Python Web Apps Cut Memory Use by 31% via Optimization

Day 65 Explored Streamlit I recently explored Streamlit, an open-source Python library that enables the development of interactive web applications without the need for frontend technologies such as HTML, CSS, or JavaScript. By leveraging simple Python scripts, it becomes possible to build user interfaces, capture user input, and display results in real time. Streamlit is particularly effective for developing data-driven applications, including dashboards and machine learning model deployments. Overall, it is a highly efficient tool for rapidly transforming ideas into functional applications with minimal complexity. #Python #Streamlit #MachineLearning

Show HN: Goempy – Ship a CPython interpreter inside your Go binary. https://ift.tt/H51WXi2 Thread: 1) What is Goempy? A design approach to bundle a CPython interpreter directly into a Go binary so Python code can run without a separate Python runtime on the host. 2) Why it matters: Deployments become simpler, with fewer moving parts. You get consistent Python behavior across environments and can ship Python tooling right alongside your Go services. 3) How it works (high level): The CPython runtime is embedded in the Go process, with a lightweight bridge to execute Python code from Go, call Python functions, and access the standard library. No external Python install needed at runtime. 4) Trade-offs to know: Larger binary size, potentially longer build times, and a more complex maintenance story (keeping CPython in sync with security patches). Debugging and profiling across the boundary can also be trickier than with standalone runtimes. 5) When to consider it: If your Go services rely on Python scripting or tooling and you want single-file deployments, reproducible environments, and reduced OS-level dependencies, this approach is worth evaluating. 6) Takeaway: This showcases a compelling pattern—embedding a runtime from one ecosystem inside another to simplify deployment while preserving language strengths. It’s a sharp idea for the right set of constraints. Read more: https://ift.tt/H51WXi2 #Go #Python #CPython #GoLang #ShowHN #OpenSource #DevTools #SoftwareEngineering. Read my thoughts: https://ift.tt/fW0KZwh

Every framework you have ever used is just design patterns written in production code. Day 06 of 30 -- Design Patterns in Python Advanced Python + Real Projects Series Django post_save is the Observer pattern. DRF renderer_classes is the Strategy pattern. logging.getLogger() is the Singleton pattern. @app.route is the Decorator pattern. Most developers use all of these every day without knowing the names. Today's Topic covers: Why patterns exist and the 3-category decision framework 6 patterns every Python backend developer must know Singleton with double-checked locking for thread safety Factory with self-registering decorator pattern Observer event bus with decorator-based subscriptions Strategy using typing.Protocol for structural subtyping Real scenario -- Factory + Strategy + Observer in one order pipeline 6 mistakes including pattern hunting and Observer without error isolation 5 best practices including why Python functions are strategies Key insight: Design patterns are not solutions you add to code. They are names for solutions already in your code. Phase 1 complete -- 6 days of Python internals done. #Python #DesignPatterns #SoftwareEngineering #BackendDevelopment #Django #FastAPI #100DaysOfCode #PythonDeveloper #TechContent #BuildInPublic #TechIndia #CleanCode #PythonProgramming #LinkedInCreator #LearnPython #PythonTutorial

🚨 Most Python developers are using concurrency WRONG. Yes, I said it. If you're still confused between Multithreading and Multiprocessing, you're probably leaving performance on the table. Let’s fix that 👇 🧵 Multithreading → Same process, shared memory → Fast & lightweight → Perfect for I/O tasks (API calls, file handling, DB queries) BUT… Thanks to Python’s GIL, threads DON'T run in true parallel for CPU tasks. 👉 Translation: Your CPU-heavy code is still slow. ⚙️ Multiprocessing → Separate processes, separate memory → Uses multiple CPU cores → TRUE parallel execution 👉 Best for: Heavy computations Data processing ML workloads 💥 The Reality: If you're using: Threads for CPU tasks ❌ (Wrong choice) Processes for simple I/O ❌ (Overkill) You're wasting resources. 🧠 Simple Rule: 👉 I/O-bound → Use Multithreading 👉 CPU-bound → Use Multiprocessing 🔥 Pro Tip: Top developers don’t just write code… They choose the RIGHT execution model. 💬 What are you using more in your projects — Threads or Processes? #Python #Multithreading #Multiprocessing #BackendDevelopment #SystemDesign #CodingTips

14/30 Performance Post 7: Async Processing Nobody likes a loading spinner. If your API is waiting for an email to send or a PDF to generate before responding to the user, you’re creating a bottleneck. Asynchronous Processing is the secret to building "snappy" apps that handle heavy workloads without breaking a sweat. Swipe through to see how to move your slow tasks to the background and keep your main thread fast! #BackendDevelopment #SystemDesign #SoftwareEngineering #Async #RabbitMQ #NodeJS #Python #LearnInPublic

Struggling with API abuse or unexpected traffic spikes? This guide breaks down rate limiting in a simple, practical way to building scalable systems. Learn key algorithms and real-world use cases, then implement it in FastAPI using middleware or dependency-based limits for efficient request control. #python #fastapi #API #BackendDevelopment #softwareengineer #SystemScalability

Microsoft has released Agent Framework 1.0 as the production-ready foundation for building AI agents and multi-agent workflows in both .NET and Python. The release brings stable APIs, long-term support and a unified path forward that builds on Semantic Kernel and AutoGen while adding enterprise-grade orchestration and multi-model support. See what Agent Framework 1.0 means for developers: https://lnkd.in/drneyWyR #Microsoft #dotNET #Python #AIagents #DeveloperTools

Python 3.4 Sets New Standard for High-Performance Concurrent Systems 📌 Python 3.4 cracked the code on high-performance concurrency by introducing asyncio, turning Python into a powerhouse for scalable web systems. With standardized async I/O and unified file paths via pathlib, it erased callback hell and messy string manipulations. Though retired in 2019, its architecture still shapes modern Python frameworks - proving that one release can redefine an entire ecosystem. 🔗 Read more: https://lnkd.in/dbW2_R5P #Python34 #Concurrency #Standardlibrary #Highperformance #Concurrentsystems

Just shipped LLMPrice 🚀 A lightweight Python + TypeScript library for LLM pricing lookup. - 2500+ models - Offline-first - Search / compare / CLI - Auto-synced pricing data Built for developers who want pricing data only, without pulling in a heavy LLM stack. Links in first comment. #Python #TypeScript #LLM #OpenSource #AIEngineering #DeveloperTools

Simple retry decorator, but honestly… what company doesn’t need a retry mechanism? Clean, reusable, and super practical for real-world systems. #Python #BackendDevelopment #SoftwareEngineering