changing how we think about our data and growing outraged at the injustices we suffer when our personal data rights are violated. Educate yourself and your friends. And if you are interested in receiving regular information relevant to the topic of user-well being and privacy, join our newsletter: . The Good Disruption Also published at https://medium.com/iotex/why-should-i-care-about-privacy-i-have-nothing-to-hide-f560b1b02164

Humans As Data Points: Here's Why You Need Better Privacy

SINGAPORE, Singapore, April 10th, 2024/Chainwire/--On April 4th, , a leading Bitcoin Layer2, and its ecosystem project, , to launch an airdrop campaign worth millions. Binance Web3 Wallet joined forces with BEVM Satoshi Protocol Users who bridge BTC to the BEVM via Binance Web3 Wallet and borrow at least $10 SAT (USD-stablecoin) will be eligible to share a pool of 500k OSHI and 10.5 million BEVM tokens. The campaign has attracted over 30,000 participants in just 3 days since its launch. is the first CDP protocol built on BEVM, live on the BEVM mainnet on March 28th. In addition to the Binance campaign, there's a referral program that allows early participants to accumulate points by borrowing the SAT and inviting friends. Satoshi Protocol What Makes BEVM Unique? is an EVM-compatible Bitcoin Layer 2 solution that stands out in the crowded market. By leveraging Taproot consensus, Schnorr signatures, MAST, and Bitcoin SPV, BEVM achieves the highest level of decentralization and security among all BTC Layer 2 solutions. BEVM Key features of BEVM: Native BTC Layer 2: BEVM utilizes BTC as the network gas and stores transaction data on the BTC mainnet, ensuring compatibility with the original Bitcoin protocol. EVM compatibility: Developers can seamlessly migrate their Ethereum-based dApps to BEVM, expanding the reach of these applications to the Bitcoin ecosystem. Decentralized and secure: BEVM employs Musig2 multi-signature aggregation and Bitcoin light nodes to achieve a trustless and secure environment. is the first Collateralized Debt Position (CDP) protocol built on BEVM, aiming at providing liquidity for BTC through the SAT dollar stablecoin, expanding the scenarios of BTCFi. Satoshi Protocol This move unlocks trillion-dollar liquidity within the Bitcoin ecosystem, offering users a way to maintain Bitcoin holdings while gaining liquidity. The Bitcoin ecosystem is experiencing a renaissance. Recent advancements like inscriptions and scaling solutions have revitalized the ecosystem. Satoshi Protocol's team views the upcoming halving and the launch of the Runes protocol as poised to attract a wave of new users. However, a critical challenge persists: the lack of a reliable, fiat-pegged crypto for seamless trading and efficient market pricing. This is where Satoshi Protocol steps in. By enabling users to borrow SAT with their Bitcoin as collateral, Satoshi Protocol offers a reliable liquidity solution within the Bitcoin ecosystem. Satoshi Protocol Milestones In the past month, Satoshi Protocol has built a strong community, with 60,000+ followers on and 70,000+ members across and . Twitter Telegram Discord Let’s see what they have achieved : February 24th: Satoshi Protocol . launched on BEVM testnet March 18th: with 100,000+ participants and . Testnet concluded 80,000+ NFT minted March 24th: Smart contracts passed security audits by and . Scalebit Supremacy March 26th: was secured from Web3Port Foundation, Waterdrip Capital and BEVM Foundation. Pre-seed funding March 28th: . Launched on BEVM Mainnet April 4th: with BEVM on Binance wallet. 500k OSHI Airdrop How does Satoshi Protocol work? The utilizes a sophisticated system to maintain a stable peg of $1 for SAT. This system combines several mechanisms: over-collateralization, permissionless liquidation, stability pools, and arbitrage. Satoshi Protocol Additionally, it features OSHI, a utility token granting holders 97.5% of the protocol's revenue. For more details about OSHI and sOSHI, refer to the official documentation: . OSHI & sOSHI OSHI distribution The interplay between SAT and OSHI forms the heart of Satoshi Protocol. Here's the complete picture: Collateralized Borrowing When borrowing SAT, users must maintain a minimum collateral ratio (MCR) of 110%. This means the borrowed amount cannot exceed 90.9% of the deposited BTC value. Liquidation A liquidation is triggered if a user's collateral value dips below 110% (MCR) due to price fluctuations. The user's BTC collateral is sold at a discount to Stability Pool providers to repay the SAT loan. This mechanism protects the protocol and prevents borrowers from taking on excessive debt. Maintaining the Peg A robust three-pronged system ensures SAT's value remains consistently pegged to the US dollar: Redemption: Arbitrage activity helps regulate SAT's price and keep it within the desired range. If SAT dips below $1, arbitrageurs can buy discounted SAT and redeem them for $1 worth of BTC from the protocol. Conversely, if SAT exceeds $1.1, users can borrow SAT at the MCR (110%), sell them at a premium on decentralized exchanges (DEXs), and pocket the profit. Over-collateralization: As mentioned earlier, over-collateralization (MCR of 110%) acts as a safety net. The protocol discourages borrowers from defaulting by requiring a higher collateral value and protects itself from price fluctuations. Stability Pool: This pool serves as a final safety measure. If a user's collateral ratio falls below the MCR, the Stability Pool provides the necessary liquidity to execute a liquidation event and maintain protocol stability. Satoshi Protocol x BEVM Airdrop With Binance Wallet BEVM and Satoshi Protocol recently joined forces with Binance Web3 Wallet to offer a total of 10.5M $BEVM and 500k $OSHI Token Airdrop. By using Binance Wallet to complete tasks like bridge to BEVM and create positions on Satoshi Protocol, you'll be eligible to share the . rewards Duration: 2024/04/04-2024/05/04 Rewards: 10,500,000 BEVM and 500,000 OHSI Tasks: Using Binance Wallet Bridge BTC to the BEVM ( 0.0004 BTC, ~$25) Create positions on Satoshi Protocol ( at least 10 SAT ) Join Binance Wallet Airdrop Campaign How to Participate in this Campaign STEP 1: Users should go to Campaign Landing Page, and connect Binance Wallet STEP 2: Withdraw BTC and bridge to BEVM Suggest withdrawing at least 0.0004 BTC to complete the task Using OmniBTC to transfer BTC from BSC to BEVM Using BEVM bridge to transfer BTC from mainnet to BEVM ( ~ 30mins ) STEP 3: Users should go to Satoshi Protocol and create a Position Users should go to Satoshi Protocol and Connect Wallet Input BTC Amount Borrow SAT ( at least 10 SAT ) (Optional) Fill in your referrer, get 150+ points Click "Approve" Click “Create Position” Binance Wallet Mobile tutorial: Create Position Using Binance Web3 Wallet Upon completing all those steps, the user becomes qualified for the BEVM and OSHI airdrop! Bridging the Future: Satoshi Protocol and Binance Wallet's Airdrop Campaign represents a glimpse into the future of Bitcoin finance. Satoshi Protocol Leveraging the BEVM and a robust CDP model allows users to borrow BTC-backed stablecoin SAT and expands the possibility of the Bitcoin Ecosystem. Binance Wallet launched an airdrop campaign with a total of 10.5M , 500K for those who bridge BTC to BEVM and create positions on Satoshi Protocol, which is also Binance Wallet's first integration with a Bitcoin Layer 2 solution. BTCFi is heading west. $BEVM $OSHI About Satoshi Protocol Built on BEVM, it's the first CDP protocol to unlock the true potential of Bitcoin. Unleash unprecedented liquidity through SAT, a stablecoin designed to supercharge the booming BTCFi market. To learn about Satoshi Protocol, users can follow: | | | | | | Website Web APP Twitter Telegram Discord Docs Blog Contact Marketing Hugo Satoshi Protocol marketing@satoshiprotocol.org This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program . here

Satoshi Protocol: First CDP On Bitcoin Layer2, 500k OSHI Airdrop With Binance Wallet And BEVM

সাতোশি প্রোটোকল: Bitcoin Layer2-এ প্রথম CDP, Binance Wallet এবং BEVM সহ 500k OSHI Airdrop

Satoshi Protocol: Erster CDP auf Bitcoin Layer2, 500.000 OSHI Airdrop mit Binance Wallet und BEVM

Protocolo Satoshi: primer CDP en Bitcoin Layer2, lanzamiento aéreo de 500k OSHI con Binance Wallet y BEVM

Protocole Satoshi : premier CDP sur Bitcoin Layer2, 500 000 OSHI Airdrop avec Binance Wallet et BEVM

सातोशी प्रोटोकॉल: बिटकॉइन लेयर 2 पर पहला CDP, बिनेंस वॉलेट और BEVM के साथ 500k OSHI एयरड्रॉप

サトシ・プロトコル：ビットコインレイヤー2初のCDP、バイナンスウォレットとBEVMによる50万OSHIエアドロップ

Satoshi 프로토콜: Bitcoin Layer2의 첫 번째 CDP, Binance Wallet 및 BEVM을 통한 500,000 OSHI 에어드롭

Protocolo Satoshi: primeiro CDP em Bitcoin Layer2, 500k OSHI Airdrop com carteira Binance e BEVM

Протокол Сатоши: первый CDP на биткойн-уровне 2, раздача 500 тыс. OSHI с помощью кошелька Binance и BEVM

Satoshi Protokolü: Bitcoin Layer2'de İlk CDP, Binance Cüzdan ve BEVM ile 500.000 OSHI Airdrop

Giao thức Satoshi: CDP đầu tiên trên Bitcoin Layer2, Airdrop 500k OSHI với ví Binance và BEVM

Satoshi 协议：比特币 Layer2 上的第一个 CDP，使用币安钱包和 BEVM 进行 500k OSHI 空投

Recommendation systems have become an integral and indispensable part of our lives. These intelligent algorithms are pivotal in shaping our online experiences, influencing the content we consume, the products we buy, and the services we explore. Whether we are streaming content on platforms like , discovering new music on , or shopping online, recommendation systems are quietly working behind the scenes to personalize and enhance our interactions. Netflix Spotify The unique element of these recommendation systems is their ability to understand and predict our preferences based on historical behavior and user patterns. By analyzing our past choices, these systems curate tailored suggestions, saving us time and effort while introducing us to content/products that align with our interests. This enhances user satisfaction and fosters discovery, introducing us to new and relevant offerings that we might not have encountered otherwise. At a high level, developers understand that these algorithms are powered by machine learning and deep learning systems (interchangeably called neural networks), but what if I tell you there is a way to build a recommendation engine without going through the pain of deploying your neural net or machine learning model? This question is specifically relevant in the context of early and mid-stage startups because they don't have tons of structured data to train their models. And as we already know, most machine learning models will not give accurate predictions without proper training data. I recently built and deployed a basic recommendation engine for a , which led to a 40% jump in our key metrics. At the time of writing this blog, the system is generating more than 30 million recommendations per month. Even though this recommendation system was built for a social network, you can apply the basic architecture to any use case, such as product recommendations, music recommendations, content recommendations on text and video platforms, or anything else. Let me start by describing the problem statement. voice-first social network Problem statement from the engineering perspective I had an extensive and because we were building the recommendation system for a product that is already used by thousands of users daily. But to keep this blog short and on point, I will write only the high-level requirements and then discuss the solution of the same. If you are building a recommendation system for your product (simple or neural net-based) and are stuck somewhere, please feel free to contact me on or and I will be more than happy to answer your questions. product requirement document subsequent engineering requirements document Twitter , Linkedin At a high level, we had the following requirements from an engineering perspective - The system should be able to capture a user's interests in the form of keywords. The system should also be able to classify the level of interest a user has with specific keywords. The system should be able to capture a user's interest in other users. It should be able to classify the level of interest a user has in content created by another user. The system should be able to generate high-quality recommendations based on a user's interests. The system should be able to ensure that the recommendations already viewed/rejected by the user shouldn't re-appear again for X number of days. The to ensure that the posts from the same creators aren't grouped on the same page. The system should try its best to ensure that if a user consumes ten posts (our page size), all of those should be from different creators. system should have logic The system should be fast. Less than 150 milliseconds of P99 latency. All the other non-functional requirements, such as high availability, scalability, security, reliability, maintainability, etc, should be fulfilled. Again, this is a highly oversimplified list of problem statements. In reality, the documents were 3000+ words long as they also covered a lot of edge cases and corner cases that can arise while integrating this recommendation engine into our existing systems. Let's move on to the solution. Solution - High-level working of the recommendation engine I will discuss the solutions to the problem one by one and then will describe the overall working of the entire system. Our first problem is capturing the user's interests and defining their interest level with a specific interest For this, we created something called a . To put it simply, a social graph stores the relationships and connections between different entities in a social network. These entities can be different users or a relationship of users with a specific interest. Social graphs are a powerful way to understand and structure the relationships within a particular system. For the sake of brevity, I will not explain the social graph in detail, but I will recommend you google it and learn more about it. Following is a simplified version of the social graph I built for our recommendation engine. social graph As you can see from the above image, we are storing a lot of information, such as the number of interactions (likes, comments, shares) and recency of these interactions (when they happened last) as relationship data between two users as well between a user and an interest. We are even storing the relationship between two different interest keywords. I used , a managed graph database by AWS, to store this social graph. You can use any other graph database, such as , etc. Amazon Neptune Neo4j, JanusGraph, ArrangoDB These interest keywords are predominantly nouns. There is a system in place that breaks down the contents of a post into these keywords(nouns). It's powered by a natural-language processing (NLP) service that uses machine learning to break text into entities, key phrases, etc. Again, you can use any managed NLP services (several available) to accomplish the same. You don't need to learn or deploy your machine-learning models! If you already understand machine learning, then you can go check . AWS comprehend; open-source NLP models as well on Huggingface Our second problem is generating high-quality recommendations based on a user's interest The following diagram is a simplified high-level representation of how the system works. While the above looks easy, there is a lot more going on at each step, and those things have to be carefully thought through and then programmed to ensure that the system is performing optimally. Let me explain step by step: Step 1 - Converting post content into vector embeddings To generate these recommendations, first, we have to convert the contents of a post into something called - . With the recent uptick in the branding of LLMs, OpenAI( the makers of ChatGPT) and , Vector embeddings are becoming an everyday term. I will not go into the details of what they are and how they work, but I highly recommend reading more about them. But generating viable candidates for a feed also has to account for things like content privacy and moderation (removing profane words, abuses, sexual content, harassment, filtering blocked users, etc). Vector embeddings Vector databases For generating the vector embeddings, you can use any prominent embedding model such as the , or any text embedding model, depending on your use case. We went with because of its friendly pricing, performance and operational ease. OpenAI embedding model Amazon titan open-source Amazon Titan Step 2 - Query the user's interest Now, this is where things get interesting. You would want to design the queries based on your specific business needs. For example, we give more weightage to the recency of engagement while querying interests than the number of engagements with a specific keyword or user. We also run multiple parallel queries to find different types of interest of the user - keyword or other user. Since we generate multiple feeds for a single user, we also run some queries promoting a specific topic according to the trend (for example, you will see many Christmas-related posts near Christmas or earthquake-related posts if some earthquake has happened). Needless to say, this topic will only come up in the query results if the user has expressed some interest in them in their journey. So, choose the logic that suits your business use case and the behavior that you want to drive and run multiple queries to get a big enough list of all the user's interests. Step 3 - Do an ANN search based on the interests found Vector databases are predominantly used for performing a particular type of search called (ANN). Again, the way you categorize various interests and whether you are doing one big ANN search or parallel difference searches should entirely be based on your use case and business requirements. I recommend doing more than cohort-based searches and then ordering the results (we will discuss this later in this blog) for the best end-user experience. What ANN search does, in this case, is find other posts on the platform that are similar (closer) to the interests of the user. Approximate nearest neighbor search Step 4 - Store the results in a cache database with ordering. Cache database because one of the problems that we need to solve is speed. We used for storing the unique IDs of the posts for a specific user. We used redis sorted sets because the order of posts in a user's feed is critical. Also, another problem that you have to solve is that the" to ensure that the posts from the same creators aren't grouped on the same page". To avoid repetition of content from the same creator, we have written a simple algorithm which ensures that if a specific creator's post is inserted at any position in a particular user's feed (sorted set), we don't insert another post from the same creator for successive ten positions (we have a page size of 10 while serving the feed to the end user, so we kept it static to avoid complexity). sorted sets redis system should have logic For deciding the order of a specific recommendation of the user, we factored in the following things - : It's determined by an arithmetic formula that takes various data points from the social graph. All of this is engagement data like the timestamp of the last likes created, number of likes created, last comment, etc. User engagement behavior is the indicator of their interest in something. The strength of the relationship with a specific interest (or another user) for this user To determine this, we have created an algorithm that takes various factors such as engagement, engagement-to-impression ratios, number of unique users who engaged, etc., to generate an engagement score of that post at a platform level. The popularity of the post on the platform: In some feeds, we prioritize popularity; in others, we prioritize the social graph. But mostly, all of them are a healthy mix of the two. How the System Works As you can see from the diagram above, the system has been intentionally kept very simple. Following is how the system works - When user A creates a post, the post service, after saving that post, triggers a pub/sub event to a queue, which is received by a background service meant for candidate generation. We use for the pub/sub functionality. Google Pub/Sub This background service receives this asynchronously and performs functionalities discussed earlier - Privacy checks, moderation checks, and keyword generation and then generates the vector embeddings and stores them in the vector database. We are using (discussed later). AstraDB as our vector database Whenever a user engages (like/comment/share, etc.) after updating our main NoSQL database, the post-service triggers a pub/sub event to the recommendation engine service. This recommendation engine service updates the graph database and then updates the recommended feed of the user in near real-time by performing the ANN search and updating the Redis database. So, the more users interact, the better the feed keeps getting. . Those checks are performed while we query the Graph database. This service also updates the engagement score asynchronously. Engagement scores are re-calculated on users viewing the post as well. There are checks to ensure that the recommendations are not biased towards a specific list of keywords Since all of the above steps are performed asynchronously behind the scenes, these computations have no impact on the end-user experience. The feed is finally served to the end user through a feed service. Since this service just performs a lookup on redis and our main NoSQL database ( ), its P99 latency is less than 110 milliseconds. Both these databases return query results in single-digit millisecond latency irrespective of scale. DyanmoDB Tools and technologies used Some services have been written in , while others have been written in (with typescript). Go programming language NodeJS We are using as our vector database. We arrived at this decision after evaluating multiple other databases, such as and . Apart from its excellent query and indexing capabilities on vector and other data types, it offers a pocket-friendly serverless pricing plan. It runs on top of a Cassandra engine, which we use as a database in several other features on our platform, and it gives a CQL query interface, which is very developer-friendly. I highly recommend trying it for your vector use cases. AstraDB by Datastax pinecone, milvus weaviate We use for our asynchronous communication because, at our current scale (few lakh total users, few thousand daily active users), it's highly cost-effective. I have run it at a scale of a few lakh users with thousands of events per second. It works well, and it's effortless to use and extend. Google pub/sub - Speed, simplicity and powerful data structure. I don't think I need to discuss why redis in 2024. Redis - Again, it is highly scalable and easy to use, and we run it in the serverless mode where, despite hundreds of thousands of queries per minute, our total bill is quite low. It also offers very powerful indexing capabilities and single-digit millisecond latency in reads and writes. DynamoDB Problems to be solved in the future As you can imagine, this same setup can be tweaked to build a basic recommendation engine for any use case. But, since ours is a social network, we will require some tweaks down the line to make this system more efficient. Machine learning/ Deep learning algorithms will be needed at the social graph level to predict the keywords and users most relevant for the user. Currently, the data set is too small to predict anything accurately as it is a very new product. However, as the data grows, we will need to replace the current simple queries and formulas with the output of machine learning algorithms. Relationships between various keywords and users must be fine-tuned and made more granular. They are at a very high level right now. But they will need to be deeper. We will need to explore the second and third-degree relationships in our graph to refine the recommendations first. We are not doing any fine-tuning in our embedding models right now. We will need to do that in the near future. End note I hope you found this blog helpful. If you have any questions, doubts or suggestions, please feel free to contact me on , or . Do share this article with your friends and colleagues. Twitter Linkedin Instagram Also published . here

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Crypto winter is over. What does the rest of the year hold? I believe 2024 is going to be a pivotal moment in blockchain finally finding mass market product fit. It’s a bit late in the year to write a prediction post, but given the movements in the crypto market, it feels timely. We were already seeing glimpses of hope in the latter half of 2023 with the price of Bitcoin beginning another ascent and some big NFT sales — perhaps most surprisingly, . thanks to Bitcoin ordinals Now, with halving imminent, with , and with the , confidence is rapidly returning. development on alternative blockchains like Solana rise of Layer 2s So, what do I mean by ? reset The product manager — and eternal optimist — in me is hopeful that, this time around, growth and adoption won’t be driven purely on hype and greed but rather on finally, living up to the promise of efficiency, ownership, and a better store of value. However, I’m also a realist. finally I recognize that, for all the breakneck speeds with which the crypto sector innovates and solves its own problems of scalability or security, it’s surprisingly slow at delivering on that initial promise. There are many excuses as to why. Regulation has often been cited as the main blocker, with . Though regulators often appear to work innovation, the reality is that regulators must defend consumer rights. If the doesn’t understand the risks, they’re the ones who are left out of pocket. And unfortunately, crypto complex — for all its promises of inclusivity, the barrier to entry remains high. regulators accused of not understanding crypto against consumer is Web3 Solves Its UX Issues So, let’s start with an obvious one. I expect that this barrier to lower even further in 2024. While I don’t mean that we’ll achieve full mass market adoption this year, we’re already seeing much easier onboarding into web3 through . wallet creation using just an email address Paired with the likelihood of another bull run, more and more people will dip their toes into the unknown — but this time, it’s going to be a smoother customer journey, which will result in more conversions. We’re a long way from when only tech natives could engage with web3. That being said, more work needs to be done to improve user experience — it’s why my job exists, after all. So here’s an easy test. Back in 2020, my then 74-year-old grandfather was asking me about Bitcoin. I thought this was a sign of mass market adoption. But now, I think the reflection of mass adoption would be if he finally tries it for himself. true Crypto Begins Delivering Value It’s our job as product managers, designers, and developers to simplify the process and clearly communicate the value. In doing so, we facilitate access for those traditionally left out of mainstream commerce — the elderly, the marginalized, and the less tech-savvy. While centralized exchanges had previously attracted these users with intuitive customer journeys, I think the have been twofold: consumers are more conscious of who’s controlling their funds, and decentralized services are more focused than ever to get user experience right. lessons of the past few years I don’t think we’re anywhere near perfect equilibrium. So, while I do foresee more cases of mismanagement in the future, I think we’re getting closer to a synergy between regulation and innovation. Mostly because I truly believe 2024 is the year crypto will finally begin delivering value. It’s stating the obvious to say that a lot of hype in the market is driven by greed. But crypto exists for a reason. There was a specific user need — a store of value — that Bitcoin originally tapped into. Other chains were built using similar technology, but for different purposes, whether that be as cross-border payments, in-game currency, micropayments, or more. Whether you’re a Bitcoin maxi or a degen, there is most likely a reason you prefer one chain over another. What I envision for 2024 is that those reasons will become more pronounced. And that depends entirely on abstraction from the technology. away Blockchain Fades Into the Background With all the focus on crypto as a medium of exchange, we often forget that the blockchain — a decentralized ledger — is actually solving non-financial problems as well. It’s being used in , in , in , and countless other industries. healthcare agriculture real estate But working in the industry, we often forget that the end user doesn’t care their pain points are being solved, what matters is that they . I really hope we’ll see more blockchain innovation in non-financial sectors in 2024, but that the story won’t be about the fact that it’s solutions, but rather how much more efficient and streamlined former processes have become. how are blockchain NFTs Figure out Digital Copyright One place we have to talk about the medium, however, is the NFT space. NFTs have , but many consumers still don’t understand them. In fact, I’ve come to realize that not many people with NFTs understand them. After all, what are they meant to give to the token holder? do already had their moment in 2021 working Like with the crypto use cases, NFTs have many variations. They can be used as , as a , or as a piece of — but when it comes to ownership, the line is blurry. Whenever we talk about web3, we talk about how web1 was , how web2 was , and how the latest iteration of the Internet is all about . provenance non-fungible community token digital property read write own NFTs are meant to be representative of this — otherwise, they’re no significant difference between a digital file and an NFT. It’s something that big NFT communities like or state outright. What’s interesting is that the same is not true for Crypto Punks, though it does appear that its are intended to give owners more monetization rights. Bored Ape Yacht Club World of Women updated licensing terms It’s also not the case in the . Purchasing an Art Blocks NFT, for instance, does not permit the token holder to sell prints — that right remains with the artist. lucrative generative art space As we move towards an increasingly phygital experience, with brands, communities, and artists looking to capture more value out of their NFTs, I believe this will become an even bigger conversation topic in 2024. I remain optimistic that there’s a way forward that ensures artists receive due compensation for their work while the NFTs remain true to the web3 ethos and enable holders to genuinely own. These predictions are, of course, in their own way, optimistic — it’s what I would like to see happen. But regardless of whether they come now or later, they are things the burgeoning industry continues to contend with. If recent market movements are anything to go by, we’re at the beginning of a new bull run, so I see 2024 as the best opportunity to learn from the past, recalibrate, and reach even greater heights in the future.

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