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My Journey with the Hyperlane Framework（1750207391683100）

A computer science junior shares their experience with the Hyperlane framework, impacting their perspective on web development.
Impressed by Hyperlane's clean Context (ctx) abstraction, which simplifies code readability and eliminates nested method calls.
Hyperlane's versatile method macros make implementing RESTful APIs straightforward, allowing focus on business logic over HTTP intricacies.
Response handling in Hyperlane is intuitive, offering features like sending responses in chunks for dealing with large files.
Middleware in Hyperlane follows the onion model, enabling isolation of cross-cutting concerns from core logic like authentication.
Dynamic routing is seamless in Hyperlane, supporting both static and dynamic routes with easy retrieval of route parameters.
Hyperlane's performance in a wrk test is commendable, ranking second only to a native Tokio implementation.
Key learnings from using Hyperlane include the importance of clean API design and the extensibility of the middleware model.
Future aspirations involve exploring Hyperlane's WebSocket support, utilizing Rust's abstractions, and building a microservices architecture.
Hyperlane has reshaped the author's programming approach by emphasizing performance and a positive development experience.

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Dev

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Unveiling the Power of Simple Networks: Understanding Feedforward Neural Networks

Feedforward Neural Networks (FNNs), also known as Multi-Layer Perceptrons (MLPs), are simple yet powerful artificial neural networks inspired by biological neural networks.
FNNs are structured in layers – input layer, hidden layer(s), and output layer – allowing them to process data by passing information forward without loops or feedback.
In FNNs, neurons in the input layer represent features of the input data, hidden layers perform calculations with weighted inputs and activation functions, and the output layer produces predictions.
FNNs excel in learning complex patterns from data, enabling them to solve challenging problems in pattern recognition, classification, and prediction across various industries.
Applications of FNNs include image recognition in self-driving cars, facial recognition, natural language processing, financial modeling, medical diagnosis, and robotics.
Challenges of FNNs include data dependency, the 'black box' nature making interpretations difficult, computational cost, overfitting, and ethical considerations regarding privacy and bias.
Ongoing research aims to enhance the efficiency, transparency, and robustness of FNNs, with techniques like deep learning expanding their capabilities.
As computational power grows, FNNs are expected to have an increasingly significant role in technology and various industries, contributing to problem-solving and innovation.
Addressing ethical concerns and ensuring responsible development will be vital in harnessing the full potential of FNNs for societal benefits.

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Dev

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📦Beginner-Friendly Guide "Divide Array Into Arrays With Max Difference" LeetCode 2966 (C++ | Python | JavaScript)

LeetCode 2966 is a medium problem involving dividing an array into smaller arrays with a maximum difference constraint.
Given an integer array and a limit parameter k, the task is to split the array into groups of size 3 where the max difference between elements is at most k.
The approach involves sorting the array to have close values together and then picking every three consecutive elements greedily.
If the max difference condition is violated in any group, return an empty array.
The provided C++ solution uses frequency counting to avoid full sorting and provides lexicographically minimal triplets.
JavaScript solution also sorts the array and slices it into groups, checking the max difference within each group.
Python code follows a similar approach of sorting, slicing, and checking the max difference within groups.
The problem showcases the use of greedy algorithms after sorting and maintaining bounded differences in grouped segments.
Efficient solutions involve sorting the array and handling fixed-size windows to ensure the constraints are met.
This problem demonstrates a practical algorithm for array partitioning tasks with specific constraints.
Readers are encouraged to like and follow for more algorithmic guides and happy coding! 🛠️

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Dev

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Best ChatGPT Alternatives for Coding in 2025: Tested and Compared

Many developers opt for free ChatGPT alternatives like Claude, Bing Copilot, Phind, Codeium, and HuggingChat for coding tasks when ChatGPT Plus is costly for occasional use.
These tools aid in code generation, refactoring, and debugging, specifically designed for software development tasks.
Criteria for a good alternative includes deep code understanding, integration within coding workflow, accuracy, responsiveness, and privacy protection.
Testing involved prompts related to code explanation, refactoring, debugging, test case writing, and documentation in JavaScript and Python.
Recommended tools include Claude for logic-heavy code, Bing Copilot for GPT-4 responses, Phind for technical queries, Codeium for IDE integration, and HuggingChat for open-source use.
Each tool has strengths and limitations based on integration, code understanding, accuracy, speed, and privacy policies.
Users are advised to select a tool based on their specific coding needs and preferences, without the need for unnecessary subscriptions.
Tools like Claude, Bing Copilot, Phind, Codeium, and HuggingChat offer a range of features from explaining code logic to providing fast GPT-4 responses.
These alternatives cater to different aspects of coding tasks and can enhance productivity and efficiency for developers.
Choosing the right tool that aligns with the user's workflow and requirements is crucial for successful coding endeavors in 2025.
Users should explore and experiment with various free ChatGPT alternatives to find the most suitable one for their coding projects.

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Medium

406

Title: Why ENSO Shortcuts Might Be the Web3 Hack You Didn’t Know You Needed

ENSO is a Web3 automation engine allowing users to create custom on-chain workflows without coding, similar to Zapier for the blockchain.
Users can automate tasks like auto-swapping tokens, moving assets between wallets, and executing DeFi strategies using ENSO.
The automation is driven by ENSO's Shortcuts feature, which offers a user-friendly drag-and-drop interface.
ENSO Shortcuts are pre-built or custom logic flows that automate on-chain processes without requiring smart contracts or technical expertise.
Users can easily set conditions for actions like moving funds based on ETH gas fees using ENSO Shortcuts.
ENSO Shortcuts help users save time and react quicker to market changes in Web3, enabling a focus on strategic decision-making.
Automation provided by ENSO allows users to streamline their Web3 activities and handle complex tasks more efficiently.
ENSO Shortcuts are designed to make blockchain automation accessible to all users, not just developers.
The tool aims to simplify the management of Web3 activities and reduce the manual effort required for repetitive tasks.
ENSO Shortcuts are compared to cheat codes for the blockchain, empowering users to automate processes and optimize their workflow.
For those feeling overwhelmed by Web3 management, ENSO Shortcuts offer a solution by handling repetitive tasks and allowing users to focus on more critical aspects.
ENSO's automation capabilities can enhance efficiency and decision-making in the Web3 space, offering users a way to automate tasks and processes effortlessly.

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Medium

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Embabel: Unleashing the Power of Advanced AI Agents in Java Applications

Embabel, founded by Rod Johnson, aims to simplify AI agent development in Java applications, offering a comprehensive platform for creating goal-oriented agents.
The platform streamlines AI integration in Java, aligning with established engineering practices and making it accessible for developers.
Embabel recognizes the need for structured agent behavior, autonomous actions, and goal achievement within applications.
AI agents play a crucial role in modern software architecture, providing dynamic capabilities beyond simple query-response interactions.
Embabel introduces foundational concepts like Actions, Goals, and Conditions for structured agent development.
Embabel utilizes Goal-Oriented Action Planning (GOAP) for sophisticated planning, allowing agents to dynamically formulate plans to achieve goals.
The platform's key features aim to enhance AI development efficiency, reliability, and integration within the Java ecosystem.
Embabel's higher-level abstraction focuses on building intelligent agents capable of performing multi-step tasks and dynamic decision-making.
It challenges the dominance of Python in AI development, enabling Java teams to leverage AI capabilities using existing skills and infrastructure.
Embabel empowers Java developers to participate in the AI revolution, allowing them to build advanced AI agents and drive innovation in enterprise applications.

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Medium

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Why Your API Feels Slow — And How to Fix It

API performance is often overlooked until it becomes a bottleneck.
Practical ways to improve API performance include optimizing network latency and implementing caching.
Performance issues commonly originate from the database, requiring proactive solutions.
Avoiding overloading tasks and optimizing the environment are crucial for faster APIs.
Monitoring and good design play pivotal roles in enhancing API performance.
Improving API performance may not necessitate a complete overhaul but rather strategies like compression, caching, and scaling.
Measuring, identifying bottlenecks, and optimizing systematically enhance overall product speed.

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Dev

170

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What Is the Walrus Operator (:=) in Python and Why It's So Useful

The walrus operator (:=) in Python allows assigning a value as part of an expression.
It enables saving a value and using it immediately in a single line, making the code cleaner and more concise.
An example use is within a while loop where user input is assigned to a variable and checked in the same line.
The operator is beneficial when combining assignment and comparison, especially in loops or list comprehensions.
It helps in avoiding the repetition of the same function calls and can enhance code readability if used appropriately.
A caution is given against overusing the walrus operator as it may complicate code readability.
A bonus example demonstrates using the walrus operator for reading a file line-by-line in a concise manner.
Overall, integrating the walrus operator into coding practices can simplify logic and improve code expressing in suitable scenarios.

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Medium

205

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AI Coding Tools: Making Programming Easier for Everyone

AI coding tools like DeepCode, Amazon CodeWhisperer, Android Studio Bot, Codiga, CodeWP, and Polycoder aim to simplify programming by assisting with bug detection, code completion, and more.
DeepCode, integrated with Snyk, uses AI to identify code issues often missed by humans, working seamlessly with platforms like Visual Studio Code.
Amazon CodeWhisperer offers real-time code completions, particularly beneficial for AWS projects, with a free individual plan available.
Android Studio Bot, now Gemini, assists Android developers by providing tailored code suggestions and explanations within Android Studio at no cost.
Codiga functions as a spell-checker for code, providing real-time feedback and enforcing coding standards, suitable for team collaboration.
CodeWP specializes in generating WordPress code snippets based on natural language input, aiding both developers and non-technical users.
Polycoder, an open-source tool trained on extensive codebases, can generate code in various languages like C, Python, and JavaScript for free.
These AI tools cater to different coding needs, from bug detection to code generation, enhancing efficiency and accessibility for developers.
Whether you're a professional developer or a novice looking to enhance your coding skills, there's a tool available in this AI coding toolset.
The tools mentioned in the article demonstrate how AI aids in streamlining coding processes and understanding complex systems effectively.
The availability of these AI tools encourages individuals to explore coding with assistance, focusing on creativity while reducing repetitive tasks.

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Medium

How to Self-Learn Python in 2025 — The Ultimate Beginner’s Guide

Python is a popular language for beginners and job-seekers alike, known for its readability and versatility.
To start learning Python in 2025, one can download the latest Python version from Python.org and use tools like Visual Studio Code, Replit, Google Colab, GitHub, and Jupyter Notebooks.
Begin by mastering foundational concepts, practicing daily, and working on small projects.
For data science or machine learning, consider using Anaconda.
Progress by picking a specific path and engaging with relevant resources.
Understand code rather than just copying and pasting it, and utilize tools like Replit, Kaggle Learn, LeetCode Python, and Official Python Docs for interactive learning.
Python offers not just programming skills, but a way to bring ideas to life.
In 2025, self-learning Python can lead to job-ready skills for various career paths, emphasizing the importance of curiosity, continuous learning, and hands-on practice.
Self-learners are encouraged to start coding, no matter how small, as a way to progress and create meaningful projects.
The article provides tools, resources, and guidance for anyone interested in self-learning Python.
Readers are invited to follow the author for more self-learning tips on tech, creativity, and DIY coding projects, and to ask any questions in the comments section.

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Dev

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WWDC 2025 - What’s new in App Store Connect

The latest App Store Connect updates introduce significant enhancements for managing app distribution, discoverability, and communication with users.
Key updates include API-driven build uploads, webhook integration for real-time monitoring, and Apple-hosted background assets for rich content experiences.
Enhanced discoverability features include machine learning-generated app store tags and custom product pages with keyword assignment.
TestFlight integration offers native mobile feedback workflows, while expanded offer code capabilities enhance monetization strategies.
User communication improvements include automated review summaries and a granular age rating system for parental control.
Accessibility nutrition labels reflect Apple's commitment to inclusive design and accurate representation of app capabilities.
Strategic implementation considerations emphasize development workflow integration, metadata strategy, testing, and architecture implications.

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Dev

109

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WWDC 2025 - Wake up to the AlarmKit API - iOS 26

Apple introduces AlarmKit in iOS 26, revolutionizing time-sensitive notifications in applications.
AlarmKit ensures critical time-based events reach users regardless of silent mode or focus settings.
It supports schedule-based alarms for specific dates/times and countdown-based alarms for intervals.
User consent is required through explicit authorization states: notDetermined, authorized, and denied.
Core architecture includes countdown duration, flexible scheduling, and presentation customization.
Live Activities integration offers responsive interfaces on Lock Screen and in StandBy mode.
Custom metadata system allows passing contextual information between scheduling and Live Activity presentation.
Advanced customization features include custom actions with App Intents and audio/system fallbacks.
AlarmKit is suitable for guaranteed delivery scenarios like cooking timers, medication reminders, and wake-up alarms.
User experience design focuses on clarity, usability, and hierarchy in alarm presentations.

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The Pragmatic Engineer

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Being an “Intrapreneur” as a software engineer

To thrive as a software engineer in today's tech market, developing intrapreneurial skills can be invaluable.
Intrapreneurship, a combination of internal and entrepreneurial skills, involves running towards problems, taking ownership, investing in relationships, and more.
Chaitali Narla, a seasoned engineering executive, highlights seven habits of successful intrapreneurs.
These habits include running towards problems, taking end-to-end ownership, investing in cross-functional relationships, seeking sponsorships, making impact visible, not fearing rejection, and being proactive in all aspects.
Developing these skills can lead to career success within a company, prepare for new opportunities, or even launch a startup in the future.
Chaitali Narla shares her journey from an intern to director at Google, emphasizing the importance of intrapreneurial leadership.
She provides practical examples from her experience, like addressing inefficiencies in data seeding and streamlining project setup processes.
The article also touches upon the significance of cross-functional relationships, translating technical aspects for non-engineering stakeholders, and building networks proactively.
It stresses the need for gaining sponsorships not just for promotions but also for overall career growth and advancement.
Overall, the article encourages software engineers to cultivate intrapreneurial skills to navigate the competitive tech market successfully.

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Dev

266

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Exploring Firestore with Micronaut and Java 21: A Scalable NoSQL Solution for Modern Applications

Choosing data storage for microservices is crucial; NoSQL can be more scalable in serverless environments.
Google Cloud Firestore is explored as a serverless NoSQL database for modern applications, particularly AI and ML workloads.
Firestore integrates well with tools like LangChain and LlamaIndex for various patterns like document loaders and vector stores.
Firestore simplifies integration with AI services, enabling features such as automated embedding generation and image classification.
Using Firestore with Micronaut and Java 21 for server-side development is detailed in the article.
Setting up the project, client libraries, and configuring Firestore client are essential steps in working with Firestore.
Code examples for creating a Firestore controller, validating feedback requests, and writing data to Firestore are provided.
Firestore is highlighted as a great choice for scalable, AI-powered microservices in a serverless environment.
The article emphasizes focusing on business value rather than infrastructure management with Firestore and Micronaut.
Firestore with its scalability and integration with Micronaut simplifies building serverless, AI-powered applications.

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Medium

371

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Why Logos Are Getting Boring

Logos in the marketing world are increasingly losing their personality and being simplified.
Visual identity encompasses more than just a logo, including colors, fonts, website designs, and image styles.
Large corporations have detailed brand manuals dictating how every visual element should be presented.
There is a trend in logos towards simplification as companies aim for a more streamlined and minimalist look.
This trend is driven by a desire for uniformity and consistency across various platforms and marketing channels.
Logos are being stripped down to their essential elements as part of this simplification process.
The concept of visual identity is often misunderstood by those outside the design field.
Small businesses might only require a logo and a few brand colors, while larger corporations have more complex visual branding requirements.
The battle for simplification in logos is a widespread phenomenon across the industry.
The shift towards minimalism in logo design is driven by the need for a cohesive brand image across different mediums.
Visual identity plays a crucial role in defining what a company stands for.
Logos form just one part of a brand's overall visual identity.
The quality of logos in marketing is perceived to be declining as they become more generic and stripped of individuality.
There is a push for logos to be more streamlined and simplified across the board.
The changing landscape of logos reflects a broader trend towards minimalist and clean design aesthetics in branding.

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