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Wives and Daughters by Elizabeth Cleghorn Gaskell, is part of the HackerNoon Books Series. You can jump to any chapter in this book . MRS. GIBSON'S LITTLE DINNER. here CHAPTER XXIV. MRS. GIBSON'S LITTLE DINNER. All this had taken place before Roger's first meeting with Molly and Cynthia at Miss Brownings'; and the little dinner on the Friday at Mr. Gibson's, which followed in due sequence. Mrs. Gibson intended the Hamleys to find this dinner pleasant; and they did. Mr. Gibson was fond of the two young men, both for their parents' sake and their own, for he had known them since boyhood; and to those whom he liked Mr. Gibson could be remarkably agreeable. Mrs. Gibson really gave them a welcome—and cordiality in a hostess is a very becoming mantle for any other deficiencies there may be. Cynthia and Molly looked their best, which was all the duty Mrs. Gibson absolutely required of them, as she was willing enough to take her full share in the conversation. Osborne fell to her lot, of course, and for some time he and she prattled on with all the ease of manner and commonplaceness of meaning which go far to make the "art of polite conversation." Roger, who ought to have made himself agreeable to one or the other of the young ladies, was exceedingly interested in what Mr. Gibson was telling him of a paper on comparative osteology in some foreign journal of science, which Lord Hollingford was in the habit of forwarding to his friend the country surgeon. Yet, every now and then while he listened, he caught his attention wandering to the face of Cynthia, who was placed between his brother and Mr. Gibson. She was not particularly occupied with attending to anything that was going on; her eyelids were carelessly dropped, as she crumbled her bread on the tablecloth, and her beautiful long eyelashes were seen on the clear tint of her oval cheek. She was thinking of something else; Molly was trying to understand with all her might. Suddenly Cynthia looked up, and caught Roger's gaze of intent admiration too fully for her to be unaware that he was staring at her. She coloured a little; but, after the first moment of rosy confusion at his evident admiration of her, she flew to the attack, diverting his confusion at thus being caught, to the defence of himself from her accusation. "It is quite true!" she said to him. "I was not attending: you see I don't know even the A B C of science. But, please, don't look so severely at me, even if I am a dunce!" "I didn't know—I didn't mean to look severely, I am sure," replied he, not knowing well what to say. "Cynthia is not a dunce either," said Mrs. Gibson, afraid lest her daughter's opinion of herself might be taken seriously. "But I have always observed that some people have a talent for one thing and some for another. Now Cynthia's talents are not for science and the severer studies. Do you remember, love, what trouble I had to teach you the use of the globes?" "Yes; and I don't know longitude from latitude now; and I'm always puzzled as to which is perpendicular and which is horizontal." "Yet, I do assure you," her mother continued, rather addressing herself to Osborne, "that her memory for poetry is prodigious. I have heard her repeat the 'Prisoner of Chillon' from beginning to end." "It would be rather a bore to have to hear her, I think," said Mr. Gibson, smiling at Cynthia, who gave him back one of her bright looks of mutual understanding. "Ah, Mr. Gibson, I have found out before now that you have no soul for poetry; and Molly there is your own child. She reads such deep books—all about facts and figures: she'll be quite a blue-stocking by-and-by." "Mamma," said Molly, reddening, "you think it was a deep book because there were the shapes of the different cells of bees in it! but it was not at all deep. It was very interesting." "Never mind, Molly," said Osborne. "I stand up for blue-stockings." "And I object to the distinction implied in what you say," said Roger. "It was not deep, , it was very interesting. Now, a book may be both deep and interesting." ergo "Oh, if you are going to chop logic and use Latin words, I think it is time for us to leave the room," said Mrs. Gibson. "Don't let us run away as if we were beaten, mamma," said Cynthia. "Though it may be logic, I, for one, can understand what Mr. Roger Hamley said just now; and I read some of Molly's books; and whether it was deep or not I found it very interesting—more so than I should think the 'Prisoner of Chillon' now-a-days. I've displaced the Prisoner to make room for Johnnie Gilpin as my favourite poem." "How could you talk such nonsense, Cynthia!" said Mrs. Gibson, as the girls followed her upstairs. "You know you are not a dunce. It is all very well not to be a blue-stocking, because gentle-people don't like that kind of woman; but running yourself down, and contradicting all I said about your liking for Byron, and poets and poetry—to Osborne Hamley of all men, too!" Mrs. Gibson spoke quite crossly for her. "But, mamma," Cynthia replied, "I am either a dunce, or I am not. If I am, I did right to own it; if I am not, he's a dunce if he doesn't find out I was joking." "Well," said Mrs. Gibson, a little puzzled by this speech, and wanting some elucidatory addition. "Only that if he's a dunce his opinion of me is worth nothing. So, any way, it doesn't signify." "You really bewilder me with your nonsense, child. Molly is worth twenty of you." "I quite agree with you, mamma," said Cynthia, turning round to take Molly's hand. "Yes; but she ought not to be," said Mrs. Gibson, still irritated. "Think of the advantages you've had." "I'm afraid I had rather be a dunce than a blue-stocking," said Molly; for the term had a little annoyed her, and the annoyance was rankling still. "Hush; here they are coming: I hear the dining-room door! I never meant you were a blue-stocking, dear, so don't look vexed.—Cynthia, my love, where did you get those lovely flowers—anemones, are they? They suit your complexion so exactly." "Come, Molly, don't look so grave and thoughtful," exclaimed Cynthia. "Don't you perceive mamma wants us to be smiling and amiable?" Mr. Gibson had had to go out to his evening round; and the young men were all too glad to come up into the pretty drawing-room; the bright little wood-fire; the comfortable easy-chairs which, with so small a party, might be drawn round the hearth; the good-natured hostess; the pretty, agreeable girls. Roger sauntered up to the corner where Cynthia was standing, playing with a hand-screen. "There is a charity ball in Hollingford soon, isn't there?" asked he. "Yes; on Easter Tuesday," she replied. "Are you going? I suppose you are?" "Yes; mamma is going to take Molly and me." "You will enjoy it very much—going together?" For the first time during this little conversation she glanced up at him—real honest pleasure shining out of her eyes. "Yes; going together will make the enjoyment of the thing. It would be dull without her." "You are great friends, then?" he asked. "I never thought I should like any one so much,—any girl I mean." She put in the final reservation in all simplicity of heart; and in all simplicity did he understand it. He came ever so little nearer, and dropped his voice a little. "I was so anxious to know. I am so glad. I have often wondered how you two were getting on." "Have you?" said she, looking up again. "At Cambridge? You must be very fond of Molly!" "Yes, I am. She was with us so long; and at such a time! I look upon her almost as a sister." "And she is very fond of all of you. I seem to know you all from hearing her talk about you so much.—All of you!" said she, laying an emphasis on "all" to show that it included the dead as well as the living. Roger was silent for a minute or two. "I didn't know you, even by hearsay. So you mustn't wonder that I was a little afraid. But as soon as I saw you I knew how it must be; and it was such a relief!" "Cynthia," said Mrs. Gibson, who thought that the younger son had had quite his share of low, confidential conversation, "come here, and sing that little French ballad to Mr. Osborne Hamley." "Which do you mean, mamma? 'Tu t'en repentiras, Colin?'" "Yes; such a pretty, playful little warning to young men," said Mrs. Gibson, smiling up at Osborne. "The refrain is— Tu t'en repentiras, Colin, Tu t'en repentiras, Car si tu prends une femme, Colin, Tu t'en repentiras. The advice may apply very well when there is a French wife in the case; but not, I am sure, to an Englishman who is thinking of an English wife." This choice of a song was exceedingly , had Mrs. Gibson but known it. Osborne and Roger knowing that the wife of the former was a Frenchwoman, and, conscious of each other's knowledge, felt doubly awkward; while Molly was as much confused as though she herself were secretly married. However, Cynthia carolled the saucy ditty out, and her mother smiled at it, in total ignorance of any application it might have. Osborne had instinctively gone to stand behind Cynthia, as she sate at the piano, so as to be ready to turn over the leaves of her music if she required it. He kept his hands in his pockets and his eyes fixed on her fingers; his countenance clouded with gravity at all the merry quips which she so playfully sang. Roger looked grave as well, but was much more at his ease than his brother; indeed, he was half-amused by the awkwardness of the situation. He caught Molly's troubled eyes and heightened colour, and he saw that she was feeling this more seriously than she needed to do. He moved to a seat by her, and half whispered, "Too late a warning, is it not?" mal-àpropos contretemps Molly looked up at him as he leant towards her, and replied in the same tone—"Oh, I am so sorry!" "You need not be. He won't mind it long; and a man must take the consequences when he puts himself in a false position." Molly could not tell what to reply to this, so she hung her head and kept silence. Yet she could see that Roger did not change his attitude or remove his hand from the back of his chair, and, impelled by curiosity to find out the cause of his stillness, she looked up at him at length, and saw his gaze fixed on the two who were near the piano. Osborne was saying something eagerly to Cynthia, whose grave eyes were upturned to him with soft intentness of expression, and her pretty mouth half-open, with a sort of impatience for him to cease speaking, that she might reply. "They are talking about France," said Roger, in answer to Molly's unspoken question. "Osborne knows it well, and Miss Kirkpatrick has been at school there, you know. It sounds very interesting; shall we go nearer and hear what they are saying?" It was all very well to ask this civilly, but Molly thought it would have been better to wait for her answer. Instead of waiting, however, Roger went to the piano, and, leaning on it, appeared to join in the light merry talk, while he feasted his eyes as much as he dared by looking at Cynthia. Molly suddenly felt as if she could scarcely keep from crying—a minute ago he had been so near to her, and talking so pleasantly and confidentially; and now he almost seemed as if he had forgotten her existence. She thought that all this was wrong; and she exaggerated its wrongness to herself; "mean," and "envious of Cynthia," and "ill-natured," and "selfish," were the terms she kept applying to herself; but it did no good, she was just as naughty at the last as at the first. Mrs. Gibson broke into the state of things which Molly thought was to endure for ever. Her work had been intricate up to this time, and had required a great deal of counting; so she had had no time to attend to her duties, one of which she always took to be to show herself to the world as an impartial stepmother. Cynthia had played and sung, and now she must give Molly her turn of exhibition. Cynthia's singing and playing was light and graceful, but anything but correct; but she herself was so charming, that it was only fanatics for music who cared for false chords and omitted notes. Molly, on the contrary, had an excellent ear, if she had ever been well taught; and both from inclination and conscientious perseverance of disposition, she would go over an incorrect passage for twenty times. But she was very shy of playing in company; and when forced to do it, she went through her performance heavily, and hated her handiwork more than any one. "Now, you must play a little, Molly," said Mrs. Gibson; "play us that beautiful piece of Kalkbrenner's, my dear." Molly looked up at her stepmother with beseeching eyes; but it only brought out another form of request, still more like a command. "Go at once, my dear. You may not play it quite rightly; and I know you are very nervous; but you're quite amongst friends." So there was a disturbance made in the little group at the piano, and Molly sate down to her martyrdom. "Please, go away!" said she to Osborne, who was standing behind her ready to turn over. "I can quite well do it for myself. And oh! if you would but talk!" Osborne remained where he was in spite of her appeal, and gave her what little approval she got; for Mrs. Gibson, exhausted by her previous labour of counting her stitches, fell asleep in her comfortable sofa-corner near the fire; and Roger, who began at first to talk a little in compliance with Molly's request, found his conversation with Cynthia so agreeable, that Molly lost her place several times in trying to catch a sudden glimpse of Cynthia sitting at her work, and Roger by her, intent on catching her low replies to what he was saying. "There, now I've done!" said Molly, standing up quickly as soon as she had finished the eighteen dreary pages; "and I think I will never sit down to play again!" Osborne laughed at her vehemence. Cynthia began to take some part in what was being said, and thus made the conversation general. Mrs. Gibson wakened up gracefully, as was her way of doing all things, and slid into the subjects they were talking about so easily, that she almost succeeded in making them believe she had never been asleep at all. 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. Elizabeth Cleghorn Gaskell (2003). Wives and Daughters. Urbana, Illinois: Project Gutenberg. Retrieved https://www.gutenberg.org/cache/epub/4274/pg4274-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

MRS. GIBSON'S LITTLE DINNER.

An investigation by The Markup found that AT&T, Verizon, EarthLink, and CenturyLink disproportionately offered lower-income and least-White neighborhoods slow internet service for the same price as speedy connections they offered in other parts of town. Credit: Joel Eastwood and Gabe Hongsdusit. Sources: The Markup analysis of AT&T, CenturyLink, Verizon; U.S. Census Bureau This Article Is Copublished With A.P. News Read on our collaborator's site A couple of years into the pandemic, Shirley Neville had finally had enough of her crappy internet service. “It was just a headache,” said Neville, who lives in a middle-class neighborhood in New Orleans whose residents are almost all Black or Latino. “When I was getting ready to use my tablet for a meeting, it was cutting off and not coming on.” Neville said she was willing to pay more to be able to Zoom without interruption, so she called AT&T to upgrade her connection. She said she was told there was nothing the company could do. In her area, AT&T only offers download speeds of 1 megabit per second or less, trapping her in a digital Stone Age. Her internet is so slow that it doesn’t meet Zoom’s minimum for group video calls, doesn’t come close to the FCC’s of broadband, currently 25 Mbps, and is worlds below median home internet speeds in the U.S., which 167 Mbps. recommended definition average “In my neighborhood, it’s terrible,” Neville said. But that’s not the case in other parts of New Orleans. AT&T offers residents of the mostly White, upper-income neighborhood of Lakeview internet speeds almost 400 times faster than Neville’s—for the same price: $55 a month. The vast gulf between the quality of service AT&T offered these neighborhoods for the same cost is not a fluke. The Markup gathered and analyzed more than 800,000 internet service offers from AT&T, Verizon, Earthlink, and CenturyLink in 38 cities across America and found that all four routinely offered fast base speeds at or above 200 Mbps in some neighborhoods for the same price as connections below 25 Mbps in others. The neighborhoods offered the worst deals had lower median incomes in nine out of 10 cities in the analysis. In two-thirds of the cities where The Markup had enough data to compare, the providers gave the worst offers to the least-White neighborhoods. These providers also disproportionately gave the worst offers to formerly redlined areas in every one of the 22 cities examined where historical maps were available. digitized These are areas a since-disbanded agency created by the federal government in the 1930s had deemed “hazardous” for financial institutions to invest in, often because the residents were Black or poor. Redlining was outlawed in 1968. By failing to price according to service speed, these companies are demanding some customers pay dramatically higher unit prices for advertised download speed than others. CenturyLink, which showed the most extreme disparities, offered some customers service of 200 Mbps, amounting to as little as $0.25 per Mbps, but offered others living in the same city only 0.5 Mbps for 400 times as much—$100 per Mbps. Residents of neighborhoods offered the worst deals aren’t just being ripped off; they’re denied the ability to participate in remote learning, well-paying remote jobs, and even family connection and recreation—ubiquitous elements of modern life. “It isn’t just about the provision of a better service. It’s about access to the tools people need to fully participate in our democratic system,” said Chad Marlow, senior policy counsel at the ACLU. “That is a far bigger deal and that’s what really worries me about what you’re finding.” Christopher Lewis, president and CEO of the nonprofit Public Knowledge, which works to expand internet access, said The Markup’s analysis shows how far behind the federal government is when it comes to holding internet providers to account. “Nowhere have we seen either the FCC nor the Congress, who ultimately has authority as well, study competition in the marketplace and pricing to see if consumers are being price gouged or if those service offerings make sense.” None of the providers denied charging the same fee for vastly different internet speeds to different neighborhoods in the same cities. But they said their intentions were not to discriminate against communities of color and that there were other factors to consider. The industry group USTelecom, speaking on behalf of Verizon, said the cost of maintaining the antiquated equipment used for slow speed service plays a role in its price. “Fiber can be hundreds of times faster than legacy broadband—but that doesn’t mean that legacy networks cost hundreds of times less,” USTelecom senior vice president Marie Johnson said in an email. “Operating and maintaining legacy technologies can be more expensive, especially as legacy network components are discontinued by equipment manufacturers.” AT&T spokesperson Jim Greer said in an emailed statement that The Markup’s analysis is “fundamentally flawed” because it “clearly ignored our participation in the federal Affordable Connectivity Program and our low-cost Access by AT&T service offerings.” The Affordable Connectivity Program was launched in 2021 and pays up to $30 a month for internet for low-income residents, or $75 on tribal lands. “Any suggestion that we discriminate in providing internet access is blatantly wrong,” he said, adding that AT&T plans on spending $48 billion on service upgrades over the next two years. Recent looking at 30 major cities found only about a third of eligible households for the federal subsidy, however, and use it to help cover cellphone bills, which also qualify, rather than home internet costs. research had signed up the majority Connectivity advocates told The Markup that it’s hard to get people to jump through the bureaucratic hoops needed to sign up for the program when service is slow. Greer declined to say how many or what percentage of AT&T’s internet customers are signed up for either the ACP or the company’s own low-cost program for low-income residents. In a to the FCC, AT&T insisted its high-speed internet deployments are driven by “household density, not median incomes.” letter But when The Markup ran a statistical test controlling for density, it still found AT&T disproportionately offered slower speeds to lower-income areas in three out of four of the 20 cities where we investigated their service. “We do not engage in discriminatory practices like redlining and find the accusation offensive,” Mark Molzen, a spokesperson for CenturyLink’s parent company, Lumen, wrote in an email. He said that The Markup’s analysis is “deeply flawed” without specifying how. He did not respond to requests for clarification. EarthLink, which doesn’t own internet infrastructure in the examined cities but rather rents capacity from other providers, did not provide an official comment despite repeated requests. Internet prices are not regulated by the federal government because unlike telephone service, internet service is not considered a utility. As a result, providers can make their own decisions about where they provide service and how much to charge. The FCC declined a request to comment on the findings. The investigation is based on service offers collected from the companies’ own websites, which contain service lookup tools that list all available plans for specific addresses, using a method pioneered by researchers at . Princeton The Markup analyzed price and speed for nearly 850,000 offers for addresses in the largest city in 38 states where these providers operate. https://themarkup.org/still-loading/2022/10/19/dollars-to-megabits-you-may-be-paying-400-times-as-much-as-your-neighbor-for-internet-service?embedable=true#in-most-cities-poorer-neighborhoods-were-offered-worse-internet-plans-more-often Las Vegas is one city where large swaths of CenturyLink’s offers were for slow service. Almost half didn’t meet the current federal definition of broadband. These fell disproportionately on Las Vegas’s lower-income and least-White areas. Las Vegas councilwoman Olivia Diaz said that in the summer of 2020, she approached families where children had stopped showing up to virtual lessons the previous school year to find out what went wrong. City schools were preparing to begin their second school year marked by COVID-19 lockdowns. “We kept hearing there were multiple children trying to connect in the household, but they weren’t able to,” said Diaz, who represents a district that’s predominantly Latino and on the lower end of the city’s income spectrum. More than 80 percent of CenturyLink’s internet offers in her district were for service slower than 25 Mbps. Education advocacy group Common Sense Media recommends at least 200 Mbps download speeds for a household to reliably conduct multiple, simultaneous video conferencing sessions. “I think it’s unfair knowing that it is slow service that we’re paying for that is not commensurate with the faster speeds that they have in the other parts of the city that are paying the same price,” Diaz said. “It just breaks my heart to know we’re not getting the best bang for our buck.” Diaz said city officials have asked CenturyLink to expand high-speed service in her district, but the company declined, citing the prohibitive cost of deploying new infrastructure in the area. CenturyLink did not respond to emails asking about this request. Some officials told The Markup they’ve been yelling for years about bad service for high prices. “If I was paying $6 a month,” Joshua Edmonds, Detroit’s director of digital inclusion, “well you get what you’re paying for.” But he objects to people being asked to pay premium rates for bad service. “What I pay versus what I get doesn’t really make sense.” In a 2018 , Bill Callahan, who runs the online accessibility organization , coined the term “tier flattening” to describe charging internet customers the same rate for differing levels of service. report Connect Your Community He said The Markup’s findings show how much of America’s internet market is based on the “basic unfairness” of internet service providers deciding to deprioritize investing in new, high-speed infrastructure in marginalized areas. “They’ve made a decision that those neighborhoods are going to be treated differently,” said Callahan. “The core reason for that is they think they don’t have enough money in those neighborhoods to sustain the kind of market they want.” The FCC under a provision of the 2021 infrastructure bill aimed at “preventing digital discrimination of access based on income level, race, ethnicity, color, religion, or national origin.” is currently drafting rules A coalition of 39 groups led by the Electronic Frontier Foundation and Center for Accessible Technology to take aggressive action rectifying broadband inequality by examining the socioeconomics of the neighborhoods getting the slowest speeds and the prices they pay—regardless of whether the companies intended to discriminate. urged the FCC https://themarkup.org/still-loading/2022/10/19/dollars-to-megabits-you-may-be-paying-400-times-as-much-as-your-neighbor-for-internet-service?embedable=true#in-most-cities-households-in-the-least-white-areas-were-offered-the-worse-internet-plans-much-more-often AT&T insisted in filings with the agency that the standard for discrimination should be explicit, deliberate efforts to avoid building infrastructure in areas that are populated by people of color or lower-income residents. It also asked for subsidies to build high-speed internet in lower-income neighborhoods because, as AT&T asserted in its to the FCC, “most or all deficiencies in broadband access appear to result not from invidious discrimination, but from ordinary business-case challenges in the absence of subsidy programs.” letter Advocates say that’s just not true. “There are very few places in the country where it is not economically feasible to deploy broadband,” said Brian Thorn, who served as a senior researcher for the Communication Workers of America, a union representing telecom employees, which has been vocal on the issue and to the FCC. filed its own comment (Full disclosure: The CWA is the parent union of The NewsGuild-CWA, which represents The Markup’s employees.) He said members are tired of seeing their employers make inequitable infrastructure deployment decisions. “We would hear from members all the time that they’re out laying lines on one side of the neighborhood and not on the other,” he said. In a to the FCC, the coalition asserted that “broadband users are experiencing discriminatory impacts of deployment that are no different than the impacts of past redlining policies in housing, banking, and other venues of economic activity.” letter The term “redlining” derives from efforts by the federal government to stem the tide of foreclosures during the Great Depression by drawing up maps, with the help of real estate agents, to identify areas that were safe for mortgage lending. Predominantly White neighborhoods were consistently rated better than less-White neighborhoods, which were shaded in red. Echoes of these maps still reverberate today in things like and . rates of home ownership prenatal mortality on the historical map explaining why one part of Kansas City, Mo., was redlined cited “Negro encroachment from the north.” Notes In that same area, AT&T offered only slow service to every single address The Markup examined. Across Kansas City, AT&T offered the worst deals to 68 percent of addresses in redlined areas, compared to just 12 percent of addresses in areas that had been rated “best” or “desirable.” https://themarkup.org/still-loading/2022/10/19/dollars-to-megabits-you-may-be-paying-400-times-as-much-as-your-neighbor-for-internet-service?embedable=true#2022-broadband_kansas_city_redlining Redlining maps frequently tracked neatly with the disparities The Markup found. Addresses in redlined areas of 15 cities from Portland to Atlanta were offered the worst deals at least twice as often as areas rated “best” or “desirable.” Minneapolis, which is served by CenturyLink, displayed one of the most striking disparities: Formerly redlined addresses were offered the worst deals almost eight times as often as formerly better-rated areas. Pamela Jackson-Walters, a 68-year-old longtime resident of Detroit’s Hope Village, said she needs the internet to work on her dissertation in organizational leadership at University of Phoenix online and to virtually attend church services. The slow speeds AT&T offered were a constant annoyance. “They still haven’t installed the high-speed internet over here,” she said. “How do we get it? Are we too poor of a neighborhood to have the better service?” Hope Village has a per capita income of just over $11,000 and is almost entirely Black. To add insult to injury, last fall, AT&T internet service across Hope Village went down for 45 days before being restored. This summer, Jackson-Walters’s internet went down again, this time for four weeks, she said. Jeff Jones, another longtime Hope Village resident, noted a bitter irony amid all the service problems. “To add to the insult, I can look out my bedroom window literally, maybe 150 yards, is the AT&T service facility,” he said with a weary laugh. “I’m like, please help me! You’re right there! How can you ignore this problem that is just right in front of your face?” Until The Markup told Hope Village residents its findings about AT&T’s pricing practices in Detroit, they didn’t know that lower-income areas were more often asked to pay the same price for slower internet. “That’s the big piece,” said Angela Siefer, the executive director of the National Digital Inclusion Alliance, which advocates for broadband access. “Folks don’t know that they’re being screwed.” Credits: & Leon Yin Aaron Sankin Also published here Photo by on Thomas Jensen Unsplash

Are You Paying 400 Times More for Your Internet Than Your Neighbor? You Might Be

In 2021, hosted the first-ever awards!! HackerNoon Startup of the Year The goal of this campaign was to give recognition to the hardworking startups who pulled through the pandemic -- growing and thriving (despite all of the challenges). More than 44,000 startups were nominated all across the globe! They were selected from over 4,553 cities, with a total number of * cough * “4 billion” votes. Insane, right? To see who is a winner in each respective city, visit and pick a city. The name at the top of the list is the winner. (Compromised awards are under review. Cities with tie votes will be updated to have two winners!) startups.hackernoon.com The will announce the winners per region in their respective cities. Startups Winners Announcement series Here are the winners in the : Asia region Top Startup Cities in East Asia Seoul The Startup of the Year in is with 377 votes! Seoul dable Central is the Startup of the Year! It came out ahead with 121 votes. UpNow Central Sheung Wan came out as the Startup of the Year in with 154 votes. Votee Sheung Wan Tokyo The Startup of the Year in is with 41 votes! Tokyo ACES Wan Chai The Startup of the Year in is with 54 votes! Wan Chai Zetl Osaka, Japan In , is the winner! Osaka Nanophoton Taiwan came out as the Startup of the Year in ! Tico Taiwan Dongcheng, China In , is declared the Startup of the Year! Dongcheng Waiju Taipei came out as the Startup of the Year in ! Tico Taipei Akasaka In , is the winner! Akasaka WAKUWAKU Chuo In , is declared the Startup of the Year! Chuo GO-NET Ginza came out as the Startup of the Year in ! Netyear Ginza Kanazawa In , is the winner! Kanazawa Granzella Minami-aoyama In , is declared the Startup of the Year! Minami-aoyama Tokyo6 Entertainment Shibuya came out as the Startup of the Year in ! Hupro Japan Shibuya Toranomon In , is the winner! Toranomon Small Worlds Toshima In , is declared the Startup of the Year! Toshima Altplus Guangzhou, China came out as the Startup of the Year in ! Leyaoyao Guangzhou Hong Kong, Hong Kong In , is declared the Startup of the Year! Hong Kong Episode Six Kunming In , is the winner! Kunming Green A Sapporo In , is declared the Startup of the Year! Sapporo Polar Star Space Shirokanedai came out as the Startup of the Year in ! Nupp1 Shirokanedai Wuhan In , is the winner! Wuhan CSICEE Baoan, China In , is declared the Startup of the Year! Baoan Jumper Baoshan, China In , is declared the Startup of the Year! Baoshan Renren Games Chengdu, China came out as the Startup of the Year in ! Ju You Xi Chengdu Fujisawa, Japan In , is the winner! Fujisawa Scohia Pharma Fukuoka, Japan In , is declared the Startup of the Year! Fukuoka Kyulux Haidian, China came out as the Startup of the Year in ! 51vv Haidian Hongkou, China In , is the winner! Hongkou Kidstone Jiading, China In , is declared the Startup of the Year! Jiading IMRobotic Kawasaki, Japan came out as the Startup of the Year in ! QD Laser, Inc. Kawasaki Kyoto, Japan In , is declared the Startup of the Year! Kyoto Hacarus Minato, Japan In , is the winner! Minato Visionary Fun Nagoya, Japan came out as the Startup of the Year in ! Prodrone Nagoya Ningbo, China The winner in is . Ningbo OlymTech Corporation Aomori In , is declared the Startup of the Year! Aomori Forte Japan Bandai-cho In , is the winner. Bandai-cho Delta-Fly Pharm Changchun In , the Startup of the Year is . Changchun Kaidi Animation Fuchu In , is declared the Startup of the Year! Fuchu TMS Fukuoka-shi The winner in is . Fukuoka-shi SkyDisc Fukushima In , the Startup of the Year is . Fukushima Matsuri Technologies Gotanda In , is the winner. Gotanda Basset Hachioji In , is declared the Startup of the Year! Hachioji Adriakaim Harbin The winner in is J . Harbin oining Technology Iwaki In , is the winner. Iwaki Tao Engineering Izumo In , the Startup of the Year is . Izumo PuREC Kagoshima In , is declared the Startup of the Year! Kagoshima Farmers Support Kamakura In , the Startup of the Year is . Kamakura KAYAC Kami-meguro The winner in is . Kami-meguro Buzzreach Kanagawa In , is the winner. Kanagawa LoiLo Kashiwa In , the Startup of the Year is . Kashiwa Intelligent Surfaces Kita In , is declared the Startup of the Year! Kita Ax Robotix Kobe The winner in is . Kobe Coconomi Koriyama In , the Startup of the Year is . Koriyama Tocoton Kumamoto In , is the winner. Kumamoto Bloom Technology Kunitachi In , is declared the Startup of the Year! Kunitachi Trys Liaocheng The winner in is . Liaocheng Jinzheng Animation Minami In , is declared the Startup of the Year! Minami ATLAB Miyazaki In , is the winner. Miyazaki Caster Nagano In , is declared the Startup of the Year! Nagano Precision Forestry Nagaoka The winner in is . Nagaoka Plant Form Nihombashi-hamacho In , the Startup of the Year is . Nihombashi-hamacho Shizen Energy Niigata In , is declared the Startup of the Year! Niigata UniBio Japan Nishi-shinagawa In , is the winner. Nishi-shinagawa Play Heart Okayama In , is the winner! Okayama Arch Farm Okinawa In , is declared the Startup of the Year! Okinawa BlogNinja Otemachi In , the Startup of the Year is . Otemachi beBit, Inc Qingdao Shi In , is the winner! Qingdao Shi Manyoubang Quanzhou In , is declared the Startup of the Year! Quanzhou KungFu Animation Group Roppongi The winner in is . Roppongi Holo-X Saitama In , the Startup of the Year is . Saitama GameTector Sendai In , is the winner! Sendai Sendai Smart Machines Setagaya In , is declared the Startup of the Year! Setagaya Skyestate Shenzhen In , the Startup of the Year is . Shenzhen Aiator Shinjuku In , is declared the Startup of the Year! Shinjuku Scalar Shizuoka In , is the winner! Shizuoka PiPhotonics Sumida The winner in is . Sumida Holotch Tachikawa In , the Startup of the Year is . Tachikawa PlusOne Takamatsu In , is declared the Startup of the Year! Takamatsu Ken Robotec Tokushima In , is the winner! Tokushima DennoKotsu Toyonaka In , the Startup of the Year is . Toyonaka FISH BIOTECH Tsukuba In , is declared the Startup of the Year! Tsukuba WARPSPACE Wakayama In , the Startup of the Year is . Wakayama 4D Sensor Xiangzhou In , is the winner! Xiangzhou ALLiN Edutech Group Xianyang The winner in is . Xianyang TONLY Xiaoshan In , is declared the Startup of the Year! Xiaoshan GONGHUI Yamaguchi In , the Startup of the Year is . Yamaguchi Archis Yangpu The winner in is . Yangpu Tmxmall Yau Tsim Mong In , is the winner! Yau Tsim Mong Airnugget Yeoksamdong In , the Startup of the Year is . Yeoksamdong WizSchool Yichang In , is declared the Startup of the Year! Yichang Hubei Kento Electronic Yokohama In , is the winner! Yokohama Next Innovation Yubei In , the Startup of the Year is . Yubei Ophiux Network Yueqing The winner in is . Yueqing Yueqing Easythink Media Zhuhai In , is the winner! Zhuhai Aijuba Hongo In , the Startup of the Year is . Hongo Smile Robotics, Inc Admiralty In , is declared the Startup of the Year! Admiralty BUNCH Southern In , the Startup of the Year is . Southern Retykle Yongdungpo In , is the winner! Yongdungpo Push News Taichung In , is declared the Startup of the Year! Taichung Wondercise Cyberport, Hong Kong In , the Startup of the Year is . Cyberport Animoca Brands Hefei, China In , is the winner! Hefei Tsinglink New Territories, Hong Kong The winner in is . New Territories, Hong Kong Zeek Dezhou In , the Startup of the Year is . Dezhou Dezhou Baji Kitakyushu In , is declared the Startup of the Year! Kitakyushu Reif Seongnam In , is the winner! Seongnam EOFlow Yanshi In , the Startup of the Year is . Yanshi CASI Vision Top Startup Cities in South Asia Hyderabad, India The Startup of the Year in is with 962 votes Hyderabad Quixy Lahore, Pakistan is the Startup of the Year! It came out ahead with 186 votes. SmartWindows Lahore Bangalore, India When it comes to , is the Startup of the Year with 210 votes! Bangalore vBridge Hub Pune, India couldn’t be beaten for Startup of the Year in , it won with 129 votes! InfraCloud Pune Jaipur, India The Startup of the Year in is with 166 votes Jaipur Emizen Tech Lucknow, India is the Startup of the Year! It came out ahead with 279 votes. Keeros Superfoods Lucknow Noida, India came out as the Startup of the Year in with 156 votes! Bagisto Noida Mumbai, India When it comes to , is the Startup of the Year with 96 votes! Mumbai Sequretek Sylhet couldn’t be beaten for Startup of the Year in , it won with 122 votes! DataXpie Sylhet Mylapore, India In , is the winner! Mylapore Yalimart Goa, India In , the Startup of the Year is Goa Filter Coffee Delhi, India In , is declared the Startup of the Year! Delhi EkAnek Networks Karachi, Pakistan The winner in is Karachi Bykea Ahmedabad, India In , is the winner Ahmedabad MageComp Dhaka, Bangladesh The winner in is Dhaka SOLshare Islamabad, Pakistan In Islamabad, is declared the Startup of the Year! TAG Gurgaon, India In , the Startup of the Year is Gurgaon ixigo Bengaluru, India In , is the winner! Bengaluru Prakshep Private Limited Haryana, India couldn’t be beaten for Startup of the Year in ! Footprints Childcare Haryana Kolkata, India In , is the winner! Kolkata Wow! Momo New Delhi, India In , is declared the Startup of the Year! New Delhi Rusk Media Indore, India The winner in is Indore Chattar Patar Kormangala In , is the winner Kormangala Sanctum Technologies Ghaziabad came out as the Startup of the Year in Craftfurnish.com Ghaziabad! Mohali In , the Startup of the Year is Mohali DataKund Mysore The Startup of the Year in is Mysore Excelsoft Karnal came out as the Startup of the Year in ! Dunar Karnal Kolhapur In , is the winner! Kolhapur Zanvar Group Patiya In , the Startup of the Year is Patiya Kloud Project Kathmandu In , is declared the Startup of the Year! Kathmandu Vairav Technology Bangalore City, India In is the winner! Bangalore City, PagarBook Ernakulam The winner in is Ernakulam Epixel Solutions Colombo, Sri Lanka couldn’t be beaten for Startup in the Year in MAs Foods Colombo Andheri, India When it comes to , is the Startup of the Year! Andheri DoorMint Kanpur, India came out as the Startup of the Year in ! ServiceWalaa Kanpur Faisalabad In , is the winner! Faisalabad DigiKhata Nasik The Startup of the Year in is Nasik Sahyadri Farms Patna came out as the Startup of the Year in ! Asvin Tech Patna Chennai, India In , is the winner! Chennai Agnikul Cosmos Faridabad, India In , is declared the Startup of the Year! Faridabad UClean Panjim, India The winner in is Panjim TempoGO Ranchi, India In , is the winner Ranchi Osam Dairy Surat, India In , is declared the Startup of the Year! Surat Wishbook Thane, India In , is the winner! Thane Delight Foods Trivandrum, India came out as the Startup of the Year in ! GenRobotic Trivandrum Vijayawada, India In , the Startup of the Year is Vijayawada Eruvaka Technologies Visakhapatnam, India In , Ltd is declared the Startup of the Year! Visakhapatnam Greenline Labs Pvt Ahmadabad In , is the winner! Ahmadabad KiwiQA Services Bandra came out as the Startup of the Year in ! Car Club Bandra Bombay In , is the winner! Bombay Locobuddy Mobile Technology (Sportobuddy) Cochin In , the Startup of the Year is Cochin Tuttifrutti Interactive Gandhinagar In , is declared the Startup of the Year! Gandhinagar Kroop AI Guwahati In , the Startup of the Year is Guwahati GNRC Hospitals Jamshedpur In , is declared the Startup of the Year! Jamshedpur Intelliber Kabul The winner in is Kabul Miracle Shop (Vocinno Technology) Karur In , is the winner Karur Karur Vysya Bank Kochi The winner in is Kochi BestDoc Kozhikode In , is declared the Startup of the Year! Kozhikode OfficeKit HR Ludhiana In , the Startup of the Year is Ludhiana Trendy Beat Network Madhapur In , is the winner! Madhapur Head Digital Works Mangalore When it comes to , is the Startup of the Year Mangalore Hangyo Nagpur came out as the Startup of the Year in ! UPrudence Agro Nagpur Rawalpindi The Startup of the Year in is Rawalpindi Pro Options Marketing Rishikesh came out as the Startup of the Year in ! Descatuk Rishikesh Secunderabad couldn’t be beaten for Startup of the Year in Prakriti Biosystems Engineering Secunderabad Tirupur In , is the winner! Tirupur Tubeknit Fashions Udupi The winner in is Udupi Krishitantra Top Startup Cities in Southeast Asia Singapore, Singapore The Startup of the Year in is with 6556 votes! Singapore Cake DeFi Hanoi, Vietnam is the Startup of the Year! It came out ahead with 683 votes. DeFi For You Hanoi Kuala Lumpur, Malaysia came out as the Startup of the Year in with 109 votes! Trambellir Kuala Lumpur Pasig, Philippines When it comes to , . is the Startup of the Year with 110 votes! Pasig Quickreach Inc Bangkok, Thailand couldn’t be beaten for Startup in the Year in , it won with 60 votes! Perception Codes Bangkok Johor Bahru In , the Startup of the Year is . Johor Bahru AGR1 Bandung, Indonesia In , is declared the Startup of the Year! Bandung Prosa Portugal The winner in is . Portugal SCEMAI Tangerang, Indonesia In , is the winner Tangerang Yummy Corp Jakarta, Indonesia In , is the winner! Jakarta Tokocrypto Dki Jakarta, Indonesia In , is declared the Startup of the Year! Dki Jakarta Limakilo Bangkok Metropolis In , is the winner! Bangkok Meticuly Medan The winner in is ! Medan DIKA Manila, Philippines In , is declared the Startup of the Year! Manila PawnHero Phnom Penh, Cambodia In , the Startup of the Year is Phnom Penh vKirirom Bali In , is the winner! Bali Radius App Penang When it comes to , is the Startup of the Year Penang EasyParcel Surabaya came out as the Startup of the Year in ! Super Surabaya Setiabudi, Indonesia The Startup of the Year in is Setiabudi Chilibeli Cebu City came out as the Startup of the Year in MiCab Cebu City! Jawa couldn’t be beaten for Startup in the Year in DAUR Jawa Ho Chi Minh City, Vietnam In , is the winner! Ho Chi Minh City Pharmacity Selangor, Malaysia is the Startup of the Year! BabyDash Selangor Jakarta Pusat, Indonesia In , is the winner! Jakarta Pusat StickEarn Semarang When it comes to , is the Startup of the Year! Semarang Food Bear Petaling Jaya, Malaysia The winner in is . Petaling Jaya Deemples Kebayoran Baru, Indonesia couldn’t be beaten for Startup in the Year in ! Yogrt Kebayoran Baru Kuningan The winner in is . Kuningan Prinzio Bukit Merah came out as the Startup of the Year in ! DC Alliance Bukit Merah Huai Khwang In , the Startup of the Year is . Huai Khwang AppMan Co.,Ltd Kuta In , is declared the Startup of the Year! Kuta Gringgo Mandaluyong came out ahead as the Startup of the Year! Work Inspire Mandaluyong Midview City In , is the winner! Midview City BioMers Phuket The Startup of the Year in is ! Phuket Sapahn Yangon, Myanmar The winner in is . Yangon Wave Money Denpasar When it comes to , is the Startup of the Year! Denpasar Revasi Nakhon Ratchasima, Thailand The winner in is . Nakhon Ratchasima Siam Lottery co., Ltd Taguig, Philippines In , is the winner! Taguig PayMongo Bogor came out as the Startup of the Year in ! TIRANYX Bogor Senai, Malaysia won as the Startup of the Year. Nutrition Technologies Senai Bekasi Kota couldn’t be beaten for Startup in the Year in ! 8villages Bekasi Kota Top Startup Cities in Middle East Tel Aviv, Israel The Startup of the Year in is with 1,065 votes! Tel Aviv hyro Beirut, Lebanon is the Startup of the Year! It came out ahead with 1,276 votes. Hovi Digital Lab Beirut Riyadh, Saudi Arabia With 350 votes, came out as the Startup of the Year in ! Flicron Riyadh Tel Aviv-yafo, Israel When it comes to , is the Startup of the Year with 167 votes! Tel Aviv-yafo Cymbio Haifa, Israel couldn’t be beaten for Startup in the Year in , it won with 183 votes! Pico Haifa Amman, Jordan In , with 141 votes, is the winner! Amman Edexy Dubai, United Arab Emirates In , the Startup of the Year is with 74 votes. Dubai Ziina Doha, Qatar In , is declared the Startup of the Year! Doha SkipCash Tehran, Iran The winner in is . BeyondBoard AR Tehran Jerusalem, Israel In , is the winner! Jerusalem SolidBlock Rishon Le Zion The Startup of the Year in is ! Rishon Le Zion Passparto Ness Ziona, Israel is the Startup of the Year! Soapy Ness Ziona Kuwait, Kuwait came out as the Startup of the Year in ! Brapper Shop Kuwait Manama, Bahrain couldn’t be beaten for Startup in the Year in ! Doctori Manama Lod, Israel is the winner in ! Phinergy Lod Agudat Israel, Israel When it comes to is the Startup of the Year with [vote ] votes! Agudat Israel, Aqaurius Engines Beersheba, Israel In , the Startup of the Year is . Beersheba Visual Estate Ltd Giv'atayim, Israel In , is declared the Startup of the Year! Giv'atayim WSC Sports Modi'in, Israel The winner in is . Modi'in Loginno Nazareth, Israel In , is the winner! Nazareth AR Guide In Medical Ramat Gan, Israel The Startup of the Year in is . Ramat Gan Datos Health Baghdad When it comes to , is the Startup of the Year! Baghdad Mishwar Khobar In , the Startup of the Year is . Khobar Garage Misgav couldn’t be beaten for Startup in the Year in ! Eden Shield Misgav Sharjah came out as the Startup of the Year in ! Bookends Sharjah Affula In , the Startup of the Year is . Affula eMotion Technologies Bene Beraq, Israel In , is the winner Bene Beraq Reflectiz Caesarea, Israel In , the Startup of the Year is . Caesarea CardiacSense Dubayy In , is declared the Startup of the Year! Dubayy North Ladder Muscat couldn’t be beaten for Startup in the Year in ! iLab Marine Muscat Tirat Carmel In , the Startup of the Year is . Tirat Carmel PassCall Advanced Technologies Hod Hasharon, Israel The winner in is . Hod Hasharon Idomoo Holon, Israel came out ahead as the Startup of the Year! OptimalPlus Holon Jeddah, Saudi Arabia When it comes to , is the Startup of the Year! Jeddah Arabian Combustion Technologies Ramat Hasharon, Israel is the Startup of the Year! Lumen Ramat Hasharon Al Khobar came out as the Startup of the Year in ! Swyft Al Khobar Bet Shemesh In , is the winner! Bet Shemesh Degania Medical Binyamina When it comes to , is the Startup of the Year! Binyamina OncoHost Makkah In , the Startup of the Year is Makkah Scimagine Or Akiva Last but not least, couldn’t be beaten for Startup in the Year in the city of ! WMT Or Akiva More Information: If you’re a winner, please answer ! If you’re a reader, participant, just a curious individual, consider answering one of our interviews . this interview here Read the FAQs about or . Startups here here To see all of the winners you may visit , pick any city and view the name that appears at the top. this site If you’re interested in partnering with us, we invite you to look through some of these or reach out to our business department. unique sponsorship options Perhaps creating your very own tickles your fancy or maybe you have a whole new idea for how we can partner! writing contest Thank you to all of our sponsors! We truly appreciate your contribution to the tech world and thus the future! The Startups Awards are sponsored by: , , , , , , , , , , Blockster Brex FreeCodeCamp LaunchDarkly Coil Udacity Udemy Vercel Asia Pacific Transgender Network Bybit Everscale Network The post was written by Kushal Tamrakar and Valerie Ejegi, edited by Hang Ngo. If you have any questions or concerns or would like to report a discrepancy please email ellen@hackernoon.com.

Meet the Winners of Startups in Asia

Introduction Ever complaint about too many emails? I think so. Not only because you clicked on this article, but I know of it being a common problem. So so often I hear my acquaintances or clients, saying “I have to do my emails!” or “I spend hours just to get ahead of things.” It´s worse after holidays. Source: https://www.pexels.com/de-de/foto/frau-schreibtisch-laptop-buro-4240504/ I have good news for you. And bad news. Good news: You can change that Bad news: You yourself have to change that You might think now, well, that´s a big say of him. He does not know about MY situation! I am so busy! I have so many emails! I have to deal with so many things! I am so important! I have to take care of everything! But are you? Do you have to? I mean, really? I don´t think so. That´s what I am going to show you in this article. Also, I have an additional bonus pro tipp at the end of my article. The stories we tell ourselves We, as humans consist of stories. Every group we interact with tells itself stories to keep them alive. Only therefore companies, money, states exist. I wrote about that by analyzing soccer clubs, which are also just stories. See . here Source: https://www.pexels.com/de-de/foto/schwarze-und-rote-schreibmaschine-1995842/ You tell yourself stories. I myself, tell me that I am not good in playing soccer (which I am really not), that I am not good in math (working on it) and many other things. When you tell yourself stories you believe in these. But you can change these stories. I changed my “I am not a morning person” into waking up at five or six am — by myself without alarm (to be honest, this is rather new). I changed “Cold showers are totally unbearable for me, big no no, I need the warmth!” into taking showers with ONLY cold water. I do that now for two years on average on 6 out of 7 days of the week. Trust me with this and read on. Or continue telling yourself your same old stories. Stay stuck and complain about it. Sound to harsh? Sorry, I read too many . So should you. Each of them. (I am working on the story of myself that I am a too diplomatic kind of guy, too. 😊) books of Nassim Taleb A different story to tell yourself Let me bring you another story, a theorem, a mental model. I wrote about mental models and what they are in connection with nightlife . here This other story is the , which basically says that increased efficiency in the usage of a resource does not lead to less but to more demand. This is because more efficiency leads to more usage. rebound effect Source: https://www.pexels.com/de-de/foto/arbeiten-muster-internet-abstrakt-1089438/ Applying this logic to you and your emails means that the better you get in reading and writing emails the more emails you will receive. For example, if you receive an email from a client and answer right away you reward her behavior by this reply. This — consciously or unconsciously — let´s her know that her action of writing an email will get her a reply back. You her behavior. reinforce You might now say or think: “But I have to reply!”. Again, do you have to? Do you have “writing emails” in your job description? If so, in case you are a communication agent or similar, better stick to it. If not? Well, this leads me to the ideas that can help you change your struggle with emails in the next section. Start with questions to ask yourself Take some time and ask yourself the following. No, not somewhen. Do it now! What´s your job? Is it writing emails? (yes, ask yourself again) What are the of your email reading and writing behavior? What kind of (deep) work do you cannot do? opportunity costs What else do you miss out on? What are the things you are missing out on worth in comparison to writing emails? If you wanna get one level deeper, close your eyes and ask yourself: What feelings come up in your body when you think of your email inbox? Listen to your bodily feelings here. What are your normal actions in response to that feeling? And finally: What do you really want to achieve with your behavior? You do not have to find answers now. Asking the questions is a good start. If answers come write them down to make `em stick. What to do now Time for some hands on ideas of what to do. Source: Based on: https://www.pexels.com/de-de/foto/motivierende-einfache-inschrift-gegen-zweifel-5238645/ Do not reply (to emails you receive), or at least a little later than usual If it´s your colleagues, suggest and do a or weekly instead daily standup When people start asking why you did not reply, tell them what you are doing — many will like the idea. If not, show them this article. If they still not like the idea, they wanna stay stuck. Let them. . And this doesn´t only apply to emails! Their expectations of you are not your problem — it´s their`s Create an “do not disturb” out of office reply in which you say something like: You can also add a link to this article. “I am changing my ways of handling emails. I thereby create time for myself to deeply work on meaningful tasks. I might get back to you, I might not. If it`s urgent, call me (is it urgent?). If it`s not, lets find some other time and means to deal with this topic.” Look at emails at certain daily fixed timeslots Delete the email app from your phone (tough one!)Disallow push notifications on your mobile your laptop (disable of them in the best case) and all Continue writing your emails but look at it differently. Stop seeing it as a necessity. This will already change a lot. Summed up: Give up the idea of inbox zero. The more you work on your emails, the more you will get. If you still don´t believe me, see another theory below to back up what I am saying. Some systems theory I am a beginner student of systems theory and follow the . He worked on his systems theory for decades. Let me try to point out the most relevant points here. If you want to dive in further, you can listen to his on Spotify (German only). Luhmann school 14 (!) introductory lectures He says that systems are autopoietic. That means that they keep themselves alive. They keep themselves alive by performing operations (like reading and writing emails). What are systems? You are a system. You, together with your colleagues are a system. Your organization is a system. Your town is. Your country is. It´s systems in systems and it´s complex. It´s “ .” turtles all the way down Source: My own creativity with the help of Power Point In general, social systems keep themselves alive by communication. A system does not consist of it´s participants, but of the established communication structures. In this case your emails. In order to change a system, you need to irritate it. This does not guarantee a change of behavior of that system but makes the change a little more likely than without the irritation. That is the reason I mentioned the actions / interventions above. With these actions you can irritate others. Attention! They might fight back because you kind of question their deeply held assumptions (stories) about themselves. And if you yourself still do not believe me, well that´s part of a waaaaay longer discussion. Way longer means, of Niklas Luhmann. And he is not the only systems thinker. 70 books (!) and more than 400 articles Bonus pro tipp “Meetings should really be phone calls, phone call should be emails, and phone calls should just be texts.” (Naval Ravikant) Does that contradict partly what I said above? Sure! Cope with it. If you are looking for one sided answers, look somewhere else. Disclaimer I am really happy that you read this far. I am rather bold with my statements in this text. The points of view are quite one sided. I am aware of that. But they are also loosely held. I am ready to give them up in a glimpse because reality is always way more complex than one line of argumentation. But it`s less fun. I am happy to discuss any of the things I stated above. Mostly, to challenge my points of view — which I do continuously. I love to have opposing views in my head at the same time — and try to function nevertheless (based on a quote of F. Scott Fitzgerald) So, feel free to connect and discuss. You find me on twitter @SirRendipity or LinkedIn https://www.linkedin.com/in/florian-schmidt-550bb820/ If you are interested in the application of mental models on seemingly unrelated topics, follow me. Sources 100 truths you will learn to late — Luca Delanna: https://www.amazon.de/Truths-Will-Learn-Late-English-ebook/dp/B07RRCXV3J Tradeoffs: The Currency of Decision Making: https://fs.blog/2019/12/tradeoffs-decision-making/ Soccer and it´s loss of meaning: https://florianschmidt-43639.medium.com/soccer-and-it-s-loss-of-meaning-165fee548bd5 Books from Nassim Taleb: https://portal.dnb.de/opac.htm?method=simpleSearch&query=124120180 Rebound effect: https://en.wikipedia.org/wiki/Rebound_effect_(conservation) Reinforcement — Burrhus Frederic Skinner: . https://en.wikipedia.org wiki/Reinforcement#:~:text=In%20behavioral%20psychology%2C%20reinforcement%20is,by%20a%20specific%20antecedent%20stimulus.&text=Reinforcement%20does%20not%20require%20an,effect%20elicited%20by%20the%20stimulus Mental Models, the nightlife and the deejay: https://florianschmidt-43639.medium.com/mental-models-the-nightlife-and-the-deejay-4d4846a603c5 Opportunity costs: https://fs.blog/2019/12/tradeoffs-decision-making/ Stand up meetings: https://en.wikipedia.org/wiki/Stand-up_meeting Systems Theory — Niklas Luhmann: https://en.wikipedia.org/wiki/Niklas_Luhmann Spotify: Carl-Auer autobahnuniversität: https://open.spotify.com/show/0HVLyjAHZkFMVr9XDATMGz?si=6dQvjt_iRzuSaPNYzi2gTQ Also published at https://florianschmidt-43639.medium.com/inbox-zero-is-a-lie-you-tell-yourself-882c790b92bc

Inbox zero is a lie you tell yourself

Every so often, my team and I set two weeks aside to work on a passion project. Regardless of the final state we launch our product into the market to get feedback and refine if there’s enough interest. Our latest product, , was no different until we decided to try something new: launch it with only $100 (went a tinge over budget 😇). Guess the Throne is an online death pool for the final season of Game of Thrones. Create your group, submit your picks, and invite your friends. We handle the scoring, rules, and everything else for free! Guess the Throne You may be asking yourself, “What’s a death pool?” Much like it sounds, it’s a bracket that lets you predict if a character lives or dies. Each correct guess is worth a point, and the most points win! It’s hard to launch a product, especially without money, but I hope our story shows you the results you can see by focusing on product with a solid ground game. Armed with DM slides, one cat picture, and a good user experience we were able to capture thousands of users within 24 hours of launching. Product Hunt Before spending cash we exhausted all of the free routes. We determined Product Hunt would be a great place to make our splash on the internet. After reading we were finished in around 20 minutes. On launch day, over 7,000 people viewed our website and 1,200 users signed up. We battled for a top five spot all day before falling short to eighth. how to make it happen This was awesome! One thing we noticed was that even though we converted over 1,200 people we didn’t get very many upvotes on Product Hunt in comparison. To battle that, we decided to personalize the experience if you came from Product Hunt by giving you a little nudge to direct you back to the site after making a group and entry. If you’re using React and would like to have the code comment below and I’ll open source it! Since we didn’t network before launching on Product Hunt or use the platform beforehand I found myself going to other people’s products commenting, interacting, and following. Don’t do this! Every time I did we would drop in the rankings which was unfortunate because I wasn’t being spammy. Of course I wanted them to come back and check out our cool new thing. 😆 Overall, Product Hunt seems like a no brainer in a $100 launch. We still get referrals from their site each day. I don’t know if we were super lucky of not, but for all future products they’ll be on there. Influencer Marketing Perhaps one of the more sketchy ideas was to slide into the DMs of influencers on Twitter and politely ask for a shout out. No one gave us one for free 😭, but you can normally negotiate a way for them to promote you in a tweet. We put our trust in the internet here and it panned out great. Within five minutes of the tweet going out we had over 80 people on our home page simultaneously and based off our analytics we converted over 250 people within two hours. The cost per acquisition was 🙌🏻. Influencer marketing is the move. 20 cents Share it Everywhere We scoured the internet for mentions of death pool and Game of Thrones together. If there was an article written about it we commented on the post. Game of Thrones Discord channel, we’re there. Facebook Groups? You got it. Reddit? Yep. If you’ve tweeted about Game of Thrones and death pool in the past three years you received a message from us. I cannot overstate the importance of this. You need to be everywhere someone looks for your product and available to answer questions. Don’t mindlessly spam your product everywhere and say it’s the greatest thing ever. Most groups have moderators and it’s always best to confirm with them that you can share your product before doing so. There is a certain way to go about it though. Submit a Post to a Publication on Medium A lot of people I’ve spoken to doesn’t know this, but you can write for publications on Medium and it’s super easy to do. Write your article as a draft, find the publication that it aligns with closest, and reach out to them to asking it to be published. I’ve never been denied from publishing content, and it performs 100x better (no exaggeration) than without submitting it to one. Caldera is my personal account and rarely receives any views. From our we estimate over 1,100 users joined. It took us an hour to write and was completely free to do! Medium post in Hacker Noon Fiverr Time! Everyone that worked on the product found one gig on Fiverr to help us promote. I Will Advertise And Promote Your Business On iHeartRadio And Social Network This was pretty awesome. An iHeartRadio host advertised us on his talk show. The commercial is an instant classic! <a href="https://medium.com/media/a70c3aa88d105837adefebe0b58bc665/href">https://medium.com/media/a70c3aa88d105837adefebe0b58bc665/href</a> Cost: $7 Impact: Hard to measure. Worth the cost because you never know who is listening. Promote Your Twitter To A Large Audience You give this guy your Twitter handle and he floods it with “real” people that like, follow, and retweet you. Cost: $7 Impact: Little to none. Though it was nice to get over the hump of 75 followers on our Twitter even if the accounts were fake. Tweet Your Message To My 330k Twitter Followers Type up a tweet and off it goes onto this guys account (he’s a cool guy — it’s in his bio). body[data-twttr-rendered="true"] {background-color: transparent;}.twitter-tweet {margin: auto !important;} Bracket busted? Create a 100% free Game of Thrones death pool instead! Create your group, add your friends, and submit your picks. We handle the scoring and everything else! #guessthethrone #winterishere https://t.co/Hla661Czsd — @ShaileshTr function notifyResize(height) {height = height ? height : document.documentElement.offsetHeight; var resized = false; if (window.donkey && donkey.resize) {donkey.resize(height); resized = true;}if (parent && parent._resizeIframe) {var obj = {iframe: window.frameElement, height: height}; parent._resizeIframe(obj); resized = true;}if (window.location && window.location.hash === "#amp=1" && window.parent && window.parent.postMessage) {window.parent.postMessage({sentinel: "amp", type: "embed-size", height: height}, "*");}if (window.webkit && window.webkit.messageHandlers && window.webkit.messageHandlers.resize) {window.webkit.messageHandlers.resize.postMessage(height); resized = true;}return resized;}twttr.events.bind('rendered', function (event) {notifyResize();}); twttr.events.bind('resize', function (event) {notifyResize();});if (parent && parent._resizeIframe) {var maxWidth = parseInt(window.frameElement.getAttribute("width")); if ( 500 < maxWidth) {window.frameElement.setAttribute("width", "500");}} Cost: $7 Impact: I’d say we converted about 60 users from it. Not bad! Do Promotion Or Shoutout On My 18k Cat Instagram Once we saw this one it had to happen. <a href="https://medium.com/media/e5e6023e23ba1f690664e9ab557972b4/href">https://medium.com/media/e5e6023e23ba1f690664e9ab557972b4/href</a> Cost: $7 Impact: Not too much. Links on Instagram are hard. Distribute Your Press Release You type up a press release and it’s immediately published on over 100 websites. This was more of a curiosity to us than anything because we can never get press on product (more on that below). This is an example of the quality you get 😂… Don’t do this. Chinese Export Business Cost: $52.50 🙄 Impact: It was shared everywhere, but we couldn’t measure any impact at all. Most of the websites were sketchy and probably hurt our SEO. A Slight Rant on Press You may be asking, “What about press?” We tried so hard to get this in the news and thought it would for sure get coverage after seeing blogs like . Out of 110 DM slides only six people responded. All said they weren’t interested. Not even Game of Thrones blogs would write about it. We sent a press kit, personalized messages, and followed up three times with updates on how we were doing. If anyone reading this has insights on how we can connect better with the press I would love to hear it. this Results Grand total spent was $130.50. Now to the numbers. We are up to on the app twelve days after launching. Here’s a snap of our Google Analytics. 5,058 users We’re overjoyed with the success that we’ve seen and feel like we’re one piece of press away from taking this to a new level. We’re projecting to hit 20,000 users by the start of the show, and would love for you to ! join us We’re actively looking for a partner to help promote Guess the Throne and create content. If this is something you’d be interested in shoot us an email at support@itsadate.app. I think the most important lesson we’ve learned is the power of product and timing. Having a timely idea with a good product makes marketing easier. And remember, as the co-founder of Airbnb once said… body[data-twttr-rendered="true"] {background-color: transparent;}.twitter-tweet {margin: auto !important;} If you launch and no one notices, launch again. We launched 3 times. — @bchesky function notifyResize(height) {height = height ? height : document.documentElement.offsetHeight; var resized = false; if (window.donkey && donkey.resize) {donkey.resize(height); resized = true;}if (parent && parent._resizeIframe) {var obj = {iframe: window.frameElement, height: height}; parent._resizeIframe(obj); resized = true;}if (window.location && window.location.hash === "#amp=1" && window.parent && window.parent.postMessage) {window.parent.postMessage({sentinel: "amp", type: "embed-size", height: height}, "*");}if (window.webkit && window.webkit.messageHandlers && window.webkit.messageHandlers.resize) {window.webkit.messageHandlers.resize.postMessage(height); resized = true;}return resized;}twttr.events.bind('rendered', function (event) {notifyResize();}); twttr.events.bind('resize', function (event) {notifyResize();});if (parent && parent._resizeIframe) {var maxWidth = parseInt(window.frameElement.getAttribute("width")); if ( 500 < maxWidth) {window.frameElement.setAttribute("width", "500");}} My name is Marcus Wood. I am the founder of Caldera, a product company focused on creating great user experiences through cutting edge tech.

We launched our product with $130. Here’s what happened.

Hyperledger Composer architecture Welcome, to part two of the “Choosing private blockchain tech” series ( ). In each article we implement a simple use case to give an overview of what an existing technology does and how it can be useful in businesses. part1 is here Originally, this article was meant to be about Hyperledger Fabric, but after learning about Hyperledger Composer, it became clear that it would be beneficial to write about it first. This will allow us to get a higher-level view of what type of business applications can be built on top of Hyperledger Fabric blockchain and make it easier to understand the purpose of Fabric itself. The business use case Let’s start with the artificially simple use case from the finance industry defined in . Here are the parties in this scenario: part1 makes investment decisions (trades). Non-custodial means they don’t actually hold a client’s money. Instead, after a trade is executed they send directions to a custodian that moves the money appropriately. Non-custodial asset manager (hedge fund): : Usually a large and trusted finance institution that holds a client’s money and executes transfers on behalf of the hedge fund. A custodian needs to know about each trade to distribute payments to counterparties. Custodian : Invests into the hedge fund. Each client needs to view trades in their own account, but not other clients’ accounts. Client(s) Let’s see how we can use Hyperledger Composer to define all participants in this scenario and enable them to share appropriate information. What is Hyperledger Composer? Hyperledger Composer is a higher-level toolset and framework made to quickly build and run applications on top of Hyperledger Fabric blockchain. If Fabric is network-level, Composer is application-level. It allows us to define the data model, business logic and access control lists for an application that can be deployed and executed inside of a . Users of such applications don’t have to run a local node and can interact with a remote node through an RPC or HTTP REST if needed. Fabric channel Composer comes with a nice which allows users to prototype applications in browser without setting up a local network. We’ll use the playground to implement and test our use case. In a future article, we will export the resulting ‘business network definition’ and deploy to a real Hyperledger Fabric blockchain. web playground Solution The full code of the solution can be found . Bellow we’ll see how to configure each piece step-by-step using the . here Composer Playground 1. Create network Select ‘Deploy a new business network’ and fill out the required info. In my case, the name of the network is ‘hedge-fund-network’ and admin card name is admin@hedge-fund-network. Select the ‘empty-business-network’ template and hit Deploy. This will create an empty business network and an admin identity that has full control over it. After the business network is created we can connect to it by pressing ‘Connect Now’. 2. Configure object model Composer has its own object modeling language that is very straightforward and easy to use. There are 4 types of resources that can be defined: . Things that are being tracked in the application. In our case we track Lots — traded blocks of securities. Assets Entities that interact with the network. Each has their own permissions. Participants. are sent to update an asset or a participant as well as to execute a custom-defined logic. Transactions can be emitted from the transaction logic and subscribed to by participants. Events Now, let’s create a model file named ‘org.acme.mynetwork.cto’ and add the following code. The file defines 3 participant types (Trader, Client, Custodian), Lot asset, Trade transaction and NewTrade event. /*** My commodity trading network*/namespace org.acme.mynetwork asset Lot identified by lotId {o String lotIdo String securityNameo Double quantityo Double price--> Client owner} participant Client identified by clientId {o String clientIdo String description} participant Custodian identified by custodianId {o String custodianIdo String description} participant Trader identified by traderId {o String traderIdo String name} transaction Trade {--> Trader trader--> Client client--> Lot lot} event NewTradeEvent {--> Lot lot} 3. Configure transaction logic Next, we’ll implement a custom logic that gets executed every time a Trade transaction is sent. Create a new script file called script.js with the code below. Composer knows to execute this code for each Trade transaction based on the @param annotation in the comment. This code does 2 things: changes the owner of a Lot and emits a NewTrade event on success. /*** Track the trade* {org.acme.mynetwork.Trade} trade — the trade to be processed* */function tradeCommodity(trade) {var factory = getFactory();trade.lot.owner = trade.client;var result = getAssetRegistry(‘org.acme.mynetwork.Lot’).then(function (assetRegistry) {return assetRegistry.update(trade.lot);});if (result) {var newTradeEvent = factory.newEvent(‘org.acme.mynetwork’, ‘NewTradeEvent’);newTradeEvent.lot = trade.lot;emit(newTradeEvent);}return result;} @param @transaction 4. Access control list (ACL) Lastly, we need to define an access control list governing what each participant type can do and see. Here we specify that traders can execute trades, clients can view their own trades, and custodians can view all trades. /* Admin */ rule NetworkAdminUser {description: "Grant business network administrators full access to user resources"participant: "org.hyperledger.composer.system.NetworkAdmin"operation: ALLresource: "**"action: ALLOW} rule NetworkAdminSystem {description: "Grant business network administrators full access to system resources"participant: "org.hyperledger.composer.system.NetworkAdmin"operation: ALLresource: "org.hyperledger.composer.system.**"action: ALLOW} /* Common */ rule CommonReadTransactionRegistry {description: "Allow all participants to read transaction registry"participant: "org.hyperledger.composer.system.Participant"operation: READresource: "org.hyperledger.composer.system.TransactionRegistry"action: ALLOW} rule CommonReadParticipantRegistry {description: "Allow all participants to read participant registry"participant: "org.hyperledger.composer.system.Participant"operation: READresource: "org.hyperledger.composer.system.ParticipantRegistry"action: ALLOW} rule CommonReadAssetRegistry {description: "Allow all participants to read asset registry"participant: "org.hyperledger.composer.system.Participant"operation: READresource: "org.hyperledger.composer.system.AssetRegistry"action: ALLOW} rule CommonReadNetwork {description: "Allow all participants to read network"participant: "org.hyperledger.composer.system.Participant"operation: READresource: "org.hyperledger.composer.system.Network"action: ALLOW} /* Trader */ rule TraderManageClient {description: "Allow traders to read all clients"participant: "org.acme.mynetwork.Trader"operation: ALLresource: "org.acme.mynetwork.Client"action: ALLOW} rule TraderManageOwnTrades {description: "Allow traders to manage their trades"participant(t): "org.acme.mynetwork.Trader"operation: ALLresource(tt): "org.acme.mynetwork.Trade"condition: (tt.trader.getIdentifier() == t.getIdentifier())action: ALLOW} rule TraderManageLots {description: "Allow traders to read and create lots"participant: "org.acme.mynetwork.Trader"operation: READ, CREATEresource: "org.acme.mynetwork.Lot"action: ALLOW} rule TraderUpdateLots {description: "Allow traders to update lots via Trade transaction"participant: "org.acme.mynetwork.Trader"operation: UPDATEresource: "org.acme.mynetwork.Lot"transaction: "org.acme.mynetwork.Trade"action: ALLOW} rule TraderReadOwnTrader {description: "Allow traders to read their own info"participant(t): "org.acme.mynetwork.Trader"operation: READresource(tt): "org.acme.mynetwork.Trader"condition: (tt.getIdentifier() == t.getIdentifier())action: ALLOW} rule TraderAddAsset {description: "Allow traders to add assets to registry"participant: "org.acme.mynetwork.Trader"operation: CREATEresource: "org.hyperledger.composer.system.AddAsset"action: ALLOW} rule TraderCreateHistorianRecord {description: "Allow traders to create historian record"participant: "org.acme.mynetwork.Trader"operation: CREATEresource: "org.hyperledger.composer.system.HistorianRecord"action: ALLOW} rule TraderReadOwnHistorianRecord {description: "Allow traders to read their own historian record"participant(t): "org.acme.mynetwork.Trader"operation: READresource(hr): "org.hyperledger.composer.system.HistorianRecord"condition: (hr.transactionType == "org.acme.mynetwork.Trade" && hr.participantInvoking.getIdentifier() == t.getIdentifier())action: ALLOW} /* Client */ rule ClientReadOwnTrades {description: "Allow clients to view their trades"participant(c): "org.acme.mynetwork.Client"operation: READresource(t): "org.acme.mynetwork.Trade"condition: (t.client.getIdentifier() == c.getIdentifier())action: ALLOW} rule ClientReadOwnEvents {description: "Allow clients to subscribe to NewTrade events"participant(c): "org.acme.mynetwork.Client"operation: READresource(e): "org.acme.mynetwork.NewTradeEvent"condition: (e.lot.owner.getIdentifier() == c.getIdentifier())action: ALLOW} rule ClientReadOwnLots {description: "Allow clients to view lots they own"participant(c): "org.acme.mynetwork.Client"operation: READresource(s): "org.acme.mynetwork.Lot"condition: (s.owner.getIdentifier() == c.getIdentifier())action: ALLOW} rule ClientReadOwnClient {description: "Allow clients to view their info"participant(c): "org.acme.mynetwork.Client"operation: READresource(cc): "org.acme.mynetwork.Client"condition: (cc.getIdentifier() == c.getIdentifier())action: ALLOW} rule ClientReadTraders {description: "Allow clients to view traders"participant: "org.acme.mynetwork.Client"operation: READresource: "org.acme.mynetwork.Trader"action: ALLOW} rule ClientReadHistorianRecord {description: "Allow clients to view their historian records"participant(c): "org.acme.mynetwork.Client"operation: READresource(hr): "org.hyperledger.composer.system.HistorianRecord"condition: (hr.transactionType == "org.acme.mynetwork.Trade" && hr.transactionInvoked.client.getIdentifier() == c.getIdentifier())action: ALLOW} /* Custodian */ rule CustodianReadAllTrades {description: "Allow custodian to view all trades"participant: "org.acme.mynetwork.Custodian"operation: READresource: "org.acme.mynetwork.Trade"action: ALLOW} rule CustodianReadAllLots {description: "Allow custodian to view all lots"participant: "org.acme.mynetwork.Custodian"operation: READresource: "org.acme.mynetwork.Lot"action: ALLOW} rule CustodianReadAllClients {description: "Allow custodian to view all clients"participant: "org.acme.mynetwork.Custodian"operation: READresource: "org.acme.mynetwork.Client"action: ALLOW} rule CustodianReadAllEvents {description: "Allow custodian to subscribe to NewTrade events"participant: "org.acme.mynetwork.Custodian"operation: READresource: "org.acme.mynetwork.NewTradeEvent"action: ALLOW} rule CustodianReadAllTraders {description: "Allow custodian to view all traders"participant: "org.acme.mynetwork.Custodian"operation: READresource: "org.acme.mynetwork.Trader"action: ALLOW} rule CustodianReadOwnCustodian {description: "Allow custodian to view their info"participant(c): "org.acme.mynetwork.Custodian"operation: READresource(cc): "org.acme.mynetwork.Custodian"condition: (cc.getIdentifier() == c.getIdentifier())action: ALLOW} rule CustodianReadHistorianRecord {description: "Allow custodian to view all trade historian records"participant(t): "org.acme.mynetwork.Custodian"operation: READresource(hr): "org.hyperledger.composer.system.HistorianRecord"condition: (hr.transactionType == "org.acme.mynetwork.Trade")action: ALLOW} After all steps are completed, we can deploy the application by clicking on the left. Update How to test in Playground Now that we have the complete definition of our application, we can test it using the Composer Playground Test module (click at the top). Test 1. Create participants First, let’s seed our application with some participants. We can create 2 clients: a custodian and a trader. 2. Create identities for each participant In order to be able to interact with the business network a participant needs an associated identity. Identities can be created in the ID registry (open the user dropdown on the top right). We’ll create an identity for each participant by specifying its name and pointing to an instance of the participant created in the previous step. At a minimum, we need 4 identities: 2 clients, 1 trader, and 1 custodian. 3. Login as Trader We can now login using the newly created identities by clicking “Use now” next to each identity in the list. First, we login as a trader and create a new Lot that doesn’t belong to any client (client ID is null). Then, we assign the ownership of this lot to client 1 via a Trade transaction. Now, let’s create one more lot and assign it to client 2 via another Trade. 4. Login as Client Then, we’ll login as client 1 and client 2 to check if we can see our Lots. Client 1 lots Client 2 lots As you can see, each client sees only lots that they have ownership of and they are not aware of trades for other clients. 5. Login as Custodian Let’s login as a custodian to check that we can view all trades. Indeed, both trades are visible, as you can see below. Custodian can also view history of all trades: Note on decentralization Keep in mind that when a transaction is broadcasted to the Hyperledger Fabric network, each configured peer is independently executing the custom transaction processing logic which ensures the correctness of the blockchain update. That’s a major architectural difference and — in many cases — an advantage over the traditional centralized, client-server approach. In the traditional approach a single organization would be responsible for managing the data and exposing it to other participants, forcing others to trust it and making it a potential single point of failure. In the blockchain approach, each peer is responsible for managing and reconciling their own copy of the data. This makes the entire network more resilient and decentralized. However, it is not completely simple to configure. There are additional privacy implications to consider since the data is now hosted in many places which creates a potentially larger threat surface. Conclusion Hopefully this has given you a better understanding of what Hyperledger Composer is and how it can be useful in sharing information and keeping track of assets and other concepts among multiple participants. Feel free to export the .bna file of your network and follow the tutorial to deploy the application to a local Hyperledger Fabric network. We’d love to hear your thoughts on Hyperledger Composer. Please, leave any questions or comments below. here

Choosing private blockchain tech: Hyperledger Composer

by Lukas Larsson This is an update to our previous blog post, Erlang 19.0 Garbage Collector . With Erlang/OTP 20.0, some things have changed, which is the reason for this updated blog post. Erlang manages dynamic memory with a . More precisely a per process generational semi-space copying collector using copy collection algorithm together with a global large object space. tracing garbage collector Cheney’s Overview Each Erlang process has its own stack and heap which are allocated in the same memory block and grow towards each other. When the stack and the heap , the garbage collector is triggered and memory is reclaimed. If not enough memory was reclaimed, the heap will grow. meet Creating Data Terms are created on the heap by evaluating expressions. There are two major types of terms: which require no heap space (small integers, atoms, pids, port ids etc) and cons or (tuple, big num, binaries etc) that do require heap space. Immediate terms do not need any heap space because they are embedded into the containing structure. immediate terms boxed terms Let’s look at an example that returns a tuple with the newly created data. data(Foo) -> Cons = [42|Foo], Literal = {text, "hello world!"}, {tag, Cons, Literal}. In this example we first create a new cons cell with an integer and a tuple with some text. Then a tuple of size three wrapping the other values with an atom tag is created and returned. On the heap tuples require a word size for each of its elements as well as for the header. Cons cells always require two words. Adding these things together, we get seven words for the tuples and 26 words for the cons cells. The string is a list of cons cells and thus requires 24 words. The atom and the integer do not require any additional heap memory since it is an . Adding all the terms together, the heap space required in this example should be 33 words. "hello world!" tag 42 immediate Compiling this code to beam assembly ( ) shows exactly what is happening. erlc -S ... {test_heap,6,1}. {put_list,{integer,42},{x,0},{x,1}}. {put_tuple,3,{x,0}}. {put,{atom,tag}}. {put,{x,1}}. {put,{literal,{text,"hello world!"}}}. return. Looking at the assembler code we can see three things; The heap requirement in this function turns out to be only six words, as seen by the instruction. All the allocations are combined to a single instruction. The bulk of the data is a . Literals, sometimes referred to as constants, are not allocated in the function since they are a part of the module and allocated at load time. {test_heap,6,1} {text, "hello world!"} literal If there is not enough space available on the heap to satisfy the instructions request for memory, then a garbage collection is initiated. It may happen immediately in the instruction, or it can be delayed until a later time depending on what state the process is in. If the garbage collection is delayed, any memory needed will be allocated in heap fragments. Heap fragments are extra memory blocks that are a part of the young heap, but are not allocated in the contigious area where terms normally reside. See for more details. test_heap test_heap The young heap The collector Erlang has a copying semi-space garbage collector. This means that when doing a garbage collection, the terms are copied from one distinct area, called the , to a new clean area, called the . The collector starts by (stack, registers, etc). from space to space scanning the root-set It follows all the pointers from the root-set to the heap and copies each term word by word to the . to space After the header word has been copied a is destructively placed in it pointing to the term in the . Any other term that points to the already moved term will and copy the referring pointer instead. For example, if the have the following Erlang code: move marker to space see this move marker foo(Arg) -> T = {test, Arg}, {wrapper, T, T, T}. Only one copy of T exists on the heap and during the garbage collection only the first time T is encountered will it be copied. After referenced by the root-set have been copied, the collector scans the and copies all terms that these terms reference. When scanning, the collector steps through each term on the and any term still referencing the is copied over to the . Some terms contain non-term data (the payload of a on heap binary for instance). When encountered by the collector, these values are simply skipped. all terms to space to space from space to space Every term object we can reach is copied to the and stored on top off the line, and then the scan stop is moved to the end of the last object. to space scan stop When marker to the marker, the garbage collection is done. At this point we can the entire and therefore reclaim the entire young heap. scan stop catches up scan start deallocate from space Generational Garbage Collection In addition to the collection algorithm described above, the Erlang garbage collector also provides generational garbage collection. An additional heap, called the old heap, is used where the long lived data is stored. The original heap is called the young heap, or sometimes the allocation heap. With this in mind we can look at the Erlang’s garbage collection again. During the copy stage anything that should be copied to the young is instead copied to the old it is . to space to space if below the high-watermark The is placed where the previous garbage collection (described in ) ended and we have introduced a new area called the old heap. When doing the normal garbage collection pass, any term that is located below the high-watermark is copied to the old instead of the young. high-watermark Overview to space In the next garbage collection, any pointers to the old heap will be ignored and not scanned. This way the garbage collector does not have to scan the long-lived terms. Generational garbage collection aims to increase performance at the expense of memory. This is achieved because only the young, smaller, heap is considered in most garbage collections. The generational predicts that most terms tend to die young, and for an immutable language such as Erlang, young terms die even faster than in other languages. So for most usage patterns the data in the new heap will die very soon after it is allocated. This is good because it limits the amount of data copied to the old heap and also because the garbage collection algorithm used is proportional to the amount of live data on the heap. hypothesis One critical issue to note here is that any term on the young heap can reference terms on the old heap but term on the old heap may refer to a term on the young heap. This is due to the nature of the copy algorithm. Anything referenced by an old heap term is not included in the reference tree, root-set and its followers, and hence is not copied. If it was, the data would be lost, fire and brimstone would rise to cover the earth. Fortunately, this comes naturally for Erlang because the terms are immutable and thus there can be no pointers modified on the old heap to point to the young heap. no To reclaim data from the old heap, both young and old heaps are included during the collection and copied to a common . Both the of the young and old heap are then deallocated and the procedure will start over from the beginning. This type of garbage collection is called a full sweep and is triggered when the size of the area under the high-watermark is larger than the size of the free area of the old heap. It can also be triggered by doing a manual call to , or by running into the young garbage collection limit set by where N is the number of young garbage collections to do before forcing a garbage collection of both young and old heap. to space from space erlang:garbage_collect() spawn_opt(fun(),[{fullsweep_after, N}]) The young heap The young heap, or the allocation heap, consists of the stack and heap as described in the Overview. However, it also includes any heap fragments that are attached to the heap. All of the heap fragments are considered to be above the high-watermark and part of the young generation. Heap fragments contain terms that either did not fit on the heap, or were created by another process and then attached to the heap. For instance if the bif binary_to_term created a term which does not fit on the current heap without doing a garbage collection, it will create a heap-fragment for the term and then schedule a garbage collection for later. Also if a message is sent to the process, the payload may be placed in a heap-fragment and that fragment is added to young heap when the message is matched in a receive clause. This procedure differs from how it worked prior to Erlang/OTP 19.0. Before 19.0, only a contiguous memory block where the young heap and stack resided was considered to be part of the young heap. Heap fragments and messages were immediately copied into the young heap before they could be inspected by the Erlang program. The behaviour introduced in 19.0 is superior in many ways — most significantly it reduces the number of necessary copy operations and the root set for garbage collection. Sizing the heap As mentioned in the Overview the size of the heap to accommodate more data. Heaps grow in two stages, first a is used starting at 233 words. Then at about 1 mega words the heap only . grows variation of the Fibonacci sequence grows in 20% increments There are two occasions when the young heap grows: if the total size of the heap + message and heap fragments exceeds the current heap size. if after a fullsweep, the total amount of live objects is greater than 75%. There are two occasions when the young heap is shrunk: if after a young collection, the total amount of live objects is less than 25% of the heap and the young heap is “big” if after a fullsweep, the total amount of live objects is less than 25% of the heap. The old heap is always one step ahead in the heap growth stages than the young heap. Literals When garbage collecting a heap (young or old) all literals are left in place and not copied. To figure out if a term should be copied or not when doing a garbage collection the following pseudo code is used: if (erts_is_literal(ptr) || (on_old_heap(ptr) && !fullsweep)) { /* literal or non fullsweep - do not copy */ } else { copy(ptr); } The check works differently on different architectures and operating systems. [erts_is_literal](https://github.com/erlang/otp/blob/OTP-19.0/erts/emulator/beam/global.h#L1452-L1465) On 64 bit systems that allow mapping of unreserved virtual memory areas (most operating systems except Windows), an area of size 1 GB (by default) is mapped and then all literals are placed within that area. Then all that has to be done to determine if something is a literal or not is . This system relies on the fact that a memory page that has not been touched yet does not take any actual space. So even if 1 GB of virtual memory is mapped, only the memory which is actually needed for literals is allocated in ram. The size of the literal area is configurable through the +MIscs erts_alloc option. two quick pointer checks On 32 bit systems, there is not enough virtual memory space to allocate 1 GB for just literals, so instead small 256 KB sized literal regions are created on demand and a card mark bit-array of the entire 32 bit memory space is then used to determine if a term is a literal or not. Since the total memory space is only 32 bits, the card mark bit-array is only 256 words large. On a 64 bit system the same bit-array would have to be 1 tera words large, so this technique is only viable on 32 bit systems. Doing is a little more expensive then just doing the pointer checks that can be done in 64 bit systems, but not extremely so. lookups in the array On 64 bit windows, on which erts_alloc cannot do unreserved virtual memory mappings, a within the Erlang term object is used to determine if something . This is very cheap, however, the tag is only available on 64 bit machines, and it is possible to do a great deal of other nice optimizations with this tag in the future (like for instance a more compact list implementation) so it is not used on operating systems where it is not needed. special tag is a literal or not This behaviour is different from how it worked prior to Erlang/OTP 19.0. Before 19.0 the literal check was done by checking if the pointer pointed to the young or old heap block. If it did not, then it was considered a literal. This lead to considerable overhead and strange memory usage scenarios, so it was removed in 19.0. Binary heap The binary heap works as a large object space for binary terms that are greater than 64 bytes (from now on called off-heap binaries). The binary heap is and a pointer to the off-heap binary is stored on the process heap. To keep track of when to decrement the reference counter of the off-heap binary, a linked list (the MSO — mark and sweep object list) containing funs and externals as well as off-heap binaries is woven through the heap. After a garbage collection is done, the and any off-heap binary that does not have a written into the header words has its reference . reference counted MSO list is swept move marker decremented and is potentially freed All items in the MSO list are ordered by the time they were added to the process heap, so when doing a minor garbage collection, the MSO sweeper only has to sweep until it . encounters an off-heap binary that is on the old heap Virtual Binary heap Each process has a virtual binary heap associated with it that has the size of all the current off-heap binaries that the process has references to. The virtual binary heap also has a limit and grows and shrinks depending on how off-heap binaries are used by the process. The same growth and shrink mechanisms are used for the binary heap and for the term heap, so first a Fibonacci like series and then 20% growth. The virtual binary heap exists in order to garbage collections earlier when potentially there is a very large amount of off-heap binary data that could be reclaimed. This approach does not catch all problems with binary memory not being released soon enough, but it does catch a lot of them. trigger Messages Messages can become a part of the process heap at different times. This depends on how the process is configured. We can configure the behaviour of each process using or we can set a default for all processes at start using the option . process_flag(message_queue_data, off_heap | on_heap) +hmqd What do these different configurations do and when should we use them? Let’s start by going through what happens when one Erlang process sends a message to another. The sending process needs to do a couple of things: The process flag , of the receiver process, controls the message allocating strategy of the sender process in step 2 and also how the message data is treated by the garbage collector. message_queue_data The procedure above is different from how it worked prior to 19.0. Before 19.0 there was no configuration option, the behaviour was always very similar to how the option is in 19.0. on_heap Message allocating strategies If set to , the sending process will first attempt to allocate the space for the message directly on the young heap block of the receiving process. This is not always possible as it requires taking the of the receiving process. The main lock is also held when the process is executing. The possibility for a lock conflict is thus likely in an intensely collaborating system. If the sending process cannot acquire the main lock, a heap fragment is instead created for the message and the message payload is copied onto that. With the option the sender process always creates heap fragments for messages sent to that process. on_heap main lock off_heap There are a bunch of different tradeoffs that come into play when trying to figure out which of the strategies you want to use. Using may seem like a nice way to get a more scalable system as you get very little contention on the main locks, however, allocating a heap fragment is more expensive than allocating on the heap of the receiving process. So if it is very unlikely that contention will occur, it is more efficient to try to allocate the message directly on the receiving process’ heap. off_heap Using will force all messages to be part of on the young heap which will increase the amount of data that the garbage collector has to move. So if a garbage collection is triggered while processing a large amount of messages, they will be copied to the young heap. This in turn will lead to that the messages will quickly be promoted to the old heap and thus increase its size. This may be good or bad depending on exactly what the process does. A large old heap means that the young heap will also be larger, which in turn means that less garbage collections will be triggered while processing the message queue. This will temporarly increase the throughput of the process at the cost of more memory usage. However, if after all the messages have been consumed the process enters a state where a lot less messages are being received. Then it may be a long time before the next fullsweep garbage collection happens and the messages that are on the old heap will be there until that happens. So while is potentially faster than the other modes, it uses more memory for a longer time. This mode is the legacy mode which is almost how the message queue was handled before Erlang/OTP 19.0. on_heap on_heap Which one of these strategies is best depends a lot on what the process is doing and how it interacts with other processes. So, as always, profile the application and see how it behaves with the different options. [1]: C. J. Cheney. A nonrecursive list compacting algorithm. Commun. ACM, 13(11):677–678, Nov. 1970. [2]: D. Ungar. Generation scavenging: A non-disruptive high performance storage reclamation algorithm. SIGSOFT Softw. Eng. Notes, 9(3):157–167, Apr. 1984. Learn more about our work with Erlang and Elixir . Originally published at www.erlang-solutions.com .

Erlang Garbage Collector

A beginner-friendly overview of the hoisting concept in means that . Hoisting JavaScript variable and function declarations are moved to the top of their containing scope What does that translate to in my code? https://unsplash.com/ The Declaration Comes Before the Assignment Let’s consider the following snippet of code to understand how hoisting works : Maybe you expected the console.log to print , since the statement (the declaration ) comes after (the assignment ). We assigned a value to the variable it was created in the code. undefined var rockStar = “Mick Jagger” rockStar before After all it is logical to think that all the lines of code written in a JavaScript program are read, interpreted, and executed from top to bottom. There is a truth to that assumption, but the behaviour we just demonstrated tells us otherwise. In real fact, just before the code is executed, it goes through a compiler : during this phase called gets defined and the . If they are defined inside a , they are moved to the top of this function, and if they are outside a function, to the top of the global scope. lexing, scope variable and function declarations are moved to the top of their scope function Consider this other piece of code : This time, although we have the variable declared and assigned, we get an response when we try to print the variable. Why is that ? rockStar undefined The best way to get your head around this is to remember that , and this is equally true for variables and functions. all declarations are processed first , and this is equally true for variables and functions. All declarations are processed first Let’s decode what it means by reading the code just as JavaScript does. When you see , you see it as one statement, but JavaScript sees it otherwise. It actually interprets it as two distinct statements: var rockStar = “Mick Jagger” In our example, JavaScript first interprets the code, sees that the variable exists and moves it to the top of the scope: this is what is called . rockStar hoisting But the second statement, the assignment, is . Then, the code is executed by JavaScript as if it were written like this: left in place In other words, : only variable and function declarations are hoisted, while the assignments are left in place, just where they were written in the code. the declaration comes before the assignment T : only variable and function declarations are hoisted, while the assignments are left in place, just where they were written in the code. he declaration comes before the assignment Function Declarations Are Hoisted, but Function Expressions Are Not We have seen examples of hoisting, now let’s focus on the behavior of . variable functions See the following piece of code : From what we understand now of hoisting, how can we rewrite this code as if it were JavaScript interpreting it ? First keep in mind that , and that here the scope of the variable is the inside of the function . The variable declaration will then be hoisted to hoisting is per-scope name local scope movieStar the top of the function, not of the global scope. However, we also the function in the , and hoisting works the same for variable and function declarations: our function declaration will be moved to the top, before the of the function. declared movieStar global scope execution The code will be interpreted by JavaScript as follows: In this example, the function call works because the function declaration is hoisted, and interpreted as being declared it was called. movieStar before Let’s take another example : What happened here? The function I assigned to the variable , a process that is called , is not hoisted this time : when I call the function, JavaScript throws a TypeError. movieStar function expression That is because functions that are assigned to variables are not hoisted. Functions that are assigned to variables are not hoisted. As a demonstration, if I now write the code as follows it is now printing the expected result : Functions Before Variables We now know that both function declarations and variable declarations are hoisted. But there is another thing to be wary of: functions are hoisted first, and then variables. Functions are hoisted first, and then variables. Consider this code where a variable and a function are declared with the same name: The function has been hoisted before the variable, so when I print the value of it outputs the function itself. businessStar And another example where the variable is assigned a value : This time the output is the value of the variable, because the variable has been assigned a value, which overrides the function declaration. If you now try to call the function , you will get a TypeError: that’s because has been assigned the string “Gary Vaynerchuck” and is no longer a function. businessStar() businessStar Conclusion Hoisting is Javascript’s default behavior of moving declarations to the top. Rather than being available right after their declaration, variables and functions might actually be available beforehand in the code, which might lead to certain behaviors that you wouldn’t expect if you knew nothing about hoisting. That is why as a rule, it is important to make sure, as often as you can, that you: → (at the top of the global scope or at the top of the function scope) Declare all of your variables at the top of their scope → Make sure you . put all your functions, if you can, also at the top of their scope Want to learn more about JavaScript basics? Check out my other articles: Understand JavaScript Variable Scope with ease Wrap your head around “this” How to use JavaScript closures with confidence Grasp “By Value” and “By Reference” in JavaScript A Quick Handbook for Dates in JavaScript Work with JavaScript arrays like a boss I hope you enjoyed this introduction to JavaScript Hoisting. Feel free to comment and like this article so that others can find it easily on Medium !

Hoisting in JavaScript: a Quick Guide

CHICAGO, United States, April 17th, 2024/Chainwire/-- Liquid Mercury, a leading crypto trading technology provider, announced today that it is providing a request for quote (RFQ) platform to GFO-X, the UK’s first regulated and centrally cleared trading venue dedicated to digital assets derivatives. Institutional traders will be able to source deep liquidity for trading listed options and futures contracts in large trade sizes that can be transacted as single-leg or complex, multi-leg strategies. Liquid Mercury’s institutional-grade platform, combined with GFO-X’s regulated derivatives marketplace, provides a secure and efficient trading environment for institutional trading firms and professional investors. This technology allows market participants to manage digital asset exposure and hedge underlying portfolio risks. Liquidity providers can provide two-sided quotes in response to RFQs generated by hedge funds, asset managers, and other buy-side participants initiated from Liquid Mercury’s professional-grade trading interface or FIX and web socket APIs. Trades will print on GFO-X’s exchange and be cleared through a GFO-X general clearing member at LCH SA, LCH Group’s continental Europe clearing house, which is part of the largest and most sophisticated clearing services in financial markets. Brokers will also use Liquid Mercury’s platform to intermediate trades between buyers and sellers in order to cross-block trades on GFO-X’s exchange and conduct other sell-side trading workflows. “Looking at the equity derivatives market, where the growth in number of options exchanges has fueled the growth in trading participants, added more liquidity in the market, and driven record volumes of contracts traded, I see GFO-X’s entrance into crypto derivatives as a key development for a more vibrant exchange landscape. This is a prime opportunity to grow the entire crypto options market,” said Liquid Mercury CEO, Tony Saliba. “In addition to providing the technology solution that will power GFO-X’s block liquidity network, Liquid Mercury will be operating as a strategic partner, introducing our clients and broad network of traditional market players as new participants to GFO-X’s exchange.”“We chose Liquid Mercury as a partner due to the team’s pedigree in building successful technology-focused franchises in traditional markets, combined with a track record of delivering institutional-grade solutions to the professional crypto trading community,” said GFO-X CEO, Arnab Sen. “Our shared vision of a regulated derivatives market that is purpose-built for digital assets using the best market standards and technology from traditional finance underpins what we expect to be a long and very fruitful strategic partnership.” For more information about Liquid Mercury’s suite of crypto trading technology solutions, users can visit www.liquidmercury.com. About Liquid Mercury Liquid Mercury powers professional crypto trading. Liquid Mercury is the #1 choice for sophisticated buy-side and institutional sell-side legacy trading professionals moving into crypto. Institutional grade infrastructure, access to deep liquidity, and best-in-class trading tools and workflow automation; Liquid Mercury was built by professionals for professionals. For more information about Liquid Mercury, visit www.liquidmercury.com. About GFO-X Global Futures and Options Ltd (GFO-X) is the UK's first regulated and centrally cleared trading venue dedicated to digital asset derivatives. UK FCA authorized and regulated GFO-X, which is connected to the world's largest financial institutions, and has partnered with leading clearing house LCH SA, solving counterparty and credit risk. GFO-X services the significant unmet need of large global institutional participants who require a highly regulated, institutional-grade trading venue for digital asset derivatives. GFO-X’s mission is to deliver enhanced liquidity, institutional connectivity, and risk mitigation through high-performance technology and optimized contract specifications. Further information can be found at www.gfo-x.com Contact Chief Commercial Officer Ryan Hansen Liquid Mercury team@liquidmercury.com This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here. CHICAGO, United States, April 17th, 2024/Chainwire/-- Liquid Mercury, a leading crypto trading technology provider, announced today that it is providing a request for quote (RFQ) platform to GFO-X , the UK’s first regulated and centrally cleared trading venue dedicated to digital assets derivatives. Institutional traders will be able to source deep liquidity for trading listed options and futures contracts in large trade sizes that can be transacted as single-leg or complex, multi-leg strategies. GFO-X GFO-X Liquid Mercury’s institutional-grade platform, combined with GFO-X’s regulated derivatives marketplace, provides a secure and efficient trading environment for institutional trading firms and professional investors. This technology allows market participants to manage digital asset exposure and hedge underlying portfolio risks. Liquidity providers can provide two-sided quotes in response to RFQs generated by hedge funds, asset managers, and other buy-side participants initiated from Liquid Mercury’s professional-grade trading interface or FIX and web socket APIs. Trades will print on GFO-X’s exchange and be cleared through a GFO-X general clearing member at LCH SA, LCH Group’s continental Europe clearing house, which is part of the largest and most sophisticated clearing services in financial markets. Brokers will also use Liquid Mercury’s platform to intermediate trades between buyers and sellers in order to cross-block trades on GFO-X’s exchange and conduct other sell-side trading workflows. “Looking at the equity derivatives market, where the growth in number of options exchanges has fueled the growth in trading participants, added more liquidity in the market, and driven record volumes of contracts traded, I see GFO-X’s entrance into crypto derivatives as a key development for a more vibrant exchange landscape. This is a prime opportunity to grow the entire crypto options market,” said Liquid Mercury CEO, Tony Saliba. “Looking at the equity derivatives market, where the growth in number of options exchanges has fueled the growth in trading participants, added more liquidity in the market, and driven record volumes of contracts traded, I see GFO-X’s entrance into crypto derivatives as a key development for a more vibrant exchange landscape. This is a prime opportunity to grow the entire crypto options market,” said Liquid Mercury CEO, Tony Saliba. “In addition to providing the technology solution that will power GFO-X’s block liquidity network, Liquid Mercury will be operating as a strategic partner, introducing our clients and broad network of traditional market players as new participants to GFO-X’s exchange.”“We chose Liquid Mercury as a partner due to the team’s pedigree in building successful technology-focused franchises in traditional markets, combined with a track record of delivering institutional-grade solutions to the professional crypto trading community,” said GFO-X CEO, Arnab Sen. “In addition to providing the technology solution that will power GFO-X’s block liquidity network, Liquid Mercury will be operating as a strategic partner, introducing our clients and broad network of traditional market players as new participants to GFO-X’s exchange.”“We chose Liquid Mercury as a partner due to the team’s pedigree in building successful technology-focused franchises in traditional markets, combined with a track record of delivering institutional-grade solutions to the professional crypto trading community,” said GFO-X CEO, Arnab Sen. “Our shared vision of a regulated derivatives market that is purpose-built for digital assets using the best market standards and technology from traditional finance underpins what we expect to be a long and very fruitful strategic partnership.” “Our shared vision of a regulated derivatives market that is purpose-built for digital assets using the best market standards and technology from traditional finance underpins what we expect to be a long and very fruitful strategic partnership.” For more information about Liquid Mercury’s suite of crypto trading technology solutions, users can visit www.liquidmercury.com . www.liquidmercury.com www.liquidmercury.com About Liquid Mercury Liquid Mercury powers professional crypto trading. Liquid Mercury is the #1 choice for sophisticated buy-side and institutional sell-side legacy trading professionals moving into crypto. Institutional grade infrastructure, access to deep liquidity, and best-in-class trading tools and workflow automation; Liquid Mercury was built by professionals for professionals. For more information about Liquid Mercury, visit www.liquidmercury.com . Liquid Mercury Liquid Mercury www.liquidmercury.com www.liquidmercury.com About GFO-X Global Futures and Options Ltd (GFO-X) is the UK's first regulated and centrally cleared trading venue dedicated to digital asset derivatives. UK FCA authorized and regulated GFO-X, which is connected to the world's largest financial institutions, and has partnered with leading clearing house LCH SA, solving counterparty and credit risk. GFO-X services the significant unmet need of large global institutional participants who require a highly regulated, institutional-grade trading venue for digital asset derivatives. Global Futures and Options Global Futures and Options GFO-X’s mission is to deliver enhanced liquidity, institutional connectivity, and risk mitigation through high-performance technology and optimized contract specifications. Further information can be found at www.gfo-x.com www.gfo-x.com www.gfo-x.com Contact Chief Commercial Officer Ryan Hansen Liquid Mercury team@liquidmercury.com This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here. This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here. here. here.

Liquid Mercury Partners With GFO-X To Provide RFQ Platform For Trading Crypto Derivatives

Decentralization Levels: Bitcoin's Proof-of-Work vs. Obyte's DAG Approach

বিকেন্দ্রীকরণ স্তর: বিটকয়েনের কাজের প্রমাণ বনাম ওবাইটের ডিএজি পদ্ধতি

Dezentralisierungsebenen: Bitcoins Proof-of-Work vs. Obytes DAG-Ansatz

Niveles de descentralización: prueba de trabajo de Bitcoin frente al enfoque DAG de Obyte

Niveaux de décentralisation : la preuve de travail de Bitcoin par rapport à l'approche DAG d'Obyte

विकेंद्रीकरण स्तर: बिटकॉइन का प्रूफ-ऑफ-वर्क बनाम ओबाइट का DAG दृष्टिकोण

分散化レベル: ビットコインのプルーフ・オブ・ワーク vs. Obyte の DAG アプローチ

분산화 수준: 비트코인의 작업 증명과 Obyte의 DAG 접근 방식

Níveis de descentralização: Prova de Trabalho do Bitcoin vs. Abordagem DAG da Obyte

Уровни децентрализации: доказательство работы Биткойна против подхода Obyte DAG

Merkezi Olmama Düzeyleri: Bitcoin'in Proof-of-Work'ü ve Obyte'nin DAG Yaklaşımı

Cấp độ phi tập trung: Bằng chứng công việc của Bitcoin so với phương pháp tiếp cận DAG của Obyte

去中心化程度：比特币的工作量证明与 Obyte 的 DAG 方法

10 Steps to Write a Compelling About Me Page

Introduction The article demonstrates the use of null-state analysis to remove compiler warnings of “check code for null safety”. Learning Objectives Discover how to set up the nullable context in your C# project or codebase. Prerequisites for Developers Familiarity with introductory-level C# programming Know how to use Visual Studio Code or Visual Studio .NET SDK version 6.0 or newer Getting Started .NET developers often face the System.NullReferenceException, which happens when a null is referenced at runtime and results in the most common exception in the .NET application. As the creator of null, Sir Tony Hoare, refers to null as the "billion-dollar mistake." Example The variable records is set to null and then immediately referenced which results in System.NullReferenceException TestRecord records = null;
    
    _ = records.ToString();
    
    record TestRecord(int Id, string Name); As the applications grow in the number of lines of code and become more complex, spotting such issues as a developer can be challenging. This is where C# compiler steps in. Define null safety In the previous example, a developer can avoid the System.NullReferenceExceptionby checking if the records variable was null as shown below. TestRecord records = null;
    
    // Check for null
    if (records is not null)
    {
        _ = records.ToString();
    }
    
    record TestRecord(int Id, string Name); Nullable Types The default value for all reference types is null. string first;                  // first is null
    string second = string.Empty   // second is not null, instead it's an empty string ""
    int third;                     // third is 0 because int is a value type
    DateTime date;                 // date is DateTime.MinValue In the example mentioned earlier: The variable “first” is null because a reference type “string” was declared but not assigned any value. The variable “second” is assigned the value “string.Empty” during declaration. The variable “third” has a value of 0 even though it was not explicitly assigned. The variable “date” is uninitialized, but its default value is “System.DateTime.MinValue.” Post C# 2.0 version, we can define nullable values using Nullable<T>. This allowed value types to be assigned with a value of null. int? first;            // first is implicitly null (uninitialized)
    int? second = null;    // second is explicitly null
    int? third = default;  // third is null as the default value for Nullable<Int32> is null
    int? fourth = new();    // fourth is 0, since new calls the nullable constructor Nullable context As per my experience, this is a must-have feature that should be enabled in every .Net application as it enables control for how the compiler understands reference type variables. There are four types of nullable contexts disable enable warnings annotations Enable nullable context it can be enabled by adding <Nullable> item to the <PropertyGroup> inside the application .csproj file as shown below <Project Sdk="Microsoft.NET.Sdk">
    
        <PropertyGroup>
            <OutputType>Exe</OutputType>
            <TargetFramework>net6.0</TargetFramework>
            <Nullable>enable</Nullable>
        </PropertyGroup>
    
        
    
    </Project> Alternatively, developers can also add scope nullable context which means the nullable context will be applicable only in the defined scope. #nullable enable Complete Code on GitHub GitHub — ssukhpinder/30DayChallenge.Net C# Programming🚀 Thank you for being a part of the C# community! Before you leave: If you’ve made it this far, please show your appreciation with a clap and follow the author! 👏️️ Follow us: | | | | | X LinkedIn Dev.to Hashnode Newsletter Tumblr Visit our other platforms: | | | | GitHub Instagram Tiktok Quora Daily.dev More content at C# Programming Also published . here

The 30-Day .NET Challenge, Day 9: Null Safety

VANCOUVER, Canada, March 27th, 2024/Chainwire/-- In a significant move to fuel the growth of decentralized artificial intelligence, ceτi AI is proud to announce the establishment of the ceτi AI Partnership Innovation Fund with an initial allocation of $500k USD. This strategic fund is designed to catalyze innovation by forging partnerships with emerging projects at the intersection of and blockchain technology. AI Overview ceτi AI, a leader in decentralized AI infrastructure, is initiating this fund to support projects that align with its mission of democratizing access to advanced AI technologies. The fund aims to facilitate collaborative efforts that push the boundaries of what's possible in decentralized AI, enhancing the ecosystem's diversity and driving technological advancement. Investment Details The Partnership Innovation Fund is a pivotal step at the beginning of ceτi AI's journey, dedicating an initial $500k USD worth of resources to empower innovation through strategic alliances. This initiative underscores ceτi AI's commitment to not just being a participant but a catalyst in the decentralized AI marketplace. Use of Proceeds The allocation of this fund will specifically target: Strategic Partnerships: Identifying and supporting projects that demonstrate the potential to revolutionize decentralized AI with innovative solutions. Technology Advancement: Encouraging the development of technologies that can seamlessly integrate with ceτi AI's infrastructure, contributing to the ecosystem's growth and scalability. Community and Ecosystem Development: Strengthening the decentralized AI community by backing projects that bring value, diversity, and innovation to the space. Why Partner with ceτi AI? ceτi AI stands at the forefront of the decentralized AI revolution, with a proven track record of innovation and a strong commitment to an open, accessible AI future. By partnering with ceτi AI through the Partnership Innovation Fund, projects can gain access to: Expertise and Collaboration: Leverage ceτi AI's deep industry knowledge and technical expertise to accelerate project development. Network and Visibility: Benefit from ceτi AI's established network and gain visibility within the broader decentralized AI and blockchain communities. Shared Vision: Users can join a collective effort to shape the future of AI, promoting a decentralized approach that prioritizes accessibility, diversity, and innovation. A Vision for Partnership and Innovation Tony Evans, Chief Strategy Officer at ceτi AI, shared his vision for the fund, "The ceτi AI Partnership Innovation Fund isn't just about financial investment; it's a testament to our belief in the transformative power of collaboration at the intersection of AI and blockchain. By supporting projects that share our vision of an open, decentralized AI future, we're not just fueling innovation; we're actively building the community and ecosystem that will drive the future of technology. This fund is our invitation to innovators and visionaries who dare to dream big and join us in shaping the next frontier of AI." Shaping the Future with ceτi AI ceτi AI invites crypto AI projects, developers, and innovators looking to make a substantial impact in the decentralized AI space to explore partnership opportunities with ceτi AI. Those interested in the Partnership Innovation Fund or seeking more detailed information can contact ceτi AI at . partnership@taoceti.ai About ceτi AI ceτi AI is pioneering the decentralization of artificial intelligence. Our mission is to build a globally distributed, high-performance, scalable AI infrastructure accessible to developers and networks across the globe, thereby accelerating the advancement of decentralized AI and fostering a more diverse and inclusive AI ecosystem. For a deeper dive into our mission, technology, and the future we’re building, users can refer to ceτi AI and visit ceτi AI website at . , , and for the latest updates and community discussions. litepaper http://taoceti.ai Follow us on X Telegram Discord Contact Information: For press inquiries, users can contact: .For inquiries regarding the Partnership Innovation Fund, users can contact: press@taoceti.ai partnership@taoceti.ai Contact Chief Strategy Officer Tony Evans ceτi AI partnership@taoceti.ai This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here.

ceτi AI Announces Partnership Innovation Fund To Accelerate Innovation In Decentralized AI

Explaining the Infinite Potential of Quantum Computing in Marvel-lous English (No Math)

মার্ভেল-লাউস ইংলিশে কোয়ান্টাম কম্পিউটিং এর অসীম সম্ভাবনা ব্যাখ্যা করা (কোন গণিত নয়)

Erklären Sie das unendliche Potenzial des Quantencomputings in wunderbarem Englisch (keine Mathematik)

Explicando el potencial infinito de la computación cuántica en un inglés bastante divertido (sin matemáticas)

Expliquer le potentiel infini de l'informatique quantique dans un anglais plutôt drôle (pas de mathématiques)

क्वांटम कंप्यूटिंग की अनंत संभावनाओं को मज़ेदार अंग्रेजी में समझाना (कोई गणित नहीं)

量子コンピューティングの無限の可能性を面白い英語で説明する (数学なし)

놀라운 영어로 양자 컴퓨팅의 무한한 잠재력 설명(수학 없음)

Explicando o potencial infinito da computação quântica em inglês bastante engraçado (sem matemática)

Kuantum Hesaplamanın Sonsuz Potansiyelini Harika İngilizceyle Açıklamak (Matematiksiz)

Giải thích tiềm năng vô hạn của máy tính lượng tử bằng tiếng Anh khá hài hước (Không có toán học)

用相当有趣的英语解释量子计算的无限潜力（无数学）

How We Built a Debit Card From Scratch in Only 4 Months

আমরা কীভাবে মাত্র 4 মাসে স্ক্র্যাচ থেকে একটি ডেবিট কার্ড তৈরি করেছি

Wie wir in nur 4 Monaten eine Debitkarte von Grund auf entwickelt haben

Cómo creamos una tarjeta de débito desde cero en solo 4 meses

Comment nous avons créé une carte de débit à partir de zéro en seulement 4 mois

हमने कैसे सिर्फ़ 4 महीनों में एक डेबिट कार्ड बनाया

わずか 4 か月でデビット カードをゼロから構築する方法

단 4개월 만에 직불카드를 처음부터 만드는 방법

Como construímos um cartão de débito do zero em apenas 4 meses

Как мы создали дебетовую карту с нуля всего за 4 месяца

Sadece 4 Ayda Sıfırdan Nasıl Banka Kartı Oluşturduk?

Cách chúng tôi xây dựng thẻ ghi nợ từ đầu chỉ trong 4 tháng

我们如何仅用 4 个月时间从零开始打造借记卡

GEORGE TOWN, Grand Cayman, April 23rd, 2024/Chainwire/-- Zircuit, a ZK rollup with parallelized circuits and AI-enabled security, today announced that its staking program has soared past $2B in TVL in only two months. As Zircuit forges ahead towards mainnet, the ZK rollup’s staking program is rapidly gaining momentum, attracting over $2B TVL in a short 2 months. Zircuit Staking allows users to earn Zircuit Points by depositing assets that can then be migrated to Zircuit's mainnet upon launch. Users can deposit ETH, liquid staking tokens (LSTs), liquid restaking tokens (LRTs), and USDe (Ethena USD) to earn Zircuit Points. In March, Zircuit launched its Build to Earn program to incentivize builders, founders, and community members who contribute to the Zircuit ecosystem. The program has already attracted over 1,000 submissions. With Zircuit's growing pre-launch TVL and community, approved projects will capture the interest of users in one of the fastest-growing L2s and benefit from a supercharged developer experience. In addition, developers and ecosystem contributors who participate in Zircuit’s Build to Earn program stand to receive rewards for approved contributions. Projects that join Zircuit through Build to Earn will join the growing list of top-tier launch partners including Ethena, Ether.fi, KelpDAO, Renzo, Hashflow, Galxe, Elixir, Blocto, Eigenpie, and many others. Zircuit is currently in testnet with plans to launch its mainnet in Summer 2024. To participate in Build to Earn, users can visit: https://build.zircuit.com/ To participate in Zircuit Staking, users can visit: https://stake.zircuit.com/ About Zircuit Zircuit is a ZK rollup with parallelized circuits and AI-enabled security at the sequencer level. Built by a team of web3 security veterans and PhDs in computer science, algorithms, and cryptography, Zircuit is backed by Pantera Capital, Dragonfly Capital, and Maelstrom. To learn more visit zircuit.com or follow us on Twitter/X @ZircuitL2 Contact Jessica Graber Zircuit jessica@zircuit.com This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here. GEORGE TOWN, Grand Cayman, April 23rd, 2024/Chainwire/-- Zircuit, a ZK rollup with parallelized circuits and AI-enabled security, today announced that its staking program has soared past $2B in TVL in only two months. staking program staking program As Zircuit forges ahead towards mainnet, the ZK rollup’s staking program is rapidly gaining momentum, attracting over $2B TVL in a short 2 months. Zircuit Staking allows users to earn Zircuit Points by depositing assets that can then be migrated to Zircuit's mainnet upon launch. Users can deposit ETH, liquid staking tokens (LSTs), liquid restaking tokens (LRTs), and USDe (Ethena USD) to earn Zircuit Points. In March, Zircuit launched its Build to Earn program to incentivize builders, founders, and community members who contribute to the Zircuit ecosystem. The program has already attracted over 1,000 submissions. With Zircuit's growing pre-launch TVL and community, approved projects will capture the interest of users in one of the fastest-growing L2s and benefit from a supercharged developer experience. In addition, developers and ecosystem contributors who participate in Zircuit’s Build to Earn program stand to receive rewards for approved contributions. Projects that join Zircuit through Build to Earn will join the growing list of top-tier launch partners including Ethena, Ether.fi, KelpDAO, Renzo, Hashflow, Galxe, Elixir, Blocto, Eigenpie, and many others. Zircuit is currently in testnet with plans to launch its mainnet in Summer 2024. To participate in Build to Earn, users can visit: https://build.zircuit.com/ https://build.zircuit.com/ https://build.zircuit.com/ To participate in Zircuit Staking, users can visit: https://stake.zircuit.com/ https://stake.zircuit.com/ https://stake.zircuit.com/ About Zircuit Zircuit is a ZK rollup with parallelized circuits and AI-enabled security at the sequencer level. Built by a team of web3 security veterans and PhDs in computer science, algorithms, and cryptography, Zircuit is backed by Pantera Capital, Dragonfly Capital, and Maelstrom. To learn more visit zircuit.com or follow us on Twitter/X @ZircuitL2 zircuit.com zircuit.com @ZircuitL2 @ZircuitL2 Contact Jessica Graber Zircuit jessica@zircuit.com This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here. This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here. here. here.

Zircuit Staking Soars Past $2B TVL In Only Two Months

Zircuit Staking মাত্র দুই মাসে $2B TVL ছাড়িয়েছে

Zircuit-Staking übersteigt in nur zwei Monaten 2 Milliarden US-Dollar TVL

La apuesta por Zircuit supera los 2.000 millones de dólares TVL en sólo dos meses

Le jalonnement de Zircuit dépasse les 2 milliards de dollars TVL en seulement deux mois

ज़िरकुइट की हिस्सेदारी केवल दो महीनों में $2B TVL से अधिक हो गई

Zircuit ステーキング、わずか 2 か月で TVL 20 億ドルを突破

지르퀴트 스테이킹은 단 2개월 만에 TVL 20억 달러를 넘어 치솟았습니다.

Zircuit Staking ultrapassa US$ 2 bilhões de TVL em apenas dois meses

Ставка Zircuit превысила $2 млрд TVL всего за два месяца

Zircuit Staking Sadece İki Ayda 2 Milyar Doları Geçti

Đặt cược Zircuit tăng vọt vượt qua 2 tỷ USD TVL chỉ sau hai tháng

Zircuit 质押总锁定金额仅两个月就突破 20 亿美元

This paper is available on Arxiv under CC 4.0 license.
Authors:
(1) Seth P. Benson, Carnegie Mellon University (e-mail: spbenson@andrew.cmu.edu);
(2) Iain J. Cruickshank, United States Military Academy (e-mail: iain.cruickshank@westpoint.edu) Table of Links Abstract and Intro Related Research Methodology Results Discussion Conclusion and References V. DISCUSSION This research yielded several significant results. Firstly, our findings suggest that it is feasible to computationally extract media bias characterizations with minimal human bias assessments. The empirical examination of news programs from three cable news networks, each with different perceived biases, generally confirmed broadly held assumptions about these programs. The biases exhibited by these programs remained consistent over time and strongly aligned with their respective networks. However, the key contribution of our work is not merely the affirmation of common media pundit observations but the fact that we reached this conclusion through an automated machine-learning model. This model required minimal human input and did not necessitate a priori bias specifications. While our findings still necessitate human interpretation of the resulting clusters, they are largely free from subjective human assessments and do not require media expertise for operation. The objective nature of this model demonstrates its potential to replace current media evaluation methods, which still heavily rely on human interpretation and assumptions. It also provides a tool for exploring realms where biases are not yet known, such as social media user biases. For political science researchers, this paper offers a novel framework for analyzing media bias and a confirmation of many implicit assumptions about biases in cable news. Another significant finding of this research is the application of stance analysis methods to cable news transcript text. Our results indicated that stance analysis, employing task-based prompting with GPT-4, was far more effective than sentiment analysis in characterizing programs’ attitudes towards topics. Our stance analysis method generated more variance in programs’ views towards topics and produced variance that better aligned with real-world expectations of programs’ viewpoints. This method not only offers an improvement for understanding cable news transcripts, but it also demonstrates a more advanced way to identify writing style bias in media generally. However, the model presented in this paper does have certain limitations. It identifies key topics within transcripts as the most frequently used named entities, which may overlook certain types of topics, such as policy discussions. Furthermore, while stance detection was a significant improvement over sentiment analysis, the stance labeling of sentences within transcripts was not always accurate. The extent of these inaccuracies remains undetermined due to the absence of a labeled dataset for the cable news transcripts we utilized. Nevertheless, it appears that certain specific subsets, like short sentences lacking key context, resulted in a higher amount of incorrect labeling. The improvement of stance detection, in both a zero-shot setting like this and on spokenword text that comprises news transcripts, remains a crucial area for future research. This paper is available on Arxiv under CC 4.0 license. This paper is available on Arxiv under CC 4.0 license. Authors: (1) Seth P. Benson, Carnegie Mellon University (e-mail: spbenson@andrew.cmu.edu); (2) Iain J. Cruickshank, United States Military Academy (e-mail: iain.cruickshank@westpoint.edu) This paper is available on Arxiv under CC 4.0 license. Authors: Authors: (1) Seth P. Benson, Carnegie Mellon University (e-mail: spbenson@andrew.cmu.edu); (2) Iain J. Cruickshank, United States Military Academy (e-mail: iain.cruickshank@westpoint.edu) Table of Links Abstract and Intro Abstract and Intro Related Research Related Research Methodology Methodology Results Results Discussion Discussion Conclusion and References Conclusion and References V. DISCUSSION This research yielded several significant results. Firstly, our findings suggest that it is feasible to computationally extract media bias characterizations with minimal human bias assessments. The empirical examination of news programs from three cable news networks, each with different perceived biases, generally confirmed broadly held assumptions about these programs. The biases exhibited by these programs remained consistent over time and strongly aligned with their respective networks. However, the key contribution of our work is not merely the affirmation of common media pundit observations but the fact that we reached this conclusion through an automated machine-learning model. This model required minimal human input and did not necessitate a priori bias specifications. While our findings still necessitate human interpretation of the resulting clusters, they are largely free from subjective human assessments and do not require media expertise for operation. The objective nature of this model demonstrates its potential to replace current media evaluation methods, which still heavily rely on human interpretation and assumptions. It also provides a tool for exploring realms where biases are not yet known, such as social media user biases. For political science researchers, this paper offers a novel framework for analyzing media bias and a confirmation of many implicit assumptions about biases in cable news. Another significant finding of this research is the application of stance analysis methods to cable news transcript text. Our results indicated that stance analysis, employing task-based prompting with GPT-4, was far more effective than sentiment analysis in characterizing programs’ attitudes towards topics. Our stance analysis method generated more variance in programs’ views towards topics and produced variance that better aligned with real-world expectations of programs’ viewpoints. This method not only offers an improvement for understanding cable news transcripts, but it also demonstrates a more advanced way to identify writing style bias in media generally. However, the model presented in this paper does have certain limitations. It identifies key topics within transcripts as the most frequently used named entities, which may overlook certain types of topics, such as policy discussions. Furthermore, while stance detection was a significant improvement over sentiment analysis, the stance labeling of sentences within transcripts was not always accurate. The extent of these inaccuracies remains undetermined due to the absence of a labeled dataset for the cable news transcripts we utilized. Nevertheless, it appears that certain specific subsets, like short sentences lacking key context, resulted in a higher amount of incorrect labeling. The improvement of stance detection, in both a zero-shot setting like this and on spokenword text that comprises news transcripts, remains a crucial area for future research. This paper is available on Arxiv under CC 4.0 license. This paper is available on Arxiv under CC 4.0 license. Arxiv

Characterizing Cable News Bias: Discussing What It All Means

This paper is available on Arxiv under CC 4.0 license.
Authors:
(1) Seth P. Benson, Carnegie Mellon University (e-mail: spbenson@andrew.cmu.edu);
(2) Iain J. Cruickshank, United States Military Academy (e-mail: iain.cruickshank@westpoint.edu) Table of Links Abstract and Intro Related Research Methodology Results Discussion Conclusion and References IV. RESULTS A. MONTHLY PROGRAM AGGREGATION Looking at the monthly level we also observe that the program clusters generally follow the networks. Table 1, which depicts the Adjusted Rand Index between clustering assignments and network, shows that this is the case for most months. Other than in January, each month has an Adjusted Rand Index greater than 0.35, indicating an association between a program’s network and its clustering assignment in our model. B. FULL YEAR RESULTS Our next results examine clusters across the year 2020. Figure 6 contains a Sankey diagram of all clustering assignments of programs across months. In this diagram, each individual line on the verticle axis represents a cluster, while moving from right to left represents going from January to December 2020. Two things are apparent. First, the clusters are consistent over time. Although there are a few periods of major movement between months (especially in the first two months), most months see very few programs change clusters. Between March and December, there are rarely more than a couple of transcripts changing clusters at a time. The programs that do frequently move tend to be those typically less thought of as partisan, such as "Fox News Sunday". Like the single months’ results, we also see over time that the clustering assignments are strongly tied to networks. With a few exceptions, almost all of the red Fox programs are in the same cluster throughout the year. Similarly, the blue MSNBC programs are largely assigned to the same cluster over time. The tan CNN programs are also mostly together, although several cross over to the MSNBC-dominated middle cluster at different points of the year. The Sankey diagram shows programs grouped by cluster and network, but we also want to determine how individual programs compare to each other in their clustering assignments. Using Principle Component Analysis (PCA), we reduce the clustering assignments from each month into two dimensions. Like the Sankey diagram, the PCA plot, Figure 7, reveals that programs are largely grouped together by network. Most of the Fox programs are tightly grouped together, with a few exceptions like Fox News Sunday placed closer to CNN and MSNBC programs. Opposite the Fox programs on the plot, MSNBC programs are also closely placed. Interestingly, CNN programs are more spread out, with some programs intermingled with the group of MSNBC programs while others are placed away from both the Fox and MSNBC groups. However, having many CNN and MSNBC programs placed closely together aligns with the conventional belief that CNN and MSNBC are similar in ideology and lean in the opposite direction of Fox. C. EFFECTIVENESS SENTIMENT VERSUS STANCE IN BIAS DETERMINATION As mentioned in the results section, using sentiment similarity to measure how programs talk about topics failed to produce meaningful variation between programs. On the other hand, stance similarity produced a much greater distinction between programs. One way to measure this is through the similarity matrix standard deviation. Given that some cable news programs are much closer to each other than others in their views on key topics and very different from others, we would expect there to be clusters of high similarity and large regions of low similarity. On the other hand, if a similarity matrix has values that are small in magnitude and close together, it would indicate that the measure of similarity is unable to distinguish between programs. Thus, having a higher similarity matrix standard deviation indicates that a model is better at capturing real-world conditions. Sentiment similarity resulted in a standard deviation (0.05 on average) that was only a third as large as the topic similarity standard deviation (0.15 on average), indicating that it captured much less variation between programs. Meanwhile, using stance resulted in a standard deviation (0.16 on average) roughly equal to the topic similarity standard deviation. Another way to compare performance between the methods is to look at the variance of programs’ sentiment and stance towards specific topics. 8 shows the distribution of program sentiment and stance towards "Trump" and "Democrats" in April 2020. Because these are highly partisan terms, we would expect the networks to differ in their coverage of them. We can see that this is not the case for sentiment, as programs from all three networks are tightly grouped together around the same average sentiment values. Comparatively, stance does a much better job of both providing variance between programs and sorting by network. The distribution for both terms covers a much wider numerical range than the sentiment results and there appears to be some sorting by the network, with CNN and MSNBC transcripts being generally more negative than Fox towards "Trump" and more positive than Fox towards "Democrats". While the variance and network sorting produced through stance might not completely match expectations, they are much more meaningful in separating out bias-driven opinions toward entities than the sentiment results. These results suggest that local sentiment does not properly identify differences in how topics are discussed on cable news. Sentiment’s weakness might be because different programs discussing the same topic are directing similar sentiments in opposing directions. For example, the phrases "the investigation into President Trump is fraudulent" and "the investigation reveals President Trump’s failures" have similar sentiments but frame the same figure very differently. Consequently, our results also reveal the relative strength of stance analysis and suggest that it is the better approach for analyzing political text. This paper is available on Arxiv under CC 4.0 license. This paper is available on Arxiv under CC 4.0 license. Authors: (1) Seth P. Benson, Carnegie Mellon University (e-mail: spbenson@andrew.cmu.edu); (2) Iain J. Cruickshank, United States Military Academy (e-mail: iain.cruickshank@westpoint.edu) This paper is available on Arxiv under CC 4.0 license. Authors: Authors: (1) Seth P. Benson, Carnegie Mellon University (e-mail: spbenson@andrew.cmu.edu); (2) Iain J. Cruickshank, United States Military Academy (e-mail: iain.cruickshank@westpoint.edu) Table of Links Abstract and Intro Abstract and Intro Related Research Related Research Methodology Methodology Results Results Discussion Discussion Conclusion and References Conclusion and References IV. RESULTS A. MONTHLY PROGRAM AGGREGATION Looking at the monthly level we also observe that the program clusters generally follow the networks. Table 1, which depicts the Adjusted Rand Index between clustering assignments and network, shows that this is the case for most months. Other than in January, each month has an Adjusted Rand Index greater than 0.35, indicating an association between a program’s network and its clustering assignment in our model. B. FULL YEAR RESULTS Our next results examine clusters across the year 2020. Figure 6 contains a Sankey diagram of all clustering assignments of programs across months. In this diagram, each individual line on the verticle axis represents a cluster, while moving from right to left represents going from January to December 2020. Two things are apparent. First, the clusters are consistent over time. Although there are a few periods of major movement between months (especially in the first two months), most months see very few programs change clusters. Between March and December, there are rarely more than a couple of transcripts changing clusters at a time. The programs that do frequently move tend to be those typically less thought of as partisan, such as "Fox News Sunday". Like the single months’ results, we also see over time that the clustering assignments are strongly tied to networks. With a few exceptions, almost all of the red Fox programs are in the same cluster throughout the year. Similarly, the blue MSNBC programs are largely assigned to the same cluster over time. The tan CNN programs are also mostly together, although several cross over to the MSNBC-dominated middle cluster at different points of the year. The Sankey diagram shows programs grouped by cluster and network, but we also want to determine how individual programs compare to each other in their clustering assignments. Using Principle Component Analysis (PCA), we reduce the clustering assignments from each month into two dimensions. Like the Sankey diagram, the PCA plot, Figure 7, reveals that programs are largely grouped together by network. Most of the Fox programs are tightly grouped together, with a few exceptions like Fox News Sunday placed closer to CNN and MSNBC programs. Opposite the Fox programs on the plot, MSNBC programs are also closely placed. Interestingly, CNN programs are more spread out, with some programs intermingled with the group of MSNBC programs while others are placed away from both the Fox and MSNBC groups. However, having many CNN and MSNBC programs placed closely together aligns with the conventional belief that CNN and MSNBC are similar in ideology and lean in the opposite direction of Fox. C. EFFECTIVENESS SENTIMENT VERSUS STANCE IN BIAS DETERMINATION As mentioned in the results section, using sentiment similarity to measure how programs talk about topics failed to produce meaningful variation between programs. On the other hand, stance similarity produced a much greater distinction between programs. One way to measure this is through the similarity matrix standard deviation. Given that some cable news programs are much closer to each other than others in their views on key topics and very different from others, we would expect there to be clusters of high similarity and large regions of low similarity. On the other hand, if a similarity matrix has values that are small in magnitude and close together, it would indicate that the measure of similarity is unable to distinguish between programs. Thus, having a higher similarity matrix standard deviation indicates that a model is better at capturing real-world conditions. Sentiment similarity resulted in a standard deviation (0.05 on average) that was only a third as large as the topic similarity standard deviation (0.15 on average), indicating that it captured much less variation between programs. Meanwhile, using stance resulted in a standard deviation (0.16 on average) roughly equal to the topic similarity standard deviation. Another way to compare performance between the methods is to look at the variance of programs’ sentiment and stance towards specific topics. 8 shows the distribution of program sentiment and stance towards "Trump" and "Democrats" in April 2020. Because these are highly partisan terms, we would expect the networks to differ in their coverage of them. We can see that this is not the case for sentiment, as programs from all three networks are tightly grouped together around the same average sentiment values. Comparatively, stance does a much better job of both providing variance between programs and sorting by network. The distribution for both terms covers a much wider numerical range than the sentiment results and there appears to be some sorting by the network, with CNN and MSNBC transcripts being generally more negative than Fox towards "Trump" and more positive than Fox towards "Democrats". While the variance and network sorting produced through stance might not completely match expectations, they are much more meaningful in separating out bias-driven opinions toward entities than the sentiment results. These results suggest that local sentiment does not properly identify differences in how topics are discussed on cable news. Sentiment’s weakness might be because different programs discussing the same topic are directing similar sentiments in opposing directions. For example, the phrases "the investigation into President Trump is fraudulent" and "the investigation reveals President Trump’s failures" have similar sentiments but frame the same figure very differently. Consequently, our results also reveal the relative strength of stance analysis and suggest that it is the better approach for analyzing political text. This paper is available on Arxiv under CC 4.0 license. This paper is available on Arxiv under CC 4.0 license. Arxiv

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