Most people think Python does the work. In reality, for backend & data-heavy systems, Python is often just the orchestration layer. NumPy, Pandas, PyTorch, OpenCV — the real computation runs in C/C++. Python mainly: Passes pointers Manages APIs Makes things developer-friendly That’s why Python can be “slow” in loops but insanely fast in vectorized ops — because those ops jump straight into optimized C/C++. Even CPython itself is written in C. Takeaway for backend engineers: Abstractions are great, but performance lives below them. Knowing what runs underneath makes you a better system designer — not just a Python user. #BackendEngineering #Systems #C++ #Python #FAANG #Performance

The wait is over—Analytica 7.0 is here, and it's a game-changer. For the first time, you can seamlessly integrate Python's vast ecosystem of libraries directly into your Analytica models. Whether you want to tap into machine learning frameworks, create specialized visualizations, or leverage third-party tools, you can now write Python code right inside Analytica variables and functions. Mix and match languages based on what works best for each task, while still enjoying Analytica's visual influence diagrams, automatic dependency tracking, and intelligent array handling. Python developers will love using Analytica as an interactive development environment, while Analytica modelers gain instant access to thousands of powerful libraries. #software #analytics #decisionmodeling #riskmanagement #Python

🐍 5 Python Libraries Every Developer Should Learn in 2026 Stop writing everything from scratch. These libraries will 10x your productivity: 1️⃣ FastAPI → Build APIs in minutes, not hours 2️⃣ Pandas → Data manipulation made ridiculously easy 3️⃣ Pydantic → Data validation that actually makes sense 4️⃣ LangChain → Build AI apps without reinventing the wheel 5️⃣ Rich → Beautiful terminal output (yes, it matters!) Which one are you learning next? 👇 Save this for later 🔖 #TechTuesday #Python #PythonDeveloper #CodingTips #AI #MachineLearning #LearnToCode #AdvikaITSolutions #IndoreIT

Watch this highlight from the Feature Store Summit showing how Chalk achieves real-time ML by accelerating Python inference to sub-10ms latency at scale. In this talk, Chase Haddleton breaks down how to build an infrastructure platform that executes complex data pipelines end-to-end, on demand, in under 10ms, making real-time ML practical in production using Python. 🎥 See full video at your own pace here: https://lnkd.in/dUpdvUxK #RealTimeAI #MLSystems #LowLatency #Python #MLOps

Python 3.14t Launches with No-GIL Support as TypeScript Remains Top on GitHub Octoverse 📌 Python 3.14t launches with No-GIL support, enabling true multi-core performance for AI and scientific computing-marking a major leap for Python’s speed and scalability. Meanwhile, TypeScript remains the top language on GitHub, driving demand for stricter typing and WebAssembly solutions to bring Python to the web. 🔗 Read more: https://lnkd.in/dhdnV42J #Python314t #Nogil #Cpython #Pep703 #Githuboctoverse

Day 27/30 – Python Challenge 🐍 | Path Sum (Binary Tree) Today’s challenge focuses on checking whether a binary tree has a root-to-leaf path where the sum of node values equals a given target. Key ideas 💡 A valid path must start at the root and end at a leaf Subtract the current node’s value as you traverse Use DFS recursion to explore all paths Return true as soon as a valid path is found Another classic binary tree problem checked off the list 🌳✅ The finish line is getting closer 🚀🔥 #Day27 #PythonChallenge #LeetCode112 #BinaryTree #PathSum #DataStructures #Algorithms #CodingJourney #30DaysOfCode #Python

🚀 Consistent DSA Practice – Learning One Problem at a Time I’ve been regularly practicing Data Structures & Algorithms using Python, focusing on understanding the logic behind problems, not just writing code. Recently worked on problems like: Good Pairs Majority Element (Boyer–Moore Voting Algorithm) Jewels and Stones Decode the Message Isomorphic Strings Key learnings from my practice: ✅ How to optimize solutions from brute force to efficient approaches ✅ Writing clean and readable code ✅ Understanding time & space complexity ✅ Solving problems step-by-step with proper dry runs Consistency is helping me build confidence and problem-solving skills every day. Looking forward to learning more and improving continuously 🚀 #Python #DSA #ProblemSolving #CodingPractice #LearningJourney #LeetCode #Consistency

🔥 Day 29 of #75DaysOfDSA - Today’s Focus: -> Topic: String -> Language: Python - What I Worked On Today: -> Solved 1 DSA easy problems - Valid Palindrome II - Key Learnings / Takeaways: -> Uses two pointers from both ends of the string. On the first mismatch, try skipping either the left or right character and check if the remaining substring is a palindrome, allowing at most one deletion. -> Time Complexity : O(n) - Each character is visited at most a constant number of times. Even with the extra palindrome checks, the total work remains linear. -> Space Complexity : O(1) - The solution uses constant extra space since all checks are done using pointers without additional data structures. 📈 Progress So Far: -> Day: 29/75 -> Consistency > Motivation On to Day 30 🚀 #75DaysOfDSAChallenge #Day29 #DSA #Python #ProblemSolving #LearnInPublic #SoftwareEngineering #CareerGrowth #TechJourney #HR

Excited to share my latest project: Rule-Based Expert System! This is a Python-based rule system that recommends project ideas based on user interest and skill level. It demonstrates basic conditional logic and beginner-friendly decision-making in Python. Features: - Takes user input (domain & skill level) - Suggests relevant project ideas - Simple and interactive Check it out on my GitHub: https://lnkd.in/gvPRKg7M #Python #AI #MachineLearning #RuleBasedSystem #Project #Coding

While developing a Python package for searching and sorting algorithms, I came across a concept I had mostly ignored before. It is invariants. At first, it sounded abstract. But soon I realized I was already using it without naming it. An invariant is simply a condition that remains true at specific points during an algorithm’s execution. In sorting algorithms, for example, we often assume that a part of the array is already sorted at each step. That assumption must always hold. That is the invariant. Why does this matter? Invariants help us reason about correctness. They act as mental checkpoints, ensuring that every iteration or recursion step keeps the algorithm on the right path. If the invariant breaks, the algorithm is broken. Once I started thinking in terms of invariants, debugging became easier, logic became clearer, and my implementations became more reliable. Small concept. Big impact. #Algorithms #DataStructures #Python #SoftwareEngineering #LearningInPublic #ComputerScience #DeveloperJourney