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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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Medium

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PUT and DELETE HTTP Verbs in IIS Not Working? Here’s Why (and How to Fix It)

PUT and DELETE HTTP verbs in IIS may not work when deployed on a Windows server with IIS.
This issue is caused by the default installation of WebDAV (Web Distributed Authoring and Versioning) in IIS, which intercepts and blocks PUT and DELETE requests.
To fix this, you can uninstall/remove WebDAV completely at the machine level or disable it just for your application by editing the web.config file.
Disabling or removing WebDAV will allow the PUT and DELETE requests to reach your application and work as expected.

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Medium

189

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How Java’s Memory Model Prevents Data Corruption in Multi-Threading

Java's memory model prevents data corruption in multi-threading by controlling how threads access and update shared data to avoid race conditions.
When multiple threads share the same data, cached values can lead to inconsistencies if changes are not immediately visible to other threads.
Java offers techniques like using volatile keyword for visibility, synchronized blocks for atomicity and visibility, and atomic variables for lock-free thread safety.
The volatile keyword ensures that updates go directly to main memory, preventing delayed visibility of changes by other threads.
Synchronized blocks help in controlling access to shared data by allowing only one thread to execute a section of code at a time, ensuring consistency.
Atomic variables in Java offer a lock-free mechanism for updating shared data efficiently without causing delays.
Java's memory model defines happens-before relationships to ensure proper sequencing of updates across threads, preventing stale reads and incomplete updates.
By using memory barriers and coordination across processor cores, Java ensures that changes to shared data are visible in the correct order to all threads.
Compilers and processors may rearrange instructions, but Java's memory model enforces rules to maintain the intended order of operations for thread consistency.
Developers can choose the appropriate synchronization mechanism based on the usage of shared data to ensure thread safety and prevent data corruption in multi-threaded programs.

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Medium

411

HOW TO TURN INSTANT GRATIFICATION TO YOUR ADVANTAGE

Good habits may not feel rewarding initially, but they become easier to stick with as you see progress.
Immediate rewards are essential to keep you excited while waiting for delayed rewards.
Using reinforcement and habit stacking can help tie your habits to immediate cues and rewards.
Immediate reinforcement is especially effective for breaking habits of avoidance.

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Idownloadblog

411

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WWDC25 is running from June 9 through June 13 at Apple Park in California

Apple has announced that its Worldwide Developers Conference (WWDC) will run from June 9-13 at the Apple Park headquarters in California.
WWDC25 will be available to stream entirely online via the Apple Developer website, app and YouTube channel.
The keynote on Monday, June 9, will preview significant advancements in iOS 19, iPadOS 19, macOS 16, and other fall updates.
The conference will provide developers with the opportunity to meet Apple's experts, gain insight into new tools, frameworks, and platform features.

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Medium

226

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LaTeX-like Syntax for JSXGraph: A JavaScript Math Visualization Revolution

JSXGraph and MathJax are two powerful JavaScript libraries that enhance the capabilities of handling mathematical content in web applications.
JSXGraph specializes in creating interactive geometry diagrams and visualizations, making it ideal for educational applications.
MathJax is a specialized library for rendering high-quality mathematical formulas and equations in a LaTeX-like syntax.
The combination of JSXGraph and MathJax allows for dynamic and engaging learning environments and powerful mathematical exploration tools.

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Javacodegeeks

316

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Event Sourcing with Axon Framework in Spring Boot

Event Sourcing, when combined with CQRS, offers a scalable and maintainable architecture for microservices by capturing changes as events stored in a log.
Axon Framework simplifies implementing CQRS and Event Sourcing in Spring Boot, providing tools for event-driven microservices like command handling and query handling.
Key concepts in Axon Framework include Commands, Events, Aggregates, Event Store, Queries, and Projections for managing application state.
To set up Axon in Spring Boot, include Axon dependencies in the project's configuration file (pom.xml/build.gradle).
Implementation involves defining Aggregates, Commands, Events, configuring Axon in Spring Boot, creating a Command Gateway, and a Query Handler.
Axon Framework offers benefits like scalability, maintainability, event-driven architecture, event sourcing, flexibility, support for CQRS pattern, replayability, extensibility, testing support, and a strong community.
Using Axon in Spring Boot enables building scalable, maintainable, and event-driven microservices that are resilient and easy to evolve over time.
Developing skills with Java and Axon Framework can help create robust microservices architectures leveraging CQRS and Event Sourcing patterns.
Axon provides a comprehensive ecosystem and tools for effective implementation of event-driven systems in Java.
Subscribe to newsletters for free eBooks and resources on Java development, covering topics like JPA, JVM troubleshooting, JUnit testing, Java interviews, and more.

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The Pragmatic Engineer

458

Image Credit: The Pragmatic Engineer

AI Engineering in the real world

AI Engineering is a versatile field, encompassing ML engineers, data scientists, software engineers, and more, utilizing language models for building applications.
Chip Huyen defines AI engineering as the bridge between software engineering and ML engineering, often starting with software engineers utilizing language model APIs.
Software engineers transitioning to AI engineering work with large language models to build features and products, facing various challenges and utilizing diverse tech stacks.
Companies like incident.io, Sentry, Augment Code, Elsevier, Wordsmith, Simply Business, and DSI are highlighted for their AI engineering applications and tools.
Common topics covered include what companies are building, onboarding tips for software engineers entering AI engineering, tech stacks used, engineering challenges, and novel tooling.
Issues like non-deterministic outputs, evaluation, latency, privacy, and cost considerations are key challenges in AI engineering projects.
Developers emphasize the importance of being able to assess the quality of AI outputs, ramping up expertise, and understanding domain-specific nuances.
The article includes insights from software engineers at various companies, sharing experiences and advice for transitioning to AI engineering roles.
Tech stacks utilized by these companies vary, with common tools like Postgres, PyTorch, LangChain, AWS Bedrock, Pinecone, Sonnet, and more being employed.
AI coding assistants, legal AI tools, chatbots, and summarization features are among the applications highlighted in the article, showcasing the diverse applications of AI engineering.
Overall, AI engineering presents unique challenges and opportunities for software engineers transitioning to this dynamic and rapidly evolving field.

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Medium

320

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No Code, No Problem: How AI is Making Coding Accessible to Everyone

AI has made coding more accessible than ever before, with AI-assisted coding, no-code platforms, and natural language programming empowering individuals with ideas to create and innovate.
AI-powered coding assistants like GitHub Copilot and others can generate working code instantly based on simple instructions, revolutionizing the coding experience for both beginners and experienced programmers.
No-code AI platforms such as Google's AutoML and Microsoft AI Builder allow anyone to build AI-powered applications quickly by uploading data and letting AI detect patterns and make predictions.
AI-powered builders like Wix ADI and Bubble enable the creation of professional websites and mobile apps without coding, democratizing participation in the digital economy.
AI-powered automation tools like Zapier and Microsoft Power Automate save time by handling repetitive tasks, making work smarter and more efficient for individuals and organizations.
Natural language coding with AI models like OpenAI's Codex is making it possible to generate code by simply describing the desired outcome in plain language, changing the coding landscape.
AI is democratizing access to software development, empowering a diverse range of individuals including researchers, teachers, small business owners, and activists to bring their ideas to life.
This technological revolution is redefining coding as an open door rather than a barrier, allowing more people to create and innovate in the world of technology.
Artificial intelligence is not replacing coders but expanding the pool of creators, thinkers, and problem-solvers who can leverage AI tools to make a difference.
In this era of accessible coding facilitated by AI, individuals are encouraged to envision and create solutions that have the potential to impact society and drive positive change.

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Hackernoon

415

Image Credit: Hackernoon

Running Cypress Tests for Node.js Apps Inside a Docker Container

The article focuses on running Cypress tests for a Node.js application inside a Docker container.
Pre-requisites include having Docker, Docker-Compose, Node.js installed, and a basic understanding of Docker.
The project setup involves creating a docker-compose file defining services like book-reader-app and cypress.
Cypress tests are created in a cypress-test directory with configuration and specifications for testing.
A simple test is defined to check if the application heading contains 'Book App'.
A Dockerfile for Cypress is created to install dependencies and run tests.
Running the tests in the Docker container can be done using 'docker-compose up --build'.
Additional tests for rendering components, animations, responsiveness, and navigation can be added.
The article concludes by emphasizing the importance of testing with Cypress for ensuring application functionality.
Using Cypress can enhance app reliability and robustness by testing UI interactions and navigation.

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Dev

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Day 0: System Architecture basics

System design starts with understanding the high-level architecture of a computer system, including bits, storage, RAM, cache memory, CPU, and motherboard.
Production-ready app architecture involves components like CI/CD pipelines, load balancers, external storage solutions, and logging and monitoring systems.
Debugging process includes error identification through logs, replication in non-production environments, analysis using tools, and hotfix deployment for temporary fixes.
Key principles of system design include scalability, maintainability, efficiency, and robustness under adverse conditions.
The CAP theorem outlines trade-offs in distributed systems, balancing consistency, availability, and partition tolerance.
Networking basics cover IP addressing, data packets, transport layer protocols like TCP and UDP, and application layer protocols such as HTTP, WebSockets, SMTP, and FTP.
API design best practices focus on RESTful principles, GraphQL flexibility, and rate limiting to control API requests.
Strategies for reducing latency in web development involve caching and CDNs for faster data access.
Proxy servers serve as intermediaries like forward proxies for client anonymity and reverse proxies for load balancing and encryption management.
Load balancing distributes workloads via strategies like round-robin, least connection, and geographical algorithms to prevent server bottlenecks.
Database fundamentals cover relational vs. NoSQL databases, sharding, replication, caching, and indexing for performance optimization.

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