🚀 pywho — a debugging painkiller for Python developers (~45 stars ⭐ on GitHub 🔥) 💡 What is pywho? A zero-dependency Python CLI that explains your environment, traces imports, and detects module shadowing. No guessing. No scattered checks. Just clear answers. ⚠️ Pain point: Debugging Python issues usually means checking: • Interpreter • Virtualenv • sys.path • pip • Import resolution 👉 All separately → slow, repetitive, and perfect for “works on my machine” problems 📊 Existing tools: • Python built-in site/path inspection • pip debug • Manual import checks 👉 Useful individually, but each shows only part of the picture 🛠️ What pywho does: One CLI that gives you: ✅ Interpreter details ✅ Virtualenv detection ✅ Import tracing ✅ Import resolution insights ✅ Module shadow scanning ✅ JSON output for CI/sharing ➡️ One place, not five ➡️ Zero dependency ➡️ Cross-platform ➡️ Built for real debugging workflows 👨💻 For all Python developers 🔗 GitHub: https://lnkd.in/dMvz9PYM 🔗 PyPI: https://lnkd.in/dM72_8rs 🔗 Docs: https://lnkd.in/dCvUBAeu ♻️ Resharing to support the Python community 🤝 💬 What’s the most confusing Python environment issue you’ve debugged? #Python #PythonDeveloper #PythonDev #PyPI #PythonTools #DebuggingTools #DeveloperTools #DevTools #CLItools #CommandLine #SoftwareEngineering #BackendDevelopment #DevOps #OpenSource #OpenSourceProject #Programming #CodingLife #BuildInPublic #TechInnovation #ProductivityTools #Automation #CI_CD #TestingTools #PythonTips #CodeQuality #SoftwareDevelopment #DevelopersLife #TechCommunity #GitHubProjects

🚀 pywho — a debugging painkiller for Python developers (30+ GitHub stars in 1 month 🔥) 💡 What is pywho? A zero-dependency Python CLI that explains your environment, traces imports, and detects module shadowing. No guessing. No scattered checks. Just clear answers. ⚠️ Pain point: Debugging Python issues usually means checking: • Interpreter • Virtualenv • sys.path • pip • Import resolution 👉 All separately → slow, repetitive, and perfect for “works on my machine” problems 📊 Existing tools: • Python built-in site/path inspection • pip debug • Manual import checks 👉 Useful individually, but each shows only part of the picture 🛠️ What pywho does: One CLI that gives you: ✅ Interpreter details ✅ Virtualenv detection ✅ Import tracing ✅ Import resolution insights ✅ Module shadow scanning ✅ JSON output for CI/sharing ➡️ One place, not five ➡️ Zero dependency ➡️ Cross-platform ➡️ Built for real debugging workflows 👨💻 For all Python developers 🔗 GitHub: https://lnkd.in/dMvz9PYM 🔗 PyPI: https://lnkd.in/dM72_8rs 🔗 Docs: https://lnkd.in/dCvUBAeu ♻️ Resharing to support the Python community 🤝 💬 What’s the most confusing Python environment issue you’ve debugged? #Python #PythonDeveloper #PythonDev #PyPI #PythonTools #DebuggingTools #DeveloperTools #DevTools #CLItools #CommandLine #SoftwareEngineering #BackendDevelopment #DevOps #OpenSource #OpenSourceProject #Programming #CodingLife #BuildInPublic #TechInnovation #ProductivityTools #Automation #CI_CD #TestingTools #PythonTips #CodeQuality #SoftwareDevelopment #DevelopersLife #TechCommunity #GitHubProjects

Are endless Python configurations slowing down your team and making project maintenance a nightmare? The days of complex, disjointed toolchains are officially over. It's time to elevate your workflow and achieve unparalleled efficiency! 😩 Introducing the Python project setup for 2026: a game-changing stack featuring `uv`, `Ruff`, `Ty`, and `Polars`. This isn't just an upgrade; it's a complete overhaul designed to deliver unparalleled speed, pristine code quality, and effortless maintainability, all unified under one roof. ✨ Imagine replacing multiple, disparate tools like pip, Black, and mypy with a single, integrated ecosystem that just *works*. This Astral-backed synergy simplifies everything from dependency management and lightning-fast linting to robust type checking and blazingly quick data processing for massive datasets. Revolutionize your development cycle and onboard new talent faster than ever before, ensuring your projects are cleaner and future-proof. 🚀 **Comment "PythonStack" to get the full article** Learn more about this streamlined Python project setup https://lnkd.in/gQQmtBnF 𝗥𝗲𝗮𝗱𝘆 𝘁𝗼 𝘀𝗲𝗲 𝘄𝗵𝗲𝗿𝗲 𝘆𝗼𝘂𝗿 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝘀𝘁𝗮𝗻𝗱𝘀 𝗶𝗻 𝘁𝗵𝗲 𝗿𝗮𝗽𝗶𝗱𝗹𝘆 𝗲𝘃𝗼𝗹𝘃𝗶𝗻𝗴 𝘄𝗼𝗿𝗹𝗱 𝗼𝗳 𝗔𝗜? 𝗧𝗮𝗸𝗲 𝗼𝘂𝗿 𝗾𝘂𝗶𝗰𝗸 𝗲𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗼𝗻 𝘁𝗼 𝗯𝗲𝗻𝗰𝗵𝗺𝗮𝗿𝗸 𝘆𝗼𝘂𝗿 𝗔𝗜 𝗿𝗲𝗮𝗱𝗶𝗻𝗲𝘀𝘀 𝗮𝗻𝗱 𝘂𝗻𝗹𝗼𝗰𝗸 𝘆𝗼𝘂𝗿 𝗽𝗼𝘁𝗲𝗻𝘁𝗶𝗮𝗹! https://lnkd.in/g_dbMPqx #Python #DevOps #CleanCode #Programming #TechStack #SaizenAcuity

🚀 pyresilience — 7 resilience patterns in 1 decorator (~1000 downloads last month 🔥) 💡 What is resilience? Your app keeps working even when dependencies fail, slow down, or overload. No crashes. No hanging. Just smart recovery. ⚠️ Pain point: Python teams often stitch together: • Retries ("tenacity") • Circuit breakers ("pybreaker") • Timeouts ("asyncio", "signal") • Rate limiting ("limits", "slowapi") • Fallbacks (custom code) 👉 These don’t coordinate → messy + inconsistent failure handling 📊 Existing tools: • "tenacity" (retries ~263.6M downloads/month) • "pybreaker" (circuit breaker ~9.6M downloads/month) 👉 Great individually, not unified ⚡pyresilience Benchmark: 🚀 pyresilience → 0.64 μs (🔥 ~10.4x faster) 🐢 tenacity → 6.64 μs 🛠️ What pyresilience does: One decorator with: ✅ Retry ✅ Circuit Breaker ✅ Timeout ✅ Fallback ✅ Bulkhead ✅ Rate Limiter ✅ Cache ➡️ Works together, not glued ➡️ Zero dependency ➡️ Sync + Async ➡️ High performance Frameworks: 🌐 FastAPI • Flask • Django 👨💻 For all Python developers 🔗 GitHub: https://lnkd.in/d-SRygNQ 🔗 PyPI: https://lnkd.in/dRg2H4D5 🔗 Docs: https://lnkd.in/dxZ4xYkw ♻️ Resharing to support the Python community 🤝 💬 How are you handling resilience in Python today? #Python #PythonDeveloper #PythonDev #BackendDevelopment #SoftwareEngineering #DistributedSystems #Microservices #SystemDesign #ResilienceEngineering #FaultTolerance #HighAvailability #ScalableSystems #PerformanceEngineering #AsyncIO #FastAPI #Django #Flask #APIEngineering #CloudNative #DevOps #SRE #OpenSource #OpenSourceProject #PyPI #PythonLibraries #DeveloperTools #TechInnovation #BuildInPublic #Programming

🚀 From Learning Python to Understanding Backend Systems When I first started learning Python, my focus was mainly on syntax and basic problem-solving. But as I explored more, I realized that backend development is where logic meets real-world application. That’s when I began diving deeper into the Python backend ecosystem. Instead of just learning tools, I started understanding how backend systems actually work—how requests are processed, how data flows between the server and database, and how APIs connect everything together. 🔧 Tools & Technologies I’m Exploring: • Python for core logic • Django for structured and scalable applications • Flask / FastAPI for lightweight API development • Relational Databases for data management • REST APIs for communication between systems • Git & GitHub for version control • JWT for authentication • Basic backend security practices • Deployment fundamentals 💡 What Changed in My Approach: Earlier, I focused on “what to learn.” Now, I focus on “how things work.” This shift helped me: • Understand backend architecture more clearly • Write better and cleaner code • Think like a developer instead of just a learner I’m still at the beginning of this journey, but I’m consistently building, experimenting, and improving every day. The goal is simple — to become a backend developer who not only writes code, but understands the system behind it. Excited for what’s ahead 🚀 #Python #BackendDevelopment #Django #Flask #FastAPI #RESTAPI #LearningJourney #SoftwareDevelopment #snsinstitutions #snsdesignthinkers #designthinking

I created language development skills (TY/PY/Rust) Lint + Design + Patterns. Extracted from Linter rulesets, Official Google and Microsoft guides. Check them out: https://lnkd.in/dEVqd_Kv Python Example: `npx skills add youssef-tharwat/lang-guidelines --skill python-guidelines -g`

A lot of Python teams use packages with binary parts every day, but rarely think about how those wheels are actually built across platforms and architectures. There is a very practical example of using cibuildwheel around compiled parts in django-modern-rest with mypyc. What makes this setup interesting is that it turns a painful release problem into a fairly structured pipeline: - define which wheels need to be built - reuse the existing build system from pyproject.toml - enable compiled builds only when needed - test the built wheel, not just the source tree - verify that compilation actually improves performance - generate CI matrix configs automatically instead of maintaining them by hand - publish releases cleanly through PyPI Trusted Publisher A nice detail here is that compiled parts can stay optional, so it is still possible to keep native Python-only builds without forcing .so artifacts everywhere. This is one of those tools that looks niche at first, but becomes very practical once you need repeatable binary builds at scale. For teams shipping Python packages across multiple environments, this kind of setup can remove a lot of manual CI pain. Based on recent work shared by Nikita Sobolev around django-modern-rest. Has your team ever needed this outside open source?

🚀 From Scripts to Systems: A Python Automation Milestone Over the past few weeks, I’ve been deliberately strengthening my Python skills by focusing on real‑world automation, not just isolated scripts or tutorials. As a capstone, I recently completed an end‑to‑end, production‑style automation project, where I built a config‑driven Python system that: • Validates and processes structured CSV data • Applies configurable business rules (PAID / DUE classification) • Generates clean, reusable reports automatically • Integrates with an external API using retries and exponential backoff • Logs every critical step for observability • Persists execution state and run metrics in JSON • Is idempotent and safe to run repeatedly Throughout this journey, I focused heavily on engineering discipline: ✅ dry‑run mindset before writing data ✅ defensive validation of inputs ✅ separation of logic from configuration ✅ graceful failure handling instead of crashes ✅ building automation that can be trusted to run unattended This experience reinforced an important lesson for me: "Automation is not about writing code fast — it’s about building systems that behave correctly when things go wrong". I’m excited to continue building on this foundation as I move deeper into backend and automation‑heavy roles, and eventually into scalable application development. Always happy to connect and learn from others building reliable systems with Python. #Python #Automation #BackendDevelopment #SoftwareEngineering #LearningByBuilding #ResilientSystems #ContinuousLearning

💡Python – Simple to Learn, Powerful to Build Python is one of the most beginner-friendly and powerful programming languages. Its clean syntax makes coding easy to read, write, and maintain, while its vast ecosystem allows developers to build anything from automation scripts to scalable web applications. To build strong Python skills for backend development with Django, Flask, and FastAPI, mastering key modules is essential. 🔹 Core Modules: os, sys, datetime, json, re, collections📐 🔹 Backend Utilities: logging, pathlib, functools, argparse 🔹 Web/API Modules: requests, hashlib, uuid, secrets🌐 🔹 Async Programming (FastAPI): asyncio, concurrent.futures🎯 🔹 Database Modules: sqlite3, sqlalchemy, psycopg2♟️🧩 With a solid understanding of these modules, developers can easily build REST APIs, automate tasks, manage databases, and develop scalable backend systems.🖥️🖲️ #Python #Django #Flask #FastAPI #BackendDevelopment #PythonDeveloper #APIDevelopment #SoftwareEngineering

🐍 The Reality of Python Developers (We’ve All Been There 😅) At some point in your journey, you’ve probably: • Copied code from Stack Overflow… and prayed it works • Debated Django vs Flask like it’s a life decision • Started learning a “new library” every other week • Spent 30 minutes writing code… and 2 hours fixing environment issues • Waited for pip install like it’s downloading the entire internet And somewhere in the middle of all this chaos… You tell yourself: “Yes, I am building something meaningful.” Here’s the truth 👇 This messy phase isn’t confusion — it’s growth in disguise. Every error you debug Every library you explore Every “why is this not working??” moment 👉 It’s shaping you into a real developer. But the people who actually move ahead are not the ones who know everything… They’re the ones who: ✔ Stick to one path long enough ✔ Build real projects (even messy ones) ✔ Focus more on creating than just consuming tutorials 💡 From chaos to clarity — that’s the journey. So if you’re still figuring things out… Good. You’re exactly where you need to be. 🚀 What’s the most “relatable Python struggle” you’ve faced? #Python #Programming #Developers #CodingLife #DataScience #WebDevelopment #Automation #LearningJourney