Final score 36-41. Barça: Emil Nielsen (9), Pol Valera (1), Dika Mem (7), Hampus Wanne, Blaz Janc (6), Timothey N’Guessan (7), Luis Frade (2) - starting seven - Gonzalo Pérez de Vargas (4), Jonathan Carlsbogard (2), Aitor Ariño (2), Aleix Gómez (1 ...

Barça 36–41 Telekom Veszprém: First loss In Europe

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The first saw some wonderful defending, but it was the attack that was most in tune. After the break, the visitors cut down the difference but an inspired Dika Mem made sure of the fourth win in as many games in the Champions League. GOG's highly attack ...

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DIKA

dika

The Brothers Karamazov by Fyodor Dostoyevsky is part of the HackerNoon Books Series. You can jump to any chapter in this book here . Smerdyakov Chapter VI. Smerdyakov He did in fact find his father still at table. Though there was a dining‐ room in the house, the table was laid as usual in the drawing‐room, which was the largest room, and furnished with old‐fashioned ostentation. The furniture was white and very old, upholstered in old, red, silky material. In the spaces between the windows there were mirrors in elaborate white and gilt frames, of old‐fashioned carving. On the walls, covered with white paper, which was torn in many places, there hung two large portraits—one of some prince who had been governor of the district thirty years before, and the other of some bishop, also long since dead. In the corner opposite the door there were several ikons, before which a lamp was lighted at nightfall ... not so much for devotional purposes as to light the room. Fyodor Pavlovitch used to go to bed very late, at three or four o’clock in the morning, and would wander about the room at night or sit in an arm‐chair, thinking. This had become a habit with him. He often slept quite alone in the house, sending his servants to the lodge; but usually Smerdyakov remained, sleeping on a bench in the hall. When Alyosha came in, dinner was over, but coffee and preserves had been served. Fyodor Pavlovitch liked sweet things with brandy after dinner. Ivan was also at table, sipping coffee. The servants, Grigory and Smerdyakov, were standing by. Both the gentlemen and the servants seemed in singularly good spirits. Fyodor Pavlovitch was roaring with laughter. Before he entered the room, Alyosha heard the shrill laugh he knew so well, and could tell from the sound of it that his father had only reached the good‐humored stage, and was far from being completely drunk. “Here he is! Here he is!” yelled Fyodor Pavlovitch, highly delighted at seeing Alyosha. “Join us. Sit down. Coffee is a lenten dish, but it’s hot and good. I don’t offer you brandy, you’re keeping the fast. But would you like some? No; I’d better give you some of our famous liqueur. Smerdyakov, go to the cupboard, the second shelf on the right. Here are the keys. Look sharp!” Alyosha began refusing the liqueur. “Never mind. If you won’t have it, we will,” said Fyodor Pavlovitch, beaming. “But stay—have you dined?” “Yes,” answered Alyosha, who had in truth only eaten a piece of bread and drunk a glass of kvas in the Father Superior’s kitchen. “Though I should be pleased to have some hot coffee.” “Bravo, my darling! He’ll have some coffee. Does it want warming? No, it’s boiling. It’s capital coffee: Smerdyakov’s making. My Smerdyakov’s an artist at coffee and at fish patties, and at fish soup, too. You must come one day and have some fish soup. Let me know beforehand.... But, stay; didn’t I tell you this morning to come home with your mattress and pillow and all? Have you brought your mattress? He he he!” “No, I haven’t,” said Alyosha, smiling, too. “Ah, but you were frightened, you were frightened this morning, weren’t you? There, my darling, I couldn’t do anything to vex you. Do you know, Ivan, I can’t resist the way he looks one straight in the face and laughs? It makes me laugh all over. I’m so fond of him. Alyosha, let me give you my blessing—a father’s blessing.” Alyosha rose, but Fyodor Pavlovitch had already changed his mind. “No, no,” he said. “I’ll just make the sign of the cross over you, for now. Sit still. Now we’ve a treat for you, in your own line, too. It’ll make you laugh. Balaam’s ass has begun talking to us here—and how he talks! How he talks!” Balaam’s ass, it appeared, was the valet, Smerdyakov. He was a young man of about four and twenty, remarkably unsociable and taciturn. Not that he was shy or bashful. On the contrary, he was conceited and seemed to despise everybody. But we must pause to say a few words about him now. He was brought up by Grigory and Marfa, but the boy grew up “with no sense of gratitude,” as Grigory expressed it; he was an unfriendly boy, and seemed to look at the world mistrustfully. In his childhood he was very fond of hanging cats, and burying them with great ceremony. He used to dress up in a sheet as though it were a surplice, and sang, and waved some object over the dead cat as though it were a censer. All this he did on the sly, with the greatest secrecy. Grigory caught him once at this diversion and gave him a sound beating. He shrank into a corner and sulked there for a week. “He doesn’t care for you or me, the monster,” Grigory used to say to Marfa, “and he doesn’t care for any one. Are you a human being?” he said, addressing the boy directly. “You’re not a human being. You grew from the mildew in the bath‐house. That’s what you are.” Smerdyakov, it appeared afterwards, could never forgive him those words. Grigory taught him to read and write, and when he was twelve years old, began teaching him the Scriptures. But this teaching came to nothing. At the second or third lesson the boy suddenly grinned. [2] “What’s that for?” asked Grigory, looking at him threateningly from under his spectacles. “Oh, nothing. God created light on the first day, and the sun, moon, and stars on the fourth day. Where did the light come from on the first day?” Grigory was thunderstruck. The boy looked sarcastically at his teacher. There was something positively condescending in his expression. Grigory could not restrain himself. “I’ll show you where!” he cried, and gave the boy a violent slap on the cheek. The boy took the slap without a word, but withdrew into his corner again for some days. A week later he had his first attack of the disease to which he was subject all the rest of his life—epilepsy. When Fyodor Pavlovitch heard of it, his attitude to the boy seemed changed at once. Till then he had taken no notice of him, though he never scolded him, and always gave him a copeck when he met him. Sometimes, when he was in good humor, he would send the boy something sweet from his table. But as soon as he heard of his illness, he showed an active interest in him, sent for a doctor, and tried remedies, but the disease turned out to be incurable. The fits occurred, on an average, once a month, but at various intervals. The fits varied too, in violence: some were light and some were very severe. Fyodor Pavlovitch strictly forbade Grigory to use corporal punishment to the boy, and began allowing him to come upstairs to him. He forbade him to be taught anything whatever for a time, too. One day when the boy was about fifteen, Fyodor Pavlovitch noticed him lingering by the bookcase, and reading the titles through the glass. Fyodor Pavlovitch had a fair number of books—over a hundred—but no one ever saw him reading. He at once gave Smerdyakov the key of the bookcase. “Come, read. You shall be my librarian. You’ll be better sitting reading than hanging about the courtyard. Come, read this,” and Fyodor Pavlovitch gave him Evenings in a Cottage near Dikanka. He read a little but didn’t like it. He did not once smile, and ended by frowning. “Why? Isn’t it funny?” asked Fyodor Pavlovitch. Smerdyakov did not speak. “Answer, stupid!” “It’s all untrue,” mumbled the boy, with a grin. “Then go to the devil! You have the soul of a lackey. Stay, here’s Smaragdov’s Universal History. That’s all true. Read that.” But Smerdyakov did not get through ten pages of Smaragdov. He thought it dull. So the bookcase was closed again. Shortly afterwards Marfa and Grigory reported to Fyodor Pavlovitch that Smerdyakov was gradually beginning to show an extraordinary fastidiousness. He would sit before his soup, take up his spoon and look into the soup, bend over it, examine it, take a spoonful and hold it to the light. “What is it? A beetle?” Grigory would ask. “A fly, perhaps,” observed Marfa. The squeamish youth never answered, but he did the same with his bread, his meat, and everything he ate. He would hold a piece on his fork to the light, scrutinize it microscopically, and only after long deliberation decide to put it in his mouth. “Ach! What fine gentlemen’s airs!” Grigory muttered, looking at him. When Fyodor Pavlovitch heard of this development in Smerdyakov he determined to make him his cook, and sent him to Moscow to be trained. He spent some years there and came back remarkably changed in appearance. He looked extraordinarily old for his age. His face had grown wrinkled, yellow, and strangely emasculate. In character he seemed almost exactly the same as before he went away. He was just as unsociable, and showed not the slightest inclination for any companionship. In Moscow, too, as we heard afterwards, he had always been silent. Moscow itself had little interest for him; he saw very little there, and took scarcely any notice of anything. He went once to the theater, but returned silent and displeased with it. On the other hand, he came back to us from Moscow well dressed, in a clean coat and clean linen. He brushed his clothes most scrupulously twice a day invariably, and was very fond of cleaning his smart calf boots with a special English polish, so that they shone like mirrors. He turned out a first‐rate cook. Fyodor Pavlovitch paid him a salary, almost the whole of which Smerdyakov spent on clothes, pomade, perfumes, and such things. But he seemed to have as much contempt for the female sex as for men; he was discreet, almost unapproachable, with them. Fyodor Pavlovitch began to regard him rather differently. His fits were becoming more frequent, and on the days he was ill Marfa cooked, which did not suit Fyodor Pavlovitch at all. “Why are your fits getting worse?” asked Fyodor Pavlovitch, looking askance at his new cook. “Would you like to get married? Shall I find you a wife?” But Smerdyakov turned pale with anger, and made no reply. Fyodor Pavlovitch left him with an impatient gesture. The great thing was that he had absolute confidence in his honesty. It happened once, when Fyodor Pavlovitch was drunk, that he dropped in the muddy courtyard three hundred‐rouble notes which he had only just received. He only missed them next day, and was just hastening to search his pockets when he saw the notes lying on the table. Where had they come from? Smerdyakov had picked them up and brought them in the day before. “Well, my lad, I’ve never met any one like you,” Fyodor Pavlovitch said shortly, and gave him ten roubles. We may add that he not only believed in his honesty, but had, for some reason, a liking for him, although the young man looked as morosely at him as at every one and was always silent. He rarely spoke. If it had occurred to any one to wonder at the time what the young man was interested in, and what was in his mind, it would have been impossible to tell by looking at him. Yet he used sometimes to stop suddenly in the house, or even in the yard or street, and would stand still for ten minutes, lost in thought. A physiognomist studying his face would have said that there was no thought in it, no reflection, but only a sort of contemplation. There is a remarkable picture by the painter Kramskoy, called “Contemplation.” There is a forest in winter, and on a roadway through the forest, in absolute solitude, stands a peasant in a torn kaftan and bark shoes. He stands, as it were, lost in thought. Yet he is not thinking; he is “contemplating.” If any one touched him he would start and look at one as though awakening and bewildered. It’s true he would come to himself immediately; but if he were asked what he had been thinking about, he would remember nothing. Yet probably he has, hidden within himself, the impression which had dominated him during the period of contemplation. Those impressions are dear to him and no doubt he hoards them imperceptibly, and even unconsciously. How and why, of course, he does not know either. He may suddenly, after hoarding impressions for many years, abandon everything and go off to Jerusalem on a pilgrimage for his soul’s salvation, or perhaps he will suddenly set fire to his native village, and perhaps do both. There are a good many “contemplatives” among the peasantry. Well, Smerdyakov was probably one of them, and he probably was greedily hoarding up his impressions, hardly knowing why. 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. Fyodor Dostoyevsky (2009). The Brothers Karamazov. Urbana, Illinois: Project Gutenberg. Retrieved October https://www.gutenberg.org/cache/epub/28054/pg28054-images.html This eBook is for the use of anyone anywhere at no cost and with almost no restrictions whatsoever. You may copy it, give it away or re-use it under the terms of the Project Gutenberg License included with this eBook or online at www.gutenberg.org , located at https://www.gutenberg.org/policy/license.html .

My Smerdyakov’s an artist

In 2021, hosted the first-ever awards!! HackerNoon Startup of the Year The goal of this campaign was to give recognition to the hardworking startups who pulled through the pandemic -- growing and thriving (despite all of the challenges). More than 44,000 startups were nominated all across the globe! They were selected from over 4,553 cities, with a total number of * cough * “4 billion” votes. Insane, right? To see who is a winner in each respective city, visit and pick a city. The name at the top of the list is the winner. (Compromised awards are under review. Cities with tie votes will be updated to have two winners!) startups.hackernoon.com The will announce the winners per region in their respective cities. Startups Winners Announcement series Here are the winners in the : Asia region Top Startup Cities in East Asia Seoul The Startup of the Year in is with 377 votes! Seoul dable Central is the Startup of the Year! It came out ahead with 121 votes. UpNow Central Sheung Wan came out as the Startup of the Year in with 154 votes. Votee Sheung Wan Tokyo The Startup of the Year in is with 41 votes! Tokyo ACES Wan Chai The Startup of the Year in is with 54 votes! Wan Chai Zetl Osaka, Japan In , is the winner! Osaka Nanophoton Taiwan came out as the Startup of the Year in ! Tico Taiwan Dongcheng, China In , is declared the Startup of the Year! Dongcheng Waiju Taipei came out as the Startup of the Year in ! Tico Taipei Akasaka In , is the winner! Akasaka WAKUWAKU Chuo In , is declared the Startup of the Year! Chuo GO-NET Ginza came out as the Startup of the Year in ! Netyear Ginza Kanazawa In , is the winner! Kanazawa Granzella Minami-aoyama In , is declared the Startup of the Year! Minami-aoyama Tokyo6 Entertainment Shibuya came out as the Startup of the Year in ! Hupro Japan Shibuya Toranomon In , is the winner! Toranomon Small Worlds Toshima In , is declared the Startup of the Year! 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Manila PawnHero Phnom Penh, Cambodia In , the Startup of the Year is Phnom Penh vKirirom Bali In , is the winner! Bali Radius App Penang When it comes to , is the Startup of the Year Penang EasyParcel Surabaya came out as the Startup of the Year in ! Super Surabaya Setiabudi, Indonesia The Startup of the Year in is Setiabudi Chilibeli Cebu City came out as the Startup of the Year in MiCab Cebu City! Jawa couldn’t be beaten for Startup in the Year in DAUR Jawa Ho Chi Minh City, Vietnam In , is the winner! Ho Chi Minh City Pharmacity Selangor, Malaysia is the Startup of the Year! BabyDash Selangor Jakarta Pusat, Indonesia In , is the winner! Jakarta Pusat StickEarn Semarang When it comes to , is the Startup of the Year! Semarang Food Bear Petaling Jaya, Malaysia The winner in is . Petaling Jaya Deemples Kebayoran Baru, Indonesia couldn’t be beaten for Startup in the Year in ! Yogrt Kebayoran Baru Kuningan The winner in is . Kuningan Prinzio Bukit Merah came out as the Startup of the Year in ! DC Alliance Bukit Merah Huai Khwang In , the Startup of the Year is . Huai Khwang AppMan Co.,Ltd Kuta In , is declared the Startup of the Year! Kuta Gringgo Mandaluyong came out ahead as the Startup of the Year! Work Inspire Mandaluyong Midview City In , is the winner! Midview City BioMers Phuket The Startup of the Year in is ! Phuket Sapahn Yangon, Myanmar The winner in is . Yangon Wave Money Denpasar When it comes to , is the Startup of the Year! Denpasar Revasi Nakhon Ratchasima, Thailand The winner in is . Nakhon Ratchasima Siam Lottery co., Ltd Taguig, Philippines In , is the winner! Taguig PayMongo Bogor came out as the Startup of the Year in ! TIRANYX Bogor Senai, Malaysia won as the Startup of the Year. Nutrition Technologies Senai Bekasi Kota couldn’t be beaten for Startup in the Year in ! 8villages Bekasi Kota Top Startup Cities in Middle East Tel Aviv, Israel The Startup of the Year in is with 1,065 votes! Tel Aviv hyro Beirut, Lebanon is the Startup of the Year! It came out ahead with 1,276 votes. Hovi Digital Lab Beirut Riyadh, Saudi Arabia With 350 votes, came out as the Startup of the Year in ! Flicron Riyadh Tel Aviv-yafo, Israel When it comes to , is the Startup of the Year with 167 votes! Tel Aviv-yafo Cymbio Haifa, Israel couldn’t be beaten for Startup in the Year in , it won with 183 votes! Pico Haifa Amman, Jordan In , with 141 votes, is the winner! Amman Edexy Dubai, United Arab Emirates In , the Startup of the Year is with 74 votes. Dubai Ziina Doha, Qatar In , is declared the Startup of the Year! Doha SkipCash Tehran, Iran The winner in is . BeyondBoard AR Tehran Jerusalem, Israel In , is the winner! Jerusalem SolidBlock Rishon Le Zion The Startup of the Year in is ! Rishon Le Zion Passparto Ness Ziona, Israel is the Startup of the Year! Soapy Ness Ziona Kuwait, Kuwait came out as the Startup of the Year in ! Brapper Shop Kuwait Manama, Bahrain couldn’t be beaten for Startup in the Year in ! Doctori Manama Lod, Israel is the winner in ! Phinergy Lod Agudat Israel, Israel When it comes to is the Startup of the Year with [vote ] votes! Agudat Israel, Aqaurius Engines Beersheba, Israel In , the Startup of the Year is . Beersheba Visual Estate Ltd Giv'atayim, Israel In , is declared the Startup of the Year! Giv'atayim WSC Sports Modi'in, Israel The winner in is . Modi'in Loginno Nazareth, Israel In , is the winner! Nazareth AR Guide In Medical Ramat Gan, Israel The Startup of the Year in is . Ramat Gan Datos Health Baghdad When it comes to , is the Startup of the Year! Baghdad Mishwar Khobar In , the Startup of the Year is . Khobar Garage Misgav couldn’t be beaten for Startup in the Year in ! Eden Shield Misgav Sharjah came out as the Startup of the Year in ! Bookends Sharjah Affula In , the Startup of the Year is . Affula eMotion Technologies Bene Beraq, Israel In , is the winner Bene Beraq Reflectiz Caesarea, Israel In , the Startup of the Year is . Caesarea CardiacSense Dubayy In , is declared the Startup of the Year! Dubayy North Ladder Muscat couldn’t be beaten for Startup in the Year in ! iLab Marine Muscat Tirat Carmel In , the Startup of the Year is . Tirat Carmel PassCall Advanced Technologies Hod Hasharon, Israel The winner in is . Hod Hasharon Idomoo Holon, Israel came out ahead as the Startup of the Year! OptimalPlus Holon Jeddah, Saudi Arabia When it comes to , is the Startup of the Year! Jeddah Arabian Combustion Technologies Ramat Hasharon, Israel is the Startup of the Year! Lumen Ramat Hasharon Al Khobar came out as the Startup of the Year in ! Swyft Al Khobar Bet Shemesh In , is the winner! Bet Shemesh Degania Medical Binyamina When it comes to , is the Startup of the Year! Binyamina OncoHost Makkah In , the Startup of the Year is Makkah Scimagine Or Akiva Last but not least, couldn’t be beaten for Startup in the Year in the city of ! WMT Or Akiva More Information: If you’re a winner, please answer ! If you’re a reader, participant, just a curious individual, consider answering one of our interviews . this interview here Read the FAQs about or . Startups here here To see all of the winners you may visit , pick any city and view the name that appears at the top. this site If you’re interested in partnering with us, we invite you to look through some of these or reach out to our business department. unique sponsorship options Perhaps creating your very own tickles your fancy or maybe you have a whole new idea for how we can partner! writing contest Thank you to all of our sponsors! We truly appreciate your contribution to the tech world and thus the future! The Startups Awards are sponsored by: , , , , , , , , , , Blockster Brex FreeCodeCamp LaunchDarkly Coil Udacity Udemy Vercel Asia Pacific Transgender Network Bybit Everscale Network The post was written by Kushal Tamrakar and Valerie Ejegi, edited by Hang Ngo. If you have any questions or concerns or would like to report a discrepancy please email ellen@hackernoon.com.

Meet the Winners of Startups in Asia

“The currently unrivaled programming language for smart contracts is Solidity for the Ethereum blockchain.” - Alfred Shaffir iOlite.io Solidity is still a new, fast-changing language which has some serious pitfalls. As with all new things, bugs and logical faults are inevitable. Luckily, the ICO craze has triggered a new movement of research into the security aspects of blockchain and high-level programming languages as certain smart contracts hold millions of dollars, making it a perfect target for hackers. This article is based on the following research paper : “Ethereum Smart Contracts: Security Vulnerabilities and Security Tools” written by Ardit Dika at the Norwegian University of Science and Technology. ERC20 critical bug ERC20 assumes two ways of performing a token transaction. The function lets you send tokens to someone’s address. transfer For depositing tokens to a smart contract, you have to use the combination ‘ ’. approve & transferFrom But, when you send tokens to a smart contract using the transfer function, the smart contract won’t detect your deposit request. This results in permanently losing your funds. A lot of novel developers have experienced this critical bug firsthand. Standards like ERC223 and ERC777 try to mitigate this bug. More specifically, ERC223 allows developers to send funds to an address or contract using the same function: . ERC223 allows developers to handle incoming token transactions, and reject non-supported tokens. At last, the ERC223 token standard is more efficient as it’s a one-step process, rather than the two-step process. In short, ERC777 focuses on adoption by offering a wide range of transaction handling mechanisms. — Source: . transfer approve & transferFrom Cointelligence Unchecked send function Kalra S., Goel S., et al have recently published a on analyzing the safety of smart contracts. They describe the problem of the function: “Since Solidity allows only 2300 gas upon a send call, a computation-heavy fallback function at the receiving contract will cause the invoking send to fail. Contracts that do not correctly handle such failed invocations and allow modifications to the global state following the failed send call, may incorrectly lead to loss of Ether.” research paper unchecked send Publicly visible data All data on the Ethereum blockchain is publicly visible to anyone. Regardless of the positive impact Ethereum has, the benefits are limited by this property. Companies cannot use Ethereum to pay the wages of their employees as that would expose their monthly income. As you can see, there is a serious need for blockchains that introduce more security properties like encryption, data obfuscation, and secure transaction handling mechanisms. The Enigma project is doing a great thing with its so-called ‘ ’. secret smart contracts Unchangeable smart contracts Once a smart contract is deployed, it’s not possible to modify or alter it. It’s advantageous as it represents a trustworthy platform. On the other side, due to this property, a significant number of smart contracts contain vulnerabilities that can be exploited gaining illegal profit. In addition, a common safety guide for ICOs is to use an arbitrary name when deploying their ICO smart contract on the testnet. It has happened before an ICO smart contract got hacked because attackers were able to locate the testnet smart contract weeks before the actual deployment on the main net, giving them enough time to find critical bugs. In 2016, a symbolic execution analysis tool ( ) was developed by Luu et al. This tool revealed 45% of the 19,366 smart contracts back then were vulnerable for at least one known security issue. Oyente Exceeding the maximum gas amount Let’s end with a slightly funny problem. The GovernMental website was doing a jackpot. A large amount of 1100 ETH could be claimed by the lucky winner. However, because of a bug in the code, a gas amount of 5057945 was needed to claim the Ether. However, back then, the maximum gas amount for a transaction is only 4712388. The problem was caused by an array that looped all creditors and deleted them one by one, returning the winner. Even Vitalik Buterin jumped in and gave this piece of advice: “In general, it seems like we need one new piece of standard advice for contract developers: make sure that if you have arrays that users can extend then either find some way for each user to separately handle the array elements associated with themselves or split up the work among several transactions.” Luckily, this problem has been solved now. Such hard-to-find problems will keep popping up in the future. Golden Tip: Use Experts and Audit Audit Audit! I don’t want to be rude, but don’t trust novel Solidity developers to code your ICO smart contract. Proper developers know the problems that have occurred in the past and know how to respond to it. Most Solidity contracts being developed will be used for ICOs, so we are speaking about millions of dollars. Have your smart contract audited by several independent organizations, it’s for your own safety. Another possibility is to use platforms that assist you in the creation of a smart contract. The iOlite product is a community-driven tool that accepts natural language or any other programming language as input for the creation of smart contracts. Solidity experts are able to define structures linked to their smart contract code. iOlite uses a modified version of the . The whole idea is based on machine training, driven by a community of Solidity experts. The benefit of iOlite is that it allows you to craft smart contracts easily and for free. Stanford Natural Language Processing engine Find out more about or join their channel. iOlite.io Telegram

Solidity Pitfalls and How to Avoid Them

The world is a much more connected place than it was 20 years ago. With the change came new expectations. Downtimes can cripple businesses. There is an expectation of privacy and security. If we post on Facebook and the post gets lost due to any kind of issue, people will not tolerate it. Data integrity and durability are more critical than ever. As an organization, we must consider multiple technological tenets—security, availability, latency, durability, integrity, reliability, and privacy. We don’t get to choose from this list; we just sort it by importance based on business needs. Business continuity Disaster can come in many flavors, ranging from natural disasters to hardware failures, misconfiguration, and cyberattacks. The problem with cyberattacks is that we risk having our data stolen and wonder if the rest of the data is trustworthy. Maybe the attack was a sleight of hand, and the primary objective was to introduce malicious data that would haunt the organization forever. After the security incident, we need to recover the data and restore the service to the level our customers expect. One of the issues we face is reconstructing the original picture and configuration. We need to understand how all the pieces work together and their interdependency. Data restoration is more than just restoring a highly scalable web service snapshot. Objective At this point, our primary objective should be to ensure that our services are available and reliable by whatever means necessary. In the context of dependency management, we need to have a comprehensive data and dependency catalog, including the relationships between the entities. This provides us with the necessary insight into what exists. We also need regular operations testing and robust disaster recovery through automation and manual runbooks. We need to cultivate a culture of disaster recovery fire drills and identify fallbacks. Maturity Model Like an individual who enters college goes through freshman, sophomore, junior, and senior years before graduating, businesses need to follow a slow, progressive approach. It’s impractical for a company or organization to go from no disaster recovery to a fully-fledged, perfectly fleshed-out plan. The road to success is incremental, and each step builds on the previous step. Maturity models examine an organization’s current state from any specific perspective, such as disaster recovery, and help the organization reach the desired state or capability. As the organization progresses toward the end goal, it goes through various checkpoints, usually called Levels. Most Maturity Models have four levels, starting from Level 1 up to Level 4. Why are we talking about maturity models? One step in such a disaster recovery maturity model requires that the organization understand dependencies among various entities and data objects. Recovery usually requires restoring the data and bringing back critical systems in a specific incremental manner, Implementation Based on the business needs, understanding dependency and its relationship can be somewhere in the middle levels of the maturity models. In a 4-level system, it is most likely in Level 2 or Level 3. The other approach is to split the Dependency Management into the same number of milestones as the number of levels. Suppose we are using a 4-level maturity model. In that case, we can split up dependency management to span all 4 Levels, starting with the most straightforward steps and going all the way to the most complicated, expensive, and involved steps. Going forward, we will only discuss the dependency management aspect of this maturity model. Level 1 - Discovering and Understanding Dependencies For many organizations, this can be the most daunting task. If dependency management was never considered necessary, there might be no institutional knowledge. We will start by discussing technical dependencies and then discuss process dependencies. Regarding technical dependencies, we need to identify the direct dependencies first. If the code is written in JavaScript and runs on a cloud service, then the direct dependencies might be JavaScript, NodeJS, AWS Lambda, etc. If we use npm for package management, the packages listed in the dependency file are direct dependencies. Next, we have transitive dependencies, which are pulled automatically from direct dependencies. In practice, this forms a directed acyclic graph. Another aspect of technical dependencies is the cloud architecture. Let’s take an example of AWS—we might use EC2, S3, API Gateway, CloudFront, ELB, etc. If the cloud resources were deployed using tools like CloudFormation or CDK, then it would be trivial to determine the dependencies. If the resources were created manually, the dependency diagram must be made manually by reading the source code and drawing conclusions. Process dependency falls under the non-technical category and defines how different stakeholders work together to move forward. It can also outline the release process or runbooks to invoke during an incident. Level 2 - Setup Continuous Dependencies Validation Dependencies can change since there are too many cooks in the kitchen. The organization's continuous integration tool should enumerate and track the direct code and library dependencies. Every time the code is pushed, a snapshot of current pinned dependencies should be saved to recreate the specific circumstances or restore to a known good state. Like the Continuous integration concept, the non-technical aspects should have some automation. Alerts should be created on metric, and using tools like PagerDuty, appropriate people should be notified of issues, and proper escalation channels should be created. It should be tested occasionally to avoid the known problem called Dogs Not Barking - a situation when you wonder if you are not getting any alerts because nothing is wrong or the alerting logic is broken. Some dependencies can be validated by using the Scream test. Under proper notification and preparation, turn off specific dependencies and see what fails. You may find a Load Bearing Mac Mini. Collect all of this configuration and manage it through some kind of provisioning tool like Chef and Terraform Level 3 - Fixing dependencies by adding, updating, or removing Once we have all the dependencies figured out, take some time and pat yourself on the back. Now, back on the issue, the work isn’t done. During the quest, you realize that some dependencies are causing more problems than they solve. One approach would be to update the dependencies. For example, if we are using old versions of OpenSSL, updating them to newer versions can solve many of the warnings raised by the build system and even help us pass any security compliance. Sometimes, the specific versions of two separate packages are incompatible. The solution is to find versions that can work together. This involves changing the version and might involve downgrading one or more packages. Sometimes, just removing a package or dependency solves the problem. It could have been a dead dependency with no further need but causing warnings and spamming the application logs. Under some circumstances, we might have to add a dependency to bring harmony. Suppose a transitive dependency is causing the problem, but we cannot update the direct dependency. In that case, explicitly adding the fixed version of the transitive dependency as a direct dependency can be an acceptable solution. Level 4 - Manual and Automated Disaster Recovery Drills This is the highest form of maturity in disaster recovery. We need runbooks that outline the roles and responsibilities of individuals involved. The runbook should also define the interdependencies and criticality of each step. The disaster recovery process should be practiced regularly by simulating a disaster and identifying possible sources of friction that get introduced when the plan is set in motion. The gold standard should be automated disaster recovery under human supervision. Automating the disaster recovery workflow ensures that humans do not make mistakes that go unnoticed. Automating has an additional advantage in that we can now run stress tests and simulation disasters by turning off certain network parts in a controlled manner to validate whether our automated disaster recovery is performing as expected. Conclusion Dependency management plays a critical role in disaster recovery, which is essential after a security incident. By following a structured and methodological approach, organizations can build their understanding progressively and control the dependencies. Each maturity model level builds on the previous one and strengthens the organization's security posture. While it can be easy to write off dependency management as a superfluous technical exercise, it is primarily about creating a comprehensive framework that brings together computer systems, people, and processes to work harmoniously. The systematic approach ensures that when disaster strikes, even in the form of a security breach, we can safely recover the data and be confident in the integrity of the said data. The world is a much more connected place than it was 20 years ago. With the change came new expectations. Downtimes can cripple businesses. There is an expectation of privacy and security. If we post on Facebook and the post gets lost due to any kind of issue, people will not tolerate it. Data integrity and durability are more critical than ever. As an organization, we must consider multiple technological tenets—security, availability, latency, durability, integrity, reliability, and privacy. We don’t get to choose from this list; we just sort it by importance based on business needs. Business continuity Disaster can come in many flavors, ranging from natural disasters to hardware failures, misconfiguration, and cyberattacks. The problem with cyberattacks is that we risk having our data stolen and wonder if the rest of the data is trustworthy. Maybe the attack was a sleight of hand, and the primary objective was to introduce malicious data that would haunt the organization forever. After the security incident, we need to recover the data and restore the service to the level our customers expect. One of the issues we face is reconstructing the original picture and configuration. We need to understand how all the pieces work together and their interdependency. Data restoration is more than just restoring a highly scalable web service snapshot. Objective At this point, our primary objective should be to ensure that our services are available and reliable by whatever means necessary. In the context of dependency management, we need to have a comprehensive data and dependency catalog, including the relationships between the entities. This provides us with the necessary insight into what exists. We also need regular operations testing and robust disaster recovery through automation and manual runbooks. We need to cultivate a culture of disaster recovery fire drills and identify fallbacks. Maturity Model Like an individual who enters college goes through freshman, sophomore, junior, and senior years before graduating, businesses need to follow a slow, progressive approach. It’s impractical for a company or organization to go from no disaster recovery to a fully-fledged, perfectly fleshed-out plan. The road to success is incremental, and each step builds on the previous step. Maturity models examine an organization’s current state from any specific perspective, such as disaster recovery, and help the organization reach the desired state or capability. As the organization progresses toward the end goal, it goes through various checkpoints, usually called Levels. Most Maturity Models have four levels, starting from Level 1 up to Level 4. Why are we talking about maturity models? One step in such a disaster recovery maturity model requires that the organization understand dependencies among various entities and data objects. Recovery usually requires restoring the data and bringing back critical systems in a specific incremental manner, Implementation Based on the business needs, understanding dependency and its relationship can be somewhere in the middle levels of the maturity models. In a 4-level system, it is most likely in Level 2 or Level 3. The other approach is to split the Dependency Management into the same number of milestones as the number of levels. Suppose we are using a 4-level maturity model. In that case, we can split up dependency management to span all 4 Levels, starting with the most straightforward steps and going all the way to the most complicated, expensive, and involved steps. Going forward, we will only discuss the dependency management aspect of this maturity model. Level 1 - Discovering and Understanding Dependencies For many organizations, this can be the most daunting task. If dependency management was never considered necessary, there might be no institutional knowledge. We will start by discussing technical dependencies and then discuss process dependencies. Regarding technical dependencies, we need to identify the direct dependencies first. If the code is written in JavaScript and runs on a cloud service, then the direct dependencies might be JavaScript, NodeJS, AWS Lambda, etc. If we use npm for package management, the packages listed in the dependency file are direct dependencies. Next, we have transitive dependencies, which are pulled automatically from direct dependencies. In practice, this forms a directed acyclic graph. Another aspect of technical dependencies is the cloud architecture. Let’s take an example of AWS—we might use EC2, S3, API Gateway, CloudFront, ELB, etc. If the cloud resources were deployed using tools like CloudFormation or CDK, then it would be trivial to determine the dependencies. If the resources were created manually, the dependency diagram must be made manually by reading the source code and drawing conclusions. Process dependency falls under the non-technical category and defines how different stakeholders work together to move forward. It can also outline the release process or runbooks to invoke during an incident. Level 2 - Setup Continuous Dependencies Validation Dependencies can change since there are too many cooks in the kitchen. The organization's continuous integration tool should enumerate and track the direct code and library dependencies. Every time the code is pushed, a snapshot of current pinned dependencies should be saved to recreate the specific circumstances or restore to a known good state. Like the Continuous integration concept, the non-technical aspects should have some automation. Alerts should be created on metric, and using tools like PagerDuty, appropriate people should be notified of issues, and proper escalation channels should be created. It should be tested occasionally to avoid the known problem called Dogs Not Barking - a situation when you wonder if you are not getting any alerts because nothing is wrong or the alerting logic is broken. Some dependencies can be validated by using the Scream test . Under proper notification and preparation, turn off specific dependencies and see what fails. You may find a Load Bearing Mac Mini . Collect all of this configuration and manage it through some kind of provisioning tool like Chef and Terraform Scream test Scream test Load Bearing Mac Mini Load Bearing Mac Mini Level 3 - Fixing dependencies by adding, updating, or removing Once we have all the dependencies figured out, take some time and pat yourself on the back. Now, back on the issue, the work isn’t done. During the quest, you realize that some dependencies are causing more problems than they solve. One approach would be to update the dependencies. For example, if we are using old versions of OpenSSL, updating them to newer versions can solve many of the warnings raised by the build system and even help us pass any security compliance. Sometimes, the specific versions of two separate packages are incompatible. The solution is to find versions that can work together. This involves changing the version and might involve downgrading one or more packages. Sometimes, just removing a package or dependency solves the problem. It could have been a dead dependency with no further need but causing warnings and spamming the application logs. Under some circumstances, we might have to add a dependency to bring harmony. Suppose a transitive dependency is causing the problem, but we cannot update the direct dependency. In that case, explicitly adding the fixed version of the transitive dependency as a direct dependency can be an acceptable solution. Level 4 - Manual and Automated Disaster Recovery Drills This is the highest form of maturity in disaster recovery. We need runbooks that outline the roles and responsibilities of individuals involved. The runbook should also define the interdependencies and criticality of each step. The disaster recovery process should be practiced regularly by simulating a disaster and identifying possible sources of friction that get introduced when the plan is set in motion. The gold standard should be automated disaster recovery under human supervision. Automating the disaster recovery workflow ensures that humans do not make mistakes that go unnoticed. Automating has an additional advantage in that we can now run stress tests and simulation disasters by turning off certain network parts in a controlled manner to validate whether our automated disaster recovery is performing as expected. Conclusion Dependency management plays a critical role in disaster recovery, which is essential after a security incident. By following a structured and methodological approach, organizations can build their understanding progressively and control the dependencies. Each maturity model level builds on the previous one and strengthens the organization's security posture. While it can be easy to write off dependency management as a superfluous technical exercise, it is primarily about creating a comprehensive framework that brings together computer systems, people, and processes to work harmoniously. The systematic approach ensures that when disaster strikes, even in the form of a security breach, we can safely recover the data and be confident in the integrity of the said data.

Dependency Management is Critical for Disaster Recovery After a Security Incident

The massive, catastrophic flash flood that occurred in Spain on October 29 was so horrific and devastating that it left the world aghast at its destructive nature. Over 200 people died, dozens went missing, and whole towns were covered in mud and debris, especially in the eastern Valencia region. In the United States alone, in 2023, 25 weather or climate disasters [caused at least $1 billion in losses and the [deaths](https://www.ncei.noaa.gov/access/billions/#:~:text=In%202023%20(as%20of%20December,and%201%20winter%20storm%20event) of 482 people, according to the National Centers for Environmental Information (NCEI). According to the US Government Accountability Office (GAO), “Severe storms, hurricanes, floods, and wildfires kill hundreds of people and cause billions of dollars in damage in the U.S. each year.” In fact, [according](https://www.ncei.noaa.gov/access/billions/#:~:text=In%202023%20(as%20of%20December,and%201%20winter%20storm%20event) to the National Oceanic and Atmospheric Administration (NOAA), “the U.S. has sustained 400 weather and climate disasters since 1980 where overall damages/costs reached or exceeded $1 billion (including CPI adjustment to 2024). The total cost of these 400 events exceeds $2.785 trillion.” India suffers from a similar problem. The frequency of flooding is getting worse due to the rapid growth of cities and climate change. Numerous other countries, especially in Africa, have seen similar catastrophes caused by unprecedented flooding. Widespread flooding in West and Central Africa, according to the World Health Organization, “has affected more than 4 million people, caused extensive damage to homes, schools, health facilities and swaths of farmland, heightening the risk of water-borne diseases, food insecurity and malnutrition.” Human-induced climate change “is likely to have significantly worsened the situation”, according to scientists at the Red Cross Climate Centre. Many experts now agree that with the advancement of artificial intelligence, this technology can and should play a bigger role in predicting those events and saving lives. And Northeastern University faculty experts predict it will. In an interview last year, Auroop R. Ganguly, professor of civil and environmental engineering and director of AI4CaS (AI for Climate and Sustainability) said, “In the next 12 months, we are going to see more and more efforts where data-driven systems and artificial intelligence come together.” One example is the Earth-2 platform, which is able to simulate global weather patterns in great detail. Taiwan is already using a similar tool to precisely predict the path and impact of typhoons. AI4CaS serves as a focus area within Northeastern University’s Institute for Experiential AI. The GAO found that AI weather forecasting models can help predict these catastrophic weather events. Machine learning specifically “can help improve model accuracy, increase warning times, and reduce uncertainty” and while some of these types of models are currently in use, “there are challenges to developing and adopting them more widely.” Google recently released an AI model that it claims can generate accurate weather forecasts at scale — while being cheaper than conventional physics-based forecasting. The "Scalable Ensemble Envelope Diffusion Sampler" (SEEDS) model is designed similarly to popular large language models (LLMs) like ChatGPT. SEEDS generates many ensembles — or multiple weather scenarios — much quicker and cheaper than traditional predicting models can. The team described its findings in a paper published March 29 in the journal Science Advances. In yet another effort to get ahead of life-threatening weather patterns, the University of Illinois and IBM have joined forces to work together on an AI model capable of anticipating heavy rainfall and flash floods in the Appalachian Mountains. Such models can triple or quadruple the warning time currently available to meteorologists and scientists. In August, NVIDIA Research, announced that it has developed a new AI model for predicting extreme weather events, called StormCast, part of a larger effort by NVIDIA to improve climate research through AI. These new AI models are financially beneficial since they can process information far quicker than older models. As extreme weather events, like flooding, become more frequent and intense, it is becoming increasingly important to be able to predict, understand, and prepare for these events. AI models will assist in predicting weather catastrophes and will help reduce not just major damage, but will also save thousands of lives. Such AI technologies will help scientists make faster and more accurate predictions, which can ultimately save lives and reduce the cost of disasters. In September, Smithsonian Magazine interviewed computer scientist and meteorologist Amy McGovern. She said that while AI will not replace human meteorologists in the near future, “We’re developing things to give them more options so that they can focus on turning the forecasts into actionable information for people to make better decisions.” “Humans are a lot better at that than AI is right now,” she said. The massive, catastrophic flash flood that occurred in Spain on October 29 was so horrific and devastating that it left the world aghast at its destructive nature. Over 200 people died, dozens went missing, and whole towns were covered in mud and debris, especially in the eastern Valencia region. In the United States alone, in 2023, 25 weather or climate disasters [caused at least $1 billion in losses and the [deaths](https://www.ncei.noaa.gov/access/billions/#:~:text=In%202023%20(as%20of%20December,and%201%20winter%20storm%20event) of 482 people, according to the National Centers for Environmental Information (NCEI). According to the US Government Accountability Office (GAO), “Severe storms, hurricanes, floods, and wildfires kill hundreds of people and cause billions of dollars in damage in the U.S. each year.” kill hundreds of people In fact, [according](https://www.ncei.noaa.gov/access/billions/#:~:text=In%202023%20(as%20of%20December,and%201%20winter%20storm%20event) to the National Oceanic and Atmospheric Administration (NOAA), “the U.S. has sustained 400 weather and climate disasters since 1980 where overall damages/costs reached or exceeded $1 billion (including CPI adjustment to 2024). The total cost of these 400 events exceeds $2.785 trillion .” The total cost of these 400 events exceeds $2.785 trillion India suffers from a similar problem. The frequency of flooding is getting worse due to the rapid growth of cities and climate change. The frequency of flooding is getting worse Numerous other countries, especially in Africa, have seen similar catastrophes caused by unprecedented flooding. Widespread flooding in West and Central Africa , according to the World Health Organization, “has affected more than 4 million people, caused extensive damage to homes, schools, health facilities and swaths of farmland, heightening the risk of water-borne diseases, food insecurity and malnutrition.” Widespread flooding in West and Central Africa Human-induced climate change “is likely to have significantly worsened the situation”, according to scientists at the Red Cross Climate Centre . Red Cross Climate Centre Many experts now agree that with the advancement of artificial intelligence, this technology can and should play a bigger role in predicting those events and saving lives. And Northeastern University faculty experts predict it will. In an interview last year, Auroop R. Ganguly, professor of civil and environmental engineering and director of AI4CaS (AI for Climate and Sustainability) said, “In the next 12 months, we are going to see more and more efforts where data-driven systems and artificial intelligence come together.” One example is the Earth-2 platform , which is able to simulate global weather patterns in great detail. Earth-2 platform Taiwan is already using a similar tool to precisely predict the path and impact of typhoons. AI4CaS serves as a focus area within Northeastern University’s Institute for Experiential AI. The GAO found that AI weather forecasting models can help predict these catastrophic weather events. Machine learning specifically “can help improve model accuracy, increase warning times, and reduce uncertainty” and while some of these types of models are currently in use, “there are challenges to developing and adopting them more widely.” Google recently released an AI model that it claims can generate accurate weather forecasts at scale — while being cheaper than conventional physics-based forecasting. The " Scalable Ensemble Envelope Diffusion Sampler " (SEEDS) model is designed similarly to popular large language models (LLMs) like ChatGPT. Scalable Ensemble Envelope Diffusion Sampler SEEDS generates many ensembles — or multiple weather scenarios — much quicker and cheaper than traditional predicting models can. The team described its findings in a paper published March 29 in the journal Science Advances . Science Advances In yet another effort to get ahead of life-threatening weather patterns, the University of Illinois and IBM have joined forces to work together on an AI model capable of anticipating heavy rainfall and flash floods in the Appalachian Mountains. joined forces Such models can triple or quadruple the warning time currently available to meteorologists and scientists. In August, NVIDIA Research, announced that it has developed a new AI model for predicting extreme weather events, called StormCast, part of a larger effort by NVIDIA to improve climate research through AI. announced These new AI models are financially beneficial since they can process information far quicker than older models. As extreme weather events, like flooding, become more frequent and intense, it is becoming increasingly important to be able to predict, understand, and prepare for these events. AI models will assist in predicting weather catastrophes and will help reduce not just major damage, but will also save thousands of lives. Such AI technologies will help scientists make faster and more accurate predictions, which can ultimately save lives and reduce the cost of disasters. In September, Smithsonian Magazine interviewed computer scientist and meteorologist Amy McGovern. She said that while AI will not replace human meteorologists in the near future, “We’re developing things to give them more options so that they can focus on turning the forecasts into actionable information for people to make better decisions.” Smithsonian Magazine interviewed “Humans are a lot better at that than AI is right now,” she said.

Will AI Ever Be Able to Predict Disasters and Save Lives?

Don’t Panic! How Clear Communication and Strategy Can Prevent Cybersecurity Disasters

Diagramming Tools: When Should You Use No Code, DSL, or Code?

Diagrams are fantastic tools for explaining complex systems. They act as a visual map, allowing viewers to grasp how various components interact and connect in a single glance. However, static diagrams sometimes lack the ability to convey intricate processes fully. Imagine presenting a complex system diagram – you can narrate, draw focus to specific elements, and guide your audience through the visual representation. However one of the primary goals of diagrams is to provide a clear explanation without needing a constant narration. Animating diagrams, particularly the flow between different components, bridges the gap between static images and detailed narration. Animations act as a visual guide, directing viewers’ attention and highlighting the order of operations within the system. Imagine seeing a building’s piping system statically on paper (which is still an amazing way to give information about, well the piping in a building). But imagine the drawing animates and shows the direction of the flow of water or sewer system. Increasing the information signal using animations, amplifies the impact of the system diagram. I recently ended up using Drawing.io that has easy way to do this. Most of the time the connections between components of the diagrams are the pieces we want to animate. I looked for this in a lot of other tools I used in the past. Both proper diagramming tools like lucid charts, mermaid, excalidraw, and miro. figma/figjam and general presentation apps like Keynote, and PowerPoint. None have easy animation options. The way I started doing easy animations, is to use my existing drawings, import them as static jpeg/png or SVG into draw.io then draw a connector line over my existing lines, give it a white color and make it thick, then check it’s “animation” option to animate white dashes lines that animate over my existing connector lines, making them animated. Beyond Animation, I like draw.io because it’s a fantastic drawing tool. It’s open-source and free to use and contribute to. Works cross-platform. It has a lot of drawing elements, and you can import your own png, svg icons to your library. You can install draw.io on your desktop (on Mac, using homebrew, as simple as brew install –cask drawio), use the online version for free, or signup for cloud plans to collaborate with your team. Diagrams are fantastic tools for explaining complex systems. They act as a visual map, allowing viewers to grasp how various components interact and connect in a single glance. However, static diagrams sometimes lack the ability to convey intricate processes fully. Imagine presenting a complex system diagram – you can narrate, draw focus to specific elements, and guide your audience through the visual representation. However one of the primary goals of diagrams is to provide a clear explanation without needing a constant narration. Animating diagrams, particularly the flow between different components, bridges the gap between static images and detailed narration. Animations act as a visual guide, directing viewers’ attention and highlighting the order of operations within the system. Imagine seeing a building’s piping system statically on paper (which is still an amazing way to give information about, well the piping in a building). But imagine the drawing animates and shows the direction of the flow of water or sewer system. Increasing the information signal using animations, amplifies the impact of the system diagram. I recently ended up using Drawing.io that has easy way to do this. Most of the time the connections between components of the diagrams are the pieces we want to animate. I looked for this in a lot of other tools I used in the past. Both proper diagramming tools like lucid charts, mermaid, excalidraw, and miro. figma/figjam and general presentation apps like Keynote, and PowerPoint. None have easy animation options. The way I started doing easy animations, is to use my existing drawings, import them as static jpeg/png or SVG into draw.io then draw a connector line over my existing lines, give it a white color and make it thick, then check it’s “animation” option to animate white dashes lines that animate over my existing connector lines, making them animated. Beyond Animation, I like draw.io because i t’s a fantastic drawing tool. It’s open-source and free to use and contribute to. Works cross-platform. It has a lot of drawing elements, and you can import your own png, svg icons to your library. i You can install draw.io on your desktop (on Mac, using homebrew, as simple as brew install –cask drawio), use the online version for free, or signup for cloud plans to collaborate with your team.

Using Draw.io to Animate Systems Diagrams

What started as a tool I developed to simplify visualizing complex database schemas quickly grew into a platform that aims to help developers and data analysts alike. The vision was simple: to create an intuitive, keyboard-friendly database design tool that anyone could use, without the friction of cumbersome clicks or endless UI navigation. The tool supports both open-source languages like ERDL and DBML, giving users the flexibility to choose their preferred modeling language for designing database diagrams. Whether you're a fan of DBML's expressive syntax or prefer the simplicity of ERDL, the tool seamlessly integrates both. How It Works: User-Friendly UI/UX: Just open DatabaseDiagram.com and start typing your schema—your diagram appears instantly, making design fast and easy.
The Toolbar: Manage diagrams, import/export schemas, and share your designs with a click.
Code Editor: Type out your database schema and see your diagram take shape in real-time. The syntax is straightforward—just type, and the diagram is generated.
Visual Diagram: Zoom, pan, and navigate through your diagram effortlessly. Creating Tables and Fields: You can define tables and fields directly in the code editor. Here's the syntax to help you get started: Creating Relationships in ERDL: < one-to-many. e.g: Customer.id < Order.Id
many-to-one. e.g: Order.Id > Customer.id
one-to-one. e.g: Customer.id - Address.id
<> many-to-many. e.g: authors.id <> books.id eg. CustomerID int FK > Customer.CustomerID Import and Export Options Whether you're working with PostgreSQL or MySQL, you can import your existing scripts to instantly visualize your database. Export your diagrams to PDF, PNG, or SQL scripts to share with your team or integrate into your workflow. I’m constantly working on improving DatabaseDiagram.com, and feedback from the community has been invaluable in shaping its evolution. If you love the tool, spread the word and share your feedback! What started as a tool I developed to simplify visualizing complex database schemas quickly grew into a platform that aims to help developers and data analysts alike. The vision was simple: to create an intuitive, keyboard-friendly database design tool that anyone could use, without the friction of cumbersome clicks or endless UI navigation. visualizing complex database schemas The tool supports both open-source languages like ERDL and DBML, giving users the flexibility to choose their preferred modeling language for designing database diagrams. Whether you're a fan of DBML's expressive syntax or prefer the simplicity of ERDL, the tool seamlessly integrates both. ERDL How It Works: How It Works: User-Friendly UI/UX: Just open DatabaseDiagram.com and start typing your schema—your diagram appears instantly, making design fast and easy. The Toolbar: Manage diagrams, import/export schemas, and share your designs with a click. Code Editor: Type out your database schema and see your diagram take shape in real-time. The syntax is straightforward—just type, and the diagram is generated. Visual Diagram: Zoom, pan, and navigate through your diagram effortlessly. User-Friendly UI/UX: Just open DatabaseDiagram.com and start typing your schema—your diagram appears instantly, making design fast and easy. DatabaseDiagram.com The Toolbar: Manage diagrams, import/export schemas, and share your designs with a click. Code Editor: Type out your database schema and see your diagram take shape in real-time. The syntax is straightforward—just type, and the diagram is generated. Visual Diagram: Zoom, pan, and navigate through your diagram effortlessly. Creating Tables and Fields: Creating Tables and Fields: You can define tables and fields directly in the code editor. Here's the syntax to help you get started: Creating Relationships in ERDL: Creating Relationships in ERDL: < one-to-many. e.g: Customer.id < Order.Id many-to-one. e.g: Order.Id > Customer.id one-to-one. e.g: Customer.id - Address.id <> many-to-many. e.g: authors.id <> books.id < one-to-many. e.g: Customer.id < Order.Id many-to-one. e.g: Order.Id > Customer.id one-to-one. e.g: Customer.id - Address.id <> many-to-many. e.g: authors.id <> books.id eg. CustomerID int FK > Customer.CustomerID Import and Export Options Whether you're working with PostgreSQL or MySQL, you can import your existing scripts to instantly visualize your database. Export your diagrams to PDF, PNG, or SQL scripts to share with your team or integrate into your workflow. Import and Export Options I’m constantly working on improving DatabaseDiagram.com , and feedback from the community has been invaluable in shaping its evolution. If you love the tool, spread the word and share your feedback! DatabaseDiagram.com

Designing Database Diagrams Effortlessly with Open-Source ERDL Language

The leading open oracle network, DIA, is taking a bold step forward in the decentralized data market with the launch of 'Lumina.' Lumina addresses the ongoing demand for fully decentralized, trustless data infrastructure in web3. This innovative design also enhances the utility of DIA’s native token, $DIA, by integrating it into a new system that rethinks the way oracles operate. DIA is a decentralized oracle network that provides data feeds for tokens, liquid staking tokens (LST), real-world assets (RWA), random numbers, and other information to all blockchains. The architecture of DIA  is permissionless, enabling nodes and stakers to engage in decentralized data sourcing that is safeguarded by cryptographic and crypto-economic methods. All essential oracle tasks are conducted on DIA's Ethereum layer 2 rollup, named 'Lasernet,' guaranteeing reliable calculations and complete auditability. Powered by Lasernet: A Rollup-based, Modular Oracle Architecture At the heart of Lumina’s advanced design is its modular architecture, which revolutionizes oracle operations from data sourcing to verification, storage, processing, and delivery. All key functions take place on DIA’s native L2 rollup, ‘Lasernet,’ leveraging Ethereum’s latest scaling technologies for optimal performance. This approach provides flexibility and scalability, ensuring oracles remain transparent, secure, and decentralized. Lumina’s architecture uses staking incentives to motivate a distributed network of nodes to collect trade data from over 100 centralized and decentralized exchanges. The native $DIA token secures these contributions, while Zero Knowledge components verify the raw data and computations. With Lasernet managing both data storage and processing, the system ensures on-chain integrity and immutability, sending data where it’s needed via a decentralized messaging protocol. DIA is known as a flexible and transparent cross-chain oracle in the industry. By integrating with over 50 L1 and L2 chains, DIA provides data support for over 200 decentralized applications (dApps) with customizable data feeds for on-chain and off-chain purposes. Zygis Marazas, DIA’s Head of Product, emphasized: "Lumina represents four years of web3 innovation, consolidating it all into one product. It is the cornerstone of our vision for a decentralized, trustless oracle network that guarantees security and flexibility for every use case." A Phased Rollout for Full Decentralization DIA’s Lumina will follow a phased rollout, beginning with the launch of the Lasernet testnet and an early bird staking campaign. Next, the Lasernet mainnet will go live in a closed environment, introducing live staking, followed by an open mainnet that will allow broad participation from the web3 community. “This redesign is a paradigm shift for the oracle space,” said DIA co-founder Paul Claudius. “With Lumina, we are realizing our founding vision of a fully decentralized, transparent oracle network that can be seamlessly integrated by our partners.” Don’t forget to like and share the story. Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program. HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR The leading open oracle network, DIA , is taking a bold step forward in the decentralized data market with the launch of 'Lumina.' Lumina addresses the ongoing demand for fully decentralized, trustless data infrastructure in web3. This innovative design also enhances the utility of DIA’s native token, $DIA, by integrating it into a new system that rethinks the way oracles operate. DIA DIA is a decentralized oracle network that provides data feeds for tokens, liquid staking tokens (LST), real-world assets (RWA), random numbers, and other information to all blockchains. The architecture of DIA is permissionless, enabling nodes and stakers to engage in decentralized data sourcing that is safeguarded by cryptographic and crypto-economic methods. All essential oracle tasks are conducted on DIA's Ethereum layer 2 rollup, named 'Lasernet,' guaranteeing reliable calculations and complete auditability. DIA Powered by Lasernet: A Rollup-based, Modular Oracle Architecture At the heart of Lumina’s advanced design is its modular architecture, which revolutionizes oracle operations from data sourcing to verification, storage, processing, and delivery. All key functions take place on DIA’s native L2 rollup, ‘Lasernet,’ leveraging Ethereum’s latest scaling technologies for optimal performance. This approach provides flexibility and scalability, ensuring oracles remain transparent, secure, and decentralized. Lumina’s architecture uses staking incentives to motivate a distributed network of nodes to collect trade data from over 100 centralized and decentralized exchanges. The native $DIA token secures these contributions, while Zero Knowledge components verify the raw data and computations. With Lasernet managing both data storage and processing, the system ensures on-chain integrity and immutability, sending data where it’s needed via a decentralized messaging protocol. DIA is known as a flexible and transparent cross-chain oracle in the industry. By integrating with over 50 L1 and L2 chains, DIA provides data support for over 200 decentralized applications (dApps) with customizable data feeds for on-chain and off-chain purposes. Zygis Marazas, DIA’s Head of Product, emphasized: "Lumina represents four years of web3 innovation, consolidating it all into one product. It is the cornerstone of our vision for a decentralized, trustless oracle network that guarantees security and flexibility for every use case." A Phased Rollout for Full Decentralization DIA’s Lumina will follow a phased rollout, beginning with the launch of the Lasernet testnet and an early bird staking campaign. Next, the Lasernet mainnet will go live in a closed environment, introducing live staking, followed by an open mainnet that will allow broad participation from the web3 community. “This redesign is a paradigm shift for the oracle space,” said DIA co-founder Paul Claudius. “With Lumina, we are realizing our founding vision of a fully decentralized, transparent oracle network that can be seamlessly integrated by our partners.” Don’t forget to like and share the story. Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program. HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program . HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR Vested Interest Disclosure: business blogging program business blogging program

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