Wing is an all in one in-person networking software for businesses 

Wing

wing

I sold my startup because of bugs: I wish I had this serverless repository! In 2021, I built a startup called Linvo, a LinkedIn automation tool. It generated good money but also had a high burn rate. I can tell you that I lost most of my money as a young entrepreneur (entirely bootstrapped) 😆 The problem with my system is that it kept on failing; I am not the best programmer in the world, but let me elaborate. Puppeteer Sucks Your Memory Linvo went into a few iterations during its lifetime. I used the tech that I know: React and NestJS. Basically, any automated cloud scraping goes into making tasks and then solving them. It looks something like this: Cron runs every 10 minutes, looks for new stuff to do, and sends it to the queue. Workers take tasks from the queue and process them. Save everything to the DB. This is a very naive solution that was running on multiple EC2 (AWS) and Redis. In theory, it should work - Yes, you would need to scale the workers horizontally, but that was not my problem. I started to experience something else - memory leaks. As you probably know, - a chromium automated browser, is heavy (same as Chrome on your computer), and I was running lots of them - they sucked both my memory - causing the servers to stop working and taking the internet bandwidth. Puppeteer Needed a Solution Refactoring sounds like - yeah, let's do that, but my Intercom support was filled with support tickets (I already had many users), and I knew I had to create some crazy refactoring. I had an idea for a solution - let's use AWS lambda. Lambda start Do the task Die (most of the time) It doesn't suck your internet. It will die and not cause a memory leak. It's a bit more expensive because Lambdas were not meant to run indefinitely—that would remove the whole concept, but I was desperate. (Well, the good thing is that you don't need to think about scale.) Here is the new infrastructure: Event bridge cron running every 1 minute (that's the minimum) Lambda that creates six events for the queue (making it like the cron runs every 10 seconds) SQS queue (bye bye, Redis) Lambdas shooting out with auto-scaling (yay?) Vendor Locking As you probably understand from the context - now I am all in with AWS, but I didn't mind - only how I would test it on my computer. Back in the day, I used . It's almost the same as to create the entire environment quickly. Sam Serverless And yes, they do bring some local testing. They can take your lambdas and turn them into endpoints! Yay! But I was using Event Bridge, SQS, and Lambdas. That was just not possible. I have encountered . It was supposed to help me build an entire AWS stack on my computer, but many things were not supported back then, such as Lambda layers. And I was overwhelmed in using it. localstack So I started to create my "own" infrastructure where I deployed one "test" SQS on AWS and ran local code with it (a function that calls a function), and it was - bad. I did not work well in production; I felt like the word was squeezing up on me back in the day. I just wanted to get rid of this startup; I even offered it to people for free, taking the customers and rid myself of that problem. Long story short, I managed to sell the system for a low price, but lately, I discovered something interesting. Winglang - Typescript-like dialect for the Cloud I guess my pain was real, and even though I would be happier to find this back in the day - I am glad it's alive today. helps you to build the same cloud infrastructure as I used, but the nice thing is: Winglang It doesn't require you to use any configuration files. You basically define the entire infrastructure with code (you can check their entire preflight and inflight documentation). You can run it locally - thank you, god, 🙏🏻🙏🏻🙏🏻🙏🏻 with all the infrastructure included. You can deploy it to any cloud provider, and you don't have to use AWS. They can literally generate any Terraform or cloud formation for easy deployment. I talked to the founder of Winglang, Elad (builder of the famous CDK), and he said something that made me twitch. You can literally work on your projects when you are on an airplane and write tests for it 🤯 I can't give them enough stars myself - but do me a favor and star their repository and of course, use it for your next project. https://github.com/winglang/wing Final Words I am angry at Winglang. I wish they had done it sooner. Now they show up. When it's all about AI? I started a new YouTube channel mostly about Tech, open-source, and marketing :) If that's something that interests you, feel free to subscribe to it here: https://www.youtube.com/@nevo-david?sub_confirmation=1

I Sold My Startup Because of Bugs: I Wish Winglang Existed Back Then

You can jump to any section of the National Aeronautics Science & Technology Priorities here. Introduction U.S. leadership in the aeronautics enterprise has long underpinned economic prosperity and national security. The aeronautics enterprise comprises civil and military aircraft and aviation systems, the air transportation management system, aviation infrastructure, and the people involved in the research, design, development, manufacture, operation and use of these aircraft and aviation systems. The aeronautics enterprise is an integral part of the U.S. economy, annually generating 4.9 percent of U.S. gross domestic product—$1.9 trillion in total economic activity in 2019—and more than 10 million jobs.[1] The aeronautics sector is also a major exporter, the second largest manufacturing export sector in the United States, generating $148 billion in exports in 2019.[2] From next-generation fuels, to advances in aircraft design, to improvements in the aviation infrastructure, the aeronautics enterprise is poised for disruption and transformation. As such, continued investment in state-of-the-art aeronautics research and development (R&D) is critical to ensure the continued vitality of this important sector. One of the major challenges facing the global aviation industry is to reduce the climate impacts of aviation. Consistent with the 2021 Aviation Climate Action Plan[3] and national security policies, the U.S. Government will prioritize efforts to reduce and ultimately achieve net-zero emissions from United States aviation by 2050. These efforts include improved efficiency in flight operations, innovations in engine and aircraft technology, and the use of emerging energy sources such as Sustainable Aviation Fuels (SAFs), electricity, and hydrogen. In light of the International Civil Aviation Organization (ICAO) goals for net-zero carbon emissions, the U.S. Government will prioritize decarbonization to maintain its global economic competitiveness, national security, and benefits to humanity. The U.S. National Airspace System (NAS) is the most complex, most efficient, and safest airspace system in the world. It accommodates approximately 2.3 million passengers and 66,000 tons of cargo on 25,000 flights daily. These numbers are projected to increase, as emerging aviation technologies such as drones, air taxis, balloons, and the increasingly frequent launch and re-entry of spacecraft, place increased strain on the NAS. Accordingly, it is imperative that the NAS safely transform into the Federal Aviation Administration’s (FAA) Next Generation Air Transportation System (NextGen). Transforming into NextGen is necessary to create an optimal operating environment for integrating new aircraft and traffic management systems, while completing upgrades to legacy systems, and enhancing connectivity and speed. New aeronautics technologies are providing users of the NAS better connectivity options, with greater speed. Drones offer the ability to perform complex operations faster and more efficiently. Large electric aircraft can move people or cargo around urban environments with greater speed. Quiet supersonic and hypersonic aircraft have the potential to traverse huge distances in a fraction of current times. Autonomous systems can improve operational and energy efficiencies, while promoting sustainability. These advances in connectivity and speed are expected to significantly benefit both economic competitiveness and national security. To that end, the U.S. Government will prioritize maintaining an agile regulatory framework that rewards innovation and technological advances, while promoting safety and sustainability. The United States will maintain its global leadership in the aeronautics enterprise by supporting R&D to: (1) achieve sustainable aviation, (2) transform the national airspace system, and (3) promote connectivity and speed. The United States will pursue these priorities as guided by seven principles: (1) Safety, (2) Environment, (3) Economic Competitiveness, (4) Innovation, (5) Security, (6) Workforce, and (7) Equity. [1] Federal Aviation Administration. “The Economic Impact U.S. Civil Aviation: 2020”. https://www.faa.gov/sites/faa.gov/files/2022-08/2022-APL 038%202022_economic%20impact_report.pdf. Released August 2022 [2] International Trade Administration. “Leading Economic Indicators Aerospace Industry”. Accessed February 21, 2023. https://www.trade.gov/leading-economic-indicators-aerospace-industry [3] Federal Aviation Administration. “United States 2021 Aviation Climate Action Plan”. https://www.faa.gov/sites/faa.gov/files/2021-11/Aviation_Climate_Action_Plan.pdf. Released November 2021 This was originally published in March 2023 on whitehouse.gov. This content has been broken-up into bite sized sections, each with unique headlines and AI-generated lead images, in line with the provisions of Creative Commons 3.0.

Wings of Prosperity: U.S. Aeronautics Leadership Anchors Economic Growth and Security

THE YELLOW-WINGED SPHEX

Field, Forest and Farm by Jean-Henri Fabre, is part of the HackerNoon Books Series. You can jump to any chapter in this book here . SHEATH-WINGED INSECTS CHAPTER XLVI. SHEATH-WINGED INSECTS “I show you here the scarab, clothed all in black. Passionate lover of the sun, it rarely strays beyond the regions bordering on the Mediterranean. It belongs to the band of scavengers, a group of handsome insects which, feeding on ordure, are charged with the sanitation of the greensward defiled by grazing herds. “Its favorite dish is the dung of horses and mules. With the toothed edges of its head it rummages in the dung; with its wide, serrate fore legs it cuts up this material, kneads it, and molds it into a ball about as large as an apricot. This done, the next thing is to seek out some quiet retreat far from the hubbub of its fellows who have been drawn to the spot for as much as a kilometer round about by the odor; and of course the booty must be trundled away to this secure retreat, there to be eaten at ease, without fear of predatory assaults from the envious. “This task is performed in couples. One hooks on to the globule in front and pulls with head up; the other pushes from behind with head down. Heave ho! It starts, it rolls, under the combined efforts of the two partners. On the down grade the load again and again runs away with the team, which [ ]falls headlong, gets up again, and catches hold of the cargo once more with an ardor that nothing can discourage. Under the rays of a scorching sun this store of provision is thus dragged a long distance over the sand, across the greensward, and over ruts. Perhaps the scarabs find their bread at first not sufficiently compact, and seek to give it consistency by rolling it on the ground. Every one according to his fancy. 236 “At last a favorable spot is selected in a sandy tract. One of the two proprietors hollows out in all haste a dining-room, while the other stands guard without over the globular treasure, ready to defend it stoutly against any chance marauder. As soon as the dining-room is ready the provisions are stored away there, after which the two colleagues shut themselves up in their domicile, safe from unwelcome visitors, closing their door with sand. So there they are at table, with their heap of victuals in front of them; and now for a good feast! When the board is bare again, the two banqueters leave their dugout to gather together a new globule and resume their feasting. “The scarab is not found everywhere, the more’s the pity, for its manner of life is very curious to watch. Wanting this manufacturer of globules, we nevertheless do have everywhere other scavengers which work in somewhat similar fashion. Out of ordure they put together little balls of the size of a cherry, and sometimes they roll away their plunder, as does the scarab, bury it in the ground, and there [ ]regale themselves on it. Their trade of making these little balls or pills has given them the expressive name of pill-mixers. 237 “Let us pass on to other kinds. This one, for example, is called the calosoma. By reason of the elegance of its form and the richness of its coloring it is one of the most beautiful insects of our region. Its back has the brilliance of a gem such as no jeweler ever possessed. One would really take it to be made of gold, but gold of a peculiar sort and much richer than ours, flashing as it does with red, green, and purple glints. There is nothing to compare with this dazzling costume. It should be added that if the insect is taken between the fingers it emits, as a means of defense, a strong odor reminding one of a chemist’s disagreeable drugs. “The calosoma does not share the scarab’s peaceful habits: it is an ardent hunter and leads a life of carnage. Its prey is the caterpillar, the bigger the better, whether smooth-skinned or hairy. If you happen to find a calosoma, put it into a good-sized bottle and give it for dinner a lusty young caterpillar as large as your finger. You will see with what ferocious satisfaction this drinker of blood will disembowel the poor worm, despite all its writhing and squirming, and will feast on its green entrails. “The carabid, which is also a passionate lover of game, has the calosoma’s activity and brilliance, but is of smaller size. Some are bronze in color, others golden, still others of a copper tint, or black edged with a superb violet. All explore with keen scrutiny [ ]the thick tufts of grass, and give chase to small prey such as larvæ, caterpillars, and worms. The most common example of this class wears a golden green coat and is a frequenter of gardens, where it makes war on all kinds of vermin. It is the little guardian of our beds of peas and beans, and of our flower borders. In honor of its services to the garden we call it the gardener. 238 “The calosoma and the carabid do not fly; they are made for running, as is evident from their long legs, their agile movements, and their lithe form. They chase the game in hot pursuit, or else lie in wait for it behind a leaf, but never pursue it on the wing. On the other hand, the scarab, the common June-bug, and a host of other insects fly very well.” “But why don’t they all fly?” asked Emile. “I will tell you,” replied his uncle. “Look carefully at the June-bug a moment. It has two kinds of wings: on the outside two large and substantial scales of horn, and beneath these two fine membranous wings, expanded during flight, but carefully folded together and concealed when not in use. The outside scales are called elytra, or sheaths. They serve as a case for enclosing and protecting the delicate membranous wings, which alone are fitted for flying. The carabid and the calosoma have sheaths of splendid brilliance, it is true, but beneath these sheaths there are no membranous wings to spread themselves in flight and fold up again in repose. Hence these two insects are unable to fly. “The dytiscus and the hydrophile, whose names [ ]signify ‘diver’ and ‘water-lover’ respectively, both frequent the waters of deep ponds, of ditches, and of pools. With their legs flattened out like oars, their very smooth bodies, arched above and keel-shaped below, they are first-rate swimmers and divers. It is a feast for the eye to follow the graceful agility of their oars when they row calmly on the surface or plunge beneath it. 239 “At the least alarm they dart quickly to the bed of the pond and take refuge amid the water plants. On the instant of diving their belly is seen to flash like a plate of polished silver. The reason of this borrowed sheen is found in a thin layer of air that they carry with them adherent to the belly. With this supply they will have air to breathe until, all danger past, they ascend again to the surface. “In the matter of costume these two master-swimmers are of modest appearance. Both are of a very somber olive green, but in addition the dytiscus wears faded gold lace on its sheaths. If the pond dries up or ceases to please them, they can quickly betake themselves to another—not on foot, for their flattened legs, excellent as oars, are worthless in walking, but by flight, with the help of their membranous wings, ordinarily hidden under the sheaths, where the water cannot reach them. “In old oak trees the larva of the capricorn-beetle, another ravager of forests, leads much the same kind of life as does the grub of the stag-beetle. Large in size, all black with gleams of chestnut, this insect is remarkable for its jointed horns, which are longer [ ]than its body. What can it do with these cumbersome ornaments? Does it wear them on its forehead to intimidate the foe? I would not venture to dispute the matter, but what I do know very well is that with its extravagantly long horns it frightens the inexperienced young pupil so that he dares not touch it, and he calls it the devil. All the same, the capricorn-beetle does not deserve the evil reputation it has got from the timid. It is perfectly harmless. 240 “Insects’ horns are called antennæ. All have them, some longer and some shorter, now of one shape, now of another. In some instances they are flexible filaments, jointed chaplets; in others, short stems ending in either a cluster of little buds or a bunch of leaves pressed one against the other. See for example the burly and magnificent insect that browses the foliage of our pine-trees on warm summer days. It is called the pine-beetle. On a chestnut background it wears a sprinkling of white spots. The antennæ carry at the end a set of little plates or scales which open and shut like the leaves of a book. “It is in place here to mention the common June-bug, furnished like the pine-beetle with antennæ bearing leaf-clusters at the end. I propose to tell you its story in detail; for, if this little creature is the joy of young people of your age, it is also the terror of the farmer. “But first one word more to conclude our short story of sheath-winged insects. Their number is immense. Nearly all have membranous wings under [ ]the protecting case formed by the sheaths; and these can fly. Others, relatively few, are unprovided with membranous wings, and hence are unfitted for flight. This entire group bears the general name of coleoptera, meaning sheath-winged. A coleopter is any insect furnished with sheaths, whether it flies or not.” 241 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. Jean-Henri Fabre (2022). Field, Forest and Farm. Urbana, Illinois: Project Gutenberg. Retrieved October https://www.gutenberg.org/cache/epub/67813/pg67813-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 www.gutenberg.org , located at https://www.gutenberg.org/policy/license.html.

SHEATH-WINGED INSECTS

Inside Musk's Social Circle: Yes-men, Media Personalities, and (a lot of) right-wing VCs

Dentro del círculo social de Musk: sí-hombres, personalidades de los medios y (muchos) capitalistas de riesgo de derecha

À l'intérieur du cercle social de Musk : les hommes du oui, les personnalités des médias et (beaucoup) les VC de droite

मस्क के सोशल सर्कल के अंदर: हाँ-पुरुष, मीडिया हस्तियां, और (बहुत सारे) दक्षिणपंथी वीसी

Musk のソーシャル サークルの内部: イエスメン、メディアパーソナリティ、および (多くの) 右翼 VC

Dentro do Círculo Social de Musk: sim-men, personalidades da mídia e (muitos) VCs de direita

Bên trong vòng kết nối xã hội của Musk: Yes-men, Media Personalies, và (rất nhiều) nhà đầu tư mạo hiểm cánh hữu

马斯克的社交圈内幕：应声虫、媒体名人和（很多）右翼风投

Astounding Stories of Super-Science April 1930, by Astounding Stories is part of HackerNoon’s Book Blog Post series. You can jump to any chapter in this book here . Monsters of Moyen - Chapter III: Wings of To-morrow Astounding Stories of Super-Science April 1930: Monsters of Moyen - CHAPTER III. Wings of To-morrow AS Prester Kleig climbed into the enclosed passenger pit of the monoplane—a Mayther—his ears seemed literally to be ringing with the drumming, mighty voice of Moyen. But now that voice, instead of merely speaking, rang with sardonic laughter. He had never heard the laughter of Moyen, but he could guess how it would sound. That airplane of the slanted wings, the bulbous, almost bulletlike fuselage, what of it? It was simple, as Kleig looked back at his memoried glimpse of it. The submarine was a metal fish made with human hands; the airplane aped the birds. The strange ship which had caused the destruction of the Stellar, was a combination fish and bird—which merely aped nature a bit further, as anyone who had ever traversed tropical waters would have instantly recognized. But what did it portend? What ghastly terrors of Moyen roamed the deeps of the Atlantic, of the Pacific, the oceans of the world? How close were some of these to the United States? The pale eyes of Moyen, he was sure, were already turned toward the West. PRESTER KLEIG sighed as he seated himself beside Carlos Kane. Then Kane pressed one of the myriad of buttons on the dash, and Kleig lifted his eyes to peer through the skylight, to where that single press of a button had set in motion the intricate machinery of the helicopter. A four-bladed fan lifted on a slender pedestal, sufficiently high above the surface of the wing for the vanes to be free of the central propeller. Then, automatically, the vanes became invisible, and the Mayther lifted from the sandy beach as lightly, and far more straightly, than any bird. As the ship climbed away for the skies, and through the transparent floor the beach and the Atlantic fell away below the ship, a sigh of relief escaped Kleig. This was living! Up here one was free, if only for a moment, and the swift wind of flight brushed all cobwebs from the tired human brain. He watched the slender needle of the altimeter, as it moved around the face of the dial as steadily as the hands of a clock, around to thirty thousand, thirty-five, forty. Then Carlos Kane, every movement as effortless as the flight of the silvery winged Mayther, thrust forth his hand to the dash again, pressed another button. Instantly the propellers vanished into a blur as the vanes of the helicopter dropped down the slender staff and the vanes themselves fitted snugly into their appointed notches atop the wing. FOR a second Carlos Kane glanced at the tiny map to the right of the dash, and set his course. It was a matter of moments only, but while Kane worked, Prester Kleig studied the instruments on the dash, for it had been months since he had flown, save for his recent half-dreamlike experience. There was a button which released the mechanism of the deadly guns, fired by compressed air, all operated from the noiseless motor, whose muzzles exactly cleared the tips of Mayther's wings, two guns to each wing, one on the entering edge, one on the trailing edge, fitted snugly into the adamant rigging. Four guns which could fire to right or left, twin streams of lead, the number of rounds governed only by the carrying power of the Mayther. Prester Kleig knew them all: the guns in the wings, the guns which fired through the three propellers, and the guns set two and two in the fuselage, to right and left of the pits, which could be fixed either up or down—all by the mere pressing of buttons. It was marvelous, miraculous, yet even as Kleig told himself that this was so, he felt, deep in the heart of him, that Moyen knew all about ships like these, and regarded them as the toys of children. Kane touched Kleig on the shoulder, signaling, indicating that the atmosphere in the pits had been regulated to their new height, and that they could remove their helmets and oxygen tanks without danger. WITH a sigh Prester Kleig sat back, and the two friends turned to face each other. "You certainly look done in, Kleig," said Kane sympathetically. "You must have been through hell, and then some. Tell me about this Moyen; that is, if you think you care to talk about him." "Talk about him!" repeated Kleig. "Talk about him? It will be a relief! There has been nothing, and nobody, on my mind save Moyen for weary months on end. If I don't talk to someone about him, I'll go mad, if I'm not mad already. Moyen? A monster with the face of an angel! What else can one say about him? A devil and a saint, a brute whose followers would go with him into hell's fire, and sing him hosannas as they were consumed in agony! The greatest mob psychologist the world has ever seen. He's a genius, Kane, and unless something is done, the Western world, all the world, is doomed to sit at the feet, listen to the commands, of Moyen! "He isn't an Oriental; he isn't a European; he isn't negroid or Indian; but there is something about him that makes one thing of all of these, singly and collectively. His body is twisted and grotesque, and when one looks at his face, one feels a desire to touch him, to swear eternal fealty to him—until one looks into his pale eyes, eyes almost milky in their paleness—and gets the merest hint of the thoughts which actuate him. If he has a failing I did not find it. He does not drink, gamble...." "And women?" queried Kane, softly. KLEIG was madly in love with the sister of Kane, Charmion, and this thing touched him nearest the heart, because Charmion was one of her country's most famous beauties, about whom Moyen must already have heard. "Women?" repeated Kleig musingly, his black eyes troubled, haunted. "I scarcely know. He has no love for women, only because he has no capacity for any love save self-love. But when I think of him in this connection I seem to see Moyen, grown to monster proportions, sitting on a mighty throne, with nude women groveling at his feet, bathed in tears, their long hair in mantles of sorrow, hiding their shamed faces! That sounds wild, doesn't it? But it's the picture I get of Moyen when I think of Moyen and of women. Many women will love him, and have, perhaps. But while he has taken many, though I am only guessing here, he has given himself to none. Another thing: His followers—well, he sets no limits to the lusts of his men, requiring only that every soldier be fit for duty, with a body strong for hardship. You understand?" Kane understood; and his face was very pale. "Yes," he said, his voice almost a whisper, "I understand, and as you speak of this man I seem to see a city in ruins, and hordes of men marching, bloodstained men entering houses ... from which, immediately afterward, come the screams of women ... terror-stricken women...." He shuddered and could not go on for the very horror of the vision that had come to him. But Kleig stared at him as though he saw a ghost. "Great God, Carl!" he gasped. "The same identical picture has been in my mind, not once but a thousand times! I wonder...." Was it an omen of the future for the West? Deep in his soul Prester Kleig fancied he could hear the sardonic laughter of the half-god, Moyen. A TINY bell rang inside the dash, behind the instruments. Kane had set direction finders, had pressed the button which signaled the Washington-control Station of the National Radio, thus automatically indicating the exact spot above land, by grid-coordinates, where the Mayther should start down for the landing. An hour later they landed on the flat roof of the new Capitol Building, sinking lightly to rest as a feather, nursed to a gentle landing by the whirring vanes of the helicopter. Prester Kleig, surrounded by uniformed guards who tried to shield him from the gaze of news-gatherers crowded there on the roof-top, hurried him to the stairway leading into the executive chambers, and through these to the Secret Chamber which only a few men knew, and into which not even Carlos Kane could follow Prester Kleig—yet. But one man, one news-gatherer, had caught a glimpse of the face of Kleig, and already he raced for the radio tower of his organization, to blazon to the Western world the fact that Kleig had come back. 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. Astounding Stories. 2009. Astounding Stories of Super-Science, April 1930. Urbana, Illinois: Project Gutenberg. Retrieved May 2022 from https://www.gutenberg.org/files/29390/29390-h/29390-h.htm#Monsters_of_Moyen 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 www.gutenberg.org , located at https://www.gutenberg.org/policy/license.html .

Wings of To-morrow

For the Story Teller: Story Telling and Stories to Tell, by Carolyn Sherwin Bailey is part of the HackerNoon Books series. You can jump to any chapter in this book here . The Little Rabbit Who Wanted Red Wings The Little Rabbit Who Wanted Red Wings Once upon a time there was a little White Rabbit with two beautiful long pink ears and two bright red eyes and four soft little feet—such a pretty little White Rabbit, but he wasn’t happy. Just think, this little White Rabbit wanted to be somebody else instead of the nice little rabbit that he was. When Mr. Bushy Tail, the gray squirrel, went by, the little White Rabbit would say to his Mammy: “Oh, Mammy, I wish I had a long gray tail like Mr. Bushy Tail’s.” And when Mr. Porcupine went by, the little White Rabbit would say to his Mammy: “Oh, Mammy, I wish I had a back full of bristles like Mr. Porcupine’s.” And when Miss Puddle-Duck went by in her two little red rubbers, the little White Rabbit would say: “Oh, Mammy, I wish I had a pair of red rubbers like Miss Puddle-Duck’s.” So he went on and on wishing until his Mammy was clean tired out with his wishing and Old Mr. Ground Hog heard him one day. Old Mr. Ground Hog is very wise indeed, so he said to the little White Rabbit: “Why don’t you-all go down to Wishing Pond, and if you look in the water at yourself and turn around three times in a circle, you-all will get your wish.” So the little White Rabbit trotted off, all alone by himself through the woods until he came to a little pool of green water lying in a low tree stump, and that was the Wishing Pond. There was a little, little bird, all red, sitting on the edge of the Wishing Pond to get a drink, and as soon as the little White Rabbit saw him he began to wish again: “Oh, I wish I had a pair of little red wings!” he said. Just then he looked in the Wishing Pond and he saw his little white face. Then he turned around three times and something happened. He began to have a queer feeling in his shoulders, like he felt in his mouth when he was cutting his teeth. It was his wings coming through. So he sat all day in the woods by the Wishing Pond waiting for them to grow, and, by and by, when it was almost sundown, he started home to see his Mammy and show her, because he had a beautiful pair of long, trailing red wings. But by the time he reached home it was getting dark, and when he went in the hole at the foot of a big tree where he lived, his Mammy didn’t know him. No, she really and truly did not know him, because, you see, she had never seen a rabbit with red wings in all her life. And so the little White Rabbit had to go out again, because his Mammy wouldn’t let him get into his own bed. He had to go out and look for some place to sleep all night. He went and went until he came to Mr. Bushy Tail’s house, and he rapped on the door and said: “Please, kind Mr. Bushy Tail, may I sleep in your house all night?” But Mr. Bushy Tail opened his door a crack and then he slammed it tight shut again. You see he had never seen a rabbit with red wings in all his life. So the little White Rabbit went and went until he came to Miss Puddle-Duck’s nest down by the marsh and he said: “Please, kind Miss Puddle-Duck, may I sleep in your nest all night?” But Miss Puddle-Duck poked her head up out of her nest just a little way and then she shut her eyes and stretched her wings out so far that she covered her whole nest. You see she had never seen a rabbit with red wings in all her life. So the little White Rabbit went and went until he came to Old Mr. Ground Hog’s hole and Old Mr. Ground Hog let him sleep with him all night, but the hole had beech nuts spread all over it. Old Mr. Ground Hog liked to sleep on them, but they hurt the little White Rabbit’s feet and made him very uncomfortable before morning. When it came morning, the little White Rabbit decided to try his wings and fly a little, so he climbed up on a hill and spread his wings and sailed off, but he landed in a low bush all full of prickles, and his four feet got mixed up with the twigs so he couldn’t get down. “Mammy, Mammy, Mammy, come and help me!” he called. His Mammy didn’t hear him, but Old Mr. Ground Hog did, and he came and helped the little White Rabbit out of the prickly bush. “Don’t you-all want your red wings?” Mr. Ground Hog asked. “No, no!” said the little White Rabbit. “Well,” said the Old Ground Hog, “why don’t you-all go down to the Wishing Pond and wish them off again?” So the little White Rabbit went down to the Wishing Pond and he saw his face in it. Then he turned around three times, and, sure enough, his red wings were gone. Then he went home to his Mammy, who knew him right away and was so glad to see him and he never, never wished to be something different from what he really was again. Southern Folk Tale. 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. Bailey, Carolyn Sherwin. 2018. For the Story Teller: Story Telling and Stories to Tell. Urbana, Illinois: Project Gutenberg. Retrieved April 2022 from https://www.gutenberg.org/files/58107/58107-h/58107-h.htm#Page_185 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 www.gutenberg.org , located at https://www.gutenberg.org/policy/license.html .

Stories for Telling: The Little Rabbit Who Wanted Red Wings

Scalability is currently one of the largest concerns for the Ethereum network. Despite being the most popular blockchain development platform, the network still suffers from scalability issues, from slow transactions to high gas costs once the network gets congested. This can have a negative effect on decentralized applications (dapps) due to high transaction costs as well as stablecoins such as USDC and USDT which require users to use ETH to pay for transactions. There are protocols that are working on tackling these issues faced by protocols on the Ethereum network, one of which is a Cardano-based decentralized exchange. The platform works as a decentralized exchange (DEX) and automated market maker (AMM), and they worked with the to provide stablecoins USDC and USDT to the Cardano mainnet ecosystem. The platform also reached a total value locked (TVL) of 62.5m ADA after launching its mainnet. WingRiders , Milkomeda Foundation The platform aims to provide a cheaper and more scalable alternative to Ethereum-based dapps and services including a decentralized exchange (DEX) with an automated market maker (AMM) as well as stablecoins (USDT and USDC). Before we look into the platform, let’s delve into the scalability concerns with Ethereum. Ethereum and its Scalability Issues Scalability is an issue for Ethereum, as we've seen with Bitcoin in previous years. The most widely used smart contract platform was released as a proof-of-work network in 2015. Despite being the most popular blockchain development platform, it has to develop an infrastructure that can be used by billions of people all over the globe without raising issues around scale or security. When it comes to the Ethereum network, one example of its scalability problem was the blockchain game CryptoKitties. CryptoKitties is an Ethereum-based blockchain game that was released in 2017. The game allows players to buy, collect, breed, and trade virtual cats. The game became very popular and users buying and selling kittens on the network caused the Ethereum network to since there were so many transactions. become congested The large number of transactions was a problem since Ethereum is only capable of processing around in its present form. Ethereum will need a much higher throughput to become truly scalable for global use. 30 transactions per second Furthermore, all of the nodes on the Ethereum network must process each transaction and smart contract. This restricts Ethereum's transaction throughput since the amount of work needed by a single node is effectively capped. Another reason is that the Ethereum platform utilizes the same blockchain for all dapps. Due to the massive volume of network transactions generated by CryptoKitties, the Ethereum platform's other dapps got clogged and delayed as well. As a consequence of this, a greater quantity of ETH was needed to be used whenever a transaction or contract was executed on the network. Due to the high volume of transactions, users were forced to compete for faster transaction speeds by spending more ETH (or gas) to incentivize nodes to process their transactions. Miners on the network automatically processed the more expensive transactions since they were more lucrative. Cardano is a Suitable Platform for Scalable Dapps Whilst Ethereum Transitions to ETH 2.0 Whilst proof-of-work is progressively being phased out of Ethereum, slow transactions, scalability issues, and other issues persist. Blockchain developers now have a choice with the Cardano blockchain's proof-of-stake model, and WingRiders makes it available to investors. Within six days of coming online on April 12, WingRiders had accumulated over , with a high of 62.5 million ADA in the first month before the significant crypto market downturn. Once yield farming was implemented, their mainnet went live and 1.6% (1,600,000 WRT)  of its token supply was allocated to the first month's worth of yield farming incentives and bonuses. $60 million in total value locked (TVL) The platform has a double yield farming functionality, which allows liquidity providers to earn additional tokens from the automatic staking in ADA-containing pools and yield farming of platform tokens. Those earnings are in addition to the standard exchange fees that liquidity pools receive. WingRiders uses the extended unspent transaction output (EUTXO) protocol. UTXO is the amount of cryptocurrency left behind after a transaction, some crypto users refer to this as "dust". In simple terms, it's like the change left over after transferring crypto out of your wallet. by allowing addresses to contain arbitrary logic in the form of scripts as well as being able to carry almost any kind of data. This in smart contract execution costs and enables a more powerful parallelization of transactions. EUTXO builds on the UTXO model increases security and predictability The platform’s DEX has also incorporated Ledger hardware wallets into its platform, making it possible to conduct transactions with increased security. In order to address the problems that have plagued DEXs on Ethereum, WingRiders's exchange platform uses the advantages of Cardano. Due to Cardano’s Ouroboros Proof-of-Stake (PoS) protocol, the DEX is less likely to experience excessive gas prices or failed transactions, issues that have been present with Ethereum-based DEXs. Conclusion WingRiders aims to address issues related to scalability of Ethereum-based dapps and protocols by using Cardano’s PoS blockchain to reduce transaction fees, improve speeds and prevent network congestion. If the platform succeeds, it could work as a suitable alternative for developers and users of decentralized applications and stablecoins. This story was submitted to HackerNoon by an independent contributor. Hence the information contained therein has also been researched and compiled independently. Disclosure:

WingRiders DEX Aims to Address Scalability Issues Faced by Ethereum Dapps

Photo by on JOSHUA COLEMAN Unsplash In early November, Facebook published its Q3 Widely Viewed Content , the second in a series meant to rebut critics who said its algorithms were boosting extremist and sensational content. Report The report declared that, among other things, the most popular informational content on Facebook came from sources like UNICEF, ABC News, or the CDC. But data collected by The Markup suggests that, on the contrary, sensationalist news or viral content with little original reporting performs just as well as—and often better than—many mainstream sources when it comes to how often it’s seen by platform users. from The Markup’s Citizen Browser project shows that during the period from July 1 to Sept. 30, 2021, outlets like The Daily Wire, The Western Journal, and BuzzFeed’s viral content arm were among the top-viewed domains in our sample. Citizen Browser is a national panel of paid Facebook users who automatically share their news feed data with The Markup. Data To analyze the websites whose content performs the best on Facebook, we counted the total number of times that links from any domain appeared in our panelists’ news feeds—a metric known as “impressions”—over a three-month period (the same time covered by Facebook’s Q3 Widely Viewed Content Report). Facebook, by contrast, chose a different metric, calculating the “most-viewed” domains by tallying only the number of users who saw links, regardless of whether each user saw a link once or hundreds of times. By our calculation, the top-performing domains were those that surfaced in users’ feeds over and over—including some highly partisan, polarizing sites that effectively bombarded some Facebook users with content. See our data . : The Markup Citizen Browser project, July 1–Sept. 30, 2021. (all cropped from the original): , by MSNBC, and , by , licensed under ; , by , licensed under . here Source Credits Rachel Maddow Ben Shapiro Steven Crowder CC BY 3.0 Tucker Carlson Mark Taylor CC BY 2.0 These findings chime with recent revelations from Facebook whistleblower Frances Haugen, who has repeatedly said the company tends to cherry-pick statistics to release to the press and the public. “They are very good at dancing with data,” Haugen . told British lawmakers during a European tour When presented with The Markup’s findings and asked whether its own report’s statistics might be misleading or incomplete, Ariana Anthony, a spokesperson for Meta, Facebook’s parent company, said in an emailed statement, “The focus of the Widely Viewed Content Report is to show the content that is seen by the most people on Facebook, not the content that is posted most frequently. That said, we will continue to refine and improve these reports as we engage with academics, civil society groups, and researchers to identify the parts of these reports they find most valuable, which metrics need more context, and how we can best support greater understanding of content distribution on Facebook moving forward.” Anthony did not directly respond to questions from The Markup on whether the company would release data on the total number of link views or the content that was seen most frequently on the platform. The Battle Over Data There are many ways to measure popularity on Facebook, and each tells a different story about the platform and what kind of content its algorithms favor. For years, the startup CrowdTangle’s “engagement” metric—essentially measuring a combination of how many likes, comments, and other interactions any domain’s posts garner—has been the most publicly visible way of measuring popularity. Facebook bought CrowdTangle in 2016 and, , has since largely tried to downplay data showing that ultra-conservative commentators like The Daily Wire’s Ben Shapiro produce the most engaged-with content on the platform. according to reporting in The New York Times Shortly after the end of the second quarter of this year, Facebook came out with its , framed in the introduction as a way to “provide clarity” on “the most-viewed domains, links, Pages and posts on the platform during the quarter.” (More accurately, the Q2 report was the first publicly released transparency report, after a Q1 report was, The New York Times reported, and only released later after details emerged.) first transparency report suppressed for making the company look bad For the Q2 and Q3 reports, Facebook turned to a specific metric, known as “reach,” to quantify the most-viewed domains. For any given domain, say or , reach represents the number of unique Facebook accounts that had at least one post containing a link to a tweet or a YouTube video in their news feeds during the quarter. youtube.com twitter.com On that basis, Facebook found that those domains, and other mainstream staples like Amazon, Spotify, and TikTok, had a wide reach. When applying this metric, The Markup found similar results in our Citizen Browser data, as detailed in-depth in our methodology. But this calculation ignores a reality for a lot of Facebook users: bombardment with content from the same site. Citizen Browser data shows, for instance, that from July through September of this year, articles from far-right news site Newsmax appeared in the feed of a 58-year-old woman in New Mexico 1,065 times—but under Facebook’s calculation of reach, this would count as one single unit. Similarly, a 37-year-old man in New Hampshire was shown 245 unique links to satirical posts from The Onion, which appeared in his feed more than 500 times—but again, he would have been counted just once by Facebook’s method. When The Markup instead counted each appearance of a domain on a user’s feed during Q3—e.g., Newsmax as 1,065 instead of 1—we found that polarizing, partisan content jumped in the performance rankings. Indeed, the same trend is true of the domains in Facebook’s Q2 report, for which analysis can be found in our data repository on . GitHub We found that outlets like The Daily Wire, BuzzFeed’s viral content arm, Fox News, and Yahoo News jumped in the popularity rankings when we used the impressions metric. Most striking, The Western Journal—which, similarly to The Daily Wire, does little original reporting and instead —improved its ranking by almost 200 places. repackages stories to fit with right-wing narratives “To me these findings raise a number of questions,” said Jane Lytvynenko, senior research fellow at the Harvard Kennedy School Shorenstein Center. “Was Facebook’s research genuine, or was it part of an attempt to change the narrative around top 10 lists that were previously put out? It matters a lot whether a person sees a link one time or if they see it 20 times, and to not account for that in a report, to me, is misleading,” Lytvynenko said. Using a narrow range of data to gauge popularity is suspect, said Alixandra Barasch, associate professor of marketing at NYU’s Stern School of Business. “It just goes against everything we teach and know about advertising to focus on one [metric] rather than the other,” she said. In fact, when it comes to the core business model of selling space to advertisers, Facebook encourages them to consider yet another metric, “ ”—how many times to show a post to each user on average—when trying to optimize brand messaging. frequency Data from Citizen Browser shows that domains seen with high frequency in the Facebook news feed are mostly news domains, since news websites tend to publish multiple articles over the course of a day or week. But Facebook’s own content report does not take this data into account. “[This] clarifies the point that what we need is independent access for researchers to check the math,” said Justin Hendrix, co-author of a report on and editor at Tech Policy Press, after reviewing The Markup’s data. social media and polarization Written by: Corin Faife Also published here

How Popular is Right-Wing Content on Facebook? 

Automated market maker (AMM)-based decentralized exchanges have become one of the most convenient ways to exchange crypto tokens. The majority of these DEXs are built on top of account-based blockchains like Ethereum, with Uniswap being the first exchange built in this style. However, there have been new innovations in how decentralized exchanges operate, with a new Cardano-based model known as the (EUTXO) model. Extended Unspent Transaction Output With constant fees and formally validated deterministic behavior, Cardano's EUTXO model addresses some of the limitations of DEXs on other blockchains. is a decentralized exchange (DEX) that uses an AMM model as well as the EUTXO protocol. WingRiders In this post we’ll look at the evolution of decentralized exchanges as well as the EUTXO protocol and the WingRiders platform. The Workings of the Financial System The financial world of today is highly centralized and regulated. This means that regulated institutions are in charge of your funds and are personally liable for them. The processes, accounting, infrastructure and security levels of these commercial entities are all audited by government authorities. Bank accounts are frequently insured, so consumers will not lose money if their bank goes bankrupt. Users have faith in third parties such as banks, as well as the authorities' ability to adequately audit banks. If the bank does not behave according to the rules and users are not satisfied, they can complain to the authorities. It is illegal and easily detectable for bankers to just take money from their customers. The government can also cancel the banks' licenses to run the service if there were severe issues. Decentralization has brought about a new type of monetary exchange and we’ll look at that in the next section. How Digital Asset Exchanges Have Evolved Trading in digital assets is still dominated by centralized exchanges (CEXs). Private keys, exchange trading settings, user information, fund security, fees, and the exchange ratio are all under their custody. These centralized exchanges use an order book to display buy and sell orders. Traders can use this order book to place orders which are then matched by a central market maker. A certain matching procedure must be followed when matching buy and sell orders. Once a match is made, the trade between the two parties can begin. Orders can be of two types on exchanges that use an order book, “market” and “limit orders”. A market order is an order to buy or sell a token at the market's current best available price. A limit order is an order to buy or sell a token at a specific price or better. Buy limit orders can only be executed at the limit price or lower, and a sell limit order can only be executed at the limit price or higher. As a result, the limit order can be held for a longer amount of time until market conditions meet the user's desired price. Decentralized Finance (DeFi) has changed how users trade cryptocurrencies. Decentralized exchanges (DEXs) provide a non-custodial solution, as well as lower fees, anonymity and global availability for users. Exchanges based on the Automated Market Maker (AMM) model offer automated asset trading through smart contracts. Users don’t need to trade with another user to fulfill their swap requests since orders are fulfilled automatically using tokens from a liquidity pool. Liquidity pools are collections or “pools” of cryptocurrencies or tokens that are locked in a smart contract and used to facilitate trades between assets on a DEX. Tokens in a liquidity pool are provided by a liquidity provider who locks their tokens in the smart contract. In return, liquidity providers either earn fees or additional tokens for providing liquidity. Liquidity Providers (LPs) need to deposit an equal amount of each cryptocurrency to their respective pools to provide liquidity. This means that, for a pool with the pair ADA and token A, a liquidity provider must deposit $100 in ADA coins and $100 in A tokens. The price of a token is set by algorithms based on a changing ratio in liquidity pools. User swap requests are directed to a pool of tokens that are identical. It is required to have enough tokens in the pools to be able to execute trades on the AMM exchange. To put it another way, pools need enough liquidity for a trader to successfully trade tokens in the respective pools. AMM-Based DEXs are a simple solution for users to trade tokens using a non-custodial system. WingRiders is an AMM-based DEX that builds on the Cardano network's stable foundations. Their goal is to provide a secure platform that allows users to exchange native Cardano tokens without the usage of intermediaries or single points of failure. The project is creating an infrastructure element with a decentralized exchange platform based on the Cardano blockchain. Animoca Brands, the company behind Axie Infinity and The Sandbox, invested into the project alongside other investors including Bitrue and Spark Digital Capital. WingRiders uses the EUTXO model to power its decentralized exchange ecosystem and we’ll look at how the EUTXO system works in the next section. How the Extended UTXO Model Works Before we can know how the EUTXO model functions, we first need to understand how the UTXO model works. The flow of assets in a UTXO model is represented as a directed acyclic graph (DAG). Nodes represent the transactions and edges represent the transaction outputs, and each additional transaction consumes part of the UTXOs while adding new ones. Users' wallets determine their balance by keeping track of a list of unspent outputs connected with all addresses they control. In many ways, UTXO is identical to cash. For example, consider the following scenario: you have $100 in your wallet. This sum could be made up of a variety of different bills: four $20 bills and two $10 bills or eight $10 bills and four $5 bills, and so on. The sum ($100) remains the same regardless of the permutations. The same is true for UTXOs. Any balance you have in your crypto wallet (for example, 500 ADA) can be built up using a variety of UTXO combinations based on prior transactions, but the total value remains the same. In other words, a wallet address's balance is the total of all unspent UTXOs from prior transactions. The extends the UTXO model in two ways: EUTXO model 1. EUTXO Uses the Lock-and-Key Analogy to Expand the Concept of 'Address.' Addresses in the EUTXO model can contain arbitrary logic in the form of scripts, rather than constraining locks to public keys and keys to signatures. When a node verifies a transaction, for example, it decides whether the transaction is permitted to use a specific output as an input. If the transaction can use the output as an input, the transaction will seek for the script provided by the output's address and execute it. 2. EUTXO Outputs Can Carry Almost Any Data The second distinction between UTXO and EUTXO is that, in addition to an address and a value, outputs can carry (nearly) any data. By allowing scripts to carry state information, they become much more powerful. EUTXO also expands on the UTXO model by allowing output addresses to contain complicated logic to determine which transactions are allowed to unlock them, as well as by allowing custom data to be added to all outputs. When validating an address, the script will look at the data carried by the output, the transaction being checked, and some additional data known as redeemers that the transaction supplies for each input. The script provides enough context to deliver a 'yes' or 'no' answer in what can be highly complex circumstances and use cases by digging up all of this information. EUTXO allows for arbitrary logic to be expressed in the form of scripts. This arbitrary logic examines the transaction and data to determine whether or not the transaction can use an input. The graph topology of the UTXO paradigm differs significantly from the account-based model employed by several existing smart-contract enabled blockchains. As a result, design patterns for DApps on account-based blockchains do not easily adapt to Cardano. New design patterns are required because the underlying representation of the data is different. Final Thoughts Decentralized exchanges offer all of the same services as centralized exchanges, but in an anonymous and non-custodial manner. Before the creation of Ethereum, traders had to rely on centralized systems to exchange cryptocurrency. However, smart contracts have ushered in a trustless and entirely open financial system. The EUTXO model adds to this system by providing increased security, predictability in smart contract execution costs (avoiding unpleasant surprises), and more powerful parallelization. When implemented appropriately, these technological advances have the potential to benefit all players economically. The content is this story is provided by an independent contributor. Disclaimer:

WingRiders is Using Cardano to Build a DEX Based on DAG-Based Technology.

is a 1990 bullet hell shoot-em-up game released for the Atari ST and Commodore Amiga 16-bit computers, developed by Eclipse Software and published by Thalion Software. What is unique about this game is that it came on a “dual boot” floppy diskette, which allowed two separate versions of the same game to coexist on the same disk together, making the game cheaper to duplicate and sell. Wings of Death The premise involves a wizard named Sagyr (pronounced “Sah-gear”) who seeks revenge on the evil witch Xandrilia (pronounced “Zan-drill-ear”) for transforming him into a shapeshifting winged creature, a hybrid between a dragon, wyvern, griffin, and phoenix, in addition to saving the world. To do so, he must traverse seven (7) stages which start off hard and get increasingly difficult to the point of extreme sadism. For the purpose of this review, I will cover the Amiga version using my custom-made 2-button arcade joystick. The Game This game, as is common for its era, uses multiple floppy disks, as hard disk drives were not commonplace until the Windows 95 era - in this case, it uses two (2) 3 1/2” double-density floppy diskettes, with the introduction and ending sequence on the first diskette and the second disk containing the game itself. When the game boots up, the player is greeted with the above image. Pressing any key on the keyboard or clicking the mouse will take the player to the opening credits accompanied by a very adrenaline-surging score by Jochen “Mad Max” Hippel. Pressing a button or key here will take the player to the main menu (after the player has swapped out the first disk for the second one). Each option is self-explanatory - the player can start a new game or continue a game if they run out of lives. The game is generous enough to provide the player with four (4) continues, the usage of which will take the player back to the beginning of the current level. The player can view the high scores, game information (credits) or choose between sound effects and music (the “Music+” option allows for both simultaneously, which is unusual for games from this era). Typing the word here enables a cheat mode, and the screen will flash red to confirm the correct code entry. With this cheat mode enabled, the player can do the following by pressing the respective keys on the keyboard (in parentheses): SPELLBINDER Change weapons (F1 to F5) Change power level for the current weapon (1 to 5) Change defensive powerup (F6 or F7) Activate temporary invincibility lasting approximately 20 to 30 seconds (F8) The Powerup System The game consists of a unique powerup system commonplace in most shoot-em-ups (mainly Japanese bullet hell games, from which this is indirectly inspired). The player can use between five (5) different weapons: - this is the player’s default weapon and initially fires a projectile in a straight line. Fires multiple triangular bullets at a 180-degree angle when fully upgraded. Split Fire - this fires a smaller and faster (but less powerful) projectile. Fires multiple circular bullets at a 360-degree angle (omnidirectional) when fully upgraded. Circle Blast - this is the most useful weapon in the game since it is the only weapon that has native fully automatic fire (does not require an autofire powerup) - it fires a deadly arc-shaped beam of energy in straight line and fires up to four (4) beams at various angles when fully upgraded. Power Beam - this is the most powerful weapon in the game, capable of killing most enemies in a single shot and can make quick work of bosses, defeating them in a matter of seconds - it fires a small flame and fires a pair of large jets of fire (which then split into opposite directions) when fully upgraded. Dragonfire - this is the most unconventional weapon in the game, as it is powerful but requires some trickery and expertise to master. It fires a volley of fireballs in a straight line when first picked up, and when fully upgraded, will fire a single line of fireballs which then splits into a pair of two separate volleys at right angles (90 degrees). Thunderball The player character will change shape and size to determine which powerup is currently equipped. Powerups can be upgraded by collecting an icon bearing the current powerup’s symbol, and this can be done up to four (4) times. Collecting a different powerup will not only grant the player a new weapon but will also reset the power level and necessitate collecting multiple icons of that weapon to upgrade it, so keep that in mind. The Player’s Defences No game would be fair without some form of self-defense for the player. In this case, the player has three (3) defenses available to find and collect: - this is a simple defense, orbiting the player and destroying enemies it comes into contact with - the most common defense. Destroyer - this actively seeks, locates, and destroys nearby targets in addition to enemies further away (sometimes going off-screen to do so) - less common than the Destroyer but more common than the Shield.  The Hunter and the Destroyer are mutually exclusive (picking up one will result in the loss of the other). Hunter - this grants the player temporary invincibility for about 20 or 30 seconds (the player will flash when invincible) - there is no damage recovery in this game (the player will continue taking damage for as long as they are in constant contact with an enemy or a hazard), so this defense is quite valuable, although scarce. Shield - this is the rarest of them all. It destroys all enemies currently on the screen. Smart Bomb Other Pickups - this simply adds 500 points to the player’s score. Very common and harmless. 500 Ball - this gives the player a speed boost, allowing them to move and fly faster. Wings - This pickup will nullify ALL upgrades to the player’s weapon and take away any defenses they have (including shields and speed boosts), resetting them to their initial state. Skull AVOID THIS!! - this resets the player’s health to its maximum value. Heart - this gives the player an additional life and is one of the rarest pickups in the game. Ankh - this equips the player’s current weapon with fully automatic fire. Picking up another Autofire powerup (or a different weapon) disables this. Autofire Now that the basic mechanics are covered, let’s explore the game’s levels. Stage 1: Sagyr’s Castle This level is fairly easy and gives the player ample time to get used to the game’s mechanics and difficulty. It is set in the protagonist’s castle and is populated primarily by evil statues and the occasional golden eagle, which will drop powerups when defeated. Sometimes the player will come across an entire formation of enemies - destroying the whole formation (killing every single enemy) will also drop a powerup. This stage’s hazards include fire and deadly statues and it is one of the best levels in the game. Boss: 3-headed Hydra Weakness: Head(s) Recommended Weapon: Power Beam Stage 2: The Jungle of Mutants This stage is a good showcase for what I consider to be the game’s main weakness: a gratuitous usage of vivid colors (most of the game’s stages look too garish and cartoony) resulting in the enemies and the background blending in with each other, making it difficult to evade projectiles. Natural hazards include deadly poison plants, rainbow pollen, and giant mosquitoes. About halfway through the level, the player encounters a giant ladybird which neither harms the player nor can the player harm it. This is one of the weaker levels in the game, in my opinion, due to the garish palette. Boss: Obese Beetle Weakness: Jaws Recommended Weapon: Dragonfire Stage 3: The Lethal Swamp After a brief visit into Hell for the color-deficient, we return to a much more subdued palette and environment for this third stage. This is where the difficulty begins to increment significantly after the first two levels’ somewhat easy ride. Enemies include giant floating Emperor Palpatine heads, angry mosquitoes, eccentric skeletons, and possessed swords that can attack the player from behind. Hazards include puddles and lakes of deadly bubbles. Boss: Legless Skeleton Weakness: Floating Head Recommended Weapon: Either Dragonfire or Power Beam Stage 4: A Scape of Fire The difficulty begins to increase with this stage, and the lack of health-based pickups (energy or life) does nothing to alleviate the constant projectile dodging the player will undertake for the majority of this level, which has a half-volcanic, half-Greco-Roman theme to its design. This is usually the point where even the most experienced of players will run out of their initial set of lives and get a game over, where they must choose between settling for a high score or continuing at the expense of a clean slate (their score is reset to 0 with each continue used). Hazards include pools of lava, jets of hot liquid magma, and active volcanoes complete with eruptions. Enemies include angry clouds, oversized mosquitoes, and (red gargoyle-like demons in Arab mythology). ifrits Boss: Cacodemon Weakness: Whole Body Recommended Weapon: Dragonfire Stage 5: Cold Lands of Doom and Despair As the name suggests, this stage has a very arctic theme to it. The player will be encountering all kinds of ice-themed enemies along the way, including frozen skull faces which spew fireballs and lethal possessed snowflakes. Natural hazards include Olympic torches, deadly frozen flowers which spew projectiles, and what appear to be a cross between a caterpillar and a pig in a blanket. This level does have a nice artistic touch involving frozen faces visible in the background (see the screenshot above). Boss: Angry Q*bert Lookalike Weakness: Whole Body Recommended Weapon: Dragonfire Stage 6: Crystal World This is the penultimate (second to last) stage of the game, and the difficulty is near its peak. As the name implies, the player is in a crystalline dimension. This level has very few powerups (and is full of hidden skulls), but it compensates with an abundance of shields and defenses. This time, the player can easily die due to too many sprites appearing on the screen at once and unleashing projectiles which are nearly impossible to avoid completely as opposed to the earlier stages’ garish colors. Enemies include flying skulls and angry scarabs, while natural hazards include glowing lava lamps and some of the crystals themselves. Boss: Dragon Weakness: Head Recommended Weapon: Either Dragonfire or Power Beam Stage 7 (Final Stage): Xandrilia’s Domain This is it. The last level of the game… and the difficulty is now at its apex, bordering on sadistic - this stage is a true test of the player’s skill, reflexes, and perseverance. There are enemies coming from all sides, next to no powerups, very few health-based pickups, and only a single defense and shield. The architecture is a fusion of the majestic and the infernal - this stage is full of natural hazards. Upon reaching Xandrilia’s fortress, the final boss of the game will appear in the form of a demonic dog along with possessed masks - defeat it, and you’ve won. Boss: Single-Headed Cerberus & Evil Face Masks Weakness: Head (Cerberus), Whole Body (Masks) Recommended Weapon: Either Dragonfire or Power Beam Here’s the ending sequence for those who find this game too hard or are simply too lazy to try: The ending text is transcribed here due to its questionable legibility thanks to the pseudo-medieval font used throughout the game: “Congratulations! You managed to get even with evil Xandrilia. Finally, you have regained your own form again and fame will follow you throughout her domain for the rest of your life!” Presentation The game’s presentation is very well developed and well executed for this era. The rudimentary cutscene at the end, in addition to the improvised opening credits when the game begins, does give this game a more epic scope and feel and is a sign that the developers care about their product. This is a game that was made for the 16-bit machines, and it shows, although there is a lack of optimization for the Amiga version since the Atari ST is the lead platform, which was commonplace in the era of these games. Porting aside, it is a very well-crafted effort. Graphics (Detail, Color) The game’s graphics are nicely realized and very well detailed. However, the color palette is inconsistent and can vary from level to level - this is more prominent in the early stages, where the colors used can cause enemies, hazards, and projectiles used to blend in with each other and make the game unnecessarily difficult. In the levels where the colors do have the right combination, however, every detail is vibrant and easily identifiable, and every enemy and hazard pop out at the player. The player sprites themselves are also uniquely colored, drawn, and shaped (in addition to having different sizes depending on the player’s weapon), giving the game some visual variety. Sound (SFX, Music) The game’s soundtrack is by Jochen “Mad Max” Hippel, a composer who is quite well-known on the Atari demoscene for converting SID tunes from the C64 to the Atari ST’s YM2149F chip, which is similar but not identical to the AY-3-8912 chip used in other computers of the time such as the MSX series, Amstrad CPC and 128K models of the Sinclair ZX Spectrum. As a result, the game’s soundtrack fares better on the Atari ST than on the Commodore Amiga, which utilizes mostly samples in addition to the actual recorded sound of varying quality as opposed to a series of bleeps and bloops. Sound effects are limited mostly to muffled explosions when enemies die, a voiceover announcing whatever items the player picks up, and an evil laugh that plays whenever the player loses a life or collects the dreaded skull pickup. The game does have music for nearly every setting, though - the opening credits and ending sequence have their own music, as do each of the game’s seven stages, in addition to a funky drum beat when the game loads the next level. Controls The controls are smooth and responsive - maybe a little bit too responsive, particularly when equipped with multiple speed upgrades, which could result in the player flying into enemy projectiles and/or hazards instead of trying to dodge them, which could be problematic in some of the levels with more garish color palettes, as everything just blends in together. Like most games from this era, only a single-button Atari-style joystick is supported, which is curious because, by this point, games were evolving to the point that usually overcame such a limitation, and the Atari ST and Amiga could support two (2) separate fire buttons (compatible games could use a Sega Master System/Mark 3 controller as it shares the same pinout save for an additional pin wired up to accommodate a second button) as well as the MSX computers (whose controllers were not compatible, as they were wired up slightly differently). The one complaint I do have is that the fire button must be pressed manually (and constantly) to ensure rapid fire, so this game is best played with a joystick or gamepad that supports turbo fire. I also feel that this game would have been more playable with a mouse instead of a joystick, which would have allowed a greater freedom of movement and precision. Gameplay (Difficulty, Fun-to-Frustration Ratio) The gameplay is highly reminiscent of bullet hell games, and as such, places emphasis on speed and skill, and the enemies will always appear in the same predetermined paths in the exact same sequence, eliminating any strategy from the player’s part and proving the very definition of insanity. The difficulty for the game is also a potential deterrent, as it starts off fairly challenging in the first stage and quickly becomes brutally difficult, and the final stage is the epitome of shoot-em-up gaming sadism. An option to choose the difficulty (easy, medium, or hard) would have been beneficial and could have made the game more accessible to a larger audience. The game’s strength lies in its powerups and upgrade system - the player MUST level up their weapon if they are to succeed and emerge victorious, with the Dragonfire (which can kill most enemies in less than a second) and the Power Beam (with its multiple arcs of energy at various angles) being the best weapons in the game. The weakest weapon, in my opinion, is the Circle Blast, as it does insufficient damage in its initial state, meaning enemies cannot be destroyed quickly enough to upgrade it. There is some strategy for the game’s bosses, though (the Stage 2 boss requires the player to circle around and go behind it in order to evade projectiles before coming back to the front to attack it, ditto the Stage 5 boss). Overall is a semi-classic from the peak era of 16-bit home computers, and it would not have been out of place had it originally been an arcade game. Rather than make the usual ST-to-Amiga quick port, the developers actually tried to make some use of the Amiga’s expanded capabilities, including the sampled sound and larger color palette. It sold well enough to warrant a sequel the following year (1991) in the form of (AKA ), which had a futuristic sci-fi theme and involved Sagyr fighting Xandrilia’s descendants in the far future. However, that game did not sell as well, largely due to the difficulty being even more cruel and sadistic than that of its predecessor. Wings of Death Lethal Xcess Lethal Xcess: Wings of Death 2 A lot of games made in the West (and even in Eastern Europe due to the proliferation of copies and clones made behind the Iron Curtain) ran the gauntlet from excellent to average to abominable, many of which were ported from one machine to another without optimizing the target system’s specifications and capabilities. , however, shows the potential that these machines have when coded properly and when ported with optimization in mind, rivaling some Japanese games in terms of addictiveness and atmosphere. This game is a must-have if you like a good challenge and a shooter with a unique medieval theme, regardless of whether you have an Atari ST or Commodore Amiga. Wings of Death Final Score: 4 out of 5.

Wings of Death (Commodore Amiga) Retro Game Review 

Spending your money and earning money are the two things that need to be managed properly in order to live a financially stable life while also being able to spend on things other than your necessities. Savings, budgets, and balance are words we grew up hearing but don’t know much about it. Below are some helpful tips for you to understand financial literacy in a much better way. Savings are very much interlinked with mindful spending. Wants and Needs It is extremely important to differentiate between one’s needs and wants. Needs are your necessities; it’s almost like a special kind of want which you must have in order to survive, for instance, food, water supply, and a roof above your head. Now when we take a look at our wants, it’s the day-to-day desires that you think of: to buy a brand new phone, a shirt because it’s trending a new car. But you know you can live without buying that new phone or that shirt. Once you learn how to control your wants, you can easily support your needs. Budgeting First, calculate the total pay you take home, the money you are earning monthly. Next, know your fixed expenses that fall under the category of needs like bills and taxes. Now you need to make a list of all the things that you spend on monthly other than your fixed expenses. This is where you will find out where you should restrict your spendings. The above two categories, once calculated, need to be subtracted from your monthly income so that you know what balance you’re left with. Now, this is your time to think about your monthly goal of savings or investments you’re doing or contribution to any financial goal you’re trying to achieve. The balance goes for the last category. The key to following your budget is taking away temptations to avoid overspending. Tackle pressure Almost every day, you find yourself in situations where there’s an offer or opportunity that can cause you to break the bank or not follow your budget properly. A trip to Hawaii, a night out with friends, or just another shopping spree. These are times when you get hit with money peer pressure, you know the only way out of this is to decline the offer, but society guilts you into making really poor financial decisions that can cause you financial stress and make you spend money you should be saving. Tackling societal pressure is hard, but the results are promising for a stable future. Hack Your Spending Habits 30-Day Rule This is a simple yet effective rule, when you see something you want, wait for at least 30 days before buying it. After 30 days, if you still feel like you have to buy that, go ahead and buy it. But if you forget about it or make up your mind that you don’t need it, you will end up saving that money. Money not spent in the wrong place is money saved. Sale, discounts & limited-time offers Sales are a huge attraction, just the thought of spending less money on something sounds convincing enough to buy something that you don’t necessarily need. Now, if you come across an item that is on sale, ask yourself if the same item was not on sale and you had to pay the full price, will you still be buying it? If yes, then you know that’s something you really need, but if the answer is no, don’t buy it as you were spending your money on it because of the sale or the “discounted price.” Prefer Cash Paying in cash will most likely help you save money and make fewer impulsive choices, paying in credit cards sounds more of a convenient option, but you’ll, in most cases, end up spending more than intended as the money is not in your hands and you’re not really paying attention to the amount. Environment & Habits Try to avoid the visual attraction in your surroundings in order to be less prone to spending excessively. You’re more likely to spend on things unnecessarily if you’re visiting places that have more malls, fast-food restaurants, and hotels. Consider this scenario, you’re going home, and on your way back, you take the route which is less cluttered with attractions like sales billboards, shopping malls. But if you go on the streets with these attractions, it is highly possible to make a pit stop in order to buy something or succumb to visual attractions surrounding you. Why Spend? Finding a balance is really important. Sometimes it is better to spend than to save when it comes to your health, happiness, and well-being. Buying what you’re saving for, things that are convenient and save your time, bring you joy should be things you spend on. Some people also spend money by investing, and that is a genius way to build wealth. Don’t let saving money, budget, and expenses consume you; go easy on yourself and make smart decisions that you don’t regret later. Spending money on yourself and your family is as important as saving.

Financial Literacy 101: How to grow wings

In 2014, was founded as a professional manufacturer for the mapping and surveying industries. Wingtra drone Its drone technology integrates the benefits of fixed-wing flight with the convenience of vertically controlled landings and takeoffs. Justina Kostinaite, the drone maker's head of marketing, spoke with Benzinga about Wingtra's VTOL — vertical take-off and landing — project. The Technology The VTOL drone “lands and takes off vertically as a multirotor and flies in the air as a fixed-wing aircraft, which is really efficient," Kostinaite said. As a result of the unique design, the drone can carry a heavier payload, allowing for the use of best-in-class cameras, she said. Operators can “generate maps and 3D models with 1 cm accuracy, which, in the surveying world, means if you map eight football fields, you can ... calculate the exact coordinates of a [fallen] coin down to 1 centimeter.” Wingtra counts 3DR and among its partners; the companies assist in gathering and processing aerial data. Sony Corp SNE 0.46% Who's It For? Wingtra places an emphasis on worksite planning. "Our main audience is actually the surveying, mining and construction industries,” Kostinaite said. In one example of a use case, the Idaho Forest Group, a lumber producer, leverages VTOL drone technology to “map hundreds of acres of forest and stockpiles of wood” for six northwestern mills, the spokeswoman said. Regulations And Worker Safety “Regulations, all around the world, are super hectic now,” Kostinaite said. They represent a bottleneck for the drone business, she said. "India is currently a country we cannot enter, [as well as] Brazil." The countries are huge untapped markets, Kostinaite said. Wingtra does not face the same privacy regulation issues as the consumer drone market, as the drones are flown in more secluded areas, she said. Kostinaite said she views the company's technology as a safety asset. "Let's say you have the biggest mine of uranium in Africa and you have to survey this mine, which takes three people two weeks," she said. "You can slide, you can fall and then at the end you can measure wrong.” With a Wingtra drone, worker safety can be maintained while gathering data faster, she said. Going Forward The industry is an exciting one, Kostinaite said. "If you’re not afraid and you kind of lose the skepticism about tech and drones, it just makes everything move further, makes people's work easier, makes it more efficient, and safer.” Wingtra is working on improving the VTOL drone technology, and it will perform work that makes jobs dangerous, she said. "We are at the forefront of a revolution." This was originally published on Benzinga.com . More of our Related Stories: Iran Shoots Down US Drone In What Trump Calls 'Very Big Mistake'; Pelosi Says 'No Appetite' For War Kratos Receives $31.8M Drone Contract Award From US Air Force Photo courtesy of Wingtra.

'At The Forefront Of A Revolution': A Chat With Drone Maker Wingtra

is a with one rotor but it can take off and land vertically. Monocopter flying machine This article is about scratch building a that looks like an airplane but with a missing wing. A picture of it is shown below. design This flies by spinning around. Its wing acts like a blade of a helicopter. A video and an image of its flight is shown below. monocopter An airborne monocopter Flight test of the monocopter Wing: I cut a rectangle out from a Dollar Tree foam board. The dimension of the rectangle is 8 inches by 30 inches. To form the wing, I cut and fold the rectangle 2 inches away from the leading edge. The wing Support structure: The motor is attached to a motor mount cut from plywood. I insert two carbon fibre rods through the motor mount. I further attach more plywood on the carbon fibre rods so that I can glue the wing on them and prevent the other end of the rods from getting loose. The motor mount and support structure Aluminum rods(7mm diameter) are used to form the legs of the monocopter. This can prevent the blades from touching the ground. Foam is used to wrap around the electronics and batteries to prevent damages. Protective gears : aluminum leg and foam container for the electroncis Load and propulsion: The whole monocopter including batteries is 8.5 oz. Weight of everything including the batteries I get the motor and propellers from Amazon. Motor used is E-flite Park 370 Brushless Outrunner Motor 1360Kv. Propeller is RAYCorp® 1045 10x4.5 Propeller. Propeller and motor Electronics: Electronics includes a RC receiver, 2S LiPo battery and an ESC speed controller. Since my ESC does not provide power, I add an additional small battery to power the RC receiver. Electronics Piloting tips: I start with half throttle to spin the monocopter on the ground. Then, I slowly increase the throttle until the monocopter is almost airborne. At that point, I quickly increase to full throttle to keep it in the air.

How to scratch build a monocopter: an airplane that can fly with a missing wing

Authors:
(1) Han Jiang, HKUST and Equal contribution (hjiangav@connect.ust.hk);
(2) Haosen Sun, HKUST and Equal contribution (hsunas@connect.ust.hk);
(3) Ruoxuan Li, HKUST and Equal contribution (rliba@connect.ust.hk);
(4) Chi-Keung Tang, HKUST (cktang@cs.ust.hk);
(5) Yu-Wing Tai, Dartmouth College, (yu-wing.tai@dartmouth.edu). Table of Links Abstract and 1. Introduction 2. Related Work 2.1. NeRF Editing and 2.2. Inpainting Techniques 2.3. Text-Guided Visual Content Generation 3. Method 3.1. Training View Pre-processing 3.2. Progressive Training 3.3. 4D Extension 4. Experiments and 4.1. Experimental Setups 4.2. Ablation and comparison 5. Conclusion and 6. References 4. Experiments For our experiments, we select from dynamic scenes in the Nvidia Dynamic Scenes Dataset [35]. Scenes in this dataset are captured using a sparse set of 12 stationary cameras located in two rows, producing images of resolution 1015×1920. The static scenes we use are taken from one frame from the dynamic scenes. For the backbone NeRF, we use static and dynamic versions of K-Planes [25] implemented in nerfstudio [30]. For each scene, we conduct inpainting by replacing a foreground object with another text-prompted object with a different geometry. We will demonstrate the effectiveness of our method by showing the qualitative intermediate and final results. In addition, we will explain different parts of our design by ablations and comparisons on our baseline. 4.1. Qualitative results 3D Examples. We show several 3D inpainting examples in figure 2. For each individual inpainting task, we show 2 renderings of the final NeRF from different views to demonstrate the multiview consistency. Additionally, we show the first seed image, another pre-processed image, as well as the RGB and depth map in the three stages: before training, after warmup training, and after convergence. These beforeand-after images demonstrate the efficacy of each stage in our method. As shown in Figure 2, a roughly consistent preprocessed image can optimize a coarse inpainted NeRF after warmup training, and the geometry (represented by depth map) converges during warmup training. Then, fine convergence across views is achieved after the final training stage. All 3D inpainting tasks are trained on a single Nvidia RTX 4090 GPU. Warmup training takes approximately 0.5–1 hour, and the main training stage with IDU takes approximately 1–2 hours. 4D Example. We show a 4D inpainting example in figure 3 to demonstrate that our method has the potential to generalize to dynamic NeRFs. In this example, we remove the foreground object in the video of the seed view using E2FGVI [11], a flow-based method with optimization by feature propagation and content hallucination. For transferring motion to the generated object, after key point tracking, we estimate a rigid transformation between the key points, and propagate the pixels along the transformation. This dynamic scene consists of 16 frames, in which the first frame includes the first seed image. As shown in the figures, we successfully obtained an overall convergence on the generated object with correct motion for all the illustrated frames. This paper is available on arxiv under CC 4.0 license. Authors: (1) Han Jiang, HKUST and Equal contribution (hjiangav@connect.ust.hk); (2) Haosen Sun, HKUST and Equal contribution (hsunas@connect.ust.hk); (3) Ruoxuan Li, HKUST and Equal contribution (rliba@connect.ust.hk); (4) Chi-Keung Tang, HKUST (cktang@cs.ust.hk); (5) Yu-Wing Tai, Dartmouth College, (yu-wing.tai@dartmouth.edu). Authors: Authors: (1) Han Jiang, HKUST and Equal contribution (hjiangav@connect.ust.hk); (2) Haosen Sun, HKUST and Equal contribution (hsunas@connect.ust.hk); (3) Ruoxuan Li, HKUST and Equal contribution (rliba@connect.ust.hk); (4) Chi-Keung Tang, HKUST (cktang@cs.ust.hk); (5) Yu-Wing Tai, Dartmouth College, (yu-wing.tai@dartmouth.edu). Table of Links Abstract and 1. Introduction Abstract and 1. Introduction 2. Related Work 2.1. NeRF Editing and 2.2. Inpainting Techniques 2.1. NeRF Editing and 2.2. Inpainting Techniques 2.3. Text-Guided Visual Content Generation 2.3. Text-Guided Visual Content Generation 3. Method 3. Method 3.1. Training View Pre-processing 3.1. Training View Pre-processing 3.2. Progressive Training 3.2. Progressive Training 3.3. 4D Extension 3.3. 4D Extension 4. Experiments and 4.1. Experimental Setups 4. Experiments and 4.1. Experimental Setups 4.2. Ablation and comparison 4.2. Ablation and comparison 5. Conclusion and 6. References 5. Conclusion and 6. References 4. Experiments For our experiments, we select from dynamic scenes in the Nvidia Dynamic Scenes Dataset [35]. Scenes in this dataset are captured using a sparse set of 12 stationary cameras located in two rows, producing images of resolution 1015×1920. The static scenes we use are taken from one frame from the dynamic scenes. For the backbone NeRF, we use static and dynamic versions of K-Planes [25] implemented in nerfstudio [30]. For each scene, we conduct inpainting by replacing a foreground object with another text-prompted object with a different geometry. We will demonstrate the effectiveness of our method by showing the qualitative intermediate and final results. In addition, we will explain different parts of our design by ablations and comparisons on our baseline. 4.1. Qualitative results 3D Examples. We show several 3D inpainting examples in figure 2. For each individual inpainting task, we show 2 renderings of the final NeRF from different views to demonstrate the multiview consistency. Additionally, we show the first seed image, another pre-processed image, as well as the RGB and depth map in the three stages: before training, after warmup training, and after convergence. These beforeand-after images demonstrate the efficacy of each stage in our method. As shown in Figure 2, a roughly consistent preprocessed image can optimize a coarse inpainted NeRF after warmup training, and the geometry (represented by depth map) converges during warmup training. Then, fine convergence across views is achieved after the final training stage. All 3D inpainting tasks are trained on a single Nvidia RTX 4090 GPU. Warmup training takes approximately 0.5–1 hour, and the main training stage with IDU takes approximately 1–2 hours. 3D Examples. 4D Example. We show a 4D inpainting example in figure 3 to demonstrate that our method has the potential to generalize to dynamic NeRFs. In this example, we remove the foreground object in the video of the seed view using E2FGVI [11], a flow-based method with optimization by feature propagation and content hallucination. For transferring motion to the generated object, after key point tracking, we estimate a rigid transformation between the key points, and propagate the pixels along the transformation. This dynamic scene consists of 16 frames, in which the first frame includes the first seed image. As shown in the figures, we successfully obtained an overall convergence on the generated object with correct motion for all the illustrated frames. 4D Example. This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

NeRF Editing and Inpainting Techniques: Experiments and Qualitative results

Authors:
(1) Han Jiang, HKUST and Equal contribution (hjiangav@connect.ust.hk);
(2) Haosen Sun, HKUST and Equal contribution (hsunas@connect.ust.hk);
(3) Ruoxuan Li, HKUST and Equal contribution (rliba@connect.ust.hk);
(4) Chi-Keung Tang, HKUST (cktang@cs.ust.hk);
(5) Yu-Wing Tai, Dartmouth College, (yu-wing.tai@dartmouth.edu). Table of Links Abstract and 1. Introduction 2. Related Work 2.1. NeRF Editing and 2.2. Inpainting Techniques 2.3. Text-Guided Visual Content Generation 3. Method 3.1. Training View Pre-processing 3.2. Progressive Training 3.3. 4D Extension 4. Experiments and 4.1. Experimental Setups 4.2. Ablation and comparison 5. Conclusion and 6. References ABSTRACT Current Neural Radiance Fields (NeRF) can generate photorealistic novel views. For editing 3D scenes represented by NeRF, with the advent of generative models, this paper proposes Inpaint4DNeRF to capitalize on state-of-the-art stable diffusion models (e.g., ControlNet [39]) for direct generation of the underlying completed background content, regardless of static or dynamic. The key advantages of this generative approach for NeRF inpainting are twofold. First, after rough mask propagation, to complete or fill in previously occluded content, we can individually generate a small subset of completed images with plausible content, called seed images, from which simple 3D geometry proxies can be derived. Second and the remaining problem is thus 3D multiview consistency among all completed images, now guided by the seed images and their 3D proxies. Without other bells and whistles, our generative Inpaint4DNeRF baseline framework is general which can be readily extended to 4D dynamic NeRFs, where temporal consistency can be naturally handled in a similar way as our multiview consistency 1. INTRODUCTION Recent development of Neural Radiance Fields (NeRF) [18] and its dynamic variants including [20, 25, 28] have shown their great potential in modeling scenes in 3D and 4D. This representation is suitable for scene editing from straightforward user inputs such as text prompts, eliminating the need for modeling and animating in detail. One important task in scene editing is generative inpainting, which refers to generating plausible content that is consistent with the background scene. Generative inpainting has a wide range of potential applications, including digital art creation, and VR/AR. Although several recent works have addressed the inpainting and text-guided content generation problem on NeRFs, they have various limitations in directly generating consistent novel content seamlessly with the background based on the text input. Specifically, [4, 24, 41] allow users to edit the appearance of an existing object in the NeRF based on the text prompt, but their generations are limited by the original target object, thus they cannot handle substantial geometry changes. In [19, 31] a given NeRF is inpainted by removing the target object and inferring the background, but their inpainting task is not generative as they do not match the inferred background with any other user input. Recent generative works [12, 21, 26, 32] produce new 3D or 4D content from the text input, but the generation is not conditioned on the existing background. While [27] can generate contents around the target object without removing it, generative inpainting on NeRF should allow users to remove target objects while filling the exposed region with plausible 3D content, which was previously totally or partially occluded in the given scene. While applying recent diffusion models [22] on generative 2D inpainting may largely solve the problem, simply extending diffusion models to higher dimensions to static or dynamic NeRFs introduces extensive challenges, including new and more complex network structures and sufficient higher-dimensional training data. On the other hand, NeRFs are continuous multiview estimations on its training images which provide a natural connection between 2D images and NeRF, enabling us to propagate the 2D inpainting results by diffusion models to the underlying scene. Thus, in this paper, we propose Inpaint4DNeRF, the first work on text-guided generative NeRF inpainting with diffusion models, which can be naturally extended to inpaint 4D dynamic NeRFs. Given a text prompt, and a target foreground region specified by the user by text or on the single individual image(s), our method first utilizes stable diffusion to inpaint a few independent training views. Next, we regard these inpainted views as seed images and infer a coarse geometry proxy from them. With the strong guidance by seed images and their geometry proxies, the other views being inpainted are constrained to be consistent with the seed images when refined with stable diffusion, which is followed by NeRF finetuning with progressive updates on the training views to obtain final multiview convergence. For dynamic cases, once generative inpainting is achieved on a static single frame, it can be regarded as the seed frame where the edited information can be propagated to other frames to naturally inpaint the scene in 4D without bells and whistles. Our generative Inpaint4DNeRF proposes a text-guided approach for inpainting the relevant background after removing static foreground objects specified by a user-supplied prompt, where multiview consistency is meticulously maintained. To summarize, Inpaint4DNeRF presents the following contributions. First, harnessing the power of recent diffusion models on image inpainting, we can directly generate textguided content with new geometry while being consistent with the context given by the unmasked background. Second, our approach infers and refines other views from initially inpainted seed images to achieve multiview consistency across all the given views, where temporal consistency is naturally enforced and achieved. This paper is available on arxiv under CC 4.0 license. Authors: (1) Han Jiang, HKUST and Equal contribution (hjiangav@connect.ust.hk); (2) Haosen Sun, HKUST and Equal contribution (hsunas@connect.ust.hk); (3) Ruoxuan Li, HKUST and Equal contribution (rliba@connect.ust.hk); (4) Chi-Keung Tang, HKUST (cktang@cs.ust.hk); (5) Yu-Wing Tai, Dartmouth College, (yu-wing.tai@dartmouth.edu). Authors: Authors: (1) Han Jiang, HKUST and Equal contribution (hjiangav@connect.ust.hk); (2) Haosen Sun, HKUST and Equal contribution (hsunas@connect.ust.hk); (3) Ruoxuan Li, HKUST and Equal contribution (rliba@connect.ust.hk); (4) Chi-Keung Tang, HKUST (cktang@cs.ust.hk); (5) Yu-Wing Tai, Dartmouth College, (yu-wing.tai@dartmouth.edu). Table of Links Abstract and 1. Introduction Abstract and 1. Introduction 2. Related Work 2.1. NeRF Editing and 2.2. Inpainting Techniques 2.1. NeRF Editing and 2.2. Inpainting Techniques 2.3. Text-Guided Visual Content Generation 2.3. Text-Guided Visual Content Generation 3. Method 3. Method 3.1. Training View Pre-processing 3.1. Training View Pre-processing 3.2. Progressive Training 3.2. Progressive Training 3.3. 4D Extension 3.3. 4D Extension 4. Experiments and 4.1. Experimental Setups 4. Experiments and 4.1. Experimental Setups 4.2. Ablation and comparison 4.2. Ablation and comparison 5. Conclusion and 6. References 5. Conclusion and 6. References ABSTRACT Current Neural Radiance Fields (NeRF) can generate photorealistic novel views. For editing 3D scenes represented by NeRF, with the advent of generative models, this paper proposes Inpaint4DNeRF to capitalize on state-of-the-art stable diffusion models (e.g., ControlNet [39]) for direct generation of the underlying completed background content, regardless of static or dynamic. The key advantages of this generative approach for NeRF inpainting are twofold. First, after rough mask propagation, to complete or fill in previously occluded content, we can individually generate a small subset of completed images with plausible content, called seed images, from which simple 3D geometry proxies can be derived. Second and the remaining problem is thus 3D multiview consistency among all completed images, now guided by the seed images and their 3D proxies. Without other bells and whistles, our generative Inpaint4DNeRF baseline framework is general which can be readily extended to 4D dynamic NeRFs, where temporal consistency can be naturally handled in a similar way as our multiview consistency Current Neural Radiance Fields (NeRF) can generate photorealistic novel views. For editing 3D scenes represented by NeRF, with the advent of generative models, this paper proposes Inpaint4DNeRF to capitalize on state-of-the-art stable diffusion models (e.g., ControlNet [39]) for direct generation of the underlying completed background content, regardless of static or dynamic. The key advantages of this generative approach for NeRF inpainting are twofold. First, after rough mask propagation, to complete or fill in previously occluded content, we can individually generate a small subset of completed images with plausible content, called seed images, from which simple 3D geometry proxies can be derived. Second and the remaining problem is thus 3D multiview consistency among all completed images, now guided by the seed images and their 3D proxies. Without other bells and whistles, our generative Inpaint4DNeRF baseline framework is general which can be readily extended to 4D dynamic NeRFs, where temporal consistency can be naturally handled in a similar way as our multiview consistency direct 1. INTRODUCTION Recent development of Neural Radiance Fields (NeRF) [18] and its dynamic variants including [20, 25, 28] have shown their great potential in modeling scenes in 3D and 4D. This representation is suitable for scene editing from straightforward user inputs such as text prompts, eliminating the need for modeling and animating in detail. One important task in scene editing is generative inpainting, which refers to generating plausible content that is consistent with the background scene. Generative inpainting has a wide range of potential applications, including digital art creation, and VR/AR. Although several recent works have addressed the inpainting and text-guided content generation problem on NeRFs, they have various limitations in directly generating consistent novel content seamlessly with the background based on the text input. Specifically, [4, 24, 41] allow users to edit the appearance of an existing object in the NeRF based on the text prompt, but their generations are limited by the original target object, thus they cannot handle substantial geometry changes. In [19, 31] a given NeRF is inpainted by removing the target object and inferring the background, but their inpainting task is not generative as they do not match the inferred background with any other user input. Recent generative works [12, 21, 26, 32] produce new 3D or 4D content from the text input, but the generation is not conditioned on the existing background. While [27] can generate contents around the target object without removing it, generative inpainting on NeRF should allow users to remove target objects while filling the exposed region with plausible 3D content, which was previously totally or partially occluded in the given scene. While applying recent diffusion models [22] on generative 2D inpainting may largely solve the problem, simply extending diffusion models to higher dimensions to static or dynamic NeRFs introduces extensive challenges, including new and more complex network structures and sufficient higher-dimensional training data. On the other hand, NeRFs are continuous multiview estimations on its training images which provide a natural connection between 2D images and NeRF, enabling us to propagate the 2D inpainting results by diffusion models to the underlying scene. Thus, in this paper, we propose Inpaint4DNeRF, the first work on text-guided generative NeRF inpainting with diffusion models, which can be naturally extended to inpaint 4D dynamic NeRFs. Given a text prompt, and a target foreground region specified by the user by text or on the single individual image(s), our method first utilizes stable diffusion to inpaint a few independent training views. Next, we regard these inpainted views as seed images and infer a coarse geometry proxy from them. With the strong guidance by seed images and their geometry proxies, the other views being inpainted are constrained to be consistent with the seed images when refined with stable diffusion, which is followed by NeRF finetuning with progressive updates on the training views to obtain final multiview convergence. For dynamic cases, once generative inpainting is achieved on a static single frame, it can be regarded as the seed frame where the edited information can be propagated to other frames to naturally inpaint the scene in 4D without bells and whistles. Our generative Inpaint4DNeRF proposes a text-guided approach for inpainting the relevant background after removing static foreground objects specified by a user-supplied prompt, where multiview consistency is meticulously maintained. To summarize, Inpaint4DNeRF presents the following contributions. First, harnessing the power of recent diffusion models on image inpainting, we can directly generate textguided content with new geometry while being consistent with the context given by the unmasked background. Second, our approach infers and refines other views from initially inpainted seed images to achieve multiview consistency across all the given views, where temporal consistency is naturally enforced and achieved. This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

NeRF Editing and Inpainting Techniques: Abstract and Introduction

Authors:
(1) Han Jiang, HKUST and Equal contribution (hjiangav@connect.ust.hk);
(2) Haosen Sun, HKUST and Equal contribution (hsunas@connect.ust.hk);
(3) Ruoxuan Li, HKUST and Equal contribution (rliba@connect.ust.hk);
(4) Chi-Keung Tang, HKUST (cktang@cs.ust.hk);
(5) Yu-Wing Tai, Dartmouth College, (yu-wing.tai@dartmouth.edu). Table of Links Abstract and 1. Introduction 2. Related Work 2.1. NeRF Editing and 2.2. Inpainting Techniques 2.3. Text-Guided Visual Content Generation 3. Method 3.1. Training View Pre-processing 3.2. Progressive Training 3.3. 4D Extension 4. Experiments and 4.1. Experimental Setups 4.2. Ablation and comparison 5. Conclusion and 6. References 3.2. Progressive Training Warmup Training. Our training image pre-processing stage provides a good initialization for rough convergence. Before fine-tuning on these images to get fine convergence, notice that the 3D object in NeRF is still the original object intact, which is quite different from what is depicted in the pre-processed training images above in geometry and appearance. Therefore, for the first stage of NeRF training, without any finetuning, we directly train the NeRF on the preprocessed images for coarse convergence. Note that when we perform fine-tuning later, the original object will have no effect because warmup training has already erased all its appearance information. Iterative Dataset Update. Given a NeRF in which the editing target has fixed overall geometry, Iterative Dataset Update (IDU), first proposed and shown effective in InstructNerf2Nerf [4], is a useful training image fine-tuning strategy which can edit the appearance and fine geometry of the target object in NeRF. In our task, warmup training has already provided a converged coarse geometry, leaving fine geometry and appearance to be determined. In their work, they use InstructPix2Pix [1] as a diffusion model strongly conditioned on the original training image. In our task, we use stable diffusion in a similar way to achieve similar objectives. The fine-tuned image should be conditioned on the pre-processed image, as well as the depth map. In detail, for pre-processed image conditioning, we adopt the same approach as in the previously introduced projected image correction. The preprocessed image and the current NeRF rendering are blended together, either in image space or in latent space. Then a small amount of noise is injected into the blended image, followed by stable diffusion denoising from an intermediate timestep. For depth map conditioning, we input the depth map of the current NeRF into ControlNet to guide the editing process. Along with training image updates and NeRF training, the underlying NeRF will gradually converge to an object with fine details, making rendering from NeRF more reliable. Meanwhile, stable diffusion correction with less noise injected into the training images introduces less multiview inconsistency to fine details. Therefore, we gradually increase the intermediate timestep of our stable diffusion correction when training NeRF, until the intermediate timestep becomes very close to the final denoise timestep. Consequently, the coarse NeRF converges to a fine one. Regularizers. To supervise NeRF training, we use the L1 photometric loss between NeRF rendering and the training images. However, sole supervision of RGB cannot avoid inaccuracies and noise in geometry. To achieve clean converged geometry, we add the following two regularizers into supervision. First, we also render a depth value along a ray, and compute the depth loss as defined in [3] between the rendered depth and our pre-processed 2-layer depth i.e., initial inpainted geometry proxies. Supervision with our planar 2-layer depth is not accurate, but it helps to avoid incorrect converged geometry, such as merging of the inpainted geometry with the background. Second, to deal with noisy RGB and depth renderings and “floaters” (artifacts hovering in the underlying NeRF volume), we use LPIPS loss [40] on RGB as a regularizing term. In practice, we found LPIPS to be effective in reducing noise and floaters. Since LPIPS is based on patches, rays selected in NeRF training are not allowed to be random, but are required to form rectangular patches. Thus we render small image patches, corresponding to rectangular areas in ground truth images, during NeRF optimization. The effect of the regularizers is also previously demonstrated in [31, 34]. This paper is available on arxiv under CC 4.0 license. Authors: (1) Han Jiang, HKUST and Equal contribution (hjiangav@connect.ust.hk); (2) Haosen Sun, HKUST and Equal contribution (hsunas@connect.ust.hk); (3) Ruoxuan Li, HKUST and Equal contribution (rliba@connect.ust.hk); (4) Chi-Keung Tang, HKUST (cktang@cs.ust.hk); (5) Yu-Wing Tai, Dartmouth College, (yu-wing.tai@dartmouth.edu). Authors: Authors: (1) Han Jiang, HKUST and Equal contribution (hjiangav@connect.ust.hk); (2) Haosen Sun, HKUST and Equal contribution (hsunas@connect.ust.hk); (3) Ruoxuan Li, HKUST and Equal contribution (rliba@connect.ust.hk); (4) Chi-Keung Tang, HKUST (cktang@cs.ust.hk); (5) Yu-Wing Tai, Dartmouth College, (yu-wing.tai@dartmouth.edu). Table of Links Abstract and 1. Introduction Abstract and 1. Introduction 2. Related Work 2.1. NeRF Editing and 2.2. Inpainting Techniques 2.1. NeRF Editing and 2.2. Inpainting Techniques 2.3. Text-Guided Visual Content Generation 2.3. Text-Guided Visual Content Generation 3. Method 3. Method 3.1. Training View Pre-processing 3.1. Training View Pre-processing 3.2. Progressive Training 3.2. Progressive Training 3.3. 4D Extension 3.3. 4D Extension 4. Experiments and 4.1. Experimental Setups 4. Experiments and 4.1. Experimental Setups 4.2. Ablation and comparison 4.2. Ablation and comparison 5. Conclusion and 6. References 5. Conclusion and 6. References 3.2. Progressive Training Warmup Training. Our training image pre-processing stage provides a good initialization for rough convergence. Before fine-tuning on these images to get fine convergence, notice that the 3D object in NeRF is still the original object intact, which is quite different from what is depicted in the pre-processed training images above in geometry and appearance. Therefore, for the first stage of NeRF training, without any finetuning, we directly train the NeRF on the preprocessed images for coarse convergence. Note that when we perform fine-tuning later, the original object will have no effect because warmup training has already erased all its appearance information. Warmup Training. Iterative Dataset Update. Given a NeRF in which the editing target has fixed overall geometry, Iterative Dataset Update (IDU), first proposed and shown effective in InstructNerf2Nerf [4], is a useful training image fine-tuning strategy which can edit the appearance and fine geometry of the target object in NeRF. In our task, warmup training has already provided a converged coarse geometry, leaving fine geometry and appearance to be determined. In their work, they use InstructPix2Pix [1] as a diffusion model strongly conditioned on the original training image. In our task, we use stable diffusion in a similar way to achieve similar objectives. The fine-tuned image should be conditioned on the pre-processed image, as well as the depth map. In detail, for pre-processed image conditioning, we adopt the same approach as in the previously introduced projected image correction. The preprocessed image and the current NeRF rendering are blended together, either in image space or in latent space. Then a small amount of noise is injected into the blended image, followed by stable diffusion denoising from an intermediate timestep. For depth map conditioning, we input the depth map of the current NeRF into ControlNet to guide the editing process. Iterative Dataset Update. Along with training image updates and NeRF training, the underlying NeRF will gradually converge to an object with fine details, making rendering from NeRF more reliable. Meanwhile, stable diffusion correction with less noise injected into the training images introduces less multiview inconsistency to fine details. Therefore, we gradually increase the intermediate timestep of our stable diffusion correction when training NeRF, until the intermediate timestep becomes very close to the final denoise timestep. Consequently, the coarse NeRF converges to a fine one. Regularizers. To supervise NeRF training, we use the L1 photometric loss between NeRF rendering and the training images. However, sole supervision of RGB cannot avoid inaccuracies and noise in geometry. To achieve clean converged geometry, we add the following two regularizers into supervision. First, we also render a depth value along a ray, and compute the depth loss as defined in [3] between the rendered depth and our pre-processed 2-layer depth i.e., initial inpainted geometry proxies. Supervision with our planar 2-layer depth is not accurate, but it helps to avoid incorrect converged geometry, such as merging of the inpainted geometry with the background. Second, to deal with noisy RGB and depth renderings and “floaters” (artifacts hovering in the underlying NeRF volume), we use LPIPS loss [40] on RGB as a regularizing term. In practice, we found LPIPS to be effective in reducing noise and floaters. Since LPIPS is based on patches, rays selected in NeRF training are not allowed to be random, but are required to form rectangular patches. Thus we render small image patches, corresponding to rectangular areas in ground truth images, during NeRF optimization. The effect of the regularizers is also previously demonstrated in [31, 34]. Regularizers. This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

NeRF Editing and Inpainting Techniques: Progressive Training

Authors:
(1) Han Jiang, HKUST and Equal contribution (hjiangav@connect.ust.hk);
(2) Haosen Sun, HKUST and Equal contribution (hsunas@connect.ust.hk);
(3) Ruoxuan Li, HKUST and Equal contribution (rliba@connect.ust.hk);
(4) Chi-Keung Tang, HKUST (cktang@cs.ust.hk);
(5) Yu-Wing Tai, Dartmouth College, (yu-wing.tai@dartmouth.edu). Table of Links Abstract and 1. Introduction 2. Related Work 2.1. NeRF Editing and 2.2. Inpainting Techniques 2.3. Text-Guided Visual Content Generation 3. Method 3.1. Training View Pre-processing 3.2. Progressive Training 3.3. 4D Extension 4. Experiments and 4.1. Experimental Setups 4.2. Ablation and comparison 5. Conclusion and 6. References 3.1. Training View Pre-processing Text-guided visual content generation is inherently a highly underdetermined problem: for a given text prompt, there are infinitely many object appearances that are feasible matches. In our method, we generate content in NeRF by first performing inpainting on its training images and then backward propagating the modified pixels of the images into NeRF. If we inpaint each view independently, the inpainted content will not be consistent across multiple views. Some prior 3D generation works, using techniques including score distillation sampling [21], have demonstrated the possibility of multiview convergence based on independently modified training views. Constraining the generation problem by enforcing the training views to be strongly related to each other will simplify convergence to a large extent. Therefore, we first inpaint a small number of seed images associated with a coarse set of cameras that covers sufficiently wide viewing angles. For the other views, the inpainted content are strongly conditioned on these seed images. Stable diffusion correction. Since our planar depth estimation is not always accurate, while the raw projected results are plausible in general, the many small artifacts make the relevant images unqualified for training. To make them look more natural, we propose to cover the projection artifacts with stable diffusion hallucinated details. First, we blend the raw projection with random noise in stable diffusion’s latent space for t timesteps, where t is relatively small to the total number of stable diffusion timesteps. Then, starting from the last t steps, we denoise with stable diffusion to generate the hallucinated image. The resulting image is then regarded as the initial training image. With this step, the training image pre-processing stage is completed. This paper is available on arxiv under CC 4.0 license. Authors: (1) Han Jiang, HKUST and Equal contribution (hjiangav@connect.ust.hk); (2) Haosen Sun, HKUST and Equal contribution (hsunas@connect.ust.hk); (3) Ruoxuan Li, HKUST and Equal contribution (rliba@connect.ust.hk); (4) Chi-Keung Tang, HKUST (cktang@cs.ust.hk); (5) Yu-Wing Tai, Dartmouth College, (yu-wing.tai@dartmouth.edu). Authors: Authors: (1) Han Jiang, HKUST and Equal contribution (hjiangav@connect.ust.hk); (2) Haosen Sun, HKUST and Equal contribution (hsunas@connect.ust.hk); (3) Ruoxuan Li, HKUST and Equal contribution (rliba@connect.ust.hk); (4) Chi-Keung Tang, HKUST (cktang@cs.ust.hk); (5) Yu-Wing Tai, Dartmouth College, (yu-wing.tai@dartmouth.edu). Table of Links Abstract and 1. Introduction Abstract and 1. Introduction 2. Related Work 2.1. NeRF Editing and 2.2. Inpainting Techniques 2.1. NeRF Editing and 2.2. Inpainting Techniques 2.3. Text-Guided Visual Content Generation 2.3. Text-Guided Visual Content Generation 3. Method 3. Method 3.1. Training View Pre-processing 3.1. Training View Pre-processing 3.2. Progressive Training 3.2. Progressive Training 3.3. 4D Extension 3.3. 4D Extension 4. Experiments and 4.1. Experimental Setups 4. Experiments and 4.1. Experimental Setups 4.2. Ablation and comparison 4.2. Ablation and comparison 5. Conclusion and 6. References 5. Conclusion and 6. References 3.1. Training View Pre-processing Text-guided visual content generation is inherently a highly underdetermined problem: for a given text prompt, there are infinitely many object appearances that are feasible matches. In our method, we generate content in NeRF by first performing inpainting on its training images and then backward propagating the modified pixels of the images into NeRF. If we inpaint each view independently, the inpainted content will not be consistent across multiple views. Some prior 3D generation works, using techniques including score distillation sampling [21], have demonstrated the possibility of multiview convergence based on independently modified training views. Constraining the generation problem by enforcing the training views to be strongly related to each other will simplify convergence to a large extent. Therefore, we first inpaint a small number of seed images associated with a coarse set of cameras that covers sufficiently wide viewing angles. For the other views, the inpainted content are strongly conditioned on these seed images. Stable diffusion correction. Since our planar depth estimation is not always accurate, while the raw projected results are plausible in general, the many small artifacts make the relevant images unqualified for training. To make them look more natural, we propose to cover the projection artifacts with stable diffusion hallucinated details. First, we blend the raw projection with random noise in stable diffusion’s latent space for t timesteps, where t is relatively small to the total number of stable diffusion timesteps. Then, starting from the last t steps, we denoise with stable diffusion to generate the hallucinated image. The resulting image is then regarded as the initial training image. With this step, the training image pre-processing stage is completed. Stable diffusion correction. t t t This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

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