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Hackernoon

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Mastering Asynchronous JavaScript Callbacks, Promises, and Async, Await for Cleaner React Components

Asynchronous JavaScript is crucial in React for handling delays and ensuring a smooth user experience.
Callbacks were the original way to manage async logic before Promises and async/await.
Callbacks involve passing functions into other functions to be executed later, leading to callback hell with deeply nested functions.
Promises provide a cleaner way to handle async code, featuring states like pending, fulfilled, and rejected, allowing for chaining with .then() and error handling with .catch().
Async/await simplifies async coding, making it read like synchronous code, using await to pause a function until a Promise resolves and try/catch for error handling.
Best practices for using async logic in React components include avoiding using async directly in useEffect, cleaning up async calls on unmount, and utilizing useEffect for async side effects.
Real-world examples showcase handling API requests with fetch(), managing loading states and errors, and ensuring cleanup of side effects.
Key takeaways include handling loading and errors, using AbortController to prevent side effects, and wrapping API calls in try-catch-finally for reliable code.
The importance of showing loading and error states, cleaning up requests, and writing fetch logic that works is highlighted in the key takeaways.
Writing clean async logic in React involves focusing on basics like handling loading, error states, and cleanup to ensure a reliable UI.
Starting with plain fetch(), understanding the problem, and solving it cleanly can lead to good code, and additional tools can be used as complexity grows.

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Javacodegeeks

325

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Spring Kafka Synchronous Request Reply Example

Apache Kafka is known for asynchronous, event-driven communication, but synchronous request-reply patterns may be necessary.
Spring Kafka simplifies synchronous request-reply messaging with features like ReplyingKafkaTemplate.
The article delves into setting up local Kafka with Docker, configuring Kafka in a Spring Boot project, and creating producer and consumer services.
The provided code example includes Kafka setup, project configuration, Kafka template setup, and handling synchronous replies.
KafkaRequestClient sends messages to request-topic and waits for replies from the reply-topic using ReplyingKafkaTemplate.
KafkaRequestConsumer listens for messages on the request-topic and sends a processed response to the reply-topic.
A REST controller exposes Kafka producer functionality as an HTTP endpoint for sending and receiving messages over Kafka.
Running the Spring Boot application triggers Kafka request-reply communication and demonstrates the flow of messages.
The usage of ReplyingKafkaTemplate in Spring Kafka enables a clean request-reply communication model for inter-service communication.
Caution is urged when using synchronous patterns over Kafka, especially in time-sensitive applications, as they should not replace standard RPC mechanisms.

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Insider

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AI will allow software engineers to be more creative and reach the 'magical flow state' easier, GitHub CEO says

GitHub CEO Thomas Dohmke believes that AI will automate the production side of software engineering, allowing engineers to focus more on creativity and reach the 'flow state' with ease.
Assistance from AI agents will help software engineers stay focused and creative during the coding process, akin to being in a 'magical flow state'.
Despite concerns over AI impacting coding jobs, Dohmke views AI as a tool that enhances the coding process rather than fundamentally changing what it means to be a software engineer.
According to Dohmke, engineers will still need to understand the fundamental processes behind their work even with AI assistance, as AI will aid in efficiency and abstraction in software development.

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Medium

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Pauthr for Teams

Using AI for issue resolution in development can provide quick solutions, but may not enhance the developer's understanding.
Relying solely on AI solutions without understanding the underlying issues can lead to a false sense of confidence.
Developers should aim for confidence in their own abilities rather than in external tools like AI.
AI tools can be beneficial but should not replace human learning and interaction.
Full-time corporate developers may struggle to find time for learning new topics due to work and personal commitments.
To maintain confidence at work, employees could have access to authors specializing in the topics they work with.
Ongoing access to subject matter experts can help teams become more knowledgeable and efficient.
Human-authored learning resources can significantly contribute to a team's success and performance.
Pauthr offers live multicast call sessions with expert authors to elevate team knowledge and skills.
Teams interested in integrating Pauthr into their learning culture can contact them for more information.

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Medium

254

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Under the hood: What Really Happens When Swift Suspends a Task

Swift's suspension of a task involves one of the most elegant parts of the Swift Concurrency system.
Async functions in Swift are transformed into coroutines, which are essentially state machines that allow for efficient multitasking.
When an await function like Task.sleep is encountered, the current job captures necessary state, creates a continuation, and hands it off to the Swift concurrency runtime.
The thread is immediately released when a task is suspended, showcasing Swift's efficient cooperative multitasking capabilities.

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Logrocket

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Build a React AI image generator with Hugging Face Diffusers

Creativity has been elevated with AI enabling image generation through text prompts and descriptions, expanding possibilities for artists and developers.
The article details building a custom offline AI image generator using React and Hugging Face Diffusers, known for diffusion models.
Hugging Face offers a wide range of AI tools, models, and datasets, focusing on NLP and machine learning solutions.
Stable Diffusion XL is highlighted as an advanced text-to-image generation model used in the project.
The implementation involves running text-to-image models locally and comparing it to using Hugging Face Inference Endpoint for performance, scalability, and cost.
Diffusion models, like Stable Diffusion XL, reverse the process of introducing static noise, allowing for precise image creation from randomness.
Hugging Face Diffusers simplifies the working process with pre-trained pipelines, offering control over diffusion for various use cases.
The application architecture comprises a React frontend making calls to a Flask backend for local AI inference using Hugging Face Diffusers.
Local inference offers full control and data privacy but requires high-end GPU hardware and complex setup.
Hugging Face Inference Endpoints provide easy deployment, scalability, and pay-as-you-go pricing, but have dependencies on external service.

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Logrocket

124

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Gemini 2.5 and the future of AI reasoning for frontend devs

Gemini 2.5 Pro by Google is a groundbreaking AI model for building rich web applications, showcasing strong reasoning and coding capabilities.
It boasts deep reasoning capabilities, allowing for complex problem-solving through multi-step reasoning without the need for patient prompting.
Gemini 2.5 Pro processes information by ingesting various data types, connecting information patterns, and producing coherent outputs.
Its native multimodal capabilities enable processing of text, images, audio, video, and PDFs within a single prompt.
Google's integrated approach to AI development, data collection, and hardware utilization gives it a competitive edge in the AI space.
Gemini 2.5 Pro has a significant impact on various fields, offering advanced reasoning and a large context window for efficient handling of information.
Users can access Gemini 2.5 Pro through platforms like Google AI Studio, enabling experimentation with AI models directly in the browser.
The model can aid in analyzing large codebases, creating 3D games, and building simple web apps with real precision.
For optimal results, it is advised to provide detailed prompts when using Gemini 2.5 Pro to maximize its capabilities.
Gemini 2.5 Pro outperforms other AI models in terms of output speed, coding, math performance, and pricing, positioning it as a top choice for web development.

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Logrocket

405

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How AI prototyping tools are changing PM workflows

Prototyping tools leveraging AI have revolutionized the development process, reducing reliance on engineers and simplifying workflows.
In the pre-AI era, prototyping involved ideation, planning, documentation, execution, and testing, leading to delays and high costs.
Now, AI streamlines the process into ideation, execution using tools like Lovable or Replit, and testing with users, accelerating the feedback loop.
Advantages of AI prototyping tools include speed, no coding knowledge required, cost-effectiveness, and shorter feedback loops for frequent iteration.
Tools like Lovable, Cursor, Replit, and Bolt offer various pricing and features catering to different PM needs, from no-code to more technical solutions.
Choosing the right tool depends on factors like technical expertise, collaboration needs, budget constraints, and project complexity.
By utilizing AI prototyping tools like Lovable, PMs can save time, create functional MVPs, and engage in rapid iteration and testing with real users.
Tips for PMs starting with AI prototyping include trying different tools for personal projects, writing clear prompts for desired outputs, and understanding tool limitations.
In conclusion, leveraging AI prototyping tools empowers PMs to build software efficiently without coding knowledge, with room for growth and improvement in the field.

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Medium

138

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Who will AI be replacing? — Answering the ultimate question

AI will replace web developers and frontend or backend developers who solely focus on tasks that can be automated, like using design tools or writing basic code snippets.
However, AI cannot replace software engineers who understand the broader aspects of technology, such as system architecture, deployment, and decision-making based on project requirements.
Software engineers play a crucial role in making complex decisions related to system design, tool selection, and integration, which AI lacks the capability to handle effectively.
Embracing AI tools can empower developers to enhance their skills, focus on core concepts, and allocate time to high-impact decision-making, leading to a positive evolution in the software engineering field.

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Medium

401

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What if AI Took an Indian Exam? — Mastering Prompt Engineering with Python

Prompting in AI is essential for providing context and instructions to the system to generate relevant responses.
Different prompt engineering techniques such as system_prompt, CoT Prompting, Persona Prompting, and Self-Consistency are explored and explained using Python and OpenAI API.
System_prompt sets the tone and persona for AI responses, CoT Prompting structures tasks, Persona Prompting defines character roles, and Self-Consistency ensures consistent and logical responses.
By utilizing these techniques, AI can be trained to provide more accurate, context-aware, and reliable answers, similar to preparing for an Indian exam.

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Dev

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Microfrontends in 2025: A Reality Check from the Trenches

Microfrontends usage has dropped from 75.4% to 23.6%, indicating a healthy market correction.
85% of teams implement microfrontends for wrong reasons, focusing on technical rather than organizational problems.
They are effective for enterprises with 15+ developers in 3+ teams, while startups may benefit more from a monolith with good architecture.
Key challenges include dependency version conflicts, CSS isolation failures, and performance regressions.
Successful microfrontend implementation was seen in a multi-brand sports platform scenario, enabling quick deployments and team autonomy.
Failed cases often involve premature optimization without facing actual scaling issues, leading to a return to monolith structures.
Recommendations emphasize monitoring distributed frontends, smart Module Federation setup, and performance optimization for successful microfrontend deployment.
Criteria for considering microfrontends include team size, release schedules, DevOps capabilities, and adherence to Conway's Law.
Future trends include server components with microfrontends, edge-side composition using CDN solutions, and AI-driven architecture analysis.
In conclusion, microfrontends should be employed judiciously, focusing on solving organizational scaling challenges rather than technical complexities.

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Dev

227

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10 Genius Python Tricks Every Beginner Should Be Using

Python has many hidden gems that can make your code cleaner, faster, and more Pythonic.
Highlighted 10 Python tricks for beginners with code examples like swapping variables without a temporary variable, using list comprehensions, merging dictionaries, using enumerate() instead of range + index, using zip() to iterate over multiple lists, etc.
These small Python tricks can improve code readability, efficiency, and style.
Keep experimenting and building projects to master Python.

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Dev

182

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🔐 From Full Stack Dev to Security Researcher: My Journey & What I Learned By Mashudul Hoque (@devmashud)

Mashudul Hoque shares his journey from Full Stack Developer to Security Researcher.
He combined full stack development with ethical hacking to create more secure and scalable apps.
He emphasized the importance of web security and bug bounties, focusing on vulnerabilities like XSS, IDOR, and Broken Authentication.
In his blog, Mashudul Hoque will cover MERN Stack Tutorials, Real-world Security Tips, Bug Bounty Writeups, Developer Productivity Hacks, and his Coding & Hacking Journey.

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Medium

343

The AI Revolution is Here — And It’s Free: 12 Game-Changing Tools That Are Quietly Transforming…

The AI revolution has brought powerful AI tools that are available for free, providing opportunities for professionals to excel and create value.
Key tools like ChatGPT, DALL-E 2, GitHub Copilot, Perplexity AI, and others are changing industries and creating income opportunities.
ChatGPT is not just a chatbot but a content creation ecosystem, enabling users to ask the right questions for quality output.
DALL-E 2 and Midjourney allow easy creation of professional visuals and illustrations, opening up revenue streams for freelancers and businesses.
GitHub Copilot revolutionizes coding by assisting developers in building applications efficiently.
Tools like Zapier with AI integration are automating tasks, saving time for businesses to focus on growth strategies.
AI tools like Runway ML for video editing, DeepL for translations, and AIVA for music composition offer professional capabilities to creators.
ElevenLabs' voice cloning technology and Tableau Public's data visualization combined with AI analysis are transforming industries.
By creatively combining these AI tools, individuals can achieve results comparable to expensive enterprise solutions.
Embracing AI tools and integrating them into workflows can lead to professional-quality outputs, automation, and enhanced productivity.

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Dev

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💡 Have You Ever Had a Brilliant Idea… Then It Got Lost in the Mess?

Many people experience the frustration of coming up with brilliant ideas that get lost in the chaos of everyday life.
After the initial spark, ideas often end up half-baked and forgotten, making it challenging to revisit and develop them further.
The struggle lies in capturing the creative flow while structuring and visualizing the idea in a clear and presentable way.
The article suggests a comprehensive workspace solution that combines AI assistance, structured documentation, and visualization tools to help refine and share ideas effectively.

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