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YouTube Clone Built with HTML & CSS Add It to Your Own Website

This article discusses the creation of a YouTube homepage clone using HTML and CSS for front-end development practice and portfolio use.
The project aims to enhance skills in layout design, responsiveness, and overall web development understanding.
The HTML and CSS code components include navigation bars, search bars, video categories, and video grid layouts.
Various elements such as buttons, images, text, and icons are styled and arranged to mimic the YouTube homepage's appearance.
The design is mobile-responsive and demonstrated through CSS classes like flex, grid, and scrollbar customization.
Categories like Home, Shorts, Subscriptions, and Library, along with channel subscriptions, are simulated in the sidebar navigation.
Explore section displays trending categories like Music, Gaming, News, Sports, and Education for user interaction.
The main content area features a sticky top navigation bar, video categories section with various genre options, and video grid layout showcasing video cards with thumbnails and information.
Additional functionalities like infinite scroll simulation and tooltip implementation are mentioned for further enhancement.
The project serves as a learning tool for web developers and enthusiasts, offering insights into front-end design and coding practices.

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VentureBeat

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Former DeepSeeker and collaborators release new method for training reliable AI agents: RAGEN

A collaborative team has introduced RAGEN, a new system for training and evaluating AI agents to make them more reliable and less brittle for real-world usage.
RAGEN focuses on interactive settings requiring adaptation, memory, and reasoning, using a custom RL framework called StarPO.
StarPO operates in rollout and update stages, guiding the model with reasoning and optimizing using cumulative rewards to improve the learning loop.
The framework was tested using Alibaba's Qwen models, showing reproducibility and consistent baseline comparisons in symbolic tasks.
StarPO-S, a stabilized version, helps prevent training collapse and improves performance across different tasks.
RAGEN evaluates agents in three symbolic environments, focusing on decision-making strategies developed during training.
RAGEN's open-source project on GitHub visualizes agent rollouts and thought processes, emphasizing transparency in decision-making.
Explicit reasoning boosts performance in single-turn tasks but decays during multi-turn training, highlighting a need for refined reward shaping.
RAGEN offers a foundation for developing AI agents that can think, plan, and evolve, shedding light on model training beyond task completion.
Questions remain regarding RAGEN's transferability outside symbolic tasks and scalability over longer horizons for sustained reasoning in tasks.

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Dev

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Everyone Loves Open Source… Until It's Time to Contribute

Most open source projects are maintained by a small group of contributors.
Developers face barriers to contributing, such as lack of documentation and unfamiliar codebases.
Contributors often lack recognition for their efforts, leading to decreased participation.
To encourage more contributions, steps can be taken such as labeling beginner-friendly issues and providing mentorship.

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Dev

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The Psychology of Programming: Why Naming, Readability & Simplicity Matter

Programming is not just about computers, but also about communicating with other humans through code, emphasizing clarity and maintainability over cleverness.
The article explores the importance of naming, readability, and simplicity in programming through Cognitive Load Theory, which suggests minimizing unnecessary mental effort.
Intrinsic load (task difficulty), extraneous load (unnecessary complexity), and germane load (schema construction) are crucial elements in reducing cognitive load in programming tasks.
Effective naming reduces extraneous load, with tips like specificity, avoiding noise words, and following conventions to enhance code understanding.
Readability is essential, as developers spend significantly more time reading code than writing it, emphasizing whitespace usage, function size limits, meaningful comments, and structures.
Simplicity in code matters to improve understanding, prevent errors, and save time on debugging, advocating for clear and straightforward code over overly clever optimizations.
Practical tips include using code reviews for teaching, reading code regularly, leveraging linters and formatters, and refactoring consistently to uphold coding principles in daily practice.
Writing clean code with empathy towards others leads to better teamwork, user experience, and future understanding, emphasizing the importance of simplicity and clarity in programming.
Programming is a blend of logic, language, art, and science, where the code's clarity and simplicity impact the cognitive load of those who interact with it.
The article concludes with a reminder to name, read, and write code with care, highlighting the soul of efficiency in simplicity and clear intentions.

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Dev

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Text-to-SQL from Scratch — Tutorial For Dummies (Using PocketFlow!)

Text-to-SQL systems help translate natural language questions into SQL queries, simplifying database interactions.
Understanding the database schema is crucial for Text-to-SQL systems to interpret queries accurately.
Large Language Models (LLMs) play a key role in transforming questions into SQL queries based on schema information.
The process involves generating SQL queries, executing them against the database, and handling errors through debugging loops.
PocketFlow simplifies the creation of Text-to-SQL systems by breaking down the workflow into manageable Nodes and a Flow manager.
Specific nodes like GetSchema, GenerateSQL, ExecuteSQL, and DebugSQL are used to map the database, translate queries, run SQL, and fix errors.
Nodes in PocketFlow have prep, exec, and post methods for handling inputs, executing tasks, and storing outputs.
The interconnected flow ensures a seamless process from understanding the schema to presenting query results.
Text-to-SQL systems empower users to interact with databases using plain English, bridging the gap between users and complex SQL queries.
PocketFlow's simplicity allows developers to build conversational database interfaces efficiently and intelligently.

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Dev

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From Uni to Dev Life: What They Don’t Teach You

A university student shares their transition into a software engineering role at a B2B marketing firm undergoing a tech transformation.
Starting as a part-time campaign executive, the student gained firsthand experience in business operations alongside full-time studies.
Moving into an internship as a software engineer, the student faced broad learning requirements beyond university teachings.
University provided foundational knowledge in programming, teamwork, and concepts but lacked real-world practicality.
The job demanded full-stack problem-solving skills, DevOps tasks, and adaptability outside traditional academic boundaries.
Working at a startup required flexibility to handle diverse tasks beyond defined roles and take ownership of challenges.
Real-world experience emphasized the importance of practical skills, soft skills, and learning from failures for personal growth.
The student advises fellow students to seek hands-on experience, ask for help, apply for roles, and expand knowledge beyond coding.
Embracing challenges in the tech industry is highlighted as a growth opportunity and a pathway to becoming a problem-solving developer.
The post encourages readers to persevere through uncertainties, errors, and learning curves, emphasizing continuous improvement and shared experiences.

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Medium

256

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The Hidden Cost of Obeying the Rules: Why AI Is Coming for Your Job

Following rules too well can lead to becoming replaceable and limiting creativity.
The reliance on AI and ready-made answers reduces the need for cognitive engagement.
Bending the rules and using judgment becomes the most valuable skill in a world dominated by AI.
Questioning rules and creating your own path leads to originality and staying relevant.

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Towards Data Science

406

Data Science: From School to Work, Part IV

Different types of tests form a pyramid structure from unit tests, integration tests, to end-to-end tests.
Unit tests focus on testing a single unit of code and are fast and automatable.
Integration tests check how different code units interact with each other and ensure subsystems work together.
Functional tests verify if the application's functionality aligns with the specifications.
End-to-end tests simulate real-world scenarios and cover a wide range of application flows.
Using frameworks like Pytest simplifies comprehensive testing processes and provides detailed feedback on test results.
Fixtures in Pytest help set up a consistent environment for testing with the same context or dataset.
Mocking with Pytest allows simulating the behavior of functions or classes, ensuring tests are independent and reliable.
Managing test directories and analyzing test coverage are essential practices for effective testing.
Balanced testing strategies involving unit, integration, functional, and end-to-end tests are crucial for ensuring software quality and reliability.

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Dev

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🛠️ I built a suite of dev tools — because we all use them more than we think!

A developer has created a personal suite of dev tools called ToolifyX.
The suite includes a JSON formatter & prettifier, a CSS box shadow generator, and a border radius previewer.
The tools are designed to be clean, fast, and distraction-free.
The developer is open to feedback, suggestions, and ideas for new tools to add.

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Medium

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A missing semicolon? Not a big deal!

Developers should not compromise on established protocols and best practices.
A missing hyphen in the code caused a failure in NASA's Mariner 1 mission, leading to a loss of $18.5 million.
Paying attention to every single character and having a QA department can prevent such costly mistakes.
Adhering to quality standards is crucial to prevent project failures and financial losses.

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VentureBeat

183

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Amazon’s SWE-PolyBench just exposed the dirty secret about your AI coding assistant

Amazon Web Services introduced SWE-PolyBench, a multi-language benchmark for evaluating AI coding assistants.
The benchmark aims to address limitations in current evaluation frameworks and assess AI agents in navigating complex codebases.
SWE-PolyBench contains over 2,000 coding challenges across Java, JavaScript, TypeScript, and Python.
It offers more diverse tasks and programming language support compared to existing benchmarks like SWE-Bench.
The new benchmark introduces advanced evaluation metrics beyond pass rate, including file-level localization and CST node-level retrieval.
Python remains the strongest language for AI agents, while performance declines with increased task complexity.
Different agents exhibit varying strengths in bug fixing, feature requests, and code refactoring tasks.
Clear issue descriptions significantly impact success rates for AI coding agents.
SWE-PolyBench is crucial for evaluating AI coding assistants as they transition from experimental to production environments.
The benchmark is publicly available for enterprise environments, providing valuable insights for real-world development scenarios.

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Dev

271

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Building AI Agents to Prioritize CVEs — A Google ADK Guide

AI agents are created using the Agent Development Kit (ADK) to prioritize vulnerabilities by enriching data from public sources like Google OSV, MITRE, KEV, and Google search.
The AI agents do not directly prioritize vulnerabilities but provide summarized information to understand their criticality.
The tutorial requires basic Python programming skills and familiarity with AI concepts.
Setting up the environment involves using PyCharm, creating a Python project with locked dependencies, and installing necessary dependencies for Google ADK.
Defining the project structure is crucial for integrating AI agents with Google ADK, requiring an API key for Google Models stored in a .env file.
Building an agent involves creating instances of classes with names and instructions, running commands for web UI or CLI, and defining tools to collect and save information.
Agents can use function tools like retrieving data from APIs and built-in tools provided by ADK.
Glueing together agents involves creating sequential agents to orchestrate the collection and summarization of vulnerability information stored in session states.
Running the agents using ADK web interface triggers the agent chain to prioritize vulnerabilities based on collected data.
AI agents have limitations but hold significant potential, with this guide serving as an entry point into developing AI agents.

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Dev

394

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Take the Blame, Pass the Praise

Wins are rarely solo acts; credit should be given to all involved.
Deliberate praise helps step up others and builds trust.
Taking responsibility when something goes wrong demonstrates leadership.
Sharing the spotlight and giving credit to others increases influence and creates a positive team culture.

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Medium

353

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Module Loading in JavaScript with ES Modules vs CommonJS

CommonJS is a module system that revolves around require() and module.exports, allowing for synchronous loading of modules in Node.js.
Modules in CommonJS are read, parsed, and cached during runtime, with module caching storing the result of module.exports in memory.
CommonJS modules are executed as they are required, with no upfront analysis of all modules.
Module caching in CommonJS allows for sharing a single instance of a module across different parts of an app.
CommonJS modules can lead to issues with circular dependencies and side effects only happening once during module loading.
Node.js wraps each CommonJS file in a function to avoid global scope pollution and provides special variables like require and module.
ES Modules introduce a standardized module system with static analysis, where imports and exports are established before any code execution.
ES Modules preload all dependencies and establish a full dependency graph before executing code, avoiding delays during runtime.
ES Modules do not support conditional loading of imports, ensuring a predictable dependency structure.
Top-level code in ES Modules is always in strict mode by default and does not pollute the global scope.

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Dev

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Geocoding Distance🐞

Geocoding involves transforming location names into geographical coordinates and vice versa. Nominatim can power search boxes on websites for free-form or structured queries.
Calculating geographical distance between two points on Earth's surface can be done using the Haversine formula, which considers Earth's spherical shape.
Delphi and Python functions are provided for calculating distances between coordinates using the Haversine formula.
The article demonstrates how to get the distance between Bern and Paris using latitude and longitude coordinates in Delphi and Python.
There is an explanation of the great-circle distance, orthodromic distance, or spherical distance, which is the distance along the great-circle arc between two points on a sphere.
Geocoding APIs like Nominatim and OpenWeather support the conversion of location names into geographical coordinates and vice versa.
The article provides code examples using Nominatim's geocoding API to retrieve coordinates and addresses for given locations.
Troubleshooting tips are given for resolving '403 Forbidden' errors with the Nominatim API, including checking API usage limits and using proper User-Agent headers.
The article discusses different User-Agent strings and their importance in API requests to comply with Nominatim's usage policy.
Different solutions are compared for geolocation distance calculations, highlighting considerations such as performance, storage, and scalability in various environments.

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