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Exploring a Python Project on GitHub

A multimodal Conversational AI system for medical diagnostics is introduced on GitHub, aiming to enhance the diagnostic process through advanced artificial intelligence.
The system utilizes Retrieval-Augmented Generation (RAG) for textual data analysis and LLaVA for processing medical imagery, such as chest X-rays, to provide comprehensive medical consultation and educational support.
Key Python files in the repository, like new_temp.py and temp.py, handle model loading and data processing to ensure efficient system response to queries.
This AI system has real-world applications in remote healthcare and medical education, potentially transforming medical diagnostics and improving online consultations using AI-powered insights.

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Dev

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How to Connect Spark to MinIO Using S3A in Java?

Many developers face challenges connecting Apache Spark to MinIO using the S3A file system.
Proper configurations and dependencies like 'hadoop-aws' and 'software.amazon.awssdk' are required for seamless operation.
The Java code example provided demonstrates how to configure Spark to connect to MinIO instance.
Key configuration parameters like endpoint URL, access and secret keys, and SSL settings need to be set correctly for successful connectivity.

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Medium

259

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How I Turned My Ideas into Stunning AI Movies

The AI MovieMaker is a powerful tool that allows anyone to create stunning 8K cinematic movies without the need for advanced editing skills or expensive equipment.
Users can craft visually impressive movies with the AI MovieMaker, adding visual effects and transitions that enhance the quality of the final product.
The tool not only saves time and money but also opens up opportunities for monetization, with some users earning profits by offering video creation services after learning to use the AI MovieMaker.
The AI MovieMaker simplifies the movie-making process, making it accessible for hobbyists and aspiring filmmakers alike, with its user-friendly interface and features like automated editing and easy scene transitions.

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Medium

355

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Indian missiles ‘strike army air bases in Pakistan

Three Pakistani air bases were targeted by Indian missiles on Friday morning amidst escalating tensions between the two countries.
Pakistan's military spokesperson accused India of aggression and warned of a strong response.
Indian warships with supersonic cruise missiles have been positioned closer to Pakistan, putting Karachi within striking range.
Both countries have engaged in missile and drone attacks, with reports of explosions at an airport in Indian-controlled Kashmir as the conflict escalates.

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Towards Data Science

137

Log Link vs Log Transformation in R — The Difference that Misleads Your Entire Data Analysis

Log transformation is commonly used to normalize extremely skewed data in data analysis.
The article discusses a project analyzing energy consumption of training AI models.
The dilemma of whether to use log-transformed response variables or log link functions is explored.
Comparison of models using AIC values and diagnostic plots is detailed, favoring log-transformed models.
The article interprets coefficients in models and discusses the impact of log transformations on interpretation.
Results show log-linked models provide more sensible outcomes compared to log-transformed models.
The importance of understanding the difference between log transformation and log link in modeling is emphasized.
Log transformation may distort variation and noise in the data, affecting model reliability.
Detailed comparisons, plots, and interpretations are presented to illustrate the impact of transformation decisions.
Utilizing log-linked models can provide more accurate and meaningful results in data analysis.

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Medium

282

Inside .NET Asynchrony and Concurrency : Threads and Thread Pool, How I/O Work Is Done, Completion…

A process is an instance of a program being executed, with its own system resources allocated by the OS.
Threads are units of execution within a process, sharing resources but having their own stack memory space.
Concurrency involves managing multiple tasks, even if not running simultaneously.
Parallelism involves executing multiple tasks in actual parallel at the same time.
Asynchrony allows executing multiple parts of a program in a non-blocking way.
Thread Pool in .NET manages threads efficiently, starting with cores' logical count.
I/O-bound operations spend time waiting for external resources, best handled asynchronously.
CPU-bound operations benefit from parallelism and multithreading for improved performance.
Worker threads handle CPU-bound tasks, while I/O threads manage completed I/O operations.
Async/await in C# allows writing non-blocking code in a sequential and readable manner.

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Dev

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Stop Fighting Python's Relative Imports: Meet `run-main` for Smoother Module Execution & Debugging

run-main is a Python utility that simplifies the execution and debugging of individual Python modules, especially those using relative imports.
It addresses issues like relative import errors, debugger misdirection, and IDE configuration overhead, making the debugging process smoother.
To get started, install run-main using pip, define a _main() function in your Python file, and then run your module using 'run-main path/to/your_module.py args'.
run-main intelligently handles relative imports and ensures the correct package context while executing your Python module's _main() function.

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Mjtsai

164

NSCache and LRUCache

NSCache's eviction strategy is not defined and it's not LRU, which can impact performance when handling memory caching.
Many developers opt to create their own LRU cache implementation like LRUCache using a Swift Dictionary with a linked list to control memory consumption effectively.
When dealing with custom LRU cache implementations, it's important to manage memory allocation carefully to prevent issues like stack overflow caused by automatic deallocation of linked list nodes.
To ensure objects are retained in the cache even when the app is backgrounded, developers can implement the NSDiscardableContent protocol within the objects stored in the cache.

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Dev

264

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Doubly Linked Lists - DSA Notes 📝

Doubly linked lists involve two pointers - next and prev - where null prev signifies the head of the list.
For insertion at the end, the tail pointer must be updated along with setting prev pointer for the new node. Time complexity: O(1).
Deletion at the end involves setting the tail pointer to the node before the current tail. Time complexity: O(1).
Access in doubly linked lists allows traversal in both directions with a time complexity of O(n).

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Dev

177

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Syncing Obsidian Notes to Notion by Python Script (Part 1) - Append Content to Existing Pages

The article discusses the transition from using Notion to Obsidian for note-taking due to the latter's simplicity in casual writing.
The author expresses the need to automate the process of transferring notes from Obsidian to Notion to alleviate time-consuming tasks.
Python scripts are identified as a solution for automating the workflow effectively, streamlining note management.
The article aims to introduce a Python script that automates the process of transferring notes from Obsidian to a Notion database.
Key points highlighted include setting up Notion by obtaining the Internal Integration Secret and Database ID, and configuring Obsidian by saving notes in specific folders.
The Python script automates the process by reading .md files, using the Notion API to create entries in the Notion database, and updating tags of transferred notes.
The script follows a specific flow of operations, extracting metadata from Obsidian notes and transferring them to the designated Notion database.
The conclusion emphasizes the collaborative use of Obsidian and Notion, encourages customization to suit individual needs, and hints at more script developments in the future.
Automating the transfer process between Obsidian and Notion enhances note-taking efficiency and productivity for users looking to merge the functionalities of both platforms.
The Python script simplifies the manual transfer process by integrating Obsidian notes with a Notion database, demonstrating the power of automation in improving workflow.
The article serves as a guide for users seeking to automate note transfers between Obsidian and Notion through Python scripts, showcasing a systematic approach to enhancing note-taking tasks.

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Dev

187

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AI Code Generators & Tools That Speed Up App Development

AI-powered code generators and development tools have revolutionized software development, drastically speeding up the process and reducing errors.
These tools use large language models to generate code that follows syntax rules, implements functionality, and includes best practices.
Leading platforms like Instance, GitHub Copilot, and Replit offer AI assistance in developing complete applications from natural language prompts.
AI tools like Tabnine, Qodo, and Codestral focus on code completion, testing, and quality improvements, respectively.
AI assistants accelerate prototyping, reduce technical debt, and aid developers in learning new languages and frameworks.
Choosing the right AI coding tool depends on factors like experience level, project requirements, and integration needs.
Best practices for using AI code generators include being specific in prompts, reviewing generated code, and providing feedback.
Future developments in AI coding tools may include enhanced domain expertise, improved collaboration between teams and AI, and better educational explanation capabilities.
While AI tools won't replace skilled developers, they enhance productivity by handling routine tasks and providing intelligent suggestions.
As these technologies advance, they will continue to democratize software development and empower developers with a competitive edge.

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Hackingwithswift

396

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What's new in Swift 6.2?

Swift 6.2 introduces a wide range of additions and improvements to the language, along with enhancements to Swift concurrency to facilitate easier adoption.
Small changes like raw identifiers, default values in string interpolation, and enumerated() conforming to Collection are expected to be widely used due to their usefulness.
Swift Testing in Swift 6.2 sees significant improvements, including exit tests and attachments, strengthening its capabilities.
Overall, Swift 6.2 enhances support for concurrency and includes pragmatic choices that aid in smoothing out the language's learning curve.

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23 Likes

Medium

123

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Programming Learning Project 2：I made a tetris using Pygame

The news is about a programming learning project where the individual created a Tetris game using Pygame.
The project is developed using coding software VS Code and the Pygame package.
The game logic involves setting up variables in window1, the main game functionality in window2, and displaying the scoreboard in window3.
The developer used matrices to represent the gameboard and tetrominos, along with implementing tetromino rotation efficiently with minimal lines of code.

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Dev

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What if adding in-app notifications to your Next.js app only took one command?

Setting up in-app notifications can be a tedious process for developers, involving installing SDKs, creating components, and setting up environment variables.
To streamline this process, a CLI tool called add-inbox was created to set up a notification inbox in Next.js or React applications quickly.
Running npx add-inbox@latest init initiates an interactive setup process that handles framework selection, package manager preference, dependency installation, component scaffolding, environment configuration, and provides next steps.
add-inbox is built using Node.js and libraries like prompts, chalk, fs, and path, aiming to simplify the setup of in-app notifications and gather feedback for further improvements.

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Dev

337

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What is Dataset in Python?

A dataset in Python refers to a structured collection of data, organized for analysis, manipulation, and visualization.
Datasets are a fundamental component in data science and machine learning workflows, providing raw material for building models and extracting insights.
Datasets in Python are often represented using libraries like Pandas for DataFrames or NumPy arrays, enabling efficient data handling and manipulation.
Common examples of datasets include the Iris dataset, MNIST dataset, ImageNet dataset, Diabetes dataset, and IMDB dataset, each serving different purposes in machine learning and data analysis tasks.

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