PyCharm, a JetBrains IDE

Dozens of models claim to be the best detector. In 2026, it comes down to these three: 🏆 RF-DETR – Highest accuracy. First real-time model to break 60 mAP on COCO. ⚡ YOLO26 – Best for edge devices. 43% faster on CPU than YOLO11n, five tasks in one model. 🔬 YOLO12 – Best for research. Attention at its core (use the original repo, not the port). But one rule beats every benchmark: COCO mAP ≠ your data. Always test on your own. #Python #ComputerVision #ObjectDetection

Classification tells you what's in an image. Detection tells you what AND where. 📦 Draw a box around each object. 🏷️ Label every box. That "where" is what runs self-driving cars, security, retail, and medical imaging. Want to go deeper? Our guide explains how detection works and breaks down the best object detection models of 2026: 🔗 jb.gg/object-detecti… 🔗 #ComputerVision #MachineLearning

Django turns 21 today. 🚀 Old enough to have onboarded a generation of developers, launched countless careers, and quietly run more of the internet than most people realize. Happy 21st birthday, @djangoproject! What was your first Django project? Tell us in the comments 👇

TypeScript 7 support is live in PyCharm 2026.2 RC – ready out of the box, no setup required. Try it: jetbrains.com/pycharm/nextve…

Full guide with benchmarks, quick-start code, and fine-tuning tips: jb.gg/object-detecti… Test these models on your own data using PyCharm and its Hugging Face integration. New to PyCharm? Try PyCharm Pro free for 30 days!

Dozens of models claim to be the best detector. In 2026, it comes down to these three: 🏆 RF-DETR – Highest accuracy. First real-time model to break 60 mAP on COCO. ⚡ YOLO26 – Best for edge devices. 43% faster on CPU than YOLO11n, five tasks in one model. 🔬 YOLO12 – Best for research. Attention at its core (use the original repo, not the port). But one rule beats every benchmark: COCO mAP ≠ your data. Always test on your own. #Python #ComputerVision #ObjectDetection

Publishing Python packages to PyPI? Seth Larson from the PSF recommends two things: 👉 Use Trusted Publishers 👉 Check your GitHub Actions workflows with Zizmor Trusted Publishers remove long-lived credentials from your workflow. Zizmor helps find and fix risky CI/CD patterns. Because attackers are looking at workflows — not just code. #Python #PyPI #OpenSource #CyberSecurity #GitHubActions

Can PyCharm help this duck walk? 🦆 Watch @iuliaferoli build the Open Duck Mini kit by Tnkr (@tnkrdotai) – an open-source animatronic duck – and use PyCharm's AI chat with Claude to work through setup errors, dependency issues, and servo calibration. Watch the complete build: 🔗 youtube.com/watch?v=tt-g_f… 🔗 #openduck #physicalai #robotics

Python 3.15 introduces explicit lazy imports. ⚡ Faster startup 📉 Lower memory use 🧹 Less upfront cost from heavy dependencies With lazy imports, modules load only when they’re first used. Read the blog post, check the PyCharm benchmark, and try it today 👇 jb.gg/lazy-imports

Want to build your own open-source duck robot? 🦆 While an animatronic duck may sound cute, the build entails a comprehensive robotics workflow: • Hardware assembly and 3D printing • Soldering and wiring • Servo motor configuration • Raspberry Pi setup • Open-source code repo cloning • Debugging in PyCharm That’s what @iuliaferoli from Back To Engineering explores with the Open Duck Mini Kit by @tnkrdotai. ▶️ Watch the video to see how it all comes together: youtube.com/watch?v=tt-g_f… #openduck #physicalai #robotics

Learning Python? Don’t spend weeks searching for the “perfect” tutorial. Pick one. Commit to it. Write the code yourself. Use AI when you get stuck — but make sure you understand every line. As @sheena_oconnell says: if it feels hard, you’re probably doing it right. Python is learned by building, not by watching. #Python #LearnPython #Coding

Rust is changing the Python ecosystem — but not always for the reason people think. As Seth Larson from the @ThePSF explains, tools like uv, Ruff, ty, and Pyrefly often choose Rust for performance: startup time, speed, and data processing. But for Python binary modules, especially when handling untrusted data, Rust’s memory safety is a serious security advantage over C/C++. Python + Rust is becoming one of the most interesting combinations in modern developer tooling. #Python #Rust #PyPI #OpenSource #CyberSecurity

🐍 What if Python could skip loading the libraries your code never actually uses? That’s what explicit lazy imports in Python 3.15 are designed to do. In this post by @cheukting_ho, we put this new feature to the test with a familiar data science setup: pandas, NumPy, and Matplotlib. The result? A single unused module had accounted for a large share of startup time – and with lazy imports, that cost disappeared. See the benchmark, the PyCharm profiling setup, and what happens inside `sys.lazy_modules` when a lazy import finally gets used 👇 jb.gg/lazy-imports

Python AI frameworks usually fall into three groups: 1. Deep learning frameworks For neural networks, images, text, audio, and GPU acceleration. Examples: TensorFlow, PyTorch, Keras 2. Classical ML frameworks For structured data, forecasting, classification, and risk scoring. Examples: scikit-learn, XGBoost 3. LLM and agent frameworks For chatbots, RAG, tool use, and agent workflows. Examples: LangChain, Hugging Face The full guide breaks down when to use each one: 🔗 jb.gg/py-ai 🔗

With Junie, you get Pytest configs picked up automatically, Django and FastAPI runs, and a debugger that uses PyCharm's index instead of grepping your repo.

A common mistake in AI projects is picking the framework before defining the job. • Training a custom model? • Building an LLM app? • Working with structured business data? • Deploying to production? Each path points to a different Python AI framework – from scikit-learn or XGBoost to PyTorch, TensorFlow, LangChain, or Hugging Face. We broke down where each one fits best in the full guide: 🔗 jb.gg/py-ai 🔗

Type checking may become more important in the AI era, not less. @BruceEckel connects type checking with something broader: effect checking — tracking not just what types flow through a program, but what side effects the code can produce. The more boundaries we can give an LLM — types, constraints, interfaces, effects — the better it can reason about what it’s generating. Without those boundaries, we hit scaling problems: code may look plausible, but it becomes harder to trust, maintain, or evolve. Could Python’s type system eventually go further and help us express effects too? Watch the full interview on YouTube: youtu.be/hj6RiSEUuoY?si…

You can now open a project from a task in Air directly in PyCharm.

You can now showcase your expertise with PyCharm, directly on your LinkedIn profile. Learn more ⬇️

AI can generate a prototype in seconds. But can a beginner tell whether it’s good software? Will Vincent joined @teachingpython to discuss Django, AI coding, and why foundational skills still matter. Not just for writing code, but for understanding architecture, security, maintainability, and production-ready software. Listen for a thoughtful look at how Python education is changing: 🎧 teachingpython.fm/158

Choosing a Python AI framework is easier when you start with one question: What are you building? Here’s how to choose the right Python framework for your project: 📊 Working with structured data? • Use scikit-learn for quick classical ML workflows. • Use XGBoost when accuracy on tabular data is the priority. Best for: forecasting, classification, risk scoring, business analytics. 🧠 Building deep learning models? • Use PyTorch for research, experimentation, and custom architectures. • Use TensorFlow when production deployment matters most. • Use Keras when you need fast prototyping. 💬 Building LLM-powered apps? • Use LangChain for RAG, agents, memory, and tool use. • Use Hugging Face to access and fine-tune pre-trained models. 🎯A simple rule of thumb: • Structured data → scikit-learn / XGBoost • Deep learning research → PyTorch • Production deployment → TensorFlow • Fast prototyping → Keras • LLM apps → LangChain / Hugging Face Read the full guide: 🔗 jb.gg/py-ai 🔗 #Python #MachineLearning #AI #DataScience #PyCharm