Russia, more or less, invested in Angola, even after the Soviet collapse political dialogues and economic cooperation have been the strategic focus in their bilateral relations.

Russian Enterprises Fostering Closer Economic Ties with Angola, Southern African Countries - Modern Diplomacy

Infographic in English on Angola and 1 other country about Protection and Human Rights; published on 3 May 2024 by UNHCR

Angola: Monthly Refugee Population Statistics, 2017 Kasai Active Refugee Population, April 2024 [EN/PT] - Angola | ReliefWeb

Angola: Monthly Refugee Population Statistics Lovua Refugee Settlement, April 2024 [EN/PT] - Angola | ReliefWeb

Russian Enterprises Fostering Closer Economic Ties with Angola, Southern 
African Countries

Agolo

agolo

THE EVENING SKY AT THE VERNAL EQUINOX

The long awaited of this series has arrived. It has taken me a significant amount of time longer to put together than I had anticipated. Now the wait is over, here it is… Part 2 of 3 This list contains companies working on artificial intelligence and machine learning products primarily for business use, non-specific to any industry. Industry specific AI will be the final part of this series. Rise of artificial intelligence is rapidly taking over the world, especially countries like Anguilla; a small island in the Eastern Caribbean with the use of their .ai top level domain for companies within the AI and robotics industry. But in all seriousness, AI is without a doubt accelerating much faster than is probably known and much more established within our daily lives than most would realise. Conducting this research has demonstrated sheer scale of the landscape and progress of the artificial intelligence industry. “Already humans and AI are co-evolving and no one has paid attention to this yet…” “When we search on Google, it’s an AI deciding what we should see. When a dating site matches two people together there’s a machine learning algorithm… that is getting people together, that then have babies. And so in effect you have this machine learning algorithm that is breeding humans.”— Sam Altman, discussing AI . here In the case of the companies listed below, business and AI are co-evolving. With so many websites to go through and dissect there may be some descriptions or names that need changing. Please let me know if that’s the case or if you think there are companies I should add to the list. Shoutout to me , and I’ll get on to it! here Artificial Intelligence for businesses (enterprise intelligence): 🔉 Audio — language transcription and recognition — ? — transcribes insights from phone calls, video footage, and online — discover more customer and employee conversations** **— makes sound searchable — critical insights about your customers conversations — building blocks to add messaging, voice, and video to web+apps Capio Clover Intelligence Deepgram Gridspace MindMeld — advanced AI to power conversational interface Nexidia — turns customer interactions into valuable insights Pop Up Archive TalkIQ Twilio 📊 Business Intelligence & Analytics — optimise and autonomously IT and business operations** ** —behavioural AI for IoT — a suite of intelligent applications for enterprise — a range of products to improve enterprise products** ** — automates repetitive tasks within your business — making complex data science easy for enterprise Arago/HIRO Arimo Ayasdi DataRobot Dataminr — discovers events and breaking information before the news — helps you to answer questions about your business — a smarter Salesforce — adds intelligent decision making to web and mobile apps — helps you index, search, visualise and analyse your data — is a framework for temporal pattern recognition and prediction — to transform raw data into useful information automatically — helps you monitor server bugs Electra by Lore Einstein Fuzzy AI Logz.io NXT AI Paxata Poweredby.ai Sundown UBIX Ruths.ai — helps you do more with your data — a marketplace for analytics — captures, categorises, and extracts key information from all your data — fast data insights using search — solves big data better, faster and at less cost — find hidden value in complex human and business ecosystems — helps to optimise pricing Exchange.ai Owl.ai AnswerRocket iSeek.ai Ecosystem.AI Prix 🍎 Core AI — a common API for many algorithms, functions and models — workbench for neural networks — advanced industry specific ML for the enterprise — advanced machine learning for enterprise — advanced machine learning for enterprise — open source machine learning and deep learning platform — advanced machine learning for enterprise — deep learning as computational system — advanced machine learning for enterprise — advanced machine learning for enterprise — range of financial, ecommerce and digital marketing AI products — open-source deep learning and ETL for enterprise on the JVM — advanced machine learning for enterprise — log Analysis for enterprises Algorithmia Arya CognitiveScale Digital Reasoning Fluid AI H2O.ai Loop AI Labs Nervana Petuum Scaled Inference Sentient Skymind Vicarious Loom Systems PipelineAI — solves the problem of production ML and AI at scale — building AI using neuroscience Ogma 📋 Data Capture — marketplace to automate simple processes — automate the discovery of objects at scale — automatically prepare any data for immediate & compliant use — helps structure data from social media and blog — automatically extract web pages as structured data — extract data from almost any website — training data, image annotation and more for enterprise** ** — tools for operations team to automate business processes Amazon Mechanical Turk CrowdAI Datalogue DataSift Diffbot Import.io Playment WorkFusion 🔬 Data Science — single platform for all predictive use cases — training data, image annotation for machine learning teams — data science platform for prototype, deploy, and run at scale — enterprise data science platform for R&D and production** ** — helps you learn, work, and play with machine learning models — makes data science teams more productive — helps DS teams put machine learning models into production — a platform to build, test, and deploy AI algorithms** **— research engine, capable of discovering complex patterns in data — makes data unification of data silos possible — helps put data into useful structures for analysis — allows data scientists to deploy and update predictive models rapidly — automate the writing of reports, websites, emails, articles and more BigML CrowdFlower Dataiku DataScience Domino Data Lab — platform for collaborating, building and deploying — makes DS accessible to analysts with OpenSource algorithms Exploratory Kaggle RapidMiner Seldon SherlockML Spark Tamr Trifacta Yhat Yseop 💻 Development — detects business incidents — develop more adaptive, trusted and programmable AI models — helps predict project timelines — adds intelligent decision making to web and mobile apps — connecting projects with the right team — augments your coding environment with web available knowledge — deep learning platform for prediction and personalisation — makes developing chatbots for your business easy — make your bot smarter, by helping it self learn — rapidly web and mobile app testing — increase server uptime and predict downtime — project management and chat software that’s easy for teams AnOdot Bonsai Deckard.ai Fuzzy.ai Gigster Kite Layer 6 AI Morph Ozz RainforestQA SignifAI Turtle Improve.ai — automatically optimises app content, design, pricing etc. — gesture recognition for developers — trains deep learning algorithms to learn like human brain — abstracts away the complexity of ML libraries like TensorFlow for more effective management of AI models — helps with decision making for designers, engineers and leaders — helps analyse algorithm Gesture.ai Cognitive Toolkit Bonsai Tangle Imandra 📂 Internal Data — helps you work together, improve productivity, and data indexing** ** — helps employees with tasks and access data using micro apps — better website analytics Alation Cycorp — a range of different AI enterprise products — takes the pain of cluster management away to focus on DS — helps predict project timelines — insight into online trends and other text analytics tools — AI platform for business Databricks Deckard.ai Gavagi IBM Watson Kyndi — helps knowledge workers process vast amounts of information — SaaS AI for risk management and operations — makes your business software smarter oneFactor Probot Sapho Sofia eContext — structure for unstructured data — create intelligent interactions between people, devices, and data — deep insights into customers and internal data —automate decision-making at scale Hayley RelativeInsight Rainbird 🤖 Machine Learning —No-code end-to-end AutoML tool that takes you from data to predictions in minutes* *__ — a range of different AI enterprise products — helps you focus your digital ad on people you want to target — analyse data in the form of images, speech, text and video — now apart of the Uber AI Labs — can do menial task work, saving time for employees — platform to unite siloed data for better recommendations — improves machine learning models 100x faster PI.Exchange — develop more adaptive, trusted and programmable AI models__ Bonsai Cycorp Datacratic deepsense.io Geometric Intelligence HyperScience Nara Logics SigOpt Amazon Machine Learn — ML-as-a-service, amongst other things — import predictive models and scale infinitely to answer existential questions Providence 📡 Sensor (IoT/IIoT) — platform delivers real-time operational insights for industry — learns about shoppers behaviour in your store — helps to unify application development and data science — sensor analytics solution for businesses — machine learning for manufacturing process optimisation — a range of workflow optimisation products for fuel and industry — helps you develop, deploy, and operate industrial apps — helps renewable energy companies utilise their data better — manufacturing analytics — automates data engineering for data science — add a voice assistant to your connected product — platform to manage the development for your IoT applications — a predictive platform for major industries — smart building management for commercial buildings Alluvium Black C3 IoT KONUX Imubit Maana Predix Planet OS Sight Machine Sentenai Snips ThingWorx Uptake Verdigris 📚 Text Analysis/Generation — creates summaries from your text and information in real-time — extract meaning from your text and visuals — text analytics and mining which works without any training** **—automatic knowledge acquisition from text — build profile and understand your users better — scaleable text analytics software — capture, measure, and act on customer feedback — scalable API to automate text classification — interprets your data more useful information — helps you find errors and inaccuracies in documents — free open-source library natural of language processing in Python — automates information extraction, management and analysis — turn text into insights — helps improve how your job ads are written — automate the writing of reports, websites, emails, articles and more Agolo AYLIEN Compreno Cortical.io — advanced language processing fido.ai IntroSpect by Lore Lexalytics Luminoso MonkeyLearn Narrative Science Qeep spaCy Salient Stride Textio Yseop 😎 Vision — add instant text capture functionality to mobile apps and more! — automated drones with vision and diagnosis capabilities — analyses subtle facial expressions to identify human emotions — makes every camera in the world smart — helps cameras to detect analyse the video feed — finds defects in wind turbines — extracts and transforms data from handwritten and typed forms — helps you to organise media libraries — visual analysis for medical and transport industry — image detection for a range of uses and industries — brand and face recognition — makes satellite imagery useful — the first intelligent cloud video platform** ** — computer vision for automative and human detection — matches similar products and helps to suggests combinations — facial recognition — uses facial recognition to help you decided on job candidates — medical data analysis and interpretation — recognises different objects and things — dedicated brand recognition for social networks — satellite image analysis — deep-learning based computer vision platform — planet monitoring and analysis using satellite imagery — satellite image analysis — is an eye and emotion tracking platform — understands and describes video content — image analysis primarily for industrial purposes ABBYY Achron Affectiva Algocian __ __i Angus.a Birds.ai Captricity Clarifai Cortica Deepomatic DeepVision Descartes Labs Flixsense FotoNation GrokStyle Haystack HireVue Lunit Inc. Matroid Netra Orbital Insight Pilot AI Labs Planet Spaceknow Sticky.ai Valossa Vidi CloudSight — high quality understanding of images within seconds — detects foreign and intentionally placed objects for security — a range of intelligent computer vision techniques Irvine Sensors Pilot.ai Originally posted → liamiscool.com

A list of artificial intelligence tools you can use today — for businesses (2/3)

“What, to you, is an example of the ideal startup culture?” I asked this question to a panel of entrepreneurs gathered at Work Bench, a venture capital firm. I expected to hear praise of Silicon Valley’s darlings — Amazon, Netflix, et al. , a co-founder of the AI , surprised me with his response: Mohamed AlTantawy startup Agolo “I spent most of my background in academia, and I wanted to bring that culture with us. What I find is that there is more openness. Sometimes you get into heated arguments. But there’s no “do this my way or you’re fired!” We’re publishing together, so we come together as a team and there’s collective responsibility.” Employees open up more in an environment where opposing viewpoints are welcome. This can bring more communication and new ideas in a company. This was also my experience working with Mohamed at Agolo. The company’s growth and employee retention speaks to the success of Mohamed’s approach. Consequences of Bad Culture In the startup world, there is a saying — Yet the culture of many startups today seems to be driven more by ego than by data. “Whoever has the best data, wins.” tech In , author paints a stark picture of his time at Facebook during its IPO phase. The culture he depicts conveys a cult-like atmosphere: employees have almost no life outside the company, deserters are scorned by the faithful, and opinions contrary to upper management are derided. Chaos Monkeys Antonio García Martinez The repercussions to firms for ignoring the impact of culture can be severe. The movement, in response to Uber’s activation of surge pricing during a pro-immigrant rally at JFK, has cost the company millions. But recent revelations of workplace harassment and discrimination could be even more damaging. #deleteUber A few weeks ago, my Facebook feed became littered with ads for Uber software engineering positions. This could be interpreted as a sign of expansion. After seeing the departure of several high-level execs in the midst of scandal, I’m more inclined to believe that employees are jumping ship. After all, how many engineers will have the motivation to work for a company that allows “rockstar” performers to get away with almost anything? Culture and Recruiting Bad culture makes it difficult to attract and retain top talent. Frank*, an engineer who recently interviewed for several Silicon Valley startups, says that the culture of some startups is “toxic.” At one interview, an employee bragged about working on the weekend without pay. At another, a recruiter told him that the company wanted “people who live to code, not code to live.” These experiences gave him the impression that the companies didn’t value work-life balance, and only wanted one-dimensional engineers. While passion for work is a desirable trait, many companies don’t realize that singular focus on work often leads to burnout and poor decision-making. “When should a company start being proactive about building culture?” The consensus from the founders I interviewed for this piece is as soon as possible. , the co-founder of an e-commerce startup, says, “The culture starts with the founders. They set the tone for everyone else.” David Mohs Mohamed admits that he didn’t always think establishing culture was a high priority. He felt that the company culture was going well, despite not having anything written down. Finally a knowledgable friend convinced him to work with his team to codify their values. “If you don’t have it written down, then it doesn’t exist,” he says. It might not be possible to articulate a company’s core values in the first shot. Like most aspects of startups, Mohamed says, culture can also be improved upon in iterations. “Just like the team, culture grows. You face setbacks, and you have to adjust it. It’s a continuous process.” While the core values of a culture will differ from company to company, recent events make clear the need to define a robust culture early on. As one of the panelists, , said, “[culture] is the most important thing any company has.” Michael Reidbord What do you think? What are ways that startups can create a strong and attractive culture? Please post your comment below! *the names of some people interviewed for this article have been kept anonymous or changed for privacy reasons **thanks to Jess Holzer, Ryan Goldenberg, David Helene, and Garret Sokoloff for reviewing this piece

Culture at Startups — Differentiator or Downfall?

Here's Why You Should Stop Over-Apologizing at Work

Kunatsa irnaqäwin jan sinti perdón mayisiñamaxa, akax akawa

Вось чаму вы павінны спыніць празмерныя прабачэнні на працы

Esta es la razón por la que deberías dejar de disculparte demasiado en el trabajo

Tässä on miksi sinun pitäisi lopettaa liiallinen anteeksipyyntö töissä

הנה למה אתה צריך להפסיק להתנצל יותר מדי בעבודה

職場で過剰な謝罪をやめるべき理由

Tala Pourquoi Osengeli Kotika Kosenga Pardon Koleka Na Mosala

Та яагаад ажил дээрээ хэт уучлалт гуйхаа болих хэрэгтэйг эндээс харж болно

Dit is waarom u moet stoppen met u te vaak te verontschuldigen op het werk

Tu je dôvod, prečo by ste sa v práci mali prestať príliš ospravedlňovať

Waa tan sababta ay tahay inaad u joojiso raaligelinta xad dhaafka ah ee shaqada

Ево зашто би требало да престанете да се превише извињавате на послу

The Murder of Roger Ackroyd by Agatha Christie, is part of the HackerNoon Books Series. You can jump to any chapter in this book here . APOLOGIA CHAPTER XXVII. APOLOGIA Five a.m. I am very tired—but I have finished my task. My arm aches from writing. A strange end to my manuscript. I meant it to be published some day as the history of one of Poirot’s failures! Odd, how things pan out. All along I’ve had a premonition of disaster, from the moment I saw Ralph Paton and Mrs. Ferrars with their heads together. I thought then that she was confiding in him; as it happened I was quite wrong there, but the idea persisted even after I went into the study with Ackroyd that night, until he told me the truth. Poor old Ackroyd. I’m always glad that I gave him a chance. I urged him to read that letter before it was too late. Or let me be honest—didn’t I subconsciously realize that with a pig-headed chap like him, it was my best chance of getting him to read it? His nervousness that night was interesting psychologically. He knew danger was close at hand. And yet he never suspected . not me The dagger was an afterthought. I’d brought up a very handy little weapon of my own, but when I saw the dagger lying in the silver table, it occurred to me at once how much better it would be to use a weapon that couldn’t be traced to me. I suppose I must have meant to murder him all along. As soon as I heard of Mrs. Ferrars’s death, I felt convinced that she would have told him everything before she died. When I met him and he seemed so agitated, I thought that perhaps he knew the truth, but that he couldn’t bring himself to believe it, and was going to give me the chance of refuting it. So I went home and took my precautions. If the trouble were after all only something to do with Ralph—well, no harm would have been done. The dictaphone he had given me two days before to adjust. Something had gone a little wrong with it, and I persuaded him to let me have a go at it, instead of sending it back. I did what I wanted to it, and took it up with me in my bag that evening. I am rather pleased with myself as a writer. What could be neater, for instance, than the following:— “The letters were brought in at twenty minutes to nine. It was just on ten minutes to nine when I left him, the letter still unread. I hesitated with my hand on the door handle, looking back and wondering if there was anything I had left undone.” All true, you see. But suppose I had put a row of stars after the first sentence! Would somebody then have wondered what exactly happened in that blank ten minutes? When I looked round the room from the door, I was quite satisfied. Nothing had been left undone. The dictaphone was on the table by the window, timed to go off at nine-thirty (the mechanism of that little device was rather clever—based on the principle of an alarm clock), and the arm-chair was pulled out so as to hide it from the door. I must admit that it gave me rather a shock to run into Parker just outside the door. I have faithfully recorded that fact. Then later, when the body was discovered, and I had sent Parker to telephone for the police, what a judicious use of words: “ ” It was quite little—just to shove the dictaphone into my bag and push back the chair against the wall in its proper place. I never dreamed that Parker would have noticed that chair. Logically, he ought to have been so agog over the body as to be blind to everything else. But I hadn’t reckoned with the trained-servant complex. I did what little had to be done! I wish I could have known beforehand that Flora was going to say she’d seen her uncle alive at a quarter to ten. That puzzled me more than I can say. In fact, all through the case there have been things that puzzled me hopelessly. Every one seems to have taken a hand. My greatest fear all through has been Caroline. I have fancied she might guess. Curious the way she spoke that day of my “strain of weakness.” Well, she will never know the truth. There is, as Poirot said, one way out.... I can trust him. He and Inspector Raglan will manage it between them. I should not like Caroline to know. She is fond of me, and then, too, she is proud.... My death will be a grief to her, but grief passes.... When I have finished writing, I shall enclose this whole manuscript in an envelope and address it to Poirot. And then—what shall it be? Veronal? There would be a kind of poetic justice. Not that I take any responsibility for Mrs. Ferrars’s death. It was the direct consequence of her own actions. I feel no pity for her. I have no pity for myself either. So let it be veronal. But I wish Hercule Poirot had never retired from work and come here to grow vegetable marrows. THE END 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 APOLOGIA

Anne of Green Gables by L. M. Montgomery, is part of the HackerNoon Books Series. You can jump to any chapter in this book here . Anne’s Apology CHAPTER X. Anne’s Apology MARILLA said nothing to Matthew about the affair that evening; but when Anne proved still refractory the next morning an explanation had to be made to account for her absence from the breakfast table. Marilla told Matthew the whole story, taking pains to impress him with a due sense of the enormity of Anne’s behavior. “It’s a good thing Rachel Lynde got a calling down; she’s a meddlesome old gossip,” was Matthew’s consolatory rejoinder. “Matthew Cuthbert, I’m astonished at you. You know that Anne’s behavior was dreadful, and yet you take her part! I suppose you’ll be saying next thing that she oughtn’t to be punished at all!” “Well now—no—not exactly,” said Matthew uneasily. “I reckon she ought to be punished a little. But don’t be too hard on her, Marilla. Recollect she hasn’t ever had anyone to teach her right. You’re—you’re going to give her something to eat, aren’t you?” “When did you ever hear of me starving people into good behavior?” demanded Marilla indignantly. “She’ll have her meals regular, and I’ll carry them up to her myself. But she’ll stay up there until she’s willing to apologize to Mrs. Lynde, and that’s final, Matthew.” Breakfast, dinner, and supper were very silent meals—for Anne still remained obdurate. After each meal Marilla carried a well-filled tray to the east gable and brought it down later on not noticeably depleted. Matthew eyed its last descent with a troubled eye. Had Anne eaten anything at all? When Marilla went out that evening to bring the cows from the back pasture, Matthew, who had been hanging about the barns and watching, slipped into the house with the air of a burglar and crept upstairs. As a general thing Matthew gravitated between the kitchen and the little bedroom off the hall where he slept; once in a while he ventured uncomfortably into the parlor or sitting room when the minister came to tea. But he had never been upstairs in his own house since the spring he helped Marilla paper the spare bedroom, and that was four years ago. He tiptoed along the hall and stood for several minutes outside the door of the east gable before he summoned courage to tap on it with his fingers and then open the door to peep in. Anne was sitting on the yellow chair by the window gazing mournfully out into the garden. Very small and unhappy she looked, and Matthew’s heart smote him. He softly closed the door and tiptoed over to her. “Anne,” he whispered, as if afraid of being overheard, “how are you making it, Anne?” Anne smiled wanly. “Pretty well. I imagine a good deal, and that helps to pass the time. Of course, it’s rather lonesome. But then, I may as well get used to that.” Anne smiled again, bravely facing the long years of solitary imprisonment before her. Matthew recollected that he must say what he had come to say without loss of time, lest Marilla return prematurely. “Well now, Anne, don’t you think you’d better do it and have it over with?” he whispered. “It’ll have to be done sooner or later, you know, for Marilla’s a dreadful deter-mined woman—dreadful determined, Anne. Do it right off, I say, and have it over.” “Do you mean apologize to Mrs. Lynde?” “Yes—apologize—that’s the very word,” said Matthew eagerly. “Just smooth it over so to speak. That’s what I was trying to get at.” “I suppose I could do it to oblige you,” said Anne thoughtfully. “It would be true enough to say I am sorry, because I am sorry now. I wasn’t a bit sorry last night. I was mad clear through, and I stayed mad all night. I know I did because I woke up three times and I was just furious every time. But this morning it was over. I wasn’t in a temper anymore—and it left a dreadful sort of goneness, too. I felt so ashamed of myself. But I just couldn’t think of going and telling Mrs. Lynde so. It would be so humiliating. I made up my mind I’d stay shut up here forever rather than do that. But still—I’d do anything for you—if you really want me to—” “Well now, of course I do. It’s terrible lonesome downstairs without you. Just go and smooth things over—that’s a good girl.” “Very well,” said Anne resignedly. “I’ll tell Marilla as soon as she comes in I’ve repented.” “That’s right—that’s right, Anne. But don’t tell Marilla I said anything about it. She might think I was putting my oar in and I promised not to do that.” “Wild horses won’t drag the secret from me,” promised Anne solemnly. “How would wild horses drag a secret from a person anyhow?” But Matthew was gone, scared at his own success. He fled hastily to the remotest corner of the horse pasture lest Marilla should suspect what he had been up to. Marilla herself, upon her return to the house, was agreeably surprised to hear a plaintive voice calling, “Marilla” over the banisters. “Well?” she said, going into the hall. “I’m sorry I lost my temper and said rude things, and I’m willing to go and tell Mrs. Lynde so.” “Very well.” Marilla’s crispness gave no sign of her relief. She had been wondering what under the canopy she should do if Anne did not give in. “I’ll take you down after milking.” Accordingly, after milking, behold Marilla and Anne walking down the lane, the former erect and triumphant, the latter drooping and dejected. But halfway down Anne’s dejection vanished as if by enchantment. She lifted her head and stepped lightly along, her eyes fixed on the sunset sky and an air of subdued exhilaration about her. Marilla beheld the change disapprovingly. This was no meek penitent such as it behooved her to take into the presence of the offended Mrs. Lynde. “What are you thinking of, Anne?” she asked sharply. “I’m imagining out what I must say to Mrs. Lynde,” answered Anne dreamily. This was satisfactory—or should have been so. But Marilla could not rid herself of the notion that something in her scheme of punishment was going askew. Anne had no business to look so rapt and radiant. Rapt and radiant Anne continued until they were in the very presence of Mrs. Lynde, who was sitting knitting by her kitchen window. Then the radiance vanished. Mournful penitence appeared on every feature. Before a word was spoken Anne suddenly went down on her knees before the astonished Mrs. Rachel and held out her hands beseechingly. “Oh, Mrs. Lynde, I am so extremely sorry,” she said with a quiver in her voice. “I could never express all my sorrow, no, not if I used up a whole dictionary. You must just imagine it. I behaved terribly to you—and I’ve disgraced the dear friends, Matthew and Marilla, who have let me stay at Green Gables although I’m not a boy. I’m a dreadfully wicked and ungrateful girl, and I deserve to be punished and cast out by respectable people forever. It was very wicked of me to fly into a temper because you told me the truth. It was the truth; every word you said was true. My hair is red and I’m freckled and skinny and ugly. What I said to you was true, too, but I shouldn’t have said it. Oh, Mrs. Lynde, please, please, forgive me. If you refuse it will be a lifelong sorrow on a poor little orphan girl, would you, even if she had a dreadful temper? Oh, I am sure you wouldn’t. Please say you forgive me, Mrs. Lynde.” Anne clasped her hands together, bowed her head, and waited for the word of judgment. There was no mistaking her sincerity—it breathed in every tone of her voice. Both Marilla and Mrs. Lynde recognized its unmistakable ring. But the former under-stood in dismay that Anne was actually enjoying her valley of humiliation—was reveling in the thoroughness of her abasement. Where was the wholesome punishment upon which she, Marilla, had plumed herself? Anne had turned it into a species of positive pleasure. Good Mrs. Lynde, not being overburdened with perception, did not see this. She only perceived that Anne had made a very thorough apology and all resentment vanished from her kindly, if somewhat officious, heart. “There, there, get up, child,” she said heartily. “Of course I forgive you. I guess I was a little too hard on you, anyway. But I’m such an outspoken person. You just mustn’t mind me, that’s what. It can’t be denied your hair is terrible red; but I knew a girl once—went to school with her, in fact—whose hair was every mite as red as yours when she was young, but when she grew up it darkened to a real handsome auburn. I wouldn’t be a mite surprised if yours did, too—not a mite.” “Oh, Mrs. Lynde!” Anne drew a long breath as she rose to her feet. “You have given me a hope. I shall always feel that you are a benefactor. Oh, I could endure anything if I only thought my hair would be a handsome auburn when I grew up. It would be so much easier to be good if one’s hair was a handsome auburn, don’t you think? And now may I go out into your garden and sit on that bench under the apple-trees while you and Marilla are talking? There is so much more scope for imagination out there.” “Laws, yes, run along, child. And you can pick a bouquet of them white June lilies over in the corner if you like.” As the door closed behind Anne Mrs. Lynde got briskly up to light a lamp. “She’s a real odd little thing. Take this chair, Marilla; it’s easier than the one you’ve got; I just keep that for the hired boy to sit on. Yes, she certainly is an odd child, but there is something kind of taking about her after all. I don’t feel so surprised at you and Matthew keeping her as I did—nor so sorry for you, either. She may turn out all right. Of course, she has a queer way of expressing herself—a little too—well, too kind of forcible, you know; but she’ll likely get over that now that she’s come to live among civilized folks. And then, her temper’s pretty quick, I guess; but there’s one comfort, a child that has a quick temper, just blaze up and cool down, ain’t never likely to be sly or deceitful. Preserve me from a sly child, that’s what. On the whole, Marilla, I kind of like her.” When Marilla went home Anne came out of the fragrant twilight of the orchard with a sheaf of white narcissi in her hands. “I apologized pretty well, didn’t I?” she said proudly as they went down the lane. “I thought since I had to do it I might as well do it thoroughly.” “You did it thoroughly, all right enough,” was Marilla’s comment. Marilla was dismayed at finding herself inclined to laugh over the recollection. She had also an uneasy feeling that she ought to scold Anne for apologizing so well; but then, that was ridiculous! She compromised with her conscience by saying severely: “I hope you won’t have occasion to make many more such apologies. I hope you’ll try to control your temper now, Anne.” “That wouldn’t be so hard if people wouldn’t twit me about my looks,” said Anne with a sigh. “I don’t get cross about other things; but I’m so tired of being twitted about my hair and it just makes me boil right over. Do you suppose my hair will really be a handsome auburn when I grow up?” “You shouldn’t think so much about your looks, Anne. I’m afraid you are a very vain little girl.” “How can I be vain when I know I’m homely?” protested Anne. “I love pretty things; and I hate to look in the glass and see something that isn’t pretty. It makes me feel so sorrowful—just as I feel when I look at any ugly thing. I pity it because it isn’t beautiful.” “Handsome is as handsome does,” quoted Marilla. “I’ve had that said to me before, but I have my doubts about it,” remarked skeptical Anne, sniffing at her narcissi. “Oh, aren’t these flowers sweet! It was lovely of Mrs. Lynde to give them to me. I have no hard feelings against Mrs. Lynde now. It gives you a lovely, comfortable feeling to apologize and be forgiven, doesn’t it? Aren’t the stars bright tonight? If you could live in a star, which one would you pick? I’d like that lovely clear big one away over there above that dark hill.” “Anne, do hold your tongue,” said Marilla, thoroughly worn out trying to follow the gyrations of Anne’s thoughts. Anne said no more until they turned into their own lane. A little gypsy wind came down it to meet them, laden with the spicy perfume of young dew-wet ferns. Far up in the shadows a cheerful light gleamed out through the trees from the kitchen at Green Gables. Anne suddenly came close to Marilla and slipped her hand into the older woman’s hard palm. “It’s lovely to be going home and know it’s home,” she said. “I love Green Gables already, and I never loved any place before. No place ever seemed like home. Oh, Marilla, I’m so happy. I could pray right now and not find it a bit hard.” Something warm and pleasant welled up in Marilla’s heart at touch of that thin little hand in her own—a throb of the maternity she had missed, perhaps. Its very unaccustomedness and sweetness disturbed her. She hastened to restore her sensations to their normal calm by inculcating a moral. “If you’ll be a good girl you’ll always be happy, Anne. And you should never find it hard to say your prayers.” “Saying one’s prayers isn’t exactly the same thing as praying,” said Anne meditatively. “But I’m going to imagine that I’m the wind that is blowing up there in those tree tops. When I get tired of the trees I’ll imagine I’m gently waving down here in the ferns—and then I’ll fly over to Mrs. Lynde’s garden and set the flowers dancing—and then I’ll go with one great swoop over the clover field—and then I’ll blow over the Lake of Shining Waters and ripple it all up into little sparkling waves. Oh, there’s so much scope for imagination in a wind! So I’ll not talk any more just now, Marilla.” “Thanks be to goodness for that,” breathed Marilla in devout relief. 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. L. M. Montgomery (2008). Anne of Green Gables. Urbana, Illinois: Project Gutenberg. Retrieved https://www.gutenberg.org/cache/epub/45/pg45-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 lead image was generated using HackerNoon's Stable Disffusion Al Image Generator feature, via the prompt "Anne of Green Fables".

Anne’s Apology

APOLOGY

Apology by Plato, is part of the HackerNoon Books Series. Read this book online for free on HackerNoon! : Apology Title : Plato Author : Benjamin Jowett Translator : February 1, 1999 [EBook #1656] Release Date : English Language Table of Links INTRODUCTION APOLOGY 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. Plato (1999). Apology. Urbana, Illinois: Project Gutenberg. Retrieved October 2022, https://www.gutenberg.org/cache/epub/1656/pg1656-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

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