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Hey Hackers! On April 19th, , the creators of the widely popular game , have announced a joint venture with augmented reality firm, Naver Z, to “create a new metaverse platform under the title Migaloo”. KRAFTON PUBG: Battlegrounds Based out of South Korea, KRAFTON was founded in 2007 and describes itself as a “collective of independent game development studios bound by a passion for creating innovative and engaging entertainment experiences for gamers worldwide”. KRAFTON consists of various gaming studios including: STUDIOS, Bluehole Studio, RisingWings, Striking Distance Studios, Dreamotion, Unknown Worlds, 5minlab, Neon Giant and KRAFTON Montréal Studio. PUBG Not much information was shared about KRAFTON’s upcoming game, “Migaloo”, but what we do know is there will be a create-to-earn (C2E) system that will allow players to create in-game NFTs that can be sold and traded to other users. KRAFTON did share that Migaloo will launch “later this year”, but did not give any specific details about the release date. “The companies have invested roughly $36.8 million USD to develop the platform, which features a Create-to-Earn (C2E) system that allows content creators to build works in the metaverse that users can purchase and own using NFTs and blockchain technology. The joint venture will be based in North America, with the name to be shared once a local corporate entity has been established. The joint venture will combine KRAFTON’s experience in developing popular games with NAVER Z’s expertise in operating metaverse social networks. KRAFTON will develop sandbox tools and blockchain systems for creators, while NAVER Z will focus on service planning and partnership acquisition. The platform is set to launch later this year, and it promises to be a new metaverse platform that incentivizes creators and offers transparency in transactions and settlements.” - Statement from KRAFTON Press Release Just yesterday I wrote about announcing their with Elixir games to push into web3 and . Square Enix strategic partnership blockchain gaming Square Enix has a NFT-based game in the works, SYMBIOGENESIS, and with KRAFTON announcing their metaverse and NFT-based game, Migaloo, we now have two blockchain games being developed by major Asian gaming studios. Interestingly, I have seen a general pushback towards blockchain gaming and NFT-based games from the United States gamers. This negative sentiment towards blockchain gaming in the United States, has lead to little innovation or development from major gaming companies. In contrast, it appears to me that Asian gamers are more receptive towards NFT-based games, as we see major Asian gaming companies developing games for blockchain and investing heavily into web3. These actions lead me to believe that the Asian gaming companies clearly see the blockchain gaming market as an edge to their United States and Western competitors. It will be interesting to keep an eye on this to see if the wider gaming industry will follow the lead of the Asian gaming companies into the world of blockchain gaming. What are your thoughts on this news? Have you ever played PUBG: Battlegrounds before? Are we seeing the beginning of a huge industry shift for gaming? Will USA gaming companies follow Asia? Let me know in the comments below! Enter my on Publish0x to win yourself some free Crypto! Monthly Giveaway Check out my Socials: https://linktr.ee/zerorequiem0x See ya! :) Also published here.

The Team Behind PUBG is Launching a Metaverse Platform

El equipo detrás de PUBG está lanzando una plataforma Metaverse

L'équipe derrière PUBG lance une plateforme Metaverse

पबजी के पीछे की टीम मेटावर्स प्लेटफॉर्म लॉन्च कर रही है

PUBG の背後にあるチームがメタバース プラットフォームを立ち上げる

A equipe por trás do PUBG está lançando uma plataforma Metaverse

Đội ngũ đằng sau PUBG đang ra mắt Nền tảng Metaverse

PUBG 背后的团队正在推出一个 Metaverse 平台

This paper is available on under CC 4.0 license. Arxiv Authors: (1) Md Masud Rana, Department of Mathematics, University of Kentucky; (2) Duc Duy Nguyen, Department of Mathematics, University of Kentucky & ducnguyen@uky.edu. Table of Links Abstract & Introduction Datasets and Results Methods Conclusion, Data and Software Availability, Competing interests, Acknowledgments & References 4 Conclusion The study of protein-protein interactions (PPIs) and the prediction of mutation-induced binding free energy changes are of great importance in understanding the molecular basis of biological processes. The application of geometric graph theory and atom-level graph coloring techniques provides a powerful framework for analyzing biomolecules and capturing their intricate relationships. By utilizing the concept of geometric subgraphs and constructing multi-scale weighted colored geometric subgraphs (MWCGS), we can effectively represent the structural and functional properties of PPIs. The site-specific MWCGS features allow us to extract meaningful patterns and characteristics, shedding light on the effects of mutations and the underlying molecular interactions. In this work, we developed a mutation-induced binding free energy change predictor, called GGL-PPI, by incorporating site-specific MWCGS features for PPIs and gradient-boosting trees. Our method demonstrates superior performance compared to existing methods. The model was validated on three datasets: AB-Bind S645, SKEMPI 1.0 S1131, and SKEMPI 2.0 S4169 and S8338, showcasing its robustness and effectiveness. Furthermore, GGL-PPI was evaluated on a blind test set, the Ssym dataset. To prevent data leakage between the test and training sets, the model was trained on a homology-reduced balanced training set Q3488. This approach ensures the reliability and fairness of the evaluation process. GGL-PPI exhibits the most unbiased and superior performance in predicting binding free energy changes for both direct and reverse mutations, outperforming other existing methods, particularly for reverse mutations. Overall, the results highlight the potential of the GGL-PPI approach in accurately predicting mutation-induced binding free energy changes in protein-protein interactions, providing valuable insights into the molecular mechanisms underlying protein-protein interactions and facilitating drug design and discovery efforts. 5 Data and Software Availability The source code is available at Github: https://github.com/NguyenLabUKY/GGL-Mutation. 6 Competing interests No competing interest is declared. 7 Acknowledgments This work is supported in part by funds from the National Science Foundation (NSF: # 2053284, # 2151802, and # 2245903), and the University of Kentucky Startup Fund. References [1] Dana Chuderland and Rony Seger. Protein-protein interactions in the regulation of the extracellular signal-regulated kinase. Mol. Biotechnol., 29:57–74, 2005. [2] Harry Jubb, Tom L Blundell, and David B Ascher. Flexibility and small pockets at protein–protein interfaces: new insights into druggability. Prog. Biophys. Mol., 119(1):2–9, 2015. [3] Mileidy W Gonzalez and Maricel G Kann. Chapter 4: Protein interactions and disease. PLoS Comput. Biol., 8(12):e1002819, 2012. [4] Cunliang Geng, Li C Xue, Jorge Roel-Touris, and Alexandre MJJ Bonvin. Finding the δδg spot: are predictors of binding affinity changes upon mutations in protein–protein interactions ready for it? Wiley Interdiscip. Rev. Comput. Mol. Sci., 9(5):e1410, 2019. [5] Rocco Moretti, Sarel J Fleishman, Rudi Agius, Mieczyslaw Torchala, Paul A Bates, Panagiotis L Kastritis, Joao PGLM Rodrigues, Mika¨el Trellet, Alexandre MJJ Bonvin, Meng Cui, et al. Community-wide evaluation of methods for predicting the effect of mutations on protein–protein interactions. 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Proteins: Structure, Function, and Bioinformatics, 79(3):830–838, 2011. [19] Brian J Bender, Alberto Cisneros III, Amanda M Duran, Jessica A Finn, Darwin Fu, Alyssa D Lokits, Benjamin K Mueller, Amandeep K Sangha, Marion F Sauer, Alexander M Sevy, et al. Protocols for molecular modeling with rosetta3 and rosettascripts. Biochemistry, 55(34):4748–4763, 2016. [20] Kurt S Thorn and Andrew A Bogan. Asedb: a database of alanine mutations and their effects on the free energy of binding in protein interactions. Bioinformatics, 17(3):284–285, 2001. [21] MD Shaji Kumar and M Michael Gromiha. Pint: protein–protein interactions thermodynamic database. Nucleic Acids Res., 34(suppl 1):D195–D198, 2006. [22] MD Shaji Kumar, K Abdulla Bava, M Michael Gromiha, Ponraj Prabakaran, Koji Kitajima, Hatsuho Uedaira, and Akinori Sarai. Protherm and pronit: thermodynamic databases for proteins and protein–nucleic acid interactions. Nucleic Acids Res., 34(suppl 1):D204–D206, 2006. [23] Iain H Moal and Juan Fern´andez-Recio. Skempi: a structural kinetic and energetic database of mutant protein interactions and its use in empirical models. Bioinformatics, 28(20):2600–2607, 2012. [24] Justina Jankauskait˙e, Brian Jim´enez-Garc´ıa, Justas Dapk¯unas, Juan Fern´andez-Recio, and Iain H Moal. Skempi 2.0: an updated benchmark of changes in protein–protein binding energy, kinetics and thermodynamics upon mutation. Bioinformatics, 35(3):462–469, 2019. [25] Sarah Sirin, James R Apgar, Eric M Bennett, and Amy E Keating. Ab-bind: antibody binding mutational database for computational affinity predictions. Protein Sci., 25(2):393–409, 2016. [26] Cunliang Geng, Anna Vangone, and Alexandre MJJ Bonvin. Exploring the interplay between experimental methods and the performance of predictors of binding affinity change upon mutations in protein complexes. Protein Eng. Des. Sel., 29(8):291–299, 2016. [27] Sherlyn Jemimah, K Yugandhar, and M Michael Gromiha. Proximate: a database of mutant protein–protein complex thermodynamics and kinetics. Bioinformatics, 33(17):2787–2788, 2017. [28] Douglas EV Pires, David B Ascher, and Tom L Blundell. mcsm: predicting the effects of mutations in proteins using graph-based signatures. Bioinformatics, 30(3):335–342, 2014. [29] Cunliang Geng, Anna Vangone, Gert E Folkers, Li C Xue, and Alexandre MJJ Bonvin. isee: Interface structure, evolution, and energy-based machine learning predictor of binding affinity changes upon mutations. Proteins: Structure, Function, and Bioinformatics, 87(2):110–119, 2019. [30] Sherlyn Jemimah, Masakazu Sekijima, and M Michael Gromiha. Proaffimuseq: sequence-based method to predict the binding free energy change of protein–protein complexes upon mutation using functional classification. Bioinformatics, 36(6):1725–1730, 2020. [31] Guangyu Zhou, Muhao Chen, Chelsea JT Ju, Zheng Wang, Jyun-Yu Jiang, and Wei Wang. Mutation effect estimation on protein–protein interactions using deep contextualized representation learning. NAR genom. bioinform., 2(2):lqaa015, 2020. [32] Alexey Strokach, Tian Yu Lu, and Philip M Kim. Elaspic2 (el2): combining contextualized language models and graph neural networks to predict effects of mutations. J. Mol. Biol., 433(11):166810, 2021. [33] Xianggen Liu, Yunan Luo, Pengyong Li, Sen Song, and Jian Peng. Deep geometric representations for modeling effects of mutations on protein-protein binding affinity. PLoS Comput. Biol., 17(8):e1009284, 2021. [34] Piero Fariselli, Pier Luigi Martelli, Castrense Savojardo, and Rita Casadio. Inps: predicting the impact of nonsynonymous variations on protein stability from sequence. Bioinformatics, 31(17):2816–2821, 2015. [35] Grant Thiltgen and Richard A Goldstein. Assessing predictors of changes in protein stability upon mutation using self-consistency. PLoS One, 7(10):e46084, 2012. [36] Fabrizio Pucci, Katrien V Bernaerts, Jean Marc Kwasigroch, and Marianne Rooman. Quantification of biases in predictions of protein stability changes upon mutations. Bioinformatics, 34(21):3659–3665, 2018. [37] Dinara R Usmanova, Natalya S Bogatyreva, Joan Ari˜no Bernad, Aleksandra A Eremina, Anastasiya A Gorshkova, German M Kanevskiy, Lyubov R Lonishin, Alexander V Meister, Alisa G Yakupova, Fyodor A Kondrashov, et al. Self-consistency test reveals systematic bias in programs for prediction change of stability upon mutation. Bioinformatics, 34(21):3653–3658, 2018. [38] Duc D Nguyen, Tian Xiao, Menglun Wang, and Guo-Wei Wei. Rigidity strengthening: A mechanism for protein– ligand binding. J. Chem. Inf. Model., 57(7):1715–1721, 2017. [39] Md Masud Rana and Duc Duy Nguyen. Geometric graph learning with extended atom-types features for proteinligand binding affinity prediction. Comput. Biol. Med., 164:107250, 2023. [40] Jian Jiang, Rui Wang, and Guo-Wei Wei. Ggl-tox: geometric graph learning for toxicity prediction. J. Chem. Inf. Model., 61(4):1691–1700, 2021. [41] Menglun Wang, Zixuan Cang, and Guo-Wei Wei. A topology-based network tree for the prediction of protein–protein binding affinity changes following mutation. Nat. Mach. Intell., 2(2):116–123, 2020. [42] Duan Chen, Zhan Chen, Changjun Chen, Weihua Geng, and Guo-Wei Wei. Mibpb: a software package for electrostatic analysis. J. Comput. Chem., 32(4):756–770, 2011. [43] Xiang Liu, Huitao Feng, Jie Wu, and Kelin Xia. Hom-complex-based machine learning (hcml) for the prediction of protein–protein binding affinity changes upon mutation. J. Chem. Inf. Model., 62(17):3961–3969, 2022. [44] Douglas EV Pires and David B Ascher. mcsm-ab: a web server for predicting antibody–antigen affinity changes upon mutation with graph-based signatures. Nucleic Acids Res., 44(W1):W469–W473, 2016. [45] Peng Xiong, Chengxin Zhang, Wei Zheng, and Yang Zhang. Bindprofx: assessing mutation-induced binding affinity change by protein interface profiles with pseudo-counts. J. Mol. Biol., 429(3):426–434, 2017. [46] Quanya Liu, Peng Chen, Bing Wang, Jun Zhang, and Jinyan Li. dbmpikt: a database of kinetic and thermodynamic mutant protein interactions. BMC bioinformatics, 19(1):1–7, 2018. [47] Bian Li, Yucheng T Yang, John A Capra, and Mark B Gerstein. Predicting changes in protein thermodynamic stability upon point mutation with deep 3d convolutional neural networks. PLoS Comp. Bio., 16(11):e1008291, 2020. [48] Lijun Quan, Qiang Lv, and Yang Zhang. Strum: structure-based prediction of protein stability changes upon single-point mutation. Bioinformatics, 32(19):2936–2946, 2016. [49] Leo Breiman. Random forests. Machine learning, 45:5–32, 2001. [50] Corinna Cortes and Vladimir Vapnik. Support-vector networks. Machine learning, 20:273–297, 1995. [51] Duc Duy Nguyen, Kaifu Gao, Menglun Wang, and Guo-Wei Wei. 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PPIs and the Prediction of Mutation-Induced Binding Free Energy Changes: What It All Means

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Scaling SpotHero and the Future of Mobility

Uber, Lyft, Waze, and DiDi are not the only one. From London, Paris, and New York, to China, Israel, and Estonia, these companies are revolutionizing the way we move around. Whether it’s about commuting to work, traveling across countries and long distances, biking around, or just moving from point A to point B in your city, trust and data seem to be key the key factors in moving the industry forward. VIA Via ( ) is trying to build the most powerful on-demand transit systems in the world. The company also partners extensively with public transit agencies, making its proprietary software available to them — like Capital Metro in Austin, Texas, Keolis in France, and Arriva in the UK. According to the company: “Using Via, a ride booked on your phone costs little more than the bus.” ridewithvia.com Via is on-demand transit on a mass scale; it’s transportation that’s friendly to our planet. The app is currently available in New York City, Chicago, and Washington DC, and the company is aggressively expanding nationally — eyeing large markets like Austin, Boston, Los Angeles, Philadelphia, and San Francisco — and abroad, where is focusing on cities like Berlin, London, Rome, Sau Paolo, and Toronto. Via is headquartered in New York City. Daniel Ramot, co-founder and CEO of Via. CITYMAPPER C ( ) has been evolving from being an app that empowers data to make commuting and moving around cities easier to developing transportation services. The company has recently launched “a social hyper-local multi-passenger pooled vehicle” and private bus services in London to cover what its data flagged as under-serviced public transport routes. itymapper citymapper.com Cities are complicated. We use the power of mobile and open transport data to help humans survive and master them. The Citymapper platform, which also allows you to book services like Uber and Mobike, is available in almost . 40 cities around the world Citymapper is headquartered in London, UK. Azmat Yusuf, founder and CEO of Citymapper, at at Techcrunch Disrupt London 2016. BLABLACAR B ( ) is a community marketplace that connects drivers with empty seats to passengers looking for a long-distance ride. According to the company: “Over 12 million people use BlaBlaCar every quarter creating an entirely new, people powered, transport network.” laBlaCar blablacar.com With a dedicated customer service, a state of the art web and mobile platform, and a fast-growing community of users, BlaBlaCar is making travel social, money-saving and more efficient for millions of members. The platform counts over 45 million members in 22 countries around the world. BlaBlaCar is headquartered in Paris, France. TAXIFY T ( ) is a fast-growing transport technology company in Europe, the Middle East, and Africa, with more than 3 million users in 20 countries. According to the company, whose app allows users “to book convenient and reliable rides at the tap of a button,” both “taxi and private driver companies join Taxify to get more customers and benefit from our market leading transport technology platform.” axify taxify.eu Taxify is literally changing how millions of people move by making on-demand transport affordable in tens of cities around the world. “We wanted to become taxi aggregator at first — we later turned it into the ride-hailing app we know today,” said co-founder Martin Villig in an interview last June. Taxify has more than 25,000 drivers in over 20 countries. Not only the company large cities like London, Paris, and Mexico City, but its platform is also available in smaller cities like Budapest, Malta, Belgrade, and even in Riyadh and Baghdad. services Taxify is headquartered in Tallinn, Estonia. , Taxify’s co-founder and chief executive officer, discusses the company’s business model with Bloomberg’s Caroline Hyde on “Bloomberg Technology.” Markus Villig DRIVY Drivy ( ) is a marketplace for car rental. The company’s platform “lets you hire a car from among the thousands parked nearby, providing a cheaper, quicker car rental service.” drivy.com Martin Villig We want to create an on-demand car service that is much more efficient than owning a car. According to Car Magazine, the platform counts more than “40,000 cars across France, Germany, Spain, Belgium, and Austria and a million members.” Drivy is headquartered in Paris, France. Paulin Dementhon, co-founder and CEO of Drivy, at NOAH16 in London. OFO AND MOBIKE As China’s bike-sharing startups Ofo and Mobike are agressively expanding into overseas markets, rumors of the two companies merging are mounting, to create “a single dominant player in the fast-growing business” worth more than $4 billion, as . Bloomberg reports Both Ofo and Mobike allow users to unlock their bikes off the street — instead of using dock systems like more traditional bike-sharing services — using just a smartphone and electronic wallet, ride them to their destination, and then dump them. “Mobike is the clear leader in the global bikesharing industry, supporting 30 million rides in 180 cities around the world every single day,” the company told Bloomberg. Ofo has said it plans to have 20 million bikes in 20 countries by the end of the year. Both companies have recently launched in the US and Europe. Ofo and Mobike are both headquartered in Beijing, China. SCOOT S ( ) operates a fleet of “shared, smartphone-enabled, electric, Vespa-style scooters” currently available in San Francisco. According to the company, the iconic red electric scooters are “expanding beyond San Francisco and choosing partners to bring the future of transportation to new cities.” One of the cities is Barcelona, Spain, where the company is currently hiring a fleet manager. coot Networks scoot.co Scoots are the perfect combination of speed, affordability, convenience, safety, and sustainability for the billions of trips that take place everyday in cities around the world. , the electric scooter rental service has been used by almost 50,000 users since it launched in 2012, and has logged more than “1,000,000 miles scooted,” . Keating has recently announced a partnership with to bring the automaker’s yet-to-be-released small electric car with swappable batteries to San Francisco. According to Scoot founder and CEO Michael Keating as of last year Chinese automotive startup CHJ Scoot is headquartered in San Francisco, CA. CHARIOT C ( ) is an on-demand shuttle service. According to the company, its mission “is to become the world’s first profitable mass transit service by crowdsourcing a network of commuter shuttle routes that provides a fast, reliable and affordable service using new Ford Transit 15-passenger vans.” hariot chariot.com We’re focused on creating the world’s first self-sustainable mass-transit system that relieves congestion while offering a comfortable, personalized commuting experience. Bought by Ford in 2016, Chariot operates its own fleet of on-demand vans in the San Francisco Bay Area, as well as Seattle, New York, and Austin. Los Angeles is among the future markets its targeting, as are international cities like London. Chariot is headquartered in San Francisco, CA. , founder and CEO of Chariot, at SXSW. Ali Vahabzadeh GRAB Grab ( ) started as a ride-hailing platform in Southeast Asia but has extended its services to include private car services, motorcycle taxis, carpooling, delivery, and mobile payments. The company says: “Working with governments, drivers, passengers, and the community, we aim to unlock the true potential of the region by solving problems that hinder progress.” grab.com We use data and technology to improve everything from transportation to payments across a region of more than 620 million people. The platform claims more than 700,000 drivers across around 40 cities in Singapore, Indonesia, the Philippines, Malaysia, Thailand, Vietnam, and Myanmar. Grab is headquartered in Singapore. GETT Gett ( ) is a cab-hailing startup with a mission “to build a transportation service loved for its quality.” gett.com We’re a global ride sharing app built on a simple idea — if you treat drivers better, they will treat riders better. Gett is in more than 100 cities in four countries; the UK, the US, Israel, and Russia. According to the company, “in London alone, nearly half of all black cabs run on Gett,” while in New York City it operates through its subsidiary Juno, and “on-track to become the second-largest player in that market.” Gett’s recent partnership with Carey International, a chauffeur and logistics business, will grow its market to 1,000 cities globally, including 100 more in the US and Europe. The company is headquartered in Tel Aviv, Israel.

11 companies disrupting the transportation industry

A while ago I installed Steempress on my WordPress blog on http://localhost, and Simply Static to export my WordPress site as a static site to local drive and the git commit it to my github.io repository. Lo and behold, after originally thinking that Steempress wasn’t working, it appears it is! I’ve even included the dblog.io dblog tag, which means they appear here: The only issue is that I have to replace the images from localhost to dunncreativess.github.io. Small price to pay! If I were to instead host my WordPress somewhere in the cloud (even on a port other than 80/443), it would work. Now, $0.06 USD in crypto over 7 posted articles might not sound like much – but in an adless presentation that solves the monetization-without-bugging-people sense, I’m now being rewarded by all these people voting on my content instead of a centralized force like Adsense and you’ll notice that my github.io and my hackernoon don’t share ads! It’s also continuously improving.. that $0.04 is for the latest article, meaning I’m gaining momentum. I mentioned the benefits of DPoS at length to the folks at Hacker Noon when they were dreaming up HN 2.0. Monetization is key. Incentivization is what rules the internet and any publication platform should be centered around ‘how do we turn content into $.’ I’ve said it before and I’ll say it again that we need to consider tokenization and blockchain in order to put the ability to upvote/downvote content (self regulate and moderate) while also allowing shareholders and prolific content producers to have the most influence on the monetary rewards for contributing authors. While Steem appears to be failing because of a misbalance in currencies producing bidbots and scammy 115% returns on promotion budgets, there are alternative sidechains to consider that did away with the SBD concept and pay everything out as VESTed income, so that people are – in turn – vested into the success of the platform. Check and . smoke.io whaleshares.io I truly believe in DPoS as a technology – although with a few tweaks it could serve far better than it does on . steemit.com When someone shares or upvotes a story, if it then becomes popular and earns a ton of rewards through the social algorithm % of inflation, that person who shared it to get it popular in the first place earns a % of that social % as a curator – as do all the other curators, weighted by when they expressed interest. This is important for a few reasons. .first, as your most prolific writers along with the team and the stakeholders would be the biggest currency holders, they can do a lot of good by spending time (or automation ways to) upvote posts they like, and get exposure to users or groups of users they think deserve it – you only get a certain number of upvotes/downvotes per day, which you can increase or decrease by increasing or decreasing the ‘strength’ of your vote (out of 100%). Second – these large token holders would also be paramount in fighting spam and self-regulating the environment. If there are people drawn to the site with the gleam in their eye of earning crypto by regurgitating content or plagiarizing, these highly vested people can ‘downvote’ said spammy posts and undo any earnings they may have accumulated – to the point that their post gets a negative up/downvote ratio and can be taken off of web + mobile + whatever UI interfaces to the chain. In short, you gain currency by sharing, creating, or upvoting content… . proportional to your VESTed interest in the platform. Moreover, there can be other % uses for the inflation of the currency that can do a lot of good – one of the integral parts of DPoS is the ‘delegated’ part where there are many nodes hosted by many people, and they can acquire votes from users that are weighted based on how vested they are into the system as a whole. Some of these people (usually the top 21) will earn a % of the inflation pie just for helping produce blocks – but how they accumulate votes is the important part. They must continually deliver value and hold the network’s priorities at heart in order to outperform those other contenders – which is good for the network. Lastly, there can be a ‘team’ or ‘reserve’ % of inflation that can be used to continually fundraise based on the current value of the coin. While everyone is earning via social algos or by being a witness, the team can have a source of continual income with which to spend on the project or improving various aspects to it. I know that HN in the past has said one of the advantages to HN 2.0 will be the ability to publish without the hassle of ads… and a DPoS-driven network is one way to ensure that HN will never have to sustain itself by running ads. If the team creates wealth through inflation and the community drives the value of that currency upwards (when really the token came from almost nowhere and represents no legal obligation) it’s basically free money… for everyone  – while empowering those with faith and trust in the new platform to exert more influence by buying more and VESTing more. @David @Dane It opens a whole lot of other doors, too. How do we add ‘real value’ to that coin? What sorts of services or products can we sell as a group or as the team that aligns the value of the coin to something? Can we create a distributed network of writers that are vouched by community consensus via a new kind of Blockchain reputation system that doesn’t yet exist in Blockchain content networks (aside from their exponential ‘rating’ applied to accounts which can be fabricated if you buy enough attention for your posts), and then pitch this new marketplace of content creators at brands and agencies for exposure and so everyone can win – while having them pay in the currency, which drives value? Swag stores? Can we set it up so that content creators on the network can hustle out communications to their subscriber list to these brands and agencies? What sort of services can we come up with that are ‘out of the box’ and apply to the hacker community? I agree wholeheartedly the incentives need to be aligned. I think a good step in the right direction would be to move rewards from liquid to VESTed amounts, which means that those who are looking for a ‘quick buck’ will be less likely to spam up the ecosystem. I really shouldn’t have said ‘free money,’ and you’re right that it’d have a huge impact on the project and the value of the coin could potentially devour what we’re trying to achieve – ‘Sure, the upside of a token is very high…but the downside is also very low.’ this is absolutely true! *** See this post on dunncreativess.github.io

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