This week's Java roundup for October 16th, 2023, features news from OpenJDK, JDK 22, BellSoft, Oracle VS Code extension, WildFly 30, Payara Platform, MicroProfile 6.1, EclipseCon and releases for GraalVM Native Build Tools,

Java News Roundup: Unnamed Variables and Patterns, WildFly 30, MicroProfile 6.1, Payara Platform

Hardware made in Christchurch will be used in the first deep-space mining mission. And it’s one that will help shape things back on Earth. Palladium, used in the manufacture of emission-reducing catalytic converters for cars,

Deep-space mining: Dawn Aerospace delivers propulsion system for AstroForge’s first asteroid mission

Alexis Ohanian is a billionaire from the United States, well-known as the co-founder of Reddit, one of the internet's most popular social media and discussion platforms. Apart from Reddit, Alexis co-founded a venture capital firm called Initialized Capital in 2011.

Understanding Alexis Ohanian's net worth: What is his valuation?

Andrew Sather, principal at Initialized Capital, on why he invested in AstroForge, a US asteroid-mining startup that will use propulsion systems supplied by Christchurch's Dawn Aerospace. Video / Astr

AstroForge's plan to mine near-Earth asteroids

Over a third of consumers said they were likely to splurge in multiple categories, as household debt sits at record highs.

Nearly 7 in 10 Shoppers Likely to Overspend This Holiday Season, Survey Finds

Gulf Cooperation Council (GCC) and Pakistan initialized a free trade agreement, the GCC said on messaging platform X on Thursday. The agreement was signed by GCC Secretary-General Jasem al-Budaiwi and Pakistani trade minister Gohar Ejaz.

Gulf Cooperation Council, Pakistan Sign Free Trade Agreement

Gulf Cooperation Council (GCC) and Pakistan "initialized" a free trade agreement, the GCC said on messaging platform X on Thursday.The agreement was signed by GCC Secretary-General Jasem al-Budaiwi and Pakistani trade minister Gohar Ejaz.

Gulf Cooperation Council says it and Pakistan 'initialize' free trade agreement

If you are looking for the best nicknames for boys and girls, this post is for you. Millions of people search the internet, but there are some websites that offer some nickname ideas for boys and girls, but in this post, I am going to share 1000+ nicknames ...

366 + Best Nicknames For boys and Girls (New & Latest) 2023

Since its launch, the MetaTrace Quest platform has accumulated over 250,000 registered users, meaning a total of at least 250,000 Trace Wallets have been initialized and created. The platform launched nearly three weeks ago and has experienced exceptional recognition since its debut.

MetaTrace Has Reached 250,000 Users on the Quest Platform

If the drive that fails to initialize is empty or contains no essential data, you should format it. Then, you can create a new partition by converting its format to GPT and assigning the new volume. It is the most recommended method for resolving the issue and has worked for many users. To accomplish that, follow these steps:

How to Fix the “Disk Unknown Not Initialized” Issue on Windows

Initialized

initialized

Ending The Crypto Winter with Garry Tan and Brett Gibson of Initialized Capital

AOP Frameworks Can Do Wonders For Your Project — Here's How

Authors:
(1) Yuwei Guo, The Chinese University of Hong Kong;
(2) Ceyuan Yang, Shanghai Artificial Intelligence Laboratory with Corresponding Author;
(3) Anyi Rao, Stanford University;
(4) Zhengyang Liang, Shanghai Artificial Intelligence Laboratory;
(5) Yaohui Wang, Shanghai Artificial Intelligence Laboratory;
(6) Yu Qiao, Shanghai Artificial Intelligence Laboratory;
(7) Maneesh Agrawala, Stanford University;
(8) Dahua Lin, Shanghai Artificial Intelligence Laboratory;
(9) Bo Dai, The Chinese University of Hong Kong and The Chinese University of Hong Kong. Table of Links Abstract and 1 Introduction 2 Work Related 3 Preliminary AnimateDiff 4.1 Alleviate Negative Effects from Training Data with Domain Adapter 4.2 Learn Motion Priors with Motion Module 4.3 Adapt to New Motion Patterns with MotionLora 4.4 AnimateDiff in Practice 5 Experiments and 5.1 Qualitative Results 5.2 Qualitative Comparison 5.3 Ablative Study 5.4 Controllable Generation 6 Conclusion 7 Ethics Statement 8 Reproducibility Statement, Acknowledgement and References ABSTRACT With the advance of text-to-image (T2I) diffusion models (e.g., Stable Diffusion) and corresponding personalization techniques such as DreamBooth and LoRA, everyone can manifest their imagination into high-quality images at an affordable cost. However, adding motion dynamics to existing high-quality personalized T2Is and enabling them to generate animations remains an open challenge. In this paper, we present AnimateDiff, a practical framework for animating personalized T2I models without requiring model-specific tuning. At the core of our framework is a plug-and-play motion module that can be trained once and seamlessly integrated into any personalized T2Is originating from the same base T2I. Through our proposed training strategy, the motion module effectively learns transferable motion priors from real-world videos. Once trained, the motion module can be inserted into a personalized T2I model to form a personalized animation generator. We further propose MotionLoRA, a lightweight fine-tuning technique for AnimateDiff that enables a pre-trained motion module to adapt to new motion patterns, such as different shot types, at a low training and data collection cost. We evaluate AnimateDiff and MotionLoRA on several public representative personalized T2I models collected from the community. The results demonstrate that our approaches help these models generate temporally smooth animation clips while preserving the visual quality and motion diversity. Codes and pre-trained weights are available at https://github.com/guoyww/AnimateDiff. 1 INTRODUCTION Text-to-image (T2I) diffusion models (Nichol et al., 2021; Ramesh et al., 2022; Saharia et al., 2022; Rombach et al., 2022) have greatly empowered artists and amateurs to create visual content using text prompts. To further stimulate the creativity of existing T2I models, lightweight personalization methods, such as DreamBooth (Ruiz et al., 2023) and LoRA (Hu et al., 2021) have been proposed. These methods enable customized fine-tuning on small datasets using consumer-grade hardware such as a laptop with an RTX3080, thereby allowing users to adapt a base T2I model to new domains and improve visual quality at a relatively low cost. Consequently, a large community of AI artists and amateurs has contributed numerous personalized models on model-sharing platforms such as Civitai (2022) and Hugging Face (2022). While these personalized T2I models can generate remarkable visual quality, their outputs are limited to static images. On the other hand, the ability to generate animations is more desirable in real-world production, such as in the movie and cartoon industries. In this work, we aim to directly transform existing high-quality personalized T2I models into animation generators without requiring model-specific fine-tuning, which is often impractical in terms of computation and data collection costs for amateur users. We present AnimateDiff, an effective pipeline for addressing the problem of animating personalized T2Is while preserving their visual quality and domain knowledge. The core of AnimateDiff is an approach for training a plug-and-play motion module that learns reasonable motion priors from video datasets, such as WebVid-10M (Bain et al., 2021). At inference time, the trained motion module can be directly integrated into personalized T2Is and produce smooth and visually appealing animations without requiring specific tuning. The training of the motion module in AnimateDiff consists of three stages. Firstly, we fine-tune a domain adapter on the base T2I to align with the visual distribution of the target video dataset. This preliminary step guarantees the motion module concentrates on learning the motion priors rather than pixel-level details from the training videos. Secondly, we inflate the base T2I together with the domain adapter and introduce a newly initialized motion module for motion modeling. We then optimize this module on videos while keeping the domain adapter and base T2I weights fixed. By doing so, the motion module learns generalized motion priors and can, via module insertion, enable other personalized T2Is to generate smooth and appealing animations aligned with their personalized domains. The third stage of AnimateDiff, also dubbed as MotionLoRA, aims to adapt the pre-trained motion module to specific motion patterns with a small number of reference videos and training iterations. We achieve this by fine-tuning the motion module with the aid of Low-Rank Adaptation (LoRA) (Hu et al., 2021). Remarkably, adapting to a new motion pattern can be achieved with as few as 50 reference videos. Moreover, a MotionLoRA model requires only approximately 30M of additional storage space, further enhancing the efficiency of model sharing. This efficiency is particularly valuable for users who are unable to bear the expensive costs of pre-training but desire to fine-tune the motion module for specific effects. We evaluate the performance of AnimateDiff and MotionLoRA on a diverse set of personalized T2I models collected from model-sharing platforms (Civitai, 2022; Hugging Face, 2022). These models encompass a wide spectrum of domains, ranging from 2D cartoons to realistic photographs, thereby forming a comprehensive benchmark for our evaluation. The results of our experiments demonstrate promising outcomes. In practice, we also found that a Transformer (Vaswani et al., 2017) architecture along the temporal axis is adequate for capturing appropriate motion priors. We also demonstrate that our motion module can be seamlessly integrated with existing content-controlling approaches (Zhang et al., 2023; Mou et al., 2023) such as ControlNet without requiring additional training, enabling AnimateDiff for controllable animation generation. In summary, (1) we present AnimateDiff, a practical pipeline that enables the animation generation ability of any personalized T2Is without specific fine-tuning; (2) we verify that a Transformer architecture is adequate for modeling motion priors, which provides valuable insights for video generation; (3) we propose MotionLoRA, a lightweight fine-tuning technique to adapt pre-trained motion modules to new motion patterns; (4) we comprehensively evaluate our approach with representative community models and compare it with both academic baselines and commercial tools such as Gen2 (2023) and Pika Labs (2023). Furthermore, we showcase its compatibility with existing works for controllable generation. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Yuwei Guo, The Chinese University of Hong Kong; (2) Ceyuan Yang, Shanghai Artificial Intelligence Laboratory with Corresponding Author; (3) Anyi Rao, Stanford University; (4) Zhengyang Liang, Shanghai Artificial Intelligence Laboratory; (5) Yaohui Wang, Shanghai Artificial Intelligence Laboratory; (6) Yu Qiao, Shanghai Artificial Intelligence Laboratory; (7) Maneesh Agrawala, Stanford University; (8) Dahua Lin, Shanghai Artificial Intelligence Laboratory; (9) Bo Dai, The Chinese University of Hong Kong and The Chinese University of Hong Kong. Authors: Authors: (1) Yuwei Guo, The Chinese University of Hong Kong; (2) Ceyuan Yang, Shanghai Artificial Intelligence Laboratory with Corresponding Author; (3) Anyi Rao, Stanford University; (4) Zhengyang Liang, Shanghai Artificial Intelligence Laboratory; (5) Yaohui Wang, Shanghai Artificial Intelligence Laboratory; (6) Yu Qiao, Shanghai Artificial Intelligence Laboratory; (7) Maneesh Agrawala, Stanford University; (8) Dahua Lin, Shanghai Artificial Intelligence Laboratory; (9) Bo Dai, The Chinese University of Hong Kong and The Chinese University of Hong Kong. Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 Work Related 2 Work Related 3 Preliminary 3 Preliminary AnimateDiff AnimateDiff 4.1 Alleviate Negative Effects from Training Data with Domain Adapter 4.1 Alleviate Negative Effects from Training Data with Domain Adapter 4.2 Learn Motion Priors with Motion Module 4.2 Learn Motion Priors with Motion Module 4.3 Adapt to New Motion Patterns with MotionLora 4.3 Adapt to New Motion Patterns with MotionLora 4.4 AnimateDiff in Practice 4.4 AnimateDiff in Practice 5 Experiments and 5.1 Qualitative Results 5 Experiments and 5.1 Qualitative Results 5.2 Qualitative Comparison 5.2 Qualitative Comparison 5.3 Ablative Study 5.3 Ablative Study 5.4 Controllable Generation 5.4 Controllable Generation 6 Conclusion 6 Conclusion 7 Ethics Statement 7 Ethics Statement 8 Reproducibility Statement, Acknowledgement and References 8 Reproducibility Statement, Acknowledgement and References ABSTRACT With the advance of text-to-image (T2I) diffusion models (e.g., Stable Diffusion) and corresponding personalization techniques such as DreamBooth and LoRA, everyone can manifest their imagination into high-quality images at an affordable cost. However, adding motion dynamics to existing high-quality personalized T2Is and enabling them to generate animations remains an open challenge. In this paper, we present AnimateDiff, a practical framework for animating personalized T2I models without requiring model-specific tuning. At the core of our framework is a plug-and-play motion module that can be trained once and seamlessly integrated into any personalized T2Is originating from the same base T2I. Through our proposed training strategy, the motion module effectively learns transferable motion priors from real-world videos. Once trained, the motion module can be inserted into a personalized T2I model to form a personalized animation generator. We further propose MotionLoRA, a lightweight fine-tuning technique for AnimateDiff that enables a pre-trained motion module to adapt to new motion patterns, such as different shot types, at a low training and data collection cost. We evaluate AnimateDiff and MotionLoRA on several public representative personalized T2I models collected from the community. The results demonstrate that our approaches help these models generate temporally smooth animation clips while preserving the visual quality and motion diversity. Codes and pre-trained weights are available at https://github.com/guoyww/AnimateDiff. 1 INTRODUCTION Text-to-image (T2I) diffusion models (Nichol et al., 2021; Ramesh et al., 2022; Saharia et al., 2022; Rombach et al., 2022) have greatly empowered artists and amateurs to create visual content using text prompts. To further stimulate the creativity of existing T2I models, lightweight personalization methods, such as DreamBooth (Ruiz et al., 2023) and LoRA (Hu et al., 2021) have been proposed. These methods enable customized fine-tuning on small datasets using consumer-grade hardware such as a laptop with an RTX3080, thereby allowing users to adapt a base T2I model to new domains and improve visual quality at a relatively low cost. Consequently, a large community of AI artists and amateurs has contributed numerous personalized models on model-sharing platforms such as Civitai (2022) and Hugging Face (2022). While these personalized T2I models can generate remarkable visual quality, their outputs are limited to static images. On the other hand, the ability to generate animations is more desirable in real-world production, such as in the movie and cartoon industries. In this work, we aim to directly transform existing high-quality personalized T2I models into animation generators without requiring model-specific fine-tuning, which is often impractical in terms of computation and data collection costs for amateur users. We present AnimateDiff, an effective pipeline for addressing the problem of animating personalized T2Is while preserving their visual quality and domain knowledge. The core of AnimateDiff is an approach for training a plug-and-play motion module that learns reasonable motion priors from video datasets, such as WebVid-10M (Bain et al., 2021). At inference time, the trained motion module can be directly integrated into personalized T2Is and produce smooth and visually appealing animations without requiring specific tuning. The training of the motion module in AnimateDiff consists of three stages. Firstly, we fine-tune a domain adapter on the base T2I to align with the visual distribution of the target video dataset. This preliminary step guarantees the motion module concentrates on learning the motion priors rather than pixel-level details from the training videos. Secondly, we inflate the base T2I together with the domain adapter and introduce a newly initialized motion module for motion modeling. We then optimize this module on videos while keeping the domain adapter and base T2I weights fixed. By doing so, the motion module learns generalized motion priors and can, via module insertion, enable other personalized T2Is to generate smooth and appealing animations aligned with their personalized domains. The third stage of AnimateDiff, also dubbed as MotionLoRA, aims to adapt the pre-trained motion module to specific motion patterns with a small number of reference videos and training iterations. We achieve this by fine-tuning the motion module with the aid of Low-Rank Adaptation (LoRA) (Hu et al., 2021). Remarkably, adapting to a new motion pattern can be achieved with as few as 50 reference videos. Moreover, a MotionLoRA model requires only approximately 30M of additional storage space, further enhancing the efficiency of model sharing. This efficiency is particularly valuable for users who are unable to bear the expensive costs of pre-training but desire to fine-tune the motion module for specific effects. We evaluate the performance of AnimateDiff and MotionLoRA on a diverse set of personalized T2I models collected from model-sharing platforms (Civitai, 2022; Hugging Face, 2022). These models encompass a wide spectrum of domains, ranging from 2D cartoons to realistic photographs, thereby forming a comprehensive benchmark for our evaluation. The results of our experiments demonstrate promising outcomes. In practice, we also found that a Transformer (Vaswani et al., 2017) architecture along the temporal axis is adequate for capturing appropriate motion priors. We also demonstrate that our motion module can be seamlessly integrated with existing content-controlling approaches (Zhang et al., 2023; Mou et al., 2023) such as ControlNet without requiring additional training, enabling AnimateDiff for controllable animation generation. In summary, (1) we present AnimateDiff, a practical pipeline that enables the animation generation ability of any personalized T2Is without specific fine-tuning; (2) we verify that a Transformer architecture is adequate for modeling motion priors, which provides valuable insights for video generation; (3) we propose MotionLoRA, a lightweight fine-tuning technique to adapt pre-trained motion modules to new motion patterns; (4) we comprehensively evaluate our approach with representative community models and compare it with both academic baselines and commercial tools such as Gen2 (2023) and Pika Labs (2023). Furthermore, we showcase its compatibility with existing works for controllable generation. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

How to Transform Static Text-to-Image Models into Dynamic Animation Generators

So wandeln Sie statische Text-zu-Bild-Modelle in dynamische Animationsgeneratoren um

Cómo transformar modelos estáticos de texto a imagen en generadores de animación dinámicos

Nola eraldatu testu-irudi eredu estatikoak animazio-sorgailu dinamikoetan

静的なテキストから画像へのモデルを動的なアニメーション ジェネレータに変換する方法

ວິທີການປ່ຽນຕົວແບບຂໍ້ຄວາມເປັນຮູບພາບຄົງທີ່ໄປສູ່ເຄື່ອງສ້າງພາບເຄື່ອນໄຫວແບບໄດນາມິກ

गतिशील एनिमेसन जेनरेटरहरूमा स्थिर पाठ-देखि-छवि मोडेलहरू कसरी रूपान्तरण गर्ने

Как преобразовать статические модели «текст-изображение» в динамические генераторы анимации

Sida Loo Beddelo Qaabka Qoraal-Ka-Sawirka Jooga Ah Loona Bedeli Karaa Animation Generators

Jinsi ya Kubadilisha Miundo Tuli ya Maandishi-hadi-Picha kuwa Jenereta za Uhuishaji Zinazobadilika

Statiki tekstden-şekil modellerini dinamiki animasiýa generatorlaryna nädip öwürmeli

Statik Metin-Görüntü Modellerini Dinamik Animasyon Oluşturucularına Nasıl Dönüştürebilirsiniz

جامد متن سے تصویری ماڈلز کو متحرک اینیمیشن جنریٹرز میں کیسے تبدیل کیا جائے

Hello JavaScript Enthusiasts! Welcome to this week’s edition of “This Week In JavaScript”! Today, we’re diving into TypeScript’s latest release candidate, a thought-provoking discussion about the future of frameworks in the AI era, and some must-know tools to boost your dev game. Let’s jump right in! Typescript 5.7 RC Lands You can now install TypeScript 5.7 Release Candidate with: npm install -D typescript@rc Here’s what’s new: Smarter Bug Detection: New checks catch never-initialized variables, even in deeply nested functions. No-Build Convenience: For ts-node and Deno users, the --rewriteRelativeImportExtensions option automatically converts .ts imports to .js, allowing developers to run TypeScript code seamlessly across multiple environments like Node.js and Deno. ECMAScript 2024: Full support for Object.groupBy and Promise.withResolvers, helping you leverage the latest ECMAScript features. The Vlt Package Manager and Serverless Registry Isaac Schlueter, the creator of npm, is back with something new—vlt and the Serverless Registry (vsr). vlt Package Manager: A faster, sleeker alternative to npm with features like improved performance and dependency query syntax. Serverless Registry: Compatible with npm, this scalable, private registry gives you full control over your package distribution. These tools could significantly streamline how developers manage JavaScript packages by offering faster performance and more control over private package distribution. Component Party If you’re a front-end dev working across multiple frameworks, Component Party is your new best friend. It’s a tool that compares how common tasks are performed across different libraries like React, Vue, Svelte, and Angular. With its latest additions—support for Svelte 5 and Angular 17—it’s more up-to-date than ever. It offers a side-by-side comparison of how common tasks—like rendering a component or managing state—are handled across libraries like React, Vue, and Svelte. This makes it easier to learn or switch frameworks without wasting time hunting through documentation. The AI Framework Debate With tools like Replit, Cursor, v0 and more that generate functional code almost instantly, a new debate is brewing: Will frameworks still matter in the future? Speed vs. Structure: AI-generated code might reduce the reliance on traditional frameworks for certain projects, but frameworks will likely remain essential for larger, complex applications. The real question is how frameworks will evolve alongside these AI tools. Future Impact: As AI advances, we might see a shift in how software is built—frameworks could evolve or become less relevant. It’s definitely something to keep an eye on! Tools & Releases YOU SHOULD KNOW ABOUT Here are some key tools and releases that deserve a spot on your radar this week: Parcel 2.13.0: This update includes faster resolver caching (which speeds up builds by improving how dependencies are resolved) and adds glob support in package.json, allowing you to match patterns for files more easily. Lexical 0.20: Lexical is an extensible text editor framework from Meta, focused on speed, accessibility, and cross-platform compatibility. It even has a Swift variant for iOS, making it a must-try if you’re building text-heavy apps. @clerk/nextjs v6: Clerk’s latest release supports Next.js 15 with async auth(), static rendering by default, and partial pre-rendering. It’s backward-compatible with Next.js 14, making it a seamless upgrade for any dev team. Storybook 8.4: Frontend devs, this is one to check out! Storybook 8.4 now features one-click component testing right in your browser, full support for Svelte 5, and React Native Storybook updates. Plus, the bundle size is now 50% smaller, making it faster and leaner. Jsep: Looking to parse expressions in JavaScript? jsep is a lightweight library that converts expressions like x * (1 + 2) into an AST. It’s flexible, lightweight, and supports custom operators, making it perfect for both client and server-side projects. Expo SDK 52: React Native developers, this one's for you! Expo SDK 52 brings React Native 0.76 with a 38% performance boost thanks to its new architecture. This release also includes new libraries, bug fixes, and ecosystem improvements. Upgrade now with npx expo install expo@^52.0.0 --fix If you found these JavaScript updates useful, give this post an upvote to help others discover it. Don’t forget to share it with your fellow developers and keep an eye out for more insights in the JavaScript world. Until next time, keep coding and stay curious! Hello JavaScript Enthusiasts! Welcome to this week’s edition of “ This Week In JavaScript ”! This Week In JavaScript Today, we’re diving into TypeScript’s latest release candidate, a thought-provoking discussion about the future of frameworks in the AI era, and some must-know tools to boost your dev game. Let’s jump right in! Typescript 5.7 RC Lands You can now install TypeScript 5.7 Release Candidate with: TypeScript 5.7 Release Candidate npm install -D typescript@rc npm install -D typescript@rc Here’s what’s new: Here’s what’s new: Smarter Bug Detection: New checks catch never-initialized variables, even in deeply nested functions. Smarter Bug Detection: No-Build Convenience: For ts-node and Deno users, the --rewriteRelativeImportExtensions option automatically converts .ts imports to .js, allowing developers to run TypeScript code seamlessly across multiple environments like Node.js and Deno. No-Build Convenience: ECMAScript 2024: Full support for Object.groupBy and Promise.withResolvers, helping you leverage the latest ECMAScript features. ECMAScript 2024: The Vlt Package Manager and Serverless Registry Isaac Schlueter , the creator of npm , is back with something new—vlt and the Serverless Registry (vsr) . Isaac Schlueter npm new—vlt and the Serverless Registry (vsr) vlt Package Manager: A faster, sleeker alternative to npm with features like improved performance and dependency query syntax. vlt Package Manager: Serverless Registry: Compatible with npm, this scalable, private registry gives you full control over your package distribution. Serverless Registry: These tools could significantly streamline how developers manage JavaScript packages by offering faster performance and more control over private package distribution. Component Party If you’re a front-end dev working across multiple frameworks, Component Party is your new best friend. It’s a tool that compares how common tasks are performed across different libraries like React , Vue , Svelte , and Angular . Component Party React Vue Svelte Angular With its latest additions—support for Svelte 5 and Angular 17 —it’s more up-to-date than ever. It offers a side-by-side comparison of how common tasks—like rendering a component or managing state—are handled across libraries like React , Vue , and Svelte . This makes it easier to learn or switch frameworks without wasting time hunting through documentation. Svelte 5 and Angular 17 React Vue Svelte The AI Framework Debate With tools like Replit , Cursor , v0 and more that generate functional code almost instantly, a new debate is brewing: Will frameworks still matter in the future? Replit Cursor v0 Speed vs. Structure: AI-generated code might reduce the reliance on traditional frameworks for certain projects, but frameworks will likely remain essential for larger, complex applications. The real question is how frameworks will evolve alongside these AI tools. Speed vs. Structure: Future Impact: As AI advances, we might see a shift in how software is built—frameworks could evolve or become less relevant. It’s definitely something to keep an eye on! Future Impact: Tools & Releases YOU SHOULD KNOW ABOUT Here are some key tools and releases that deserve a spot on your radar this week: Parcel 2.13.0: This update includes faster resolver caching (which speeds up builds by improving how dependencies are resolved) and adds glob support in package.json, allowing you to match patterns for files more easily. Parcel 2.13.0: Lexical 0.20: Lexical is an extensible text editor framework from Meta, focused on speed, accessibility, and cross-platform compatibility. It even has a Swift variant for iOS, making it a must-try if you’re building text-heavy apps. Lexical 0.20: @clerk/nextjs v6: Clerk’s latest release supports Next.js 15 with async auth(), static rendering by default, and partial pre-rendering. It’s backward-compatible with Next.js 14, making it a seamless upgrade for any dev team. @clerk/nextjs v6: Storybook 8.4: Frontend devs, this is one to check out! Storybook 8.4 now features one-click component testing right in your browser, full support for Svelte 5, and React Native Storybook updates. Plus, the bundle size is now 50% smaller, making it faster and leaner. Storybook 8.4: Jsep: Looking to parse expressions in JavaScript? jsep is a lightweight library that converts expressions like x * (1 + 2) into an AST. It’s flexible, lightweight, and supports custom operators, making it perfect for both client and server-side projects. Jsep: x * (1 + 2) Expo SDK 52: React Native developers, this one's for you! Expo SDK 52 brings React Native 0.76 with a 38% performance boost thanks to its new architecture. This release also includes new libraries, bug fixes, and ecosystem improvements. Expo SDK 52: Upgrade now with npx expo install expo@^52.0.0 --fix npx expo install expo@^52.0.0 --fix If you found these JavaScript updates useful, give this post an upvote to help others discover it. Don’t forget to share it with your fellow developers and keep an eye out for more insights in the JavaScript world. Until next time, keep coding and stay curious!

TypeScript 5.7 RC Released, New npm Alternative, Future of JavaScript Frameworks - This Week in JS

Winter(Physics) is Coming It now looks like Large Language Models running on the GPT technology may be nearing a plateau in their development. This is from popular Physicist Dr. Sabine Hossenfelder’s latest YouTube video. https://www.youtube.com/watch?v=AqwSZEQkknU&embedable=true Dr. Sabine cites a couple of sources, including Bill Gates, who said last year that GPT 5 won’t be much better than GPT 4. And Prof. Gary Marcus says he has been drumming this particular beat for years. This goes against all the Ray Kurzweilian hype by Sam Altman and Ilya Sutskever as seen in the video. But if merely beautiful mathematics won’t spur the progress of Physics, as Dr. Sabine always reminds us, then merely wishful group-thinking won’t give us a superhuman artificial general intelligence (AGI). Why isn’t throwing as much data, computing power, and human supervision as can be afforded, going to birth us a superhuman AGI? Dr. Sabine has a ready answer. The problem, she explains, is the Decoupling of Scales. The Decoupling of Scales The world is broken up into scales. In the physical world, there is the scale of land animals like us humans. Then below that we have microbes, then molecules, atoms, and subatomic particles. In the opposite direction, we have aircraft and skyscrapers, mountains, planets, stars, solar systems, galaxies, etc. In the world of data, we have high-resolution data e.g. categories like “objects that can be touched”, “animals”, “cats”. Then low-resolution data to further expand the above categories e.g. “cat species”, “cat patterns”, “cat behavior at 8:00 am on Monday,” etc. There are other taxonomies, however. Some data is online and some is offline in books, papers, and people’s minds. We may have all the knowledge we desire at the high-resolution scale of human language creation on the internet. But that doesn’t equate to the much bigger, low-resolution data source that is human language creation in the world. Thus, Sam Altman’s AI is like Aristotle in the image below. Hoping to discover the Standard Model of Physics by looking at his hand. But without something like the Large Hadron Collider to poke subatomic particles and see the strange new data hiding within, Aristotle is at a loss on how to proceed. A Large Hadron Collider for AI GPT architecture, unveiled to the world only 2 years ago with the phenomenal ChatGPT, is likely nearing the end of its “improvement-ness”. Perhaps it is time to seriously consider passing the baton, and the VC funding, to something else. I am rooting for thermodynamic computing, currently being explored by companies like Extropic, which works by leveraging thermal noise to create random samples of data as in Stable Diffusion technology, then denoising using statistical analysis. In fact, in any neural network, the initial weights are usually initialized with random noise. During training, backpropagation shapes the final weights from this random noise - writes Laszlo Fazekas. The advantage lies in the chips operating at scales smaller than those of traditional digital computing, where quantum effects result in abundant thermal noise. Thermal noise has always been plenty, anyway. My kitchen stove is full of thermal noise. The real problem has always been how to understand it better through statistical chunking of its data, without throwing away data during regression analysis. My guess? Some sort of massive cloud computational AI training leveraging smartphones, bitcoin nodes, TV sets, radios, name it. Currently, allowing the AI some “thinking time'“ so that it runs longer inferences on the same data, is how AI companies are squeezing out some more juice. For example with ChatGPT’s o1 model. But maybe even training time should be extended. Maybe to get great AI, like a child, it needs decades of training from data collected from all over. Collected and slowly siphoned into a condensed AI model in a few servers. Sam Altman might need those trillions of government dollars for such a plan. Alas, they will likely not be easy to get. The D.O.G.E might not bankroll him (if xAI is suffering the same troubles). Meanwhile, Bitcoin is still growing As the above graphic shows, very few people are using Bitcoin. Yet it is already at $90k. Data from Bitbo shows that in fact, only 400,000 people are using Bitcoin daily. And only 106 million own Bitcoin. Contrast this with the 314 million people who have used AI tools as per data from Statista. ChatGPT alone has millions of users per day. Probably more than the entire Bitcoin blockchain combined! What’s more? AI amassed the bulk of these users in less than a year. This feels wrong. Bitcoin took years to grow. People sweated, lost, DCA’d, had faith. Maybe that’s it. ChatGPT has grown too fast. Now it has slowed down and everybody is sour. Sorry, ya’ll, there is still work to do. AGI will not be so easy to grasp, and GPT scientists should learn to hodl like Bitcoiners. Winter will come and go, but right now, it is time to pay the piper. Winter(Physics) is Coming It now looks like Large Language Models running on the GPT technology may be nearing a plateau in their development. This is from popular Physicist Dr. Sabine Hossenfelder’s latest YouTube video. https://www.youtube.com/watch?v=AqwSZEQkknU&embedable=true https://www.youtube.com/watch?v=AqwSZEQkknU&embedable=true Dr. Sabine cites a couple of sources, including Bill Gates, who said last year that GPT 5 won’t be much better than GPT 4. And Prof. Gary Marcus says he has been drumming this particular beat for years. This goes against all the Ray Kurzweilian hype by Sam Altman and Ilya Sutskever as seen in the video. said last year says But if merely beautiful mathematics won’t spur the progress of Physics, as Dr. Sabine always reminds us, then merely wishful group-thinking won’t give us a superhuman artificial general intelligence (AGI). Why isn’t throwing as much data, computing power, and human supervision as can be afforded, going to birth us a superhuman AGI? Dr. Sabine has a ready answer. The problem, she explains, is the Decoupling of Scales . Decoupling of Scales The Decoupling of Scales The world is broken up into scales. In the physical world, there is the scale of land animals like us humans. Then below that we have microbes, then molecules, atoms, and subatomic particles. In the opposite direction, we have aircraft and skyscrapers, mountains, planets, stars, solar systems, galaxies, etc. In the world of data, we have high-resolution data e.g. categories like “objects that can be touched”, “animals”, “cats”. Then low-resolution data to further expand the above categories e.g. “cat species”, “cat patterns”, “cat behavior at 8:00 am on Monday,” etc. There are other taxonomies, however. Some data is online and some is offline in books, papers, and people’s minds. We may have all the knowledge we desire at the high-resolution scale of human language creation on the internet . But that doesn’t equate to the much bigger, low-resolution data source that is human language creation in the world . human language creation on the internet human language creation in the world Thus, Sam Altman’s AI is like Aristotle in the image below. Hoping to discover the Standard Model of Physics by looking at his hand. But without something like the Large Hadron Collider to poke subatomic particles and see the strange new data hiding within, Aristotle is at a loss on how to proceed. A Large Hadron Collider for AI GPT architecture, unveiled to the world only 2 years ago with the phenomenal ChatGPT, is likely nearing the end of its “improvement-ness”. Perhaps it is time to seriously consider passing the baton, and the VC funding, to something else. I am rooting for thermodynamic computing , currently being explored by companies like Extropic, which works by leveraging thermal noise to create random samples of data as in Stable Diffusion technology, then denoising using statistical analysis. thermodynamic computing In fact, in any neural network, the initial weights are usually initialized with random noise. During training, backpropagation shapes the final weights from this random noise - writes Laszlo Fazekas. in any neural network , the initial weights are usually initialized with random noise. During training, backpropagation shapes the final weights from this random noise - neural network backpropagation The advantage lies in the chips operating at scales smaller than those of traditional digital computing, where quantum effects result in abundant thermal noise. Thermal noise has always been plenty, anyway. My kitchen stove is full of thermal noise. The real problem has always been how to understand it better through statistical chunking of its data, without throwing away data during regression analysis. My guess? Some sort of massive cloud computational AI training leveraging smartphones, bitcoin nodes, TV sets, radios, name it. Currently, allowing the AI some “thinking time'“ so that it runs longer inferences on the same data, is how AI companies are squeezing out some more juice. For example with ChatGPT’s o1 model. But maybe even training time should be extended. Maybe to get great AI, like a child, it needs decades of training from data collected from all over. Collected and slowly siphoned into a condensed AI model in a few servers. Sam Altman might need those trillions of government dollars for such a plan. Alas, they will likely not be easy to get. The D.O.G.E might not bankroll him (if xAI is suffering the same troubles). Meanwhile, Bitcoin is still growing As the above graphic shows, very few people are using Bitcoin. Yet it is already at $90k . already at $90k Data from Bitbo shows that in fact, only 400,000 people are using Bitcoin daily. And only 106 million own Bitcoin. Bitbo Contrast this with the 314 million people who have used AI tools as per data from Statista . ChatGPT alone has millions of users per day . Probably more than the entire Bitcoin blockchain combined! data from Statista has millions of users per day What’s more? AI amassed the bulk of these users in less than a year. This feels wrong. Bitcoin took years to grow. People sweated, lost, DCA’d, had faith. Maybe that’s it. ChatGPT has grown too fast. Now it has slowed down and everybody is sour. Sorry, ya’ll, there is still work to do. AGI will not be so easy to grasp, and GPT scientists should learn to hodl like Bitcoiners. Winter will come and go, but right now, it is time to pay the piper.

The AI Winter is Near, Courtesy of Physics

Der KI-Winter steht vor der Tür, mit freundlicher Genehmigung der Physik

El invierno de la IA se acerca, cortesía de la física

به لطف فیزیک، زمستان هوش مصنوعی نزدیک است

Tekoälytalvi on lähellä, fysiikan luvalla

एआई शीतकाल निकट है, भौतिकी के सौजन्य से

物理学のおかげでAIの冬が近づいている

AI қысы жақын, физиканың рұқсатымен

Зима искусственного интеллекта уже близко. Предоставлено Physics

Iyo AI Chando Yava Pedyo, Nekuda kweFizikisi

Dimri i AI është afër, me mirësjellje të fizikës

AI குளிர்காலம் அருகில் உள்ளது, இயற்பியல் உபயம்

AI qishi yaqin, fizika izni bilan

How to Manage Permissions in a Langflow Chain for LLM Queries Using Permit.io

This article outlines how to transition from singletons to dependency injection in a SwiftUI app with minimal effort. If you're here, you likely already know that you should avoid singletons with various of reasons like: Hidden Dependencies: Classes rely on singletons without explicit indicators, making it harder to track app dependencies and leading to unintended coupling.
Circular Dependencies: Singletons depending on each other can create cycles, leading to initialization deadlocks as instances wait for each other.
Testing Challenges: Using singletons complicates testing and SwiftUI previews since singletons can't easily be swapped for mocks. Lack of Flexibility: Singletons make it difficult to provide different implementations for different environments (e.g., live vs. mock versions for testing). And many others. Dependency Injection To overcome most issues we can use Dependency injection (DI). Using it, swapping in a new implementation becomes much easier, as components don’t rely on a single shared instance but on protocols that can be implemented in different ways. This promotes flexibility, easier testing, and adaptability as app requirements evolve. Constructor Injection Constructor injection is a straightforward technique for injecting dependencies by passing them through initializers. While effective, it often increases the size and complexity of initializers. A common challenge with constructor injection is passing dependencies through a deep UI hierarchy. If a dependency is only needed on a specific screen but must be passed down through multiple intermediary screens, this can quickly become cumbersome. One way to handle this is by using the Coordinator pattern, which centralizes navigation and manages dependencies more efficiently. Check out this article to learn how to make it. Why Not Use @EnvironmentObject? While @EnvironmentObject simplifies dependency sharing in SwiftUI, it has limitations: It only works with ObservableObject
It doesn’t allow using protocols for implementation switching.
It only works in SwiftUI views hierarchy
It only checks for service existence when accessed, increasing the risk of unintentional omissions in views. Dependency injection with property wrappers This is similar to the @EnvironmentObject solution where the dependency management happens inside of property wrappers, but without drawbacks. DependencyContainer This small library contains only a couple files, can be copied directly into your project or integrated via SPM. Please, make sure you put a star on it if you like it. Here is the link on GitHub. Let’s create an example of the service we need to pass as a dependency. As we are assuming that we are going to make a mock for testing, we need to implement our service as a protocol. Also we make it as ObservableObject, to review additional features of the framework. import Combine

protocol DataRepository: Actor, ObservableObject {
    
    func fetchData() async throws
}

actor DataRepositoryImp: DataRepository {
    
    func fetchData() async throws {
        // do real stuff
    }
}

actor DataRepositoryMock: DataRepository {
    
    func fetchData() async throws {
        // do mocked stuff
    }
} Defining a DI container Once integrated, you’ll set up a DI container to manage your services. Each service requires a unique key. You have no limitations in creating keys with the same service type. import DependencyContainer

extension DI {
    static let data = Key<any DataRepository>()
} Next let’s create a function for registering service in DI Container. extension DI.Container {
    
    static func setup() {
        register(DI.data, DataRepositoryImp())
    }
} Then, call this function at app startup. @main
struct ExampleApp: App {
    
    @MainActor final class AppState: ObservableObject {
        
        init() {
            DI.Container.setup()
        }
    }
    @StateObject var state = AppState()
    
    var body: some Scene {
        WindowGroup {
            MainView()
        }
    }
} Injecting Dependencies The actual injection is done with several property wrappers. The most common one is @DI.Static. You just need to pass a key we defined earlier. struct MainView: View {
    
    @DI.Static(DI.data) var data
    
    var body: some View {
        Color.clear.task {
            try? await data.fetchData()
        }
    }
} The points we need to make attention to: No Need to Specify Type. As you may have noticed we don’t need to write a type here, because Swift gets the type from the key.
It’s very convenient in case you start with a concrete type for your service and later decide to use a protocol to enable mocking. You only need to update the type in the key definition. This change propagates automatically without requiring updates to any code referencing that service. Dependency Existence Checked on Initialization. The property wrapper checks that the service exists when initialized. This helps prevent accidental circular dependencies, where services might try to reference each other. The framework enforces a clear initialization order, encouraging thoughtful dependency management and avoiding deadlocks. Additional property wrappers: @DI.Observed Use this in SwiftUI views when your service is ObservableObject. Views will be updated when the service is changed. struct MainView: View {
    
    //updates the view when data is posting objectWillChange
    @DI.Observed(DI.data) var data
    
    var body: some View {
        Color.clear.task {
            try? await data.fetchData()
        }
    }
} @DI.RePublished Should be used in ObservableObject. If your service is ObservableObject, the update will be republished further by the owner of the property wrapper. struct MainView: View {
    
    final class State: ObservableObject {
        
        // Republishes the data.objectWillChange to the State.objectWillChange
        // The view will be updated
        @DI.RePublished(DI.data) var data
    }
    
    @StateObject var state = State()
    
    var body: some View {
        Color.clear.task {
            try? await data.fetchData()
        }
    }
} In addition, if you have nested services you can use a key path in property wrappers. In this case the observing logic uses the service referenced by a keypath. @DI.Static(DI.data, \.innerService) var innerService

@DI.Observed(DI.data, \.innerService) var innerService

@DI.RePublished(DI.data, \.innerService) var innerService Mocking To swap out implementations for testing or previews, you have two options: Initialize with mocked dependency
MainView(data: .init(value: DataRepositoryMock())) Replace service with mock in DI Container
DI.Container.register(DI.data, DataRepositoryMock())
    
MainView() Conclusion This guide provides a straightforward approach to adopting DI using a DependencyContainer framework and moving away from singletons, enhancing flexibility, modularity, and testability in your iOS apps. This article outlines how to transition from singletons to dependency injection in a SwiftUI app with minimal effort. singletons dependency injection minimal If you're here, you likely already know that you should avoid singletons with various of reasons like: reasons Hidden Dependencies: Classes rely on singletons without explicit indicators, making it harder to track app dependencies and leading to unintended coupling. Circular Dependencies: Singletons depending on each other can create cycles, leading to initialization deadlocks as instances wait for each other. Testing Challenges: Using singletons complicates testing and SwiftUI previews since singletons can't easily be swapped for mocks. Hidden Dependencies: Classes rely on singletons without explicit indicators, making it harder to track app dependencies and leading to unintended coupling. Hidden Dependencies: Circular Dependencies: Singletons depending on each other can create cycles, leading to initialization deadlocks as instances wait for each other. Circular Dependencies: Testing Challenges : Using singletons complicates testing and SwiftUI previews since singletons can't easily be swapped for mocks. Testing Challenges Lack of Flexibility : Singletons make it difficult to provide different implementations for different environments (e.g., live vs. mock versions for testing). Lack of Flexibility And many others. Dependency Injection Dependency Injection To overcome most issues we can use Dependency injection (DI). Using it, swapping in a new implementation becomes much easier, as components don’t rely on a single shared instance but on protocols that can be implemented in different ways. This promotes flexibility, easier testing, and adaptability as app requirements evolve. Dependency injection protocols Constructor Injection Constructor Injection Constructor injection is a straightforward technique for injecting dependencies by passing them through initializers . While effective, it often increases the size and complexity of initializers. initializers A common challenge with constructor injection is passing dependencies through a deep UI hierarchy . If a dependency is only needed on a specific screen but must be passed down through multiple intermediary screens, this can quickly become cumbersome. deep UI hierarchy One way to handle this is by using the Coordinator pattern, which centralizes navigation and manages dependencies more efficiently. Check out this article to learn how to make it. Coordinator this article this article this article Why Not Use @EnvironmentObject ? Why Not Use @EnvironmentObject ? While @EnvironmentObject simplifies dependency sharing in SwiftUI, it has limitations : @EnvironmentObject limitations It only works with ObservableObject It doesn’t allow using protocols for implementation switching. It only works in SwiftUI views hierarchy It only checks for service existence when accessed, increasing the risk of unintentional omissions in views. It only works with ObservableObject ObservableObject It doesn’t allow using protocols for implementation switching. protocols It only works in SwiftUI views hierarchy views It only checks for service existence when accessed, increasing the risk of unintentional omissions in views. existence Dependency injection with property wrappers Dependency injection with property wrappers This is similar to the @EnvironmentObject solution where the dependency management happens inside of property wrappers , but without drawbacks. @EnvironmentObject property wrappers DependencyContainer DependencyContainer This small library contains only a couple files , can be copied directly into your project or integrated via SPM . couple files SPM Please, make sure you put a star on it if you like it. star Here is the link on GitHub . link on GitHub link on GitHub link on GitHub link on GitHub Let’s create an example of the service we need to pass as a dependency. As we are assuming that we are going to make a mock for testing, we need to implement our service as a protocol. Also we make it as ObservableObject , to review additional features of the framework. ObservableObject import Combine

protocol DataRepository: Actor, ObservableObject {
    
    func fetchData() async throws
}

actor DataRepositoryImp: DataRepository {
    
    func fetchData() async throws {
        // do real stuff
    }
}

actor DataRepositoryMock: DataRepository {
    
    func fetchData() async throws {
        // do mocked stuff
    }
} import Combine

protocol DataRepository: Actor, ObservableObject {
    
    func fetchData() async throws
}

actor DataRepositoryImp: DataRepository {
    
    func fetchData() async throws {
        // do real stuff
    }
}

actor DataRepositoryMock: DataRepository {
    
    func fetchData() async throws {
        // do mocked stuff
    }
} Defining a DI container Defining a DI container Once integrated, you’ll set up a DI container to manage your services. Each service requires a unique key. You have no limitations in creating keys with the same service type . DI container unique key. service type import DependencyContainer

extension DI {
    static let data = Key<any DataRepository>()
} import DependencyContainer

extension DI {
    static let data = Key<any DataRepository>()
} Next let’s create a function for registering service in DI Container. function extension DI.Container {
    
    static func setup() {
        register(DI.data, DataRepositoryImp())
    }
} extension DI.Container {
    
    static func setup() {
        register(DI.data, DataRepositoryImp())
    }
} Then, call this function at app startup . app startup @main
struct ExampleApp: App {
    
    @MainActor final class AppState: ObservableObject {
        
        init() {
            DI.Container.setup()
        }
    }
    @StateObject var state = AppState()
    
    var body: some Scene {
        WindowGroup {
            MainView()
        }
    }
} @main
struct ExampleApp: App {
    
    @MainActor final class AppState: ObservableObject {
        
        init() {
            DI.Container.setup()
        }
    }
    @StateObject var state = AppState()
    
    var body: some Scene {
        WindowGroup {
            MainView()
        }
    }
} Injecting Dependencies Injecting Dependencies The actual injection is done with several property wrappers. The most common one is @DI.Static . You just need to pass a key we defined earlier. injection common @DI.Static key struct MainView: View {
    
    @DI.Static(DI.data) var data
    
    var body: some View {
        Color.clear.task {
            try? await data.fetchData()
        }
    }
} struct MainView: View {
    
    @DI.Static(DI.data) var data
    
    var body: some View {
        Color.clear.task {
            try? await data.fetchData()
        }
    }
} The points we need to make attention to: attention No Need to Specify Type. As you may have noticed we don’t need to write a type here, because Swift gets the type from the key.
It’s very convenient in case you start with a concrete type for your service and later decide to use a protocol to enable mocking. You only need to update the type in the key definition. This change propagates automatically without requiring updates to any code referencing that service. No Need to Specify Type. As you may have noticed we don’t need to write a type here, because Swift gets the type from the key. It’s very convenient in case you start with a concrete type for your service and later decide to use a protocol to enable mocking. You only need to update the type in the key definition. This change propagates automatically without requiring updates to any code referencing that service. No Need to Specify Type. As you may have noticed we don’t need to write a type here, because Swift gets the type from the key. No Need to Specify Type. type It’s very convenient in case you start with a concrete type for your service and later decide to use a protocol to enable mocking. You only need to update the type in the key definition. This change propagates automatically without requiring updates to any code referencing that service. concrete type protocol Dependency Existence Checked on Initialization. The property wrapper checks that the service exists when initialized. This helps prevent accidental circular dependencies, where services might try to reference each other. The framework enforces a clear initialization order, encouraging thoughtful dependency management and avoiding deadlocks. Dependency Existence Checked on Initialization. The property wrapper checks that the service exists when initialized . This helps prevent accidental circular dependencies, where services might try to reference each other. The framework enforces a clear initialization order, encouraging thoughtful dependency management and avoiding deadlocks . Dependency Existence Checked on Initialization. initialized deadlocks Additional property wrappers: @DI.Observed Use this in SwiftUI views when your service is ObservableObject . Views will be updated when the service is changed. @DI.Observed ObservableObject struct MainView: View {
    
    //updates the view when data is posting objectWillChange
    @DI.Observed(DI.data) var data
    
    var body: some View {
        Color.clear.task {
            try? await data.fetchData()
        }
    }
} struct MainView: View {
    
    //updates the view when data is posting objectWillChange
    @DI.Observed(DI.data) var data
    
    var body: some View {
        Color.clear.task {
            try? await data.fetchData()
        }
    }
} @DI.RePublished Should be used in ObservableObject . If your service is ObservableObject , the update will be republished further by the owner of the property wrapper. @DI.RePublished ObservableObject ObservableObject struct MainView: View {
    
    final class State: ObservableObject {
        
        // Republishes the data.objectWillChange to the State.objectWillChange
        // The view will be updated
        @DI.RePublished(DI.data) var data
    }
    
    @StateObject var state = State()
    
    var body: some View {
        Color.clear.task {
            try? await data.fetchData()
        }
    }
} struct MainView: View {
    
    final class State: ObservableObject {
        
        // Republishes the data.objectWillChange to the State.objectWillChange
        // The view will be updated
        @DI.RePublished(DI.data) var data
    }
    
    @StateObject var state = State()
    
    var body: some View {
        Color.clear.task {
            try? await data.fetchData()
        }
    }
} In addition, if you have nested services you can use a key path in property wrappers. In this case the observing logic uses the service referenced by a keypath. key path observing logic @DI.Static(DI.data, \.innerService) var innerService

@DI.Observed(DI.data, \.innerService) var innerService

@DI.RePublished(DI.data, \.innerService) var innerService @DI.Static(DI.data, \.innerService) var innerService

@DI.Observed(DI.data, \.innerService) var innerService

@DI.RePublished(DI.data, \.innerService) var innerService Mocking Mocking To swap out implementations for testing or previews, you have two options: Initialize with mocked dependency
MainView(data: .init(value: DataRepositoryMock())) Initialize with mocked dependency MainView(data: .init(value: DataRepositoryMock())) Initialize with mocked dependency MainView(data: .init(value: DataRepositoryMock())) MainView(data: .init(value: DataRepositoryMock())) Replace service with mock in DI Container
DI.Container.register(DI.data, DataRepositoryMock())
    
MainView() Replace service with mock in DI Container DI.Container.register(DI.data, DataRepositoryMock())
    
MainView() Replace service with mock in DI Container DI.Container.register(DI.data, DataRepositoryMock())
    
MainView() DI.Container.register(DI.data, DataRepositoryMock())
    
MainView() Conclusion Conclusion This guide provides a straightforward approach to adopting DI using a DependencyContainer framework and moving away from singletons, enhancing flexibility, modularity, and testability in your iOS apps. DependencyContainer DependencyContainer DependencyContainer DependencyContainer

How to Stop Making Singletons in Swift: A Dependency Injection Guide

Artificial Minds, Human Consequences: Explaining AI’s Impact on Our Education, Cognition, and More

Welcome to a new edition of "This Week in JavaScript"! Today, we’ve got some HUGE new updates for the JavaScript language from TC39, Deno 2’s Official Release, TypeScript 5.7 Beta and a lot more, so let’s get right into it! TC39 Updates: What's Coming to JavaScript? The recent TC39 meeting in Tokyo brought some great updates that will make working with JavaScript even better. JavaScript features go through six different "stages" before becoming available, and here are some highlights: Iterator Helpers: Just reached stage 4! These will make handling large data sets a breeze, adding more tools to process data.
Import Attributes & JSON Modules: This update will make importing JSON files smoother, improving performance and flexibility.
Promise.try: Now in stage 4, it simplifies error handling in promises, reducing the need for extra code.
Other cool updates include Math.sumPrecise and Shared Structs, both aimed at enhancing JavaScript's efficiency. Deno 2 Official Release Deno 2just dropped, and it's packed with new features! Built to simplify JavaScript and TypeScript development, Deno 2 is now backward compatible with Node.js, which means you can run your existing Node projects smoothly. Some standout features include: 90% faster package manager: With a hot cache, Deno’s package manager is much quicker than npm, so no more waiting around for installs.
Workspaces and Monorepos: Managing large projects is now easier, especially when working with both Deno and Node.
JSR (JavaScript Registry): Makes sharing TypeScript modules across environments seamless. TypeScript 5.7 Beta TypeScript 5.7 Beta has landed, bringing with it some helpful updates: New check for never-initialized variables: A handy feature to catch bugs early.
Path rewrites for relative imports: Simplifies building and running code across Deno, Bun, and Node.js. TypeScript 5.7 is nearly feature-stable, so expect bug fixes and performance improvements in the final release. New Tools and Releases Bun v1.1.30: Now supports bundling multiple CSS files, introduces a new bun publish command, and offers faster startup times.
Node.js v20.18.0: Features experimental network inspection, helping you monitor network activity in real time.
DOCX 9.0: Allows easy generation and editing of Word files via JavaScript or TypeScript.
TinyJS: A lightweight library for DOM manipulation, perfect for simplifying HTML elements in your projects. If you found these JavaScript updates useful, give this post an upvote to help others discover it. Don’t forget to share it with your fellow developers and keep an eye out for more insights in the JavaScript world. Until next time, keep coding and stay curious! Welcome to a new edition of "This Week in JavaScript"! Today, we’ve got some HUGE new updates for the JavaScript language from TC39, Deno 2’s Official Release, TypeScript 5.7 Beta and a lot more, so let’s get right into it! TC39 Updates: What's Coming to JavaScript? TC39 Updates: What's Coming to JavaScript? The recent TC39 meeting in Tokyo brought some great updates that will make working with JavaScript even better. JavaScript features go through six different "stages" before becoming available, and here are some highlights: TC39 Iterator Helpers: Just reached stage 4! These will make handling large data sets a breeze, adding more tools to process data. Import Attributes & JSON Modules: This update will make importing JSON files smoother, improving performance and flexibility. Promise.try: Now in stage 4, it simplifies error handling in promises, reducing the need for extra code. Other cool updates include Math.sumPrecise and Shared Structs, both aimed at enhancing JavaScript's efficiency. Iterator Helpers: Just reached stage 4! These will make handling large data sets a breeze, adding more tools to process data. Iterator Helpers: Import Attributes & JSON Modules: This update will make importing JSON files smoother, improving performance and flexibility. Import Attributes & JSON Modules: Promise.try: Now in stage 4, it simplifies error handling in promises, reducing the need for extra code. Promise.try: Other cool updates include Math.sumPrecise and Shared Structs, both aimed at enhancing JavaScript's efficiency. Other cool updates Deno 2 Official Release Deno 2 Official Release Deno 2 just dropped, and it's packed with new features! Deno 2 Built to simplify JavaScript and TypeScript development, Deno 2 is now backward compatible with Node.js, which means you can run your existing Node projects smoothly. Some standout features include: Some standout features include: 90% faster package manager: With a hot cache, Deno’s package manager is much quicker than npm, so no more waiting around for installs. Workspaces and Monorepos: Managing large projects is now easier, especially when working with both Deno and Node. JSR (JavaScript Registry): Makes sharing TypeScript modules across environments seamless. 90% faster package manager: With a hot cache, Deno’s package manager is much quicker than npm, so no more waiting around for installs. 90% faster package manager: Workspaces and Monorepos: Managing large projects is now easier, especially when working with both Deno and Node. Workspaces and Monorepos: JSR (JavaScript Registry): Makes sharing TypeScript modules across environments seamless. JSR (JavaScript Registry): TypeScript 5.7 Beta TypeScript 5.7 Beta TypeScript 5.7 Beta has landed, bringing with it some helpful updates: TypeScript 5.7 Beta New check for never-initialized variables: A handy feature to catch bugs early. Path rewrites for relative imports: Simplifies building and running code across Deno, Bun, and Node.js. New check for never-initialized variables: A handy feature to catch bugs early. New check for never-initialized variables: Path rewrites for relative imports: Simplifies building and running code across Deno, Bun, and Node.js. Path rewrites for relative imports: TypeScript 5.7 is nearly feature-stable , so expect bug fixes and performance improvements in the final release. feature-stable New Tools and Releases New Tools and Releases Bun v1.1.30: Now supports bundling multiple CSS files, introduces a new bun publish command, and offers faster startup times. Node.js v20.18.0: Features experimental network inspection, helping you monitor network activity in real time. DOCX 9.0: Allows easy generation and editing of Word files via JavaScript or TypeScript. TinyJS: A lightweight library for DOM manipulation, perfect for simplifying HTML elements in your projects. Bun v1.1.30: Now supports bundling multiple CSS files, introduces a new bun publish command, and offers faster startup times. Bun v1.1.30: Node.js v20.18.0: Features experimental network inspection, helping you monitor network activity in real time. Node.js v20.18.0: DOCX 9.0: Allows easy generation and editing of Word files via JavaScript or TypeScript. DOCX 9.0: TinyJS: A lightweight library for DOM manipulation, perfect for simplifying HTML elements in your projects. TinyJS: If you found these JavaScript updates useful, give this post an upvote to help others discover it. Don’t forget to share it with your fellow developers and keep an eye out for more insights in the JavaScript world. Until next time, keep coding and stay curious!

Stories, News & Mentions about INITIALIZED

Initialized’s Stories on HackerNoon

Initialized's News & Mentions