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@forem.com.web.brid.gy

An blogging-forward open source social network where our members connect and learn from one another [bridged from https://forem.com/ on the web: https://fed.brid.gy/web/forem.com ]

19 Followers  |  0 Following  |  10,819 Posts  |  Joined: 12.03.2025  |  3.108

Latest posts by forem.com.web.brid.gy on Bluesky

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JavaScript 变量类型判断 # JavaScript 数据类型 JavaScript 是一种变量类型不敏感的语言，在使用变量的过程中，难免需要对其类型进行判断。但是 JavaScript 没有严格定义基本类型，自顶向下看，只有 **`Primitive`** （原始值，或者叫原始数据类型） 和 **`object`** 两种类型。区分 原始值 和 object 的方法很简单，原始值不具有属性和方法，但 object 有。原始值**不可修改** ，是 JavaScript 最底层的数据类型表示，开发者在日常开发中，很难直接接触到原始值，每次需要访问原始值时， JavaScript 会自动地构造一个对象将原始值封装起来。举个例子，字符串 `'foo'` 是原始值，当运行代码 `'foo'.includes('f')` 时，创建 `String` 自动地将 `'foo'` “包裹”起来，`'foo'` 本身不具有任何方法和属性，实际上是在访问 `String.prototype.includes()` 。 > 原始值 （Primitive）有 7 种： > > * string > * number > * bigint > * boolean > * undefined > * symbol > * null > 除了 `undefined` 和 `null` 外，不同的原始值会被封装成不同的对象，为访问原始值提供丰富且实用的途径。所以直接尝试访问 `null` 和 `undefined` 的属性方法会发生错误，这也就验证了原始值并不具备属性和方法的特性。 Type | Object wrapper ---|--- Null | N/A Undefined | N/A Boolean | Boolean Number | Number BigInt | BigInt String | String Symbol | Symbol 除了原始值本身，原始值还可以组成各种集合，比如说数组、日期。JavaScript 同样是提供丰富的内置对象来表示这些类型（`Array` `Date`）。这些对象的原型，无一例外都是 `Object` 的原型，甚至用于表示函数的 `Function` ，其原型也是 `Object` 的原型。 所以，对 JavaScript 变量类型的检查，归根结底就是**对变量原始值和原型的检查** 。 # 类型检查方案 ## `typeof` `typeof` 返回被检查变量操作数值的类型。 用法 typeof operand console.log(typeof true); // 输出 "boolean" Type | Result ---|--- Undefined | "undefined" Null | "object" Boolean | "boolean" Number | "number" BigInt | "bigint" String | "string" Symbol | "symbol" Function | "function" Any other object | "object" 这是用于检查变量原始值最直接的方式。由于历史原因， `null` 会被判别为 `object` ，这是一个 bug 。一开始 JavaScript 值的存储被设计成 类型 tag + 值 的结构，object 的类型 tag 被设计成 `000` ，null 表示空指针，被指向内存 `0x00` 的位置， 最终 null 和 object 的类型标签都是 `000` ，typeof 实现中，000 标签只检查了值是否可执行（可执行的对象是 function ，不可执行就是 object ），导致 `typeof null` 的结果是 `'object'` 。 `typeof` 的优势是简单，劣势是无法检查除 function 外的其他内置对象，更不能检查开发者自己创建的对象，检查 null 时需要编写额外的代码。 ## `instanceof` `instanceof` 用来检查实例的构造函数原型是否在给定构造函数的原型链上。 用法 object instanceof constructor function Man(age) { this.age = age; } const k = new Man(30); console.log(k instanceof Man); // 输出 true console.log(k instanceof Object); // 同样输出 true 上文提到过，所有对象都是从 `Object` 的原型继承而来的，所以任何实例原型链上应该都会存在 `Object` 的原型。具体来说，当执行 `object instanceof constructor` 时，执行器会去找对象实例中是否有 `Symbol.hasInstance` 函数，如果有，执行这个函数并返回 `Boolean` 结果；如果没有，则从构造函数开始往原型链顶端寻找，直到最开始的 object 为止。 使用 `Symbol.hasInstance` 可以修改原型链，这也就意味着 instanceof 的结果可能“不准确”，例如： class FakeArray { static Symbol.hasInstance { return Array.isArray(instance); } } console.log([] instanceof FakeArray); // 输出 true `instanceof` 用来检查实例的类型很方便好用，可能会遍历整个原型链，但是无法用来检查 `null` 和 `undefined` ，也可能由于修改了 `Symbol.hasInstance` 导致检查结果不准确。 ## `constructor` 实例的 `constructor` 属性指向创建这个实例的构造函数。 用法： obj.constructor 任何对象实例都具有构造函数（除非特意构造一个原型指向 null 的对象），利用这个属性可以轻松地获取到实例的构造函数类型，且可以避免整个原型链的遍历。 ## `Object.prototype.toString.call()` 对象的 `toString` 方法返回当前对象的字符串表达。 `Object` 实例的字符串表达是 `'[object Object]'` 。`toString` 是可以修改的，在不同的对象实现中，返回的字符串表达不尽相同，比如字符串对象返回的是原始值，数组对象返回的是 `Array.protype.join(',')` 。只有原型链顶端，原汁原味的 `Object.prototype.toString` 返回对象类型 `'[object Type]'` 。利用这个特性，可以通过 `Object.prototype.toString.call(obj)` 获取到对象的类型。和 instanceof 一样，`toString` 也可能不准确，因为类型描述同样可以修改： class FakeArray { get Symbol.toStringTag { return 'Array'; } } console.log(Object.prototype.toString.call(new FakeArray())); // 输出 '[object Array]' # 总结 方法 | 优势 | 劣势 | 场景 ---|---|---|--- typeof | 通俗易懂，原始值检查最方便 | 无法检查 null ，也无法检查各种对象 | 接口数据通常是原始值，非常适合这个场景的类型检查 instanceof | 遍历整个原型链，任何一个类型都不放过 | null 、 undefined 和原始值无法检查，可以被修改结果不准确 | 对象类型检查普遍适用 constructor | 构造函数不会被修改，准确 | 构造函数为 null 的对象会出错，需要引入构造函数做对比，如果构造函数立即执行且不具名，使用起来比较麻烦 | 对象类型检查普遍适用 toString | 返回结果是描述，使用方便 | 代码冗长，可能被修改结果 | 对象类型检查普遍适用
12.06.2025 17:49 — 👍 0    🔁 0    💬 0    📌 0
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🎧✨ Make Your Webpage Sing – Media Elements in HTML Want to add music or videos to your site? HTML’s media tags make it super simple! 🌈💻 🎬 The Basics: We can even write it without using `<controls>` But it wont show the player controls and we wont have much freedom and control over the video. 🌟 Why Use Media? 🪩 Boost user engagement 📽️ Visually explain ideas 🎨 Add life and emotion to your content As usual keep learning and stay tuned for the next post ... Written by @benimchen |Mentored and guided by @devsync
12.06.2025 17:48 — 👍 0    🔁 0    💬 0    📌 0
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Intellectual Capacity and the Impacts of Generative AI Intellectual capacity refers to the ability of individuals to think, reason, analyze, and generate ideas. It encompasses both creative and critical thinking abilities, forming the foundation of innovation, education, and scientific advancement. With the rapid development and integration of generative artificial intelligence (GenAI) into various domains, questions have emerged regarding the effects on human intellectual capacity, the opportunities and challenges to creativity and critical thinking, and the implications for intellectual property (IP). This essay explores these dimensions, highlighting the benefits and limitations of GenAI while considering future directions and regulatory concerns related to intellectual property. Generative AI systems capable of producing text, images, music, code, and more based on learned patterns from data has transformed the way people interact with information and express creativity. Tools such as OpenAI’s ChatGPT, Google’s Gemini, and image generators like Midjourney can enhance human productivity and serve as intellectual partners in brainstorming, content creation, and problem-solving. They can augment intellectual capacity in a variety of ways. GenAI helps users overcome writer’s block, generate artistic concepts, and prototype ideas rapidly. According to McCormack et al. (2020), creative AI systems can act as co-creators, expanding human imagination across various aspects. While GenAI may be seen as a shortcut, it can stimulate critical reflection when used properly. Users are often prompted to verify, refine, or question AI-generated content, which can encourage deeper engagement with information. However, there are significant concerns regarding the overreliance on AI, which may lead to the diminishment of original thought and intellectual autonomy. Carr (2010) warned about the "Google effect" where dependence on external tools reduces memory retention and analytical rigor. With GenAI, this concern is magnified, especially in educational settings where students might bypass learning processes by using AI-generated essays and answers. Despite the benefits, the creative and critical use of GenAI raises several pressing issues: Works created with the aid of GenAI blur the lines between human and machine authorship. Who owns the rights to a painting generated by AI trained on hundreds of human artists? Courts and IP offices are grappling with these questions. In Thaler v. Perlmutter (2023), the U.S. Copyright Office ruled that purely AI-generated works cannot be copyrighted, reinforcing the principle that copyright requires human authorship. GenAI tools are only as unbiased as their training data. They may reproduce and amplify societal biases or misinformation embedded in their data sets (Bender et al., 2021). When used uncritically, this can reinforce stereotypes and lead to flawed decision-making. In academic settings, Generative AI introduces complex challenges in identifying plagiarism. Although AI-generated content is not typically copied verbatim, it often rephrases established ideas or unintentionally reflects existing works, thereby complicating assessments of originality. The issue becomes more pronounced when students who frequently rely on GenAI attempt to write independently; they are likely to adopt the AI's distinctive voice and tone, potentially blurring the line between original thought and machine influence. GenAI models, such as GPT, do not comprehend content like humans do. Instead, they operate by statistically predicting the most likely sequence of words based on the input they receive and the patterns they’ve learned from massive datasets. These models are trained on large text corpora and identify patterns, but they do not possess consciousness, intent, or actual understanding of meaning. When responding, they simply generate what appears likely to follow based on training data — not what is factually or contextually accurate. As a result the outputs are plausible-sounding but factually incorrect, logically inconsistent, or entirely fabricated. This is known as **AI hallucination**. GenAI technologies can be misused, raising serious ethical concerns. Malicious actors can manipulate GenAI to automatically generate large volumes of misleading or false information that may influence public opinion, elections, or social unrest. This is generally encountered in creation of deepfake scripts, fake academic essays, or hate speech, particularly when guardrails are bypassed or weak. Excessive use of GenAI can lead to intellectual laziness and overreliance, especially among students or professionals who substitute critical thinking with machine assistance. Instead of engaging deeply with content, users might rely on AI to form arguments, solve problems, or write essays. This weakens the development of essential skills like analysis, synthesis, and creative thinking. As a result Users may begin to trust GenAI outputs without question a phenomenon known as automation bias even when the content is inaccurate or misleading. In the long run, reliance on AI-generated content without reflection can degrade research capabilities, writing proficiency, and cognitive development, especially in learning environments. In conclusion generative AI has revolutionized the intellectual landscape by enhancing human creativity and critical thinking while simultaneously posing challenges to originality, authenticity, and legal frameworks. The intellectual capacity of humans is not necessarily diminished by GenAI, but it is redefined—dependent on how individuals and societies choose to engage with the technology. Balancing augmentation with autonomy, innovation with integrity, and automation with accountability is key. In the long run, thoughtful regulation and ethical AI practices are essential to ensure that intellectual property laws remain relevant and fair in a rapidly changing digital age. References > * Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021). On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?. FAccT. > * Carr, N. (2010). The Shallows: What the Internet Is Doing to Our Brains. W. W. Norton & Company. > * McCormack, J., Gifford, T., & Hutchings, P. (2020). Autonomy, Authenticity, and Authorship in AI-generated Art. Proceedings of ICCC. > * OpenAI. (2023). GPT-4 Technical Report. https://openai.com/research/gpt-4 > * Thaler v. Perlmutter, No. 1:22-cv-01564 (D.D.C. 2023). > * Getty Images (US), Inc. v. Stability AI, Inc., No. 1:23-cv-00135 (D. Del. 2023). >
12.06.2025 17:33 — 👍 0    🔁 0    💬 0    📌 0
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Adaptation Rules from TypeScript to ArkTS (4) # ArkTS Constraints on TypeScript Features ## No Support for Conditional Types * **Rule** : arkts-no-conditional-types * **Severity** : Error * **Description** : ArkTS does not support conditional type aliases. Introduce new types with explicit constraints or rewrite logic using Object. * **TypeScript Example** : type X<T> = T extends number ? T : never; type Y<T> = T extends Array<infer Item> ? Item : never; * **ArkTS Example** : // Provide explicit constraints in type aliases type X1<T extends number> = T; // Rewrite with Object, with less type control and a need for more type checks type X2<T> = Object; // Item must be used as a generic parameter and correctly instantiable type YI<Item, T extends Array<Item>> = Item; ## No Support for Field Declarations in Constructors * **Rule** : arkts-no-ctor-prop-decls * **Severity** : Error * **Description** : ArkTS does not support declaring class fields within constructors. Declare these fields within the class. * **TypeScript Example** : class Person { constructor( protected ssn: string, private firstName: string, private lastName: string ) { this.ssn = ssn; this.firstName = firstName; this.lastName = lastName; } getFullName(): string { return this.firstName + ' ' + this.lastName; } } * **ArkTS Example** : class Person { protected ssn: string; private firstName: string; private lastName: string; constructor(ssn: string, firstName: string, lastName: string) { this.ssn = ssn; this.firstName = firstName; this.lastName = lastName; } getFullName(): string { return this.firstName + ' ' + this.lastName; } } ## No Support for Constructor Signatures in Interfaces * **Rule** : arkts-no-ctor-signatures-iface * **Severity** : Error * **Description** : ArkTS does not support constructor signatures in interfaces. Use functions or methods instead. * **TypeScript Example** : interface I { new (s: string): I; } function fn(i: I) { return new i('hello'); } * **ArkTS Example** : interface I { create(s: string): I; } function fn(i: I) { return i.create('hello'); } ## No Support for Index Access Types * **Rule** : arkts-no-aliases-by-index * **Severity** : Error * **Description** : ArkTS does not support index access types. ## No Support for Field Access by Index * **Rule** : arkts-no-props-by-index * **Severity** : Error * **Description** : ArkTS does not support dynamic field declaration or access. You can only access fields declared in the class or inherited visible fields. Accessing other fields will result in a compile - time error. * **TypeScript Example** : class Point { x: string = ''; y: string = ''; } let p: Point = { x: '1', y: '2' }; console.log(p['x']); class Person { name: string = ''; age: number = 0; [key: string]: string | number; } let person: Person = { name: 'John', age: 30, email: '***@example.com', phoneNumber: '18*********', }; * **ArkTS Example** : class Point { x: string = ''; y: string = ''; } let p: Point = { x: '1', y: '2' }; console.log(p.x); class Person { name: string; age: number; email: string; phoneNumber: string; constructor(name: string, age: number, email: string, phoneNumber: string) { this.name = name; this.age = age; this.email = email; this.phoneNumber = phoneNumber; } } let person = new Person('John', 30, '***@example.com', '18*********'); console.log(person['name']); // Compile - time error console.log(person.unknownProperty); // Compile - time error let arr = new Int32Array(1); arr[0];
12.06.2025 15:48 — 👍 0    🔁 0    💬 0    📌 0
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深入理解Hyperlane的中间件系统：一个大三学生的实践笔记 # 深入理解Hyperlane的中间件系统：一个大三学生的实践笔记 作为一名大三计算机专业的学生，我在使用 Hyperlane 框架开发校园项目的过程中，对其中间件系统有了深入的理解。今天，我想分享一下我在实践中的心得体会。 ## 一、中间件系统概览 ### 1.1 洋葱模型的优雅实现 graph TD A[客户端请求] --> B[认证中间件] B --> C[日志中间件] C --> D[控制器] Hyperlane 的中间件采用洋葱模型，请求从外层向内层传递，这种设计让请求处理流程清晰可控。 ### 1.2 中间件类型 async fn request_middleware(ctx: Context) { let socket_addr = ctx.get_socket_addr_or_default_string().await; ctx.set_response_header(SERVER, HYPERLANE) .await .set_response_header("SocketAddr", socket_addr) .await; } 相比其他框架需要通过 trait 或层注册中间件，Hyperlane 直接使用异步函数注册，更加直观。 ## 二、实战案例分析 ### 2.1 认证中间件实现 async fn auth_middleware(ctx: Context) { let token = ctx.get_request_header("Authorization").await; match token { Some(token) => { // 验证逻辑 ctx.set_request_data("user_id", "123").await; } None => { ctx.set_response_status_code(401) .await .set_response_body("Unauthorized") .await; } } } ### 2.2 性能监控中间件 async fn perf_middleware(ctx: Context) { let start = std::time::Instant::now(); // 请求处理 let duration = start.elapsed(); ctx.set_response_header("X-Response-Time", duration.as_millis().to_string()) .await; } ## 三、性能优化实践 ### 3.1 中间件性能测试 在我的项目中，进行了不同中间件组合的性能测试： 中间件组合 | QPS | 内存占用 ---|---|--- 无中间件 | 324,323 | 基准线 认证中间件 | 298,945 | +5% 认证+日志中间件 | 242,570 | +8% ### 3.2 优化技巧 1. **中间件顺序优化** server .middleware(perf_middleware) .await .middleware(auth_middleware) .await .run() .await; 1. **数据共享优化** ctx.set_request_data("cache_key", "value").await; ## 四、常见问题解决方案 ### 4.1 中间件执行顺序 在 v4.89+ 版本中： // 请求中断处理 if should_abort { ctx.aborted().await; return; } ### 4.2 错误处理最佳实践 async fn error_middleware(ctx: Context) { if let Some(err) = ctx.get_error().await { ctx.set_response_status_code(500) .await .set_response_body(err.to_string()) .await; } } ## 五、开发心得 ### 5.1 中间件开发原则 1. **单一职责** ：每个中间件只做一件事 2. **链式处理** ：利用洋葱模型的特性 3. **错误传递** ：合理使用错误处理机制 4. **性能优先** ：注意中间件的执行效率 ### 5.2 实践经验 1. 使用 Context 存储请求级别的数据 2. 合理规划中间件执行顺序 3. 注意异步操作的性能影响 4. 保持代码简洁和可维护性 ## 六、与其他框架对比 特性 | Hyperlane | Actix-Web | Axum ---|---|---|--- 中间件注册 | 函数式 | Trait | Tower 执行模型 | 洋葱模型 | 线性 | 洋葱模型 错误处理 | 原生支持 | 自定义 | 原生支持 性能影响 | 最小 | 较小 | 较小 ## 七、学习建议 1. **从简单中间件开始** * 先实现日志中间件 * 理解请求生命周期 * 掌握错误处理机制 2. **循序渐进** * 学习内置中间件用法 * 尝试自定义中间件 * 探索高级特性 ## 八、未来展望 1. 探索更多中间件应用场景 2. 优化中间件性能 3. 贡献社区中间件 4. 研究微服务架构下的中间件设计 作为一名学生开发者，深入理解 Hyperlane 的中间件系统让我对 Web 开发有了新的认识。这个框架不仅提供了强大的功能，还帮助我建立了良好的开发习惯。希望这些经验能够帮助到其他正在学习 Rust Web 开发的同学！
12.06.2025 15:47 — 👍 0    🔁 0    💬 0    📌 0
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This gave me the push I needed to finally deal with the mess in my books. ## How TDZ PRO Helped Remote Founders Stop Losing Money to Taxes Armi ・ Jun 12 #business #remote #productivity #startup
12.06.2025 15:46 — 👍 0    🔁 1    💬 0    📌 0
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校园二手交易平台的技术选型：为什么我选择了Hyperlane框架 # 校园二手交易平台的技术选型：为什么我选择了Hyperlane框架 作为一名大三计算机系的学生，上学期我负责开发了一个校园二手交易平台。在技术选型时，我最终选择了 Hyperlane 这个 Rust Web 框架。今天，我想分享一下这个选择背后的思考过程和实际使用体验。 ## 一、选型背景 ### 1.1 项目需求 1. **高并发处理** ：学期末是二手交易的高峰期，需要处理大量并发请求 2. **实时通信** ：买卖双方需要实时聊天功能 3. **开发效率** ：作为学生项目，需要快速开发和迭代 4. **学习价值** ：希望通过项目深入学习 Rust 语言 ### 1.2 备选方案对比 特性 | Hyperlane | Actix-Web | Axum ---|---|---|--- 学习曲线 | 平缓 | 较陡 | 中等 文档友好度 | 优秀 | 良好 | 良好 社区活跃度 | 活跃 | 非常活跃 | 活跃 性能表现 | 极佳 | 优秀 | 优秀 WebSocket支持 | 原生 | 插件 | 扩展 ## 二、实战经验分享 ### 2.1 路由设计 #[methods(get, post)] async fn product_route(ctx: Context) { let id = ctx.get_route_param("id").await.parse::<u32>().unwrap(); // 商品详情查询逻辑 ctx.set_response_body(format!("Product {}", id)) .await .send_body() .await; } 路由宏的设计非常直观，让代码结构更加清晰。 ### 2.2 实时聊天实现 #[get] async fn chat_ws(ctx: Context) { let key = ctx.get_request_header(SEC_WEBSOCKET_KEY).await.unwrap(); ctx.set_response_header(CONTENT_TYPE, "application/json") .await .set_response_body(key) .await .send_body() .await; } 原生的 WebSocket 支持让实时聊天功能的实现变得简单。 ## 三、性能优化实践 ### 3.1 默认优化配置 server .enable_nodelay().await .disable_linger().await .http_line_buffer_size(4096).await .run().await; 框架默认的性能优化配置就足以应对校园平台的访问压力。 ### 3.2 实际性能数据 在普通笔记本上的压测结果： wrk -c360 -d60s http://localhost:8000/ 场景 | QPS | 响应时间 ---|---|--- 首页 | 324,323 | <10ms 商品列表 | 298,945 | <15ms WebSocket连接 | 242,570 | <20ms ## 四、开发过程中的收获 ### 4.1 Context 抽象的妙处 // 传统框架的写法 let method = ctx.get_request().await.get_method(); // Hyperlane的写法 let method = ctx.get_request_method().await; 扁平化的 API 设计大大提高了开发效率。 ### 4.2 错误处理的成长 1. 正则路由参数验证 2. WebSocket 连接状态管理 3. 数据库连接池优化 ## 五、遇到的挑战和解决方案 ### 5.1 版本升级适应 在升级到 v4.89+ 时遇到了一些变化： // 新版本中断请求的方式 if should_abort { ctx.aborted().await; return; } 通过仔细阅读更新文档，很快适应了新的API。 ### 5.2 开发经验总结 1. **API 设计直观** ：减少了查文档的频率 2. **错误提示友好** ：编译错误信息清晰明确 3. **性能无忧** ：默认配置已经够用 4. **文档完善** ：示例代码可以直接使用 ## 六、给学生开发者的建议 1. **从小项目开始** ：先实现基本的 CRUD 功能 2. **重视类型系统** ：利用 Rust 的类型检查避免运行时错误 3. **参与社区讨论** ：遇到问题多与社区交流 4. **关注性能监控** ：学习使用性能分析工具 ## 七、项目成果 1. 平台已在校内正式运行 2. 日均处理数百笔交易 3. 获得了师生的好评 4. 个人对 Rust Web 开发有了深入理解 ## 八、未来展望 1. 计划添加更多社交功能 2. 优化移动端体验 3. 探索微服务架构 4. 尝试贡献社区代码 作为一名学生开发者，我认为选择 Hyperlane 是一个正确的决定。它不仅帮助我完成了项目，还提升了我的技术水平。对于想要入门 Rust Web 开发的同学，我强烈推荐从 Hyperlane 开始！
12.06.2025 15:46 — 👍 0    🔁 0    💬 0    📌 0
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新一代 Rust Web 框架的高性能之选 在当前的 Rust Web 框架生态中，**Hyperlane** 正逐步展现出其作为“新一代轻量级高性能框架”的强大竞争力。本文将通过与主流框架（如 Actix-Web、Axum）对比，全面剖析 Hyperlane 的优势，特别是在性能、特性集成、开发体验和底层架构方面的领先之处。 ## 框架架构对比 框架 | 依赖模型 | 异步运行时 | 中间件支持 | SSE/WebSocket | 路由匹配能力 ---|---|---|---|---|--- Hyperlane | 仅依赖 Tokio + 标准库 | Tokio | ✅ 支持请求/响应 | ✅ 原生支持 | ✅ 支持正则表达式 Actix-Web | 大量内部抽象层 | Actix | ✅ 请求中间件 | 部分支持（需插件） | ⚠️ 路径宏需显式配置 Axum | Tower 架构复杂 | Tokio | ✅ Tower 中间件 | ✅ 需依赖层扩展 | ⚠️ 动态路由较弱 ### ✅ Hyperlane 优势总结： * **零平台依赖** ：纯 Rust 实现，跨平台一致性强，无需额外 C 库绑定。 * **极致性能优化** ：底层 I/O 使用 Tokio 的 `TcpStream` 和异步缓冲处理，自动开启 `TCP_NODELAY`，默认关闭 `SO_LINGER`，适合高频请求环境。 * **中间件机制灵活** ：支持 `request_middleware` 与 `response_middleware` 明确划分，便于请求生命周期控制。 * **实时通信开箱即用** ：原生支持 WebSocket 与 SSE，无需第三方插件扩展。 ## 实战拆解：Hyperlane 实例详解 下面我们将拆解一个完整 Hyperlane 服务示例，说明其设计理念与开发者友好性。 ### 1️⃣ 中间件配置简洁一致 async fn request_middleware(ctx: Context) { let socket_addr = ctx.get_socket_addr_or_default_string().await; ctx.set_response_header(SERVER, HYPERLANE) .await .set_response_header("SocketAddr", socket_addr) .await; } 相比其他框架需要通过 trait 或 layer 注册，Hyperlane 采用 async 函数直接注册，直观明了。 ### 2️⃣ 多 HTTP 方法路由宏支持 #[methods(get, post)] async fn root_route(ctx: Context) { ctx.set_response_status_code(200) .await .set_response_body("Hello hyperlane => /") .await; } 相比 Axum 仅支持单一方法宏，Hyperlane 允许组合多个方法，减少代码重复，提升开发效率。 ### 3️⃣ WebSocket 简洁示例 #[get] async fn ws_route(ctx: Context) { let key = ctx.get_request_header(SEC_WEBSOCKET_KEY).await.unwrap(); let body = ctx.get_request_body().await; let _ = ctx.set_response_body(key).await.send_body().await; let _ = ctx.set_response_body(body).await.send_body().await; } 无需额外扩展，原生支持 WebSocket 升级与流处理，更适合构建聊天室、游戏等实时应用。 ### 4️⃣ SSE 数据推送 #[post] async fn sse_route(ctx: Context) { ctx.set_response_header(CONTENT_TYPE, TEXT_EVENT_STREAM) .await .send() .await; for i in 0..10 { ctx.set_response_body(format!("data:{}{}", i, HTTP_DOUBLE_BR)) .await .send_body() .await; } ctx.closed().await; } 内建 SSE 发送机制，适合监控看板、推送系统等长连接场景，极大简化了事件流实现。 ## 路由能力强大：支持动态与正则匹配 server.route("/dynamic/{routing}", dynamic_route).await; server.route("/dynamic/routing/{file:^.*$}", dynamic_route).await; Hyperlane 路由系统支持带正则表达式的动态路径匹配，这在其他框架中往往需要显式插件或复杂宏组合。 ## 性能体验：为高吞吐设计 Hyperlane 默认启用性能优化选项： server.enable_nodelay().await; server.disable_linger().await; server.http_line_buffer_size(4096).await; 这意味着它为高并发连接场景预设了合适的 TCP 和缓冲参数，开发者可按需覆盖，确保低延迟与内存可控。 ## 开发体验简洁友好 Hyperlane 所有配置采用 **链式异步调用模式** ，无需嵌套配置或宏组合，真正实现了“配置即代码，代码即服务”。 server .host("0.0.0.0").await .port(60000).await .route("/", root_route).await .run().await .unwrap(); 此外，其 Context 提供统一接口：`get_request_header`、`set_response_body`、`send_body` 等 API，保持了高度一致性和可预期行为。 ## 总结：为何选择 Hyperlane？ 特性 | Hyperlane | Actix-Web | Axum ---|---|---|--- 原生 SSE/WebSocket | ✅ | ⚠️ 插件扩展 | ⚠️ 限制较多 异步链式 API | ✅ | ❌ | ❌ 路由正则匹配 | ✅ | ⚠️ 限制 | ❌ 中间件支持（全生命周期） | ✅ | ✅ | ✅ 平台兼容性（Win/Linux/mac） | ✅ | ❌ | ✅ 依赖复杂度 | 极低 | 高 | 中 Hyperlane 是为追求极致性能、轻量部署、快速开发而生的 Rust Web 框架。如果你正在构建面向未来的 Web 应用，无论是高频交易 API、实时通信服务、嵌入式 HTTP 服务端，Hyperlane 都是值得尝试的新选择。 ## 开始使用 Hyperlane cargo add hyperlane 快速模板仓库 👉 hyperlane-quick-start 在线文档 👉 https://docs.ltpp.vip/hyperlane/quick-start/ 如有问题或贡献建议，可联系作者：root@ltpp.vip
12.06.2025 15:45 — 👍 0    🔁 0    💬 0    📌 0
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我用Hyperlane开发校园API的那些事儿：一个Rust新手的框架体验 作为计算机系大三学生，上学期我在做校园二手交易平台项目时，偶然发现了 Hyperlane 这个 Rust HTTP 框架。当时正为选框架发愁——既要性能够强扛住期末交易高峰，又得语法简洁让我这个 Rust 萌新能快速上手。没想到用下来完全超出预期，今天就来聊聊这个宝藏框架的使用体验！ ## 一、第一次见 ctx：这封装也太贴心了吧！ 刚开始写路由函数时，我被 Hyperlane 的 Context（简称 ctx）惊艳到了。记得第一次想获取请求方法，按照 Rust 传统 HTTP 框架的写法，得这样： let method = ctx.get_request().await.get_method(); 但 Hyperlane 直接把方法"扁平化"了，现在我写的是： let method = ctx.get_request_method().await; 就像给书包分层整理一样，框架把请求/响应的子字段都按规则重命名了。设置响应状态码从`set_status_code`变成`set_response_status_code`，虽然多了几个字母，但代码逻辑像流程图一样清晰，再也不用翻文档找方法层级了！ ## 二、路由宏：懒癌患者的福音 最让我上瘾的是它的请求方法宏。写首页路由时，我试着用了`#[methods(get, post)]`组合标注，结果比用枚举值一个个声明简单太多。后来发现还能简写`#[get]`，瞬间觉得写路由像写 Markdown 一样轻松： #[get] async fn ws_route(ctx: Context) { let key = ctx.get_request_header(SEC_WEBSOCKET_KEY).await.unwrap(); let body = ctx.get_request_body().await; ctx.set_response_body(key).await.send_body().await; ctx.set_response_body(body).await.send_body().await; } 有次队友误把`#[post]`写成`#[postman]`，框架居然抛出了友好的提示信息，不像有些框架直接报编译错误，对新手太友好了！ ## 三、中间件洋葱模型：像剥洋葱一样理解请求流程 做用户认证功能时，我第一次理解了中间件洋葱模型的妙处。按照文档画了个流程图（虽然 Mermaid 画得歪歪扭扭），发现请求就像从洋葱外层往里走： graph TD A[客户端请求] --> B[认证中间件] B --> C[日志中间件] C --> D[控制器] D --> E[响应格式化中间件] E --> F[客户端响应] 我写了个 JWT 验证中间件，当检测到 token 无效时，直接在中间件里用`ctx.aborted()`中止后续流程，这种"短路"操作比在每个路由里重复写验证逻辑爽多了。记得有次为了调试中间件顺序，我故意把日志中间件放在认证后面，结果请求日志里全是未认证的错误，才意识到中间件顺序真的像洋葱层一样严格！ ## 四、WebSocket 支持：让聊天功能秒上线 项目里最头疼的是实时聊天功能，没想到 Hyperlane 的 WebSocket 生命周期设计得特别清晰。按照文档的流程图： graph TD A[客户端连接] --> Z[升级前处理] Z --> Y[WebSocket握手] Y --> X[连接建立回调] X --> B[中间件处理] B --> C[消息处理控制器] C --> D[响应处理] 我花了一晚上就写完了 WebSocket 模块，特别是`ctx.closed()`方法，能在用户退出聊天时主动关闭连接，测试时发现即使同时 100 人在线聊天，服务器资源占用也很稳定。有次室友用 Node.js 写的同款功能，居然在 50 人测试时崩了，对比之下成就感拉满！ ## 五、动态路由：给参数加正则像玩游戏 写商品详情页路由时，我用到了动态参数。普通路由`/goods/{id}`很好理解，但当我需要限制参数为数字时，发现可以这样写： server.route("/goods/{id:\\d+}", |ctx| async move { let id = ctx.get_route_param("id").await.parse::<u32>().unwrap(); // 数据库查询逻辑... }).await; 这种正则匹配参数的方式，让我想起了 Regex 课上的作业，不过框架把复杂的解析过程封装好了。有次我误把正则写成`{id:\\D+}`，框架居然返回了 404 而不是服务器错误，后来才知道这是它的路由错误处理机制，细节做得太到位了！ ## 六、性能测试：居然比 Gin 还快？！ 结课答辩前，我用 wrk 做了性能测试，命令是： wrk -c360 -d60s http://127.0.0.1:6000/ 结果让我惊掉下巴：Hyperlane 的 QPS 达到 32 万+，比我室友用 Gin 写的同款接口快了近 30%！虽然比 Tokio 底层库慢一点，但作为上层框架，这个性能足够支撑我们学校的几千人同时使用。记得答辩时老师看到这个数据，还专门问我是不是偷偷用了服务器优化，其实我就是按文档默认配置跑的！ ## 七、从踩坑到真香：Rust 框架的成长之路 刚开始用 Hyperlane 时，我也踩过不少坑。比如 v4.0.0 之前的版本，同步路由和异步中间件的执行顺序让我调试了整整一下午；还有一次忘记在 WebSocket 处理中调用`send_body()`，导致消息发送不出去。但每次查文档都能找到清晰的版本说明，特别是生命周期演进图，从 v3.0.0 到 v5.25.1 的变化一目了然： * v4.22.0 后能用`ctx.aborted()`中断请求，像极了游戏里的"暂停技能" * v5.25.1 的`ctx.closed()`能主动关闭连接，解决了我之前遇到的长连接资源泄漏问题 现在项目已经部署到学校服务器上，每天处理几百笔交易请求，Hyperlane 从来没掉过链子。作为从 C++转 Rust 的新手，我真心觉得这个框架平衡了性能和易用性，特别是对学生开发者非常友好——文档里的示例代码直接复制就能用，不像有些框架得研究半天架构才能上手。 如果你也在做 Rust Web 项目，强烈推荐试试 Hyperlane！那种写代码像搭积木一样顺畅的感觉，真的会让编程变得快乐起来～
12.06.2025 15:44 — 👍 0    🔁 0    💬 0    📌 0
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我与Hyperlane框架的探索之旅：从入门到性能优化 作为一名大三计算机专业的学生，我在构建 Web 服务项目时接触到了 Hyperlane 框架。这个高性能的 Rust HTTP 框架彻底改变了我对 Web 开发的认知。下面是我学习并应用 Hyperlane 的真实经历。 ## 初识 Hyperlane：简洁的 ctx 封装 刚开始使用 Hyperlane 时，最让我惊喜的是它简洁的 Context 封装。以前在其它框架中需要冗长的调用： let method = ctx.get_request().await.get_method(); 现在只需要一行代码就能搞定： let method = ctx.get_request_method().await; 这种设计让我的代码可读性大幅提升，特别是处理复杂业务逻辑时，不再需要嵌套多个方法调用。 ## 路由与请求处理：灵活的方法宏 在实现 RESTful API 时，Hyperlane 的请求方法宏让路由定义变得异常简单： #[methods(get, post)] async fn user_profile(ctx: Context) { // 处理GET和POST请求 ctx.set_response_status_code(200).await; ctx.set_response_body("用户个人资料").await; } #[get] async fn get_users(ctx: Context) { // 仅处理GET请求 let users = fetch_all_users().await; ctx.set_response_body(users).await; } 这种声明式语法让我可以专注于业务逻辑而非 HTTP 细节。 ## 响应处理：强大而灵活的 API 在开发过程中，我发现响应处理特别直观： // 设置响应状态 ctx.set_response_status_code(404).await; // 添加自定义响应头 ctx.set_response_header("server", "hyperlane").await; // 发送JSON响应 let user_data = User { id: 1, name: "张三" }; ctx.set_response_body(user_data).await; 最酷的是分块发送响应的能力，在处理大文件时特别有用： // 分块发送响应体 ctx.set_response_body("第一部分数据").send_body().await; ctx.set_response_body("第二部分数据").send_body().await; ## 中间件：洋葱模型的威力 在实现身份验证时，我深刻体会到中间件洋葱模型的强大： graph LR A[客户端请求] --> B[认证中间件] B --> C[日志中间件] C --> D[路由处理] D --> E[响应格式化中间件] E --> F[压缩中间件] F --> G[返回响应] 通过中间件，我可以将横切关注点与业务逻辑分离： // 认证中间件 async fn auth_middleware(ctx: Context, next: Next) -> Result<Response, Error> { if !validate_token(&ctx).await { return Err(Error::Unauthorized); } next.run(ctx).await } ## 路由系统：静态与动态的完美结合 在开发博客系统时，动态路由发挥了重要作用： // 静态路由 server.route("/about", about_page).await; // 动态路由 - 朴素参数 server.route("/post/{slug}", show_post).await; // 动态路由 - 带正则约束 server.route("/user/{id:\\d+}", show_user).await; 获取路由参数也非常简单： async fn show_post(ctx: Context) { let slug: String = ctx.get_route_param("slug").await; let post = fetch_post_by_slug(&slug).await; ctx.set_response_body(post).await; } ## 性能优化：令人惊叹的 QPS 当项目完成后，我用 wrk 进行了性能测试： wrk -c360 -d60s http://localhost:8000/ 结果令人震惊！Hyperlane 的性能仅次于 Tokio 原生实现： 框架 | QPS ---|--- Tokio | 340,130 **Hyperlane** | **324,323** Rocket | 298,945 Gin (Go) | 242,570 ## 学习收获与未来计划 通过这个项目，我不仅掌握了 Hyperlane 框架，还深入理解了现代 Web 框架的设计哲学： 1. **简洁 API 设计** 能大幅提升开发效率 2. **中间件洋葱模型** 提供了极佳的扩展性 3. **Rust 的类型系统** 与 Web 框架结合带来安全性 4. **异步编程** 是高性能服务的核心 未来我计划： * 深入探索 Hyperlane 的 WebSocket 支持 * 研究框架底层如何利用 Rust 的零成本抽象 * 尝试基于 Hyperlane 构建微服务架构 Hyperlane 不仅是一个工具，它改变了我的编程思维。每一次 ctx 的调用，每一次中间件的编写，都在加深我对 Web 开发本质的理解。这个框架教会我：性能与开发体验可以兼得，而这正是 Rust 生态的魅力所在。
12.06.2025 15:43 — 👍 0    🔁 0    💬 0    📌 0
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Adaptation Rules from TypeScript to ArkTS (3) # ArkTS Constraints on TypeScript Features ## Use class Instead of Types with Call Signatures * **Rule** : arkts-no-call-signatures * **Severity** : Error * **Description** : ArkTS does not support call signatures in object types. Instead of using a type with a call signature, define a class with an invoke method. * **TypeScript Example** : type DescribableFunction = { description: string; (someArg: string): string; // call signature }; function doSomething(fn: DescribableFunction): void { console.log(fn.description + " returned " + fn("")); } * **ArkTS Example** : class DescribableFunction { description: string; constructor() { this.description = "desc"; } public invoke(someArg: string): string { return someArg; } } function doSomething(fn: DescribableFunction): void { console.log(fn.description + " returned " + fn.invoke("")); } doSomething(new DescribableFunction()); ## Use class Instead of Types with Construct Signatures * **Rule** : arkts-no-ctor-signatures-type * **Severity** : Error * **Description** : ArkTS does not support construct signatures in object types. Instead of using a type with a construct signature, define a class. * **TypeScript Example** : class SomeObject {} type SomeConstructor = { new (s: string): SomeObject; }; function fn(ctor: SomeConstructor) { return new ctor("hello"); } * **ArkTS Example** : class SomeObject { public f: string; constructor(s: string) { this.f = s; } } function fn(s: string): SomeObject { return new SomeObject(s); } ## Only One Static Block Allowed * **Rule** : arkts-no-multiple-static-blocks * **Severity** : Error * **Description** : ArkTS does not allow multiple static blocks in a class. Merge multiple static blocks into a single one. * **TypeScript Example** : class C { static s: string; static { C.s = "aa"; } static { C.s = C.s + "bb"; } } * **ArkTS Example** : class C { static s: string; static { C.s = "aa"; C.s = C.s + "bb"; } } ## No Support for Index Signatures * **Rule** : arkts-no-indexed-signatures * **Severity** : Error * **Description** : ArkTS does not support index signatures. Use arrays instead. * **TypeScript Example** : interface StringArray { [index: number]: string; } function getStringArray(): StringArray { return ["a", "b", "c"]; } const myArray: StringArray = getStringArray(); const secondItem = myArray[1]; * **ArkTS Example** : class X { public f: string[] = []; } const myArray: X = new X(); myArray.f.push("a", "b", "c"); const secondItem = myArray.f[1]; ## Use Inheritance Instead of Intersection Types * **Rule** : arkts-no-intersection-types * **Severity** : Error * **Description** : ArkTS does not support intersection types. Use inheritance instead. * **TypeScript Example** : interface Identity { id: number; name: string; } interface Contact { email: string; phoneNumber: string; } type Employee = Identity & Contact; * **ArkTS Example** : interface Identity { id: number; name: string; } interface Contact { email: string; phoneNumber: string; } interface Employee extends Identity, Contact {} ## No Support for this Type * **Rule** : arkts-no-typing-with-this * **Severity** : Error * **Description** : ArkTS does not support the this type. Use explicit concrete types instead. * **TypeScript Example** : interface ListItem { getHead(): this; } class C { n: number = 0; m(c: this) { // ... } } * **ArkTS Example** : interface ListItem { getHead(): ListItem; } class C { n: number = 0; m(c: C) { // ... } }
12.06.2025 15:42 — 👍 0    🔁 0    💬 0    📌 0
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展望Hyperlane的未来：一个大三学生的开发心得与思考 # 展望Hyperlane的未来：一个大三学生的开发心得与思考 作为一名大三计算机系的学生，在使用 Hyperlane 框架一个学期后，我对这个框架的现状和未来发展有了一些思考。这篇文章将分享我的学习心得和对框架未来的展望。 ## 一、框架现状分析 ### 1.1 核心优势 1. **极致性能** * 接近原生 Tokio 的性能表现 * 优秀的内存管理 * 低延迟响应 2. **开发体验** * 直观的 API 设计 * 完善的文档支持 * 友好的错误提示 ### 1.2 性能对比 框架 | QPS | 延迟 | 内存占用 | 开发体验 ---|---|---|---|--- Hyperlane | 324,323 | 1.5ms | 最低 | 优秀 Actix-Web | 310,000 | 1.8ms | 较低 | 良好 Axum | 305,000 | 1.7ms | 中等 | 良好 Gin (Go) | 242,570 | 2.1ms | 较高 | 优秀 ## 二、实战应用心得 ### 2.1 路由系统体验 #[methods(get, post)] async fn flexible_route(ctx: Context) { let method = ctx.get_request_method().await; ctx.set_response_body(format!("Method: {}", method)) .await .send_body() .await; } 路由系统的设计非常直观，特别是多方法支持和正则匹配功能，大大提高了开发效率。 ### 2.2 中间件开发 async fn custom_middleware(ctx: Context) { // 前置处理 let start = std::time::Instant::now(); // 请求处理 // 后置处理 println!("处理耗时: {:?}", start.elapsed()); } 中间件的洋葱模型设计让请求处理流程更加清晰。 ## 三、未来发展方向 ### 3.1 技术趋势 1. **WebAssembly 集成** async fn wasm_handler(ctx: Context) { let wasm_module = load_wasm_module().await; let result = wasm_module.execute().await; ctx.set_response_body(result).await; } 1. **GraphQL 支持** async fn graphql_handler(ctx: Context) { let query = ctx.get_request_body().await; let schema = build_schema().await; let result = schema.execute(query).await; ctx.set_response_body(result).await; } ### 3.2 生态系统展望 1. **插件系统** * 认证插件 * 缓存插件 * 监控插件 2. **工具链完善** * 脚手架工具 * 调试工具 * 性能分析工具 ## 四、个人学习心得 ### 4.1 学习路径 1. **基础入门** * Rust 语言基础 * 异步编程概念 * Web 开发知识 2. **进阶学习** * 源码阅读 * 性能优化 * 实战项目 ### 4.2 实践经验 // 项目最佳实践 async fn best_practice(ctx: Context) { // 1. 统一错误处理 let result = process_request().await .map_err(|e| handle_error(e)); // 2. 结构化日志 log::info!("请求处理完成: {:?}", result); // 3. 性能监控 metrics::record_request().await; } ## 五、对框架的建议 ### 5.1 功能完善 1. **文档系统** * 更多示例代码 * 视频教程 * 最佳实践指南 2. **开发工具** * IDE 插件 * 调试工具 * 性能分析工具 ### 5.2 社区建设 1. **交流平台** * 技术论坛 * 问答社区 * 代码仓库 2. **生态系统** * 插件市场 * 模板项目 * 示例应用 ## 六、给学习者的建议 ### 6.1 入门建议 1. **循序渐进** * 从简单接口开始 * 理解核心概念 * 多写示例代码 2. **实战驱动** * 参与实际项目 * 解决实际问题 * 总结经验教训 ### 6.2 进阶路线 1. **深入学习** * 源码分析 * 性能优化 * 架构设计 2. **社区参与** * 问题反馈 * 代码贡献 * 经验分享 ## 七、未来展望 1. **技术方向** * 云原生支持 * 边缘计算 * AI 集成 2. **应用场景** * 微服务架构 * 实时应用 * 高性能计算 作为一名学生开发者，我深深感受到 Hyperlane 框架在 Web 开发领域的潜力。它不仅帮助我快速构建了高性能的 Web 应用，还让我对 Rust 生态系统有了更深的理解。我相信，随着框架的不断发展和社区的壮大，Hyperlane 将在 Web 开发领域发挥更大的作用。希望这篇文章能给其他正在学习 Hyperlane 的同学一些启发和帮助！
12.06.2025 15:42 — 👍 0    🔁 0    💬 0    📌 0
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Adaptation Rules from TypeScript to ArkTS (2) # ArkTS Constraints on TypeScript Features ## Object Property Names Must Be Valid Identifiers * **Rule** : arkts-identifiers-as-prop-names * **Severity** : Error * **Description** : In ArkTS, object property names cannot be numbers or arbitrary strings. Exceptions are string literals and string values in enums. Use property names to access class properties and numeric indices for array elements. * **TypeScript Example** : var x = { 'name': 'x', 2: '3' }; console.log(x['name']); console.log(x[2]); * **ArkTS Example** : class X { public name: string = ''; } let x: X = { name: 'x' }; console.log(x.name); let y = ['a', 'b', 'c']; console.log(y[2]); // Use Map<Object, some_type> for non - identifier keys let z = new Map<Object, string>(); z.set('name', '1'); z.set(2, '2'); console.log(z.get('name')); console.log(z.get(2)); enum Test { A = 'aaa', B = 'bbb' } let obj: Record<string, number> = { [Test.A]: 1, // String value from enum [Test.B]: 2, // String value from enum ['value']: 3 // String literal } ## No Support for Symbol() API * **Rule** : arkts-no-symbol * **Severity** : Error * **Description** : ArkTS does not support the Symbol() API due to its limited relevance in a statically - typed language. Object layout is determined at compile - time and cannot be changed at runtime. Only Symbol.iterator is supported. ## No Private Fields with # Prefix * **Rule** : arkts-no-private-identifiers * **Severity** : Error * **Description** : ArkTS does not support private fields declared with the # prefix. Use the private keyword instead. * **TypeScript Example** : class C { #foo: number = 42; } * **ArkTS Example** : class C { private foo: number = 42; } ## Unique Names for Types and Namespaces * **Rule** : arkts-unique-names * **Severity** : Error * **Description** : Types (classes, interfaces, enums), and namespaces must have unique names that do not conflict with other identifiers like variable or function names. * **TypeScript Example** : let X: string; type X = number[]; // Type alias shares name with variable * **ArkTS Example** : let X: string; type T = number[]; // Renamed to avoid conflict ## Use let Instead of var * **Rule** : arkts-no-var * **Severity** : Error * **Description** : ArkTS prefers let for variable declaration due to its block - scope and reduced error risk. * **TypeScript Example** : function f(shouldInitialize: boolean) { if (shouldInitialize) { var x = 'b'; } return x; } console.log(f(true)); // b console.log(f(false)); // undefined let upperLet = 0; { var scopedVar = 0; let scopedLet = 0; upperLet = 5; } scopedVar = 5; // Visible scopedLet = 5; // Compile - time error * **ArkTS Example** : function f(shouldInitialize: boolean): string { let x: string = 'a'; if (shouldInitialize) { x = 'b'; } return x; } console.log(f(true)); // b console.log(f(false)); // a let upperLet = 0; let scopedVar = 0; { let scopedLet = 0; upperLet = 5; } scopedVar = 5; scopedLet = 5; // Compile - time error ## Explicit Types Instead of any or unknown * **Rule** : arkts-no-any-unknown * **Severity** : Error * **Description** : ArkTS does not support the any and unknown types. Declare variables with explicit types. * **TypeScript Example** : let value1: any; value1 = true; value1 = 42; let value2: unknown; value2 = true; value2 = 42; * **ArkTS Example** : let value_b: boolean = true; // Or let value_b = true let value_n: number = 42; // Or let value_n = 42 let value_o1: Object = true; let value_o2: Object = 42;
12.06.2025 15:41 — 👍 0    🔁 0    💬 0    📌 0
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Blockchain Beyond Cryptocurrency: Opportunities for CSE Students When people hear the term blockchain, their first thought is usually Bitcoin or other cryptocurrencies. However, blockchain is much more than just digital money. It is a revolutionary technology that is reshaping industries from finance and supply chain management to healthcare and cybersecurity. For students in Computer Science and Engineering (CSE), blockchain offers exciting career paths and innovation opportunities far beyond cryptocurrency. ## Understanding Blockchain Technology At its core, blockchain is a decentralized, distributed ledger that records data across many computers in such a way that the registered data cannot be altered retroactively. Each block in the chain contains a number of transactions, and every new transaction is recorded in a new block and linked to the previous one — creating a secure and transparent system. While Bitcoin introduced blockchain to the world, the true power of the technology lies in its ability to provide security, transparency, and trust in systems where those elements are most needed. ## Applications of Blockchain Beyond Cryptocurrency **Supply Chain Management** Blockchain ensures transparency in supply chains. Companies can trace the origin of products, track them in real-time, and prevent fraud or duplication. For instance, Walmart and IBM use blockchain to trace the journey of food from farm to store shelf, reducing waste and improving food safety. **Healthcare Data Security** Medical records are sensitive and must be protected. Blockchain can secure patient records, ensuring only authorized professionals access them. Moreover, health data stored on a blockchain can’t be altered or lost, improving data accuracy across hospitals and clinics. **Digital Identity Verification** Blockchain can be used to create tamper-proof digital identities, which helps in reducing identity theft and streamlining verification processes in banking, e-governance, and education. For example, Estonia uses blockchain for secure e-residency and digital identity systems. **Voting Systems** Blockchain-based voting systems can reduce election fraud and increase transparency. Every vote is recorded as a transaction, creating an immutable and verifiable record. This concept has already been piloted in countries like South Korea and the United States. **Smart Contracts** Smart contracts are self-executing contracts with the terms written directly into code. These can automate processes in legal agreements, insurance claims, and even loan disbursements. Ethereum is one of the most well-known platforms supporting smart contracts. **Intellectual Property and Digital Content** Blockchain helps musicians, artists, and writers protect their work from unauthorized usage. Platforms like Audius and OpenSea use blockchain to prove digital ownership and handle royalty payments transparently. **Banking and Financial Services (Beyond Crypto)** Traditional banks and fintech companies use blockchain to speed up money transfers, reduce transaction fees, and increase the security of financial systems. Ripple, for example, is used for real-time cross-border payment systems. ## Why CSE Students Should Pay Attention For students pursuing Computer Science and Engineering, blockchain presents a multi-disciplinary area that combines programming, cryptography, distributed computing, data structures, and cybersecurity. Learning blockchain can open doors to roles such as: Blockchain Developer Smart Contract Engineer Blockchain Architect Decentralized App (DApp) Developer Blockchain Consultant Security Auditor for Blockchain Systems Top tech companies like IBM, Accenture, Deloitte, Infosys, and TCS have already set up blockchain-focused teams and are recruiting skilled professionals in this field. ## What Skills Do You Need? To enter the blockchain domain, CSE students should build a strong foundation in: Programming languages like Solidity, JavaScript, Python, or Go Understanding of cryptographic algorithms (hashing, digital signatures, encryption) Distributed systems and peer-to-peer networking Working with platforms like Ethereum, Hyperledger Fabric, or Solana Building and deploying smart contracts and decentralized applications (DApps) At Solamalai College of Engineering, students have access to modern labs, coding clubs, and technical workshops that can help them explore blockchain through projects, competitions, and certifications. ## How Solamalai Supports Blockchain Aspirants The **CSE department** at Solamalai College encourages students to explore emerging technologies through: Tech clubs and hackathons on blockchain, Web3, and cybersecurity Guest lectures and webinars by blockchain industry experts Final-year projects focused on blockchain-based solutions Opportunities to earn certifications from platforms like IBM Blockchain, Coursera, and Blockchain Council Tie-ups with industry for internships and hands-on training ## Conclusion Blockchain is no longer just a buzzword associated with cryptocurrency. It is a game-changing technology that is transforming the way we live, work, and interact with digital systems. For CSE students at Solamalai College of Engineering, this is the right time to explore blockchain, build skills, and prepare for exciting careers in this fast-growing domain.
12.06.2025 13:49 — 👍 0    🔁 0    💬 0    📌 0
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How We Built our API Multimodal Summary Engine I’m the founder of Fidget, an AI-powered video summarizer. Today’s post covers our multimodal engine’s architecture, complete with code examples. When we set out to build our **Multimodal Summary Engine** , the idea was clear: ingest data from many sources (e.g. video, audio, metadata etc…) and use it to produce a neat, human-readable summary. If you rely on off-the-shelf summarizers, you still end up manually parsing transcripts and missing slide cues. That’s why Fidget’s multimodal AI engine was built from day one to capture every visual and audio nuance. Instead of simply transcribing audio, Fidget will listen for tonal emphasis, detects slide changes, and integrate on-screen text all in real time. ### Building the Architecture for the Multimodal Engine Firstly we needed a home for our new system, so we spec’d out the Fidget API. We knew developers didn’t want extra complexity, so Fidget exposes a single endpoint to handle incoming requests. However, we found that an API without guardrails is like a candy store without lockable cases — rate limiting and user permissions became a top priority. So, from day one, it was planned that **every** request through the endpoint gets checked against per-user quotas, tokens and roles. A typical request might look like this: `curl -X POST https://api.getfidget.pro/v1/summarize \ -H "Authorization: Bearer sk-f9b9ba37-33b6-40e6-840e-e874d38e04a4" \ -H "Content-Type: application/json" \ -d '{"video_url": "https://example.com/video.mp4", "language": "en"}'` And have the response: `{ "success": true, "request_id": "fd7c9a1b-e8f2-4d3a-b8c5-2e7f3d8a9b1c", "processing_time": "0.87s", "video_metadata": { "title": "The Future of AI in Healthcare: Breakthroughs and Ethical Considerations", "duration": "15:42", "creator": "MedTech Insights", "language": "en", "topics": ["healthcare", "artificial intelligence", "ethics", "medical imaging", "drug discovery"] }, "summary": { "executive_summary": "This comprehensive presentation explores how AI is transforming healthcare through advanced diagnostics, personalized treatment plans, and predictive analytics. The speaker appears optimistic and highlights recent breakthroughs in medical imaging analysis that have achieved 97.3% accuracy in early cancer detection, outperforming human radiologists by 11%. The discussion covers how machine learning has accelerated drug discovery timelines by 60% and how predictive analytics now forecast patient outcomes with 85% accuracy across multiple conditions.", "chapter_breakdown": [ { "title": "Introduction to AI in Healthcare", "timestamp": "00:00 - 03:12", "summary": "Overview of current AI adoption in healthcare and historical context. The speaker appears happy and is standing against a whiteboard." }, { "title": "Medical Imaging Breakthroughs", "timestamp": "03:13 - 07:45", "summary": "Detailed analysis of how AI systems detect patterns in medical images with 97.3% accuracy. Various x-ray images are shown to highlight the points being made by the speaker." }, { "title": "Drug Discovery Revolution", "timestamp": "07:46 - 11:30", "summary": "Various scentists are shown working inside a lab performing medical tasks. The speaker is explaining the exploration of machine learning's role in accelerating pharmaceutical research" }, { "title": "Ethical Considerations", "timestamp": "11:31 - 15:42", "summary": "The video takes a more serious tone while discussion of privacy concerns, algorithmic bias, and regulatory frameworks. The speaker is attempting to stay optimistic but they appear pensive." } ], "key_insights": [ "AI systems can detect patterns in medical images that humans might miss, with 97.3% accuracy", "Machine learning has accelerated drug discovery timelines by 60%", "Predictive analytics can forecast patient outcomes with 85% accuracy", "Ethical frameworks must evolve alongside technological capabilities" ], "sentiment_analysis": { "overall": "positive", "confidence": 0.87, "segments": { "technological_advancements": "very positive", "ethical_considerations": "neutral", "future_outlook": "positive" } }, "related_topics": [ "precision medicine", "neural networks in diagnostics", "healthcare data privacy" ] }, "model_version": "fidget-v2.3.1", "tokens_processed": 5842 }` If the input video is unavailable or otherwise unreadable, our API returns an HTTP 400 status with an error code and clients can try again. After the initial API design we sketched out our **system flow**. Imagine a request arriving at `/v1/summarize`: it first passes through an auth layer, then a rate-limiter and finally lands at a dispatcher that invokes the right downstream processes (we ended up calling them “modules.”) These gates ensure that a rogue client can’t soak up everyone else's resources or bypass business rules. This isn’t just about security; it also helps us maintain predictable performance as more users discover the Fidget API and allows us to scale up performantly. System diagram of the Fidget Multimodal Summary Engine Underpinning all of this is a **strict interface** between components, which is especially important because we anticipate adding new “modalities” down the road (more on that soon). Every module, whether it handles video frames or audio transcripts, exposes a stable set of input and output parameters. A clearly defined interface means modules talk to each other in a universal dialect: JSON objects with named fields, standardized error codes, and documented versioning. This interface can (and probably will) change with new major versions of the API e.g. `/v1/summarize`, `/v2/summarize` etc… but we always plan to keep supporting all versions in-line by keeping the same modules around. ### Defining Modal Sources (or Modalities) within the API A “modality” is just a fancier word for “data type” or “context source.” But not every piece of data is created equal — so we asked ourselves: **what makes a good context source?** * **Relevance:** If a video file’s metadata says it’s 2160p at 60 fps with a 10 Mbps bitrate, that’s interesting to our engine because it hints at video quality and length (for example.) * **Availability:** We prioritized sources that we could reliably extract at scale (e.g. standard container formats, well-defined audio codecs etc…) * **Signal-to-Noise Ratio:** A YouTube “tags” list might be partially user-generated and messy, while the actual audio waveform is unstructured but raw. We needed a sense of which fields tend to carry real, actionable value. Once we identified our candidate sources — things like **video metadata** (duration, resolution, codec, description text), **audio tracks** (bits of speech or music) and **key-frame snapshots** (image frames at specific intervals), we had to decide how to **interpret the data**. Metadata often comes as JSON, so parsing fields like duration or bitrate is straightforward. But when we hit audio or visual data, things get messier: speech transcripts can be filled with filler words and images can be grainy or dark. That’s where our logic to **handle noisy data** kicks in. For instance, silent parts of audio get flagged and skipped, low-confidence speech segments are marked “uncertain,” and blurred frames are discarded or given a low relevance score. ### Extracting Data from Distinct Modalities (audio, video, metadata, YouTube) With our modalities defined, we built **unique modules** for each one. Each of these modules lives inside the API using that **strict interface** we mentioned earlier. In the end we ended up with three core services: 1. **Metadata Extractor:** Peels out raw JSON from tools like _ffprobe_ for video or _id3v2_ for audio. 2. **Audio Transcriber:** Pulls audio tracks out of containers and sends them to our **custom GPT-style omni model** for processing. 3. **Frame Snapshotter:** Grabs “key frames” every few seconds or a configurable interval depending on confidence scores. Each of these modules share a **common set of input/output parameters**. For example, every module accepts a payload like: `{ "resource_id": "abc123", "input_path": "/tmp/abc123/source.mp4", "settings": { /* e.g., sampling_interval: 10 */ } }` …and produces something like: `{ "resource_id": "abc123", "output_path": "/tmp/abc123/frames/", "summary_path": "/tmp/abc123/frame_summaries.json" }` The **magic** is that any new modality we create in future e.g. OCR’d subtitles, social media comments, links in the description etc… just needs to implement the same interface in the Fidget engine. From there, we needed to **plumb each module** together by registering it in a central “pipeline orchestrator.” When a request for summarization arrives, the orchestrator fans out to each active modality module simultaneously, waits asynchronously for each of their individual responses, and moves to the next stage. This approach means we can add or remove a modality with minimal friction. ### The Video Summary AI Combinator Once each module finishes its work, we collect everything into a staging area — which (for simplicity sake), ends up being a simple directory structure with JSON files and optional assets. To fuse these pieces, we built what we affectionately call **“The Combinator.”** It’s kind of like a blender where each ingredient (modality) gets measured by a weight slider (relevance or confidence.) First, we had to **define modality weights**. Some data types are inherently more relevant for particular tasks. For a news clip, speech transcripts might matter most; for a “how-to” cooking video, key frames and on-screen text could carry more weight. We set up a configuration file where we can assign relative weights like: `audio_transcript: 0.4 key_frame_text: 0.3 metadata: 0.3` …to quickly and easily see how the different modalities affect the final output. Eventually, this will be automatic based on an initial scan and determined confidence values of the actual content. When the Combinator runs, it **pulls data from all the modules** in a single step. Under the hood, it reads in _audio_transcript.json, frame_summaries.json_ and _metadata.json._ It then normalizes fields (e.g. converting timestamps to a uniform “seconds since start” format) and constructs a consolidated in-memory representation like: `{ "resource_id": "abc123", "modalities": { "audio_transcript": [...], "frame_summaries": [...], "metadata": {...} }, "weights": { "audio_transcript": 0.4, "frame_summaries": 0.3, "metadata": 0.3 } }` Finally, the Combinator churns out a combined data‐set ready for the next stage: either on‐the‐fly summarization or feeding into a training pipeline. ### Adding Modalities to AI Training Data With the Combinator’s output in hand, we **add modalities as context** for our **custom GPT-style AI model**. The idea is that each modality module’s data becomes part of the **training context**. For example, our LM sees: `[METADATA] Title: “How to Bake Bread”; Duration: 00:05:32 [AUDIO] 0:00–0:03: “Welcome to my bakery show...” [FRAME] 0:05: Frame description: “Chef kneads dough.” ...` By feeding the LM a structured, modality‐tagged dataset, we teach it how to correlate, say, a mention of “kneading” in audio with the corresponding visual frame. During model training, we employ techniques like **contextual embedding** where each modality’s tokens get their own positional encoding. We also up-weight or down-weight entire modalities based on the Combinator’s weights in this step. This ensures the final LM doesn’t drown in a flood of irrelevant information — no one wants a summarizer that fixates on bitrates instead of human speech! **In early tests, our prototype processed one hour of lecture video in under six minutes, with near 100% accuracy.** Once the training data is prepared, we hit the familiar “train” button (submitting jobs to our internal ML instances.) Over multiple cycles, the model learns to generate coherent summaries that weave together the information its been provided. Information like metadata blurbs, spoken dialogue, and visual descriptions all get combined and correlated. At this stage we also monitor validation loss carefully, making sure the model doesn’t overfit to one modality at the expense of others. However, as mentioned previously, we’re hoping to further automate this in future and have it feed back into the weighting system. Once all of this is done, we bundle it together in nice, neat, JSON format along with some other data relevant to the task (e.g. tokens processed, model used, time taken etc…) and return the response to the client with a lovely HTTP 200 status. ### What’s Next for the API? We’re currently in **alpha** with Fidget’s Multimodal Summary Engine, rolling it out to a handful of pilot customers at the moment. We’re aiming for a Summer 2025 public launch, where we’ll be monitoring the wider reception and community carefully. So far, our post-launch roadmap includes: 1. **Feedback Loops:** We’d like to add surveys and usage telemetry within the UX so that users can flag wildly inaccurate summaries or suggest new modalities themselves (like on-screen text recognition etc…) 2. **New Modalities on Deck:** Imagine live chat comments for livestreams, social sentiment scores from Reddit posts or even things like linking into real-world news stories that are mentioned inside a video. 3. **Fine-Tuning & Iteration:** We’ll iteratively tweak modality weights, refine our noise filters, and periodically update the underlying language model to keep pace with slang, jargon and evolving content trends. 4. **Scalability & Availability:** We’re working hard to make every part of the Fidget API scalable, both in terms of usage and performance. We’ll be making this a top priority post-launch so you’ll always have Fidget available 24/7. In short, we’ve laid a robust, extensible foundation; an API that enforces permissions and rate limits, a set of plug-and-play modality extractors, a clever Combinator to merge it all and a training pipeline that teaches our models the _context_ behind the _content_. The journey from a raw video link to a concise, readable summary is now as smooth as butter. **👉 Want to shape Fidget’s roadmap?** Join our API waitlist and receive early access, priority support and input into the development of Fidget. We can’t wait to see what you build using the Fidget API!
12.06.2025 13:49 — 👍 0    🔁 0    💬 0    📌 0
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From Red to Green: What I Learned Diving into Test-Driven Development (TDD) I’ve been hands-on with Test-Driven Development (TDD)—a practice where you write tests before you write production code. What initially seemed backwards, ended up completely transforming how I think about building reliable software. I used to write code like this: 1. Hack together a feature 2. Manually test it in the browser/Postman 3. Fix bugs 4. Repeat until it mostly works Then I discovered Test-Driven Development (TDD), and everything changed. Now, I write code like this: 1. Write a failing test (Red) 2. Make it pass with minimal code (Green) 3. Clean up without fear (Refactor) And guess what? I ship fewer bugs, refactor with confidence, and actually enjoy coding more. If that sounds like magic, let me break it down. ## Why Write Tests First? * _Clarify expected behavior before diving into implementation._ * _Avoid untested code, which reduces hidden bugs._ * _Maintain cleaner code by validating it continuously._ ## What Is TDD? Test-driven development (TDD) involves writing tests for your production code before writing the actual code. The **Red** → **Green** → **Refactor** cycle isn't just workflow; it's a cognitive framework that: 1. **Red Phase:** _Forces explicit requirement articulation through failing assertions_ 2. **Green Phase:** _Drives minimal viable implementation_ 3. **Refactor Phase:** _Enables fearless architectural evolution with regression safety nets_ ## TDD Workflow 1. **Red Phase (Fail First)** _Before implementing the ShoppingCart class, write a test that describes the expected behavior: "After adding a $10 item, the cart total should be $10."_ _Watch it fail (red test) - because the ShoppingCart class has not been implemented yet._ describe("ShoppingCart", () => { it("should have a total of $10 after adding a $10 item", () => { const cart = new ShoppingCart(); cart.addItem({ price: 10 }); expect(cart.total).to.equal(10); //This will fail (RED) if ShoppingCart isn't implemented }); }); **Why?** Because ShoppingCart doesn’t exist yet! 1. **Green Phase (Make It Work)** _Write just enough code to pass this one test Not perfect code - just functional code_ class ShoppingCart { constructor() { this.total = 0; } addItem(item) { this.total += item.price; // Now passes (GREEN) } } **No over-engineering.** Just make the test pass. 1. **Refactor Phase (Make It Nice)** _Now improve the code with confidence Your test tells you if you break anything_ class ShoppingCart { constructor() { this.items = []; // Better structure! this.total = 0; } addItem(item) { this.items.push(item); this.total = this.items.reduce((sum, item) => sum + item.price, 0); } } **Run the test again.** Still passes? Great. Broke something? Fix it now, not in production. ## The Testing Pyramid 🔺 E2E Tests (5-10%) ├─ Contract Tests ├─ API Integration Tests 🔺 Integration Tests (20-30%) ├─ Service Layer Tests ├─ Database Integration Tests 🔺 Unit Tests (60-70%) ├─ Pure Function Tests ├─ Class Behavior Tests └─ Mock-based Isolation Tests * **Unit Tests:** _Focus on small, isolated pieces like functions._ * **Integration Tests:** _Ensure different modules work together (e.g., DB + API), Third-party service integration points_ * **End-to-End Tests:** _Simulate real user flows in the app, Critical business process flows, Cross-service communication paths, UI/UX interaction patterns._ ## The RITE Framework * **Readable** : _Understandable by anyone on the team._ describe('Invoice Generation', () => { it('should include 10% early payment discount for payments within 15 days', () => { // Arrange: Set up the scenario const invoice = createInvoice({ amount: 1000, dueDate: '2024-01-15' }); const paymentDate = '2024-01-10'; // 5 days early // Act: Execute the behavior const finalAmount = calculatePaymentAmount(invoice, paymentDate); // Assert: Verify the outcome expect(finalAmount).to.equal(900); // 10% discount applied }); }); * **Isolated** : _Doesn’t rely on other tests._ // Tests that depend on each other let userId; it('should create user', () => { userId = createUser({ email: 'test@example.com' }); }); it('should update user email', () => { updateUser(userId, { email: 'new@example.com' }); }); // Self-contained tests it('should update user email', () => { const user = createTestUser({ email: 'test@example.com' }); updateUser(user.id, { email: 'new@example.com' }); expect(getUserEmail(user.id)).to.equal('new@example.com'); }); * **Thorough** : _Covers edge cases, not just the happy path._ describe('Password Validation', () => { // Happy path it('should accept valid strong passwords', () => { expect(validatePassword('SecurePass123!')).to.be.true; }); // Edge cases that matter it('should reject passwords shorter than 8 characters', () => { expect(validatePassword('Pass1!')).to.be.false; }); it('should reject passwords without special characters', () => { expect(validatePassword('Password123')).to.be.false; }); it('should handle empty and null inputs gracefully', () => { expect(validatePassword('')).to.be.false; expect(validatePassword(null)).to.be.false; }); }); * **Explicit** : _All requirements are visible, no hidden setup._ // Hidden test setup beforeEach(() => { setupDatabase(); createAdminUser(); seedTestData(); }); // Explicit test context it('should allow admin users to delete orders', () => { const adminUser = createUser({ role: 'admin' }); const order = createOrder({ status: 'pending', userId: 123 }); const result = deleteOrder(order.id, adminUser); expect(result.success).to.be.true; expect(getOrder(order.id)).to.be.null; }); ## Tools I Used For testing in JavaScript, stack included: **Mocha** : _Test runner_ **Chai** : _Assertion library_ **Sinon** : _For mocks/stubs_ **Supertest** : _For HTTP testing_ **NYC** : _For code coverage_ ## Final Thoughts TDD isn't just about preventing bugs—it's about elevating the entire discipline of software engineering. It's about building systems that are not just functional, but predictable, maintainable, and evolutionarily robust. The question isn't whether you can afford to adopt TDD—it's whether you can afford not to in an industry where software complexity grows exponentially. Start your TDD journey today: start small, write clearly, and trust the process. Pick one critical module, write that first failing test, and experience the paradigm shift firsthand. **_Give it a shot!_** _Want to See TDD in Action? Check My Code! I’ve published a practical TDD example on GitHub:_ https://github.com/ChandanaPrabhakar/TDD-Workspace.git _Clone it, run the tests, and hack around!_
12.06.2025 13:43 — 👍 0    🔁 0    💬 0    📌 0
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How Much Does OpenAI’s o3 API Cost Now? (As of June 2025) The o3 API—OpenAI’s premier reasoning model—has recently undergone a significant price revision, marking one of the most substantial adjustments in LLM pricing. This article delves into the latest pricing structure of the o3 API, explores the motivations behind the change, and provides actionable insights for developers aiming to optimize their usage costs. ## What is the o3 API and why does its cost matter? ### Defining the o3 API The o3 API represents OpenAI’s flagship reasoning model, renowned for its advanced capabilities in coding assistance, mathematical problem-solving, and scientific inquiry. As part of OpenAI’s model hierarchy, it occupies a tier above the o3-mini and o1-series models, delivering superior accuracy and depth of reasoning. ### Importance of pricing in AI adoption Cloud-based LLMs operate on pay-as-you-go models, where token consumption directly translates to expense. For startups and research teams operating on tight budgets, even marginal cost differentials can influence technology selection, development velocity, and long-term sustainability. ## What are the latest updates to O3 API pricing? OpenAI announced on June 10, 2025, the arrival of **O3-Pro** , a powerful extension of the O3 family designed to prioritize reliability and advanced tool use over raw speed. Alongside this launch, the company **cut the price of the standard O3 API by 80%** , making it substantially more accessible for large-scale deployments .The price cut applies uniformly to both input and output tokens, with previous rates slashed by four-fifths. This adjustment represents one of the largest single price drops in the history of OpenAI’s API offering . ### Standard O3 price cut * **Original cost (pre-June 2025):** Approximately $10 input / $40 output per 1 M tokens. * **New cost (post-cut):** $2 input / $8 output per 1 M tokens, representing an 80% reduction . ### What about discounts for repeated inputs? OpenAI didn’t stop at a straight price cut. They’ve also introduced a **cached-input discount** : if you feed the model text that’s identical to what you’ve already sent before, you only pay **\$0.50 per million tokens** for that repeat content . That’s a clever way to reward workflows where you’re iterating on similar prompts or reusing boilerplate. ### Is there a flex mode for balancing speed and cost? Yes! In addition to the standard O3 tier, there’s now a **“flex processing”** option that gives you more control over latency vs. price. Flex mode runs at **\$5 per million input tokens** and **\$20 per million output tokens** , letting you dial up performance when you need it without defaulting to the top-tier O3 Pro model. ### Batch API considerations For workloads that tolerate asynchronous processing, OpenAI’s Batch API offers an additional 50% discount on both inputs and outputs. By queuing tasks over a 24-hour window, developers can further reduce costs to approximately \$1 per million input tokens and \$4 per million output tokens. ## How does O3 compare to its competitors? ### Where does it sit against Google’s Gemini 2.5 Pro? Gemini 2.5 Pro charges anywhere from **\$1.25 to \$2.50 per million input tokens** , plus **\$10 to \$15 per output million**. On paper, at its highest input rate, Gemini can be on-par with O3’s **\$2** input rate—but Gemini’s output fees tend to be steeper. O3’s **\$8 per million outputs** undercuts Gemini’s entry-level **\$10** while delivering deep reasoning performance. ### How about Anthropic’s Claude Opus 4? Claude Opus 4 comes in hot at **\$15 per million input** and **\$75 per million output** , with additional charges for read/write caching (around **\$1.50–\$18.75**). Even with batch-processing discounts, Claude remains significantly pricier—meaning if you’re cost-sensitive, O3 is now a far more budget-friendly choice for complex tasks. ### Are there ultra-low-cost alternatives to consider? Emerging players like DeepSeek-Chat and DeepSeek-Reasoner offer aggressively low rates—sometimes as little as **\$0.07** per cache “hit” and **\$1.10** per output during off-peak hours. But those savings often come with trade-offs in speed, reliability, or tool integrations. Now that O3 sits at a comfortable mid-range price with top-tier reasoning, you can get robust capabilities without a prohibitively high fee . ## How Does o3 Pricing Compare to Other OpenAI Models? Let’s put itss cost in context with other popular choices. ### o3 vs. GPT-4.1 Model | Input (per 1M tokens) | Output (per 1M tokens) ---|---|--- **o3** | \$2 | \$8 **GPT-4.1** | \$1.10 | \$4.40 > GPT-4.1 remains cheaper per token, but its superior reasoning on coding, math, and science tasks often offsets the difference in real-world usage. ### o3 vs. o1 (Original Reasoning Model) * **o1 input** : \$10 per 1M tokens * **o1 output** : \$40 per 1M tokens Even before the cut, o3 was positioned as a premium reasoning model—and now it’s a steal at 20% of o1’s price points. ## What factors should developers consider when estimating API expenses? ### Token usage patterns Different applications consume tokens at varying rates: * **Chatbots** : Frequent back-and-forth interactions can accumulate large input and output tokens. * **Batch processing** : Large prompts or document summarization may incur high upfront input token costs. ### Context window size The expanded 200K-token context window of o3 allows for processing longer documents in a single call, potentially reducing per-unit prompt fragmentation and overall cost by minimizing repeated overhead. ### Caching and reuse Employing a caching layer for repetitive prompts or common query patterns can dramatically lower input token consumption. Cached tokens are billed at a reduced rate (25% of standard input pricing when using Batch API), amplifying savings. ## How can developers optimize costs when using o3 API? ### Leverage the Batch API By routing non-time-sensitive tasks through the Batch API, teams can halve their per-token expense without sacrificing model performance. ### Implement prompt engineering * **Concise prompts** : Streamline instructions to minimize superfluous tokens. * **Template reuse** : Standardizing prompt structures reduces variation and enhances cache hit rates. ### Monitor and analyze usage Integrating usage dashboards or automated alerts when token consumption exceeds thresholds allows proactive adjustments. Regular audits of prompt design and call frequency can unearth inefficiencies. ### Explore fine-tuning judiciously While fine-tuned models incur additional training costs, a well-tuned variant can reduce token usage per task by delivering more precise outputs, potentially offsetting the initial investment. ## Getting Started CometAPI provides a unified REST interface that aggregates hundreds of AI models—under a consistent endpoint, with built-in API-key management, usage quotas, and billing dashboards. Instead of juggling multiple vendor URLs and credentials. Developers can access O3 API(model name: `o3-2025-04-16`) through CometAPI, the latest models listed are as of the article’s publication date. To begin, explore the model’s capabilities in the Playground and consult the API guide for detailed instructions. Before accessing, please make sure you have logged in to CometAPI and obtained the API key. CometAPI offer a price far lower than the official price to help you integrate. ## Conclusion The 80% price cut for the o3 API marks a watershed moment in the commercialization of advanced AI models. By lowering per-token expenses to \$2 for inputs and \$8 for outputs, OpenAI has signaled its commitment to broadening access while maintaining high performance standards. Developers can further optimize costs through the Batch API, prompt engineering, and strategic caching. As the AI landscape continues to mature, such pricing innovations will likely catalyze a new wave of applications, driving both technological progress and economic value creation.
12.06.2025 13:43 — 👍 0    🔁 0    💬 0    📌 0
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Gemini 2.5 Pro vs OpenAI’s GPT-4.1: A Complete Comparison The competition between leading AI developers has intensified with Google’s launch of Gemini 2.5 Pro and OpenAI’s introduction of GPT-4.1. These cutting-edge models promise significant advancements in areas ranging from coding and long-context comprehension to cost-efficiency and enterprise readiness. This in-depth comparison explores the latest features, benchmark results, and practical considerations for selecting the right model for your needs. ## What’s new in Gemini 2.5 Pro? ### Release and integration Google rolled out the **Gemini 2.5 Pro Preview 06-05** update in early June 2025, branding it their first “long-term stable release” and making it available via AI Studio, Vertex AI, and the Gemini app for Pro and Ultra subscribers. ### Enhanced coding and Deep Think One standout feature is **“configurable thinking budgets,”** which let you control how much compute the model spends on each task—great for optimizing costs and speed in your apps. Google also introduced **Deep Think** , an advanced reasoning mode that evaluates multiple hypotheses before answering, boosting performance on complex reasoning challenges . ### Multimodal reasoning and long-form coherence Beyond raw code, Gemini 2.5 Pro strengthens multimodal understanding, achieving 84.8 percent on the Video-MME benchmark and 93 percent on long-context MRCR at 128 K tokens. The model also addresses previous weaknesses in long-form writing—improving coherence, formatting, and factual consistency—making it a compelling choice for tasks such as document drafting or conversational agents requiring sustained, context-aware dialogues. ## What’s new in GPT-4.1? ### API launch and availability On April 14, 2025, OpenAI officially introduced the **GPT-4.1** , **GPT-4.1 mini** , and **GPT-4.1 nano** families in their API, immediately deprecating the GPT-4.5 preview three months later (July 14, 2025) to give developers time to transition . All paid ChatGPT tiers now include GPT-4.1, while GPT-4.1 mini replaced GPT-4o mini as the default even for free users. ### Performance gains GPT-4.1 shows **major improvements** over its predecessor: * **Coding:** Scored **54.6 percent** on SWE-bench Verified, a 21.4 point jump over GPT-4o . * **Instruction following:** Achieved **38.3 percent** on Scale’s MultiChallenge, up 10.5 points . ### Token window and efficiency Perhaps the most exciting upgrade is the **one-million token context window** , compared to 128 K in GPT-4o. This lets you feed massive documents at once—something I’ve been eager to try for analyzing long technical manuals! Plus, GPT-4.1 often responds faster and at lower cost, thanks to optimized inference pipelines. ## How do they compare in key benchmarks? ### Coding and programming * **Gemini 2.5 Pro** leads on the Aider Polyglot coding benchmark, outperforming rivals with its latest updates. * **GPT-4.1** dominates SWE-bench Verified and Codeforces problems, with clear margins over both GPT-4o and Gemini in some user tests . ### Instruction following and reasoning * **Deep Think** in Gemini adds depth by evaluating multiple reasoning chains, which can help in complex Q&A scenarios . * **GPT-4.1** shows stronger performance on standardized multi-step reasoning tests like ARC and GPQA Gemini 2.5 Pro Preview 06-05 Thinking recently outperformed OpenAI’s o3 and Anthropic’s Claude Opus 4 on multiple reasoning and scientific benchmarks, including WebDev Arena and LMArena leaderboards . The update also demonstrated superior performance in advanced scientific question answering, showcasing Google’s investment in domain-specific reasoning capabilities. GPT-4.1 has not published head-to-head comparisons on those exact leaderboards, but internal OpenAI benchmarks indicate it outperforms GPT-4o across reasoning, instruction following, and coding tests by substantial margins . Independent tests also show marked gains in long-context understanding and multi-turn coherence. ### Context length Both models now support **very long contexts** (hundreds of thousands to a million tokens), but GPT-4.1 currently has the edge with its formal million-token window. ### multimodality Gemini 2.5 Pro retains Gemini 2.5 Flash’s strong multimodal core—processing text, images, and audio—and adds **Native Audio Output** , generating human-like speech directly from the API . Developers can integrate audio responses into applications without third-party text-to-speech services. Combined with **Deep Think** , this makes Gemini 2.5 Pro suitable for interactive voice assistants that require sophisticated reasoning. GPT-4.1 continues OpenAI’s multimodal trajectory, handling text and images with fine-tuned precision inherited from GPT-4o. While it does not yet offer native audio generation, it integrates seamlessly with existing OpenAI audio services (Whisper and TTS) for multimodal applications. Moreover, GPT-4.1 mini and nano variants enable deployment in resource-constrained environments, making multimodal AI more accessible to edge devices and mobile apps . ## Which model fits your use case? ### Developers and coding If you’re building interactive web apps or automated coding agents, **Gemini 2.5 Pro** ’s configurable budgets and tight Google Cloud integration (AI Studio/Vertex) are a boon. But if raw coding accuracy and access via ChatGPT are your priority, **GPT-4.1** ’s SWE-bench leadership makes it my go-to . ### Long-form writing and conversation For extended chat sessions or drafting long reports, I find **GPT-4.1** ’s stable million-token context window highly reliable. However, if you value more natural audio responses and richer multimodal exchanges, **Gemini** still leads with native voice and image understanding. ### Enterprise integration Both platforms offer enterprise features—Gemini via Google Workspace plugins and Scheduled Actions, and GPT-4.1 via API with Direct Preference Optimization (DPO) for fine-tuning to your team’s style. You can’t go wrong either way, but your choice may hinge on whether you’re already committed to Google Cloud or Azure/OpenAI infrastructure . Here’s how I see it: Criterion | Gemini 2.5 Pro | GPT-4.1 ---|---|--- Coding accuracy | Top-tier (Aider Polyglot leader) | Excellent (outperforms GPT-4o) Context window | Up to 1–2 million tokens | 1 million tokens Cost control | Configurable thinking budgets | 26 % cheaper API calls; 75 % prompt-caching Availability | Google AI Studio, Vertex AI (beta → GA soon) | OpenAI API, ChatGPT Plus/Pro/Team, Azure Integration | Best for Google Cloud environments | Best for OpenAI/Azure ecosystems Automation features | Scheduled Actions, Deep Think (beta) | N/ Maximum Output Tokens | 64K tokens | 32,768 tokens ## Getting Started CometAPI provides a unified REST interface that aggregates hundreds of AI models—under a consistent endpoint, with built-in API-key management, usage quotas, and billing dashboards. Instead of juggling multiple vendor URLs and credentials. Developers can access Gemini 2.5 Pro Preview API (model name: **`gemini-2.5-pro-preview-06-05`**)and GPT-4.1 API(model name: `gpt-4.1 ;gpt-4.1-mini; gpt-4.1-nano`)through CometAPI, the latest models listed are as of the article’s publication date. To begin, explore the model’s capabilities in the Playground and consult the API guide for detailed instructions. Before accessing, please make sure you have logged in to CometAPI and obtained the API key. CometAPI offer a price far lower than the official price to help you integrate. **Wrapping up** , I hope this comparison helps clarify the current landscape: Google’s Gemini 2.5 Pro excels in massive context, coding depth, and cloud-native automation, while OpenAI’s GPT-4.1 shines in instruction-following, cost-effective API access, and broad ecosystem support. Ultimately, you—and your team—know best what features matter most. Whichever path you choose, you’ll be tapping into some of the most advanced AI models available today. If you’re already using one of these platforms, give the new versions a spin and let me know how they perform in your own workflows!
12.06.2025 13:41 — 👍 0    🔁 0    💬 0    📌 0
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Why Every Developer Should Learn Prompt Engineering In the age of AI, the keyboard is no longer your only interface — **your words are**. Welcome to the era of **Prompt Engineering** — where how you ask is just as important as what you know. ## What Is Prompt Engineering? Prompt engineering is the **art and science of communicating with AI tools effectively** — like ChatGPT, GitHub Copilot, Midjourney, Claude, etc. > It’s not coding. It’s commanding AI to _code for you_ , _design for you_ , _debug for you_ , and more. ## 🛠 Why Developers MUST Learn It ### 1. Work 10x Faster Prompting helps you: * Generate code faster (using Copilot or ChatGPT) * Scaffold components, APIs, or tests in seconds * Focus more on logic, less on boilerplate ### 2. Collaborate Better with AI AI is your new pair programmer. * You write the logic → AI turns it into code * You describe a bug → AI offers a fix * You explain a UI → AI gives a design layout ### 3. Superpower for Junior Devs You may not know the syntax, but **you can explain your need in plain English** — and the AI helps you code it correctly. Perfect for: * Freshers * Self-taught developers * Non-CS backgrounds ### 4. Build Faster Prototypes Need a React login page with Firebase? One clear prompt → working code. Need 10 dummy blog posts in Markdown? Prompt → done. > It turns your _ideas into code_ faster than ever. ## 📚 How to Get Started with Prompt Engineering ### Learn the Basics: * What makes a good vs bad prompt? * Use role-based prompts (e.g., “You are a senior React dev…”) * Be specific (frameworks, use cases, output formats) * Give examples + context ### Try It Hands-on: * ChatGPT (for code, regex, docs, UI ideas) * GitHub Copilot (inline AI assistant) * Gemini, Claude, or TypingMind for long-form ## Examples of Good Prompts 🔸 “Generate a responsive React component for a pricing table with 3 tiers and TailwindCSS.” 🔸 “Explain the difference between useEffect and useLayoutEffect with examples.” 🔸 “Create 10 blog post ideas for JavaScript interview prep.”
12.06.2025 13:41 — 👍 0    🔁 0    💬 0    📌 0
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Introducing teltonika-go: A Go Package for Parsing and Communicating with Teltonika Devices If you've ever worked with Teltonika GPS tracking devices, you know that parsing their proprietary protocol can be a bit of a challenge. Whether you're building a fleet management system, a custom IoT platform, or just tinkering with real-time vehicle telemetry, understanding and communicating with these devices is critical. That's why I created teltonika-go — an open-source Go package that simplifies parsing Teltonika messages and enables communication with their devices over TCP. ### 📦 What is teltonika-go? teltonika-go is a lightweight, idiomatic Go library designed to help developers decode, parse, and interpret the binary protocol used by Teltonika GPS trackers like the FMB series. It provides building blocks for server-side communication with these devices, which typically send AVL (Automatic Vehicle Location) data over TCP. With teltonika-go, you can: * Read and parse AVL packets sent by devices * Understand GPS, IO, and timestamp data * Handle device handshakes and codec types (including Codec8, Codec8 Extended and Codec16) Build your own custom server or integrate Teltonika device communication into an existing Go application ### ✅ Why Use It? Teltonika devices are widely used in the GPS tracking industry, but documentation is limited and most of it is not tailored to developers. If you're a Go developer, teltonika-go provides: * A clean and idiomatic API * No external dependencies (other than the Go standard library) * Open-source flexibility (MIT License) ### 🔧 Getting Started Install the package: go get github.com/danieljvsa/teltonika-go Example: Parse a TCP packet from a Teltonika FMB device. package main import ( "fmt" pkg "github.com/danieljvsa/teltonika-go/pkg" //for general functions tools "github.com/danieljvsa/teltonika-go/tools" //for functions that used in package to help in case only want to decode specific tram code ) func main() { // example binary data from a Teltonika device rawLogin := []byte{ /* login packet */ } rawTram := []byte{ /* AVL packet */ } // Decode login packet login := pkg.LoginDecoder(rawLogin) if login.Error != nil { fmt.Println("Login decode error:", login.Error) } else { fmt.Printf("Login decoded: %+v\n", login.Response) } // Decode AVL/tram packet tram := pkg.TramDecoder(rawTram) if tram.Error != nil { fmt.Println("Tram decode error:", tram.Error) } else { fmt.Printf("Tram decoded: %+v\n", tram.Response) } } ### 🧱 Project Structure The project is still in its early stages, but currently supports: * Decode login packets * Parse AVL records using Codecs 08, 8E and 16 * Validate and interpret Teltonika TCP/UDP headers Feel free to check out the repo: https://github.com/danieljvsa/teltonika-go ### 🚧 Roadmap The project is under active development, and here are some features on the horizon: * Full support for Codec 12 and Codec 14 * Full support for encoding for commands with codecs 12, 13, 14, 15. * Tools to send commands to devices (e.g., engine cut-off, configuration updates) * Improved error handling and documentation If you're working with Teltonika devices and using Go, I'd love your feedback and contributions! #### 🤝 Contributing Contributions are welcome! If you find a bug, have a feature request, or just want to improve the code, feel free to open an issue or a pull request. #### 🙌 Final Thoughts Parsing Teltonika’s binary protocol doesn’t need to be a pain. With teltonika-go, you can start building robust applications that speak Teltonika's language — all in idiomatic Go. #### 👉 Check out the project on GitHub: danieljvsa/teltonika-go ⭐ Star it if you find it useful, and let's make working with Teltonika devices easier together! Source code: https://github.com/danieljvsa/teltonika-go
12.06.2025 13:40 — 👍 0    🔁 0    💬 0    📌 0
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The Scaling Gauntlet: The Art of Query Archaeology It started, as most tech crises do, with an announcement and a pastry. You were three bites into a blueberry muffin when the CTO, burst into the dev pit, eyes wide, voice too loud, radiating the kind of giddy terror usually reserved for space launches and wedding proposals. > “We did it. We landed **GigaGym**.” A hush fell over the room. Someone from Sales whispered, “No way,” like they were invoking a forbidden name. You set down your muffin, dreading the next words. > “They’re onboarding next month. They’re bringing **100,000 concurrent users**.” Applause erupted. People hugged. Marketing began updating the pitch deck with fireworks emojis. But not you. Because you know the truth: your poor database, let’s call him Postgres Pete, is already sweating through his metaphorical t-shirt handling 50 users during peak lunch traffic. The last time someone ran a report and clicked “export CSV,” Pete let out a wheeze and crashed like a Windows 98 desktop. And now? **100,000 users. Concurrent. From a fitness company that livestreams biometric yoga data to AI coaches and 12K smart mirrors.** GigaGym isn’t just a client. It’s a stress test wrapped in venture funding and Bluetooth-enabled shame. So congratulations. You’ve entered the Scaling Gauntlet™. Welcome to _Database Scaling, Part One_ , where we explore the ancient ruins of your query planner, tune connection pools like it’s F1 season, and prepare your system to survive a tidal wave of abs and analytics. ## Chapter 1: Reading the Ruins ### "EXPLAIN. Like I'm five." It all begins here, in the wreckage of a slow-loading dashboard and a pile of unexplained `EXPLAIN` outputs. Your system just got hit with the news that **GigaGym** is coming, bringing 100,000 concurrent users to a database that's already wheezing at 50. Panic is setting in. But deep down, you know what you have to do: **You must descend into the ancient ruins of your queries and uncover what sins past developers have committed.** You run your first `EXPLAIN ANALYZE`, expecting insight. It's generally safe in production environments—but be careful with write-heavy or long-running mutation queries, as it will execute them. Instead, it reads like a debug log from a sentient compiler having a minor panic attack: Nested Loop (cost=0.85..15204.13 rows=14 width=48) (actual time=0.049..404.375 rows=1109 loops=1) -> Seq Scan on users u (cost=0.00..35.50 rows=2550 width=4) (actual time=0.008..1.074 rows=2550 loops=1) -> Index Scan using idx_orders_user_id on orders o (cost=0.43..5.90 rows=1 width=44) (actual time=0.051..0.149 rows=1 loops=2550) You don't need to understand it all yet. Just know this: when you see 'Seq Scan' or 'Nested Loop' and your rows look inflated or your execution time skyrockets, it usually means your query is doing far more work than it should., something is wrong. #### The PostgreSQL Decoder Ring: * **Seq Scan on a large table** : Your DB is scanning every row. This is fine for tiny tables, not for joins across millions of rows. * **Nested Loop with high row counts** : You may be joining two large sets without indexes. Watch out for multiplying costs. * **Sort spilling to disk** : Sort operations that don’t fit in memory slow everything down. Tune work_mem or refactor. * **Hash Join with disk I/O** : Hash joins are fast in memory, but once they spill, it’s slog city. **Example:** If your query plan says: Sort (cost=104.33..104.84 rows=204 width=56) (actual time=42.173..42.257 rows=300 loops=1) Sort Key: orders.created_at Sort Method: quicksort Memory: 38kB That’s fine. But if you see: Sort Method: external merge Disk: 560MB You’ve got a problem. That’s a sign you’re sorting too much data in memory that’s too small. Fix your query, or tune your DB settings. Consider adding a LIMIT clause, filtering earlier in your query, or increasing `work_mem` in your DB configuration to allow more memory for in-memory sorts. ### Index Design That Doesn't Suck (and When to Skip Them) Not all indexes are created equal. Let's look at three that _actually_ help. #### 1. The "Covering Index" – Bring What You Need CREATE INDEX idx_user_orders_covering ON orders (user_id, created_at) INCLUDE (total, status, product_id); Why? The query gets everything it needs _from the index itself_. No need to go back to the main table. ### 2. The "Partial Index" – Don't Index Trash CREATE INDEX idx_active_orders ON orders (user_id, created_at) WHERE status IN ('pending', 'processing'); Why? If 90% of rows are completed orders you'll never query, this keeps the index lean and fast. ### 3. The "Expression Index" – For the Creative WHERE Clause CREATE INDEX idx_user_email_lower ON users (LOWER(email)); Why? Case-insensitive lookups are fast now, instead of soul-crushing. #### ⚠️ But Wait. When NOT to Index Adding indexes isn't free. Every insert, update, or delete now has to update those indexes too. Indexes cost storage and write performance. Skip the index if: * The column is low-cardinality (e.g. status = 'active') and queried infrequently. * You write far more often than you read. * You're indexing a column just because “we might need it later.” (You probably won’t.) Choose wisely. Indexes are powerful, but they’re not coupons. You don’t need to collect them all. ### Query Rewriting Kung Fu Instead of this query that makes your DB cry: SELECT DISTINCT u.name, (SELECT COUNT(*) FROM orders WHERE user_id = u.id) as order_count FROM users u WHERE u.created_at > '2024-01-01'; Try this: SELECT u.name, COALESCE(o.order_count, 0) as order_count FROM users u LEFT JOIN ( SELECT user_id, COUNT(*) as order_count FROM orders GROUP BY user_id ) o ON u.id = o.user_id WHERE u.created_at > '2024-01-01'; It’s cleaner, faster, and your database won’t develop abandonment issues. ### The N+1 Problem: Death by Papercuts You’re fetching 100 users. Then 100 more queries to get their orders. You're making your database do burpees for no reason. # This looks fine. It is not fine. users = User.objects.filter(active=True) for user in users: print(user.orders.count()) Instead: users = User.objects.filter(active=True).prefetch_related('orders') for user in users: print(user.orders.count()) One query for users. One for orders. That’s it. Your DB breathes a sigh of relief. ### TL;DR: Your First Scalability Wins * Avoid full table scans unless you're absolutely sure it’s cheap * Use covering or partial indexes that match your query pattern * Rewrite nested subqueries into joins when possible * Avoid N+1 queries through prefetching or eager loading * Use `EXPLAIN ANALYZE` to verify query plans, not guess them Next up: **Connection Pooling** and how to stop your app from DDoSing your own database. But for now, take a breath. You just started the journey from query chaos to performance Zen. _And remember: every slow dashboard is just a poorly indexed story waiting to be rewritten._
12.06.2025 13:40 — 👍 0    🔁 0    💬 0    📌 0
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My Name is Ahmad, and I Represent LinkNova — A Results-Focused Digital Marketing Agency Hello! I’m Ahmad, and I proudly represent LinkNova, a digital marketing agency built on one mission — helping brands improve their online visibility, search engine rankings, and domain authority through smart SEO, powerful link building, and authentic guest posting. Whether you’re a startup looking to gain traction or an established brand aiming to dominate your niche, we at LinkNova are here to support your growth with strategic, ethical, and effective solutions. You can reach me anytime at 📧 ahmadfarazlinkbuilder@gmail.com — let’s start transforming your digital presence. ## The Importance of Visibility in Today’s Online Landscape In 2025, digital competition is fiercer than ever. Businesses are investing heavily in content, design, and development — but without proper SEO and backlinks, their websites remain buried under thousands of others. That’s where our expertise comes into play. At LinkNova, we focus on building sustainable, search-friendly foundations for your business. We don’t sell generic solutions. Instead, we deliver targeted digital marketing strategies that actually drive results. ## What Makes LinkNova Different? Unlike cookie-cutter SEO agencies, LinkNova offers handcrafted marketing solutions that combine real strategy, deep industry knowledge, and long-term value. Here’s a closer look at what we offer: ## ✅ SEO That Drives Real Results SEO is more than just keywords and traffic. It’s about optimizing your entire digital ecosystem to make sure your website is not only discoverable but also trusted by users and search engines alike. Our SEO services include: Keyword targeting based on user intent and search volume On-page optimization including metadata, headers, and internal links Technical SEO audits to fix crawl issues and improve performance Content SEO strategies to improve existing pages and build new ones Competitor analysis to uncover hidden ranking opportunities Every step is tailored to your website’s current position and future goals. ## ✅ High-Quality, Niche-Relevant Link Building Links are the backbone of a strong SEO profile. But not just any links — contextual, white-hat, niche-relevant links are what search engines reward. At LinkNova, we build backlinks that matter: We target real websites with organic traffic, not fake blogs or PBNs All outreach is manual and personalized, ensuring higher placement success We place links within useful, well-written content that brings value to readers Our links are designed to boost authority and trust, not just manipulate rankings We believe every backlink should add real SEO value — and that’s what we deliver. ## ✅ Guest Blog Posting with Purpose Guest blogging is one of the most powerful ways to get your brand in front of new audiences, while also earning high-quality links. But too many agencies focus only on the backlink — and ignore the value of good content and real exposure. Our guest posting services include: Outreach to high-authority, industry-relevant blogs Custom-written articles created by skilled writers Guaranteed do-follow backlinks placed naturally within content Permanent placements on active, trusted domains We handle everything — from strategy to writing to publishing — so you can focus on growing your business. Who We Work With Over the years, we’ve worked with a wide variety of clients: SaaS companies looking to grow organic leads E-commerce businesses aiming for better Google rankings Agencies needing help with white-label SEO services Affiliate marketers building niche sites Startups trying to get early visibility and traction No matter your industry, we can craft a link building or SEO strategy that aligns with your goals. ## Why Clients Trust LinkNova At LinkNova, we build more than backlinks — we build trust. Here's what sets us apart: 🔹 **Personalized Attention** You’re not just another order or project number. I work closely with every client to understand your goals and ensure every campaign is aligned with your brand and audience. 🔹 **Ethical and Transparent** We strictly follow Google’s best practices. No black-hat tactics, no shady links, no shortcuts — just clean, ethical work that delivers lasting SEO results. 🔹 **Proven Track Record** We’ve helped dozens of businesses improve their rankings and traffic. Our results speak for themselves, and our clients stick with us because we deliver what we promise. ## What Clients Say “Working with Ahmad at LinkNova has been a game-changer. His team helped us land links on top-tier blogs and improve our keyword rankings across the board.” – Mia K., Digital Product Owner “The guest posts from LinkNova are some of the best I’ve seen — real blogs, excellent writing, and strong SEO value.” – Raj P., Marketing Manager ## Ready to Get Started? If you’re serious about improving your rankings, building high-quality backlinks, and getting your business in front of the right audience, then it’s time to connect. 📧 Contact me today at ahmadfarazlinkbuilder@gmail.com Let’s create a custom SEO or link building strategy that works for you. ## Final Thoughts You don’t need a big budget to compete online — you just need the right strategy and the right partner. At LinkNova, we help you grow smarter with proven techniques, ethical practices, and a hands-on approach that prioritizes your long-term success. Whether you need SEO guidance, backlinks that move the needle, or guest post placements that actually help, I’m here to help you make it happen. I’m Ahmad, and I look forward to helping your business rise above the noise — one link at a time.
12.06.2025 13:38 — 👍 0    🔁 0    💬 0    📌 0
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Efficient Nested Resolvers in AWS AppSync with Lambda Batching GraphQL has emerged as a modern alternative to RESTful APIs, offering a more flexible and efficient way for clients to query data. Unlike REST, where clients often make multiple requests to different endpoints and receive fixed response structures, GraphQL allows clients to request exactly the data they need — and nothing more — in a single round trip. This reduces the issues of over-fetching and under-fetching common in REST, and gives frontend developers more control over the shape of the response. AWS AppSync is a managed service that helps developers build scalable, real-time GraphQL APIs with minimal operational overhead. It integrates seamlessly with various AWS data sources, including DynamoDB, Lambda, RDS, and OpenSearch, and supports features such as offline access, subscriptions, and fine-grained authorization. AppSync takes care of scaling and security, allowing teams to focus on defining their data and resolvers. In AppSync, resolvers are the core building blocks that connect GraphQL fields to data sources. Each field in a GraphQL query — including nested fields — can have its own resolver. When a query is executed, AppSync invokes these resolvers individually, mapping request and response data using Velocity templates (VTL) or direct Lambda functions. While this resolver-per-field model gives developers flexibility, it can introduce a performance challenge known as the N+1 problem when working with nested data. In this post, we’ll explore what the N+1 problem looks like in AWS AppSync, why it becomes a bottleneck at scale, and how to architect efficient resolvers to solve it using batch resolvers and Lambda optimizations. ## Understanding the N+1 Problem in AppSync When working with GraphQL, it’s common to request nested data in a single query. AppSync supports this by allowing each field — including deeply nested ones — to have its own resolver that fetches data from a backend data source. While this design provides flexibility and modularity, it can lead to an inefficient execution pattern known as the **N+1 problem**. ### What Is the N+1 Problem? The N+1 problem usually occurs with list queries. When your GraphQL API ends up making one query to fetch the root items (1), and N additional queries for each nested field, where N is the number of root items returned in the list. Let’s take an example to understand this clearly. query { books { name title author { firstnName lastName } } } Here’s what typically happens behind the scenes in AppSync 1. books resolver fetches a list of books — let’s say 100 (N) items. 2. For each of those 100 books, the author resolver is called individually (resulting in 100 calls). In total, that’s 1 + 100 = 101 (N+1) resolver invocations for a single client query. If you have even more nested queries, this becomes worse. In the below query, there is an additional field (address) that requires more resolver invocations. query { books { name title author { firstName lastName address { city state } } } } 1. books resolver fetches a list of books — let’s say 100 items. 2. For each of those 100 books, the author resolver is called individually (resulting in 100 calls). 3. Then for each author, the address resolver is called again (another 100 calls). In total, that’s **1 + 100 + 100 = 301** resolver invocations for a single client query. ## Why Is This a Problem? This approach scales poorly: * Performance degrades linearly with the number of parent items. * It results in high latency due to the number of sequential or parallel resolver invocations. * It increases backend load and pressure on data sources like Lambda, RDS, or DynamoDB. * It can quickly hit throttling limits or increase costs when using Lambda or other pay-per-request services. While this might be acceptable for small datasets or low traffic, the N+1 pattern becomes a serious performance bottleneck at scale. Imagine serving thousands of queries per second — this inefficient pattern can overwhelm backend systems, increase response times, and degrade the user experience. ## Solving N+1 with Batch Resolvers in AppSync One of the most effective ways to overcome the N+1 problem in AWS AppSync is by using batch resolvers. The idea is simple: instead of resolving nested fields one-by-one (which results in many resolver calls), we batch them together into a single call , usually handled by a Lambda function. Let’s explore how this works and why it’s such a powerful pattern. ### How Batch Resolvers Work In AppSync, each nested field (such as author or address) can have its own resolver, which is typically invoked for each parent object. To convert this into a batch operation: * Instead of calling the author resolver N times (once for each book), you configure the author field to invoke a single Lambda function that accepts a list of book IDs (or author IDs). * This Lambda function fetches all the authors in one go and returns the results mapped back to their respective books. Think of it as “fan-in” batching: one resolver invocation processes multiple parent objects. Let’s apply this to our previous query and see how this works. query { books { name title author { firstName lastName } } } If you use a batch resolver for the author field: * AppSync groups all books[*].author field resolvers into one Lambda call. * You receive an array of bookIds or authorIds within the lambda function. * The Lambda fetches and returns the authors in bulk. With this optimization, you’ve reduced the number of records from N+1 to just 2, which is a substantial improvement. Moreover, it’s now independent of the number of records. Here are some additional benefits of this solution: * **Fewer Resolver Invocations:** Reduces hundreds of resolver calls to just one. This saves you from the lambda concurrency limit and also reduces the pressure on downstream services. * **Faster Performance:** Lower network overhead and latency. * **Clean Separation:** Keeps resolver responsibilities modular while still optimizing performance. * **Cost-Efficient:** Fewer Lambda invocations result in reduced AWS costs. ### Enabling Batch Resolvers Batch resolvers are currently only compatible with Lambda data sources, and this feature is not enabled by default. However, enabling this is very straightforward. 1. Create a Lambda datasource as usual 2. Go to create a resolver and select the created Lambda datasource 3. Enable batching and set the batching size 4.Update the resolver request function to use the BatchInvoke operation export function request(ctx) { return { operation: 'BatchInvoke', payload: { ctx: ctx }, }; } 5.Now your lambda function will receive not a single context, but an array of contexts for each listed item. You can update the lambda function logic to do a batch get and return the results. You must ensure the returned items are in the same order as the received context order. It’s as simple as that, but it offers significant performance gains to your GraphQL service. ## Conclusion Optimizing GraphQL queries in AWS AppSync is essential when building scalable and performant APIs — especially when dealing with nested data structures. The N+1 problem, while subtle, can lead to serious performance bottlenecks if left unaddressed. By leveraging batch resolvers, you can drastically reduce the number of resolver calls, minimize round-trips to your data source, and deliver faster, more efficient responses to your clients. Whether you choose direct Lambda resolvers or pipeline resolvers, designing with batching in mind ensures your AppSync APIs are ready to perform at scale. As your application grows, keeping an eye on resolver patterns and query performance becomes even more important. With the right strategy, tools, and architecture in place, you can build GraphQL services that are both elegant and efficient.
12.06.2025 13:36 — 👍 0    🔁 0    💬 0    📌 0
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Stop Fighting with Configs! A Guide to Tunneling, Plus a Game-Changing Ace Up Your Sleeve ## The `localhost` Struggle is Real. Hey, to all my fellow developers in the trenches of code! Let me guess if this "universal crisis" sounds familiar: You're at your desk, staring proudly at that page on `localhost:3000` that has consumed countless hours of your life (and a good chunk of your hair). Suddenly, a notification "dings" on your screen. It's your boss/client/product manager: "How's that new feature coming along? Send me a link so I can check it out on my phone." For a moment, the world freezes. Your internal monologue probably goes something like this: "Check it out? How? Should I mail you my laptop?!" You can't exactly ask them to huddle around your screen, and you certainly don't want to go through the whole tedious process of deploying to a staging server just for a quick preview. The feeling is like you've prepared a Michelin-star gourmet meal, but they're asking for takeout from across the country. This is where a magical term, glowing with technical brilliance, comes to save the day: **Reverse Proxy**. Simply put, local tunneling is like hiring a magic courier for your `localhost`. No matter where your "diner" (boss/client) is, this courier can instantly deliver the hot, fresh-out-of-the-oven results of your code directly to their device. The market is full of these "couriers," each with their own special skills. Today, we're hosting a battle royale to see which of the "Big Four" of local tunneling—ngrok, frp, Cloudflare Tunnel, and pinggy—is the right one for you. ## The Four Titans Take the Stage: A Head-to-Head Review ### ngrok: The Polished, High-Priced Consultant * **Persona:** ngrok is like a star consultant from Wall Street. He's sharply dressed, articulate, and offers premium services for a premium price. You just tell him what you need, and he'll handle everything flawlessly, requiring almost zero brainpower from you. But if you want a custom address or other VIP perks? That'll cost you. * **Ease of Use:** ⭐⭐⭐⭐⭐ (Five stars. Ridiculously easy.) Download, unzip, and run one command: `ngrok http 3000`. Done. A public URL instantly appears in your terminal, ready to be shared with anyone. * **Features & Performance:** Powerful and stable. Paid plans offer enterprise-grade features like custom subdomains, reserved domains, TCP tunnels, and high-concurrency connections. It's a highly reliable service, being one of the pioneers in this field. * **Cost:** The free plan is sufficient for basic use but comes with "consultant-style" limitations. For instance, you get a random domain every time you start it, and connections are time-limited. If you restart, the URL changes, forcing you to send a new link to your boss, which can be a bit awkward. * **Security:** Quite good, with encrypted traffic. * **Best For:** Beginners, quick temporary demos, and personal project debugging. When you need the fastest, most hassle-free way to share a local service, this is the consultant to call. ### frp: The Hardcore LEGO Technic Master * **Persona:** frp is like a massive LEGO Technic set. It's completely free and packed with an incredible variety of parts (features), allowing you to build anything from a spaceship to a remote-controlled tractor. The only catch is that you need the patience and skill to read the thick instruction manual (config file) or even figure things out on your own (but ServBay can solve the problem ). * **Ease of Use:** ⭐⭐ (Two stars. For hardcore tinkerers only.) You need your own server with a public IP to act as the server-side (frps) and then configure the client-side (frpc) on your local machine. The process involves editing INI-style config files, which is not friendly for beginners. But once you get it working, the sense of accomplishment is immense. _However, with ServBay, even total beginners can configure it easily._ * **Features & Performance:** Insanely powerful. If you can think of it, frp can probably do it. It supports various protocols (TCP, UDP, HTTP, HTTPS), custom domains, load balancing, access control, high availability... The performance limit is your server's hardware. Maximum freedom. * **Cost:** The software itself is completely free and open source. The main cost is a public cloud server (which isn't usually a problem for developers). * **Security:** Highly controllable. You can configure TLS encryption, token authentication, and more. You're building your own security fortress, so its strength depends on your craftsmanship. * **Best For:** DIY enthusiasts and power users who have their own server and demand high customization and full control; for exposing internal services on a long-term, stable basis. ### Cloudflare Tunnel: The Armored Corporate Bodyguard * **Persona:** Cloudflare Tunnel is like an elite bodyguard team from a global security giant (Cloudflare itself). He not only opens a line of communication for you but also wraps you in three layers of body armor (WAF), sets up machine guns (DDoS protection), and establishes a strict identity checkpoint at the door (Zero Trust). He's incredibly reliable and can even speed things up for you, but you have to play by his rules. * **Ease of Use:** ⭐⭐⭐ (Three stars. More complex than ngrok, simpler than frp.) Configuration is done via the `cloudflared` CLI tool and requires logging into your Cloudflare account for authorization. While there are a few more steps, the official documentation is clear and logical, making it feel like you're setting up a professional-grade communication line. * **Features & Performance:** Its core strengths are **security** and **integration**. It's seamlessly integrated with the Cloudflare ecosystem, giving you CDN acceleration, Argo Smart Routing, DDoS protection, and a powerful WAF right out of the box. With the Zero Trust model, you can implement granular access control, like allowing access only from your corporate network or specific email domains. * **Cost:** Extremely generous! Its core features are essentially free for individuals and small teams. All you need is a domain managed by Cloudflare. * **Security:** ⭐⭐⭐⭐⭐+ (Off the charts.) This is its trump card. Traffic is encrypted by default, and all requests are filtered and validated by Cloudflare's global network first. Its security is an industry benchmark. * **Best For:** Projects and teams with high security and stability requirements; scenarios that require exposing internal services to specific groups securely over the long term. ### pinggy: The Flashy Sticky-Note Kid * **Persona:** pinggy is like a sticky note or a cup of instant noodles. When you're in a desperate hurry and starving for a solution, it solves your problem at lightning speed. You just have to shout (i.e., type one command), and it's there. Just don't expect to host a state dinner with it. * **Ease of Use:** ⭐⭐⭐⭐⭐ (Five stars. Deceptively simple.) Its slogan is "Get a public URL with a single SSH command." And they're not kidding. You don't even need to download a client! Just type `ssh -p 443 -R0:localhost:3000 a.pinggy.io` in your terminal, and... that's it. You've got your URL. * **Features & Performance:** It does one thing and does it well: tunneling. It's built for "temporary" and "fast." The performance is more than enough for quick debugging and sharing. * **Cost:** Free to use, providing basic TCP and HTTP/S tunnels. * **Security:** Basic encrypted transport. Due to its temporary nature, it's not recommended for transmitting sensitive data or for long-term service exposure. * **Best For:** Any situation where you need a temporary URL "RIGHT NOW." For example, when you're in a new environment without any tools pre-installed, or you just want to quickly show a colleague something. It's the ultimate fire-and-forget tool. ## The Showdown Summary: A Chart is Worth a Thousand Words Tool Name | Persona | Ease of Use | Core Strength | Main Drawback | Cost | Best For ---|---|---|---|---|---|--- **ngrok** | The Polished Consultant | ★★★★★ | Out-of-the-box, stable | Random/timed domains on free plan | Freemium | Beginners, individuals, quick demos **frp** | The Hardcore LEGO Master | ★★☆☆☆ | FOSS, powerful, highly customizable | Complex setup, requires own server | Free Software, Server Cost | DIY enthusiasts, power users, full control **Cloudflare Tunnel** | The Armored Bodyguard | ★★★☆☆ | Top-tier security, CF ecosystem | Setup is a bit involved, CF-dependent | Core is Free | Security-conscious teams, enterprise projects **pinggy** | The Sticky-Note Kid | ★★★★★ | Extremely simple, no client needed | Single-purpose, for temporary use | Free | Anyone needing an instant, ephemeral URL ## The Ultimate Answer: ServBay Staring at this list, are you already feeling the choice paralysis creeping in? > "Oh great, now I'm more confused. Sometimes I just want to show a colleague something, and pinggy is the fastest. But for a client demo, ngrok's stable domain is more professional. My side project needs to be online 24/7 with top security, so Cloudflare Tunnel is perfect. And then sometimes I just want to tinker with advanced setups, where frp is king... Am I supposed to install four different tools, keep four different documentation links on my desktop, and pray I don't mix up the commands?" Relax! The whole point of technology is to make life easier for us "lazy" developers. What if there was a tool, like a Swiss Army knife, that could bring all these "masters" together into your personal guard, ready to be summoned and switched with a single click? It might sound like a dream, but **ServBay** actually did it. ServBay isn't just another local tunneling tool; it's a master integrator, a "command center" built for the modern developer workflow. * **The Master Integrator:** ServBay's toolbox comes pre-loaded with all the mainstream local tunneling tools, including frp, ngrok, Cloudflare Tunnel, and pinggy. No more hunting down downloads and configuring environment variables. * **One-Click Install & Management:** Forget complex install scripts and config files. In ServBay's elegant GUI, you just click "Install" on the tool you want, and then click "Start." Your world has never been this quiet. * **Seamless Switching:** Does Project A need Cloudflare Tunnel's top-tier security today, while Project B needs some advanced frp tricks tomorrow? In ServBay, switching tools is as easy as changing a song in your music player. No commands to remember, no config files to edit. * **And That's Not All: Your All-in-One Dev Environment:** Most importantly, local tunneling is just the tip of the iceberg. ServBay is a powerful, integrated development environment that includes almost everything you need for web development: PHP, Node.js, MariaDB/PostgreSQL, Redis/MongoDB, and more. It offers a silky-smooth, one-stop experience from **coding** -> **local debugging** -> **one-click sharing**. ## Conclusion: It's Time to Unleash Your Talent! Looking back at this battle royale, there's no single winner, only the right tool for the right job. ngrok's convenience, frp's freedom, Cloudflare Tunnel's security, and pinggy's speed—they all shine in their own right. The arrival of ServBay isn't about replacing them. It's about elegantly ending the mental energy we waste on choosing and managing these tools. It organizes these "masters" so that we, the developers, can truly focus on what matters most: creating. Stop letting tunnel configuration drain your talent. Leave the tedious stuff to ServBay. **You just focus on building great things.**
12.06.2025 11:53 — 👍 1    🔁 0    💬 0    📌 0
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gravity jump Check out this Pen I made!
12.06.2025 11:53 — 👍 0    🔁 0    💬 0    📌 0
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🔥 Angular Pro Tips: Creating a Custom Pipe for Human-Readable Numbers (K, M, B Format) Displaying large numbers in dashboards or reports can clutter your UI and overwhelm users. Let’s solve this by creating a custom Angular pipe that converts numbers into a more readable format — like 1,500 to 1.5K and 2,500,000 to 2.5M. In this post, you'll learn how to build a clean, reusable pipe that formats large numbers using suffixes like K, M, and B. ## 🧠 Why Use a Custom Pipe? Angular comes with built-in pipes (like date, currency, and number) — but sometimes you need more control. A custom pipe: * Keeps templates clean * Promotes reusability * Keeps formatting logic separated from business logic ## ⚙️ Step 1: Generate the Pipe In your Angular project, run the following command: ng generate pipe numberSuffix This creates a new file: number-suffix.pipe.ts. ## 🛠 Step 2: Add the Logic Open **number-suffix.pipe.ts** and replace the contents with: import { Pipe, PipeTransform } from '@angular/core'; @Pipe({ name: 'numberSuffix' }) export class NumberSuffixPipe implements PipeTransform { transform(value: number): string { if (value == null) return ''; if (value < 1000) return value.toString(); const suffixes = ['', 'K', 'M', 'B']; const tier = Math.floor(Math.log10(value) / 3); if (tier === 0) return value.toString(); const suffix = suffixes[tier]; const scale = Math.pow(10, tier * 3); const scaled = value / scale; return scaled.toFixed(2).replace(/\.00$/, '') + suffix; } } ## 💡 What’s Happening Here: * We find the “tier” (thousands, millions, billions) based on the number’s size. * We scale the number down and attach the appropriate suffix. * .toFixed(2).replace(/.00$/, '') ensures clean output — e.g., 1.00K becomes 1K. ## 🧪 Step 3: Use the Pipe in Your Template Example usage in a component template: <p>{{ 950 | numberSuffix }}</p> <!-- Output: 950 --> <p>{{ 1500 | numberSuffix }}</p> <!-- Output: 1.5K --> <p>{{ 2000000 | numberSuffix }}</p> <!-- Output: 2M --> <p>{{ 7250000000 | numberSuffix }}</p> <!-- Output: 7.25B --> ## ✨ Optional Enhancements You can extend the pipe to: * Support custom decimal places (toFixed(1) or toFixed(0)) * Add support for trillions (T) or other units * Format negative numbers or currency strings Let your use case guide you! ## 🏁 Conclusion We just built a handy little utility that can instantly improve the readability of your data-heavy UIs. ✅ Clean ✅ Reusable ✅ Declarative in templates ## 👀 Up Next in Angular Pro Tips In the next post, we’ll build a smart loader using HTTP interceptors — showing one loading indicator no matter how many requests fire off at once. Follow me for more Angular and AI programming insights — and feel free to drop questions or suggestions in the comments!
12.06.2025 11:52 — 👍 0    🔁 0    💬 0    📌 0
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Why Sleep Gummies Are Preferred Other Than Supplements? Sick of sleepless nights and groggy mornings? If so, you're not alone. Many people struggle with restless sleep, insomnia, and other sleep problems that can leave them feeling exhausted and irritable during the day. While there are many sleep supplements on the market, more and more people are turning to sleep gummies with melatonin as a preferred solution. So why are sleep gummies becoming the go-to choice for those in need of better sleep? Let’s start, Sleep gummies offer a flavorful and convenient method to obtain the rest you require. Unlike traditional supplements that may be difficult to swallow or have a bitter taste, sleep gummies are like a treat for your taste buds. With delicious flavors like cherry and strawberry, you'll look forward to taking your sleep gummies every night. But taste isn't the only reason to choose sleep gummies over other supplements. Many sleep gummies, especially those with melatonin, are specifically formulated to help you fall asleep faster and stay asleep longer. Melatonin is a hormone that helps regulate your sleep-wake cycle, making it a natural and effective solution for those who struggle with insomnia or restless sleep. . By incorporating melatonin into a palatable gummy form, you can reap the benefits of this sleep aid without the inconvenience of swallowing pills. Additionally, sleep gummies are often easier on the stomach than traditional supplements. Some people experience digestive issues or discomfort when taking certain sleep aids, but gummies are gentle and soothing, making them a more tolerable option for those with sensitive stomachs. _Overall, **Sleep Gummies** with melatonin are a preferred choice for many individuals who are seeking a natural and delicious solution to their sleep problems._ So why settle for tossing and turning night after night when you can enjoy a peaceful night's sleep with the help of sleep gummies? Give them a try and witness the improvement they can bring to your sleep quality.
12.06.2025 11:48 — 👍 0    🔁 0    💬 0    📌 0
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A solution for implementing an asymmetric rounded corner component based on Canvas in HarmonyOS In modern UI design, there is often a need for unconventional rounded corner styles. This article provides an in-depth analysis of a dynamic Canvas-based rendering solution that can perfectly achieve hybrid effects combining inner and outer rounded corners through the combination of positive and negative radius values. ### Core Implementation Principles Conditional Logic: When all four corners are either inner rounded corners or outer rounded corners, directly utilize ArkUI's standard borderRadius property: if ((this.topRadius >= 0 && this.bottomRadius >= 0) || (this.topRadius < 0 && this.bottomRadius < 0)) { Column() .height('100%') .width('100%') .borderRadius(Math.abs(this.topRadius + this.bottomRadius) / 2) .backgroundColor(this.active ? this.activeColor : this.inactiveColor) .onClick(() => { this.action(); }) } Otherwise, use Canvas for drawing: else { Canvas(this.context).height('100%').width('100%') .onReady(() => { this.drawCanvas(); }) } ### Hybrid Rounded Corner Rendering Algorithm Based on the combination of positive and negative parameters, enter one of two rendering modes: ### Mode One: Inner Rounded Corners at the Top + Outer Rounded Corners at the Bottom if (this.topRadius >= 0 && this.bottomRadius < 0) { let p1: Point = { x: Math.abs(this.bottomRadius) + this.topRadius, y: this.topRadius } let p2: Point = { x: this.context.width - Math.abs(this.bottomRadius) - this.topRadius, y: this.topRadius } let p3: Point = { x: 0, y: this.context.height - Math.abs(this.bottomRadius) }; let p4: Point = { x: this.context.width, y: this.context.height - Math.abs(this.bottomRadius) } this.context.moveTo(0, this.context.height); this.context.arc(p3.x, p3.y, Math.abs(this.bottomRadius), Math.PI / 2, 0, true); this.context.lineTo(p1.x - this.topRadius, p1.y); this.context.arc(p1.x, p1.y, this.topRadius, Math.PI, -Math.PI / 2); this.context.lineTo(p2.x, p2.y - this.topRadius); this.context.arc(p2.x, p2.y, this.topRadius, -Math.PI / 2, 0); this.context.lineTo(p4.x - Math.abs(this.bottomRadius), p4.y); this.context.arc(p4.x, p4.y, Math.abs(this.bottomRadius), Math.PI, Math.PI / 2, true); this.context.stroke(); this.context.fill(); } ### Mode Two: Outer Rounded Corners at the Top + Inner Rounded Corners at the Bottom else if (this.topRadius < 0 && this.bottomRadius >= 0) { let p1: Point = { x: 0, y: Math.abs(this.topRadius) } let p2: Point = { x: this.context.width, y: Math.abs(this.topRadius) } let p3: Point = { x: this.bottomRadius + Math.abs(this.topRadius), y: this.context.height - this.bottomRadius }; let p4: Point = { x: this.context.width - this.bottomRadius - Math.abs(this.topRadius), y: this.context.height - this.bottomRadius } this.context.moveTo(0, 0); this.context.arc(p1.x, p1.y, Math.abs(this.topRadius), -Math.PI / 2, 0); this.context.lineTo(Math.abs(this.topRadius), this.context.height - this.bottomRadius); this.context.arc(p3.x, p3.y, this.bottomRadius, Math.PI, Math.PI / 2, true); this.context.lineTo(p4.x, this.context.height); this.context.arc(p4.x, p4.y, this.bottomRadius, Math.PI / 2, 0, true); this.context.lineTo(p2.x - Math.abs(this.topRadius), p2.y); this.context.arc(p2.x, p2.y, Math.abs(this.topRadius), Math.PI, -Math.PI / 2); this.context.lineTo(0, 0); this.context.stroke(); this.context.fill(); } A complete example code of the encapsulated component: import { Point } from "@ohos.UiTest" @ComponentV2 export struct XBorderRadiusButtonBackground { @Param topRadius: number = 20 @Param bottomRadius: number = 20 @Param activeColor: ResourceStr = '#ffffffff' @Param inactiveColor: ResourceStr = '#ff8a8a8a' @Param action: () => void = () => { } private settings: RenderingContextSettings = new RenderingContextSettings(true) private context: CanvasRenderingContext2D = new CanvasRenderingContext2D(this.settings) @Param active: boolean = true @Monitor('topRadius','bottomRadius','activeColor','inactiveColor','active') drawCanvas() { this.context.clearRect(0, 0, this.context.width, this.context.height); this.context.lineWidth = 0; this.context.fillStyle = (this.active ? this.activeColor : this.inactiveColor) as string; this.context.strokeStyle = (this.active ? this.activeColor : this.inactiveColor) as string; if (this.topRadius >= 0 && this.bottomRadius < 0) { // _______ // / \ // | | // ___/ \___ // Center Point 1 let p1: Point = { x: Math.abs(this.bottomRadius) + this.topRadius, y: this.topRadius } // Center Point 2 let p2: Point = { x: this.context.width - Math.abs(this.bottomRadius) - this.topRadius, y: this.topRadius } // Center Point 3 let p3: Point = { x: 0, y: this.context.height - Math.abs(this.bottomRadius) }; // Center Point 4 let p4: Point = { x: this.context.width, y: this.context.height - Math.abs(this.bottomRadius) } this.context.moveTo(0, this.context.height); this.context.arc(p3.x, p3.y, Math.abs(this.bottomRadius), Math.PI / 2, 0, true); this.context.lineTo(p1.x - this.topRadius, p1.y); this.context.arc(p1.x, p1.y, this.topRadius, Math.PI, -Math.PI / 2); this.context.lineTo(p2.x, p2.y - this.topRadius); this.context.arc(p2.x, p2.y, this.topRadius, -Math.PI / 2, 0); this.context.lineTo(p4.x - Math.abs(this.bottomRadius), p4.y); this.context.arc(p4.x, p4.y, Math.abs(this.bottomRadius), Math.PI, Math.PI / 2, true); this.context.stroke(); this.context.fill(); } else if (this.topRadius < 0 && this.bottomRadius >= 0) { // Center Point 1 let p1: Point = { x: 0, y: Math.abs(this.topRadius) } // Center Point 2 let p2: Point = { x: this.context.width, y: Math.abs(this.topRadius) } // Center Point 3 let p3: Point = { x: this.bottomRadius + Math.abs(this.topRadius), y: this.context.height - this.bottomRadius }; // Center Point 4 let p4: Point = { x: this.context.width - this.bottomRadius - Math.abs(this.topRadius), y: this.context.height - this.bottomRadius } this.context.moveTo(0, 0); this.context.arc(p1.x, p1.y, Math.abs(this.topRadius), -Math.PI / 2, 0); this.context.lineTo(Math.abs(this.topRadius), this.context.height - this.bottomRadius); this.context.arc(p3.x, p3.y, this.bottomRadius, Math.PI, Math.PI / 2, true); this.context.lineTo(p4.x, this.context.height); this.context.arc(p4.x, p4.y, this.bottomRadius, Math.PI / 2, 0, true); this.context.lineTo(p2.x - Math.abs(this.topRadius), p2.y); this.context.arc(p2.x, p2.y, Math.abs(this.topRadius), Math.PI, -Math.PI / 2); this.context.lineTo(0, 0); this.context.stroke(); this.context.fill(); } } build() { if ((this.topRadius >= 0 && this.bottomRadius >= 0) || (this.topRadius < 0 && this.bottomRadius < 0)) { Column() .height('100%') .width('100%') .borderRadius(Math.abs(this.topRadius + this.bottomRadius) / 2) .backgroundColor(this.active ? this.activeColor : this.inactiveColor) .onClick(() => { this.action(); }) } else { Canvas(this.context).height('100%').width('100%') .onReady(() => { this.drawCanvas(); }) } } }
12.06.2025 11:47 — 👍 0    🔁 0    💬 0    📌 0
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A Practical Guide to MLOps on AWS: Transforming Raw Data into AI-Ready Datasets with AWS Glue (Phase 02) In Phase 01, we built the ingestion layer of our Retail AI Insights system. We streamed historical product interaction data into Amazon S3 (Bronze zone) and stored key product metadata with inventory information in DynamoDB. Now that we have raw data arriving reliably, it's time to clean, enrich, and organize it for downstream AI workflows. ### Objective * Transform raw event data from the Bronze zone into: * Cleaned, analysis-ready Parquet files in the Silver zone * Forecast-specific feature sets in the Gold zone under `/forecast_ready/` * Recommendation-ready CSV files under `/recommendations_ready/` This will power: * Demand forecasting via Amazon Bedrock * Personalized product recommendations using Amazon Personalize ## What We'll Build in This Phase * AWS Glue Jobs: Python scripts to clean, transform, and write data to the appropriate S3 zone * AWS Glue Crawlers: Catalog metadata from S3 into tables for Athena & further processing * AWS CDK Stack: Provisions all jobs, buckets, and crawlers * Athena Queries: Run sanity checks on the transformed data ## Directory & Bucket Layout We'll now be working with the following S3 zones: * `retail-ai-bronze-zone/` → Raw JSON from Firehose * `retail-ai-silver-zone/cleaned_data/` → Cleaned Parquet * `retail-ai-gold-zone/forecast_ready/` → Aggregated features for forecasting * `retail-ai-gold-zone/recommendations_ready/` → CSV with item metadata for Personalize You'll also notice a fourth bucket: `retail-ai-zone-assets/`, this stores scripts, and training dataset. ## Step 1 - Creating Glue Resources via CDK Now that we've set up our storage zones and uploaded the required ETL scripts and datasets, it's time to define the Glue resources with AWS CDK. We'll create: * 3 Glue Jobs * **DataCleaningETLJob** → Cleans raw JSON into structured Parquet for the Silver Zone. * **ForecastGoldETLJob** → Transforms cleaned data with features for demand prediction. * **RecommendationGoldETLJob** → Prepares item metadata CSV for Amazon Personalize. * Four Crawlers * Validate everything with Athena From the project root, generate the construct file: mkdir -p lib/constructs/analytics && touch lib/constructs/analytics/glue-resources.ts Make sure your local scripts/ and dataset/ directories are present, then upload them to your S3 assets bucket: aws s3 cp ./scripts/sales_etl_script.py s3://retail-ai-zone-assets/scripts/ aws s3 cp ./scripts/forecast_gold_etl_script.py s3://retail-ai-zone-assets/scripts/ aws s3 cp ./scripts/user_interaction_etl_script.py s3://retail-ai-zone-assets/scripts/ aws s3 cp ./dataset/events_with_metadata.csv s3://retail-ai-zone-assets/dataset/ aws s3 cp ./scripts/inventory_forecaster.py s3://retail-ai-zone-assets/scripts/ ### Define Glue Jobs & Crawlers in CDK Now, open the `lib/constructs/analytics/glue-resources.ts` file and define the full CDK logic to create: * A Glue job role with required permissions * The three ETL jobs with their respective scripts * Four crawlers with S3 targets pointing to Bronze, Silver, Forecast, and Recommendation zones Open the `lib/constructs/analytics/glue-resources.ts` file, and add the following code: import { Construct } from "constructs"; import * as cdk from "aws-cdk-lib"; import { Bucket } from "aws-cdk-lib/aws-s3"; import { CfnCrawler, CfnJob, CfnDatabase } from "aws-cdk-lib/aws-glue"; import { Role, ServicePrincipal, ManagedPolicy, PolicyStatement, } from "aws-cdk-lib/aws-iam"; interface GlueProps { bronzeBucket: Bucket; silverBucket: Bucket; goldBucket: Bucket; dataAssetsBucket: Bucket; } export class GlueResources extends Construct { constructor(scope: Construct, id: string, props: GlueProps) { super(scope, id); const { bronzeBucket, silverBucket, goldBucket, dataAssetsBucket } = props; // Glue Database const glueDatabase = new CfnDatabase(this, "SalesDatabase", { catalogId: cdk.Stack.of(this).account, databaseInput: { name: "sales_data_db", }, }); // Create IAM Role for Glue const glueRole = new Role(this, "GlueServiceRole", { assumedBy: new ServicePrincipal("glue.amazonaws.com"), }); bronzeBucket.grantRead(glueRole); silverBucket.grantReadWrite(glueRole); goldBucket.grantReadWrite(glueRole); glueRole.addToPolicy( new PolicyStatement({ actions: ["s3:GetObject"], resources: [`${dataAssetsBucket.bucketArn}/*`], }) ); glueRole.addManagedPolicy( ManagedPolicy.fromAwsManagedPolicyName("service-role/AWSGlueServiceRole") ); // Glue Crawler (for Bronze Bucket) new CfnCrawler(this, "DataCrawlerBronze", { name: "DataCrawlerBronze", role: glueRole.roleArn, databaseName: glueDatabase.ref, targets: { s3Targets: [{ path: bronzeBucket.s3UrlForObject() }], }, tablePrefix: "bronze_", }); // Glue ETL Job new CfnJob(this, "DataCleaningETLJob", { name: "DataCleaningETLJob", role: glueRole.roleArn, command: { name: "glueetl", pythonVersion: "3", scriptLocation: dataAssetsBucket.s3UrlForObject( "scripts/sales_etl_script.py" ), }, defaultArguments: { "--TempDir": silverBucket.s3UrlForObject("temp/"), "--job-language": "python", "--bronze_bucket": bronzeBucket.bucketName, "--silver_bucket": silverBucket.bucketName, }, glueVersion: "3.0", maxRetries: 0, timeout: 10, workerType: "Standard", numberOfWorkers: 2, }); // Glue Crawler (for Silver Bucket) new CfnCrawler(this, "DataCrawlerSilver", { name: "DataCrawlerSilver", role: glueRole.roleArn, databaseName: glueDatabase.ref, targets: { s3Targets: [ { path: `${silverBucket.s3UrlForObject()}/cleaned_data/`, }, ], }, tablePrefix: "silver_", }); // Glue Crawler (for Gold Bucket) new CfnCrawler(this, "DataCrawlerForecast", { name: "DataCrawlerForecast", role: glueRole.roleArn, databaseName: glueDatabase.ref, targets: { s3Targets: [{ path: `${goldBucket.s3UrlForObject()}/forecast_ready/` }], }, tablePrefix: "gold_", }); // Glue Crawler (for Gold Bucket) new CfnCrawler(this, "DataCrawlerRecommendations", { name: "DataCrawlerRecommendations", role: glueRole.roleArn, databaseName: glueDatabase.ref, targets: { s3Targets: [ { path: `${goldBucket.s3UrlForObject()}/recommendations_ready/` }, ], }, tablePrefix: "gold_", }); // Glue ETL Job to output forecast ready dataset new CfnJob(this, "ForecastGoldETLJob", { name: "ForecastGoldETLJob", role: glueRole.roleArn, command: { name: "glueetl", pythonVersion: "3", scriptLocation: dataAssetsBucket.s3UrlForObject( "scripts/forecast_gold_etl_script.py" ), }, defaultArguments: { "--TempDir": silverBucket.s3UrlForObject("temp/"), "--job-language": "python", "--silver_bucket": silverBucket.bucketName, "--gold_bucket": goldBucket.bucketName, }, glueVersion: "3.0", maxRetries: 0, timeout: 10, workerType: "Standard", numberOfWorkers: 2, }); // Glue ETL Job to output recommendation ready dataset new CfnJob(this, "RecommendationGoldETLJob", { name: "RecommendationGoldETLJob", role: glueRole.roleArn, command: { name: "glueetl", pythonVersion: "3", scriptLocation: dataAssetsBucket.s3UrlForObject( "scripts/user_interaction_etl_script.py" ), }, defaultArguments: { "--TempDir": silverBucket.s3UrlForObject("temp/"), "--job-language": "python", "--silver_bucket": silverBucket.bucketName, "--gold_bucket": goldBucket.bucketName, }, glueVersion: "3.0", maxRetries: 0, timeout: 10, workerType: "Standard", numberOfWorkers: 2, }); } } Wire it up on the `retail-ai-insights-stack.ts` file /** * Glue ETL Resources **/ new GlueResources(this, "GlueResources", { bronzeBucket, silverBucket, goldBucket, dataAssetsBucket, }); Once deployed via `cdk deploy`: 1. Navigate to AWS Glue > ETL Jobs - You should see: 1. Go to AWS Glue > Data Catalog > Crawlers – Ensure four crawlers exist: ### Step 2 - Run Glue Jobs to Transform Raw Data Now that our Glue jobs and crawlers are deployed, let’s walk through how we run the ETL flow across the Bronze, Silver, and Gold zones. #### Locate Raw Data in Bronze Bucket 1. Go to the Amazon S3 Console, open the `retail-ai-bronze-zone bucket`. 2. Drill down through the directories until you see the file, note the tree structure, in my case it's `dataset/2025/05/26/20` 3. Copy this full prefix path. #### Update the ETL Script Input Path Open the `sales_etl_script.py` inside VSCode. On line 36, update the input_path variable to reflect the directory path you just copied: input_path = f"s3://{bronze_bucket}/dataset/2025/05/26/20/" Re-upload the modified script to your S3 data-assets bucket: aws s3 cp ./scripts/sales_etl_script.py s3://retail-ai-zone-assets/scripts/ Because versioning is enabled on the bucket, this will replace the previous file while preserving version history. #### Run the ETL Jobs Now let’s kick off the transformation pipeline: Run `DataCleaningETLJob` * Go to AWS Glue Console > ETL Jobs. * Select the `DataCleaningETLJob` and click Run Job. * This job will: * Read raw JSON data from the Bronze bucket. * Clean, cast, and convert it to Parquet. * Store the results in the `retail-ai-silver-zone` bucket under `cleaned_data/` Once successful, navigate to the `retail-ai-silver-zone` bucket and confirm: Run `ForecastGoldETLJob` * Go to AWS Glue Console > ETL Jobs. * Select the `ForecastGoldETLJob` and click Run Job. * This job will: * Read the cleaned data from `retail-ai-silver-zone/cleaned_data/` * Aggregate daily sales * Output the transformed data to `retail-ai-gold-zone/forecast_ready/` Once completed, visit the Gold bucket and confirm the forecast files are present in that directory. Run `RecommendationGoldETLJob` * Go to AWS Glue Console > ETL Jobs. * Select the `RecommendationGoldETLJob` and click Run Job. * This job will: * Read cleaned product data from the Silver zone * Output only the required item metadata in CSV format * Save to `retail-ai-gold-zone/recommendations_ready/` After the job runs successfully, go to the Gold bucket and verify the structure and CSV file. ### Run All Glue Crawlers Once the Glue crawlers are deployed, you’ll see four of them listed in the Glue Console > Data Catalog > Crawlers: 1. Select all four crawlers. 2. Click Run. 3. Once completed, look at the "Table changes on the last run" column each should say "1 created". #### Validate Table Creation Navigate to Glue Console > Data Catalog > Databases > Tables. You should now see four new tables, each corresponding to a specific zone: Each table has an automatically inferred schema, including columns like `user_id`, `event_type`, `timestamp`, `price`, `product_name`, and more. ### Query with Amazon Athena Now let’s run SQL queries against these tables: Open the Amazon Athena Console. If it's your first time, you’ll see a pop-up: Choose your `retail-ai-zone-assets` bucket. Click Save. #### Sample Athena Query In the query editor, trying running simple SQL queries: Select * from sales_data_db.<TABLE_NAME> Try this query on the `bronze_retail_ai_bronze_zone` table: Select * from sales_data_db.bronze_retail_ai_bronze_zone Try this query on the `silver_cleaned_data` table: Select * from sales_data_db.silver_cleaned_data Try this query on the `gold_forecast_ready` table: Select * from sales_data_db.gold_forecast_ready Try this query on the `gold_recommendations_ready` table: Select * from sales_data_db.gold_recommendations_ready ## What You’ve Just Built In this phase, you've gone beyond basic ETL. You’ve engineered a production-grade data lake with: * Multi-zone architecture (Bronze, Silver, Gold) * Automated ETL pipelines using AWS Glue * Schema discovery and validation through Crawlers * Interactive querying via Amazon Athena All of this was done infrastructure-as-code first using AWS CDK, with clean separation of storage, processing, and access layers, exactly how real-world cloud data platforms are designed. But this isn’t just about organizing data. You’re now sitting on a foundation that’s: * AI-ready * Model-friendly * Cost-efficient * And built for scale ### What’s Next? In Phase 3, we’ll unlock this data’s real potential, using Amazon Bedrock to power AI-based demand forecasting, running nightly on an EC2 instance and storing predictions back into our pipeline. You’ve built the rails, now it’s time to run intelligence through them. ## Complete Code for the Second Phase To view the full code for the second phase, checkout the repository on GitHub 🚀 **Follow me onLinkedIn for more AWS content!**
12.06.2025 11:36 — 👍 0    🔁 0    💬 0    📌 0
Preview
GRUB Configuration for Dual-Boot Arch Linux and Windows 10 This guide explains how to edit and configure GRUB on Arch Linux for a dual-boot setup with Windows 10, ensuring the GRUB menu displays correctly for selecting Arch Linux or Windows. ## Prerequisites * Arch Linux and Windows 10 installed on a UEFI system. * GRUB bootloader installed (`sudo pacman -S grub`). * `os-prober` installed to detect Windows (`sudo pacman -S os-prober`). ## Step-by-Step Instructions ### 1. Edit GRUB Configuration The main GRUB configuration file is `/etc/default/grub`. Edit it to customize boot behavior. 1. **Open the Configuration File** : sudo nano /etc/default/grub 1. **Key Settings to Modify** : * **Timeout and Menu Display** : GRUB_TIMEOUT=5 GRUB_TIMEOUT_STYLE=menu - `GRUB_TIMEOUT=5`: Shows GRUB menu for 5 seconds. - `GRUB_TIMEOUT_STYLE=menu`: Ensures the menu is visible. * **Kernel Parameters** : GRUB_CMDLINE_LINUX_DEFAULT="loglevel=3" * `loglevel=3`: Reduces boot verbosity but keeps essential messages. * **Graphical Resolution** : GRUB_GFXMODE=auto GRUB_GFXPAYLOAD_LINUX=keep * `GRUB_GFXMODE=auto`: Sets GRUB resolution automatically. * `GRUB_GFXPAYLOAD_LINUX=keep`: Maintains graphical mode for the kernel. * **Windows Detection** : GRUB_DISABLE_OS_PROBER=false * Ensures `os-prober` detects Windows Boot Manager. * **Optional GRUB Theme** (for a custom GRUB menu look): GRUB_THEME="/usr/share/grub/themes/Vimix/theme.txt" * Install a theme: `yay -S grub-theme-vimix`. 1. **Save Changes** : * In `nano`, press `Ctrl+O`, `Enter`, then `Ctrl+X` to save and exit. ### 2. Update GRUB Configuration Regenerate the GRUB configuration to apply changes: sudo grub-mkconfig -o /boot/grub/grub.cfg This detects Arch Linux (`/boot/vmlinuz-linux`, `/boot/initramfs-linux.img`) and Windows Boot Manager (e.g., on `/dev/nvme0n1p2@/efi/Microsoft/Boot/bootmgfw.efi`). ### 3. (Optional) Reinstall GRUB If GRUB isn’t displaying or booting correctly: sudo grub-install sudo grub-mkconfig -o /boot/grub/grub.cfg Ensure your EFI partition is mounted at `/boot/efi` before running `grub-install`. ### 4. Reboot Reboot to apply changes: reboot You should see the GRUB menu with entries for Arch Linux, Windows Boot Manager, and a UEFI Firmware Settings option. ### 5. Troubleshooting * **Backup Configuration** : sudo cp /etc/default/grub /etc/default/grub.bak * **Check GRUB Output** : Run `sudo grub-mkconfig -o /boot/grub/grub.cfg` and verify it detects both Arch Linux and Windows. * **Manually Edit at Boot** : Press `e` at the GRUB menu to temporarily edit boot parameters. * **Check Logs** : journalctl -b Look for errors related to GRUB. * **Verify UEFI Mode** : ls /sys/firmware/efi If the directory exists, you’re in UEFI mode. ### Notes * Your system uses `/boot/intel-ucode.img` for Intel microcode updates, which is correctly detected. * Ensure `os-prober` is installed to detect Windows Boot Manager. * If the GRUB menu doesn’t display, check your Dell laptop’s BIOS/UEFI settings to ensure UEFI boot is enabled and Secure Boot is disabled if needed. * For GPU issues (e.g., display glitches), install appropriate drivers: sudo pacman -S nvidia nvidia-utils # For NVIDIA sudo pacman -S mesa xf86-video-amdgpu # For AMD sudo pacman -S mesa xf86-video-intel # For Intel
12.06.2025 11:33 — 👍 0    🔁 0    💬 0    📌 0