Microsoft Open Sources File to Markdown Converter for LLMs

Python has become the go-to language for developers across industries, powering everything from data science and machine learning to web development and automation. With its popularity comes a critical challenge: maintaining a secure and reliable supply chain for Python packages while ensuring they stay up-to-date. Tools like Anaconda make this process easier, but organizations can also take control by hosting their own internal Python package server, giving full oversight of dependencies, security, and version management. https://lnkd.in/guZAVxgy

🚨 Confused about how to install Python? You’re not alone. When I started learning Python, I found multiple ways to install it on my machine: 🔹 Direct Python install 🔹 Anaconda 🔹 UV At first, I wondered 🤔 — why so many options? Turns out, each method is designed for a specific use case. Here’s a quick breakdown to help you choose the right one 👇 🐍 1. Direct Python Install ✅ Pros Comes pre-installed with Ubuntu Simple and lightweight ❌ Cons Manual environment management Managing multiple Python versions is painful pip is slower compared to modern tools like UV 🎯 Best for Small projects When multiple Python versions are not required 📦 2. Anaconda ✅ Pros All-in-one package: Python + hundreds of prebuilt libraries Great for Data Science / ML (NumPy, pandas, Jupyter pre-installed) Built-in environment management (conda env) ❌ Cons Very heavy (3–5 GB depending on version) Slower compared to newer tools 🎯 Best for Data Science / ML / Research Beginners who want everything pre-configured ⚡ 3. UV ✅ Pros Extremely fast (10–100x faster than pip) Lightweight Combines: Package manager (pip) Virtual environment manager Python version manager ❌ Cons No built-in support for complex system-level dependencies (like conda) 🎯 Best for Backend development (APIs, microservices) Teams that care about speed and performance 💡 Final Thought There is no one-size-fits-all solution. Choose based on your use case, not popularity. 📌 I’ll share step-by-step installation guides for all three methods for Windows, Mac, Linux in my next post — stay tuned! 🙏 Thanks for reading! 👉 Follow me for more practical tech insights #Python #Anaconda #UV #SoftwareDevelopment #BackendDevelopment #DataScience #MachineLearning #DevOps #Programming #Developers #TechLearning #CareerGrowth #CodingTips #PythonTips

From Microsoft Community Hub, Let's Create Our First Microsoft 365 Agent SDK using Python - For Single Tenant, by meenakshiBalekar "Step 1: Set Up Your Development Environment I am using VS Code , so you don’t need to manually install Python on your..." https://lnkd.in/enDF5JX5

As of today, there is a new Python library available: kibana-py. It is a pythonic wrapper for the Kibana REST API, designed to feel familiar to anyone who has used the official elasticsearch-py library. This was my capstone project with AI-assisted development. I used the project's evolution to test GitHub Copilot, Kiro, Antigravity, and Claude Code along the way. This allowed me to see how each handled architectural decisions, refactoring, and documentation in a real-world scenario. Building demos is a good starting point for learning, but building something functional is far more gratifying (and frustrating, in a healthy way). While the library does not yet cover the entire Kibana REST API, it provides a clean, idiomatic way to interact with the endpoints it does support and, importantly, includes bootstrapped OpenTelemetry instrumentation to provide out-of-the-box observability. The screenshot shows how easy it is to use (there are plenty of other examples in the Github repo). This is a totally unofficial library, but I hope others find it useful for their automation workflows. To the team at Elastic: if you ever want to take this under your wing, just say the word. The code is available on GitHub and the documentation is live on ReadTheDocs. (Links in the comments)

Colormath was a popular Python library for several years before it was abandoned 3 years ago. The code was built from a whitepaper or refactored from code built from that whitepaper, but much of the algorithm was not implemented. That's not a bad thing in itself. The "missing" parts have to do with parameters only a chemist or color scientist would know how to use. They aren't useful to a typical programmer or artist who is constrained to the palette visible on a computer screen. The unfortunate part is that these parameters were present in the code, even if they could not be used. The functionality had been decreased ("streamlined"), but the complexity under the hood had not. That complexity persisted through ten years, two interfaces (OO and procedural), seventeen contributors, two major Python versions, and countless projects. I revisited the original problem and re-implemented the "streamlined" solution with streamlined code. Without all the cruft, I was able to INCREASE features and speed (14x) because I didn't have to wrap my head around the complexity of a whitepaper written for chemists in 1931. Simplicity is rocket fuel. https://lnkd.in/gtX7jNah

I started learning Rust recently, using LLMs to help me along the way. At first, it was just curiosity. But something felt different. The code was stricter, clearer, and surprisingly… easier to trust. Strong typing and clean memory management made LLM outputs feel more reliable. Then I stumbled on something unexpected. A Python package manager called uv (https://lnkd.in/diP_EW-2). It replaces pip, pip-tools, pipx, poetry, pyenv, twine, virtualenv and more. What caught my eye wasn’t just that it’s written in Rust. It was this : ~83k GitHub stars Still not even version 1.0 (0.11.4 Currently) pip is around 10k. npm is under 5k. pnpm and yarn is around 40k, brew at 47k: Even Python itself is close - 72k. So why is this new tool growing so fast? It didn’t feel like hype. It felt like a signal. Maybe developers are getting tired of “good enough.” Maybe speed, simplicity, and better design are finally winning. And maybe… tools built with more care are getting noticed faster than ever. Feels like something is changing. Are we seeing the beginning of a shift in how developers choose tools? #Rust #Python #OpenSource #DeveloperTools #LLM

Machine Learning Geospatial Data using rasterio #machinelearning #datascience #geospatialdata #rasterio Rasterio is a Python library that provides functionality for reading, writing, and manipulating geospatial raster datasets. It serves as a bridge between raster data formats and Python's scientific ecosystem, offering convenient tools for geospatial analysis and visualization. Rasterio: access to geospatial raster data Geographic information systems use GeoTIFF and other formats to organize and store gridded raster datasets such as satellite imagery and terrain models. Rasterio reads and writes these formats and provides a Python API based on Numpy N-dimensional arrays and GeoJSON. Before Rasterio there was one Python option for accessing the many different kind of raster data files used in the GIS field: the Python bindings distributed with the Geospatial Data Abstraction Library, GDAL. These bindings extend Python, but provide little abstraction for GDAL’s C API. This means that Python programs using them tend to read and run like C programs. For example, GDAL’s Python bindings require users to watch out for dangling C pointers, potential crasher of programs. This is bad: among other considerations we’ve chosen Python instead of C to avoid problems with pointers. What would it be like to have a geospatial data abstraction in the Python standard library? One that used modern Python language features and idioms? One that freed users from concern about dangling pointers and other C programming pitfalls? Rasterio’s goal is to be this kind of raster data library – expressing GDAL’s data model using fewer non-idiomatic extension classes and more idiomatic Python types and protocols, while performing as fast as GDAL’s Python bindings. High performance, lower cognitive load, cleaner and more transparent code. This is what Rasterio is about. https://lnkd.in/dwHurYbc

🚀 Built a File Management System using Python & Streamlit! (#CRUD) 🌐 Live Demo: https://lnkd.in/gkrzQ_pQ I recently worked on a project where I converted a command-line (CLI) Python application into a fully functional web app using Streamlit. 📌 Project: FileForge – Smart File Management System 🔗 GitHub: https://lnkd.in/g-iw2QNM 💡 What this project does: Create, read, update, and delete files Rename, overwrite, and append file content Provides a simple web interface for file operations 🛠️ Python Concepts & Skills Used: File Handling (read, write, append, delete) Object-oriented and modular coding Error handling using try-except Working with pathlib for file paths Converting CLI logic into UI-based workflow Basic UI development using Streamlit ⚙️ Tech Stack: Python | Streamlit | pathlib | Git | GitHub ⚡ Challenges I Faced: 🔴 Converting CLI to Web App The biggest challenge was replacing input() and print() logic with UI components. 🔴 File Path Issues Handling file paths correctly across directories caused multiple errors. 🔴 Environment & Setup Errors Faced issues with pip, streamlit command, and system PATH. 🔴 Recording Demo Issues OBS screen recording failed due to encoder and configuration problems. 🛠️ How I Solved Them: ✅ Replaced CLI inputs with Streamlit widgets like text_input, selectbox, text_area ✅ Used pathlib (Path.cwd()) for reliable file handling ✅ Fixed environment issues using python -m commands and virtual environments ✅ Switched encoding settings and fallback tools for recording demo 📈 Key Learnings: How to convert backend logic into user-facing applications Importance of environment setup and debugging Building and deploying real-world Python projects Using GitHub to manage and showcase projects 💬 I’m currently building more data science projects and improving my skills in Python, ML, and real-world problem solving. Would love your feedback and suggestions! 🙌 #Python #DataScience #Streamlit #GitHub #BeginnerProjects #MachineLearning #LearningInPublic #SoftwareDevelopment

Microsoft's Agent Framework has arrived. This powerful open-source SDK combines Semantic Kernel and AutoGen, offering capabilities for both .Net and Python. It is compatible with various model APIs. Excited to see the innovative projects that will emerge from this! #Microsoft #MAI #AgentFramework

Excited to share something I've been building: Slop Report — a GitHub Action that automatically posts a code quality summary on every Python pull request. Instead of asking reviewers to hunt down coverage gaps, dependency risks, or performance regressions, Slop Report surfaces the signal directly in the PR comment thread:   Change Risk — % of modified lines covered by your test suite   Blast Radius — how many modules are affected by the change   Performance — per-test timing vs. the base branch   Maintainability — MI regression on modified files + quality of newly added code It plugs into your existing CI in minutes, never blocks a merge, and gives reviewers the data they need without leaving GitHub. Now available on the GitHub Marketplace: https://lnkd.in/eyFJvnRu Would love feedback from anyone working on Python projects or developer tooling — what metrics would you want to see next? #DevTools #GitHub #GitHubActions #Python #CodeQuality #OpenSource