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Java Application Containerization and Deployment

Containerization of applications provides a way to combine all required application resources — including program and configuration files, environment variables, networking settings, and more — in a single, standardized, easily manageable package.
Multiple functionally identical containers can be started, run, managed, and terminated from a single container image, ensuring consistency from the point of image creation onward.
While there are numerous benefits to app containerization, many roll up into a single word: consistency.
Containerization solves nearly all of those externalities.
If you're just beginning to work with containerization, starting with a smaller, but full, operating system (OS) is a great first step.
There are various schools of thought on this, but if you're just beginning to work with containerization, starting with a smaller, but full, operating system (OS) is a great first step.
Many developers begin their containerization efforts by poring over the official Dockerfile reference documentation.
Multi-stage builds provide the means to reduce the size of container images if you have files required for the build that aren't required for the final output.
For applications that are largely self-contained, deploying to production can be as simple as a single command, assuming the deployment target is ready to accept a containerized application.
Containerization enables developers to combine all required application resources and supporting services in one or more container image(s) and to deploy, run, and manage them more easily.

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Prodevelopertutorial

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Chapter 6: Asymptotic Notation Big Omega and Theta

Big Omega notation (Ω) is used to represent tight lower bound.
If a function f(n) is equal to Ω(g(n)), it means f(n) is always greater than C(g(n)).
Big Theta notation (Ø) represents the average case for a function.
If a function f(n) is equal to Ø(g(n)), it means f(n) is greater than C1(g(n)) and less than C2(g(n)) for all n>= 0.

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Prodevelopertutorial

334

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Chapter 5: Asymptotic Notation Big O

Big O notation is used to calculate the complexity of an algorithm.
It defines that a function f(n) is equal to O(g(n)) if it is always less than c * g(n).
Reducing f(n) to g(n) helps in understanding the rate of growth of the function.
Examples of functions and their corresponding Big O notations are provided.

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Logrocket

442

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Working with URLs in JavaScript

The URL API, supported in all modern browsers, provides a way to parse and manipulate URLs. Understanding the parts of a URL
With modern JavaScript, we can parse URLs and extract these different parts as needed. In older browsers, before the URL API was available, one way that developers parsed URLs was by using an element.
If you’re interested in the value of a particular parameter in the query string, you can use its get method to get the parameter by its name.
Let’s look at a simple case first:
If you call the URL constructor with a relative URL and specify an invalid or incomplete URL for the base URL, you’ll get an error.
Using the URLPattern API to match patterns within URLs can be useful for things like client-side routing in a single-page application (SPA).
The URL API is a versatile interface for constructing, validating, and manipulating URLs in JavaScript.
By using a URLSearchParams object, you can build a query string without worrying about string concatenation or encoding special characters.
Creating relative URLs - Normally, the argument to the URL constructor will throw an error if it’s not a full, valid URL.
You might be familiar with the window.location object, which represents the current page’s URL.

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Prodevelopertutorial

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Chapter 4: Asymptotic Notations

AN are mathematical functions that are used to calculate running time and rate of growth of an algorithm.
Order of growth of computing time: logN, N, NlogN, N^2, 2^n.
Factors affecting running time: Speed of computer, compiler used, programming language, algorithm of choice, types and size of input/output.
Constants are ignored to focus on algorithm performance for larger values of input.
Types of Asymptotic Notations: Big O (worst case), Big Omega (lower bound), Bit Theta (average case).

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Dev

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LeetCode Challenge: 42. Trapping Rain Water - JavaScript Solution 🚀

To solve LeetCode 42: Trapping Rain Water, we need to calculate the total amount of water that can be trapped between elevation heights after it rains.
This problem is best tackled using the two-pointer approach, which has O(n) time complexity and O(1) space complexity.
The implementation of the solution involves initializing two pointers and two variables to store the maximum height seen so far from the left and right.
Then, the algorithm compares the heights at the left and right pointers, calculates the water trapped based on the maximum heights, and moves the pointers inward until they meet.

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Medium

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Death to all zombies: crafting weapons for Zombie State

The team behind weapon design for the mobile roguelike shooter Zombie State shared the insights from their gun creation journey.
To design and configure 15 entirely new guns in a short timeframe, a lot of significant changes needed to be made under tight deadlines.
The art team focused on the visuals and behavior of the guns to create an emotional attachment to each weapon.
The Zombie State weapons were inspired by existing firearms but reimagined in a post-apocalyptic style with a handmade, garage-crafted feel.
Each gun carries with it a visually identifiable backstory, enabling players to envision the artistically created narrative simply by looking at it.
The team created innovative designs that consumed minimal resources to give each gun a unique personality.
To create a realistic shooting experience, the team developed multiple animations for each weapon and fine-tuned the parameters for transitioning between these animations.
Four types of ammunition, the automatic firing system, impulse settings for the ragdoll animations, and bullet spread calibrated to external factors significantly enhance the shooting feel.
Players can immediately gauge the capabilities of their newly acquired weapon as all weapons automatically reload when transitioning between rooms, and picking up a new weapon always provides one loaded magazine.
The team had to bypass the creation of 2D concepts and began blocking out designs in 3D, which proved to be more efficient and visually informative.

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Prodevelopertutorial

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Performance analysis of an algorithm: Time Complexity

In this chapter, we learn about calculating time complexity.
Example 1: Time complexity of calculating sum of 'n' numbers in an array is O(n).
Example 2: Time complexity of matrix addition is O(n^2).
Different types of time functions include O(1), O(n log n), O(n), O(n^2), O(n^3), O(2^n), O(3^n), O(n^n).
The increasing order of time consumption is O(1) < O(n log n) < O(n) < O(n^2) < O(n^3) < O(2^n) < O(3^n) < O(n^n).

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Self-Learning-Java

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How to Send Data with the Fetch API in React: A Guide to POST Requests?

This tutorial guides you on how to send data with the Fetch API in React using POST requests.
It demonstrates how to create a React component that allows users to submit new posts and display a list of the posts they've published.
The tutorial uses the JSONPlaceholder API to simulate publishing posts.
The provided code shows the step-by-step procedure to set up the React project and implement the functionality.

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Dev

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When 0.1 + 0.2 = 0.30000000000000004: A Java Developer's Guide to Trust Issues 🤔

Java's floating-point arithmetic can lead to precision issues, as seen in calculations like 0.1 + 0.2.
Binary representation and rounding errors contribute to inaccurate financial calculations.
Using BigDecimal in Java can provide precise results for financial calculations.
Best practices include using BigDecimal for money, specifying scale and rounding mode, and avoiding double for currency calculations.

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Self-Learning-Java

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Building a Simple Queue in JavaScript Using Array Functions

A queue is a linear data structure that follows the First In, First Out (FIFO) principle.
Key operations of a Queue include enqueue, dequeue, peek, isEmpty, and size.
In JavaScript, queues can be implemented using arrays and functions like push() and shift().
Advantages of using queues include order maintenance, simplicity, and efficiency.

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Logrocket

370

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Use case vs. user story: How and when to use each

As a product manager, you express customer needs to your development teams so that you can work together to build the best possible solution.
Two common approaches to requirements are use cases and user stories. Use cases are more detailed and explain how the system should be working, whereas, user stories focus on a feature’s result.
Use cases let you build understanding stakeholders because you write out a structured requirement flow for the system behavior. On the other hand, use cases are time consuming and complex products require lots of cases.
User stories need to be as simple as possible to prevent misunderstanding between stakeholders. They represent acceptance criteria and desired features that provide value to customers.
Use cases and user stories are both important parts of the software development cycle and each describe a specific feature. User stories are more user centric and use cases are more system behavior centric.
When to use each approach and how to implement. As a product manager, if you want to explain a user need or a product goal, you need to write user stories. Typical use cases have actors, description, preconditions, main flow, alternative flows, and dependencies components.
User stories tend to be the preference for agile, but don’t think that means that agile doesn’t require details. When you find yourself needing detailed requirements, turn towards use cases instead. And sometimes you can even mix the two methods together.
Although the road you take for the two methods differs, they both focus on understanding your customer’s needs. Choose the method that best suits your product or team style and remember that you can change them to adapt to new needs or circumstances.
Either way, understanding requirements gives you the best chance at building successful, sustainable products.

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Dev

433

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Cursor + PiecesOS = Unlimited Context Window

Cursor + PiecesOS is a powerful combination that provides unlimited context window for developers.
Cursor is an IDE with AI capabilities, and when paired with Pieces as an extension, it offers enhanced functionality.
The combination solves various developer problems:
- Unlimited access to AI assistants like Claude 3.5 Sonnet, GPT-4, and Gemini 1.5 Pro.

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Prodevelopertutorial

Performance analysis of an algorithm: Space Complexity

Space complexity is about calculating the amount of space consumed by algorithm during the course of its execution.
There are two types of space complexity, fixed or constant space complexity and linear space complexity.
Fixed or constant space complexity refers to cases where the size of space consumed does not increase with the increase of input size.
Linear space complexity refers to cases where the space consumed increases with the increase of input size.

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Medium

252

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Detecting Plant Weeds with CNN Models: A Deep Dive into ResNet and Inception-v3

Through this journey, a comparative study was conducted on ResNet-50, ResNet-34, and Inception-v3 architectures to solve the problem of distinguishing between eight weed species from over 17,000 images.
The selected weed species are local to pastoral grasslands in Queensland and include Chinee apple, Snakeweed, Lantana, Prickly acacia, Siam weed, Parthenium, Rubber vine, and Parkinsonia.
Pretrained Inception-v3 achieved the highest validation accuracy (92%) and the lowest validation loss (~0.2), indicating superior generalization.
A detailed classification report revealed key findings about the model performance.

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