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Goodbye Async-Std, Welcome Smol

The async Rust ecosystem saw a major shift as the popular async-std crate was officially deprecated.
Async-std users are advised to migrate to smol, a lightweight and efficient alternative with a similar API structure.
Smol prioritizes simplicity and predictable performance, making it a natural successor to async-std.
The transition from async-std to smol is straightforward due to their shared API layout.
Smol allows async operations without the need for manually setting up an executor.
The deprecation of async-std signifies a step towards a more consolidated async Rust ecosystem.
Kani Rust Verifier is a model checker for Rust that enhances code scrutiny and reliability beyond traditional testing.
CodeCrafters offers advanced Rust courses covering real-world projects like Git, Docker, Kafka, and more, for skill enhancement.
Readers are encouraged to participate in challenges like reversing words in place and exploring Rust projects and courses.
Overall, the newsletter highlights the evolution of async Rust tools, verification methods, and educational resources for Rust developers.

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Medium

442

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Depth Perception with Stereo Cameras: Epipolar Geometry and Disparity Map

Stereo imaging involves capturing a scene from two different angles to determine depth information, similar to human vision.
Stereo camera systems use two cameras at a fixed distance to calculate object depth based on the positional disparity between images.
Rectification aligns epipolar lines horizontally, simplifying matching processes and enabling easier disparity calculation.
The Depth from Disparity Formula, based on the difference in x-coordinate of objects, helps calculate distance in stereo vision.
Epipolar geometry establishes the geometric relationship between images from different angles, facilitating point matching.
The Fundamental and Essential matrices aid in finding the transformation between cameras and calculating accurate epipolar lines.
Stereo rectification corrects images for alignment, crucial for accurate depth calculations in stereo vision systems.
The process for depth calculation involves steps like epipolar geometry computation, homography, disparity matching, and depth calculation.
Cameras parallelly aligned simplify matching calculations, while angled cameras complicate epipolar geometry and demand additional rectification.
Understanding epipolar geometry is crucial for successful stereo imaging applications and accurate depth map generation.

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Dev

248

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Configurations? Forget about it!

Setting up development environment involves configurations like local DB host, port, file paths, and service URLs.
Typically, developers face challenges with configurations leading to cycles of adjustments and server restarts.
Reading configuration from files like application.properties is a common practice but can be cumbersome.
A more efficient approach involves a 'util' class to read config values by key.
Changing config values dynamically without server restarts can be achieved through file reloading or caching.
Performance improvements can be made by implementing caching or storing config data in a database.
Concurrency in accessing config values can be managed through file system settings or DB transactions.
Permissions for config access can be controlled in a DB based on roles and teams.
Kiponos.io offers a solution with a Java SDK and Web Admin for real-time config management with WebSocket communication.
The service provides a centralized platform for config updates based on user roles, team membership, and active environments.

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Dev

248

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Trig.js Featured on CodePen Homepage! 🎉

Trig.js, a library showcasing animations with dynamic direction changes, has been featured on the CodePen homepage.
The showcased Pen demonstrates how to control Trig.js CSS scroll animations with JavaScript to enable dynamic movement while scrolling.
The creator of Trig.js appreciates the community's support and invites more individuals to try the library and share their creative animations.
The Trig.js library can be accessed on GitHub.

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Dev

401

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Tech Tip Wednesday: 5 Ways to Speed Up Your Next.js App

Use Dynamic Imports & Lazy Loading
Optimize Images with Next.js Image Component
Enable Automatic Static Optimization
Reduce Bundle Size with Code Splitting
Leverage Edge Functions & Middleware

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Dev

325

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Understanding Hoisting in JavaScript: Why Can We Use `var` Before Declaration?

Hoisting is JavaScript’s default behavior of moving variable and function declarations to the top of their scope before execution.
Variables declared with var are hoisted but initialized with undefined, meaning you can reference them before declaration, but they won't hold their actual value until the assignment line is executed.
let and const variables are hoisted but not initialized, so accessing them before declaration results in a ReferenceError.
Function declarations are fully hoisted, meaning they can be called before being defined.

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Dev

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Understanding Your Audience as a Software Engineer

Software engineering involves more than just coding, requiring collaboration and understanding of diverse audiences.
Effective collaboration in software engineering is crucial, necessitating social skills and adaptability within a team environment.
Developing the skill of understanding different audiences requires real-world experience in recognizing patterns and adapting to situations.
Interacting with roles such as CEO/CTO, Engineering Managers, Product Managers, and other team members is essential in software engineering.
Core audiences for a software engineer include users, other developers, and computers, each requiring a unique approach.
Coding primarily serves humans rather than machines, emphasizing the importance of clear, maintainable code for effective collaboration among developers.
Collaboration with machines also plays a vital role in coding, focusing on aspects like memory management, error handling, scalability, and efficient use of resources.
Mastering debugging, leveraging observability tools, and utilizing the right toolset are essential for effective collaboration with machines in software development.
Respecting and prioritizing core audiences by writing intuitive, maintainable, and optimized code is crucial for the success of users, developers, and machines.

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Medium

302

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Why Real Programmers Don’t Vibe: A Satirical Deep Dive

Real Programmers, a breed of developers that take their craft extremely seriously, reject the idea of 'vibing' while coding.
They perceive coding as a serious pilgrimage and believe that if you're not suffering for your code, you're not doing it right.
The term 'vibe coding' was coined to describe a laid-back, AI-assisted approach to programming, which was met with disdain by the Real Programmers.
Real Programmers deem fun, aesthetics, and enjoying oneself while coding as heresy.

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PlanetPython

108

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PyCoder��s Weekly: Issue #674: LangGraph, Marimo, Django Template Components, and More (March 25, 2025)

LangGraph is a Python library for stateful, cyclic, and multi-actor Large Language Model (LLM) applications.
Marimo is a Jupyter replacement using Python source, improving notebook issues.
Temporal's Python SDK for creating agentic AI workflows will be covered in a live demo on April 3.
Django 5.2 introduces the Simple Block tag for templated components.
IPython 9, Python 3.14.0a6, and Django 5.2 RC1 releases are announced.
Structural pattern matching and Django query optimization methods are discussed.
Various useful Python projects like compress_json, pysqlscribe, shorts_maker, and more are highlighted.
Events include Real Python Office Hours, SPb Python Drinkup, and PyLadies Amsterdam meetups.
A discussion about the implications of companies scraping the web for LLMs on user costs.
PEP 758 on Except expressions, instance vs. class vs. static methods in Python, and decorator challenges are covered.

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Medium

293

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3/21 Price Analysis: BTC, ETH, XRP, BNB, SOL, ADA, DOGE, TON, LINK, LEO

The Bitcoin community speculates about 'Spoofy the Whale' manipulating Bitcoin's price.
Arthur Hayes predicts a solid bottom for Bitcoin at $77K.
Robbie Mitchnick suggests economic downturns could drive Bitcoin's price up.
The market is currently muddled with bearish trends and resistance for altcoins.

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Mjtsai

Matching Drop Shadows

The article discusses the differences in drop shadow rendering between CSS on the web, Android, and iOS.
The dark and extreme shadow is used to make the differences more pronounced.
The CSS and Android examples appear similar, but a closer look reveals that the Android shadow is slightly blurrier.
Android's shadow rendering doesn't match CSS due to historical differences with Safari's rendering approach.

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Dev

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Orchestrating Models: Machine Learning with Docker Compose

Docker Compose is a tool for defining and running multi-container Docker applications using a YAML file to configure services.
It simplifies creating and configuring environments where services interact, allowing you to manage multiple containers with a single command.
Docker Compose is ideal for development and testing, quickly spinning up services like databases, APIs, and web apps locally.
In contrast, Kubernetes is suited for managing large-scale production applications, providing stability and scalability.
Docker Compose handles local development efficiently, while Kubernetes ensures application availability and resource management in production.
A sample ML project with Docker Compose involves a machine learning service and a PostgreSQL database for storing results.
The project structure includes defining services in a docker-compose.yml file, creating Dockerfiles for services, and configuring dependencies.
The ML service involves training a model, creating a Flask API for predictions, and storing results in PostgreSQL.
Running the project with Docker Compose builds images, starts containers, and runs the Flask model on port 5000.
Using Docker Compose simplifies managing multiple services for machine learning projects, offering scalability and centralized control.

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Medium

198

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The Genius Little Secret Behind Killer Machine Learning Pipelines (Plus Stats That’ll Leave You…

Machine learning (ML) is the invisible engine behind various applications like Netflix, fraud-proof banks, and stock markets.
Building a top-tier ML pipeline requires a 6-step grind that anyone can learn, starting with data collection and ending with deployment.
Key steps include data cleaning to handle missing values, duplicates, encoding, and scaling.
The importance of exploratory data analysis (EDA) is highlighted, as it helps uncover outliers, trends, and feature cuts before model development.

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Dev

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3394. Check if Grid can be Cut into Sections

Given an n x n grid and a set of rectangles, determine if it is possible to make two horizontal or two vertical cuts on the grid such that each resulting section contains at least one rectangle.
The approach involves collecting intervals in both horizontal and vertical directions, merging overlapping intervals, and checking if the number of merged intervals is at least three, indicating the possibility of making the required cuts.
The solution is implemented in PHP and has a time complexity of O(n log n) due to sorting and merging intervals.
Link to contact the author is provided for further support or information.

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Towards Data Science

A Clear Intro to MCP (Model Context Protocol) with Code Examples

MCP (Model Context Protocol) aims to standardize the way AI agents call tools across different providers, similar to REST APIs bringing order to chaos in data retrieval.
MCP provides context for AI models in a standardized way and enables systems to talk to each other consistently, avoiding mayhem in tool calling.
The standardized approach of MCP can enhance AI system safety by providing easier access to well-tested tools, reducing security risks and potential malicious code.
MCP offers a shared language for organizing, sharing, and invoking tools, which can lead to the democratization of tool calling.
Understanding how MCP works can make AI systems safer and more scalable as concerns regarding security and compatibility arise.
MCP components include Host (where the agent operates), Client (sends tool call requests), Server (centralizes tools), Agent (initiates tool calls), and Tools (functions that execute tasks).
Servers register tools, expose metadata, and agents discover tools using MCP, with an execution process involving forming tool call requests in a standardized format and executing the functions.
Utilizing the beeAI framework, a code example demonstrates leveraging MCP in a Re-Act Agent to interact with the Brave MCP server and discover and call tools.
Challenges for MCP adoption include dependency on server uptime, potential points of failure, and security considerations, though the protocol offers advantages like reduced development overhead and interoperable standards.
As more tool providers adopt MCP and organizations integrate AI agents, understanding and adopting MCP early can provide significant advantages as AI solutions scale.
MCP faces challenges such as maintaining compatibility, addressing security concerns, and minimizing latency, but its standardized approach can benefit developers, AI researchers, and organizations developing agent-based systems.

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