How to boost your CP-Uintensive tasks with polyglot architectures

Hey everyone! 👋 As part of my new series “1% Smarter with Python Daily”, here’s Day 1. If you’re using print() statements for debugging and monitoring your Python code, you’re definitely not alone. But here’s the thing — for anything beyond quick one-time scripts, print() often falls short. That’s where the built-in logging module comes in. Here’s why using logging rather than print() can level up your code: With logging, you get severity levels (DEBUG, INFO, WARNING, ERROR, CRITICAL) — so you can distinguish between normal messages vs alerts. You can direct output not just to console, but to files, sockets, or external systems — much harder when you scatter print()s. Stack Overflow+1 You get contextual information automatically: timestamps, module name, line number — helps when debugging later rather than just in the moment. print() is fine for small throwaway scripts — but once your codebase grows, you’ll thank yourself for not relying exclusively on print(). Why this is better than print(): If you want to silence debug logs in production you just change the log level, without removing/changing calls in code. All logs go through the same centralized system; you can redirect to a file, filter by module, tag by severity. Helps you maintain code clarity, production readiness, and better observability. ✅ Takeaway Challenge: Instead of writing print("something happened:", x) when you’re debugging or logging events — try replacing it with logger.info() or logger.debug() (depending on severity) and configure your logging as shown. I challenge you: in your next script, swap out one print() and replace it with logging. See how it changes the flow, how you filter logs, and what it gives you. #Python #Coding #DeveloperTips #Logging #SoftwareEngineering #TechTips #PythonTips #CleanCode

Struggling with slow, blocking HTTP calls in Python when you need to make many at once? Tom Reid demonstrates how to use httpx for concurrent operations and shows the significant performance difference in this guide.

🐍 𝗣𝘆𝘁𝗵𝗼𝗻 𝗣𝗮𝗰𝗸𝗮𝗴𝗲 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁 𝗝𝘂𝘀𝘁 𝗚𝗼𝘁 𝗮 𝗠𝗮𝗷𝗼𝗿 𝗨𝗽𝗴𝗿𝗮𝗱𝗲. 𝐅𝐨𝐫 𝐲𝐞𝐚𝐫𝐬, the phrase 𝗽𝗶𝗽 𝗶𝗻𝘀𝘁𝗮𝗹𝗹 𝗻𝘂𝗺𝗽𝘆 𝗽𝗮𝗻𝗱𝗮𝘀 has been a fundamental part of every Python developer's workflow. It's an essential command we've all typed countless times. However, a new and exciting tool, 𝐮𝐯, is rapidly changing the landscape of Python package management. Built with 𝗥𝘂𝘀𝘁, 𝐮𝐯 offers a significant leap in performance, transforming installation times from minutes down to mere seconds. 𝗪𝗵𝘆 𝘁𝗵𝗲 𝗦𝗵𝗶𝗳𝘁 𝘁𝗼 𝐮𝐯？ 𝐮𝐯 streamlines the developer experience and addresses several common pain points associated with the traditional 𝘃𝗲𝗻𝘃 and 𝐩𝐢𝐩 workflow: •⚡ 𝐁𝐥𝐚𝐳𝐢𝐧𝐠-𝐅𝐚𝐬𝐭 𝐏𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞: Thanks to its Rust foundation, installations and resolutions are exceptionally quick, dramatically improving productivity. • ✅ 𝐀𝐮𝐭𝐨𝐦𝐚𝐭𝐢𝐜 𝐄𝐧𝐯𝐢𝐫𝐨𝐧𝐦𝐞𝐧𝐭 𝐌𝐚𝐧𝐚𝐠𝐞𝐦𝐞𝐧𝐭: Say goodbye to manually creating and activating virtual environments. 𝐮𝐯 handles this process automatically and seamlessly. •🛠️ 𝐂𝐥𝐞𝐚𝐧𝐞𝐫 𝐃𝐞𝐩𝐞𝐧𝐝𝐞𝐧𝐜𝐲 𝐇𝐚𝐧𝐝𝐥𝐢𝐧𝐠: It promotes a clearer approach to dependency management, often working well with standard files like 𝗽𝘆𝗽𝗿𝗼𝗷𝗲𝗰𝘁.𝘁𝗼𝗺𝗹 •𝐒𝐢𝐦𝐩𝐥𝐢𝐟𝐢𝐞𝐝 𝐖𝐨𝐫𝐤𝐟𝐥𝐨𝐰: The core interaction is simplified. Instead of a multi-step process, you can achieve the same result with: 💻 𝗖𝗼𝗺𝗺𝗮𝗻𝗱: 👉 uv add numpy pandas This change represents a more modern, efficient, and integrated approach to managing project dependencies. Many developers who have made the switch find it a significant boost to their daily coding efficiency. 𝗛𝗮𝘃𝗲 𝘆𝗼𝘂 𝗵𝗮𝗱 𝗮 𝗰𝗵𝗮𝗻𝗰𝗲 𝘁𝗼 𝘁𝗿𝘆 𝘂𝘃 𝗶𝗻 𝘆𝗼𝘂𝗿 𝗽𝗿𝗼𝗷𝗲𝗰𝘁𝘀 𝘆𝗲𝘁? We'd be interested to hear about your experience and how it compares to your traditional pip workflow. #Python #uv #pip #Rust #Developers #Coding #DataScience #Productivity

Last Tuesday, a 30-line Python script reconciled 3,200 invoices before our board meeting. The week before, a different script almost sent 5,000 duplicate emails because the test ran on the live list. Python giveth, Python taketh away. There is a reason Python sits at or near the top of the TIOBE Index and shows up across Stack Overflow surveys. It is perfect for the quick wins founders and teams need, especially for data cleanup, integrations, and back-office automation. A few guardrails that have saved me: - Start with a dry_run flag and sample data. - Add a hard stop like max_rows or confirm prompts. - Rate limit outbound calls and add retries with backoff. - Log everything and alert on errors. - Make it safe to run twice so it does not double charge. - Keep scripts in version control and run code reviews, even for small tools. Power tools deserve safety goggles. When Python saves the day, celebrate. When it misbehaves, learn and add a checklist. Your turn. What is your most dramatic Python save or near-miss? Funniest bug-fix tale? #Python #Automation #TechHumor #DeveloperLife #Startups #DevOps #DataEngineering #Productivity

🚀 Piton v0.5.0: Modernizing the Bridge Between Elixir & Python I'm excited to announce a major upgrade to Piton, the open-source library that lets you run Python code from Elixir while bypassing the GIL! After months of work, v0.5.0 is here with a completely modernized stack. 🎉 🔧 The Modernization: We've brought Piton into 2025 with: ✅ Elixir 1.19 + OTP 27 support ✅ Python 3 only (Python 2 retired) ✅ Built-in JSON - removed Poison dependency ✅ GitHub Actions CI/CD - automated testing & publishing ✅ Latest dependencies - erlport 0.11, ex_doc 0.39 All 13 tests passing ✅ | Fully automated | Production ready 💡 Why This Matters: The real power isn't just the tech stack - it's what you can build with it. Real-world scenarios where Piton shines: 🔹 ML/AI in Phoenix Apps Run TensorFlow or PyTorch models directly from your LiveView without blocking the BEAM 🔹 Data Science Pipelines Leverage NumPy, Pandas, and SciPy while maintaining Elixir's fault-tolerance 🔹 Legacy Python Integration Migrate to Elixir gradually - wrap existing Python services without rewriting everything 🔹 Parallel Processing True parallelism - run multiple Python algorithms concurrently, bypassing the GIL using Erlang's process model 🔹 API Enrichment Call Python NLP libraries, image processing tools, or scientific computing packages from your Phoenix APIs 🎯 The Elixir + Python Sweet Spot: You get: •🏃♂️ Elixir's concurrency without the GIL limitation •🐍 Python's rich ecosystem (350K+ packages) •🛡️ Fault tolerance - Python crashes won't take down your app •⚡ Performance - modern OTP 27 optimizations •🤖 DevOps ready - full CI/CD automation Whether you're building ML-powered Phoenix apps, migrating Python workloads, or just want the best of both worlds - Piton v0.5.0 is ready. 📦 Get it: https://lnkd.in/ecarHYk 📚 Docs: https://hexdocs.pm/piton 💻 GitHub: https://lnkd.in/dkk9W8M #Elixir #Python #OpenSource #MachineLearning #AI #WebDevelopment #Phoenix #DataScience #SoftwareDevelopment #DevOps #ElixirLang #FunctionalProgramming

🚀 #Day9 of "Prompt Patterns for Developers": Unlocking the Power of Chain of Thought! 🚀 Today, we explored Chain of Thought (CoT) prompting, a game-changer for getting LLMs to think like us – step-by-step. The Concept: CoT prompting guides LLMs to break down complex problems into manageable, sequential steps, revealing their reasoning process. This is incredibly valuable for tasks requiring logical deduction, like code debugging or complex problem-solving. It's like asking a colleague to "show their work" rather than just giving an answer. 💡 Practice: Debugging with Step-by-Step Reasoning 💡 We put CoT into action by asking an LLM to debug a Python stacktrace with the explicit instruction: "Explain step by step." The difference in the output was remarkable – instead of just a solution, we got a clear, logical walkthrough of the error's origin and resolution. 🎯 Prompt Example: Debugging a Python Stacktrace Analyze the following Python stacktrace and explain, step-by-step, the likely cause of the error and how to fix it. Python Stacktrace: Traceback (most recent call last): File "main.py", line 10, in <module> result = divide(10, 0) File "main.py", line 5, in divide return a / b ZeroDivisionError: division by zero Explain step by step: This prompt clearly sets the context, provides the problem (the stacktrace), and most importantly, explicitly requests a "step-by-step" explanation. This small addition significantly enhances the quality and usefulness of the LLM's response. How do you use step-by-step reasoning in your own problem-solving? Share your insights below! #PromptEngineering #AIforDevelopers #LLMs #ChainOfThought #Debugging #Python #TechTips #DeveloperLife #MachineLearning

Switching to uv for Python and How It Cut My Docker Build Time by 60% I’ve been hearing a lot about uv, the new Python package manager built by Astral (the team behind Ruff). Everyone was talking about its speed, so I finally decided to try it in one of my FastAPI production backend projects. And honestly… I didn’t expect the impact to be this big. ⚡ The First Surprise: Pure Speed uv is written in Rust, and that alone gives it a massive performance boost. In practice, that means: - Creating a virtual environment in ~50 ms - Installing packages with parallel downloads - Running CLI tools almost instantly I immediately noticed the difference in my local development workflow. 📦 The Real Game Changer: Global Caching uv uses a global shared cache, so if a package version is already downloaded once, it never downloads it again. No network hit. No repeated wheel builds. No waiting. Just instant installs. 🐳 The Biggest Impact: Faster Docker Builds Here’s where uv really surprised me. After switching from pip to uv inside my Dockerfile for a FastAPI backend, my Docker image build time dropped from: ~3 minutes → just slightly above 1 minute That’s more than 60% faster, and for frequent deployments, this is huge. It also made my CI/CD pipeline noticeably faster and more reliable. If you work with Python especially FastAPI or backend development, I genuinely recommend trying uv. It’s fast, efficient, and modern, and it feels like the package manager Python should’ve had all along. #Python #FastAPI #BackendDevelopment #SoftwareEngineering #DevOps #Docker #RustLang #WebDevelopment #PythonDevelopers #CloudComputing #CI_CD #OpenSource #Productivity #uv

🚀 A classic Python “aha!” moment about environments Today I hit one of those “everything works… but also doesn’t” mysteries. My virtual environment had all the right packages: python-dotenv, langchain-core, langchain-openai. Running the project from the terminal? Perfect. ✅ Opening it in VS Code or Cursor? Endless “unresolved import” errors. 🤯 Here’s the twist - it had nothing to do with Python versions. The issue was that my IDE wasn’t using my project’s virtual environment for its background tasks like IntelliSense, linting, and autocomplete. There are two environments at play: 1. Runtime Python - what actually runs your code when you execute it in the terminal. 2. IDE Python Environment - what your editor uses (pylance) to analyze your code and provide smart hints. My terminal was running from ./env/bin/python, but the IDE was analyzing code with a different environment that didn’t include my installed packages. 💡 The fix: Press Cmd + Shift + P Search for Python: Select Interpreter Choose the one pointing to your project’s ./env/bin/python Instantly, all the “missing imports” disappeared. 🎯 Moral of the story: If your code runs perfectly in the terminal but your IDE claims it’s broken - check which Python environment your editor is actually using. #Python #VSCode #Cursor #LangChain #Debugging #DevTips #SoftwareEngineering