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Why You Should Quit Programming

Most people miss out on programming, because they never take effort to see how beautiful it can be
Coding should be approached with deliberate effort, just like relationships and fitness
You should not do coding, if you know you are a person who would not do coding if they have enough money to live out their life
Do what you like, no matter how hard it is. Stop complaining, it's a privilege.
Pick one thing and absolutely go at it. It is the ultimate experience to give up looking around and loving something to its absolute potential
If you stick with coding long enough, you will love it. Everything will disappear
Earn more money to have less resistance in enjoying what you do. Not to relax.
It's the ultimate suffering, to carry the flame of youth to your deathbed
People who are doing something they like will have no regrets. They will have enjoyed their youth, and they will be at peace forever
Stop wasting time and do what makes you happy

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Dev

353

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Working with XML in Python Requests library

XML stands for Extensible Markup Language, used for storing structured data and grouping items.
XML allows the creation of tags with any names, commonly used for Sitemaps and RSS feeds.
XML files use tags to structure data, such as the example of a breakfast menu.
Python's Requests library and XML ElementTree module can be used to work with XML data.

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Dev

274

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How to Develop the Right Mindset as a Programmer

Programming is a field of constant learning; embracing a curiosity-driven approach is essential.
Accepting failure as part of the process and using it as an opportunity for improvement is crucial.
Problem-solving skills and logical thinking are essential for programmers.
Collaboration, time management, and adaptability are vital for success in programming.

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Medium

418

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The Rise of AI in Programming: What Developers Need to Know

AI-driven tools are making debugging and testing processes more efficient.
AI is enabling the creation of adaptive and efficient algorithms in various fields.
Developers should grasp the basics of machine learning to gain an edge.
The rise of AI in programming calls for developers to embrace lifelong learning and treat AI as an assistant.

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Medium

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LiveAPI Devlogs Part 3: Transforming User Onboarding with 3 Industry-Inspired Methods

Swagger is not a complete solution for user onboarding and can introduce security threats.
LiveAPI provides a convenient solution to onboard users without friction.
Three methods inspired by other products are used to address user onboarding difficulties.
Existing products in the market offer solutions for user onboarding.

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Prodevelopertutorial

235

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Fractional knapsack tutorial

Introduction to Fractional Knapsack problems.
Different ways to solve knapsack problem
Understanding Fractional Knapsack with an example
Implementation of Fractional Knapsack

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Prodevelopertutorial

283

Boyer Moore algorithm

Boyer Moore algorithm is used for pattern searching inside a string.
It follows a set of rules when a mismatch occurs between the pattern and the string.
The algorithm starts searching from the rightmost character in the string.
If a mismatch occurs, it either skips the whole word in the string or skips until a character in the pattern matches a character in the string.

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Prodevelopertutorial

239

Introduction to Rabin Karp algorithm

Rabin Karp algorithm uses a hash function and brute force approach to check if a pattern is present inside a string.
Get the hash of the pattern and the hash of the whole string, and check if any hash value from substring matches our pattern hash.
If the character are not same, it is called as spurious hit. The prime number can be random and larger the prime number, less chances of getting the same output from the hash function.
In the above two steps, we are actually rolling over character by character, at any point we would have already calculated value for 2 characters.
This method is called as the rolling hash method.
When we find a match, we check if all the characters are the same. If the same we got out search pattern.
If the hash value is the same but strings are different, we conclude that it is a spurious hit and check if there exist another hash value that is the same as our pattern hash.
The Rabin-Karp algorithm uses a hash function to calculate the hash of the pattern.
Then, it uses the hash of the pattern to compare with the hash of every n-length substring of the text using the rolling hash method.
If a match is found using the hash values, the actual strings are compared by brute force to avoid spurious hits.

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Prodevelopertutorial

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Knuth Morris Pratt String matching algorithm

Given a string “s” and a pattern ‘p’, the task is to find if the pattern is present in the string “s”.
A brute force approach compares one letter after another, till we find the sub pattern or we reach end of the string.
There are 3 pattern matching algorithms, Knuth Morris Pratt(KMP) is the first algorithm in them.
KMP Algorithm is bit difficult to understand, and has 2 parts: partial match table and string matching.
We create a partial match table which will help us to skip the number of characters when there is a mismatch, eliminating checking of all the characters one by one.
To search the string, take 2 variables, i.e., i = string [str[0]] & j = pattern[0].
If the match is found, increment the index of both I and j. If there is a mismatch, move j to the location in PMT. If j = 0, then increment i index.
KMP Algorithm follows a high-level working approach to find the sub-pattern in a given string using the partial match table.
C++ implementation of KMP Algorithm which creates a partial match table based on the pattern and runs the KMP algorithm pattern p on target t, returns a Boolean if or not in the target.
KMP Algorithm is a more efficient way to search the pattern in the target in comparison to the brute-force approach.

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Prodevelopertutorial

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Floyd Warshalls algorithm

This tutorial discusses Floyd-Warshall's algorithm which is used to find the shortest path between all pairs of vertices in a graph.
The algorithm is based on dynamic programming approach. It is the third type of algorithm to find the shortest path for all vertex to every other vertex.
The article discusses previous two algorithms: Dijkstra's algorithm for finding the shortest path from one node to all other nodes/vertices and Bellman Ford's Algorithm to find the shortest path from one node to all other nodes.
Based on DP approach, the algorithm finds the shortest path by breaking the problem into simple sub-problems and solving them.
This article explains the algorithm using an example graph and by constructing distance matrices to solve the problem.
The article also provides implementation details using a C program with steps to calculate the shortest path between all pairs of vertices in a graph.
The output of the Floyd Warshall's algorithm is a shortest path matrix that provides the shortest distance between pairs of vertices in the graph.
The algorithm works for both undirected and directed graphs and can handle negative edge weights.
The time complexity of the algorithm is O(V^3), where V is the number of vertices in the graph.
Floyd-Warshall's algorithm is widely used in network routing and traffic analysis.

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RealPython

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Strings and Character Data in Python

Python strings are a sequence of characters used for handling textual data.
Strings in Python are immutable, meaning once you define a string, you can’t change it.
To access specific elements of a string, you use indexing, where indices start at 0 for the first character.
You can join all elements in a list into a single string using .join().

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RealPython

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Working With JSON Data in Python

Python’s json module provides you with the tools you need to effectively handle JSON data.
JSON is a widely-used text-based format for data interchange.
Python provides robust tools to facilitate the managing of JSON data efficiently.
JSON is the predominant standard for the exchange of information between systems.
Python makes it straightforward to work with JSON data.
JSON’s straightforward syntax allows both humans and computers to read and write JSON data effortlessly.
JSON format is text-based and has a simplistic syntax that is easy to read.
Python data types can be converted to a JSON-formatted string with json.dumps().
JSON data can be parsed with json.loads().
Python’s json.tool module can minify and prettify JSON files.

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RealPython

117

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Using Python's pip to Manage Your Projects' Dependencies

pip is a package manager for Python, used to install and manage libraries that aren’t part of the Python standard library.
pip stands for “pip installs packages”, indicating its primary function.
You use pip to manage dependencies and install packages from the Python Package Index (PyPI).
You can do a lot with pip, but the Python community is very active and has created some neat alternatives to pip.
The Python installer gives you the option to install pip when installing Python on your system.
On some Linux (Unix) systems like Ubuntu, pip comes in a separate package called python3-pip, which you need to install with sudo apt install python3-pip.
You can verify that pip is available by looking for the pip3 executable on your system.
Instead of running your system pip directly, you can also run it as a Python module.
Running pip as a module you can ensure that your system default Python version runs the pip command.
Use requirements files to manage projects’ dependencies.

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Medium

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A cap is a kind of headwear,

Caps, particularly baseball caps, are the most popular style of headwear that originated in the United States in the late 19th century.
They were initially designed for baseball players to protect their faces from the sun and later became a global fashion trend.
Caps also have important roles in professional and organizational contexts, such as military caps, police caps, academic caps, and chef's caps.
In sports, being capped refers to representing one's country in a real game, such as in cricket.

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Dev

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Optimizing Go Applications: Advanced Caching Strategies for Performance and Scalability

Caching is a crucial technique for improving the performance and scalability of Go applications.
In-memory caches store data directly in the application's memory, allowing for extremely fast access times.
Distributed caching systems like Redis or Memcached allow us to share cache data across multiple application instances and persist data between restarts.
One important aspect of caching is cache invalidation.
When dealing with large amounts of data, it's often beneficial to implement a multi-level caching strategy.
Go's expvar package can be useful for exposing cache performance metrics like cache hit rates, latency, and memory usage.
The golang.org/x/sync/singleflight package can be incredibly useful in these scenarios, helping us avoid the "thundering herd" problem.
Implementing efficient caching strategies in Go applications involves a combination of choosing the right tools, understanding trade-offs, and carefully considering specific needs.
Caching requires ongoing monitoring and tuning based on real-world usage patterns.
Caching can be a powerful tool in our Go development toolkit, helping us build faster, more scalable applications.

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