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Automate WhatsApp Channel Messages Using PHP

Automate WhatsApp Channel Messages Using PHP
Automating the process of managing and messaging WhatsApp channels saves time, boosts engagement, improves consistency, and scales communication.
This guide explains how to create a WhatsApp channel using PHP and send messages to the channel, including text messages and media messages.
By scheduling messages, you can send updates at the perfect time automatically.

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Dev

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Singleton Pattern: The Lonely Chapter of Design Patterns

The Singleton pattern ensures that a class has only one instance, offering a unified access point in software applications.
It serves as a control tower, bringing order and efficiency by avoiding conflicts and resource waste.
Unique Instance and Global Access Point are key features of the Singleton pattern.
In daily life analogy, it resembles a unique landmark or a central post office for the software world.
The pattern structure includes a private constructor, static variable, and static method for instance access.
The UML class diagram visualizes the Singleton pattern's construction and uniqueness.
Implementing the Singleton pattern involves creating a single instance for consistent behavior and data.
A practical exercise demonstrates using Singleton for global state management, like a lighthouse guiding ships at sea.
The Singleton pattern finds applications in configuration management, logging, thread pools, cache, and hardware access.
To overcome Singleton pattern limitations, Dependency Injection is suggested for flexible testing and code improvement.

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Medium

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The Hunger Beyond the Stars

Tobias, a welding inspector, enjoys his job and spends his free time stargazing.
He recently went to Black Rock, Nevada's desert to observe the stars.
While searching for the exoplanet WASP-12b, he stumbled upon an unknown planet, possibly a water world.
The planet had a mostly blue color with small strange purple spots.

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Dev

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OData with .NET core: Essential Concepts & Best Practices

OData (Open Data Protocol) is a protocol for querying and manipulating data over RESTful APIs, developed by Microsoft.
Key features of OData include querying data, CRUD operations, batch requests, metadata information, and pagination.
To enable OData in an ASP.NET Core Web API, install the OData package and configure it in Program.cs.
IEdmModel in OData defines the data model structure and is essential for OData route configuration in ASP.NET Core.
OData queries can be performed using $filter, $orderby, $select, $expand, etc., once IEdmModel is configured.
Pagination in OData is supported using $top and $skip to limit the number of records returned.
$expand in OData is used to fetch related data, similar to JOINs in SQL.
Batch requests in OData allow multiple API calls in a single HTTP request, reducing network overhead.
OData offers advantages over standard REST APIs in terms of querying data, pagination, metadata, and batch requests.
Security in OData APIs can be implemented using authentication, authorization, rate limiting, and restricting unused query options.

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Self-Learning-Java

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The Importance of Data Normalization in Machine Learning

Data normalization is a crucial pre-processing technique in machine learning to bring numerical data to a common scale for enhanced algorithm performance.
Techniques like Min-Max Normalization, Z-score normalization, Robust Scaling, Max Absolute Scaling, and Logarithmic normalization are commonly used for data normalization.
Min-Max Normalization scales data to a specific range (often 0 to 1) using a simple formula that involves the minimum and maximum values in the dataset.
Z-score normalization normalizes data to have a mean of 0 and a standard deviation of 1 by subtracting the mean and dividing by the standard deviation.
Robust scaling, similar to Z-score scaling, utilizes the median and interquartile range (IQR) instead of the mean and standard deviation, making it less sensitive to outliers.
Max Absolute Scaling, implemented by MaxAbsScaler, scales data by dividing each value by the maximum absolute value, resulting in values between -1 and 1.
Logarithmic normalization involves taking the logarithm of a feature, either natural logarithm or base 10 logarithm, to transform the data.
Implementation examples using Python and libraries like NumPy and Scikit-learn demonstrate the practical application of these normalization techniques.

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Medium

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AI Can Code, But It Can’t Think — That’s Why You Still Have a Job

Coding is just the tip of the iceberg in data science and software engineering fields.
The real work involves problem-solving, understanding the problem's significance, and determining the approach.
AI struggles in figuring out what problem to solve and how to approach it effectively.
Generic code snippets produced by AI have little relevance to the actual problem.

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Dev

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Exploring NFT-Java-Utils: Bridging Blockchain Innovation and Java Simplicity

NFT-Java-Utils is an open source utility library designed to simplify NFT operations in Java.
The project streamlines blockchain integration in Java applications and exemplifies a sustainable open source funding model powered by community contributions, grants, and corporate sponsorships.
NFT-Java-Utils provides a simplified yet powerful framework for NFT operations in Java, supporting seamless NFT creation, blockchain agnosticism, secure cryptographic routines, and community engagement.
The project's transparent and community-driven approach sets a standard for open source funding and highlights the role of community-driven development in the future of digital technology.

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Dev

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How to Optimize SQL Queries for Better Performance

Optimizing SQL queries is crucial for web developers to ensure high-performance applications by reducing execution time and resource consumption.
Key strategies include using indexes wisely, avoiding SELECT *, optimizing JOIN operations, leveraging query execution plans, and limiting results.
Indexes help speed up queries but should be used judiciously to maintain balance and avoid slowing down write operations.
Avoiding SELECT * and specifying needed columns improves query performance by reducing unnecessary data transfer.
Optimizing JOINs with indexed columns, normalized schemas, and INNER JOIN over OUTER JOIN enhances query efficiency.
Analyzing query execution plans and using tools like EXPLAIN provide insights to optimize query performance further.
Limiting and paginating results, avoiding subqueries when possible, and using stored procedures aid in improving SQL query performance.
Strategic normalization/denormalization and regular monitoring with tools like EXPLAIN, pt-query-digest, and pgAdmin ensure ongoing optimization.
Tools like EXPLAIN, pt-query-digest, pgAdmin, and SQL Server Management Studio assist in optimizing SQL queries efficiently.
Optimizing SQL queries not only enhances application performance but also establishes developers as skilled professionals capable of handling database challenges.
Regularly monitoring query performance and implementing optimization strategies are essential to maintain efficient database operations as applications scale.

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Dev

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Exploring Solana-Java-NFT: The Intersection of Java, Blockchain, and Open Source Innovation

Solana-Java-NFT is a Java library that simplifies NFT creation and management on the Solana blockchain.
The project embraces an open source funding model, incorporating sponsorships, crowdfunding, and decentralized funding approaches.
Solana-Java-NFT offers a comprehensive ecosystem that combines Java practices with the scalability and efficiency of the Solana blockchain.
The project emphasizes best practices, modular design, and secure coding standards for developers.

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Medium

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Importance of Input Encoding in Webapps

Input encoding is important in web applications to prevent security vulnerabilities like SQL injection attacks.
Input encoding converts user input to a pre-determined format, ensuring that the input is not misinterpreted.
Common encoding techniques, such as UTF-8, add escape characters before special characters to avoid misinterpretation of data.
Canonicalization, which refers to using one consistent format for data communication, further safeguards applications from attacks.

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Self-Learning-Java

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Using the Interquartile Range (IQR) to Identify Outliers

IQR stands for interquartile range, it is a measure of variability that is used to describe the middle 50% of a data set.
Q1 and Q3 are calculated using the formula, where Q1 is the first quartile and Q3 is the third quartile.
The IQR is the difference between Q3 and Q1.
The IQR is not affected by outliers, making it a robust measure of variability.

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Self-Learning-Java

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Variance: A Measure of Data Spread

Variance is used to calculate how much individual data points differ from the mean (average) of the dataset.
A larger variance indicates that the data points are more spread out from the mean, while a smaller variance indicates that the data points are closer to the mean.
In practice, the primary measure used to assess the spread or variability of data is the standard deviation.
There is no mandatory need to calculate the variance unless you specifically require it for a particular statistical analysis.

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Self-Learning-Java

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Standard Deviation: A Measure of Variability

Standard deviation quantifies the amount of variation in the data set.
A low standard deviation indicates that the data points tend to be very close to the mean.
A high standard deviation indicates that the data points are spread out over a wider range of values.
Real-world use cases include using standard deviation to evaluate stock price fluctuation or house prices in a neighborhood.

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Medium

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The joy in writing Go

The author, a software development student, shares their love for exploring programming languages.
Among the languages they have tried, their favorites are Go and Rust.
While Rust can be more complex, Go brings joy and excitement to the author.
The author highlights Go's simplicity, elegance, and strong support as reasons for their enjoyment in writing Go code.

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Self-Learning-Java

Understanding animation-timing-function in CSS

The animation-timing-function property in CSS defines the speed curve of an animation.
It controls how the animation progresses through its duration and specifies the intermediate states.
Common values for animation-timing-function are linear, ease, ease-in, ease-out, ease-in-out, cubic-bezier(n, n, n, n), and steps(n, [start | end]).
These values determine the speed pattern or step divisions of an animation.

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