We connect two sides of the video game industry that are mutually beneficial: game developers and content creators

NOIZ

PARIS, France, April 10th, 2024/Chainwire/-- Sui Basecamp, the inaugural global conference for the Sui ecosystem, opened its doors today in Paris, welcoming developers and entrepreneurs from around the world as they celebrate Paris Blockchain Week. A two-day event across two venues, Sui Basecamp features speakers across web2 and web3, spanning industry verticals from payments to gaming to e-commerce to major league sports. A celebration and exploration of the latest developments in web3, attendees are invited to presentations on cryptography from some of the world’s leading technologists, fireside chats on increasing market liquidity, and panels on real-world technological use cases. Highlight announcements from the conference included: Introducing the SuiPlay0x1 – a lightweight handheld gaming device designed in collaboration with Playtron. SuiPlay0x1 runs Playtron’s device-agnostic gaming operating system, which is building up compatibility with various hardware configurations, gaming storefronts and direct-download game partners. The SuiPlay is the first handheld gaming device with native web3 capabilities and will be available in stores worldwide in 2025. First Digital Labs is launching FDUSD, the fastest-growing stablecoin in crypto, on Sui, becoming the ecosystem’s first native stablecoin. Originally deployed on Ethereum and BNB, with plans to expand to other blockchains, Sui becomes the first blockchain FDUSD has expanded to since its inception. FDUSD launched in August 2023 and has already amassed a market cap of over $3.5 billion. Over the last week, FDUSD has seen the fifth-highest trading volume across the entire industry, including over $8 billion of volume in a single day. Enoki – Mysten Labs, the original contributor to Sui, has announced the launch of the Enoki platform, the gateway to next-gen customer experiences. Inside the Enoki portal, enterprises can access, leverage, and embed public ledger solutions inside their applications, products, and services, thanks to SDKs powered by Sui’s native feature, zkLogin. Partners integrating Enoki at launch include: Bluefin: A perpetual DEX with blazing-fast settlement and a seamless trading experience. Drife: A decentralized ride-hailing platform powered by blockchain with the intent of empowering both drivers and commuters. Quantum Temple: The digital platform for immersive cultural travel. The event is a two-day affair that begins at 9:30 AM local time on Wednesday, April 10, and concludes at 4:15 PM CET on Thursday, April 11. On Wednesday night, registered guests are invited to attend the Sui Soiree, an evening of cocktails, refreshments, and dancing with a performance by German electronic music record producer, songwriter, and DJ, Boys Noize. For more information, please visit sui.io/basecamp. About Sui Sui is a first-of-its-kind Layer 1 blockchain and smart contract platform designed from the bottom up to make digital asset ownership fast, private, secure, and accessible to everyone. Its object-centric model, based on the Move programming language, enables parallel execution, sub-second finality, and rich on-chain assets. With horizontally scalable processing and storage, Sui supports a wide range of applications with unrivaled speed at low cost. Sui is a step-function advancement in blockchain and a platform on which creators and developers can build amazing, user-friendly experiences. Learn more: https://sui.io About Mysten Labs Mysten Labs is a team of leading distributed systems, programming languages, and cryptography experts whose founders were senior executives and lead architects of pioneering blockchain projects. The mission of Mysten Labs is to create foundational infrastructure for web3. Learn more: https://mystenlabs.com About First Digital Labs First Digital Labs is the brand name of FD121 Limited, a Hong Kong-registered subsidiary under the First Digital Group. First Digital Labs focuses on cutting-edge research and development, specializing in the innovation and advancement of digital assets. First Digital Labs is the issuer of the FDUSD stablecoin. To learn more about First Digital Lab, visit https://firstdigitallabs.com/. About Playtron Playtron is building a lightweight gaming OS optimized for a new generation of powerful handheld gaming PCs and beyond. Play all your games from every store: Steam, Epic, GOG, and more. Playtron's founders built Android for 1B users while simultaneously overseeing a multi-hundred-person operational system team in an attempt to unseat the incumbent mobile duopoly. Playtron is backed by Samsung Next, Polychain, Circle, Mysten Labs, Alumni Ventures and more. Playtron-powered devices will start to ship worldwide later this year, ready to onboard the next 100 million core and casual gamers. Contact Sui Foundation media@sui.io This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program . here

Gaming, Stablecoins And Product Innovation Take The Stage At Sui Basecamp

গেমিং, স্টেবলকয়েন এবং প্রোডাক্ট ইনোভেশন সুই বেসক্যাম্পে স্টেজ নেয়

Gaming, Stablecoins und Produktinnovationen stehen im Mittelpunkt des Sui Basecamps

Los juegos, las monedas estables y la innovación de productos suben al escenario en Sui Basecamp

Les jeux, les pièces stables et l'innovation produit entrent en scène au Sui Basecamp

गेमिंग, स्टेबलकॉइन और उत्पाद नवाचार सुई बेसकैंप में मंच पर हैं

ゲーム、ステーブルコイン、製品イノベーションがSui Basecampで登場

게임, 스테이블코인 및 제품 혁신이 Sui Basecamp에서 무대에 오릅니다.

Jogos, stablecoins e inovação de produtos sobem ao palco no Sui Basecamp

Игры, стейблкоины и инновации в продуктах выходят на сцену Sui Basecamp

Oyun, Stablecoinler ve Ürün İnovasyonu Sui Basecamp'ta Sahneye Çıkıyor

Trò chơi, Stablecoin và đổi mới sản phẩm chiếm ưu thế tại Sui Basecamp

游戏、稳定币和产品创新在 Sui Basecamp 上亮相

In Facebook’s recent earnings call with investors and analysts, CEO Mark Zuckerberg repeatedly mentioned the word “metaverse.” He announced his expectation for Facebook to transition into a metaverse company within the upcoming years. This is not the first time the Facebook CEO has expressed his ambitious new venture. In June, Zuckerberg announced to his employees that Facebook would go further than just building interconnected social apps. Zuckerberg’s future of the internet would be a maximalist version of Facebook, converging the worlds of socializing, work, commerce, entertainment, and more. Putting aside the number of times Zuckerberg has raised public concern, from Facebook’s data and privacy breaches to allegations that Facebook is contributing to vaccine misinformation, there is good reason for him to push Facebook towards becoming the biggest player in a new technological era - developing the metaverse. What is the Metaverse? First coined by science fiction author Neal Stephenson in his dystopian novel “Snow Crash,” the metaverse is described as a virtual shared space for people to interact using avatars. It is a virtual reality experience that, nearly three decades later, becomes an entity that every major tech company wants to commercialize. Microsoft’s CEO Satya Nadella described the metaverse “as the virtual and physical worlds converge” - a platform for simulated environments and mixed reality. Instead of attending a Zoom meeting, you can attend your meeting in your avatar form in a simulated office space, surrounded by colleagues from anywhere in the world. When the work meeting is over, you can choose to attend a virtual concert with your friends or shop for items that are only available digitally in virtual stores. Everything is interconnected on the metaverse. There is no need to hop onto a different website or application. Matthew Ball, a venture capitalist and expert on the metaverse, said this of the metaverse, “More of our lives will be online, rather than just data relating to our lives.” Of course, when so many aspects of our lives can be lived online, this naturally translates to huge economic opportunities amounting to billions. Digital advertising is expected to hit $455 billion. The market for non-fungible tokens (NFT) - digital assets that can be exchanged for real-world cash, is rising in popularity and amounts to $2.5 billion in the first quarter of 2021. Together with other fields such as gaming, the metaverse is easily worth more than a trillion. The Metaverse Is Already Manifesting While big tech companies like Facebook and Microsoft are trying to incorporate new technology to form this futuristic utopia, other companies are already ahead of the game in creating metaverse experiences, if not developing advanced software and hardware that can accommodate these experiences. Discord has grown from a social media platform for gamers to a platform for its 150 million users to converge in topic-based spaces called servers. Users can use it for various ways of interaction - chat, send and receive files, watch live streams and listen to music together. Here, we can see an early version of the metaverse, especially in the socialization aspect. In terms of software, companies like Epic Games and Nvidia Corp. are leading with technology that can run virtual world simulations allowing large multiplayer experiences. Video gamers on Fortnite, Roblox, or Animal Crossing are already experiencing metaverse experiences with the ability to create their virtual worlds. Another essential building block for the metaverse is blockchain technology. It allows a seamless, secure process when transporting assets between platforms or from the real world to the digital world. Real-world assets can be transformed into NFTs, which can be easily purchased using cryptocurrency, increasing the liquidity of these assets. Hence companies incorporating blockchain are better positioned in ushering the metaverse than traditional big names in tech that have yet to embrace blockchain. Play-to-Earn Games Are Accelerating The Revolution While the metaverse in its full form will involve cutting-edge VR and AR technology, Internet users need to see the appeal in joining platforms that will eventually form the metaverse. The pandemic has accelerated the process of making people more comfortable in spending more aspects of their lives online, but the catalyst for mainstream adoption lies in play-to-earn games. For starters, gaming has been touted as a great use case for encouraging mass adoption of blockchain technology. Blockchain is already disrupting the $176 billion industry, allowing players to enjoy more immersive experiences and establishing new norms for in-game currency and trading gaming assets. Back then, people could spend a lot of money on in-game assets but never truly own them or make money out of them. With blockchain allowing true ownership of digital assets, play-to-earn games have seen a meteoric rise in popularity. Players can earn in-game assets such as native tokens or NFTs through game participation, which can be cashed out on open marketplaces. With the pandemic causing financial difficulties for many, earning an income through play-to-earn games from the comfort of their own homes instead of working day jobs have become a reality for many players in developing countries. For example, people in the Phillippines and Vietnam have quit their jobs to focus on trading NFT pets and native tokens on the NFT pet breeding game Axie Infinity, which saw daily users reaching 800,000 in July. More people become attracted to play-to-earn games and their open economies, bringing value to games while generating personal revenue. As a result, gaming companies receive investments from various institutions and investors, which drives the current bullish market. Gaming companies are also evolving in their competition to offer more social and immersive experiences apart from games. Decentral Games - Rising Star In The Metaverse Take blockchain entertainment company Decentral Games ( ), for example. The Ethereum-based brand started as the first community-owned metaverse gaming platform and has expanded its repertoire to involve hosting regular music and art events and a thriving NFT market demand. Within a few months from its official launch, DG has secured partnerships with some of the biggest names in the entertainment industry, such as and . DG video game pioneer Atari iconic nightclub Amnesia Ibiza How did DG achieve this success?  Unlike traditional gaming models where players and the house have what DG founder Miles Anthony called an “adversarial relationship,” DG users enjoy games through a provably fair, play-to-earn model where incentives are aligned between player and house. Notably, DG is the first DAO that owns, governs, and monetizes virtual land. DG players who own DG’s native token are essentially part of DG governance, allowing them to participate in DG’s development-related decisions. Combined with the 3D environment to play in, DG provides a whole new, unparalleled gaming experience where users are empowered to “be the house” while enjoying immersive, social experiences. Users can also find employment in this virtual space starting early this year, marking DG as the first metaverse employer. Within this month alone, DG has announced the launch of - BAYC, DexTools, and Aquarium, proving its appeal among brands in building metaverse venues. Notably, DG has also developed virtual headquarters for prominent crypto projects like Polygon, Coingecko, Rarible in the metaverse. three new B2B metaverse gaming venues in partnership with popular brands DG has started tapping into the wider entertainment scene, curating unique music and arts events for its users. Thousands of users turned up for electronic artist Dillon Francis’ live set during the . On the same day, DG also hosted the much-anticipated auction of Dillon Francis’ Gerald, the pinata-themed NFTs, designed by visual artist Merda. Following the success of this event, DG has gone on to host more successful events featuring prolific names in the dance music industry, such as German producer Boys Noize and Chicago-based trio Autograf. grand opening of Atari Casino powered by DG DG utilizes sophisticated virtual infrastructure on the backend combining Ethereum, Polygon (Matic), and the Binance Smart Chain networks, ensuring scalability concerns are addressed to accommodate more users. Backed by a team of experts and strategic partnerships, DG has specialized technological expertise that allows users to benefit from faster transaction speeds and lower transaction costs. Likely even before Facebook realizes its metaverse dream, DG’s innovative contributions across several verticals make it a promising player in this race to develop the metaverse. References: https://www.cnbc.com/2021/07/26/facebook-creates-exec-team-to-work-on-metaverse.html https://www.theverge.com/22588022/mark-zuckerberg-facebook-ceo-metaverse-interview https://www.technologyreview.com/2021/03/11/1020600/facebook-responsible-ai-misinformation/ https://www.crn.com/slide-shows/cloud/ceo-satya-nadella-s-12-boldest-remarks-at-microsoft-build-2021/11 https://cointelegraph.com/news/the-metaverse-mark-zuckerberg-s-brave-new-world https://www.emarketer.com/content/worldwide-digital-ad-spending-2021 https://www.reuters.com/technology/nft-sales-volume-surges-25-bln-2021-first-half-2021-07-05/ https://www.nasdaq.com/articles/best-stocks-etf-for-the-trillion-dollar-metaverse-2021-08-16 https://newzoo.com/insights/articles/global-games-market-to-generate-175-8-billion-in-2021-despite-a-slight-decline-the-market-is-on-track-to-surpass-200-billion-in-2023/ https://kr-asia.com/what-does-axie-infinitys-meteoric-rise-tell-us-about-the-play-to-earn-game-industry https://decentral.games/blog/decentral-games-launches-three-new-partnered-venues-alongside-bored-ape-yacht-club-dextools-and-vega Disclaimer: The author of this article owns $DG tokens. This article is part of The hosted by HackerNoon in partnership with Gaming Metaverse Writing Contest . The Sandbox Submit your #gaming-metaverse story today for your chance to win up to $2000 in SAND tokens.

Manifesting The Metaverse With Play-to-Earn Games

Pirates. Bounties and bounty hunters. Nope, not your new Netflix/Hulu series but trendy blockchain/bitcoin topic. I’ve been swimming in murky waters of bounty programs for ICOs last couple of weeks. While navigation is often times hard if you navigate properly you can come across shiny genuinely amazing programs which in all reality can earn you a chunk rather than a ton of money. However, as any experienced bounty hunter will tell you, there is a lot of garbage out there. And navigating through it can be an overwhelming task. But, what are bounties? Essentially, bounty is a marketing strategy used by blockchain startups interested in stirring up communities, getting the word out about them so to speak, by investing a part of their ‘ CryptoAssets’ into communities in return for spreading the word. In simple terms, blockchain startups offer users a fair compensation for promoting and/or improving their ICO product. Users are required to perform different tasks such as social media posting/sharing etc for which they get either ICOs native token, other cryptocurrencies, or even fiat currency. Huh, what? Let’s give just a few examples shall we? TeslaWatt Bounty Program TeslaWatt main goal is to build a solar farm to provide our new and existing customers access to independent energy. For the purpose of realizing their plans, they’ve built an infrastructure for a utility token TSW that will make this happen. TeslaWatt token #TSW is an Ethereum-based token of value. As a utility token, TSW will enable holders to continuously use TeslaWatt’s solutions, as well as to trade the utilities. A SOCIAL MEDIA BOUNTY CAMPAIGN (on Twitter, Telegram, BitcoinTalk forum, Facebook and LinkedIn) is live, with the total allocation of their 250.000 TSW tokens! Find out more at the official . BitcoinTalk page here BlockScart Bounty Program BlockScart is set to be a global e-commerce marketplace powered by blockchain technology. They operate with BLXS coin and participants will get their hands on BLXS which is a Waves Sponsored Asset at a rate traded via the DEO (decentralized exchange offering)at $0.05. They are running their campaign on Telegram, Facebook, Twitter, Instagram and Reddit. Find out more at the official . BitcoinTalk page here While it is one of the best ways for blockchain startups get the word out, for those participating in the program how can you be certain this isn't a scam? While I don’t have all the answers in my experience these things work in defining the good ones from the rest:- First and foremost study the rules and the length they’ve taken to explain and promote purpose of their bounty program.- Do a research before you jump on their bandwagon. Find out everything you can that is official about the company behind bounty (headquarters address, check Google map for it, Google my Business listing, official press releases and articles etc).- Dig a bit deeper about the company and people running it and I’m not talking about their official website and soc profiles. Check out the social noizz, forum talk etc. See if there is a into what they are trying to promote.- Use their Telegram channel to get familiar with them. Ask the dumbest question you can think off and them go into tech specifics.- Check out what is the main purpose of their token. Ask around if their explanation makes sense.- you either follow or have in your community.- Check out who shared/re-twitted/posted etc. You’ll be surprised how many fake profiles do that (usually low in number of followers/friends). Once you find real profiles send them a message. Ask what they did and why. consistency of interest ASK EXPERTS Having done all that won’t make you a successful bounty hunter in an instant. But it’ll give you a nice beginners framework which can help you become one. BTW, a well-versed and competent bounty hunters are in demand right now. Seize the moment and happy hunting. Read on…the links :-) There are people who could explain things far better than me and whose posts helped me a lot. While I share experience they share the knowledge. Tokenomics: A Business Guide to Token Usage, Utility and Value The ultimate guide to ICO bounty campaigns by Pascal Thellmann Utility vs. Security Tokens: Why Not Both? How To Make Money From Crypto Bounty Programs The Complete Guide by Mindmaster If you enjoyed this story, please click the 👏 button, follow me at , and and share it to help others find it! Medium Twitter Facebook Have I missed something? Let me know. Share your thoughts in the comment section below.

Colorful World Of Bounty Programs

In this story I will go in depth and try to give you an all-around picture how we prepared for non-paid efforts in order to generate traction, exposure, grow the community and ride that wave for our SaaS tool Reportz.io Before we start… is a clean and simple KPI reporting that helps agencies, freelancers, entrepreneurs and all other save time, automate client reporting and slay mortal enemies! Reportz.io tool badasses We created it for our in-house needs since at time and all the tools we tried (and we tried them all) were not serving our needs as a full stack agency we are or were just shamelessly upselling every click on their platform — which we really didn’t like… we were serving more than 300 clients on a monthly basis Bootstrapping. Reportz is just one of our tools, and every one of them is 100% bootstrapped. We never wanted somebody to sit on our head telling us what to do, and since everything is paid from our pocket — our main strategy is to: “USE EVERY CHANNEL OF ACQUISITION WE CAN” …before jumping into burning that PPC budget. You know how it goes… We also have that situation that we are competing with some of the biggest and well-funded companies, (the main difference between us and Klipfolio is that we didn’t want that 12 million seed funding investment 😛) so we really had in order to stand a chance which you will get the opportunity to find out later in the text since I really covered it all. to focus on doing “that something” better than everyone else Pre-Preparation. The first campaign we did was on Product Hunt. After our Product Hunt launch, we acquired an initial 531 users. The campaign was executed really well, with a little help from our friend Nichole Elizabeth De Mere (you know her from GrowthHackers and Zest.is), we ended as 🏅, but in the end, less than 100 people out of those 531 became paid users, because our initial onboarding + nurturing efforts were a disaster. 💩 3 Most Upvoted Product of the day Product Hunt helped us see the issues, collect feedback and modify our onboarding. It also helped me realize that we have to before jumping into any acquisition effort create a clear vision of where we are and what are the necessities in order to reach our goals… Our onboarding was shit and we had to improve it Mapping customers journey and evaluating the pain points Creating ICP (Ideal Customer Persona) and segmenting all the activities regarding unique segments Realizing that customer success will be a crucial part of our conversion % raise since nature/functionality/complexity of the tool. For a couple of months, we were , building integrations, building relationships with partners and working on the development preparing the educational material. We wanted to , and that meant as much as possible while making useful content and shitload of other educational material in order to help our users get on-board. avoid ending in SaaS Graveyard by any means optimizing the onboarding and reducing the general complexity You don’t want to end up there… Preparation. Industry issues. As mentioned, we had to figure out that will make us stand out from the fierce competition. what is the single thing we do better Much like Shakira's hips, data doesn’t lie. , we realized that there are 5 main issues people are not happy with, regarding reporting tools: After surveying and talking with our users Complexity Prices Upsells Educational material Customer success & support How we solved that? We didn’t create predefined metrics, and instead just integrated RAW metrics everyone could recognize Our pricing model is so a user is just paying the “ ” pay per use amount of dashboard he uses on a monthly level — whether you are a freelancer or enterprise, which perfectly aligns with our pricing model We have NO upsells Everything is included, We created a strong base of educational material in various formats + we have a “Demo with Founder” option which is free for all users Every person from CEO to support manager has slots on Intercom, so if there are users that are “too much” for rookies, somebody experienced can jump in Mapping channels of promotion Traditionally, we mapped channels as: Owned Blog Newsletter Earned Facebook groups Influencers Platforms LinkedIn Quora Reddit YouTube Guest Posts Publishes Partnerships Paid Facebook Ads AdWords BingAds Quora Ads PR articles As mentioned, our main goal is to make the most out of 1. and 2. and to play smart. To make all hassle worth it, we needed a and non-paid efforts, something that will help us generate that will help us foundation for our future campaigns advocacy, receive constant feedback and make the necessary noize get the attention of big shots. It came to my mind just like that. Since I am the creator of , we knew exactly that owning an engaged community of raving fans will help us a lot. the biggest e-commerce community in the Balkan I also know that the best way to create a community is to: Make it an interesting place Make it a valuable place Find a big source of traffic that you will redirect to it Usually, we would do a as always… quick influencer campaign … but this time we needed something different. P.S. We wrote a complete “How To Article” about building a community on Reportz blog , check it out if you want to know more. AppSumo Since said “Fuck you Facebook”, and went to pursue his young-boy-dreams, AppSumo was one of the most profitable sectors of Sumo Inc. Noah Khagan Fun fact: During our AppSumo preparation, I found a case study with some interesting results I am sure you are familiar with AppSumo, but in case you don’t know, it basically is a Groupon for entrepreneurs, offering crazy discounts on various SaaS tools for their audience… Now, personally, I am not a fan of discount campaigns, but is not a bad start, I have to admit. getting in front of 1M+ entrepreneurs worldwide Introductions were made, and I ended up on a 45-minute call with Olman, Head of Business Development of the AppSummo. Everything was arranged fast since the preparations were done way before the call happened. I do. We went live on May 7th. Note: I created a dedicated post regarding our AppSumo launch, so I am not going to jump into details about AppSumo here, but… Before we go any way further you should know a thing or two about… Running a deal on AppSumo: AppSumo takes from 60%-70% of the profit They run ads, hire influencers and affiliates Most common price per coupon is $49 for LTD (lifetime deal) Average sales per campaign vary from 2.5k — 3.5k of coupons Average refund rate varies from 12–16% No question refund period lasts for 60 days You can offer a “stackable deal” so that one user can buy more of them Most of the audience are freelancers and small size teams but there are some exceptions Also, be sure to: Hire and train support people since it will get crazy Check your server status since it will crash if you don’t have enough capacity Prepare ALL the help articles, materials and handouts regarding your tool Train your support team and arm them with patience and empathy Monitor all LTD communities and engage with users, reply to questions and provide them with help Create a (scratch) public roadmap since a lot of guys will ask for it Learn about that specific audience, know their pain points and value P.S. We helped quite a few of guys land on the AppSumo after our campaign and skyrocket their product. If you want me to help you regarding AppSumo, reach me out HERE . Our stats: Coupons sold AFTER the refund rate: 5,304 Refund rate - 11.69% And with we ended as the at the moment. Reportz.io best campaign for 2018 They are talking about us… TRUE VALUE If you think that 💸 is the reason you should go to the AppSumo, you should scroll a bit up and read my AppSumo part once more… The true value of these campaigns comes from: Getting enormous exposure and credibility The chance to create a community of early adopters, advocates and affiliates that will help you execute other campaigns The chance to get testimonials and feedback about your product, integrations and feature updates …and generally ridding that wave in order to reach your higher, long-term goals. 🌊 Riding the wave 1. Getting enormous exposure and credibility ( Stats shown through Reportz.io) Creating that momentum is essential for your outreach, partnership, affiliate and influencer efforts… From May 1st to June 10th, we got… Of course I am using Reportz.io We used this exposure to “push” some other activities. My good friend Rad Basta, who is also a CEO of Reportz.io got published on Forbes on May 29th. The exposure after the AppSumo promo gave us enough credibility to outreach our B2B targets and attracted some bigger fishes… FE. Closing big deals is fun. 100 regular dashboards + my generous offer of 150 dashboards = $560 + $599=$1159 per month just from this single user…which is $13.908 per year, just from this single user… Got me into podcast with one of my favourite LinkedIn “value bombers”, Andrei Zinkevich And many other cool things… 2. Chance to create a community of early adopters, advocates and affiliates that will help you execute every other campaign. Reportz Community grew 100% organicly from 5 to 987 members. We got a lot of feedback how to improve the standard features… We used the “voice of the community” to get in touch with guys from other tools we plan integrations with… We used it to create the inital base of affiliates that will promote Reportz… And we used it as one of the main channels of communication and nurture efforts, beside newsletter. Also, durign the AppSumo launch, and post launch, we got 100+ testemonials (listing just the favourite ones)… We don’t speak about competition but our users do. We are known as generous guys… Listen to this man. Indeed. :) Listen to this man. Remember when I told you about our Industry issues… Complexity Prices Upsells Educational material Customer success & support Well, from previous screenshots you can see that we managed to overcome all of them…yet, the biggest issue was the sole complexity of the tool. That is why we still do . FREE DEMO WITH FOUNDERS And in 30 days it looked like this… In 60 days we had: 264 demoes booked 227 demos done During launch and after-launch we had: And generally, we really pivoted all of our focus on users and their learning curve reduction. Ending word. Don’t look on AppSumo as your profit source. Sure you will make some numbers, but remember that AppSumo takes the biggest part + this is the “once of” payment, which we don’t like… Instead, try to use that AppSumo brings to make your other moves work. the momentum We used AppSumo and massive impact it brings to help us execute: B2B outreach campaigns Social campaigns Approach and negotiate with inlfuencers Get PR coverage for free Approach and create initial affiliate base Grow our community Grow the newsletter and AppSumo was just the wave we needed. 🌊 If you want to give Reportz.io a spin, you are more then welcome to do so. Be sure to reach out to me and I will personally walk you through everything, show you tips and tricks and help you put that client reporting on autopilot! Cheers! 🍻

mnaHow We Acquired 7000+ Users in Less than a Month

Authors: (1) Ruohan Zhang, Department of Computer Science, Stanford University, Institute for Human-Centered AI (HAI), Stanford University & Equally contributed; zharu@stanford.edu; (2) Sharon Lee, Department of Computer Science, Stanford University & Equally contributed; zharu@stanford.edu; (3) Minjune Hwang, Department of Computer Science, Stanford University & Equally contributed; zharu@stanford.edu; (4) Ayano Hiranaka, Department of Mechanical Engineering, Stanford University & Equally contributed; zharu@stanford.edu; (5) Chen Wang, Department of Computer Science, Stanford University; (6) Wensi Ai, Department of Computer Science, Stanford University; (7) Jin Jie Ryan Tan, Department of Computer Science, Stanford University; (8) Shreya Gupta, Department of Computer Science, Stanford University; (9) Yilun Hao, Department of Computer Science, Stanford University; (10) Ruohan Gao, Department of Computer Science, Stanford University; (11) Anthony Norcia, Department of Psychology, Stanford University (12) Li Fei-Fei, 1Department of Computer Science, Stanford University & Institute for Human-Centered AI (HAI), Stanford University; (13) Jiajun Wu, Department of Computer Science, Stanford University & Institute for Human-Centered AI (HAI), Stanford University. Table of Links Abstract & Introduction Brain-Robot Interface (BRI): Background The NOIR System Experiments Results Conclusion, Limitations, and Ethical Concerns Acknowledgments & References Appendix 1: Questions and Answers about NOIR Appendix 2: Comparison between Different Brain Recording Devices Appendix 3: System Setup Appendix 4: Task Definitions Appendix 5: Experimental Procedure Appendix 6: Decoding Algorithms Details Appendix 7: Robot Learning Algorithm Details Appendix 7: Robot Learning Algorithm Details Object and skill learning details We utilize pre-trained R3M as the feature extractor. Our training procedure aims to learn a latent representation of an input image for inferring the correct object-skill pair in the given scene. The feature embedding model is a fully-connected neural network that further encodes the outputs of the foundation model. Model parameters and training hyperparameters are summarized in Table 7. Collecting human data using a BRI system is expensive. To enable few-shot learning, the feature embedding model is trained using a triplet loss [80], which operates on three input vectors: anchor, positive (with the same label as the anchor), and negative (with a different label). Triplet loss pulls inputs with the same labels together by penalizing their distance in the latent space, as well as pushes inputs with different labels away. The loss function is defined as: where a is the anchor vector, p the positive vector, n the negative vector, f the model, and α = 1 is our separation margin. We test our algorithm in the following generalization settings: Generalization test set. For position generalization, we randomize the initial positions of all objects in the scene with fixed orientation and collect 20 different trajectories. For the pose generalization, we randomize both the initial positions and orientations of all objects. Position and pose. . The context-level generalization refers to placing the target object in different environments, defined by different backgrounds, object orientations, and the inclusion of different objects in the scene. We collect 20 different trajectories with these variations. Context . The instance generalization aims to assess the model’s capability to generalize across different types of objects present in the scene. For our target task (MakePasta), we collect 20 trajectories with 20 different kinds of pasta with different shapes, sizes, and colors. Instance . To understand the separability of latent representations generated by our object-skill learning model, we visualize the 1024-dimensional final image representations using the t-SNE data visualization technique [81]. Results for the MakePasta pose generalization test set are shown in Figure 7. The model can well separate each of the different stages of the task, allowing us to retrieve the correct object-skill pair for an unseen image. Latent representation visualization One-shot parameter learning details We empirically found that using DINOv2’s ViT-B model, alongside a 75x100 feature map and a 3x3 sliding window, with cosine similarity as the distance metric, resulted in the best performance for our image resolutions. Design choices. We test the generalization ability of our algorithm on 1008 unique training and test pairs, encompassing four types of generalizations including 8 position trials, 8 orientation trials, 32 context trials, and 960 instance trials. Generalization test set The position and orientation generalizations, shown in Fig. 8 and Fig. 9 respectively, are tested in isolation, e.g. when the position is varied, the orientation is kept the same. Position and orientation The context-level generalization, shown in Fig. 10, refers to placing the target object in different environments, e.g. the training image might show the target object in the kitchen while the test image shows the target object in a workspace. Here, we allow for position and orientation to vary as well. Context To test our algorithm’s capability of instance-level generalization, shown in Fig. 11, we collected a set of four different object categories, each containing five unique object instances. Our object categories consist of mug, pen, bottle, and medicine bottle, whereas the bottle and medicine bottle categories consist of images from both the top-down and side views. We test all permutations within each object category including train and test pairs with different camera views. Here, we allow for position and orientation to vary as well. Instance We test our method against baselines on 1080 unique training and test pairs, encompassing four types of generalizations including 8 position trials, 8 orientation trials, 32 context trials, 960 instance trials, 48 trials where we vary all four generalizations simultaneously, and 24 trials from the SetTable task. Test set for comparing our method against baselines Finally, we test our algorithm’s ability to generalize when all four variables differ between the training and test image, shown in Fig. 12. Here, the only object category we use is a mug. Position, orientation, context, and instance simultaneously. This paper is under CC 4.0 license. available on arxiv

NOIR: Neural Signal Operated Intelligent Robots for Everyday Activities: Appendix 7

Authors: (1) Ruohan Zhang, Department of Computer Science, Stanford University, Institute for Human-Centered AI (HAI), Stanford University & Equally contributed; zharu@stanford.edu; (2) Sharon Lee, Department of Computer Science, Stanford University & Equally contributed; zharu@stanford.edu; (3) Minjune Hwang, Department of Computer Science, Stanford University & Equally contributed; zharu@stanford.edu; (4) Ayano Hiranaka, Department of Mechanical Engineering, Stanford University & Equally contributed; zharu@stanford.edu; (5) Chen Wang, Department of Computer Science, Stanford University; (6) Wensi Ai, Department of Computer Science, Stanford University; (7) Jin Jie Ryan Tan, Department of Computer Science, Stanford University; (8) Shreya Gupta, Department of Computer Science, Stanford University; (9) Yilun Hao, Department of Computer Science, Stanford University; (10) Ruohan Gao, Department of Computer Science, Stanford University; (11) Anthony Norcia, Department of Psychology, Stanford University (12) Li Fei-Fei, 1Department of Computer Science, Stanford University & Institute for Human-Centered AI (HAI), Stanford University; (13) Jiajun Wu, Department of Computer Science, Stanford University & Institute for Human-Centered AI (HAI), Stanford University. Table of Links Abstract & Introduction Brain-Robot Interface (BRI): Background The NOIR System Experiments Results Conclusion, Limitations, and Ethical Concerns Acknowledgments & References Appendix 1: Questions and Answers about NOIR Appendix 2: Comparison between Different Brain Recording Devices Appendix 3: System Setup Appendix 4: Task Definitions Appendix 5: Experimental Procedure Appendix 6: Decoding Algorithms Details Appendix 7: Robot Learning Algorithm Details Appendix 6: Decoding Algorithms Details For both SSVEP and MI, we select a subset of channels and discard the signals from the rest, as shown in Figure 6. They correspond to the visual cortex for SSVEP, and the motor and visual areas for MI (with peripheral areas). For muscle tension (jaw clenching), we retain all channels. . To predict the object of interest, we apply Canonical Correlation Analysis (CCA) as shown in [77] to the collected SSVEP data. As each potential object of interest is flashing at a different frequency, we are able to generate reference signals Yf for each frequency fi: SSVEP n where f is the sampling frequency and N is the number of samples. s s By calculating the maximum correlation ρfn for each frequency f used for potential objects of interest, we are then able to predict the output class by finding argmax (ρf ) and matching the result to the object of interest with that frequency. n fn n Furthermore, we are able to return a list of predicted objects of interest in descending order of likelihood by matching each object to a list of descending maximum correlations ρf . n Motor imagery. To perform MI classification, we first band-pass filter the data between 8Hz - 30Hz, as that is the frequency range that includes the µ-band and β-band signals relevant to MI. The data is then transformed using the Common Spatial Pattern (CSP) algorithm. CSP is a linear transformation technique that applies a rotation to the data to orthogonalize the components where the over-timestep variance of the data differs the most across classes. We can then use the logvariance of each time series after rotation as features and perform QDA. Thereafter, we extract features by taking the normalized variance of this transformed data (called “CSP-space data”). We then perform Quadratic Discriminant Analysis (QDA) on this data. To calculate our calibration accuracy, we perform K-fold cross validation with KCV = 4, but we use the entire calibrate dataset to fit the classifier for deployment at task-time. Facial muscle tension results in a very significant high-variance signal across almost all channels that is very detectable using simple variance-based threshold filters without having to perform any frequency filters. Recall that we record three 500ms-long trials for each class (“Rest”, “Clench”). In short, for each of the calibration time-series, we take the variance of the channel with the median variance; call this variance m. Then, we just take the mid-point between the maximum m between the rest samples, and the minimum m between the clench samples, and have this be our threshold variance level. This paper is under CC 4.0 license. available on arxiv

NOIR: Neural Signal Operated Intelligent Robots for Everyday Activities: Appendix 6

Authors: (1) Ruohan Zhang, Department of Computer Science, Stanford University, Institute for Human-Centered AI (HAI), Stanford University & Equally contributed; zharu@stanford.edu; (2) Sharon Lee, Department of Computer Science, Stanford University & Equally contributed; zharu@stanford.edu; (3) Minjune Hwang, Department of Computer Science, Stanford University & Equally contributed; zharu@stanford.edu; (4) Ayano Hiranaka, Department of Mechanical Engineering, Stanford University & Equally contributed; zharu@stanford.edu; (5) Chen Wang, Department of Computer Science, Stanford University; (6) Wensi Ai, Department of Computer Science, Stanford University; (7) Jin Jie Ryan Tan, Department of Computer Science, Stanford University; (8) Shreya Gupta, Department of Computer Science, Stanford University; (9) Yilun Hao, Department of Computer Science, Stanford University; (10) Ruohan Gao, Department of Computer Science, Stanford University; (11) Anthony Norcia, Department of Psychology, Stanford University (12) Li Fei-Fei, 1Department of Computer Science, Stanford University & Institute for Human-Centered AI (HAI), Stanford University; (13) Jiajun Wu, Department of Computer Science, Stanford University & Institute for Human-Centered AI (HAI), Stanford University. Table of Links Abstract & Introduction Brain-Robot Interface (BRI): Background The NOIR System Experiments Results Conclusion, Limitations, and Ethical Concerns Acknowledgments & References Appendix 1: Questions and Answers about NOIR Appendix 2: Comparison between Different Brain Recording Devices Appendix 3: System Setup Appendix 4: Task Definitions Appendix 5: Experimental Procedure Appendix 6: Decoding Algorithms Details Appendix 7: Robot Learning Algorithm Details Appendix 5: Experimental Procedure . In our experiments, we use the 128-channel HydroCel Geodesic SensorNet from Magstim EGI, which has sponge tips in its electrode channels. Prior to experiments, the EEG net is soaked in a solution containing dissolved conductive salt (Potassium Chloride) and baby shampoo for 15 minutes. After the soaking, the net is worn by the experiment subject, and an impedance check is done. This impedance check entails ensuring that the impedance of each channel electrode is ≤ 50.0 kΩ, by using a syringe to add more conductive fluid between the electrodes and the scalp. We then carefully put on a shower cap to minimize the drying of conductive fluid over the course of the experiment. EEG device preparation Before commencing the experiments, subjects are given instructions on how to execute the SSVEP, MI, and muscle tension (jaw-clenching) tasks. For SSVEP, they are instructed to simply focus on the flickering object of interest without getting distracted by the other objects on the screen. For MI, similar to datasets such as BCI Competition 2003 [75], and as per extensive literature review [76], we instruct subjects to either imagine continually bending their hands at the wrist (wrist dorsiflexion) or squeezing a ball for the hand actions (“Left”, “Right”), and to imagine depressing a pedal with both feet (feet dorsiflexion) for the “Legs” action. For the “Rest” class, as is common practice in EEG experiments in general, we instruct users to focus on a fixation cross displayed on the screen. Subjects were told to stick with their actions of choice throughout the experiment, for consistency. For muscle tension, subjects were told to simply clench their jaw without too much or too little effort. Instructions to subjects. . For SSVEP, subjects are told in writing on the screen to focus on the object of interest. Thereafter, a scene image of the objects with flickering masks overlaid on each object is presented, and we immediately begin recording the EEG Data over this period of time. For MI, the cues are different during calibration and task-time. During calibration, subjects are presented with a warning symbol (.) on screen for 1 second, before being presented with the symbol representing the action they are to imagine (<-: “Left”, ->: “Right”, v: “Legs”, +: “Rest”), which lasts on screen for 5500 ms. We record the latter 5000 ms of EEG data. After which, there is a randomized period of rest the lasts between 0.5 and 2 seconds, before the process repeats for another randomly chosen action class. This is done in 4 blocks of 5 trials per action, for a total of 20 trials per action. This procedure is again similar to datasets like BCI Competition 2003 [75], that use non-linguistic cues and randomization of rest / task. At task-time, similar to SSVEP, subjects are told in writing on the screen to perform MI to select a robot skill to execute. Thereafter, a written mapping of a class symbol ({<-, ->, v, +}) to skill ({pick from top, pick from side, ...}) is presented, and we begin recording EEG Data after a 2-second delay. For muscle tension, there is also a calibration phase, similar to MI, which entails collecting three 500ms-long trials for each class (“Rest”, and “Clench”) at the start of each experiment. The cues are written on the screen in words. At task time, when appropriate, written prompts are also presented on the screen (e.g. “clench if incorrect”), followed by a written countdown, after which the user has a 500ms window to clench (or not). Interface This paper is under CC 4.0 license. available on arxiv

NOIR: Neural Signal Operated Intelligent Robots for Everyday Activities: Appendix 5

Authors: (1) Ruohan Zhang, Department of Computer Science, Stanford University, Institute for Human-Centered AI (HAI), Stanford University & Equally contributed; zharu@stanford.edu; (2) Sharon Lee, Department of Computer Science, Stanford University & Equally contributed; zharu@stanford.edu; (3) Minjune Hwang, Department of Computer Science, Stanford University & Equally contributed; zharu@stanford.edu; (4) Ayano Hiranaka, Department of Mechanical Engineering, Stanford University & Equally contributed; zharu@stanford.edu; (5) Chen Wang, Department of Computer Science, Stanford University; (6) Wensi Ai, Department of Computer Science, Stanford University; (7) Jin Jie Ryan Tan, Department of Computer Science, Stanford University; (8) Shreya Gupta, Department of Computer Science, Stanford University; (9) Yilun Hao, Department of Computer Science, Stanford University; (10) Ruohan Gao, Department of Computer Science, Stanford University; (11) Anthony Norcia, Department of Psychology, Stanford University (12) Li Fei-Fei, 1Department of Computer Science, Stanford University & Institute for Human-Centered AI (HAI), Stanford University; (13) Jiajun Wu, Department of Computer Science, Stanford University & Institute for Human-Centered AI (HAI), Stanford University. Table of Links Abstract & Introduction Brain-Robot Interface (BRI): Background The NOIR System Experiments Results Conclusion, Limitations, and Ethical Concerns Acknowledgments & References Appendix 1: Questions and Answers about NOIR Appendix 2: Comparison between Different Brain Recording Devices Appendix 3: System Setup Appendix 4: Task Definitions Appendix 5: Experimental Procedure Appendix 6: Decoding Algorithms Details Appendix 7: Robot Learning Algorithm Details Appendix 4: Task Definitions For systematic evaluation of task success, we provide formal definitions of our tasks in the format of BEHAVIOR Domain Definition Language (BDDL) language [69, 71]. BDDL is a predicate logic-based language that establishes a symbolic state representation built on predefined, meaningful predicates grounded in physical states [71]. Each task is defined in BDDL as an initial and goal condition parametrizing sets of possible initial states and satisfactory goal states, as shown in the figures at the end of the appendix. Compared to scene- or pose-specific definitions which are too restricted, BDDL is more intuitive to humans while providing concrete evaluation metrics for measuring task success. This paper is under CC 4.0 license. available on arxiv

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