API POST Requests & Headers in Python

Still using pip?! I recently switched to uv (a modern, high-performance package manager by Astral) — and for someone who codes heavily in Python, the upgrade feels practical and overdue. Why switching to uv? 1. Project initialization friction 𝐖𝐢𝐭𝐡 𝐩𝐢𝐩 (𝐭𝐲𝐩𝐢𝐜𝐚𝐥 𝐟𝐥𝐨𝐰): Create project folder → Manually create virtual environment (python -m venv venv) → Activate it (different commands per OS) → Upgrade pip (optional but common) → Install dependencies → Sometimes fix path or interpreter issues → Maintain separate requirements file It works — but it’s fragmented and repetitive. 𝐖𝐢𝐭𝐡 𝐮𝐯: Initialize project in one step -- That's it. The virtual environment handled automatically, Dependencies managed cleanly, Lockfile(UV's requirements.txt) generated for reproducibility Less ceremony. Fewer steps. Cleaner workflow. 2. Faster installs → pip installs packages sequentially. → uv performs parallel installation and dependency resolution. The difference becomes obvious in larger projects or fresh environment setups. 3. Shipping accuracy pip + requirements.txt often lists package names only conflicts when different python version is being used. uv generates a lockfile with exact resolved versions, ensuring consistent installs across machines and deployments. Better reproducibility. Fewer works on my machine issues. For anyone working in AI/ML, backend, automation, or tooling-heavy Python workflows, uv reduces overhead and speeds up iteration. Check out the Complete guide by Corey Schafer (one of the most respected educators in the Python community) to know more about it: https://lnkd.in/gN2BbbSy stay tuned for more updates✌️. #Python #PythonDevelopment #UV #PythonTools #DevTools #SoftwareDevelopment #BackendDevelopment #AIML #DataScienc #DeveloperProductivity #OpenSource #Programming #TechCommunity #BuildInPublic #ModernDevelopment

Day 40 of #90DaysOfCode — Backend Automation Project: Flight Price Alert System (Python) Built a production-style Python backend system that monitors flight prices using live APIs and sends automated email alerts when user-defined target prices are met. The focus of this project was on real-world backend engineering practices rather than tutorial workflows. Emphasis was placed on input validation, API reliability, fallback strategies, secure configuration using environment variables, and maintainable modular architecture. Key aspects: • Dynamic IATA airport code resolution • Direct-flight prioritization with indirect fallback handling • Cheapest-flight selection with stop analysis • Automated email notifications via SMTP • Persistent data storage using Sheety API • Secure secret management and environment-based configuration This project reinforced the importance of designing backend systems that are resilient, predictable, and safe to run in real environments. GitHub repository: https://lnkd.in/gKMT7SEv #Python #BackendDevelopment #Automation #APIs #SoftwareEngineering

🚀 Project Showcase: Real-Time EMA Crossover Stock Monitoring & Prediction System I’m excited to share my latest project built using Django and Python. This system monitors live stock market data, detects EMA crossovers across multiple timeframes, and provides real-time trading signals. It also includes a machine learning module that predicts future stock trends using historical CSV data. 🔹 Key Highlights: 🔐 OTP-based secure authentication   📈 Real-time EMA crossover detection   ⏱ Multi-timeframe analysis (15m, 30m, 1h, 1d)   🤖 Background stock monitoring bot   📊 Live dashboard with AJAX updates   🧠 ML-based trend prediction  This project helped me gain hands-on experience with real-time systems, background processing, and AI-based decision support. 🔗 GitHub Repository: https://lnkd.in/d9in7qck 🎥 Demo video below 👇 I’d love to hear your feedback! #Django #Python #MachineLearning #StockMarket #WebDevelopment #StudentProject #FinalYearProject #GitHub

Still working on my Python Client Management System. After using it for a bit, I noticed something: viewing all clients wasn’t enough. I needed smarter ways to track things. So today I added new features: • Search for a client instantly using name or phone number • View only clients who haven’t paid yet • Automatically show jobs that are overdue based on due dates This is starting to feel less like a script and more like a real admin system. The best part? Every new feature comes from a real problem I face while using it. I’m learning that the best way to improve at coding is to build tools that solve your own problems. More improvements coming soon. #Python #ProblemSolving #BuildInPublic #CodingJourney #Automation

🚀 Day 38 of #90DaysOfCode — Workout Tracking Automation with Python Today, I built a Workout Tracker Automation using Python that converts natural language exercise input into structured workout data. The script integrates multiple APIs to analyze exercises and automatically log workout details such as duration and calories. This project helped me understand real-world automation workflows and API-based data processing. 🔧 What I practiced: • Working with natural language exercise APIs • Processing and structuring API responses • Secure API authentication • Automating data logging workflows • Writing clean and maintainable code 🧠 Key takeaway: Automation becomes impactful when unstructured input is transformed into useful data. 📁 Project Repository: 👉 https://lnkd.in/guTF3kQ7 Building consistently — from learning APIs to building health-focused automation 🚀 #Python #90DaysOfCode #Day38 #LearnInPublic #PythonProjects #Automation #APIs #HealthTech

Hundreds of lines of Python worth of functionality. Zero code written. John Capobianco demonstrates what building AI agents looks like when you separate reasoning from execution using Itential FlowAI: describe the outcome in natural language, bind MCP with guardrails, deploy. His first FlowAgent tested every interface dynamically, generated tests, and delivered results to Slack and email automatically. No Python. No Ansible. No REST APIs. No manual parsing. ▶️ Watch how he did it: https://bit.ly/4rAjn2Q

🤖 403s, 429s, or “verification pages” instead of data? Anti-bots are doing their job. If your scraper keeps getting blocked, running a real browser session can make all the difference. In our latest ZenRows guide, we walk through how to scrape with Pydoll, an async, CDP-based Python library that controls Chromium without WebDriver and helps bypass modern anti-bot checks. Inside the blog: 🔹 What Pydoll is and why CDP beats WebDriver for stealth 🔹 How to scrape dynamic pages with async Chromium sessions 🔹 Bypassing Cloudflare-style challenges using session reuse 🔹 Real limitations of browser-based scraping at scale 🔹 When to switch to a fully managed solution like ZenRows Perfect if you want to understand how anti-bot bypass works and when it is smarter to let infrastructure handle it for you. 👉 Read the blog: https://lnkd.in/gRF6qtvG #WebScraping #Python #Automation #AntiBot #Developers #DataEngineering #ZenRows

MikeRecognex/mcp-codebase-index: 17 Essential MCP Query Tools for Codebase Navigation – Explore Functions, Classes, Imports, Dependency Graphs, and Change Impact with Zero Dependencies, Achieving 87% Token Reduction. https://lnkd.in/dUgrBhgC Unlock Your Codebase's Potential with mcp-codebase-index 🚀 Transform how you develop with mcp-codebase-index, a powerful tool designed for AI-assisted coding. Built with zero runtime dependencies using Python's AST module and regex, it streamlines code analysis across Python and TypeScript/JS. Key Features: Structural Metadata Indexing: Parses source files into functions, classes, imports, and dependency graphs. 17 Query Tools: Exposes tools through the Model Context Protocol (MCP) for efficient navigation of codebases. Seamless Integration: Compatible with OpenClaw and Claude Code for easy project setup. Installation Made Easy: Use pip install for quick setup. Access through console scripts or Python modules to fit your workflow. Whether you're tackling small projects or large codebases, mcp-codebase-index enhances your development speed and precision. 👉 Intrigued? Share your thoughts below and explore further! Source link https://lnkd.in/dUgrBhgC

𝐅𝐚𝐬𝐭𝐀𝐏𝐈: 𝐀 𝐌𝐨𝐝𝐞𝐫𝐧 𝐒𝐭𝐚𝐧𝐝𝐚𝐫𝐝 𝐟𝐨𝐫 𝐇𝐢𝐠𝐡-𝐏𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞 𝐏𝐲𝐭𝐡𝐨𝐧 𝐀𝐏𝐈𝐬 🚀 FastAPI has rapidly become a go-to framework for building production-grade APIs in Python, and for good reason. It strikes a remarkable balance between speed, code readability, and developer efficiency that few frameworks can match. Some of the standout advantages: • 𝐁𝐥𝐚𝐳𝐢𝐧𝐠 𝐟𝐚𝐬𝐭 𝐩𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞 powered by Starlette and Uvicorn • 𝐀𝐮𝐭𝐨𝐦𝐚𝐭𝐢𝐜 𝐫𝐞𝐪𝐮𝐞𝐬𝐭 𝐯𝐚𝐥𝐢𝐝𝐚𝐭𝐢𝐨𝐧 using Python type hints and Pydantic • 𝐈𝐧𝐭𝐞𝐫𝐚𝐜𝐭𝐢𝐯𝐞 𝐀𝐏𝐈 𝐝𝐨𝐜𝐮𝐦𝐞𝐧𝐭𝐚𝐭𝐢𝐨𝐧 (Swagger & ReDoc) generated out of the box • 𝐍𝐚𝐭𝐢𝐯𝐞 𝐚𝐬𝐲𝐧𝐜 𝐬𝐮𝐩𝐩𝐨𝐫𝐭 for handling high concurrency efficiently • 𝐂𝐥𝐞𝐚𝐧, 𝐦𝐚𝐢𝐧𝐭𝐚𝐢𝐧𝐚𝐛𝐥𝐞 𝐜𝐨𝐝𝐞 that scales well with growing systems With more teams prioritizing speed, scalability, and developer efficiency, is FastAPI on track to become a long-term industry standard for Python backend development? #Python #FastAPI #BackendDevelopment #APIs #SoftwareEngineering #CloudNative

I spent way too long fighting a "missing" package that was actually right in front of me. 🤦♂️ I was trying to get a new Agentic AI project started using strands-agents, and I kept hitting “ERROR: No matching distribution found for strands-agents.” I knew I had Python 3.12 installed. I knew I was in a virtual environment. I even checked my path. Everything looked "fine," but nothing was working. Then I realized the 𝘀𝗶𝗹𝗹𝘆 mistake I made. When I created the environment, I typed: python3 -m venv .venv 𝗧𝗵𝗲 𝗣𝗿𝗼𝗯𝗹𝗲𝗺: On macOS, python3 defaults to the system’s factory version (3.9.6). So, even though I thought I was working on newer version (python 3.12), I had accidentally built a "new" environment on older version (python 3.9). The library I needed requires python 3.10+, so pip just gave up on me. Also the (.venv) was not visible in my terminal due to some actions I had performed during the debugging (issue in the .zshrc file). The fix was 10 seconds of typing: • Delete the old env: rm -rf .venv • Be specific: python3.12 -m venv .venv • Fix my .zshrc so I can actually see the (.venv) bracket again for some peace of mind. 𝗡𝗼𝘁𝗲 𝘁𝗼 𝘀𝗲𝗹𝗳: Don't trust the python3 alias. Be specific with your versions or your dependencies will let you know about it the hard way. #Python #Coding #AI #DevOps #SoftwareDevelopment