Scaling Python AI with Rust: RestWs for Long-Running Tasks

𝐏𝐲𝐭𝐡𝐨𝐧 𝐈𝐭𝐞𝐫𝐚𝐭𝐢𝐨𝐧 𝐄𝐱𝐩𝐥𝐚𝐢𝐧𝐞𝐝 (List → Iterator → Generator → yield) If you understand these 4 concepts, you understand how Python loops actually work. Most developers use them every day… but rarely think about how they are connected. Let’s break it down simply. 1️⃣ 𝐋𝐢𝐬𝐭 — 𝐬𝐭𝐨𝐫𝐞𝐬 𝐞𝐯𝐞𝐫𝐲𝐭𝐡𝐢𝐧𝐠 𝐟𝐢𝐫𝐬𝐭 A list prepares all values in memory before you start using them. Example numbers = [1, 2, 3, 4] This is simple and fast for small datasets. But if the dataset is very large (logs, API data, millions of records), memory usage grows quickly. 2️⃣ 𝐈𝐭𝐞𝐫𝐚𝐭𝐨𝐫 — 𝐫𝐞𝐭𝐫𝐢𝐞𝐯𝐞𝐬 𝐯𝐚𝐥𝐮𝐞𝐬 𝐨𝐧𝐞 𝐛𝐲 𝐨𝐧𝐞 An iterator returns the next element when asked. When you write a loop like: for n in numbers: print(n) Python internally uses something similar to: next(iterator) Each call retrieves the next value. Think of it like pressing a Next button. 3️⃣ 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐨𝐫 — 𝐜𝐫𝐞𝐚𝐭𝐞𝐬 𝐯𝐚𝐥𝐮𝐞𝐬 𝐨𝐧 𝐝𝐞𝐦𝐚𝐧𝐝 A generator is simply an easy way to create an iterator. Instead of storing values, it produces them only when needed. Example def count(n): for i in range(n): yield i Now Python generates numbers one by one. This is perfect for: • large files • streaming APIs • big datasets • data pipelines 4️⃣ 𝐓𝐡𝐞 𝐤𝐞𝐲 𝐝𝐢𝐟𝐟𝐞𝐫𝐞𝐧𝐜𝐞: 𝐫𝐞𝐭𝐮𝐫𝐧 𝐯𝐬 𝐲𝐢𝐞𝐥𝐝 Normal functions use 𝙧𝙚𝙩𝙪𝙧𝙣 𝙧𝙚𝙩𝙪𝙧𝙣 → function finishes immediately. Generators use 𝙮𝙞𝙚𝙡𝙙 𝙮𝙞𝙚𝙡𝙙 → pause the function produce a value resume later from the same place. This is why generators are memory efficient. 🧠 Mental model List → store everything Iterator → get next item Generator → create items on demand yield → pause & continue later Once this clicks, many Python features suddenly make sense. Curious to hear from other developers. When did generators finally “click” for you? #Python #AutomationTesting #TestAutomation #QAEngineering #SDET #LearnPython

🚀 Microsoft Agent Framework is now Release Candidate 🤖 If you’re building agents with Semantic Kernel or AutoGen, now’s the time to migrate. Agent Framework unifies both into a single, stable framework for .NET and Python, with a consistent model for building and orchestrating AI agents 🔧🧠 ✅ Stable APIs ✅ Unified agent model ✅ Built for production 🔗 Read more: https://lnkd.in/eBQuxxKv #AI #Agents #SemanticKernel #AutoGen #Microsoft #GenAI

🌶️ 💪 Modern API workloads aren’t “one user, one request” anymore—they’re bursts of concurrent traffic, mixed fast/slow calls, and unforgiving tail-latency expectations. That’s why I’m excited to share our new post on Select AI for Python 1.3 and a major step forward for production-grade concurrency: connection pooling. https://lnkd.in/eze4sUCb With 1.3, developers can now pool connections using: select_ai.create_pool() select_ai.create_pool_async() In the blog, learn what changed from standalone connections, what we measured by integrating pooling into a FastAPI service, and how to think about choosing a pool size that fits your workload. The results: better throughput, improved p95/p99 latency, and more predictable behavior under load—exactly what matters in real-world services. If you’re running (or planning) concurrent Python services with Select AI, this is one of the simplest, highest-impact upgrades you can make. #Oracle #Database #SelectAI #OracleAI #Python #FastAPI #Concurrency #ConnectionPooling

🚀 Boosting AI app concurrency with smarter database connections The latest update to Oracle Autonomous Database Select AI for Python shows how connection pooling can significantly improve concurrency and throughput for AI-powered applications. Instead of opening a new database connection for every AI request, connection pools reuse a small set of connections, reducing overhead and enabling many concurrent AI calls from Python apps. Why it matters: ⚡ Higher concurrency for AI workloads 🔁 Reused connections reduce latency and overhead 🧠 Better performance for NL2SQL, RAG, and generative AI apps built on Autonomous Database For developers building AI-driven data apps in Python, this means more scalable, responsive AI pipelines with minimal code changes. #AI #Python #Databases #GenAI #AutonomousDatabase

The Python ecosystem is moving incredibly fast this quarter. If you are building AI workflows, automating data pipelines, or developing interactive web apps, your toolkit is likely evolving by the week. Here are the most impactful Python library updates and releases you should be paying attention to right now: Daggr (New Release): Debugging multi-step AI pipelines just got easier. Daggr is a brand-new open-source library for building "inspectable" AI workflows. It lets you write workflow nodes entirely in Python while automatically generating a visual UI to inspect states, inputs, and cached results. It’s a massive win for rapid prototyping without losing code-first flexibility. Streamlit's ASGI Evolution (v1.53+): Streamlit is blurring the line between rapid prototyping and full web frameworks. The recent experimental introduction of st.App brings an ASGI-compatible entry point, meaning you can now integrate custom HTTP routes, FastAPI middleware, and advanced API endpoints directly into your Streamlit apps. Additionally, native support for Pydantic sequences makes rendering structured AI outputs completely seamless. jstark (New Release): Just announced this week, jstark is a new Python library designed to automate and standardize feature generation on top of PySpark. If you are building predictive models, this library introduces a consistent naming convention and automated time-bound feature calculation, saving hours of manual data engineering. pip 26.0 strict filtering: A major quality-of-life update for Python infrastructure. The new --only-final flag gives developers strict control to exclude pre-release packages, and native support for inline script metadata (PEP 723) makes managing dependencies for standalone automation scripts significantly cleaner. The barrier to building reliable, production-ready AI applications and automated tools continues to drop. Which of these updates are you most excited to try? #Python #PythonDeveloper #ArtificialIntelligence #DataEngineering #Streamlit #MachineLearning #TechUpdates #PythonLibraries #Automation #SoftwareEngineering #DataScience #PySpark #AIWorkflows

ONE Language. Endless Possibilities. Why Python Dominates🐍 Ever noticed how Python shows up everywhere? That’s because it’s more than a programming language — it’s a powerful ecosystem. Here’s how Python connects directly to real-world impact: 📊 Data Analysis → Pandas 📈 Visualization → Matplotlib 🎨 Advanced Visuals → Seaborn 🤖 Machine Learning → TensorFlow 🌐 Web Scraping → BeautifulSoup ⚙️ Browser Automation → Selenium 🚀 High-Performance APIs → FastAPI 🗄️ Database Access → SQLAlchemy 🌍 Lightweight Web Apps → Flask 🏗️ Full Web Frameworks → Django 👁️ Computer Vision → OpenCV From data and AI to automation and web apps — Python scales with your ambition. If someone asks, “Is Python worth learning in 2026?” The better question is: What can’t you build with it? Tag someone who’s thinking about learning Python 👇 #Python #DataScience #MachineLearning #WebDevelopment #Automation #AI #Programming #TechCareers #iamuzairmehmood

We're moving heavily toward an AI-First stack, and I've noticed something interesting on the backend. When integrating complex, stateful logic—especially around data transformation or when connecting to tools like LangChain or sophisticated ML models—I keep reaching for Django or FastAPI over Node.js. The structure Python offers

What if a single Python library was costing you 16GB of RAM? At SpectrumEffect, we run an elevation service that provides terrain height data used by our internal algorithms. It worked fine, but the  Python library it depended on (srtm.py) loads every elevation tile into memory and never releases them — requiring 16GB of memory per pod.  That's expensive infrastructure for a single microservice.  Same service, same tests, same results — but now it runs on 130MB instead of 16GB. And it's at least twice as fast. I built the core Rust library in  one day with Claude AI as my co-pilot — from architecture to deployment.  The library is open source: https://lnkd.in/gGAKDiPi  AI isn't just writing boilerplate code. It's enabling engineers to ship production-grade systems in new languages, faster than ever. That's  real ROI.  #AI #Rust #CloudOptimization #Engineering #OpenSource

🚀 Built a Backtracking Puzzle Solver (N-Queens) Desktop Application using Python I recently developed a Backtracking Puzzle Solver Desktop App that solves the classic N-Queens problem using a recursive backtracking algorithm and an interactive graphical interface. The goal of this project was to demonstrate how algorithmic problem-solving techniques like backtracking can be visualized through a simple desktop application. 🔎 What the Application Does The program places N queens on an N×N chessboard so that no two queens attack each other (no same row, column, or diagonal conflicts). It automatically searches for a valid configuration using recursive backtracking. ⚙️ Key Features • Interactive GUI built with Python Tkinter • User input for board size (N = 4 to 12) • Automatic puzzle solving using backtracking • Clean chessboard visualization of the solution • Algorithm statistics (attempts, placements, backtracks) • Reset functionality for testing different board sizes 🧠 Concepts Demonstrated • Backtracking algorithms • Recursion and search space exploration • Constraint checking (row and diagonal safety) • Python GUI development with Tkinter • Algorithm performance tracking This project is a great example of combining Data Structures & Algorithms with practical Python applications. I enjoy building projects that connect algorithms, AI concepts, and interactive software tools. Looking forward to exploring more projects in Python, Machine Learning, and algorithmic problem solving. #Python #Algorithms #Backtracking #NQueens #SoftwareDevelopment #ComputerScience #PythonProjects #Tkinter #ProblemSolving https://lnkd.in/gSQC78py https://lnkd.in/gdmMVxBA

Meet Elysia: A New Open-Source Python Framework Redefining Agentic RAG Systems with Decision Trees and Smarter Data Handling If you’ve ever tried to build a agentic RAG system that actually works well, you know the pain. You feed it some documents, cross your fingers, and … https://ift.tt/ZoITQLR Más noticias de #IA, #tecnología, #internet y #elearning en https://ift.tt/DFhai4w