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What 600+ LeetCode Problems Taught Me: Honest Reflections & Advice ‍

Start with pattern recognition, not memorization, by identifying the underlying pattern or main topic before diving into code.
Master one data structure at a time to build genuine understanding before moving to others.
Don't focus on difficulty labels; concentrate on understanding and growth instead.
Follow the naive-to-optimal progression by starting with a naive approach and working towards optimal solutions.
Resist peeking at solutions too early and train yourself to convert thoughts into code.
Use AI tools for learning, not just answer-getting, and explore community solutions for insights.
Learn from other developers' approaches to see multiple ways to solve problems.
Problem-solving can become addictive, so focus on consistent practice over intense sessions.
Solving popular and frequently asked problems is beneficial for interview preparation.
Solve at least one problem daily and participate in contests strategically for practice.

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Dev

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Building Remote MCP Servers with .NET and Azure Container Apps

The article discusses creating remote MCP servers using .NET and Azure Container Apps to provide context to Large Language Models (LLMs) for interacting with data sources like the Australian Football League (AFL).
MCP servers can use Streamable HTTP transport for client-server communication, enabling multiple client connections and streaming server messages using POST and GET requests with Server-Sent Events (SSE).
The ModelContextProtocol.AspNetCore package in the MCP C# SDK assists in implementing MCP server capabilities, allowing for the deployment of MCP servers on Azure Container Apps.
The article covers setting up MCP server endpoints, containerizing the MCP server with a Dockerfile, deploying to Azure Container Apps, and testing the remote MCP server using an MCP client in Visual Studio Code.
Future articles will delve into security implementation for MCP servers and the ongoing work on Authorization support in the C# SDK.
The author encourages exploring the full code sample on GitHub and invites inquiries about the topic on BlueSky.

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Medium

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10 AI video translators that make you sound fluent in any language

In 2025, creators are going global by translating smarter with the help of AI video translators, allowing content to reach wider audiences effortlessly.
These AI tools cater not just to YouTubers but also to various content creators like educators, startup founders, coaches, and TikTokers looking to expand their reach globally.
The tools mentioned in the guide work directly with videos and spoken content, offering features like voice cloning, high-quality text-to-speech, and full voiceovers or dubs.
They are beneficial for creators across different fields such as YouTubers, educators, podcasters, indie developers, and startup marketers, eliminating the need for expensive production studios.
Most tools provide free tiers or trial versions, making it accessible for creators to test the multilingual capabilities before investing.
The AI video translators offer features like voice and lip synchronization, multilingual dubbing, subtitle translation to audio, studio-level dubbing, and unique voice translations for multiple speakers.
Creators can turn English content into various languages, expand market reach, grow faster in global regions, and engage international audiences by using these tools effectively.
While these tools offer powerful capabilities, it's important to note that they may not always provide a perfect translation, requiring creators to QC the output for accuracy.
These AI video translators are seen as essential tools for content creators to repurpose existing content, increase accessibility, and standardize training materials in multiple languages.
Overall, the tools serve as valuable assets for creators wanting to maximize the impact of their content globally with minimal effort.

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Dev

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HellaJS - A Tiny Reactive Framework

HellaJS is a tiny reactive framework that offers fast DOM updates, composability, lightweight nature, declarative UI writing, and simplicity.
In this tutorial, a simple Todo app is built using HellaJS.
Core concepts covered include handling state and reactivity, managing input and adding todos, building the UI declaratively, and mounting the app to the DOM.
State management in HellaJS is achieved using signals.
Handling user input and adding todos are fundamental functionalities demonstrated.
The UI is constructed using html, forEach, and show functions for a declarative approach.
The final Todo app example includes signal usage, effects, input handling, and rendering the UI.
HellaJS simplifies building reactive and declarative web apps with minimal complexity and optimal performance.
The composable API and direct DOM updates contribute to the framework's ease of use and efficiency.
HellaJS is recommended for projects where reactive behavior and declarative UI are desired.
For more information, refer to the HellaJS documentation, GitHub repository, and performance benchmarks.

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Dev

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3443. Maximum Manhattan Distance After K Changes

Given a string s representing movements on an infinite grid with 'N', 'S', 'E', and 'W' characters, find the maximum Manhattan distance from the origin that can be achieved by changing at most k characters.
Approach involves iterating through the string, updating position based on directions, calculating Manhattan distance at each step, accounting for changes allowed, and updating the maximum distance accordingly.
The solution efficiently determines the maximum Manhattan distance with the constraint of at most k changes to the movement directions in the string.
The algorithm runs in linear time, making it suitable for large inputs.
Example scenarios such as changing 'S' to 'N' in 'NWSE' to get a distance of 3 and changing 'NSWWEW' result in an output of 6 are provided.
Constraints include 1 <= s.length <= 105 and 0 <= k <= s.length, with hints suggesting brute forcing directions and maximizing distance changes.
The algorithm involves optimizing movements by potentially changing directions to increase the Manhattan distance from the origin.
Code example in PHP showcases the implementation and testing with test cases to demonstrate the solution.
The approach efficiently balances distance optimization with the constraints of allowed changes and total movements in the string.
The solution offers a systematic way to determine the maximum Manhattan distance achievable with limited direction changes.

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Medium

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15 AI tools that turn side hustlers into full-time machines

AI tools are often flashy but not always useful for making money, except for a select few now helping profit-driven individuals.
A group is using AI to build websites, sell products, automate work, and create apps without extensive coding skills.
These tools are accessible to motivated individuals without the need for a team, funding, or high costs.
Tools highlighted focus on launching, selling, automating, freelancing, and productizing to help side hustlers turn a profit.
Examples of AI tools include those for generating blog posts, voice-overs, graphic designs, video content, chatbots, and more.
Users combine these tools to create efficient workflows for tasks like marketing, content creation, client onboarding, and product development.
The emphasis is on using these tools in combination to maximize productivity and validate multiple ideas quickly.
Utilizing AI tools efficiently involves selecting those that offer real value without high paywalls and focusing on actual ROI.
Success with AI tools requires active participation, marketing efforts, and iterative processes in addition to tool usage.
The highlighted AI tools are currently helping individuals make money online in various capacities, emphasizing practical application over grand ideas.
Users are encouraged to pick a few tools, combine them, build something, sell it, and repeat for continued success.

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Dev

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The New Generation of High-Performance Web Frameworks（1750382925210600）

Hyperlane is emerging as a strong contender in the Rust Web frameworks scene, focusing on performance and lightweight design.
The framework is compared with Actix-Web and Axum in terms of architecture, middleware support, and routing capabilities.
Hyperlane's advantages include zero platform dependency, extreme performance optimization, flexible middleware mechanism, and built-in support for real-time communication.
The practical examination highlights Hyperlane's straightforward middleware configuration, support for multiple HTTP method route macros, WebSocket example, and SSE data push.
Hyperlane's routing capabilities include support for dynamic and regular expression matching, enhancing its versatility.
Performance optimization options in Hyperlane enable high throughput with pre-configured TCP and buffer parameters for high-concurrency scenarios.
It offers a developer-centric experience with an asynchronous chain API and unified interface for consistent behavior.
Hyperlane is recommended for its features like native SSE/WebSocket support, asynchronous chain API, regular expression routing, full middleware support, platform compatibility, and low dependency complexity.
Hyperlane is ideal for developing high-performance, lightweight, and future-oriented Web applications.

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Part 1: Python + Go Project: Exploring Multi-Language Development

The project involves exploring multi-language development using Python and Go to create a Monster Breeder application.
Go is responsible for handling monster creation, DNA, mutation, and returns clean JSON, while Python manages the frontend, displaying monsters, sending requests, and eventually saving them.
The project is structured in VS Code with a clean folder layout and a go.mod file to manage dependencies and package structure.
Go is utilized for creating the monsters with each having specific traits.
The project involved converting the result into JSON and running the program to generate random monsters.
Go is considered more strict and low-level compared to Python but is faster and more efficient for APIs and backend processing.
Python communicates with Go to retrieve the monsters through the /new route using the requests module.
The Python script uses a GUI to generate and breed monsters based on the data received from Go.
The Python script currently displays the JSON data and future work involves generating sprites for the monsters.
The project is a learning experience in making Python and Go languages communicate within the same project.
The goal is to build a Monster Breeder application with elements of randomness and evolution, akin to Pokemon.
Both Python and Go are used for their respective strengths: Go for backend logic and Python for frontend UI.
The project progresses with testing the communication between Python and Go, with Python displaying the monsters generated by Go.
The application includes functionality to breed and evolve the monsters, with buttons triggering the relevant functions.
The developer expresses excitement in tackling the challenge of generating sprites for the monsters in the future.
The project showcases the cooperation between Python and Go in a multi-language development environment for a creative and engaging application.

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Dev

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Mastering iOS Development: Your Essential Resource List

iOS development offers a world of possibilities with Swift, Objective-C, and Xcode for creating high-performing applications.
Curated list of resources for mastering iOS development, covering from foundational knowledge to best practices and advanced topics.
Includes guides on Swift programming, choosing between Swift and Objective-C, and best practices for user experience.
Features essential tips for boosting productivity, creating standout iOS apps, and designing cloud-native mobile apps with Swift 5.
Explores broader mobile development context with trends, app development process guides, and software engineering principles.
Resource emphasizes continuous learning, experimentation with SwiftUI and UIKit, and striving for clean code and excellent app design.

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Dev

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Getting Started with Clap: A Beginner's Guide to Rust CLI Apps

Rust makes building powerful, safe, and efficient command-line apps easy, especially with the clap crate.
Clap is the most popular Rust crate for building CLI apps, handling parsing arguments, input validation, help messages, subcommands, and more.
Clap can be defined either by describing a struct (derive-style) or building it manually (builder-style).
Other ways to parse CLI args in Rust include std::env::args(), getopts, and structopt.
Traits like Parser and ValueEnum in Rust help with CLI parsing and enum handling.
The Parser trait in clap provides features like parsing CLI args, error handling, and custom parsing sources.
Attributes like #[command(...)] and #[arg(...)] in clap define app metadata and fine-tune CLI options.
ValueEnum in clap allows parsing enum values from strings and provides error handling for invalid values.
Common derive traits in Rust include Parser, ValueEnum, Debug, and Clone for functionality and debugging support.
Clap supports both derive and builder styles for defining CLI apps.
Recap of clap functionalities: defining CLI arguments, parsing inputs, validating, using enums, getting help/version output, and adding app metadata.
An example of a file checker CLI app using clap is provided.
Clap is recommended for building clean, safe, and powerful CLI tools in Rust, offering ease of use and minimal boilerplate code.
Traits like Parser and ValueEnum in clap facilitate building command-line interfaces with minimal setup.
Clap provides a clean, declarative API, rich features, built-in validation, active development, and modern ergonomics for CLI app development.
Newcomers to Rust can leverage clap to create robust CLI tools efficiently, benefiting from the library's ergonomic design and functionality.

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📝 Beginner-Friendly Guide "Maximum Manhattan Distance After K Changes" LeetCode 3443 (C++ | Python | JavaScript)

LeetCode 3443 is a medium-level problem involving navigating a grid with restrictions on direction changes.
The task is to maximize the Manhattan distance (|x| + |y|) achievable at any point during the movement process.
By considering different dominant directions and adjusting movements, the goal is to reach the farthest edge.
The solution involves greedily spending direction changes to optimize the movement and track maximum distance.
In C++, a solution using a greedy approach with linear scanning has been provided.
The time complexity of the C++ solution is O(n) and the space complexity is O(1).
A JavaScript solution has also been presented using a similar greedy strategy for maximizing the distance.
Additionally, a Python solution implementing the same logic with a focus on direction changes and distance tracking is available.
This problem showcases a clever blend of grid simulation and a greedy approach to achieve optimal results.
It efficiently handles a large number of operations and encourages algorithmic intuition development.

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Medium

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Node.js SMS Integration Made Easy

Node.js SMS integration made easy with cheaposms.com API services.
Sending SMS from a Node.js application using a provided piece of code.
The code can be named app.js and requires the destination phone number.
Run the script on the command line to send the SMS.
Status response 'SMS Queued successfully' confirms the message has been sent.
Features include sending and receiving messages, support for multi-part SMS.
Simple scheduling of SMS messages is also supported.
Visit www.cheaposms.com for more information.
Developed in America for businesses, offering streamlined customer interactions.
The developer provides ways to stay connected and follow their work.
Links to Medium, YouTube, GitHub, Udemy, and LinkedIn profiles are provided for further engagement.

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Medium

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Silent Guardians: Advanced Threat Hunting in Critical Infrastructure

Critical infrastructure, vital for our world, faces growing cyber threats targeting life-sustaining systems.
Passive defense strategies are insufficient against sophisticated adversaries.
Threat hunters play a crucial role in proactively identifying and thwarting attacks.
Key drivers include creativity and precision in threat hunting.
Techniques such as Time Drift Detection and Firmware Integrity Validation are deployed.
ICS Command Frequency Heatmaps and Out-of-Band Wireless Scanning are used to detect anomalies.
USB Device Traps are employed to identify insider threats or malicious activities.
Threat hunting in critical infrastructure is a multi-tiered strategy to protect lives.
ICS threat hunters are more than technicians - they are strategists and scientists.
Global policy should mandate proactive threat detection in infrastructure.
Threat hunters face challenges in ICS/OT environments and play a vital role in defending civilization.

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Dev

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"From 'NOT NULL constraint failed' to Success: Debugging My Django DRF Order Creation API"

The article discusses the journey of a developer in debugging a Django DRF order creation API.
The challenge faced was a NOT NULL constraint error for the total_amount field in the Order model.
Unit testing confirmed the correctness of Product and Customer models but revealed issues in API serialization.
The solution involved making calculated fields read-only, initializing total_amount during order creation, and handling nested relationships.
After implementing the changes, the API calls were successful, marking a significant achievement.
Key takeaways include the importance of thorough unit testing, understanding DRF mechanics, ensuring data integrity, and utilizing Django signals.
The developer is seeking opportunities in Python, Django, and backend development roles.
The article provides GitHub and LinkedIn profiles for further exploration of the developer's work.
Strategies for handling calculated fields in DRF and debugging IntegrityErrors are discussed, inviting readers to share their thoughts.

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Swift Sendable: Mastering Thread Safety in iOS Development

When working with Swift concurrency, developers often encounter compiler warnings about 'sending non-Sendable types across actor boundaries.'
Understanding Sendable is crucial for building reliable concurrent iOS applications.
Sendable protocol in Swift ensures safe transfer of data across concurrency boundaries without causing data races.
The Core Problem Sendable Solves: Preventing data races and unpredictable behavior in concurrent operations.
Sendable distinguishes between safe data that can be shared without synchronization and unsafe data requiring careful handling.
Types like value types with Sendable properties, enums with Sendable associated values, and most of Swift's fundamental types are automatically Sendable.
Making custom types Sendable involves ensuring all stored properties are Sendable, with specific considerations for classes.
Classes can be made Sendable by using immutable classes or adding internal synchronization mechanisms.
Actors in Swift provide built-in thread safety and are automatically Sendable, offering a natural solution for concurrent access.
Functions that work with concurrent code should specify Sendable requirements, ensuring safe data sharing.

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