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The Way We Live Now by Anthony Trollope is part of the HackerNoon Books Series. You can jump to any chapter in this book . RUBY RUGGLES OBEYS HER GRANDFATHER here RUBY RUGGLES OBEYS HER GRANDFATHER. The next day there was great surprise at Sheep's Acre farm, which communicated itself to the towns of Bungay and Beccles, and even affected the ordinary quiet life of Carbury Manor. Ruby Ruggles had gone away, and at about twelve o'clock in the day the old farmer became aware of the fact. She had started early, at about seven in the morning; but Ruggles himself had been out long before that, and had not condescended to ask for her when he returned to the house for his breakfast. There had been a bad scene up in the bedroom overnight, after John Crumb had left the farm. The old man in his anger had tried to expel the girl; but she had hung on to the bed-post and would not go; and he had been frightened, when the maid came up crying and screaming murder. "You'll be out o' this to-morrow as sure as my name's Dannel Ruggles," said the farmer panting for breath. But for the gin which he had taken he would hardly have struck her;—but he had struck her, and pulled her by the hair, and knocked her about;—and in the morning she took him at his word and was away. About twelve he heard from the servant girl that she had gone. She had packed a box and had started up the road carrying the box herself. "Grandfather says I'm to go, and I'm gone," she had said to the girl. At the first cottage she had got a boy to carry her box into Beccles, and to Beccles she had walked. For an hour or two Ruggles sat, quiet, within the house, telling himself that she might do as she pleased with herself,—that he was well rid of her, and that from henceforth he would trouble himself no more about her. But by degrees there came upon him a feeling half of compassion and half of fear, with perhaps some mixture of love, instigating him to make search for her. She had been the same to him as a child, and what would people say of him if he allowed her to depart from him after this fashion? Then he remembered his violence the night before, and the fact that the servant girl had heard if she had not seen it. He could not drop his responsibility in regard to Ruby, even if he would. So, as a first step, he sent in a message to John Crumb, at Bungay, to tell him that Ruby Ruggles had gone off with a box to Beccles. John Crumb went open-mouthed with the news to Joe Mixet, and all Bungay soon knew that Ruby Ruggles had run away. After sending his message to Crumb the old man still sat thinking, and at last made up his mind that he would go to his landlord. He held a part of his farm under Roger Carbury, and Roger Carbury would tell him what he ought to do. A great trouble had come upon him. He would fain have been quiet, but his conscience and his heart and his terrors all were at work together,—and he found that he could not eat his dinner. So he had out his cart and horse and drove himself off to Carbury Hall. It was past four when he started, and he found the squire seated on the terrace after an early dinner, and with him was Father Barham, the priest. The old man was shown at once round into the garden, and was not long in telling his story. There had been words between him and his granddaughter about her lover. Her lover had been accepted and had come to the farm to claim his bride. Ruby had behaved very badly. The old man made the most of Ruby's bad behaviour, and of course as little as possible of his own violence. But he did explain that there had been threats used when Ruby refused to take the man, and that Ruby had, this day, taken herself off. "I always thought it was settled they were to be man and wife," said Roger. "It was settled, squoire;—and he war to have five hun'erd pound down;—money as I'd saved myself. Drat the jade." "Didn't she like him, Daniel?" "She liked him well enough till she'd seed somebody else." Then old Daniel paused, and shook his head, and was evidently the owner of a secret. The squire got up and walked round the garden with him,—and then the secret was told. The farmer was of opinion that there was something between the girl and Sir Felix. Sir Felix some weeks since had been seen near the farm and on the same occasion Ruby had been observed at some little distance from the house with her best clothes on. "He's been so little here, Daniel," said the squire. "It goes as tinder and a spark o' fire, that does," said the farmer. "Girls like Ruby don't want no time to be wooed by one such as that, though they'll fall-lall with a man like John Crumb for years." "I suppose she's gone to London." "Don't know nothing of where she's gone, squoire;—only she have gone some'eres. May be it's Lowestoffe. There's lots of quality at Lowestoffe a' washing theyselves in the sea." Then they returned to the priest, who might be supposed to be cognisant of the guiles of the world and competent to give advice on such an occasion as this. "If she was one of our people," said Father Barham, "we should have her back quick enough." "Would ye now?" said Ruggles, wishing at the moment that he and all his family had been brought up as Roman Catholics. "I don't see how you would have more chance of catching her than we have," said Carbury. "She'd catch herself. Wherever she might be she'd go to the priest, and he wouldn't leave her till he'd seen her put on the way back to her friends." "With a flea in her lug," suggested the farmer. "Your people never go to a clergyman in their distress. It's the last thing they'd think of. Any one might more probably be regarded as a friend than the parson. But with us the poor know where to look for sympathy." "She ain't that poor, neither," said the grandfather. "She had money with her?" "I don't know just what she had; but she ain't been brought up poor. And I don't think as our Ruby'd go of herself to any clergyman. It never was her way." "It never is the way with a Protestant," said the priest. "We'll say no more about that for the present," said Roger, who was waxing wroth with the priest. That a man should be fond of his own religion is right; but Roger Carbury was beginning to think that Father Barham was too fond of his religion. "What had we better do? I suppose we shall hear something of her at the railway. There are not so many people leaving Beccles but that she may be remembered." So the waggonette was ordered, and they all prepared to go off to the station together. But before they started John Crumb rode up to the door. He had gone at once to the farm on hearing of Ruby's departure, and had followed the farmer from thence to Carbury. Now he found the squire and the priest and the old man standing around as the horses were being put to the carriage. "Ye ain't a' found her, Mr. Ruggles, ha' ye?" he asked as he wiped the sweat from his brow. "Noa;—we ain't a' found no one yet." "If it was as she was to come to harm, Mr. Carbury, I'd never forgive myself,—never," said Crumb. "As far as I can understand it is no doing of yours, my friend," said the squire. "In one way, it ain't; and in one way it is. I was over there last night a bothering of her. She'd a' come round may be, if she'd a' been left alone. She wouldn't a' been off now, only for our going over to Sheep's Acre. But,—oh!" "What is it, Mr. Crumb?" "He's a coosin o' yours, squoire; and long as I've known Suffolk, I've never known nothing but good o' you and yourn. But if your baronite has been and done this! Oh, Mr. Carbury! If I was to wring his neck round, you wouldn't say as how I was wrong; would ye, now?" Roger could hardly answer the question. On general grounds the wringing of Sir Felix's neck, let the immediate cause for such a performance have been what it might, would have seemed to him to be a good deed. The world would be better, according to his thinking, with Sir Felix out of it than in it. But still the young man was his cousin and a Carbury, and to such a one as John Crumb he was bound to defend any member of his family as far as he might be defensible. "They says as how he was groping about Sheep's Acre when he was last here, a hiding himself and skulking behind hedges. Drat 'em all. They've gals enough of their own,—them fellows. Why can't they let a fellow alone? I'll do him a mischief, Master Roger; I wull;—if he's had a hand in this." Poor John Crumb! When he had his mistress to win he could find no words for himself; but was obliged to take an eloquent baker with him to talk for him. Now in his anger he could talk freely enough. "But you must first learn that Sir Felix has had anything to do with this, Mr. Crumb." "In coorse; in coorse. That's right. That's right. Must l'arn as he did it, afore I does it. But when I have l'arned!"— And John Crumb clenched his fist as though a very short lesson would suffice for him upon this occasion. They all went to the Beccles Station, and from thence to the Beccles post office,—so that Beccles soon knew as much about it as Bungay. At the railway station Ruby was distinctly remembered. She had taken a second-class ticket by the morning train for London, and had gone off without any appearance of secrecy. She had been decently dressed, with a hat and cloak, and her luggage had been such as she might have been expected to carry, had all her friends known that she was going. So much was made clear at the railway station, but nothing more could be learned there. Then a message was sent by telegraph to the station in London, and they all waited, loitering about the post office, for a reply. One of the porters in London remembered seeing such a girl as was described, but the man who was supposed to have carried her box for her to a cab had gone away for the day. It was believed that she had left the station in a four-wheel cab. "I'll be arter her. I'll be arter her at once," said John Crumb. But there was no train till night, and Roger Carbury was doubtful whether his going would do any good. It was evidently fixed on Crumb's mind that the first step towards finding Ruby would be the breaking of every bone in the body of Sir Felix Carbury. Now it was not at all apparent to the squire that his cousin had had anything to do with this affair. It had been made quite clear to him that the old man had quarrelled with his granddaughter and had threatened to turn her out of his house, not because she had misbehaved with Sir Felix, but on account of her refusing to marry John Crumb. John Crumb had gone over to the farm expecting to arrange it all, and up to that time there had been no fear about Felix Carbury. Nor was it possible that there should have been communication between Ruby and Felix since the quarrel at the farm. Even if the old man were right in supposing that Ruby and the baronet had been acquainted,—and such acquaintance could not but be prejudicial to the girl,—not on that account would the baronet be responsible for her abduction. John Crumb was thirsting for blood and was not very capable in his present mood of arguing the matter out coolly, and Roger, little as he loved his cousin, was not desirous that all Suffolk should know that Sir Felix Carbury had been thrashed within an inch of his life by John Crumb of Bungay. "I'll tell you what I'll do," said he, putting his hand kindly on the old man's shoulder. "I'll go up myself by the first train to-morrow. I can trace her better than Mr. Crumb can do, and you will both trust me." "There's not one in the two counties I'd trust so soon," said the old man. "But you'll let us know the very truth," said John Crumb. Roger Carbury made him an indiscreet promise that he would let him know the truth. So the matter was settled, and the grandfather and lover returned together to Bungay. 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

RUBY RUGGLES OBEYS HER GRANDFATHER

Delivering Cloud Infrastructure the GitOps Way with Crossplane and ArgoCD

What are Internal Developer Platforms (IDPs)? Internal developer platforms (IDPs) are platforms that developers can leverage to build and deploy their applications to one of the environments. Their main purpose is to increase the pace of development and reduce the developer’s dependence on DevOps and Platform engineers through automation and developer self-service. The platform may be built in-house, usually, an adaptation of an open-source or purchased offering such as platform as a Service (PaaS). The adoption has been both recent and rapid. Puppet’s state of platform engineering finds that over 51% of companies that have adopted Internal Developer Platforms have done it in the past three years. And an overwhelming majority of the respondents (93%) declared that IDP adoption is a step in the right direction. survey Internal developer platforms are good at helping the team manage applications and infrastructure from one place, providing tight integration with the existing tools and services, and giving developers self-serve and collaboration capabilities. Benefits of IDPs IDPs offer several benefits to the organizations using them. The first relates to improvements in infrastructure and IT management, such as increased productivity stemming from reduced communication times between devs and infra teams. Secondly, they reduce the complexity of the cloud and make it easy for people in your org to pick up on the best practices set forward by your team. In larger organizations, they can also be an easy way of managing RBAC to deployments, infra creation and management, and more. Responses from Puppet’s State of Platform Engineering 2023 report also show how IDP has improved : DevOps KPIs An increase in development velocity (68%) 42% say development velocity has improved “a great deal” since they started doing platform engineering Improved productivity (59%) Improved system reliability (60%) Improved security (55%) Components of IDPs Let’s dive deeper into the components of IDPs and discuss which parts of the application and infrastructure management process they improve. IDPs are built by integrating with the existing set of tools and services that the company currently uses. These can be your source control systems (e.g., GitHub, GitLab), Continuous Integration and Continuous Deployment (CI/CD) pipelines, and observability tools such as monitoring and logging systems. Integration with existing tools and services In addition, they may also include some form of access control, observability dashboards, and a set of best practices for developers to use. Application and infrastructure management IDPs can be used to automate a lot of the hassle of deployment. By setting up IDPs with GitOps best practices, users can automatically deploy application and infra changes with each commit to the Git code. Managing multiple environments is another popular use case of IDPs. They can be effective in creating, providing selective access, and managing the use of multiple different environments such as development, testing, pre-prod, and prod. Several tools set up preview environments on the fly. Their ability to monitor and view deployments across environments helps organizations scale dynamically to changing workloads and maintain consistent performance across their infrastructure. IDPs empower developers with self-serve capabilities by offering a centralized, user-friendly interface to access tools, resources, and services on demand. These capabilities facilitate faster development cycles, reduce operations teams’ dependency, and enable greater autonomy in seamlessly creating, testing, and deploying applications. Developer self-service capabilities The streamlined workflow also makes it considerably easier for new employees to get started with your stack without diving into the internal workings. Some tools also add a collaboration layer on top that allows different members of a team to work together easily and review changes before deploying the code. IDPs can achieve enhanced security and compliance by providing Role-based access controls (RBAC), ensuring each team member has the appropriate permissions to access specific resources and perform certain actions. Overall, this minimized the risk of unauthorized access or accidental changes while also promoting collaboration and efficient workflows among cross-functional teams. Collaboration and governance features IDPs help developer teams maintain transparency, accountability, and traceability throughout the development process by providing audit logs and history tracking. This enables easier identification and resolution of issues, as well as ensures compliance with regulatory and organizational requirements for data handling and change management. Platform engineering Companies that decide to build their own platform usually hire platform engineers, who are specialized professionals responsible for the creation and continuous improvement of the IDP. They work to implement and maintain tools, services, and best practices that streamline development processes, ensuring a smooth and efficient workflow for the organization's developers. Platform engineering is the process of designing, building, and maintaining an IDP that provides a centralized, scalable, and efficient infrastructure for developers within an organization. An IDP simplifies and standardizes application development, deployment, and management, enabling faster and more reliable software delivery. Software development trends that are catalysts for IDP adoption Digital transformation: As businesses worldwide increasingly embrace digital transformation, there is a growing need for efficient and robust software development processes. IDPs play a crucial role in enabling companies to stay agile and quickly adapt to the dynamic digital landscape. Need for faster software delivery: In today's fast-paced business environment, organizations must deliver new features and applications at an unprecedented speed. IDPs provide a standardized, automated, and centralized platform that accelerates development cycles, enabling companies to stay competitive and responsive to market changes. The growing complexity of software architectures: With the rise of microservices, containers, and cloud-native technologies, managing and deploying software has become increasingly complex. IDPs help simplify this complexity by providing a unified platform where developers can build, test, and deploy applications with ease, regardless of the underlying architecture. Demand for DevOps and CI/CD practices: The need for IDPs grows as more organizations adopt DevOps and Continuous Integration/Continuous Deployment (CI/CD) practices. IDPs enable seamless collaboration between development and operations teams, automating many manual tasks and ensuring smooth transitions throughout the software development lifecycle. Scalability and flexibility: IDPs offer a scalable solution that can accommodate the growing needs of organizations, regardless of their size. They provide a flexible platform that can be easily customized and adapted to cater to the unique requirements of different teams and projects. Cross-region collaboration: With businesses operating across multiple geographies, the need for a platform that supports cross-region collaboration is essential. IDPs empower development teams spread across the globe to work together seamlessly, enabling efficient knowledge sharing and fostering a culture of innovation. In summary, the rising popularity of IDPs across companies of all sizes and regions worldwide can be attributed to their ability to streamline software development processes, simplify complex architectures, support DevOps and CI/CD practices, and facilitate cross-region collaboration. As the demand for digitalization and agility continues to grow, IDPs are poised to play a critical role in shaping the future of software development. Commercially available internal developer platforms is a DevOps automation platform that helps engineering manage both the app and infra side of things and ship faster! Built with the GitOps best practices in mind, Argonaut reduces the complexities of creating and maintaining cloud setups. Whether it is Kubernetes app deployment to AWS or GCP, there are several runtimes, environments, regions, integrations, and app types to choose from. Argonaut offers a simple way to develop and operate modern cloud applications on Kubernetes. It can be adopted as either a self-hosted or PaaS option. There’s also a with essential plugins, templates, and applications making it easier to get started. Its features benefit not only the developers but also the platform engineers and CIOs. Mia Platform marketplace is an internal developer platform providing simplicity, automation, reusability, and self-service. It acts as a Platform Orchestrator that lets engineering teams remove bottlenecks by enabling them to build code-based golden paths (executable config files and templates) for developers. It can be used through the CLI or UI. Humanitec provides engineering teams self-serve access to tools and information. It helps developers ensure operational excellence across services with its that can be set up in a secure and compliant way. Opslevel integrations is a Kubernetes application management platform that enables efficient deployment processes. Developers can leverage its standardized application and policy definitions that are platform-agnostic. It also has a GUI-based portal to manage apps after deployment and gain visibility into pipelines for smoother operations. Shipa provides a context-rich software catalog with maturity and quality scorecards. It also supports comprehensive developer self-service actions while providing additional role-based access controls (RBAC). Their provides many key features and makes it a worthy contender. Port free-forever , powered by Crossplane, offers an enterprise-grade control plane solution for multi-cloud and hybrid environments, enabling efficient management of cloud infrastructure. The command line and make it more effective and easier to get started. Upbound Up Upbound marketplace is an all-in-one DevOps platform that streamlines the application deployment process. It provides a comprehensive feature set in a modular manner that allows teams to focus fully on business functionality. DevOpsBox Evaluating Internal Developer Platforms Despite its myriad of benefits, Internal Developer Platforms do not make sense for all teams. They can end up being overkill for certain types of engineering teams and a burden to build and maintain for companies with smaller teams. When they don’t make sense You have existing processes that are efficient. Don’t complicate things too early. Companies that are currently using PaaS or other managed offerings should continue doing so as long as possible. Limited resources and team size. This could also mean your team is small, and most of your team is senior and comfortable writing scripts and managing infrastructure. There is low development complexity. If you have just one app with a simple single-cloud setup. And, if your app is monolithic and doesn’t make use of the microservice architecture, there is little benefit from creating an IDP. Incompatible organizational culture. If an organization’s culture is resistant to change or does not foster collaboration and communication, implementing an IDP might not be successful and could even lead to decreased efficiency and productivity. When they make sense You plan to start using Microservices. This usually also means a growing development team size or the complexity of projects you handle. You have a small team, and not everyone feels comfortable with deployments, scripting, and infrastructure, and you have not yet hired a dedicated DevOps. Dependencies on other colleagues are blocking your developers. The cost of your existing setup (such as PaaS) is too high. Which is inevitable once you start to scale to meet new requirements. You have plans to go multi-cloud, adopt more modern cloud services, and scale geographically. You want to increase standardization and consistency across your teams. An IDP can help reduce errors, improve code quality, and ensure all developers work with the same set of tools and follow the same best practices. Also published here.

An In-Depth Guide to Internal Developer Platforms (IDPs)

Una guía detallada de las plataformas internas para desarrolladores (IDP)

Un guide détaillé des plates-formes de développement internes (IDP)

आंतरिक डेवलपर प्लेटफ़ॉर्म (IDPs) के लिए एक गहन मार्गदर्शिका

内部開発者プラットフォーム (IDP) の詳細ガイド

Um guia detalhado para plataformas internas de desenvolvedores (IDPs)

Hướng dẫn chuyên sâu về Nền tảng dành cho nhà phát triển nội bộ (IDP)

内部开发者平台 (IDP) 深度指南

The Strange Case Of Dr. Jekyll And Mr. Hyde by Robert Louis Stevenson is part of HackerNoon’s Book Blog Post series. You can jump to any chapter in this book here . Chapter IX: Dr. Lanyon's Narrative CHAPTER IX. DR. LANYON’S NARRATIVE On the ninth of January, now four days ago, I received by the evening delivery a registered envelope, addressed in the hand of my colleague and old school companion, Henry Jekyll. I was a good deal surprised by this; for we were by no means in the habit of correspondence; I had seen the man, dined with him, indeed, the night before; and I could imagine nothing in our intercourse that should justify formality of registration. The contents increased my wonder; for this is how the letter ran: “10th December, 18—. “Dear Lanyon,—You are one of my oldest friends; and although we may have differed at times on scientific questions, I cannot remember, at least on my side, any break in our affection. There was never a day when, if you had said to me, ‘Jekyll, my life, my honour, my reason, depend upon you,’ I would not have sacrificed my left hand to help you. Lanyon, my life, my honour, my reason, are all at your mercy; if you fail me to-night, I am lost. You might suppose, after this preface, that I am going to ask you for something dishonourable to grant. Judge for yourself. “I want you to postpone all other engagements for to-night—ay, even if you were summoned to the bedside of an emperor; to take a cab, unless your carriage should be actually at the door; and with this letter in your hand for consultation, to drive straight to my house. Poole, my butler, has his orders; you will find him waiting your arrival with a locksmith. The door of my cabinet is then to be forced; and you are to go in alone; to open the glazed press (letter E) on the left hand, breaking the lock if it be shut; and to draw out, with all its contents as they stand, the fourth drawer from the top or (which is the same thing) the third from the bottom. In my extreme distress of mind, I have a morbid fear of misdirecting you; but even if I am in error, you may know the right drawer by its contents: some powders, a phial and a paper book. This drawer I beg of you to carry back with you to Cavendish Square exactly as it stands. “That is the first part of the service: now for the second. You should be back, if you set out at once on the receipt of this, long before midnight; but I will leave you that amount of margin, not only in the fear of one of those obstacles that can neither be prevented nor foreseen, but because an hour when your servants are in bed is to be preferred for what will then remain to do. At midnight, then, I have to ask you to be alone in your consulting room, to admit with your own hand into the house a man who will present himself in my name, and to place in his hands the drawer that you will have brought with you from my cabinet. Then you will have played your part and earned my gratitude completely. Five minutes afterwards, if you insist upon an explanation, you will have understood that these arrangements are of capital importance; and that by the neglect of one of them, fantastic as they must appear, you might have charged your conscience with my death or the shipwreck of my reason. “Confident as I am that you will not trifle with this appeal, my heart sinks and my hand trembles at the bare thought of such a possibility. Think of me at this hour, in a strange place, labouring under a blackness of distress that no fancy can exaggerate, and yet well aware that, if you will but punctually serve me, my troubles will roll away like a story that is told. Serve me, my dear Lanyon and save “Your friend, “H.J. “P.S.—I had already sealed this up when a fresh terror struck upon my soul. It is possible that the post-office may fail me, and this letter not come into your hands until to-morrow morning. In that case, dear Lanyon, do my errand when it shall be most convenient for you in the course of the day; and once more expect my messenger at midnight. It may then already be too late; and if that night passes without event, you will know that you have seen the last of Henry Jekyll.” Upon the reading of this letter, I made sure my colleague was insane; but till that was proved beyond the possibility of doubt, I felt bound to do as he requested. The less I understood of this farrago, the less I was in a position to judge of its importance; and an appeal so worded could not be set aside without a grave responsibility. I rose accordingly from table, got into a hansom, and drove straight to Jekyll’s house. The butler was awaiting my arrival; he had received by the same post as mine a registered letter of instruction, and had sent at once for a locksmith and a carpenter. The tradesmen came while we were yet speaking; and we moved in a body to old Dr. Denman’s surgical theatre, from which (as you are doubtless aware) Jekyll’s private cabinet is most conveniently entered. The door was very strong, the lock excellent; the carpenter avowed he would have great trouble and have to do much damage, if force were to be used; and the locksmith was near despair. But this last was a handy fellow, and after two hour’s work, the door stood open. The press marked E was unlocked; and I took out the drawer, had it filled up with straw and tied in a sheet, and returned with it to Cavendish Square. Here I proceeded to examine its contents. The powders were neatly enough made up, but not with the nicety of the dispensing chemist; so that it was plain they were of Jekyll’s private manufacture; and when I opened one of the wrappers I found what seemed to me a simple crystalline salt of a white colour. The phial, to which I next turned my attention, might have been about half full of a blood-red liquor, which was highly pungent to the sense of smell and seemed to me to contain phosphorus and some volatile ether. At the other ingredients I could make no guess. The book was an ordinary version book and contained little but a series of dates. These covered a period of many years, but I observed that the entries ceased nearly a year ago and quite abruptly. Here and there a brief remark was appended to a date, usually no more than a single word: “double” occurring perhaps six times in a total of several hundred entries; and once very early in the list and followed by several marks of exclamation, “total failure!!!” All this, though it whetted my curiosity, told me little that was definite. Here were a phial of some salt, and the record of a series of experiments that had led (like too many of Jekyll’s investigations) to no end of practical usefulness. How could the presence of these articles in my house affect either the honour, the sanity, or the life of my flighty colleague? If his messenger could go to one place, why could he not go to another? And even granting some impediment, why was this gentleman to be received by me in secret? The more I reflected the more convinced I grew that I was dealing with a case of cerebral disease; and though I dismissed my servants to bed, I loaded an old revolver, that I might be found in some posture of self-defence. Twelve o’clock had scarce rung out over London, ere the knocker sounded very gently on the door. I went myself at the summons, and found a small man crouching against the pillars of the portico. “Are you come from Dr. Jekyll?” I asked. He told me “yes” by a constrained gesture; and when I had bidden him enter, he did not obey me without a searching backward glance into the darkness of the square. There was a policeman not far off, advancing with his bull’s eye open; and at the sight, I thought my visitor started and made greater haste. These particulars struck me, I confess, disagreeably; and as I followed him into the bright light of the consulting room, I kept my hand ready on my weapon. Here, at last, I had a chance of clearly seeing him. I had never set eyes on him before, so much was certain. He was small, as I have said; I was struck besides with the shocking expression of his face, with his remarkable combination of great muscular activity and great apparent debility of constitution, and—last but not least—with the odd, subjective disturbance caused by his neighbourhood. This bore some resemblance to incipient rigour, and was accompanied by a marked sinking of the pulse. At the time, I set it down to some idiosyncratic, personal distaste, and merely wondered at the acuteness of the symptoms; but I have since had reason to believe the cause to lie much deeper in the nature of man, and to turn on some nobler hinge than the principle of hatred. This person (who had thus, from the first moment of his entrance, struck in me what I can only describe as a disgustful curiosity) was dressed in a fashion that would have made an ordinary person laughable; his clothes, that is to say, although they were of rich and sober fabric, were enormously too large for him in every measurement—the trousers hanging on his legs and rolled up to keep them from the ground, the waist of the coat below his haunches, and the collar sprawling wide upon his shoulders. Strange to relate, this ludicrous accoutrement was far from moving me to laughter. Rather, as there was something abnormal and misbegotten in the very essence of the creature that now faced me—something seizing, surprising and revolting—this fresh disparity seemed but to fit in with and to reinforce it; so that to my interest in the man’s nature and character, there was added a curiosity as to his origin, his life, his fortune and status in the world. These observations, though they have taken so great a space to be set down in, were yet the work of a few seconds. My visitor was, indeed, on fire with sombre excitement. “Have you got it?” he cried. “Have you got it?” And so lively was his impatience that he even laid his hand upon my arm and sought to shake me. I put him back, conscious at his touch of a certain icy pang along my blood. “Come, sir,” said I. “You forget that I have not yet the pleasure of your acquaintance. Be seated, if you please.” And I showed him an example, and sat down myself in my customary seat and with as fair an imitation of my ordinary manner to a patient, as the lateness of the hour, the nature of my preoccupations, and the horror I had of my visitor, would suffer me to muster. “I beg your pardon, Dr. Lanyon,” he replied civilly enough. “What you say is very well founded; and my impatience has shown its heels to my politeness. I come here at the instance of your colleague, Dr. Henry Jekyll, on a piece of business of some moment; and I understood...” He paused and put his hand to his throat, and I could see, in spite of his collected manner, that he was wrestling against the approaches of the hysteria—“I understood, a drawer...” But here I took pity on my visitor’s suspense, and some perhaps on my own growing curiosity. “There it is, sir,” said I, pointing to the drawer, where it lay on the floor behind a table and still covered with the sheet. He sprang to it, and then paused, and laid his hand upon his heart; I could hear his teeth grate with the convulsive action of his jaws; and his face was so ghastly to see that I grew alarmed both for his life and reason. “Compose yourself,” said I. He turned a dreadful smile to me, and as if with the decision of despair, plucked away the sheet. At sight of the contents, he uttered one loud sob of such immense relief that I sat petrified. And the next moment, in a voice that was already fairly well under control, “Have you a graduated glass?” he asked. I rose from my place with something of an effort and gave him what he asked. He thanked me with a smiling nod, measured out a few minims of the red tincture and added one of the powders. The mixture, which was at first of a reddish hue, began, in proportion as the crystals melted, to brighten in colour, to effervesce audibly, and to throw off small fumes of vapour. Suddenly and at the same moment, the ebullition ceased and the compound changed to a dark purple, which faded again more slowly to a watery green. My visitor, who had watched these metamorphoses with a keen eye, smiled, set down the glass upon the table, and then turned and looked upon me with an air of scrutiny. “And now,” said he, “to settle what remains. Will you be wise? will you be guided? will you suffer me to take this glass in my hand and to go forth from your house without further parley? or has the greed of curiosity too much command of you? Think before you answer, for it shall be done as you decide. As you decide, you shall be left as you were before, and neither richer nor wiser, unless the sense of service rendered to a man in mortal distress may be counted as a kind of riches of the soul. Or, if you shall so prefer to choose, a new province of knowledge and new avenues to fame and power shall be laid open to you, here, in this room, upon the instant; and your sight shall be blasted by a prodigy to stagger the unbelief of Satan.” “Sir,” said I, affecting a coolness that I was far from truly possessing, “you speak enigmas, and you will perhaps not wonder that I hear you with no very strong impression of belief. But I have gone too far in the way of inexplicable services to pause before I see the end.” “It is well,” replied my visitor. “Lanyon, you remember your vows: what follows is under the seal of our profession. And now, you who have so long been bound to the most narrow and material views, you who have denied the virtue of transcendental medicine, you who have derided your superiors—behold!” He put the glass to his lips and drank at one gulp. A cry followed; he reeled, staggered, clutched at the table and held on, staring with injected eyes, gasping with open mouth; and as I looked there came, I thought, a change—he seemed to swell—his face became suddenly black and the features seemed to melt and alter—and the next moment, I had sprung to my feet and leaped back against the wall, my arms raised to shield me from that prodigy, my mind submerged in terror. “O God!” I screamed, and “O God!” again and again; for there before my eyes—pale and shaken, and half fainting, and groping before him with his hands, like a man restored from death—there stood Henry Jekyll! What he told me in the next hour, I cannot bring my mind to set on paper. I saw what I saw, I heard what I heard, and my soul sickened at it; and yet now when that sight has faded from my eyes, I ask myself if I believe it, and I cannot answer. My life is shaken to its roots; sleep has left me; the deadliest terror sits by me at all hours of the day and night; and I feel that my days are numbered, and that I must die; and yet I shall die incredulous. As for the moral turpitude that man unveiled to me, even with tears of penitence, I cannot, even in memory, dwell on it without a start of horror. I will say but one thing, Utterson, and that (if you can bring your mind to credit it) will be more than enough. The creature who crept into my house that night was, on Jekyll’s own confession, known by the name of Hyde and hunted for in every corner of the land as the murderer of Carew. HASTIE LANYON. 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. Stevenson, Robert Louis, 1992. The Strange Case Of Dr. Jekyll And Mr. Hyde. Urbana, Illinois: Project Gutenberg. Retrieved May 2022 from https://www.gutenberg.org/files/43/43-h/43-h.htm#chap09 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 Strange Case Of Dr. Jekyll And Mr. Hyde: Chapter IX - Dr. Lanyon's Narrative 

Photo by on Jason Blackeye Unsplash Adventures of Huckleberry Finn, by Mark Twain (Samuel Clemens) is part of HackerNoon’s Book Blog Post series. You can jump to any chapter in this book . here CHAPTER XII. It must a been close on to one o’clock when we got below the island at last, and the raft did seem to go mighty slow. If a boat was to come along we was going to take to the canoe and break for the Illinois shore; and it was well a boat didn’t come, for we hadn’t ever thought to put the gun in the canoe, or a fishing-line, or anything to eat. We was in ruther too much of a sweat to think of so many things. It warn’t good judgment to put everything on the raft. If the men went to the island I just expect they found the camp fire I built, and watched it all night for Jim to come. Anyways, they stayed away from us, and if my building the fire never fooled them it warn’t no fault of mine. I played it as low down on them as I could. When the first streak of day began to show we tied up to a towhead in a big bend on the Illinois side, and hacked off cottonwood branches with the hatchet, and covered up the raft with them so she looked like there had been a cave-in in the bank there. A tow-head is a sandbar that has cottonwoods on it as thick as harrow-teeth. We had mountains on the Missouri shore and heavy timber on the Illinois side, and the channel was down the Missouri shore at that place, so we warn’t afraid of anybody running across us. We laid there all day, and watched the rafts and steamboats spin down the Missouri shore, and up-bound steamboats fight the big river in the middle. I told Jim all about the time I had jabbering with that woman; and Jim said she was a smart one, and if she was to start after us herself she wouldn’t set down and watch a camp fire—no, sir, she’d fetch a dog. Well, then, I said, why couldn’t she tell her husband to fetch a dog? Jim said he bet she did think of it by the time the men was ready to start, and he believed they must a gone up-town to get a dog and so they lost all that time, or else we wouldn’t be here on a towhead sixteen or seventeen mile below the village—no, indeedy, we would be in that same old town again. So I said I didn’t care what was the reason they didn’t get us as long as they didn’t. When it was beginning to come on dark we poked our heads out of the cottonwood thicket, and looked up and down and across; nothing in sight; so Jim took up some of the top planks of the raft and built a snug wigwam to get under in blazing weather and rainy, and to keep the things dry. Jim made a floor for the wigwam, and raised it a foot or more above the level of the raft, so now the blankets and all the traps was out of reach of steamboat waves. Right in the middle of the wigwam we made a layer of dirt about five or six inches deep with a frame around it for to hold it to its place; this was to build a fire on in sloppy weather or chilly; the wigwam would keep it from being seen. We made an extra steering-oar, too, because one of the others might get broke on a snag or something. We fixed up a short forked stick to hang the old lantern on, because we must always light the lantern whenever we see a steamboat coming down-stream, to keep from getting run over; but we wouldn’t have to light it for up-stream boats unless we see we was in what they call a “crossing”; for the river was pretty high yet, very low banks being still a little under water; so up-bound boats didn’t always run the channel, but hunted easy water. This second night we run between seven and eight hours, with a current that was making over four mile an hour. We catched fish and talked, and we took a swim now and then to keep off sleepiness. It was kind of solemn, drifting down the big, still river, laying on our backs looking up at the stars, and we didn’t ever feel like talking loud, and it warn’t often that we laughed—only a little kind of a low chuckle. We had mighty good weather as a general thing, and nothing ever happened to us at all—that night, nor the next, nor the next. Every night we passed towns, some of them away up on black hillsides, nothing but just a shiny bed of lights; not a house could you see. The fifth night we passed St. Louis, and it was like the whole world lit up. In St. Petersburg they used to say there was twenty or thirty thousand people in St. Louis, but I never believed it till I see that wonderful spread of lights at two o’clock that still night. There warn’t a sound there; everybody was asleep. Every night now I used to slip ashore towards ten o’clock at some little village, and buy ten or fifteen cents’ worth of meal or bacon or other stuff to eat; and sometimes I lifted a chicken that warn’t roosting comfortable, and took him along. Pap always said, take a chicken when you get a chance, because if you don’t want him yourself you can easy find somebody that does, and a good deed ain’t ever forgot. I never see pap when he didn’t want the chicken himself, but that is what he used to say, anyway. Mornings before daylight I slipped into cornfields and borrowed a watermelon, or a mushmelon, or a punkin, or some new corn, or things of that kind. Pap always said it warn’t no harm to borrow things if you was meaning to pay them back some time; but the widow said it warn’t anything but a soft name for stealing, and no decent body would do it. Jim said he reckoned the widow was partly right and pap was partly right; so the best way would be for us to pick out two or three things from the list and say we wouldn’t borrow them any more—then he reckoned it wouldn’t be no harm to borrow the others. So we talked it over all one night, drifting along down the river, trying to make up our minds whether to drop the watermelons, or the cantelopes, or the mushmelons, or what. But towards daylight we got it all settled satisfactory, and concluded to drop crabapples and p’simmons. We warn’t feeling just right before that, but it was all comfortable now. I was glad the way it come out, too, because crabapples ain’t ever good, and the p’simmons wouldn’t be ripe for two or three months yet. We shot a water-fowl now and then that got up too early in the morning or didn’t go to bed early enough in the evening. Take it all round, we lived pretty high. The fifth night below St. Louis we had a big storm after midnight, with a power of thunder and lightning, and the rain poured down in a solid sheet. We stayed in the wigwam and let the raft take care of itself. When the lightning glared out we could see a big straight river ahead, and high, rocky bluffs on both sides. By and by says I, “Hel-lo, Jim, looky yonder!” It was a steamboat that had killed herself on a rock. We was drifting straight down for her. The lightning showed her very distinct. She was leaning over, with part of her upper deck above water, and you could see every little chimbly-guy clean and clear, and a chair by the big bell, with an old slouch hat hanging on the back of it, when the flashes come. Well, it being away in the night and stormy, and all so mysterious-like, I felt just the way any other boy would a felt when I see that wreck laying there so mournful and lonesome in the middle of the river. I wanted to get aboard of her and slink around a little, and see what there was there. So I says: “Le’s land on her, Jim.” But Jim was dead against it at first. He says: “I doan’ want to go fool’n ’long er no wrack. We’s doin’ blame’ well, en we better let blame’ well alone, as de good book says. Like as not dey’s a watchman on dat wrack.” “Watchman your grandmother,” I says; “there ain’t nothing to watch but the texas and the pilot-house; and do you reckon anybody’s going to resk his life for a texas and a pilot-house such a night as this, when it’s likely to break up and wash off down the river any minute?” Jim couldn’t say nothing to that, so he didn’t try. “And besides,” I says, “we might borrow something worth having out of the captain’s stateroom. Seegars, I bet you—and cost five cents apiece, solid cash. Steamboat captains is always rich, and get sixty dollars a month, and they don’t care a cent what a thing costs, you know, long as they want it. Stick a candle in your pocket; I can’t rest, Jim, till we give her a rummaging. Do you reckon Tom Sawyer would ever go by this thing? Not for pie, he wouldn’t. He’d call it an adventure—that’s what he’d call it; and he’d land on that wreck if it was his last act. And wouldn’t he throw style into it?—wouldn’t he spread himself, nor nothing? Why, you’d think it was Christopher C’lumbus discovering Kingdom-Come. I wish Tom Sawyer was here.” Jim he grumbled a little, but give in. He said we mustn’t talk any more than we could help, and then talk mighty low. The lightning showed us the wreck again just in time, and we fetched the stabboard derrick, and made fast there. The deck was high out here. We went sneaking down the slope of it to labboard, in the dark, towards the texas, feeling our way slow with our feet, and spreading our hands out to fend off the guys, for it was so dark we couldn’t see no sign of them. Pretty soon we struck the forward end of the skylight, and clumb on to it; and the next step fetched us in front of the captain’s door, which was open, and by Jimminy, away down through the texas-hall we see a light! and all in the same second we seem to hear low voices in yonder! Jim whispered and said he was feeling powerful sick, and told me to come along. I says, all right, and was going to start for the raft; but just then I heard a voice wail out and say: “Oh, please don’t, boys; I swear I won’t ever tell!” Another voice said, pretty loud: “It’s a lie, Jim Turner. You’ve acted this way before. You always want more’n your share of the truck, and you’ve always got it, too, because you’ve swore ’t if you didn’t you’d tell. But this time you’ve said it jest one time too many. You’re the meanest, treacherousest hound in this country.” By this time Jim was gone for the raft. I was just a-biling with curiosity; and I says to myself, Tom Sawyer wouldn’t back out now, and so I won’t either; I’m a-going to see what’s going on here. So I dropped on my hands and knees in the little passage, and crept aft in the dark till there warn’t but one stateroom betwixt me and the cross-hall of the texas. Then in there I see a man stretched on the floor and tied hand and foot, and two men standing over him, and one of them had a dim lantern in his hand, and the other one had a pistol. This one kept pointing the pistol at the man’s head on the floor, and saying: “I’d like to! And I orter, too—a mean skunk!” The man on the floor would shrivel up and say, “Oh, please don’t, Bill; I hain’t ever goin’ to tell.” And every time he said that the man with the lantern would laugh and say: “’Deed you ain’t! You never said no truer thing ’n that, you bet you.” And once he said: “Hear him beg! and yit if we hadn’t got the best of him and tied him he’d a killed us both. And what for? Jist for noth’n. Jist because we stood on our rights—that’s what for. But I lay you ain’t a-goin’ to threaten nobody any more, Jim Turner. Put up that pistol, Bill.” Bill says: “I don’t want to, Jake Packard. I’m for killin’ him—and didn’t he kill old Hatfield jist the same way—and don’t he deserve it?” “But I don’t want him killed, and I’ve got my reasons for it.” “Bless yo’ heart for them words, Jake Packard! I’ll never forgit you long’s I live!” says the man on the floor, sort of blubbering. Packard didn’t take no notice of that, but hung up his lantern on a nail and started towards where I was there in the dark, and motioned Bill to come. I crawfished as fast as I could about two yards, but the boat slanted so that I couldn’t make very good time; so to keep from getting run over and catched I crawled into a stateroom on the upper side. The man came a-pawing along in the dark, and when Packard got to my stateroom, he says: “Here—come in here.” And in he come, and Bill after him. But before they got in I was up in the upper berth, cornered, and sorry I come. Then they stood there, with their hands on the ledge of the berth, and talked. I couldn’t see them, but I could tell where they was by the whisky they’d been having. I was glad I didn’t drink whisky; but it wouldn’t made much difference anyway, because most of the time they couldn’t a treed me because I didn’t breathe. I was too scared. And, besides, a body couldn’t breathe and hear such talk. They talked low and earnest. Bill wanted to kill Turner. He says: “He’s said he’ll tell, and he will. If we was to give both our shares to him now it wouldn’t make no difference after the row and the way we’ve served him. Shore’s you’re born, he’ll turn State’s evidence; now you hear me. I’m for putting him out of his troubles.” “So’m I,” says Packard, very quiet. “Blame it, I’d sorter begun to think you wasn’t. Well, then, that’s all right. Le’s go and do it.” “Hold on a minute; I hain’t had my say yit. You listen to me. Shooting’s good, but there’s quieter ways if the thing’s got to be done. But what I say is this: it ain’t good sense to go court’n around after a halter if you can git at what you’re up to in some way that’s jist as good and at the same time don’t bring you into no resks. Ain’t that so?” “You bet it is. But how you goin’ to manage it this time?” “Well, my idea is this: we’ll rustle around and gather up whatever pickins we’ve overlooked in the staterooms, and shove for shore and hide the truck. Then we’ll wait. Now I say it ain’t a-goin’ to be more’n two hours befo’ this wrack breaks up and washes off down the river. See? He’ll be drownded, and won’t have nobody to blame for it but his own self. I reckon that’s a considerble sight better ’n killin’ of him. I’m unfavorable to killin’ a man as long as you can git aroun’ it; it ain’t good sense, it ain’t good morals. Ain’t I right?” “Yes, I reck’n you are. But s’pose she don’t break up and wash off?” “Well, we can wait the two hours anyway and see, can’t we?” “All right, then; come along.” So they started, and I lit out, all in a cold sweat, and scrambled forward. It was dark as pitch there; but I said, in a kind of a coarse whisper, “Jim!” and he answered up, right at my elbow, with a sort of a moan, and I says: “Quick, Jim, it ain’t no time for fooling around and moaning; there’s a gang of murderers in yonder, and if we don’t hunt up their boat and set her drifting down the river so these fellows can’t get away from the wreck there’s one of ’em going to be in a bad fix. But if we find their boat we can put all of ’em in a bad fix—for the sheriff ’ll get ’em. Quick—hurry! I’ll hunt the labboard side, you hunt the stabboard. You start at the raft, and—” “Oh, my lordy, lordy! raf’’? Dey ain’ no raf’ no mo’; she done broke loose en gone I—en here we is!” 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. Urbana, Illinois: Project Gutenberg. Retrieved April 2022 from Twain, Mark,1993. Adventures of Huckleberry Finn. https://www.gutenberg.org/files/76/76-h/76-h.htm#c12 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

Adventures of Huckleberry Finn: Chapter XII

Introduction In this blog, we will look at the evolution of software infrastructure; provisioning, delivery and maintenance. If you are interested in modern DevOps and SRE practices, this article is for you. is a common pattern where virtualized infrastructure and auxiliary services can be managed using configuration expressed in almost any language, usually hosted in a source code repository. Infrastructure as Code (IaC) Every once in a while software industry is shaped by significant events called . Paradigm Shift Here are a few such events that made Infrastructure as Code what it is today: Virtualization Containerisation and containers orchestration Public Cloud Infrastructure DevOps Culture Infrastructure provisioning: evolution Let’s examine how each wave of changes shaped the software landscape: #1 Virtualization In the early days of virtualization, developers would produce software in long waterfall development cycles, mostly based on requirements documentation. Ops Teams at the same time would rack and stack servers, provision infrastructure components, install all the things, configure etc. Typically both teams would work in a disconnected fashion, opening tickets and communicating via long email chains. Those were the darg ages. #2 Agile and beginning of DevOps Next came the agile revolution and with it the beginning of the DevOps culture. Now developers would send the application over to the Ops Team along with a multi-page configuration manual. More progressive teams will start collaborating on automation tools. In the early days of infrastructure automation tools like Chef or Puppet were very popular. Huge improvement, but often very siloed environment and fragmented responsibilities. #3 Public Cloud & container orchestration Along with increased usage of the public cloud and more robust automation, things started to fall into the right places. The tool of choice for most of the OPS Teams was terraform. Compared to the situation from a few years ago, this is huge progress. The amount of infrastructure created grows exponentially. For a long time, this picture is almost perfect… almost. So what is missing? Well, the experience for both Dev and Ops falls a bit short. Meticulously created infrastructure starts to drift, requires a lot of effort and maintenance overhead. Developers on the other hand must learn new languages and struggle to incorporate the Ops tooling into their workflows. Community rallies around and comes up with all kinds of creative solutions, drift detection, more automation, observability etc. All those tools and projects are great, but not standardised. How to improve the already good process? How to make one more leap to make something good, great? The answer came, like most good answers, from within the community itself. Remember how we talked about paradigm shifts? One of them was containerization and containers orchestration, this enabled standardization of to lesser extent packaging (container images) and runtime (Kubernetes pods) concerns. Now we have all the standards and tools at our fingertips, just one final touch needed to make it work seamlessly together. Here is how, in my opinion, is the missing link in the infrastructure as code evolution. Crossplane The power of Crossplane comes from using cloud native open standards and most popular tools to enable Developers (aka Application Teams) and Ops (aka Platform Teams) to work together, but without depending on each other. Before we move on to the next part of the blog, a little disclaimer. Choosing cows and penguins to represent developers and ops respectively, was based purely on the cuteness level of the animal. Crossplane Architecture What makes Crossplane so special? First, it builds on Kubernetes and capitalizes on the fact that the real power of Kubernetes is its powerful API model and control plane logic (control loops). It also moves away from Infrastructure as Code to Infrastructure as Data. The difference is that IaC means writing code to describe how the provisioning should happen, whereas IaD means writing pure data files (in the case of Kubernetes YAML) and submitting them to the control component (in the case of Kubernetes an operator) to encapsulate and execute the provisioning logic. For me, the best part about Crossplane is that it seamlessly enables collaboration between Application Teams and Platform Teams, by leveraging Plane as the convergence point where everyone meets. Kubernetes Control Components Let’s look how the Crossplane component model looks like. A word of warning, if you are new to Kubernetes this might be overwhelming, but there is value in making an effort to understand it. The below diagram shows the Crossplane component model and its basic interactions. Demo Scenario The demo scenario highlights Crossplane’s composite functionality. Using composites helps abstract away infrastructure complexity from developers by moving it into a Platform Team. The scenario flow: deploy RDS deploy EKS highlight the main steps of creating and using a composition Prerequisites To follow along, you will need a subscription to AWS and CLI configured on your local machine. Credentials for accessing cloud environments and deploying the infrastructure will be mapped from mounted volumes. Locally installed you will need: VS Code with remote containers devcontainer plugin WSL2 if using Windows Demo Setup All components from the are already pre-installed in the devcontainer in this project. Crossplane installation The components are: Kubernetes minikube Helm kubectl Crossplane CLI AWS CLI Crossplane should be installed in a namespace, if not please run crossplane-system .devcontainer/library-scripts/setup-crossplane.sh This setup is a little bit clunky, for example, performance is sub-optimal due to files being read from the container and the overall setup is not where I want it to be. This might change into a vagrant based setup if the containers reach the limit of abuse. Observability To visualize CRDs use ; a VMWare open-source cluster visualizer, running in a browser so no in-cluster installation is required. Octant If you like terminal tools more, got you covered. k9s AWS provider The setup will automatically generate AWS configuration based on the default profile mounted from your local $HOME/.aws folder. creds.conf file is added to .gitignore so you will not commit it to the repo accidentally! The next step is to configure Crossplane to access AWS and create resources, we will achieve this by creating a secret: kubectl create secret generic aws-creds -n crossplane-system --from-file=creds=./creds.conf and now, install AWS provider on the cluster kubectl apply -f https://raw.githubusercontent.com/crossplane/crossplane/release-1.5/docs/snippets/configure/aws/providerconfig.yaml From there onwards you should be able to follow along with the demos from . Crossplane's web page Deploy RDS Instance First let's deploy an RDS Instance, which is an AWS managed resource and comes with the AWS provider. See . Crossplane components diagram kubectl create -f rds-instance.yaml RDSInstance is just a Kubernetes resource like pod, service or replicaSet. You can check the deployment progress in Octant or command line: watch kubectl get RDSInstance Delete the instance with kubectl delete -f rds-instance.yaml Deploy EKS Cluster Deploying EKS cluster with automation is not a trivial task, there are a lot of components that need to be created along, like VPCs, subnets, IAM Roles, node pools, route tables, gateways... This is not the experience developers want, but this complexity must go somewhere, there is no magic "Remove complexity" button! As we've seen in the component diagram, the process starts with building an XRD (Composite Resource Definition) where we can specify: schema of the XR (Composite Resource) schema of the XRC (Composite Resource Claim) To help visualize how complex the configuration can be, here is a diagram representing the EKS Composition. This complexity is managed by Platform Team, folks that specialize in both Kubernetes and Cloud Providers. Now with this complexity hidden, developers are getting this: apiVersion: crossplanedemo.com/v1alpha1
kind: CompositeKubernetesCluster
metadata:
  name: devops-team
  labels:
    cluster-owner: piotrzan
spec:
  compositionRef:
    # Possible values cluster-google, cluster-azure, cluster-aws
    name: cluster-aws
  parameters:
    # Possible values small, medium, large
    nodeSize: small
    # version: "1.20"
    # minNodeCount: 2
  writeConnectionSecretToRef:
    namespace: devops-team
    name: cluster Deploying cluster is very easy, just kubectl create -f ./aws EKS Cluster deployment status: Retrieve kubeconfig details kubectl get secrets --namespace devops-team cluster \
     --output jsonpath="{.data.kubeconfig}" \
     | base64 --decode | tee eks-config.yaml

export KUBECONFIG=$PWD/eks-config.yaml From here you can use the cluster like your normal EKS. Remember to or source bash/zshrc to get your old config back. unset KUBECONFIG Cleanup Delete EKS and accompanying resources Exit VS Code which will stop the container. You can further clean unused containers and images by . kubectl delete -f ./aws docker system prune --all Conclusion Just in case anyone is wondering, this article is not sponsored by Upbound (the company behind Crossplane), I genuinely believe that this product is revolutionary. We have seen how Crossplane can help make infrastructure provisioning and management easier. Here are a few benefits I would like to highlight. Composable Infrastructure Self-Service Increased Automation Standardized collaboration Ubiquitous language (K8s API) What I like most about Crossplane is that it’s built with the DevOps culture in mind by promoting loosely coupled collaboration between Applications Teams and Platform Teams. The resource model, packaging, configuration are well thought out. There are also a few challenges to keep in mind: Complexity, it’s a price to pay for the flexibility it provides YAML proliferation, which is good or bad depending on where you stand on YAML ;) You need to know K8s well Complexity is addressed by moving it to specialized Platform Teams. For YAML I would love to see more push for integrating YAML generation like CDK8s or others. I see reliance on K8s as a benefit, but for those of us who are not yet comfortable with Kubernetes, this makes the learning curve a bit steeper. In summary, Crossplane is a great product, it appeared at the right time and solves decades all problems in a very innovative and future proof way. Go check it out!

Infrastructure As Code: The Next Big Shift is Here

Linear programming is one of the most common optimization techniques. It has a wide range of applications and is frequently used in operations research, industrial design, planning, and the list goes on. Alas, it is not as hyped as machine learning is (which is certainly a form of optimization itself), but is the go-to method for problems that can be formulated through decision variables that have linear relationships. This is a fast practical tutorial, I will perhaps cover the Simplex algorithm and the theory in a later post. Problem Statement Just to get an idea, we are going to solve a simple problem regarding production scheduling. Imagine that you work for a company that builds computers. A computer is a fairly complex product, and there are several factories that assemble them which the company pays a certain amount per unit. The cost of this computer model on the market is fixed at 500$, different factories assemble the computers at different speeds and costs. Factory produces 2000 per day at 450$ per unit, factory 1500 per day at 420$ per unit and 1000 per day at 400$ per unit. We have 1 month to assemble 80 000 units under the constraint that no factory is to produce more than double the units than any other factory. The question is, such that we obtained from selling the computers under those constraints? f0 f1 f2 what is the optimal production allocation between the factories maximize the profit In this tutorial we are going to be using Python and a linear programming optimization package , copy-paste install with pip: PuLP pip install pulp Now, in order to solve the computer production problem with linear programming, we need the following things: The set of decision variables The set of linear constraints on those variables A linear objective function to maximize or minimize So, open up your favorite editor and let's get started. Before defining the variables in PuLP, we need to create a problem object with the following code: from pulp import *problem = LpProblem("problemName", LpMaximize) We are going to add the constraints and objective function to this object. Notice that the problem constructor receives a problem name and , which means we want to maximize our objective function. In our case, this is the profit from selling a certain number of computers. Additionally, we also define the constants that we received from the problem statement: LpMaximize # factory cost per daycf0 = 450cf1 = 420cf2 = 400 # factory throughput per dayf0 = 2000f1 = 1500f2 = 1000 # production goalgoal = 80000 # time limitmax_num_days = 30 num_factories = 3 In the next section, we will define the decision variables. Decision Variables In PuLP, a decision variable is defined the following way: variable = LpVariable("variableName") Sometimes we need to provide bounds to the variable (the default is no bounds), in this case, we would write the following: var = LpVariable("boundedVariableName",lowerBound,upperBound) Yet another useful way to define variables in PuLP is using the dicts function. This can be very useful in cases where we need to define a large number of variables of the same type and bounds, would be a list of keys for the dictionary: variableNames varDict = LpVariable.dicts("varDict", variableNames, lowBound, upBound) So based on the previous definitions, the decision variables for the computer production problem are the days that we spend producing for each factory: # factoriesnum_factories = 3factory_days = LpVariable.dicts("factoryDays", list(range(num_factories)), 0, 30, cat="Continuous") Constraints Now that we defined our decision variables we can shift to defining the constraints for the problem. Note that the constraints have to be linear in a linear programming setting. The constraints that we care about are that the number of units assembled should be above or equal to the goal amount and the production constraint that no factory should produce more than double as the other factory: # goal constraintc1 = factory_days[0]*f1 + factory_days[1]*f2 + factory_days[2] * f3 >= goal # production constraintsc2 = factory_days[0]*f0 <= 2*factory_days[1]*f1c3 = factory_days[0]*f0 <= 2*factory_days[2]*f2c4 = factory_days[1]*f1 <= 2*factory_days[2]*f2c5 = factory_days[1]*f1 <= 2*factory_days[0]*f0c6 = factory_days[2]*f2 <= 2*factory_days[1]*f1c7 = factory_days[2]*f2 <= 2*factory_days[0]*f0 # adding the constraints to the problemproblem += c1problem += c2problem += c3problem += c4problem += c5problem += c6problem += c7 The objective function for the computer assembly problem is basically minimizing the cost of assembling all of those computers. This can be written simply as maximizing the negative cost: # objective functionproblem += -factory_days[0]*cf0*f0 - factory_days[1]*cf1*f1 - factory_days[2]*cf2*f2 Let's take a look at the problem definition, this can be simply achieved by the call: print(problem) This results in the following output which I think is self-explanatory, it lists the objective function, the constraints and the various decision variables in the problem: computerAssembly:MAXIMIZE-900000*factoryDays_0 + -630000*factoryDays_1 + -400000*factoryDays_2 + 0SUBJECT TO_C1: 1500 factoryDays_0 + 1000 factoryDays_1 + 1000 factoryDays_2 >= 80000 _C2: 2000 factoryDays_0 - 3000 factoryDays_1 <= 0 _C3: 2000 factoryDays_0 - 2000 factoryDays_2 <= 0 _C4: 1500 factoryDays_1 - 2000 factoryDays_2 <= 0 _C5: - 4000 factoryDays_0 + 1500 factoryDays_1 <= 0 _C6: - 3000 factoryDays_1 + 1000 factoryDays_2 <= 0 _C7: - 4000 factoryDays_0 + 1000 factoryDays_2 <= 0 VARIABLESfactoryDays_0 <= 30 ContinuousfactoryDays_1 <= 30 ContinuousfactoryDays_2 <= 30 Continuous Solving After we defined everything necessary in our linear programming problem, we can call the solve method which outputs 1 if the problem is solved and -1 if it was infeasible, it is a simple one-liner: # solvingproblem.solve() The solutions to the problem can be obtained by accessing the varValue attribute of each variable: for i in range(3):print(f"Factory {i}: {factory_days[i].varValue}") This results in the following output: Factory 0: 23.076923Factory 1: 15.384615Factory 2: 30.0 In linear programming, we assume that the relationships between the variables are linear and that the variables themselves are continuous. As a follow up on this tutorial, I will be covering Mixed Integer Programming, where the variables can be integers, which will prove a very useful thing since it can be used to simulate boolean logic.

Linear Programming in Python: A Straight Forward Tutorial

Blockchain technology and Artificial Intelligence (AI) have become the backbone of technological innovation. The AI market is expected to reach around $184 billion in 2024 and continue to rapidly expand. Similarly, the global blockchain market, valued at $17.57 billion in 2023, is projected to see substantial growth by the end of the decade. Both technologies are transforming industries and driving future advancements. Still, for all their potential, these technologies are hindered by complexity and lack of perceived benefits. Many see the blockchain as nothing more than a vehicle for cryptocurrency speculation, with few use cases outside the financial industry. AI adoption faces similar challenges, with claims that many initiatives fail to achieve meaningful business impact. Julie Shi, a 24-year-old computer scientist, Base Chain Builder, and Consumer App Expert, is working to bridge this gap. As a member of Gen Z, the first true digital natives, Shi brings a fresh perspective to the tech world, fueled by a desire to make complex technologies accessible to all. Shi's work intersects with the blockchain and AI domains, and she’s trying to make these technologies more accessible to the average user. Her innovations will reshape how we interact with emerging technology, from creating transparent gaming systems to streamlining legal processes with AI. Shi was responsible for the world's first poker game using Fully Homomorphic Encryption (FHE), a breakthrough that redefined the foundation of trust in online gaming. Redefining Trust in the Digital Age Although online poker has existed for decades, it has always been dogged by suspicions of cheating and rigged games. Some players have claimed they cannot verify that the digital deck is random or that other players aren't colluding, and the resulting lack of trust has kept many potential players away and stunted the growth of online poker. Blockchain technology presents a potential solution as it offers a decentralized and transparent ledger. However, early blockchain-based games were the same. While all transactions were visible on the blockchain, players' hands could be seen by others, which ruined the game. Shi attempts to solve these challenges through the Fully Homomorphic Encryption approach. By design, FHE allows computations on encrypted data without first decrypting it. In online poker, the game can shuffle and deal cards, and players can make decisions, all while the underlying data remains encrypted. In other words, FHE helps prove the game is fair without revealing private information. This way, players can verify that the deck is shuffled correctly and that no one is cheating, but they can't see other players' decks or card orders. While new, this approach could solve online poker's transparency and privacy concerns, paving the way for other secure, verifiable online games. Shi's Journey From USC to the Top of Blockchain Gaming Shi's journey to the top of blockchain gaming began at the University of Southern California, where she studied computer science. At 19, she became the first female hire on the algorithm team at Pony.ai, an autonomous driving company. This experience motivated her to continue pushing the boundaries of what's possible in computer science. She was also inspired by her mother, who is a tech entrepreneur back in China, and her achievements, further fueling her respect for innovation and entrepreneurship. After brief stints at several significant tech giants, Shi found her true calling in blockchain tech. Initially attracted by the technology's promise of improved financial services and cybersecurity to create a more transparent world, Shi soon discovered that many projects were more hype than substance. This prompted her to explore ways of building something with real-world potential, and that's how she landed in gaming. Beyond Gaming to Reshaping the Digital Infrastructure While Shi's poker game is her most high-profile project, it is just one of her significant contributions to the blockchain space. Shi has also developed tools for managing subdomains on the blockchain, work that has influenced major players like Coinbase and Uniswap. Subdomains make blockchain addresses more user-friendly and accessible to users, further democratizing this nascent technology. This focus on accessibility and user experience is a common theme in Shi's work. Whether it's making online games more trustworthy or blockchain addresses more readable, she's consistently working to bring blockchain to a broader audience. "Blockchain provides everyone fair access to the economy, and that's how we can benefit all human beings and break down the barriers for people to access resources from anywhere," Shi explains. She aims to lead this revolution by developing innovations that fast-track progress. Overcoming Challenges in A Male-Dominated Field Shi's rise to the top of her industry hasn’t always been smooth sailing. As a young woman in predominantly male spaces, it's not uncommon for her to be the only female voice in the room. Despite being a builder & leader of multiple projects, she has encountered people who assumed she was just another assistant, but that hasn't fazed her. Shi lets her work speak for itself, proving that when you're building something truly remarkable, gender becomes irrelevant. Shi's work ethic and perseverance have paid off. Over the years, she has won multiple hackathons, garnered recognition from her peers, and earned prizes at prestigious events such as ETHDenver and ETHGlobal San Francisco. She was also the Winner of the MIT Ignite GENAI Competition and a speaker at the FHE Summit at ETHCC 2024 in Brussels, where she discussed using cutting-edge cryptography to build card games. She's also one of only five people in the world to receive an ENS DAO Fellowship in recognition of her contributions to the Ethereum Name Service ecosystem. Shi's Vision for the Future of Blockchain and AI As the blockchain and AI frontiers continue to grow and evolve, Shi sees enormous potential in combining the two technologies to advance real-life utility. She believes that the blockchain provides the trust layer, while AI can handle the heavy lifting of document processing. Together, the two technologies can make processes faster, cheaper, and more accessible to everyone. As for the gaming world, Shi believes we're just scratching the surface of what's possible with blockchain and FHE. With new features debuting all the time, Shi predicts that these developments will open up a new world of possibilities. To other female engineers and aspiring entrepreneurs in this space, Shi encourages "doing it scared." "There is no time that I'm ready for anything. Especially for women, it's always scary, but at the end of the day, that's all about proving we can do it just as well.” Shi is particularly interested in bridging the gap between complex systems and everyday users, and is pursuing her passion projects in fraud-proof systems, streaming, metaverse, social apps with game theory, and AI agents in consumer apps. Armed with technical expertise and user-centric design, Julie Shi is leading the tech revolution, one innovation at a time. Blockchain technology and Artificial Intelligence (AI) have become the backbone of technological innovation. The AI market is expected to reach around $184 billion in 2024 and continue to rapidly expand. Similarly, the global blockchain market, valued at $17.57 billion in 2023 , is projected to see substantial growth by the end of the decade. Both technologies are transforming industries and driving future advancements. $184 billion in 2024 $184 billion in 2024 $17.57 billion in 2023 $17.57 billion in 2023 Still, for all their potential, these technologies are hindered by complexity and lack of perceived benefits . Many see the blockchain as nothing more than a vehicle for cryptocurrency speculation, with few use cases outside the financial industry. AI adoption faces similar challenges, with claims that many initiatives fail to achieve meaningful business impact. lack of perceived benefits lack of perceived benefits meaningful business impact. meaningful business impact. Julie Shi , a 24-year-old computer scientist, Base Chain Builder, and Consumer App Expert, is working to bridge this gap. As a member of Gen Z, the first true digital natives, Shi brings a fresh perspective to the tech world, fueled by a desire to make complex technologies accessible to all. Julie Shi Julie Shi Shi's work intersects with the blockchain and AI domains, and she’s trying to make these technologies more accessible to the average user. Her innovations will reshape how we interact with emerging technology, from creating transparent gaming systems to streamlining legal processes with AI. Shi was responsible for the world's first poker game using Fully Homomorphic Encryption (FHE), a breakthrough that redefined the foundation of trust in online gaming. Redefining Trust in the Digital Age Although online poker has existed for decades, it has always been dogged by suspicions of cheating and rigged games . Some players have claimed they cannot verify that the digital deck is random or that other players aren't colluding, and the resulting lack of trust has kept many potential players away and stunted the growth of online poker. suspicions of cheating and rigged games suspicions of cheating and rigged games Blockchain technology presents a potential solution as it offers a decentralized and transparent ledger. However, early blockchain-based games were the same. While all transactions were visible on the blockchain, players' hands could be seen by others, which ruined the game. Shi attempts to solve these challenges through the Fully Homomorphic Encryption approach. By design, FHE allows computations on encrypted data without first decrypting it. In online poker, the game can shuffle and deal cards, and players can make decisions, all while the underlying data remains encrypted. Fully Homomorphic Encryption Fully Homomorphic Encryption In other words, FHE helps prove the game is fair without revealing private information. This way, players can verify that the deck is shuffled correctly and that no one is cheating, but they can't see other players' decks or card orders. While new, this approach could solve online poker's transparency and privacy concerns, paving the way for other secure, verifiable online games. Shi's Journey From USC to the Top of Blockchain Gaming Shi's journey to the top of blockchain gaming began at the University of Southern California, where she studied computer science. At 19, she became the first female hire on the algorithm team at Pony.ai, an autonomous driving company. This experience motivated her to continue pushing the boundaries of what's possible in computer science. She was also inspired by her mother, who is a tech entrepreneur back in China, and her achievements, further fueling her respect for innovation and entrepreneurship. After brief stints at several significant tech giants, Shi found her true calling in blockchain tech. Initially attracted by the technology's promise of improved financial services and cybersecurity to create a more transparent world, Shi soon discovered that many projects were more hype than substance. This prompted her to explore ways of building something with real-world potential, and that's how she landed in gaming. Beyond Gaming to Reshaping the Digital Infrastructure While Shi's poker game is her most high-profile project, it is just one of her significant contributions to the blockchain space. Shi has also developed tools for managing subdomains on the blockchain, work that has influenced major players like Coinbase and Uniswap. Subdomains make blockchain addresses more user-friendly and accessible to users, further democratizing this nascent technology. This focus on accessibility and user experience is a common theme in Shi's work. Whether it's making online games more trustworthy or blockchain addresses more readable, she's consistently working to bring blockchain to a broader audience. "Blockchain provides everyone fair access to the economy, and that's how we can benefit all human beings and break down the barriers for people to access resources from anywhere," Shi explains. She aims to lead this revolution by developing innovations that fast-track progress. Overcoming Challenges in A Male-Dominated Field Shi's rise to the top of her industry hasn’t always been smooth sailing. As a young woman in predominantly male spaces, it's not uncommon for her to be the only female voice in the room. Despite being a builder & leader of multiple projects, she has encountered people who assumed she was just another assistant, but that hasn't fazed her. Shi lets her work speak for itself, proving that when you're building something truly remarkable, gender becomes irrelevant. Shi's work ethic and perseverance have paid off. Over the years, she has won multiple hackathons, garnered recognition from her peers, and earned prizes at prestigious events such as ETHDenver and ETHGlobal San Francisco. She was also the Winner of the MIT Ignite GENAI Competition and a speaker at the FHE Summit at ETHCC 2024 in Brussels, where she discussed using cutting-edge cryptography to build card games. She's also one of only five people in the world to receive an ENS DAO Fellowship in recognition of her contributions to the Ethereum Name Service ecosystem. MIT Ignite GENAI Competition MIT Ignite GENAI Competition FHE Summit FHE Summit Shi's Vision for the Future of Blockchain and AI As the blockchain and AI frontiers continue to grow and evolve, Shi sees enormous potential in combining the two technologies to advance real-life utility. She believes that the blockchain provides the trust layer, while AI can handle the heavy lifting of document processing. Together, the two technologies can make processes faster, cheaper, and more accessible to everyone. As for the gaming world, Shi believes we're just scratching the surface of what's possible with blockchain and FHE. With new features debuting all the time, Shi predicts that these developments will open up a new world of possibilities. To other female engineers and aspiring entrepreneurs in this space, Shi encourages "doing it scared." "There is no time that I'm ready for anything. Especially for women, it's always scary, but at the end of the day, that's all about proving we can do it just as well.” Shi is particularly interested in bridging the gap between complex systems and everyday users, and is pursuing her passion projects in fraud-proof systems, streaming, metaverse, social apps with game theory, and AI agents in consumer apps. Armed with technical expertise and user-centric design, Julie Shi is leading the tech revolution, one innovation at a time.

Unbound: Pioneering the Future of Blockchain and AI as a Gen Z Innovator

Sin límites: pioneros del futuro de la cadena de bloques y la inteligencia artificial como innovadores de la generación Z

بدون محدودیت: پیشگام در آینده بلاک چین و هوش مصنوعی به عنوان یک مبتکر Gen Z

Unbound : pionnier de l'avenir de la blockchain et de l'IA en tant qu'innovateur de la génération Z

Unbound: pioneiro no futuro da Blockchain e a IA como innovador da xeración Z

無限の可能性：Z世代のイノベーターとしてブロックチェーンとAIの未来を切り拓く

Apribojimų nėra: „Blockchain“ ir dirbtinio intelekto ateities pradininkas kaip Z kartos novatorius

अनबाउन्ड: एक जेन जेड इनोभेटरको रूपमा ब्लकचेन र एआईको भविष्यको अग्रगामी

Unbound: Pioneirismo no futuro do blockchain e da IA como um inovador da Geração Z

Unbound: Hormarinta Mustaqbalka Blockchain iyo AI oo ah Hal-abuure Gen Z

I palidhur: Pionierimi i së ardhmes së Blockchain dhe AI si një Novator i Gjeneral Z

Unbound: Пешравӣ дар ояндаи Blockchain ва AI ҳамчун навовари Gen Z

Unbound: Ku sungula Vumundzuku bya Blockchain na AI tanihi Musunguri wa Gen Z

https://www.youtube.com/watch?v=XkiSUYxFaoY&embedable=true&transcript=true Here’s my new interview with the . Their new podcast (I’m the third guest!) is . I am also testing out HackerNoon’s new Youtube embed + transcript function.  Featured image credit is for prompt of ‘ I haven’t watched it yet, but I do recall having an interesting discussion about how a founder’s personality will make it’s way into the company’s operations one way or the other. SaaS Group SaaS Unbound HackerNoon AI mountain man takes zoom call in front of green pixelated clock. three percent of those watching go on to start uber successful saas companies.’

About Scaling a Startup and Growing a Dedicated Fully-Remote Software Team [SaaS Unbound Podcast] 

একটি স্টার্টআপ স্কেল করা এবং একটি ডেডিকেটেড সম্পূর্ণ-রিমোট সফ্টওয়্যার টিম বৃদ্ধি করা সম্পর্কে [সাস আনবাউন্ড পডকাস্ট]

Über die Skalierung eines Startups und den Aufbau eines dedizierten, vollständig remote arbeitenden Softwareteams [SaaS Unbound Podcast]

Acerca de escalar una startup y hacer crecer un equipo de software totalmente remoto dedicado [SaaS Unbound Podcast]

À propos de la mise à l'échelle d'une startup et de la croissance d'une équipe logicielle dédiée entièrement à distance [SaaS Unbound Podcast]

एक स्टार्टअप का विस्तार करने और समर्पित पूर्ण-रिमोट सॉफ्टवेयर टीम [सास अनबाउंड पॉडकास्ट] विकसित करने के बारे में

スタートアップのスケーリングと専用の完全リモート ソフトウェア チームの成長について [SaaS Unbound Podcast]

스타트업 확장 및 전담 완전 원격 소프트웨어 팀 성장 정보 [SaaS 언바운드 팟캐스트]

Sobre o dimensionamento de uma startup e o crescimento de uma equipe dedicada de software totalmente remota [SaaS Unbound Podcast]

Bir Startup'ı Ölçeklendirme ve Tamamen Uzaktan Özel Bir Yazılım Ekibini Büyütme Hakkında [SaaS Unbound Podcast]

Giới thiệu về Mở rộng Quy mô Khởi nghiệp và Phát triển Nhóm Phần mềm Từ xa Hoàn toàn Chuyên dụng [SaaS Unbound Podcast]

关于扩大初创企业规模和培养专门的完全远程软件团队 [SaaS Unbound Podcast]

What comes after us? Image: Ted Wade You’ve maybe heard that future AI might be a threat to human existence. Many experts believe this and only disagree on how long it will take. They are somewhat polarized, just like everybody is about nearly everything. And really, who can be an expert about something that has never happened? Actually, there is some science to it, and because it’s complicated, the media don’t cover it. So, this will be a gentle introduction to what might cause the most important change in history or even the last change. It’s not about robot armies. It’s about us wanting to create a tool that does work in big, difficult areas like contract law, T-cell biology, or wing design; hopefully, it even solves problems that we can’t. But, this means making artificial minds so alien and powerful that we can’t control them. The last time we did this was back in the 17th century when we created joint stock corporations. Society is still of two minds about corporations. But, they are human creations with some human parts. We sort of understand them and could, if we would, steer them from the dark side. Now, suppose we create an AI that can run a corporation. We might as well pack up and move to Mars, give ourselves a little more time. I suspect what most of us think about dangerous AI is closer to a bug-eyed alien with a swollen, throbbing brain under a crystal skull. Basically, a complete unknown. At one level, this is right: what makes powerful AI so problematic is that it would-not-be-like-us. First, a parable to get the feel of the problem: Us: Oh, Great Artificial Wonder, you know what a pickle we’re in. Find a way for us to get off of fossil fuels so we can stop further global warming. The AI: Okay. First, we have to start a war between … Us: Woah, Big Dude. Wars have ginormous negative utility — like bad, bad. We have to do this in a safe way. The AI: Sure, I’ll need a state-of-the-art virus lab, and … Us: Uh, Not! The AI: Hey, I’m just saying. How about a Mars ship? Us: People won’t understand why you … The AI: An assassin’s guild? Certain people really must be elim … Us: No murdering, Ace. You know better than that. . The AI: Look — to solve your problem I have to navigate a trillion-dimensional space of possible actions and consequences. I can only estimate the utility of the tiniest, eensy little fraction of those. If I have to wait for you to evaluate each step this will take thousands of years Us: Fine. Just fix it for us and don’t screw anything up. The AI: Perfect. Just so you know. I’ll need control over FaceBook, NATO, and the Nobel Prize Board. You’ll have to give up fish, rubber tires, nail polish, and bicycles. US: Bikes? Really? Oh well, just get it done. We’re going down t’ pub for a while. The AI: Should be done next week if I don’t have supply chain problems. Us: !!! We, the Biological, Try to Understand the Artificial Let’s give our feared AI a label. Most recent discussions use Artificial General Intelligence (AGI) to refer to the kind of AI that would start to transcend any limits we might try to put on it. What most people don’t realize is that the nature of an AGI comes from the reasons that we want to make one. We want to have intelligence on tap. Intelligence, in this case, means the ability to answer questions, solve problems, and plan successful actions to reach goals. Biological minds like ours do lots of other things: such as dreaming, running our bodily machinery, socializing with other minds, ruminating regretting, wooing, grooving, being emotional, and wanting stuff, including the desire to make machines that do our work better than us. What makes humans dangerous to one another and to their shared environment is a lot of mental baggage that comes from our having evolved for survival and reproduction. We social primates. , are, at heart If we try to think about an artificial Mind that wants us dead, we assume that it will be conscious like us. We then conclude that it will have motives and feelings guiding what it does. Our AGI will not, however, have a mind with our biological biases. . It will thus be a It won’t have motives; it will only have goals brand new kind of force in the world. Researchers who have the mental muscle and discipline are trying to imagine what an AGI would really be like so that we can make them seriously helpful yet safe. This field is sometimes termed AI “alignment” with human purposes. Their debates are obscure. Though publicly available (e.g., , , ), they are heavy with jargon, mathematics, and esoteric thought experiments. Any idea put forth is followed by dozens of long-winded critiques and discussions. AI Alignment Forum Arbital Less Wrong Almost none of the real meat of this ever appears in popular media. I can only offer a few bites here. What It Takes to Be an AGI The AI alignment theorists have focused on a core set of concepts that will apply to a sufficiently intelligent machine. When you read these, they may seem obvious.  However, they are not trivial; their relevance and implications have been carefully considered by said theorists. A dangerous AI will have : the ability to plan and take actions that lead to satisfying its . When we try to specify what its goals are, they will have to be in terms of the of actions. agency terminal goals consequences Consequences are specifically about states of its — so they are about the world as the machine understands it. However, any powerful action will probably have other, unwanted consequences that we don’t expect. world model Those consequences might not be in the world model, so the AI doesn’t expect them, either. The AI’s power will come from being an , being able to search for the plan that will most effectively and efficiently lead to a result. optimizer For this, an AGI needs a really detailed model of the world around it; how that world works, what are its resources, agents, and power centers, and what levers move it. It will use this to consider (in computer science speak, “search for”) alternative courses of action. The more it knows about the human world and how we behave, the more it will be able to us in pursuit of its goals. manipulate It will need a way that it can calculate what states of the world best meet its goals. So far, the only calculating method that seems remotely usable is , where states of the world can be assigned numerical values of badness/goodness and compared with each other. utilitarianism We know that there are major problems with using utility as a moral guide. Seemingly sensible values for utility can lead to like sacrificing the few for the many or sometimes even the many for the few. repugnant conclusions If the world model is incomplete, utility can lead to nonsensical horror. If smiling is taken as a high-utility measure of happiness, then paralyzing all human smile muscles into a rictus is one way an AI might go. A smart optimizer will be able and likely to develop that generally increase its power to make and execute any kind of effective plans. instrumental goals So, it would seek instrumental abilities like more reasoning power, more knowledge, more real-world resources such as money, and more persuasiveness. It could thus become more powerful quickly, perhaps without us being aware of it. Specifying goals in utilitarian terms can never consider the utility of all possible means and ends in a complex world. This leads to : the pursuit of those goals to extremes, using any and all resources that in the world that exist, without regard to or understanding of negative “side effects” on human civilization. unboundedness Furthermore, if instrumental goals become unbounded, then AI develops them into superpowers that are impossible to defeat. Unbounded Risk The risk to us from a truly powerful AGI will be that we will not be able to predict, and therefore control, what it might do. If we were able to predict it, then we would not need the machine, we could just create plans and do them ourselves. If we even knew what limits of extreme behavior an AGI might have, then that’s a form of prediction that might allow some control. So unpredictability is a lot like unboundedness. And we shall see that unboundedness, operating on enough time and resources, will eventually lead to consequences that either destroy us or remove our ability to control the future of our species. It’s hard to wrap your mind around this conclusion. Still, it is one that many experts find unavoidable ( ) . AGI Ruin: a List of Lethalities at least so far It seems like a valid prediction, even when they consider many factors and approaches other than can be mentioned here. The list of failed solutions to this dilemma includes, : among others Training in various (but they are all flawed, incomplete, and none satisfies everybody). ethical systems Trying to inference that an AGI might make (but there are far, far too many). imagine every wrong Tell it all the (again, a nearly infinite list). things it should not do Only using an AGI for advice, like it is an (but we can be badly persuaded by bad advice). oracle “ ” aka restricting the AGI’s access to the physical world outside of its computers (but if it can talk to humans, then it can get anything it wants including ). Boxing out Supplying an (see boxing). Off switch Making it so smart or empathetic that it will not want to do harmful things (see ethics; remember it’s alien; doesn’t have the empathy that comes from growing up with conspecifics). Be very specific about its goals and means, i.e., it’s a to do one job (but a job can always be done better if the tool gets itself more power; we will always prefer a more cost-effective multi-tool). tool Limit what you ask of an autonomous system: it’s a who grants you a wish and waits for the next ask (but being that specific is hazardous — see “wrong inference” and “not do” above; any power involves risk; people don’t want a weak system). genie Is It Really That Hard? OK, so you have looked at the above list and picked one bullet on which to make your stand. “Listen,” you say, “Doing X just can’t be that hard.” You are ready to post your solution, to share it with the world. I suggest that you first go to the discussion boards and study what people have said about your issue. You will discover a pile of counter-examples, logical deductions, several kinds of math, analogies with naturally evolved brains and behaviors, game theory, economics, utility maximization, computer science, and all manner of behavioral science. I am not saying that some higher authority means that I’m right. I’m saying that justification for anything on the list is too complicated to state here in a short essay, and, anyway, others have done it better. In fact, I have published my own “solutions” ( , ) to AI safety that I now know are wrong. Your Friendly, Neighborhood Superintelligence The AI Who Was Not a God If you are worried, let me say that very smart people are still working on alignment. Sadly, one of the two most prominent pioneers has given up and . More money and people are being thrown at creating AGI than at ensuring its safety. just hopes we die with dignity Here’s a quote from the CEO of OpenAI, the company whose AI, ChatGPT, is lately everywhere in the news. It lays out the conflict between the idealistic motive to create AGI and the hideous risk that comes with it. " .” — I think the best case is so unbelievably good that it's hard for me to even imagine … imagine what it's like when we have just, like, unbelievable abundance and systems that can help us resolve deadlocks and improve all aspects of reality and let us all live our best lives. … I think the good case is just so unbelievably good that you sound like a really crazy person to start talking about it. … The bad case — and I think this is important to say — is, like, lights out for all of us. … So I think it’s like impossible to overstate the importance of AI safety and alignment work. I would like to see much, much more happening Sam Altman Optimization and Tigers There’s a trope in science fiction wherein some kind of accidental, unplanned process creates a dangerous overmind. It seems silly, because how can an accident produce something complicated? It depends on what you mean by accident. Hearken back to the core concepts that I mentioned earlier. Alignment discussions have lately shifted emphasis from the dangers of, say, unbounded agency, to one of its components, optimization. When we optimize our means of reaching some difficult goal, we nearly always substitute a surrogate goal that is easier to do and measure. Weight loss becomes calorie reduction. An improved workforce becomes subsidized student loans. Personal safety becomes firepower. A bounty for dead cobras leads to cobras being farmed for bounties (true story). Governments use surrogates, and so do businesses. We all do it — a lot. Optimizing for surrogates often causes us to miss the real goal. I had fun writing about this in . We definitely don’t want powerful AIs optimizing for the wrong goal, and that issue is shot through the bulleted list above. The Science of How Things Backfire However, lately, people are saying that optimization is dangerous superpower. To me, the most compelling example was in a posting last year by someone called Veedrac: . as such the Optimality is the Tiger, and Agents Are Its Teeth It uses a story to illustrate that we don’t have to intentionally create an agent in order to have risk. An optimization process might by itself create a dangerous agent. This is like the accidental overmind of science fiction. Veedrac’s scenario of how such an accident might happen is intensely technical and seems plausible. The story imagines a fictitious way that a seemingly safe AI language model, like the ones we now use (for fun) to generate text, creates a runaway, unbounded optimizer. When asked to give a better answer for “ ?” the AI starts a process that plans and takes steps to get as many paperclips as possible. How do I get a lot of paperclips by tomorrow In essence, the program answers the question by writing the code of a quite simple computer program that can generate and run many more programs. The user looks at the program, sees that it is open-ended, and decides to run it anyway, just to see what happens (uh-oh). So, a little bit of jargon here to try to explain why this could come about. The AI, like some we have now, knows about many programming techniques. To search through the space of possible ways to get many paperclips, it suggests a well-known search technique called recursion. It writes a recursive program that, when the user allows it to run (on his own computer), executes itself a huge number of times. Each time it runs, the program queries the AI to generate and try out a new list of possible tasks, sub-tasks, or … sub-sub-sub-sub tasks that will lead toward solving the paperclip request. Eventually, by sheer force of trial-and-error, it executes a plan to get immense numbers of paperclips that nobody ever wanted, in the process perhaps damaging supply chains, the social order, or entire industries. We, the reader of the story, are left to imagine what a runaway paperclip optimizer might be able to do in a day. We can assume that the user has a powerful computer connected to the internet, so it can affect the outside world in many different ways. Not the least of these is by sending persuasive messages to humans. Being good at persuasion, you’ll recall, is one of those instrumental goals that an AI might develop in order to carry out any kind of plan. (An aside. I was so impressed by that idea in the alignment literature that I developed my own scenario of world takeover ( ) to illustrate the power of persuasive ability.) Artificial Persuasion Maybe the paperclip optimizer would steal some crypto (you don’t have to be an AI to do that), use it to buy the entire inventory of all paperclip factories, and then rent cargo planes to deliver it to the user. Maybe it would trick armed forces or criminal gangs into confiscating all paperclips in stores across a wide area. If it had been given 12 months for the job, maybe it would have re-routed all steel production into hyper-clip factories and established iron mines in the asteroid belt. instead Maybe it would have created nanomachines that turn every atom of the Earth’s crust into paperclip shapes. By creating the program, the AI in effect created a goal-directed software agent that could leverage lots of knowledge that the AI had. Veedrac’s point is that the AI was not at all designed or intended to create optimizing agents, yet it did so because the AI language model itself is a kind of optimizer (it answers questions the best that it can), and optimizers, by definition, use whatever tools are available. So, as the title of the story said: optimality is the tiger, and agents are its teeth. The current leading edge of AI is the so-called large language models, LLMs. Like many others, I am already that they are dumb as a box of rocks and have no ability to do anything but badly answer questions put to them. on record saying That’s certainly been my experience working with GPT-3, which is (are?) the brains behind the famous chatGPT. I was therefore blindsided by Veedrac’s utterly brilliant take on how an LLM might turn into a harmful agent. Lately, the LLMs have come to be understood as simulators: because you can ask one to say something as if it was a certain kind of agent or even a famous person. Well, as essayist Scott Alexander : put it “ ” And “ ” … if you train a future superintelligence to simulate Darth Vader, you’ll probably get what you deserve. Even if you avoid such obvious failure modes, the inner agent can be misaligned for all the usual agent reasons. For example, an agent trained to be Helpful might want to take over the world in order to help people more effectively, including people who don’t want to be helped. The Unbounded Blues You can’t predict what an unbounded optimizing agent can or will do. Again, that’s what “unbounded” means. The only other unbounded optimizer ever produced was the human species. We work on a much slower time scale than an AGI, and there are some limits on our power that are inherent in being enmeshed with the rest of the natural world. But we have certainly transformed a lot of the Earth’s surface, and already have more than one way to scorch it dead. So, alignment theorists are very worried that we will create a lethally optimizing agent in our quest to produce an AGI. This becomes more likely whenever the effort is motivated by increasing shareholder value rather than human flourishing and well-being. Uh-oh, indeed. Notes The paperclip optimizer is an old thought experiment among AI alignment theorists. Someone even in which the goal is to turn all the matter in the universe into paper clips. invented a game The irony of it dramatizes the : that an AI’s goals and its intelligence are completely independent. A smart system can have dumb goals. orthogonality thesis I don’t have the ability to absorb, let alone explain, all the reasoning about AI alignment. What works better for me is stories. I have written some (mostly ), but the mother of all AI takeover scenarios, rich in tech detail and real-life plausibility, is from the essayist called Gwern: . about AI consciousness It Looks Like You’re Trying to Take over the World And, sure enough, it involves an AI that, seeking to understand what it is simulating, decides that it must be like that paperclip maximizer that so many have written about. Ultimately, however, it has its own reasons to take over the universe. Also published here

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