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The Noonification: These 13 Hidden Open-Source Libraries Will Help You Become an AI Wizard 🧙‍♂️🪄  (7/27/2024)

These 13 Hidden Open-Source Libraries Will Help You Become an AI Wizard 🧙‍♂️🪄 

এই 13টি লুকানো ওপেন-সোর্স লাইব্রেরি আপনাকে এআই উইজার্ড হতে সাহায্য করবে 🧙‍♂️🪄 

Diese 13 versteckten Open-Source-Bibliotheken helfen Ihnen, ein KI-Zauberer zu werden 🧙‍♂️🪄

Estas 13 bibliotecas ocultas de código abierto te ayudarán a convertirte en un mago de la IA 🧙‍♂️🪄 

Ces 13 bibliothèques open source cachées vous aideront à devenir un assistant d'IA 🧙‍♂️🪄 

ये 13 छुपी हुई ओपन-सोर्स लाइब्रेरीज़ आपको AI जादूगर बनने में मदद करेंगी 🧙‍♂️🪄

これら 13 個の隠れたオープンソース ライブラリは、AI ウィザードになるために役立ちます 🧙‍♂️🪄

이 13개의 숨겨진 오픈 소스 라이브러리는 당신이 AI 마법사가 되는 데 도움이 될 것입니다 🧙‍♂️🪄 

Estas 13 bibliotecas ocultas de código aberto ajudarão você a se tornar um assistente de IA 🧙‍♂️🪄 

Эти 13 скрытых библиотек с открытым исходным кодом помогут вам стать волшебником искусственного интеллекта 🧙‍♂️🪄 

Bu 13 Gizli Açık Kaynak Kitaplığı Bir Yapay Zeka Sihirbazı Olmanıza Yardımcı Olacak 🧙‍♂️🪄 

13 thư viện mã nguồn mở ẩn này sẽ giúp bạn trở thành phù thủy AI 🧙‍♂️🪄 

这 13 个隐藏的开源库将帮助你成为 AI 奇才🧙‍♂️🪄

Hey everyone! and just like you, I’ve been fascinated with the recent progress of artificial intelligence. Realizing that I needed to stay abreast with all the developments happening, I decided to embark on a personal journey of learning, thus was born! With this series, I will be learning about LLMs and share ideas, experiments, opinions, trends & learnings through my blog posts. You can follow along the journey on HackerNoon or my personal website . In today’s article, we’ll be looking to build a Semantic Kernel with the help of GPT-4. , I’m Nataraj 100 days of AI here here If you have been following the generative AI or the LLM space you would have already heard about Finetuning. In this post lets try to understand what finetuning is and what is its role in developing gen AI applications. What is Finetuning? Finetuning is a process of modifying a general purpose base model to make it work for a specialized use case. For example take gpt-3 model by Open AI. Gpt-3 is a base model which was finetuned for the purpose of being a chat bot which resulted in what people now refer to as chat-gpt application. Another example would be modifying GPT-4 model to be a copilot for coders, which was done to create GitHub co-pilot. Why do we have to finetune the base models? Finetuning allows us to overcome the limitation of base models. Base models like Open AI’s gpt-3 or Meta’s Llama are usually trained on the entire internet’s data. But they do not have the context of the data that is internal to your organization. And giving all the data that is relevant to your organization or proprietary use case via prompt is not possible. Finetuning allows us to fit in a lot more data than prompt engineering allows us. Finetuning also allows the model to generate consistent outputs, reduce hallucinations & customize the model for a particular use case. How is finetuning different from prompt engineering? We have seen how powerful prompt engineering can be in the . So how is finetuning different? Finetuning is for enterprise applications use case while prompt engineering is for general use cases and doesn’t require data. It can be used with additional data with RAG as a technique but it cannot be used with large data that exists in enterprise use cases. Finetuning allows for unlimited data, makes the model learn new information, it can also be used along with a RAG. previous posts Comparing Finetuned vs Non-Finetuned response Lets take an example that might give you a better intuition of the difference between finetuned vs non-finetuned models. I am using ‘s libraries to call both finetuned and non-finetuned Llama models to show the difference. You will need the api key from Lamini for this purpose. Lamini provides a simple and easy way to interact with open source LLMs. Lamini Check it out here if you want to learn more about it. In this example I have asked both the models the same question “What do you think of death?” and here are the responses. Response from Non-Finetuned Llama Model: Response from Finetuned Llama Model: You will notice that the first response was just repetition of a single line, while second response much more coherent response. Before talking about what is happening here lets take another example where I ask the model “What is your first name?”. Here’s what I got. Response from Non-Finetuned Llama Model: Response from Finetuned Llama Model: In non-finetuned model responses, the responses are weird because the model is just doing one thing. It is trying to predict the next probable text based on your input text, it is not also realizing that you posed it a question. Based models which are trained on the internet’s data are text predicting machines and try to predict the next best text. With finetuning the model is trained to base its response by giving additional data and it learns new behavior that is to act as a chat bot meant to answer questions. Also note that most of the closed models like Open AI’s gpt-3 or gpt-4 we do not exactly know what data they are trained on. But there are some cool open data sets out there which can be used to train your models. More on that later. That’s it for Day 11 of 100 Days of AI. . I write a newsletter called Above Average where I talk about the second order insights behind everything that is happening in big tech. If you are in tech and don’t want to be average, subscribe to it Follow me on , or for latest updates on 100 days of AI. If you are in tech you might be interested in joining my community of tech professionals . Twitter LinkedIn HackerNoon here

100 Days of AI, Day 11: Becoming a Finetuning Wizard in Gen AI Applications

এআই-এর 100 দিন, 11 দিন: জেনারেল এআই অ্যাপ্লিকেশনে ফাইনটিউনিং উইজার্ড হওয়া

100 Tage KI, Tag 11: Werden Sie zum Feinabstimmungsassistenten für Gen-KI-Anwendungen

100 días de IA, día 11: convertirse en un asistente de ajuste en aplicaciones de IA de generación

100 jours d'IA, jour 11 : devenir un assistant de réglage fin dans les applications Gen AI

एआई के 100 दिन, दिन 11: जनरल एआई अनुप्रयोगों में फाइनट्यूनिंग विज़ार्ड बनना

100 日間の AI、11 日目: Gen AI アプリケーションの微調整ウィザードになる

AI 100일, 11일차: Gen AI 애플리케이션의 미세 조정 마법사 되기

100 dias de IA, dia 11: Tornando-se um assistente de ajuste fino em aplicativos Gen AI

100 дней искусственного интеллекта, день 11: Станьте мастером тонкой настройки приложений Gen AI

100 Gün Yapay Zeka, 11. Gün: Nesil Yapay Zeka Uygulamalarında İnce Ayar Sihirbazı Olmak

100 Ngày AI, Ngày 11: Trở thành chuyên gia tinh chỉnh trong các ứng dụng Gen AI

AI 100 天，第 11 天：成为 Gen AI 应用程序中的微调向导

The rise of large language models like ChatGPT, with their ability to generate highly fluent responses, has been remarkable. However, these models are hampered by hallucination, the generation of incorrect or nonsensical text that is semantically or syntactically plausible. LLM Hallucinations Reinforcement learning systems can be far more accurate and cost-effective than large language models because they learn by doing. While some experts like Ilya Sutskever of OpenAI believe that reinforcement learning with human feedback can eliminate LLM hallucinations, others like Yann LeCun of Meta and Geoff Hinton formerly at Google argue that a more fundamental flaw in large language models is at work. Both experts highlight gaps in the capabilities of seemingly all-conquering large language models where other AI approaches can be more effective. Nowhere is this more important than in writing computer code, which requires absolute accuracy to work. Software developers spend around 35% of their time testing software, so automating this task not only saves time but increases productivity. Large language models can write code suggestions, and much has been made of their usefulness in unit testing. However, because LLMs trade accuracy for generalization, the best they can do is suggest code to developers, who then must check the code for effectiveness. GitHub's Copilot, which uses OpenAI’s Generative Pre-trained Transformer model, a derivative of GPT-3, does not explicitly generate unit tests, but it can suggest code snippets for testing. For example, if a developer is writing a method that takes a list as an input, Copilot may suggest code snippets that test the method with an empty list, a list with one item, and a list with multiple items. These suggested snippets can be used as a starting point for writing more comprehensive unit tests. So, while Copilot can be helpful in generating some initial test cases, it is not a replacement for a comprehensive testing strategy. Microsoft Research, the University of Pennsylvania, and the University of California, San Diego have proposed TiCoder (Test-driven Interactive Coder), which leverages user feedback to generate code based on natural language inputs consistent with user intent. It uses natural language processing and machine learning algorithms to assist developers in generating unit tests. When a developer writes code, TiCoder asks the coder a series of questions to refine its understanding of the coder’s intent. It then provides suggestions and autocomplete options based on the code's context, syntax, and language. It generates test cases based on the code being written, suggesting assertions and testing various scenarios. Both Copilot and TiCoder, as well as other LLM-based tools, may speed up the writing of unit tests, but they are fundamentally AI assistants to human coders who check their work, rather than productive AI-based coders in their own right. So is there a better way? Geoff Hinton, who left Google early this year, points out that we learn to play basketball by throwing the ball so it goes through the hoop. We don’t learn the skill by reading about basketball – we learn by trial and error. And that’s the core idea behind Reinforcement Learning, an area of AI that has demonstrated impressive performance in tasks like game-playing. Diffblue Cover, for example, uses reinforcement learning to write executable unit tests without human intervention, making it possible to automate complex, error-prone tasks at scale. The product uses reinforcement learning to search the space of all possible test methods, write the test code automatically for each method, and select the best test among those written. The reward function for reinforcement learning is based on various criteria, including coverage of the test and aesthetics, which include coding styles that look as if a human has written them. The tool creates tests for each method in an average of one second and delivers the best test for a unit of code within one or two minutes at most. Diffblue Cover is more similar to AlphaGo, DeepMind’s automatic system for playing the game Go, than Copilot or TiCoder. AlphaGo identifies areas of a huge search space where there are potential moves to win the game and then uses reinforcement learning on these areas to select which move to make next. Diffblue Cover does the same with unit test methods, coming up with potential tests, evaluating them to find the best test, and repeating this operation until it has built a full test suite. If the goal is to automate the writing of 10,000 unit tests for a program no single person understands, reinforcement learning is the only real solution. Large deep-learning models just can’t compete – not least because there’s no way for humans to effectively supervise them and correct their code at that scale, and making models larger and more complicated doesn’t fix that. While large language models like ChatGPT have wowed the world with their fluency and depth of knowledge, for precise tasks like unit testing, reinforcement learning is a more accurate and cost-effective solution. Mathew Lodge ( ) is the CEO of Diffblue, an Oxford, UK-based AI startup. mathew.lodge@diffblue.com

From AI Assistants to Code Wizards: Can Reinforcement Learning Outcode GPT Models?

Algorithmic Wizardry: Exploiting Graph Theory in the Foreign Exchange 

অ্যালগরিদমিক জাদুকর: বৈদেশিক মুদ্রায় গ্রাফ থিওরি শোষণ

Algorithmische Zauberei: Nutzung der Graphentheorie im Devisenhandel

Magia algorítmica: explotación de la teoría de grafos en el mercado de divisas

Magie algorithmique : exploiter la théorie des graphes dans le domaine des changes

एल्गोरिथम विजार्ड्री: विदेशी मुद्रा में ग्राफ़ सिद्धांत का शोषण

アルゴリズムの魔術: 外国為替におけるグラフ理論の活用

알고리즘의 마법: 외환에서 그래프 이론 활용

Magia algorítmica: explorando a teoria dos grafos no câmbio

Алгоритмическое волшебство: использование теории графов на валютной бирже

Algoritmik Sihirbazlık: Dövizde Grafik Teorisinden Yararlanmak

Thuật toán thuật toán: Khai thác lý thuyết đồ thị trong ngoại hối

算法魔法：在外汇交易中利用图论

Chris V Gomes Muffat has always had an uncanny knack for transforming complex, futuristic concepts into profitable realities. This entrepreneur, businessman, and AI virtuoso is yet again commanding the spotlight. His latest marvel? An AI platform that's shaking the advertising industry to its foundations. But let's pause for a moment. If you're picturing a rigid machine pumping out sterile, standardized images, erase that image. ’s platform is more akin to a skilled artist, interpreting the complex orchestra of user interactions and crafting visuals that hit the bullseye every time. It's about an AI that doesn't just see data but understands it. Chris V Gomes Muffat Breaking down the innovation behind , he explains, "FocalStudio.ai offers a fast solution to create tailored images and illustrations for ads, using continuous learning and A/B testing for maximum brand alignment and audience engagement. Through extensive A/B testing, the AI absorbs data like a sponge and refines images to attract specific demographics. It's like having a Picasso who's also a mind-reader.” FocalStudio.ai Now that's a rare find and a project that could mean a seismic shift in how marketers strategize. After all, who wouldn't want an AI assistant that paints not just what we can see but what we can't? And if AI can genuinely understand and predict consumer behavior, the implications are both profound and lucrative. The platform’s impact lies not only in the enhanced effectiveness of marketing campaigns but also in the cost-cutting potential. Traditional advertising methods often require substantial investments in market research, design, and extensive human resources. However, with the AI platform at their disposal, companies can streamline their marketing efforts, saving both time and resources. “By leveraging an AI assistant that understands and predicts consumer behavior, businesses can minimize trial and error, delivering targeted advertisements with a higher probability of resonating with their audience,” Muffat explains. Never one to rest on his laurels, has two other ventures up his sleeve. , his AI content creation brainchild, targets to polish the rough edges of AI language processing technology. "Promptify.com is a unique AI hub that employs advanced language processing to generate intricate, sector and audience-specific content. It makes sure that the AI understands the nuances of your question before shooting out an answer. It's about refining interactions to a whole new level," shares Muffat, his eyes gleaming with excitement. Chris V Gomes Muffat Promptify.com Then there's , a platform that is all set to be the Airbnb for AI solutions. It hosts a variety of AI options and makes it easy-peasy for organizations to integrate them. "Think of it as an AI watchdog that also makes the AI accessible for you," he elaborates. “It is the first AI Access Security Broker (AIASB) on the market that empowers organizations to evaluate AI ethics and bias, enforce policies, and customize models with their own data for specific needs, while maintaining compliance.” Aithos.ai From cybersecurity and data protection to artificial intelligence, Chris V Gomes Muffat has covered a spectrum of tech arenas. But ask him about his favorite, and he doesn't miss a beat: "AI," he declares, "is where my heart is at the moment. It's a thrilling journey, and I'm enjoying every step." If there's one thing we could learn from Muffat’s' journey, it's this - in the ever-evolving tech landscape, the wizards are the ones who dare to innovate. And as for Muffat himself? He's a wizard worth watching. His passion is evident, and the world should be waiting to see where his ventures will take him — and the world as well. Writer: Olivia Li Presenter: Ryan Kim This story was distributed as a release by Ascend under HackerNoon’s Brand As An Author Program. Learn more about the program here: https://business.hackernoon.com/brand-as-author

Revolutionizing Marketing: AI Ad Wizardry by Chris V Gomes Muffat & FocalStudio's Image Sorcery

Revolucionando el marketing: AI Ad Wizardry de Chris V Gomes Muffat y Image Sorcery de FocalStudio

Révolutionner le marketing : AI Ad Wizardry par Chris V Gomes Muffat et Image Sorcery de FocalStudio

क्रांतिकारी विपणन: क्रिस वी गोम्स मफैट और फोकलस्टूडियो की इमेज सोरसेरी द्वारा एआई विज्ञापन विजार्ड्री

マーケティングに革命を起こす: AI Ad Wizardry by Chris V Gomes Muffat と FocalStudio の Image Sorcery

Revolucionando o Marketing: AI Ad Wizardry por Chris V Gomes Muffat & FocalStudio's Image Sorcery

Cách mạng hóa tiếp thị: Phù thủy quảng cáo AI của Chris V Gomes Muffat & Phép thuật hình ảnh của FocalStudio

营销革命：人工智能广告魔法 Chris V Gomes Muffat 和 FocalStudio 的 Image Sorcery

So let’s start! Tell us a bit about yourself. For example, name, profession, and personal interests. My name is Andrey Didovskiy. I am an investor in digital assets that loves to communicate exotic ideas by adding a creative flare to otherwise complex subject matters in order to engage & excite people to want to learn more! My passion is found at the intersection of blockchain, cryptocurrency, and Web3. Underpinning these two exotic fields of interest are things such as game theory, economics, & social psychology which can really stimulate the mind and expand a person’s worldview. Interesting! What was your latest Hackernoon Top story about? My latest top story on HackerNoon was where I discussed one of the most misunderstood aspects of the industry, Consensus Mechanisms. “Mastering Consensus Mechanisms: 15 Powerful Forms of Distributed Agreements in Crypto” Many more to come! Do you usually write on similar topics? If not, what do you usually write about? All of my writing will be focused on the intersection of human evolution & technology, with blockchain/crypto as the focal point for that conversation. Maybe a sprinkle of AI just to spice things up a bit. Great! What is your usual writing routine like (if you have one?) Routine, what’s that? I’m just kidding 😁. I aim to publish 1-2 articles every week. Typically, I will lay out a blueprint of the writing I want to do, then I will fill in the blueprint as much as possible on the first round. After taking 24 hours to freshen my perspective, I return to the piece, re-read it & make edits/adjustments. Then, all aboard, as we ship it to the public! Being a writer in tech can be a challenge. It’s not often our main role, but an addition to another one. What is the biggest challenge you have when it comes to writing? Having nobody to create with. Often times, ideas must be born by their original thinkers, & the subconscious mind can start to eat away at the certainty in domain knowledge. Nevertheless, you must stride on, and through practice, master the art of written communication. What is the next thing you hope to achieve in your career? Publishing something that moves a million people. Of course, there are many steps between where I am now to where I want to go, but given consistency, intelligent effort, & some time, nothing will stop me from getting there. Wow, that’s admirable. Now, something more casual: What is your guilty pleasure of choice? I love to build things. A little obsessive at times, working on a project with a big vision literally sets me on fire & makes me want to lose sleep. Do you have a non-tech-related hobby? If yes, what is it? Fitness & Chess. I keep to a strong 4/5 day routine of working out & playing sports (namely American handball). Chess is a tool for greatness, it sharpens the mind & humbles the player. Sound mind, sound body, baby (and vice versa). What can the HackerNoon community expect to read from you next? How to knit socks using ChatGPT’s API plugin 😂. I’m just messing around. Stay tuned & find out what technology can really do to shape the future of the world we live in! What’s your opinion on HackerNoon as a platform for writers? Magnificent. Thanks for taking the time to join our “ ” series. It was a pleasure. Do you have any closing words? Meet the writer Whoever you are, wherever you are. May your chalice always be full and overflowing. May you Live Long and Prosper. See you on the other side! 🥂

Meet the Writer: Andrey Didovskiy, Crypto Content Wizard & Blockchain Systems Architect

As a longtime Harry Potter fan, I was beyond excited to finally get my hands on Hogwarts Legacy. And let me tell you, the game did not disappoint. From the moment I started playing, I was transported to the magical world of Hogwarts. The game stays true to the original book story source, which only adds to the immersive experience. World Building in Hogwarts Legacy First, let's talk about world-building. As a fan of the Harry Potter books and movies, I was thrilled to see how well the game captured the magic and wonder of Hogwarts. From the familiar locations like the Great Hall and the Forbidden Forest to the more obscure places like the Black Lake and the Hogsmeade village, every inch of the game is filled with detail and care. The attention to detail in is truly impressive and is sure to delight fans of the series. Hogwarts Legacy The story and plot are also incredibly engaging. Without giving too much away, the game takes place in the 1800s and follows a new character who starts their journey at Hogwarts. The game is full of twists and turns that kept me on the edge of my seat. As a fan of RPG games, I appreciated the depth of the story and how it was woven seamlessly into the gameplay. Gaming Visuals in the wizarding world But what really blew me away were the graphics. I've never played a game with such stunning visuals and special effects before. The graphics are truly breathtaking! The game's developers have clearly put a lot of effort into creating a visually stunning world. The lighting effects, particle effects, and attention to detail in the character models are all top-notch. I found myself stopping frequently just to take in the scenery and appreciate the artistry of the game. Dueling Mechanics in Hogwarts Legacy Another aspect of the game that I enjoyed was the . The duels are just like any other battle game, but because the story is so deeply integrated into the gameplay, you don't even notice it. The game also features a number of other quests and activities to explore, such as learning new spells, taking care of magical creatures, and interacting with other characters. dueling mechanic Final Thoughts Overall, I think Hogwarts Legacy is a fantastic game that does justice to the Harry Potter universe. It's clear that the developers put a lot of love and care into crafting this experience, and it shows in every aspect of the game. As someone who has always been a fan of the Harry Potter universe, I am very hopeful for the sequels and future game improvements that are sure to come. Hogwarts Legacy has exceeded my expectations and I can't wait to see what's next. If you're a fan of the wizarding world, this game is an absolute must-play.

Exploring the Wizarding world: My Magical Experience Playing Hogwars Legacy

Taking the first step towards something new is never easy. It can be intimidating to embark on a journey that is unknown, and it is natural to feel uncertain and unsure of yourself. But it is important to remember that everyone starts from the beginning, and that the first step is always the most important one. As a beginner UX designer, you may feel overwhelmed by the amount of information and resources available on the subject. However, with the right approach and mindset, you can quickly develop the skills and knowledge you need to excel in this field. Here are some tips for beginner UX designers: Start by understanding the basics of user-centered design and the principles of usability. This will help you to think about design from the perspective of the user, rather than just focusing on aesthetics. Learn about the different methods and techniques used to evaluate user experience, such as user testing and usability testing. This will help you to identify problems with your designs and find solutions. Get familiar with the tools and software used in UX design, such as wireframing and prototyping tools. This will enable you to create and test your designs quickly and efficiently. Practice, practice, practice! The best way to improve your UX skills is to constantly be working on real-world design challenges and projects. Join a community of UX designers and professionals, such as online forums or local meetups. This will provide you with opportunities to learn from others, share your own knowledge, and get feedback on your work. Keep up to date with the latest developments in UX design, such as new tools, techniques, and best practices. This will help you to stay relevant and competitive in the field. Don’t be afraid to take risks and try new things in your designs. UX design is an iterative process, and you will learn from your mistakes as well as your successes. Finally, remember that the most important thing is to always put the needs of the user first. This will help you to create designs that are not only beautiful, but also effective and easy to use. And don’t give up! You can develop the skills and knowledge you need to succeed as a UX designer just keep learning, practicing, and networking, and you will be on your way to a successful career in this exciting and rewarding field.

Become a UX Wizard With These Simple Tips for Beginner Designers

Code generators do our hard work. But we don’t need them anymore. Free lines of code A few years ago working in the industry I took part on the demo of a programming product in which a or was used. The presenter boasted of writing less code, leaving the machine to write the text for her. Based on the templates that she was declaring. The seduction argument consisted of writing less code. Today we know that this approach is completely wrong. wizard code generator If we have to tell the machine what the meta code is, it is because we are not being declarative enough. Photo by Phil Desforges on Unsplash There are several reasons: a) We have not yet reached the level of abstraction and we have to teach the interpreter to make syntactic manipulations on our models. If there is a certain redundancy, the usual approach would be to model that redundancy by searching in real world against the abstract concept we should match. b) Some code generators are based on the (almost always incorrect) excuse that generated code is faster than the more declarative one used when including an indirection or abstraction. The performance argument almost always loses its support against a real benchmark. As a counterpart we can argue that to read or debug a function in general, the bunch of computer generated code confuses the novice programmer (and the experienced one wastes lots of time). What is (wrong with) software? Currently, human time is more valuable and scarce than machine time. c) Some programming languages ​​impose arbitrary type restrictions that are only saved with intermediate code generation such as Java generics or templates in c++. These are language limitations and not model restrictions. Non-declarative languages ​​like C++ or Java offer us with tools like or that Templates Generics look different but are similar. < :isEmpty() {
 }
}; Linked_List<T>::append(T value) {
 generic_node<T> *new_node = generic_node<T>;
} template > <T>: class T bool Linked_List void new Photo by Shahadat Rahman on Unsplash d) The interpreter uses meta-programming to resolve these hints, although this is discouraged, among other reasons, for leaving obscure references, almost impossible to refactor and easily erasable by mistake. Laziness Chapter I: Meta-Programming e) The code generated with Wizards or with meta-programming is much darker, much more difficult to follow and violates the principle of failing fast because it does not usually know how to defend itself against possible invalid constructions. We have the choice not to use it if we want to build good software models. f) The code generated by the wizards, templates and is of very low quality. It is not usually documented and generates coupling as it is a repetitive . In case of finding an error in one of the generated fragments and, due to said coupling, we will have to correct it in multiple places violating the Fail Fast Philosophy, Explained automatic code generation tools cut&paste DRY principle. Coupling: The One and Only Software Designing Problem g) The generated code has little added value, and often encourages bad design practices like automatic generation. setters and getters Nude Models - Part I : Setters h) Finally, if computers can make inferences about the repetition of our design patterns, it is because we are not contributing the creative part (and for now not replicable) and we will be out of work in the short-term. (Most) Programmers are losing our jobs very soon The solution is on the usual shelf Given the symptom of finding a pattern that is repeated in our code, the diagnosis is almost always the same: There is an abstraction or generalization in the domain of the problem that we have not yet discovered. The trivial solution is to go, with the help of our favorite domain expert, to discover what that encompasses repetition or pattern that the wizard or template did not allow us to discover. is the real-world concept Photo by Volodymyr Hryshchenko on Unsplash Conclusions Dynamic code generators were a fad at a time when productivity was measured based on the lines of code generated. To avoid them, we must stay true to the rule of bijection and look for abstractions within the real world in the problem domain. Part of the objective of this series of articles is to generate spaces for debate and discussion on software design. We look forward to comments and suggestions on this article.

Laziness Chapter II: Code Wizards

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