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Dev
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📱Tech News to Your Ears: WhatsApp Voice Notes Powered by Python, AWS & Twilio🧠🎙️
- A Python application was built to fetch the latest tech news, convert it into audio, and deliver it as a WhatsApp message.
- The project combined services like NewsData.io for news, AWS Polly for TTS, S3 for file storage, and Twilio's WhatsApp API for messaging.
- Key features included a simple web interface using Flask and HTML form, automatic fetching of the top 5 headlines, voice conversion with AWS Polly, cloud storage in AWS S3, and delivery via Twilio's WhatsApp API.
- Amazon Q Developer was used to streamline backend operations, enhance automation, manage S3 files, set up cloud-based monitoring, automate routine operations, and improve workflow for dynamic content retrieval and system health checks.
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Dev
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Revise Python in 14 days for ML (2025) : Day 1
- Day 1 of the 14-day Python revision challenge for machine learning commenced with a focus on basic Python concepts like output, variables, and strings.
- Basic Output with print() function was covered, demonstrating how to customize separators and line endings for text output.
- Variables were explained as data containers storing values, with insights on memory address, references, and size.
- String manipulations and methods like converting input to numbers, changing case, splitting text, and finding characters within strings were highlighted for text processing.
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Dev
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What Does {sys.executable} -m Mean in Python Code?
- The term {sys.executable} in Python code refers to the Python interpreter being used, enabling the selection of the correct Python executable file, especially in environments with multiple installations.
- The -m flag, which stands for 'module name,' allows running library modules as scripts by specifying the module name to be executed, advantageous for installing packages in Python.
- The --user flag appended at the end of commands like pip install
--user indicates installing the package locally for the current user, useful when lacking administrative rights and preventing system-wide installations. - Using {sys.executable} along with -m and --user options simplifies Python library management, particularly in Jupyter Notebooks, ensuring precise package installations customized to the current user's environment.
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Dev
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Crushing the Command Line: Automating Email Reading with Python & Amazon Q Developer
- A student created a Python script to automate reading Gmail emails in the command line for quick access.
- The project uses Amazon Q Developer CLI to streamline development processes and seek coding assistance directly in the terminal.
- The script logs into Gmail securely, fetches and decodes unread emails, and displays sender, subject, and body in a formatted manner in the terminal.
- The use of IMAP, python-dotenv, and .gitignore for secure credential storage was emphasized, along with the benefits of real-time debugging with Amazon Q Developer.
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Dev
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How to Safely Kill Python Subprocesses Without Zombies?
- Managing Python subprocesses effectively is crucial to prevent zombie processes and resource wastage.
- Killing subprocesses with child processes requires special attention to avoid leaving zombie processes behind.
- Using os.killpg() to kill process groups in Python may lead to zombie processes if not handled correctly.
- Recommended solutions include using subprocess.Popen with a Process Manager, proper cleanup with wait(), and leveraging Python's signal module for efficient process control.
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Javacodegeeks
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[FREE EBOOKS] Offensive Security Using Python, Learn Computer Forensics — 2nd edition & Four More Best Selling Titles
- Offensive Security Using Python book offers insights, real-world examples, and hands-on activities to help master offensive security using Python for threat mitigation and web security.
- Learn Computer Forensics — 2nd edition book provides skills to work in a technical environment, investigate crime, acquire digital evidence, and develop forensic reports.
- Bulletproof Problem Solving book introduces a systematic approach to problem-solving, based on a visual, logic-tree method, applicable to various fields and industries.
- Productivity Reimagined challenges common business myths to improve workplace productivity through collaborative cultures and sustainable growth, backed by scientific evidence and real-world examples.
- HTML, CSS, & JavaScript All-in-One For Dummies is a comprehensive guide for beginners to learn website design using HTML, CSS, and JavaScript, covering static, dynamic websites, and essential web development skills.
- Joy Through the Journey by Amberly Lago offers insights into finding joy in life's challenges through mindfulness, resilience, and gratitude, providing practical solutions and exercises for personal growth.
- The book offers a range of free eBooks, including JPA Mini Book, JVM Troubleshooting Guide, JUnit Tutorial for Unit Testing, Java Annotations Tutorial, Java Interview Questions, Spring Interview Questions, and Android UI Design.
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Dev
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217
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Python GUI full course — Learn GUI development in 15 mins
- Learning GUI development in Python allows applications to come to life with windows, buttons, inputs, and interactions.
- Tkinter is recommended for beginners in Python GUI development, and this guide focuses on using Tkinter for GUI development.
- Widgets are essential elements in GUI development, including buttons, labels, text inputs, checkboxes, dropdowns, and sliders.
- Layouts in Python GUI are managed using geometry managers like pack(), grid(), and place() for organizing widgets.
- The root widget in Tkinter serves as the main window for holding GUI elements and the mainloop() function keeps the app running.
- mainloop() is crucial for Tkinter applications' interactivity by listening for user actions and keeping the window open.
- Getting started with Tkinter involves creating a root window and adding widgets between the root and mainloop() for visibility.
- Pack layout manager in Tkinter arranges widgets relative to each other, and grid layout manager offers precise control over widget placement.
- PyUiBuilder simplifies the process of utilizing pack and grid layouts, making GUI development easier with drag-and-drop functionalities.
- Joining a Discord server for assistance in tkinter or Python GUI development is recommended for further help and guidance.
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Dev
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163
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Secure by design in Python: A FastAPI app with 5 DevSecOps tools and a real time SSTI vulnerability remediation
- Security should not be an afterthought in software development, but a core principle baked into design, code, and CI/CD workflows.
- This article demonstrates an intentionally insecure FastAPI app with 5 open-source security tools focusing on vulnerability scanning, static code analysis, API fuzzing, container image scanning, and dynamic app security testing.
- The tools discussed include pip-audit for dependency vulnerability scanning, Bandit for static code analysis, Schemathesis for API fuzzing, Trivy for container image vulnerability scanning, and OWASP ZAP for dynamic app security testing.
- The article emphasizes the importance of incorporating security practices early in the development process and highlights remediation steps for a real Server-Side Template Injection (SSTI) vulnerability within FastAPI.
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Medium
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Exploratory Data Analysis: Radiation Monitoring with Python and Geiger Counter
- Background radiation is always present, originating from various sources like uranium, thorium, radon, nuclear accidents, and cosmic rays.
- Exploratory data analysis can help uncover patterns in radiation levels and fluctuations using tools like anomaly detection.
- The article demonstrates collecting radiation data with a Geiger counter and a Raspberry Pi, processing it using Python and Pandas.
- For those interested in using the same data, a link to a Kaggle dataset is provided at the end of the article.
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Dev
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149
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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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167
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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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Dev
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176
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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
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335
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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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Dev
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381
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From Code to Cash: Monetizing Python AI Agents ⚡
- The journey from creating a successful AI agent to monetizing it involves challenges like client convincing and deployment complexities.
- Outcome-based pricing, where customers pay based on results achieved, aligns interests and provides transparency in ROI.
- Evidence shows outcome-based pricing can lead to shorter sales cycles and higher conversion rates.
- Implementing outcome-based pricing requires precise metering and analytics tools like Stripe.
- An example lead enrichment agent showcases the process of enriching leads using Google Sheets and Python scripts.
- Setting up Stripe involves creating products, adding customers, and setting up subscriptions for billing.
- Preparing AI agents for deployment involves wrapping core logic in a Flask endpoint and defining type-safe models for data extraction.
- Deploying agents as API services using platforms like Itura simplifies execution monitoring, GitHub integration, and billing integration.
- Customers trigger the agent with a unique URL, and usage is metered for billing through Itura's integration with Stripe.
- Transitioning a Python AI agent into a consistent revenue stream involves challenges but can be overcome with outcome-based pricing and proper tools.
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The Truth About Cars
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385
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Python 5000: The Ultimate Pothole Fixer
- The Python 5000 is a cutting-edge pothole patching vehicle that can fill and flatten a pothole in just 2 minutes.
- Operated by one person using a joystick from inside the cabin, it eliminates the need for a 2-3 person crew as in traditional methods.
- The process involves clearing debris with air blast, deploying asphalt, leveling with a rake, and compacting with a roller for a smooth finish.
- Capable of carrying up to 5 tons of asphalt, the Python 5000 even uses engine exhaust to maintain the warmth of the asphalt.
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