ROGER

roger

ROGER HAMLEY'S CONFESSION.

ROGER CARBURY AND HIS TWO FRIENDS

We need not follow Sir Roger to his grave

Sir Roger Dies

Mark Twain's Speeches by Mark Twain, is part of the HackerNoon Books Series. You can jump to any chapter in this book . ROGERS AND RAILROADS here ROGERS AND RAILROADS AT A BANQUET GIVEN MR. H. H. ROGERS BY THE BUSINESS MEN OF
          NORFOLK, VA., CELEBRATING THE OPENING OF THE VIRGINIAN RAILWAY,
          APRIL, 3, 1909

          Toastmaster:

          “I have often thought that when the time comes, which must come
          to all of us, when we reach that Great Way in the Great Beyond,
          and the question is propounded, ‘What have you done to gain
          admission into this great realm?’ if the answer could be
          sincerely made, ‘I have made men laugh,’ it would be the surest
          passport to a welcome entrance.  We have here to-night one who
          has made millions laugh—not the loud laughter that bespeaks
          the vacant mind, but the laugh of intelligent mirth that helps
          the human heart and the human mind.  I refer, of course, to
          Doctor Clemens.  I was going to say Mark Twain, his literary
          title, which is a household phrase in more homes than that of
          any other man, and you know him best by that dear old title.” I thank you, Mr. Toastmaster, for the compliment which you have paid me, and I am sure I would rather have made people laugh than cry, yet in my time I have made some of them cry; and before I stop entirely I hope to make some more of them cry. I like compliments. I deal in them myself. I have listened with the greatest pleasure to the compliments which the chairman has paid to Mr. Rogers and that road of his to-night, and I hope some of them are deserved. It is no small distinction to a man like that to sit here before an intelligent crowd like this and to be classed with Napoleon and Caesar. Why didn’t he say that this was the proudest day of his life? Napoleon and Caesar are dead, and they can’t be here to defend themselves. But I’m here! The chairman said, and very truly, that the most lasting thing in the hands of man are the roads which Caesar built, and it is true that he built a lot of them; and they are there yet. Yes, Caesar built a lot of roads in England, and you can find them. But Rogers has only built one road, and he hasn’t finished that yet. I like to hear my old friend complimented, but I don’t like to hear it overdone. I didn’t go around to-day with the others to see what he is doing. I will do that in a quiet time, when there is not anything going on, and when I shall not be called upon to deliver intemperate compliments on a railroad in which I own no stock. They proposed that I go along with the committee and help inspect that dump down yonder. I didn’t go. I saw that dump. I saw that thing when I was coming in on the steamer, and I didn’t go because I was diffident, sentimentally diffident, about going and looking at that thing again—that great, long, bony thing; it looked just like Mr. Rogers’s foot. The chairman says Mr. Rogers is full of practical wisdom, and he is. It is intimated here that he is a very ingenious man, and he is a very competent financier. Maybe he is now, but it was not always so. I know lots of private things in his life which people don’t know, and I know how he started; and it was not a very good start. I could have done better myself. The first time he crossed the Atlantic he had just made the first little strike in oil, and he was so young he did not like to ask questions. He did not like to appear ignorant. To this day he don’t like to appear ignorant, but he can look as ignorant as anybody. On board the ship they were betting on the run of the ship, betting a couple of shillings, or half a crown, and they proposed that this youth from the oil regions should bet on the run of the ship. He did not like to ask what a half-crown was, and he didn’t know; but rather than be ashamed of himself he did bet half a crown on the run of the ship, and in bed he could not sleep. He wondered if he could afford that outlay in case he lost. He kept wondering over it, and said to himself: “A king’s crown must be worth $20,000, so half a crown would cost $10,000.” He could not afford to bet away $10,000 on the run of the ship, so he went up to the stakeholder and gave him $150 to let him off. I like to hear Mr. Rogers complimented. I am not stingy in compliments to him myself. Why, I did it to-day when I sent his wife a telegram to comfort her. That is the kind of person I am. I knew she would be uneasy about him. I knew she would be solicitous about what he might do down here, so I did it to quiet her and to comfort her. I said he was doing well for a person out of practice. There is nothing like it. He is like I used to be. There were times when I was careless—careless in my dress when I got older. You know how uncomfortable your wife can get when you are going away without her superintendence. Once when my wife could not go with me (she always went with me when she could—I always did meet that kind of luck), I was going to Washington once, a long time ago, in Mr. Cleveland’s first administration, and she could not go; but, in her anxiety that I should not desecrate the house, she made preparation. She knew that there was to be a reception of those authors at the White House at seven o’clock in the evening. She said, “If I should tell you now what I want to ask of you, you would forget it before you get to Washington, and, therefore, I have written it on a card, and you will find it in your dress-vest pocket when you are dressing at the Arlington—when you are dressing to see the President.” I never thought of it again until I was dressing, and I felt in that pocket and took it out, and it said, in a kind of imploring way, “Don’t wear your arctics in the White House.” You complimented Mr. Rogers on his energy, his foresightedness, complimented him in various ways, and he has deserved those compliments, although I say it myself; and I enjoy them all. There is one side of Mr. Rogers that has not been mentioned. If you will leave that to me I will touch upon that. There was a note in an editorial in one of the Norfolk papers this morning that touched upon that very thing, that hidden side of Mr. Rogers, where it spoke of Helen Keller and her affection for Mr. Rogers, to whom she dedicated her life book. And she has a right to feel that way, because, without the public knowing anything about it, he rescued, if I may use that term, that marvellous girl, that wonderful Southern girl, that girl who was stone deaf, blind, and dumb from scarlet-fever when she was a baby eighteen months old; and who now is as well and thoroughly educated as any woman on this planet at twenty-nine years of age. She is the most marvellous person of her sex that has existed on this earth since Joan of Arc. That is not all Mr. Rogers has done; but you never see that side of his character, because it is never protruding; but he lends a helping hand daily out of that generous heart of his. You never hear of it. He is supposed to be a moon which has one side dark and the other bright. But the other side, though you don’t see it, is not dark; it is bright, and its rays penetrate, and others do see it who are not God. I would take this opportunity to tell something that I have never been allowed to tell by Mr. Rogers, either by my mouth or in print, and if I don’t look at him I can tell it now. In 1893, when the publishing company of Charles L. Webster, of which I was financial agent, failed, it left me heavily in debt. If you will remember what commerce was at that time you will recall that you could not sell anything, and could not buy anything, and I was on my back; my books were not worth anything at all, and I could not give away my copyrights. Mr. Rogers had long enough vision ahead to say, “Your books have supported you before, and after the panic is over they will support you again,” and that was a correct proposition. He saved my copyrights, and saved me from financial ruin. He it was who arranged with my creditors to allow me to roam the face of the earth for four years and persecute the nations thereof with lectures, promising that at the end of four years I would pay dollar for dollar. That arrangement was made; otherwise I would now be living out-of-doors under an umbrella, and a borrowed one at that. You see his white mustache and his head trying to get white (he is always trying to look like me—I don’t blame him for that). These are only emblematic of his character, and that is all. I say, without exception, hair and all, he is the whitest man I have ever known. 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. Mark Twain (2004). Mark Twain's Speeches. Urbana, Illinois: Project Gutenberg. Retrieved October 2022 https://www.gutenberg.org/cache/epub/3188/pg3188-images.html This eBook is for the use of anyone anywhere at no cost and with almost no restrictions whatsoever. You may copy it, give it away or re-use it under the terms of the Project Gutenberg License included with this eBook or online at , located at . www.gutenberg.org https://www.gutenberg.org/policy/license.html

ROGERS AND RAILROADS

Sir Roger Is Unseated

Sir Roger's Will

Sir Roger Scatcherd

The Way We Live Now by Anthony Trollope is part of the HackerNoon Books Series. You can jump to any chapter in this book . MENTOR here MENTOR. Lady Carbury's desire for a union between Roger and her daughter was greatly increased by her solicitude in respect to her son. Since Roger's offer had first been made, Felix had gone on from bad to worse, till his condition had become one of hopeless embarrassment. If her daughter could but be settled in the world, Lady Carbury said to herself, she could then devote herself to the interests of her son. She had no very clear idea of what that devotion would be. But she did know that she had paid so much money for him, and would have so much more extracted from her, that it might well come to pass that she would be unable to keep a home for her daughter. In all these troubles she constantly appealed to Roger Carbury for advice,—which, however, she never followed. He recommended her to give up her house in town, to find a home for her daughter elsewhere, and also for Felix if he would consent to follow her. Should he not so consent, then let the young man bear the brunt of his own misdoings. Doubtless, when he could no longer get bread in London he would find her out. Roger was always severe when he spoke of the baronet,—or seemed to Lady Carbury to be severe. But, in truth, she did not ask for advice in order that she might follow it. She had plans in her head with which she knew that Roger would not sympathise. She still thought that Sir Felix might bloom and burst out into grandeur, wealth, and fashion, as the husband of a great heiress, and in spite of her son's vices, was proud of him in that anticipation. When he succeeded in obtaining from her money, as in the case of that £20,—when, with brazen-faced indifference to her remonstrances, he started off to his club at two in the morning, when with impudent drollery he almost boasted of the hopelessness of his debts, a sickness of heart would come upon her, and she would weep hysterically, and lie the whole night without sleeping. But could he marry Miss Melmotte, and thus conquer all his troubles by means of his own personal beauty,—then she would be proud of all that had passed. With such a condition of mind Roger Carbury could have no sympathy. To him it seemed that a gentleman was disgraced who owed money to a tradesman which he could not pay. And Lady Carbury's heart was high with other hopes,—in spite of her hysterics and her fears. The "Criminal Queens" might be a great literary success. She almost thought that it would be a success. Messrs. Leadham and Loiter, the publishers, were civil to her. Mr. Broune had promised. Mr. Booker had said that he would see what could be done. She had gathered from Mr. Alf's caustic and cautious words that the book would be noticed in the "Evening Pulpit." No;—she would not take dear Roger's advice as to leaving London. But she would continue to ask Roger's advice. Men like to have their advice asked. And, if possible, she would arrange the marriage. What country retirement could be so suitable for a Lady Carbury when she wished to retire for awhile,—as Carbury Manor, the seat of her own daughter? And then her mind would fly away into regions of bliss. If only by the end of this season Henrietta could be engaged to her cousin, Felix be the husband of the richest bride in Europe, and she be the acknowledged author of the cleverest book of the year, what a Paradise of triumph might still be open to her after all her troubles! Then the sanguine nature of the woman would bear her up almost to exultation, and for an hour she would be happy, in spite of everything. A few days after the ball Roger Carbury was up in town, and was closeted with her in her back drawing-room. The declared cause of his coming was the condition of the baronet's affairs and the indispensable necessity,—so Roger thought,—of taking some steps by which at any rate the young man's present expenses might be brought to an end. It was horrible to him that a man who had not a shilling in the world or any prospect of a shilling, who had nothing and never thought of earning anything, should have hunters! He was very much in earnest about it, and quite prepared to speak his mind to the young man himself,—if he could get hold of him. "Where is he now, Lady Carbury;—at this moment?" "I think he's out with the Baron." Being "out with the Baron" meant that the young man was hunting with the stag hounds some forty miles away from London. "How does he manage it? Whose horses does he ride? Who pays for them?" "Don't be angry with me, Roger. What can I do to prevent it?" "I think you should refuse to have anything to do with him while he continues in such courses." "My own son!" "Yes;—exactly. But what is to be the end of it? Is he to be allowed to ruin you, and Hetta? It can't go on long." "You wouldn't have me throw him over." "I think he is throwing you over. And then it is so thoroughly dishonest,—so ungentlemanlike! I don't understand how it goes on from day to day. I suppose you don't supply him with ready money." "He has had a little." Roger frowned angrily. "I can understand that you should provide him with bed and food, but not that you should pander to his vices by giving him money." This was very plain speaking, and Lady Carbury winced under it. "The kind of life that he is leading requires a large income of itself. I understand the thing, and know that with all I have in the world I could not do it myself." "You are so different." "I am older of course,—very much older. But he is not so young that he should not begin to comprehend. Has he any money beyond what you give him?" Then Lady Carbury revealed certain suspicions which she had begun to entertain during the last day or two. "I think he has been playing." "That is the way to lose money,—not to get it," said Roger. "I suppose somebody wins,—sometimes." "They who win are the sharpers. They who lose are the dupes. I would sooner that he were a fool than a knave." "O Roger, you are so severe!" "You say he plays. How would he pay, were he to lose?" "I know nothing about it. I don't even know that he does play; but I have reason to think that during the last week he has had money at his command. Indeed I have seen it. He comes home at all manner of hours and sleeps late. Yesterday I went into his room about ten and did not wake him. There were notes and gold lying on his table;—ever so much." "Why did you not take them?" "What; rob my own boy?" "When you tell me that you are absolutely in want of money to pay your own bills, and that he has not hesitated to take yours from you! Why does he not repay you what he has borrowed?" "Ah, indeed;—why not? He ought to if he has it. And there were papers there;—I. O. U.'s, signed by other men." "You looked at them." "I saw as much as that. It is not that I am curious, but one does feel about one's own son. I think he has bought another horse. A groom came here and said something about it to the servants." "Oh dear;—oh dear!" "If you could only induce him to stop the gambling! Of course it is very bad whether he wins or loses,—though I am sure that Felix would do nothing unfair. Nobody ever said that of him. If he has won money, it would be a great comfort if he would let me have some of it,—for, to tell the truth, I hardly know how to turn. I am sure nobody can say that I spend it on myself." Then Roger again repeated his advice. There could be no use in attempting to keep up the present kind of life in Welbeck Street. Welbeck Street might be very well without a penniless spendthrift such as Sir Felix, but must be ruinous under the present conditions. If Lady Carbury felt, as no doubt she did feel, bound to afford a home to her ruined son in spite of all his wickedness and folly, that home should be found far away from London. If he chose to remain in London, let him do so on his own resources. The young man should make up his mind to do something for himself. A career might possibly be opened for him in India. "If he be a man he would sooner break stones than live on you," said Roger. Yes, he would see his cousin to-morrow and speak to him;—that is if he could possibly find him. "Young men who gamble all night, and hunt all day are not easily found." But he would come at twelve as Felix generally breakfasted at that hour. Then he gave an assurance to Lady Carbury which to her was not the least comfortable part of the interview. In the event of her son not giving her the money which she at once required he, Roger, would lend her a hundred pounds till her half year's income should be due. After that his voice changed altogether, as he asked a question on another subject, "Can I see Henrietta to-morrow?" "Certainly;—why not? She is at home now, I think." "I will wait till to-morrow,—when I call to see Felix. I should like her to know that I am coming. Paul Montague was in town the other day. He was here, I suppose?" "Yes;—he called." "Was that all you saw of him?" "He was at the Melmottes' ball. Felix got a card for him;—and we were there. Has he gone down to Carbury?" "No;—not to Carbury. I think he had some business about his partners at Liverpool. There is another case of a young man without anything to do. Not that Paul is at all like Sir Felix." This he was induced to say by the spirit of honesty which was always strong within him. "Don't be too hard upon poor Felix," said Lady Carbury. Roger, as he took his leave, thought that it would be impossible to be too hard upon Sir Felix Carbury. The next morning Lady Carbury was in her son's bedroom before he was up, and with incredible weakness told him that his cousin Roger was coming to lecture him. "What the Devil's the use of it?" said Felix from beneath the bedclothes. "If you speak to me in that way, Felix, I must leave the room." "But what is the use of his coming to me? I know what he has got to say just as if it were said. It's all very well preaching sermons to good people, but nothing ever was got by preaching to people who ain't good." "Why shouldn't you be good?" "I shall do very well, mother, if that fellow will leave me alone. I can play my hand better than he can play it for me. If you'll go now I'll get up." She had intended to ask him for some of the money which she believed he still possessed, but her courage failed her. If she asked for his money, and took it, she would in some fashion recognise and tacitly approve his gambling. It was not yet eleven, and it was early for him to leave his bed; but he had resolved that he would get out of the house before that horrible bore should be upon him with his sermon. To do this he must be energetic. He was actually eating his breakfast at half-past eleven, and had already contrived in his mind how he would turn the wrong way as soon as he got into the street,—towards Marylebone Road, by which route Roger would certainly not come. He left the house at ten minutes before twelve, cunningly turned away, dodging round by the first corner,—and just as he had turned it encountered his cousin. Roger, anxious in regard to his errand, with time at his command, had come before the hour appointed and had strolled about, thinking not of Felix but of Felix's sister. The baronet felt that he had been caught,—caught unfairly, but by no means abandoned all hope of escape. "I was going to your mother's house on purpose to see you," said Roger. "Were you indeed? I am so sorry. I have an engagement out here with a fellow which I must keep. I could meet you at any other time, you know." "You can come back for ten minutes," said Roger, taking him by the arm. "Well;—not conveniently at this moment." "You must manage it. I am here at your mother's request, and can't afford to remain in town day after day looking for you. I go down to Carbury this afternoon. Your friend can wait. Come along." His firmness was too much for Felix, who lacked the courage to shake his cousin off violently, and to go his way. But as he returned he fortified himself with the remembrance of all the money in his pocket,—for he still had his winnings,—remembered too certain sweet words which had passed between him and Marie Melmotte since the ball, and resolved that he would not be "sat upon" by Roger Carbury. The time was coming,—he might almost say that the time had come,—in which he might defy Roger Carbury. Nevertheless, he dreaded the words which were now to be spoken to him with a craven fear. "Your mother tells me," said Roger, "that you still keep hunters." "I don't know what she calls hunters. I have one that I didn't part with when the others went." "You have only one horse?" "Well;—if you want to be exact, I have a hack as well as the horse I ride." "And another up here in town?" "Who told you that? No; I haven't. At least there is one staying at some stables which has been sent for me to look at." "Who pays for all these horses?" "At any rate I shall not ask you to pay for them." "No;—you would be afraid to do that. But you have no scruple in asking your mother, though you should force her to come to me or to other friends for assistance. You have squandered every shilling of your own, and now you are ruining her." "That isn't true. I have money of my own." "Where did you get it?" "This is all very well, Roger; but I don't know that you have any right to ask me these questions. I have money. If I buy a horse I can pay for it. If I keep one or two I can pay for them. Of course I owe a lot of money, but other people owe me money too. I'm all right, and you needn't frighten yourself." "Then why do you beg her last shilling from your mother, and when you have money not pay it back to her?" "She can have the twenty pounds, if you mean that." "I mean that, and a good deal more than that. I suppose you have been gambling." "I don't know that I am bound to answer your questions, and I won't do it. If you have nothing else to say, I'll go about my own business." "I have something else to say, and I mean to say it." Felix had walked towards the door, but Roger was before him, and now leaned his back against it. "I am not going to be kept here against my will," said Felix. "You have to listen to me, so you may as well sit still. Do you wish to be looked upon as a blackguard by all the world?" "Oh,—go on." "That is what it will be. You have spent every shilling of your own,—and because your mother is affectionate and weak, you are now spending all that she has, and are bringing her and your sister to beggary." "I don't ask them to pay anything for me." "Not when you borrow her money?" "There is the £20. Take it and give it her," said Felix, counting the notes out of the pocket-book. "When I asked her for it, I did not think she would make such a row about such a trifle." Roger took up the notes and thrust them into his pocket. "Now, have you done?" said Felix. "Not quite. Do you purpose that your mother should keep you and clothe you for the rest of your life?" "I hope to be able to keep her before long, and to do it much better than it has ever been done before. The truth is, Roger, you know nothing about it. If you'll leave me to myself, you'll find that I shall do very well." "I don't know any young man who ever did worse, or one who had less moral conception of what is right and wrong." "Very well. That's your idea. I differ from you. People can't all think alike, you know. Now, if you please, I'll go." Roger felt that he hadn't half said what he had to say, but he hardly knew how to get it said. And of what use could it be to talk to a young man who was altogether callous and without feeling? The remedy for the evil ought to be found in the mother's conduct rather than the son's. She, were she not foolishly weak, would make up her mind to divide herself utterly from her son, at any rate for a while, and to leave him to suffer utter penury. That would bring him round. And then when the agony of want had tamed him, he would be content to take bread and meat from her hand and would be humble. At present he had money in his pocket, and would eat and drink of the best, and be free from inconvenience for the moment. While this prosperity remained it would be impossible to touch him. "You will ruin your sister, and break your mother's heart," said Roger, firing a last harmless shot after the young reprobate. When Lady Carbury came into the room, which she did as soon as the front door was closed behind her son, she seemed to think that a great success had been achieved because the £20 had been recovered. "I knew he would give it me back, if he had it," she said. "Why did he not bring it to you of his own accord?" "I suppose he did not like to talk about it. Has he said that he got it by—playing?" "No,—he did not speak a word of truth while he was here. You may take it for granted that he did get it by gambling. How else should he have it? And you may take it for granted also that he will lose all that he has got. He talked in the wildest way,—saying that he would soon have a home for you and Hetta." "Did he;—dear boy!" "Had he any meaning?" "Oh; yes. And it is quite on the cards that it should be so. You have heard of Miss Melmotte." "I have heard of the great French swindler who has come over here, and who is buying his way into society." "Everybody visits them now, Roger." "More shame for everybody. Who knows anything about him,—except that he left Paris with the reputation of a specially prosperous rogue? But what of him?" "Some people think that Felix will marry his only child. Felix is handsome; isn't he? What young man is there nearly so handsome? They say she'll have half a million of money." "That's his game;—is it?" "Don't you think he is right?" "No; I think he's wrong. But we shall hardly agree with each other about that. Can I see Henrietta for a few minutes?" 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. Anthony Trollope (2004). The Way We Live Now. Urbana, Illinois: Project Gutenberg. Retrieved October 2022 https://www.gutenberg.org/cache/epub/5231/pg5231-images.html This eBook is for the use of anyone anywhere at no cost and with almost no restrictions whatsoever. You may copy it, give it away or re-use it under the terms of the Project Gutenberg License included with this eBook or online at , located at . www.gutenberg.org https://www.gutenberg.org/policy/license.html

Lady Carbury's desire for a union between Roger and her daughter

ROGER CARBURY AND PAUL MONTAGUE

The Murder of Roger Ackroyd by Agatha Christie is part of the HackerNoon Books Series. Read this book online for free on HackerNoon! : The murder of Roger Ackroyd Title : Agatha Christie Author : October 2, 2022 [eBook #69087] Release date : English Language Table of Links CHAPTER I. DR. SHEPPARD AT THE BREAKFAST TABLE II. WHO’S WHO IN KING’S ABBOT III. THE MAN WHO GREW VEGETABLE MARROWS IV. DINNER AT FERNLY V. MURDER VI. THE TUNISIAN DAGGER VIII. LEARN MY NEIGHBOR’S PROFESSION VIIII. INSPECTOR RAGLAN IS CONFIDENT IX. THE GOLDFISH POND X. THE PARLORMAID XI. POIROT PAYS A CALL XII. ROUND THE TABLE XIII. THE GOOSE QUILL XIV. MRS. ACKROYD XV. GEOFFREY RAYMOND XVI. AN EVENING AT MAH JONG XVII. PARKER XVIII. CHARLES KENT XIX. FLORA ACKROYD XX. MISS RUSSELL XXI. THE PARAGRAPH IN THE PAPER XXII. URSULA’S STORY XXIII. POIROT’S LITTLE REUNION XXIV. RALPH PATON’S STORY XXV. THE WHOLE TRUTH XXVI. AND NOTHING BUT THE TRUTH XXVII. APOLOGIA 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. Agatha Christie (2006). The Murder of Roger Ackroyd. Urbana, Illinois: Project Gutenberg. Retrieved https://www.gutenberg.org/cache/epub/69087/pg69087-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 .

The Murder of Roger Ackroyd by Agatha Christie - Table of Links

How do you pitch a business that doesn’t yet have a product, or even a prototype? For serial entrepreneur , it’s about selling possibility, with an emphasis on his track record of innovation. Roger Dickey As a prolific angel investor, Roger knows that big ideas aren’t enough. Investors look for founders who can build strong teams and deliver tested, on-trend products. Once he nailed down a pitch that worked, he captured the imagination of investors from his previous startup as well as VIPs from Silicon Valley, Hollywood and the Pro Football Hall of Fame. Roger’s latest venture, , is a “search lab” focused on the consumer space. It’s a “full-time, funded effort to systematically launch and test business ideas until a promising candidate for a company is found,” Roger writes in his on Medium. Untitled Labs launch announcement It’s his third foray into the search lab concept. His first lab, Curiosoft, launched social game Dope Wars in 2007, the precursor to 2008’s mega-viral Mafia Wars. His next lab, Liquid Labs, spawned “liquid workforce” platform Gigster in 2014. Inspired by venture capital search funds, a search lab has some similarities to studio models and incubators. But it doesn’t fund or create any independent companies. Instead, its small, super-flexible internal R&D team tests ideas until one emerges as the winner. “We’re experimenting with ideas,” Roger says. “So far, we’ve raised about $3 million to run five to 10 prototypes with a team of five to 10 folks. When we find an idea that we think works really well based on early metrics, we’ll shut off everything else and focus the entire team on that one idea. Then we’ll raise a Series A.” Here’s his blueprint, including his biggest mistake (hint: priced rounds are awkward), and the reason why he’s going after fewer investors this time ‘round. Even angels iterate “The fundraising process was, to be honest, a little stressful,” says Roger. “Because I didn’t really know how to position it. I’d never raised money for something like this.” He and his team previously raised for Liquid Labs, but they centered the pitch on an actual product, which they called Get Experts. “It’s hilarious — we had this janky web application that hardly worked,” Roger says with a chuckle. “We went in to pitch Sequoia and they graciously laughed us out of the room. They’re amazing. You’re an amazing firm when you say no to people, but they still come away having had a five-star experience.” Now a few years older, wiser and bolder, Roger’s tactics were different for selling Untitled to investors. “This time, I told people, ‘I know how to experiment. We have no first idea. We don’t have anything built. There’s no team — just me. I’m going to build a team, and we’re going to go experiment.’” It took a few tries to perfect his pitch. “At the first pitch meeting, I started by saying, ‘here’s a big list of ideas,’ and I just read down an unstructured list. That was a little confusing for investors. They didn’t really know what to do with that.” With the second firm he pitched, Roger took a new approach: “I planted a flag and said, ‘we’re doing one of these ideas. We’re doing this one first and maybe there are some others.’” The problem with that, he says, was that his pitch still lacked specificity. “We were neither here nor there. We didn’t have a real company or product. So that was kind of a fail.’” By the time he met with a third firm, he got it right. “I narrowed it down to three themes and I said, ‘look, I’m not going to pitch you on a whole idea list. There are three things I believe about the world, and I want to go study these. Within each of these themes, here’s a specific idea. I did pitch one idea per theme, but I spent more time talking about the themes and the team I was going to build, based on my background.” That was the kicker, he says. “We saw a better success rate with that pitch. “I didn’t really talk to that many people,” he notes, adding that they got “a lot of quick ‘yeses’” from investors who had funded one of his previous companies. Attracting A-list investors Building on those ‘yeses’, Roger persuaded a few Silicon Valley heavyweights to invest. Leading the seed round squad was , the playground of acquisition-exit billionaires like , and . But Roger was really attracted to the firm’s newer blood. Founders Fund Peter Thiel Brian Singerman Ken Howery “I really love, and wanted to work with, ,” he says. “And they just added , which is incredibly exciting news.” Cyan Banister Keith Rabois Other notable entries on Untitled’s roster include football great Joe Montana, who’s the established seed-stage force behind crowdsourced beef startup and cannabis venture . Crowd Cow Caliva Enter stage left: and his fund . Its portfolio includes Spotify, Airbnb, Shazam and Soundcloud. Ashton Kutcher A-Grade Investments “Ashton invested in Gigster and was very helpful. He even came into the office and did a fireside chat with me and the team” Roger says. The price is … wrong How does Roger contrast and compare raising money for his previous company Liquid Labs versus Untitled? “It definitely was different,” he says. “We raised it as a price round, which I wouldn’t recommend. It was more expensive and took more time than it should have.” Roger tried to complete the round in a one- to two-week period. “It created some time pressure for folks to make decisions sooner,” he says. But settling on valuation after fundraising was an “awkward process.” His early investors agreed to fund the company without a valuation, but that wasn’t ideal, he says. “Afterwards, I had to go back and say, ‘by the way, here’s the price.’ Most stayed in, but they had questions. I think it would’ve been better for us to lead and set the price earlier.” Roger recommends that startups in his position fundraise via instead. “That would have alleviated a bit of stress,” he says. convertible note But when the rubber meets the road, he’s woke about his privilege. “It’s really hard for me to provide more advice that would be valuable because I’m in a fortunate position, given my track record,” he says. Digging into deeper pockets “There’s such a healthy Series A, Series B ecosystem that there’s not really any signaling risk if you get an entirely new set of investors in those rounds,” Roger says. For Roger, target investors had deeper pockets than the ones he tapped for Gigster, a company that had approximately 25 angels in its seed round. “I wanted a smaller group of people with more skin in the game for Untitled,” he says. “We have about eight investors … we needed people who could write half-million dollar checks.” Why catch a few big fish instead of a net full of small ones? “If everybody’s written a $50,000 check, nobody really cares if you fail. They’re not going to help you. You’re still selling 15, 20, or 30 percent of your company to investors. That’s a part of your cap table you can never get back. “This time around, I wanted to bring on people who felt motivated to be helpful.” Nathan Beckord is the CEO of Foundersuite.com , makers of the leading fundraising CRM and investor database for startup founders. Foundersuite has helped entrepreneurs raise over $1.2 billion in seed and venture capital since 2016. This article is based on an episode of Foundersuite’s How I Raised It podcast , a behind-the-scenes look at how startup founders have raised capital.

The New Model For Venture Creation: Inside Roger Dickey's "Untitled Labs" Startup Factory 

Episode 22 of the : An interview with Alex Rogers, CEO of . Hacker Noon Podcast ICBC the International Cannabis Business Conference Listen to the interview on , or , or watch on . iTunes Google Podcast YouTube In this episode and Alex Rogers discuss the cannabis industry, legalization, regulations, and what is happening internationally around the World as cannabis moves out of prohibition. Trent Lapinski “Our tech stuff is still considered fairly pedestrian in our industry, but that’s what makes it so exciting of an opportunity.” “We see a lot of tech guys, and business guys coming into the cannabis space and they’ve seen it before, they know what is going on. It is just a simple analogy.” “You see a lot of marriages between tech and business in the cannabis space that are creating some amazing breakthroughs and developments.” — Alex Rogers _Episode 22 of the Hacker Noon Podcast: An interview with Alex Rogers, CEO of ICBC the International Cannabis Business…_www.podbean.com The Cannabis Conference Hack with Alex Rogers P.S. If you dig the new Hacker Noon Podcast , consider giving us a 5 star review on iTunes. Also check out , and . January top stories the latest stories today’s homepage

The Cannabis Conference Hack with Alex Rogers

The State Of The Market — October 10, 2018 : $6,566.22 (-0.75%) : $225.95 (-0.48%) : $0.466 (-1.89%) BTC ETH XRP Since forming a double top near $6,730, Bitcoin has consistently posted lower highs and yesterday the cryptocurrency dropped all the way to $6,513. Bitcoin and Ethereum continue to suffer from decreasing purchasing volume and the situation is unlikely to reverse until this issue is resolved. Furthermore, a somber report by suggests that the crypto slaughter is far from over and FundStrat Head of Research , advises that right now is not the time to purchase Bitcoin. Juniper Research Robert Sluymer In other news, shoppers at Walmart and Home Depot might be able to pay for their goods using Bitcoin Cash and that transmits data via a fully decentralized blockchain network. Pundi X unveiled a blockchain-powered phone He said that he intends to introduce this functionality before the end of the year and that its users would get a 1% discount every time they shopped. Roger also stated that Bitcoin.com plans to integrate privacy features onto its wallet thus preventing transaction analytics. ( ) 1) In a recent interview with CryptoGlobe, Roger Ver, the CEO of Bitcoin.com revealed that users of the Bitcoin.com wallet would soon be able to spend Bitcoin Cash (BCH) at outlets such Walmart, Walgreens, Home Depot, and Safeway. Read More The ICOs will work as alternatives for companies to raise money. Obaid Said al-Zaabi, Chief Executive at the Securities and Commodities Authority in the UAE, said: “The board of the Emirates Securities & Commodities Authority has approved considering ICOs as securities. As per our plan, we should have regulations on the ground in the first half of 2019.” Furthermore, the EMSCA is reportedly drafting up said regulations with international advisers as well as with the Dubai and Abu Dhabi stock markets. ( ) 2) According to Reuters, The United Arab Emirates intends to officially introduce initial coin offerings (ICOs) and to set forth a fully-fledged regulatory framework in 2019. Read More The patent states, “There is a need for a technological solution to provide a partitioned blockchain that is capable of storing multiple transaction formats and types in a single blockchain.” According to the patent, the partitions, or “subnets,” would hold different transaction types allowing one blockchain to receive information about different transaction types from different sources, creating a more robust blockchain with greater utility. ( ) 3) The US Patent office has awarded Mastercard with a cryptocurrency related patent that points to the ability to store different types of transactions on a single blockchain. Read More Subscribe to the Berminal Brief Newsletter Download Berminal App for Free The Berminal Website Berminal Official Telegram Berminal Twitter

Lackluster Volume Pulls Bitcoin well Below $6,600 and Roger Ver Announces that Shoppers will soon…

Identifying Axion Conversion in Compact Star Magnetospheres With Radio-Wave Polarization Signatures

Narrative Resilience in Online Conflict Coverage: Insights from Twitter Analysis

This is paper is available on arxiv under CC 4.0 DEED license. Authors: (1) Leah Chrestien, Czech Technical University in Prague; (2) Tomá˘s Pevný, Czech Technical University in Prague, and Gen Digital, Inc.; (3) Stefan Edelkamp, Czech Technical University in Prague; (4) Antonín Komenda, Czech Technical University in Prague. Table of Links Abstract & Introduction Preliminaries Conditions on strictly optimally efficient heuristic Loss functions Related Work Experiments Conclusion and References Appendix 7 Conclusion The motivation of this paper stemmed from the observation that even the cost-to-goal, considered to be an optimal heuristic, fails to guarantee a strictly optimally efficient search. Since a large body of existing research optimizes this quantity, we are effectively lost with respect to what should be optimized. To fill this gap, we have stated the necessary and sufficient conditions guaranteeing the forward search to be strictly optimally efficient. These conditions show that the absolute value of the heuristic is not important, but that the ranking of states in the Open list is what controls the efficiency. Ranking can be optimized by minimizing the ranking loss functions, but its concrete implementation needs to correspond to a variant of the forward search. In case of mismatch, the resulting heuristic can perform poorly, which has been demonstrated when the heuristic optimized for BGFS search was used with A* search. The other benefit of ranking losses is that from the point of view of statistical learning theory, they solve a simpler problem than ubiquitous regression in estimating the cost-to-goal. We do not question the existence of a strictly optimally efficient heuristic. Given our experiments, we believe that if the space of heuristic functions over which the loss is optimized is sufficiently rich, the result will be sufficiently close to the optimal for the needs of the search. 7.1 Acknowledgements This work has been supported by project numbers 22-32620S and 22-30043S from Czech Science Foundation and OP VVV project CZ.02.1.01/0.0/0.0/16_019/0000765 "Research Center for Informatics". This work has also received funding from the European Union’s Horizon Europe Research and Innovation program under the grant agreement TUPLES No 101070149. References [1] Forest Agostinelli, Stephen McAleer, Alexander Shmakov, and Pierre Baldi. Solving the rubik’s cube with deep reinforcement learning and search. Nature Machine Intelligence, 1(8):356–363, 2019. [2] Thomas Anthony, Zheng Tian, and David Barber. Thinking fast and slow with deep learning and tree search. In Isabelle Guyon, Ulrike von Luxburg, Samy Bengio, Hanna M. Wallach, Rob Fergus, S. V. N. Vishwanathan, and Roman Garnett, editors, Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, December 4-9, 2017, Long Beach, CA, USA, pages 5360–5370, 2017. [3] Shahab Jabbari Arfaee, Sandra Zilles, and Robert C Holte. Learning heuristic functions for large state spaces. Artificial Intelligence, 175(16-17):2075–2098, 2011. [4] Jeff Bezanson, Alan Edelman, Stefan Karpinski, and Viral B Shah. Julia: A fresh approach to numerical computing. SIAM review, 59(1):65–98, 2017. [5] Mohak Bhardwaj, Sanjiban Choudhury, and Sebastian Scherer. Learning heuristic search via imitation. In Conference on Robot Learning, pages 271–280. PMLR, 2017. [6] Blai Bonet and Héctor Geffner. Planning as heuristic search. Artificial Intelligence. 2001 Jun; 129 (1-2): 5-33., 2001. [7] Vladimir Cherkassky, Xuhui Shao, Filip M Mulier, and Vladimir N Vapnik. Model complexity control for regression using vc generalization bounds. IEEE transactions on Neural Networks, 10(5):1075–1089, 1999. [8] Leah Chrestien, Tomáš Pevný, Antonín Komenda, and Stefan Edelkamp. Heuristic search planning with deep neural network using imitation, attention and curriculum learning. In PRLIJCAI workshop, 2021. [9] Lukás Chrpa. Combining learning techniques for classical planning: Macro-operators and entanglements. In ICTAI, pages 79–86. IEEE Computer Society, 2010. [10] Rina Dechter and Judea Pearl. The optimality of A* revisited. In Michael R. Genesereth, editor, AAAI, pages 95–99. AAAI Press, 1983. [11] Edsger W. Dijkstra. A note on two problems in connexion with graphs. Numerische Mathematik, 1:269–271, 1959. [12] Stefan Edelkamp. Automated creation of pattern database search heuristics. In Model Checking and Artificial Intelligence, pages 35–50, 2006. [13] Stefan Edelkamp and Stefan Schrödl. Heuristic Search - Theory and Applications. Academic Press, 2012. [14] Ariel Felner, Uzi Zahavi, Robert Holte, Jonathan Schaeffer, Nathan Sturtevant, and Zhifu Zhang. Inconsistent heuristics in theory and practice. Artificial Intelligence, 175(9):1570–1603, 2011. [15] Dieqiao Feng, Carla P. Gomes, and Bart Selman. Solving hard AI planning instances using curriculum-driven deep reinforcement learning. In Christian Bessiere, editor, Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, IJCAI 2020, pages 2198–2205. ijcai.org, 2020. [16] Patrick Ferber, Malte Helmert, and Jörg Hoffmann. Neural network heuristics for classical planning: A study of hyperparameter space. In ECAI, pages 2346–2353. IOS Press, 2020. [17] Santiago Franco, Álvaro Torralba, Levi H. S. Lelis, and Mike Barley. On creating complementary pattern databases. In IJCAI, pages 4302–4309, 2017. [18] Caelan Reed Garrett, Leslie Pack Kaelbling, and Tomás Lozano-Pérez. Learning to rank for synthesizing planning heuristics. page 3089–3095, 2016. [19] Edward Groshev, Aviv Tamar, Maxwell Goldstein, Siddharth Srivastava, and Pieter Abbeel. Learning generalized reactive policies using deep neural networks. 2018. [20] Arthur Guez, Théophane Weber, Ioannis Antonoglou, Karen Simonyan, Oriol Vinyals, Daan Wierstra, Rémi Munos, and David Silver. Learning to search with mctsnets. In International conference on machine learning, pages 1822–1831. PMLR, 2018. [21] Peter E Hart, Nils J Nilsson, and Bertram Raphael. A formal basis for the heuristic determination of minimum cost paths. IEEE transactions on Systems Science and Cybernetics, 4(2):100–107, 1968. [22] Patrik Haslum, Adi Botea, Malte Helmert, Blai Bonet, and Sven Koenig. Domain-independent construction of pattern database heuristics for cost-optimal planning. In AAAI, pages 1007–1012, 2007. [23] Malte Helmert and Gabriele Röger. How good is almost perfect? In AAAI, pages 944–949. AAAI Press, 2008. [24] Robert C. Holte and Sandra Zilles. On the optimal efficiency of cost-algebraic A. In The Thirty-Third AAAI Conference on Artificial Intelligence, AAAI 2019, pages 2288–2295. AAAI Press, 2019. [25] Michael Katz, Shirin Sohrabi, Horst Samulowitz, and Silvan Sievers. Delfi: Online planner selection for cost-optimal planning. IPC-9 planner abstracts, pages 57–64, 2018. [26] Diederik P Kingma and Jimmy Ba. Adam: A method for stochastic optimization. arXiv:1412.6980, 2014. [27] Donald E Knuth and Ronald W Moore. An analysis of alpha-beta pruning. Artificial intelligence, 6(4):293–326, 1975. [28] Richard E. Korf. Iterative-deepening-A*: An optimal admissible tree search. In IJCAI, pages 1034–1036. Morgan Kaufmann, 1985. [29] Richard E Korf. Macro-operators: A weak method for learning. Artificial intelligence, 26(1):35–77, 1985. [30] Šimon Mandlík, Matej Ra ˇ cinsk ˇ y, Viliam Lis ` y, and Tomáš Pevn ` y. Jsongrinder. jl: automated ` differentiable neural architecture for embedding arbitrary json data. Journal of Machine Learning Research, 23(298):1–5, 2022. [31] Drew V. McDermott. A heuristic estimator for means-ends analysis in planning. In AIPS, pages 142–149. AAAI, 1996. [32] David Ménager, Dongkyu Choi, Mark Roberts, and David W. Aha. Learning planning operators from episodic traces. In 2018 AAAI Spring Symposia. AAAI Press, 2018. [33] Ionut Moraru, Stefan Edelkamp, Santiago Franco, and Moisés Martínez. Simplifying automated pattern selection for planning with symbolic pattern databases. In KI, pages 249–263, 2019. [34] Christian Muise. Collection of pddl files of classical problems. https://github.com/AI-Planning/classical-domains, 2023. [35] XuanLong Nguyen, Martin J Wainwright, and Michael I Jordan. On surrogate loss functions and f-divergences. The Annals of Statistics, pages 876–904, 2009. [36] Laurent Orseau, Levi Lelis, Tor Lattimore, and Théophane Weber. Single-agent policy tree search with guarantees. Advances in Neural Information Processing Systems, 31, 2018. [37] Laurent Orseau and Levi HS Lelis. Implementation of the algorithms described in "policyguided heuristic search with guarantees" by l. orseau and l. lelis, published at aaai’21. https://github.com/levilelis/h-levin, 2021. [38] Laurent Orseau and Levi HS Lelis. Policy-guided heuristic search with guarantees. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 35, pages 12382–12390, 2021. [39] Arthur L Samuel. Some studies in machine learning using the game of checkers. ii—recent progress. IBM Journal of research and development, 11(6):601–617, 1967. [40] Max-Philipp B. Schrader. gym-sokoban. https://github.com/mpSchrader/gym-sokoban, 2018. [41] Jendrik Seipp, Florian Pommerening, and Malte Helmert. New optimization functions for potential heuristics. In ICAPS, 2015. [42] Jendrik Seipp and Silvan Sievers. Fast downward stone soup 2023. [43] Jendrik Seipp, Álvaro Torralba, and Jörg Hoffmann. PDDL generators. https://doi.org/10.5281/zenodo.6382173, 2022. [44] William Shen. Strips-hgn. https://github.com/williamshen-nz/STRIPS-HGN, 2021. [45] William Shen, Felipe Trevizan, and Sylvie Thiébaux. Learning domain-independent planning heuristics with hypergraph networks. In ICAPS, volume 30, pages 574–584, 2020. [46] David Silver, Julian Schrittwieser, Karen Simonyan, Ioannis Antonoglou, Aja Huang, Arthur Guez, Thomas Hubert, Lucas Baker, Matthew Lai, Adrian Bolton, et al. Mastering the game of go without human knowledge. nature, 550(7676):354–359, 2017. [47] Kristian Smith. Maze creator / solver. https://github.com/ravenkls/Maze-Generator-and Solver, 2022. [48] Gustav Sourek, Vojtech Aschenbrenner, Filip Zelezny, Steven Schockaert, and Ondrej Kuzelka. Lifted relational neural networks: Efficient learning of latent relational structures. Journal of Artificial Intelligence Research, 62:69–100, 2018. [49] Simon Ståhlberg, Blai Bonet, and Hector Geffner. Learning general optimal policies with graph neural networks: Expressive power, transparency, and limits. In Akshat Kumar, Sylvie Thiébaux, Pradeep Varakantham, and William Yeoh, editors, Proceedings of the Thirty-Second International Conference on Automated Planning and Scheduling, ICAPS 2022, Singapore (virtual), June 13-24, 2022, pages 629–637. AAAI Press, 2022. [50] Ingo Steinwart. Consistency of support vector machines and other regularized kernel classifiers. IEEE transactions on information theory, 51(1):128–142, 2005. [51] Takeshi Takahashi, He Sun, Dong Tian, and Yebin Wang. Learning heuristic functions for mobile robot path planning using deep neural networks. In ICAPS, volume 29, pages 764–772, 2019. [52] Alvaro Torralba, Vidal Alcázar, Daniel Borrajo, Peter Kissmann, and Stefan Edelkamp. SymBA*: A symbolic bidirectional A* planner. In International Planning Competition, pages 105–108, 2014. [53] Sam Toyer, Sylvie Thiébaux, Felipe Trevizan, and Lexing Xie. Asnets: Deep learning for generalised planning. Journal of Artificial Intelligence Research, 68:1–68, 2020. [54] Vladimir Vapnik. Principles of risk minimization for learning theory. Advances in neural information processing systems, 4, 1991. [55] Marin Vlastelica, Michal Rolinek, and Georg Martius. Neuro-algorithmic policies enable fast combinatorial generalization. In International Conference on Machine Learning, pages 10575– 10585. PMLR, 2021. [56] Xuemei Wang. Learning planning operators by observation and practice. In Kristian J. Hammond, editor, AIPS, pages 335–340. AAAI, 1994. [57] Christopher Wilt and Wheeler Ruml. When does weighted A* fail? In International Symposium on Combinatorial Search, volume 3, 2012. [58] Christopher Makoto Wilt and Wheeler Ruml. Effective heuristics for suboptimal best-first search. J. Artif. Intell. Res., 57:273–306, 2016. [59] Yuehua Xu, Alan Fern, and Sungwook Yoon. Learning linear ranking functions for beam search with application to planning. Journal of Machine Learning Research, 10(7), 2009. [60] Ryo Yonetani, Tatsunori Taniai, Mohammadamin Barekatain, Mai Nishimura, and Asako Kanezaki. Path planning using neural A* search. In International Conference on Machine Learning, pages 12029–12039. PMLR, 2021. [61] Ziqi Zhang and Florian Geißer. Extending graph neural networks for generalized stochastic planning. In Bridging the Gap Between AI Planning and Reinforcement Learning. 2021. [62] Tan Zhi-Xuan. PDDL. jl: An Extensible Interpreter and Compiler Interface for Fast and Flexible AI Planning. PhD thesis, Massachusetts Institute of Technology, 2022. [63] Juntang Zhuang, Tommy Tang, Yifan Ding, Sekhar C Tatikonda, Nicha Dvornek, Xenophon Papademetris, and James Duncan. Adabelief optimizer: Adapting stepsizes by the belief in observed gradients. Advances in neural information processing systems, 33:18795–18806, 2020.

Optimize Planning Heuristics to Rank, not to Estimate Cost-to-Goal: Conclusion and References

has revolutionized the development and deployment of modern software systems, offering unprecedented scalability, flexibility, and maintainability. Microservice architecture Despite the fact that microservices are considered a cutting-edge development industry, the term was first used 20 years ago — in 2005, Peter Rogers invented the term "micro-web services" during a presentation on cloud computing. Rogers created a functional model of microservices that has become a reality over time. Using , a powerful and versatile programming language, allows developers to leverage the full potential of microservices. We will briefly review each step and reveal the technical nuances that contribute to the successful implementation of your microservice application. Java Step 1: Define the boundaries of your services The cornerstone of any microservice architecture is clearly defining service boundaries. This critical step involves: Carefully analyzing the business domain Identifying holistic components Defining the interactions between them. Microservices should encapsulate specific business capabilities, ensuring high cohesion and low coupling between modules. Domain-centric design principles, such as bounded contexts and ubiquitous language, provide a solid foundation for defining service boundaries and promote common understanding among stakeholders. In practice, tools such as Domain-Driven Design (DDD) can help model complex domains by helping developers define the aggregates, entities, and value objects that form the building blocks of microservices. Moreover, techniques such as event-storming facilitate collaborative exploration of events and domain business processes, paving the way for a well-defined microservices architecture. Step 2: Select appropriate tools and frameworks Choosing the right tools and frameworks is critical to building the technological foundation of the microservices ecosystem. In the Java ecosystem, several frameworks and libraries offer robust features for building microservices, each with its own strengths and trade-offs. , popular among developers, provides a comprehensive set of tools for rapid development, dependency injection, and integration with various libraries. In addition, frameworks such as and offer lightweight, cloud-native alternatives optimized for microservice deployments. Spring Boot Micronaut Quarkus technologies such as Docker and Kubernetes play a key role in creating scalable and resilient microservice architectures. Containerization Docker containers provide lightweight, portable deployment units, and Kubernetes orchestrates containerized applications by automating deployment, scaling, and management tasks. Step 3: Establish cross-service communication Effective communication between microservices is essential for orchestrating complex workflows and ensuring seamless operations. Depending on the nature of the communication and requirements, different communication patterns such as synchronous , asynchronous message queues, and event-driven architectures can be used. RESTful API RESTful APIs serve as the foundation of many microservices architectures, offering a standardized approach to communicating over HTTP. Frameworks such as allow developers to create reactive, non-blocking REST APIs ideal for handling high-performance, real-time workloads. Spring WebFlux For asynchronous communication, message brokers such as and provide reliable message delivery and separate communication between services. Kafka's distributed architecture and fault-tolerant design make it well-suited for event source and stream processing applications. Apache Kafka RabbitMQ Service mesh technologies such as and provide a layer of abstraction for managing interactions between services, addressing tasks such as service discovery, load balancing, and traffic routing. By shifting these responsibilities to individual microservices, service grids simplify development and operation while improving reliability and observability. Istio Linkerd Step 4: Ensure scalability and fault tolerance Scalability and fault tolerance are critical aspects in designing microservice architectures that can handle diverse workloads and maintain high availability. Horizontal scalability, achieved through containerization and orchestration, allows microservices to dynamically expand in response to increasing demand. Fault tolerance mechanisms such as circuit breakers, retries, and revert strategies help mitigate failures and ensure smooth degradation under adverse conditions. Libraries such as and provide robust implementations of these patterns, allowing developers to build resilient microservices that can gracefully withstand failures. Netflix/Hystrix resilience4j Distributed caching using technologies such as or can reduce performance bottlenecks by caching frequently used data closer to the application, reducing latency and improving scalability. In addition, database partitioning technologies allow data to be horizontally shared across multiple nodes, allowing microservices to horizontally scale database access and efficiently handle growing datasets. Redis Memcached Step 5: Continuous Integration and Deployment (CI/CD) Achieving fast and reliable software delivery requires a robust CI/CD pipeline that automates the build, test, and deployment processes. Tools such as , , and provide powerful automation capabilities, allowing developers to frequently integrate code changes and confidently deploy them to production. Jenkins GitLab CI CircleCI Containerization, combined with container registries such as or , provides consistency across environments and simplifies deployment across infrastructures. With Docker, developers can package their applications and dependencies into lightweight, portable containers, enabling seamless deployment across development, test, and production environments. Docker Hub Amazon ECR Deploying microservices in a cloud-native environment such as AWS, Azure, or Google Cloud Platform offers additional benefits in terms of scalability, resiliency, and operational efficiency. Cloud-native services such as provide managed Kubernetes offerings, abstracting away the complexity of infrastructure management and allowing developers to focus on building and deploying microservices. AWS ECS, Azure Kubernetes Service, and Google Kubernetes Engine (GKE) Monitoring and observability are important aspects of operating microservice architectures in production. Tools such as (Elasticsearch, Logstash, Kibana) provide comprehensive visibility into system health, performance metrics, and logs, enabling proactive monitoring, troubleshooting, and optimization. Prometheus and Grafana, and ELK Stack Conclusion In conclusion, building a microservices architecture in Java requires careful consideration of service boundaries, tool selection, interaction patterns, scaling measures, and CI/CD practices. By following these steps and utilizing the right technologies and best practices, developers will be able to build fault-tolerant, scalable, and flexible systems that can meet the demands of modern software development. Microservices, combined with the reliability and versatility of Java, enable organizations to innovate quickly, adapt to changing market conditions, and deliver exceptional value to customers in today's rapidly evolving digital landscape.

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