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CHRISTMAS IS COMING!!

The submission is for the Frontend Challenge - December Edition, Glam Up My Markup: Winter Solstice.
The developer created an interactive Christmas tree decoration page using HTML, CSS, and JavaScript.
The goal was to create a playful and festive experience for users using front-end technologies.
The biggest challenge was creating a smooth drag-and-drop feature for decorating the tree.

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329

Search a 2D Matrix in C++

To search for a value in a 2D matrix, there are two efficient algorithms.
The first algorithm involves eliminating one row or column at a time based on the comparison between the target value and the top right element of the current array.
The second algorithm treats the matrix as a sorted array and applies binary search to find the target value.
Both algorithms have their own advantages and can be used based on the specific requirements.

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Hackernoon

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December Brings Free GitHub Copilot

TypeScript is dominating the JavaScript landscape with 67% of developers using it more than traditional JavaScript.
GitHub Copilot AI is now free for GitHub users, offering code completions and chat messages within Visual Studio Code and GitHub.
Tailwind CSS 4 has introduced Lightning CSS for faster builds, but be cautious of potential challenges in maintaining code.
New tools and updates include JerryScript 3.0 for IoT, Transformers.js v3.2 for browser-based machine learning, Bun 1.1.39 & 1.1.40 enhancements, and ESLint v9.17.0 with improved features.

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Jump Game II in CPP

Given an array of non-negative integers, you are initially positioned at the first index of the array.
Each element in the array represents your maximum jump length at that position.
Your goal is to reach the last index in the minimum number of jumps.
The solution is a greedy approach; we need to go to end of the list with minimum jump.

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Simplify Path CPP

Given an absolute path for a file (Unix-style), simplify it.
Use stack to simplify the path and handle cases like '..', '/', '/./', etc.
Ignore redundant slashes and use stack to handle '..' by popping the top element.
Return the simplified path as the result.

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Medium

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Mastering Code: 7 Simple Strategies to Skyrocket Your Programming Skills

Practice by building projects to apply what you learn.
Understanding Git and version control is crucial.
Master debugging techniques and use helpful tools.
Utilize documentation and stay informed through learning resources.

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Medium

394

Understanding K-Means Clustering and PCA: Unraveling the Power of Data Science Techniques

K-Means Clustering and PCA are powerful data science techniques used for dimensionality reduction and data exploration.
K-Means Clustering groups data points into clusters based on similarity, while PCA reduces features for easier analysis and visualization.
K-Means Clustering helps identify patterns in data, while PCA reveals variance and improves machine learning algorithms.
Both techniques have limitations, such as assuming certain data structures or relationships.

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Javacodegeeks

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Introduction to Apache Iceberg

Apache Iceberg is an open-source table format for huge analytic datasets. It provides capabilities like schema evolution, ACID transactions, etc.
Iceberg is designed to work efficiently with big data engines like Apache Spark, Trino, Hive, Presto, and others.
Apache Iceberg was originally developed at Netflix to address limitations in Hive tables for big data processing.
The key motivations for creating Iceberg include scalability issues with Hive Metastore, inflexible schema evolution, and a lack of ACID compliance.
Iceberg addressed these problems by providing a flexible table format, efficient metadata management, and full support for ACID transactions.
The architecture of Apache Iceberg consists of three main components: Metadata Layer, Data Layer, and Manifests.
Apache Iceberg provides a range of features that make it a robust choice for modern data lake management, including ACID Transactions, Schema Evolution, Time Travel, Partition Evolution, etc.
The code example shared demonstrates how to use Apache Iceberg with a Hadoop catalog to create a table programmatically and perform CRUD (Create, Read, Update, Delete) operations.
Apache Iceberg provides a robust and scalable solution for managing large datasets with advanced capabilities.
Using Iceberg with Java and other big data engines offer flexibility, performance, and reliability in handling data-intensive applications.

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Dev

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The 5 Must-Have Tools I'll Stick With in 2025

Qodo is a code quality platform designed to enhance the developer's development process.
Notion AI integrates powerful AI tools directly into your workspace, boosting productivity.
Supabase is a backend solution for quick application development without managing infrastructure.
Canva simplifies visual content creation with easy-to-use features and a wide array of templates.
Excalidraw is a tool for brainstorming and sketching ideas with simplicity and collaboration features.

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Dev

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Week 10 of My #100DaysOfCode Challenge: Mastering JavaScript and Building Projects! 🚀

Week 10 of the #100DaysOfCode challenge focused on mastering JavaScript and building the B's Diner Restaurant Web App.
Key JavaScript topics covered during the week included map() vs forEach(), for loop, innerHTML dangers, function expressions & hoisting, and arrow functions.
The week also delved into advanced JavaScript features like import & export, reduce(), default parameters, ternary operator, and rest parameter.
The B's Diner Restaurant Web App was successfully completed, featuring DOM manipulation, local storage, dynamic rendering, event listeners, and user experience design.

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Medium

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Why Do We Celebrate Christmas on December 25? ✨

December 25 was chosen as the date to celebrate Christmas in the 4th century by Roman Christians.
The date coincided with the winter solstice and other pagan festivals, making it easier to convert people to Christianity.
Ancient Romans celebrated Saturnalia, a week-long festival, around the same time as Christmas.
Another pagan celebration, Sol Invictus, also occurred on December 25.

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Logrocket

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Applying the principles of design variety in UX designs

The principle of design variety states that multiple contrasting elements can capture user attention. This is based on the human desire to indulge and explore and the fear of missing out.
Unity, balance, and contrast are similar sounding design principles that differ from variety. Designers can use variety to capture user attention, but too much variety can lead to poor usability.
Designers can use variety in multiple ways in UX designs such as using multiple colors, experimenting with a different font family, playing with size and space, using shapes, and using interactive animations.
Visual hierarchy can also create variety within the design.
Using excess variety can lead to clutter and frustration, which is why designers have to use it sparingly and with intention.

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Hackernoon

285

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OpenAI Makes it Easier to Build Your Own AI Agents With API

OpenAI's Assistance API provides a solution to make it easier for building AI agents for advanced integrations, document retrieval, executing python code for computations, larger context limit, and more. It addresses limitations of chat completion models such as lack of persistent message history, direct handling of large documents, challenging tasks for coding, limited context windows, and synchronous processing. Through OpenAI Assistance API, users can create sophisticated AI assistants featuring tools like code interpreters, function calling, and thread handling.
Chat completion models like GPT-4o and GPT-4 are simple as they expect a sequence of messages as input. These models are synchronous and expect a single response after the question is asked where in the Assistance API, you can request multiple requests in parallel and combine the results without careful orchestration.
The Assistance API dynamically selects which message is to be included as context and thus reduces the distance between the previous conversation and current, making it possible for the API to process longer and larger interaction smoothly.
With the Assistance API, document retrieval involves dividing the text into small chunks, converting them into embeddings, storing them in a vector database, and retrieving the relevant chunks at query time.
Code interpretation allows the assistant to use Python code in response to requests like reversing a string, finding today's date. The assistant, in turn, does not solely rely on token predictions.
The OpenAI Assistance API keeps track of message history, supports document retrieval, executes Python code, manages larger contexts and enables function calling for advanced integrations.
The threaded messaging feature of the Assistance API allows the previous message content to be stored in threads, allowing assistants to keep the conversation context in multiple turns of conversation. Assistance API current supports GPT-4 (1106 preview) and will support custom fine-tuned models in the future.
The Assistance API offers solutions that address the core limitations of standard chat completions in real-time computation, document-based Q&A, or dynamic interactions in AI applications.
By using instructions, threads, tools, and function calling, users can create AI assistants that can easily handle everything from reversing string to advanced integrations.
OpenAI Assistance API brings new possibilities for building sophisticated AI-driven systems for real-world scenarios and making it easier to build AI agents.

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Hackernoon

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You Don't Need More Than 2 Weeks to Learn Basic SQL

SQL is a query language that's used to retrieve data based on the rules defined.
Benefits of learning SQL: employability, high paying jobs in data analytics, data science, software engineering, and database management.
Beginner level SQL concepts like SELECT, INSERT, UPDATE, and DELETE can be learned in a few days.
Intermediate level SQL concepts take around 1 to 2 years to master properly. One can learn SQL functions, JOINs, Subqueries, and CTEs at this level.
Advanced level topics take years to learn. Specialised knowledge of database performance and stored procedures is required.
Effective learning strategies for SQL include reading books,taking courses, practicing real-world scenarios, and getting involved with communities.
For programming beginners, it can take around two weeks to learn basic SQL skills.
If you have prior programming experience, learning SQL could be much faster.
It generally takes around 1 year of dedicated work to have advanced SQL skills.
Don't be too hard on yourself, remember it takes time to master SQL.

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