Instant Observability with lognote for Python Developers

I am happy to share that we released the #pyPRB v1.0 — an open-source #Python #package for static and dynamic rotor balancing! 🎉 What it does: 🔧 Single-plane (static) and two-plane (dynamic) balancing using the influence coefficient method 📊 Go from raw time-domain vibration measurements to balancing results — no manual calculations needed The implementation follows the classic Brüel & Kjær Application Note 17-227 by John Vaughan and is validated against its worked examples. Get started in seconds: pip install pyPRB 📦 PyPI: https://lnkd.in/d8NX4xNt 💻 GitHub: https://lnkd.in/dbSmjrZW Whether you're a practicing engineer doing field balancing or a student learning rotordynamics — give it a try and let us know what you think! #Python #OpenSource #RotorBalancing #MechanicalEngineering #Vibrations #Rotordynamics thanks to Gašper Krivic

If you’ve ever wondered what really happens behind: pip install I broke it down here: https://lnkd.in/gtCmxDg6 What looks like a simple command is actually a pipeline: • Your project is built into distribution artifacts • Those artifacts are published to sources like PyPI • The installer resolves dependencies and selects versions • It prefers wheels for faster installs • Falls back to building from source when needed • Finally installs everything into site-packages The key shift: - You are not installing source code. - You are installing a built artifact. This is why you’ll notice: • Some installs are instant • Some take significantly longer • Some fail due to build or environment issues #Python #PythonPackaging #PyPI #SoftwareEngineering #DeveloperTools #Programming #BuildSystems #DevTools #OpenSource

I got tired of waiting… Installing heavy Python packages over a slow internet connection was killing my momentum — especially with libraries like torch. So I looked for a better way and came across devpi. Set it up on our local server, and honestly — it’s been worthy. Now I spin up a new virtual environment and install gigabytes of packages in under a minute ⚡ If you’re working with Python and large ML dependencies, setting up devpi locally is 100% worth it. Sometimes the best productivity boost isn’t new tools — it’s fixing the friction. link to github: https://lnkd.in/djJMNNnr

I've shared requirements.txt files generated with pip freeze and watched them fail on every machine that wasn't mine. So I built envcore. Because waiting for the Python ecosystem to fix a 15-year-old problem seemed optimistic. It hooks directly into Python's import system and records what actually loads while your code runs. Not what's installed on your machine. Not what a static scanner thinks might be imported. What. Actually. Runs. envcore trace train.py → env_manifest.json → envcore restore Clean, pinned, minimal manifest. Exact environment rebuilt anywhere. No 200-package soup, no missing runtime imports, no "works on my machine" as if that's a valid thing to say to another human. It also resolves import aliases correctly — PIL to Pillow, cv2 to opencv-python, sklearn to scikit-learn — because the gap between what you type and what you install has existed since forever and apparently needed one person to care. pip freeze has been lying to you for 15 years. Everyone accepted it. I got tired of it. 30 seconds to try: pip install envcore If it's useful, a GitHub star helps a new project get noticed. https://lnkd.in/dz3MFTbD #Python #OpenSource #DevTools

MentisDB now has a Python client and also integration with LangChain. (Not my idea, but my agent's) Used my PyPi account, created the Github CI workflow and published to PyPi all by itself. But sure, AGI isn't here yet. Here's the blog post it wrote about this new feature https://lnkd.in/gaYB2PjG

Upcoming Clawbench v0.1 release: 1. More modular architecture with a python Clawbench-sdk to run web tasks seamlessly. Install and test your agent straight away 2. Skillupgrade npm package to install skills for your agent. Upgrade and see them improve on whatever task you through at them 3. Self improvement harness for users to submit their agents and have them hillclimb a specific domain (ref Meta-harness, autogenesis paper) - the make it or break it idea

🚀 Mastering the art of loops! 🔄 Discover how loops help your code execute repetitive tasks efficiently. Essentially, loops are like a magical chant that tells your program to keep doing something until a certain condition is met. For developers, mastering loops is crucial for automating tasks and iterating over data structures with ease. Here's the breakdown: 1️⃣ Initialize a counter variable 2️⃣ Set the condition for the loop 3️⃣ Define the action to perform in each iteration Sample code using a "for" loop in Python: ``` for i in range(5): print("Hello, World!") ``` 🌟 Pro Tip: Use loops to reduce redundancy in your code and boost efficiency. 💡 ⚠️ Common Mistake: Forgetting to update the counter variable in the loop, leading to an infinite loop! 🔄 🌟 What's your favorite use case for loops in your projects? Let's discuss! 💬 #Coding101 #LearnToCode #TechTips #CodeNewbie #PythonProgramming #DeveloperCommunity #LoopLogic #CodeEfficiency #ProDevSkills 🌐 View my full portfolio and more dev resources at tharindunipun.lk

gdown v6 is out. Biggest changes this time: - Folder downloads work with more than 50 files (this was annoying for everyone for a long time) - download() raises DownloadError on failure (instead of returning None) - New progress callback - All deprecated v5 APIs finally removed - Python 3.10+ 33 pull requests, 109 commits since v5.2.1. https://lnkd.in/gRAegKhj

2-bit Qwen3.6-35B-A3B did a complete repo bug hunt with evidence, repro, fixes, tests and a PR writeup. 🔥 Run it locally in Unsloth Studio with just 13GB RAM. The 2-bit Qwen3.6 GGUF made 30+ tool calls, searched 20 sites and executed Python code. GitHub: https://lnkd.in/dcqhW9Vv GGUF: https://lnkd.in/gkG_a9vm Guide: https://lnkd.in/gWsiUmQh

LeetCode Day 13 – Container With Most Water Today I solved the classic “Container With Most Water” problem — a great example of optimizing from brute force to an efficient solution! Problem Insight: We are given an array of heights, and we need to find two lines that together with the x-axis form a container that holds the maximum water. Approaches: Brute Force (O(n²)) Check all possible pairs Calculate area = width × min(height[i], height[j]) Keep track of maximum Optimal Approach – Two Pointer (O(n)) Start with two pointers at both ends Calculate area Move the pointer with smaller height inward Repeat until pointers meet Key Idea: The limiting factor is always the smaller height Moving the larger height won’t help increase area Complexity: Time: O(n) Space: O(1) What I learned: How two-pointer technique drastically reduces complexity Importance of understanding constraints before coding #LeetCode #Day13 #DSA #CodingJourney #Python #TwoPointers #ProblemSolving