Best of Jupyter — 2025

  1. 1
    Article
    Avatar of modal_labsModal·37w

    Introducing Notebooks

    Modal launches Notebooks, a collaborative cloud-based computing environment that provides instant GPU-enabled Python kernels starting in under 5 seconds. The platform offers real-time collaborative editing, automatic resource scaling from 0.125 CPUs to 8 H100/B200 GPUs, and seamless integration with Modal's existing infrastructure including Volumes and Functions. Key features include fast cold-start times, automatic idle shutdown to reduce costs, shared environments across teams, and modern development tools like LSP support and AI completions.

  2. 2
    Video
    Avatar of jherrJack Herrington·1y

    FASTER JavaScript In 2025 With Sets

    Sets are a high-performance data structure in JavaScript that only allow unique values, providing efficient O(1) time complexity for operations. This post discusses how to create and manipulate sets, including operations like add, delete, clear, and how to convert sets back to arrays. It also explores new methods for comparing sets such as union, intersection, difference, and symmetric difference. The use of Jupyter Notebooks with JavaScript through Deno and their benefits for coding and experimenting with sets is detailed.

  3. 3
    Article
    Avatar of lpythonLearn Python·47w

    Think Python

    Think Python offers an interactive learning experience through live Jupyter notebooks that allow readers to read explanations, run code examples instantly, and modify exercises in real time. This hands-on approach makes it particularly suitable for beginners who prefer learning through practice rather than passive reading.

  4. 4
    Article
    Avatar of devblogsDevBlogs·1y

    Python in Visual Studio Code

    The March 2025 release of Python and Jupyter extensions for Visual Studio Code introduces improvements such as enhanced shell integration, a new auto test discovery file pattern setting, inline values on hover, and automatic quotation insertion for long strings. Several other enhancements and bug fixes are also included, aimed at improving the overall development experience.

  5. 5
    Article
    Avatar of huggingfaceHugging Face·37w

    Jupyter Agents: training LLMs to reason with notebooks

    Hugging Face developed Jupyter Agent, a system that trains small language models to perform data science tasks by executing code in Jupyter notebooks. They created a comprehensive pipeline starting with 2TB of Kaggle notebooks, applied deduplication and quality filtering, generated synthetic question-answer pairs, and fine-tuned Qwen3-4B models. The approach achieved 75% accuracy on easy DABStep benchmark tasks, demonstrating that smaller models can become effective data science agents with proper training data and scaffolding. The project includes open-source datasets, trained models, and a simplified 200-line scaffolding system.

  6. 6
    Video
    Avatar of TechWithTimTech With Tim·42w

    Learn Pandas in 30 Minutes - Python Pandas Tutorial

    A comprehensive beginner tutorial covering pandas fundamentals including dataframe creation, data loading from CSV files, basic operations like head/tail/info, column and row indexing with iloc/loc, filtering data with conditions, updating and deleting entries, data cleaning methods, and basic analysis functions like groupby and value_counts. The tutorial demonstrates both regular Python files and Jupyter notebooks for data manipulation workflows.

  7. 7
    Article
    Avatar of pamelafoxPamela Fox·48w

    Proficient Python: A free interactive online course

    Pamela Fox has launched ProficientPython.com, a free interactive Python course that takes a functions-first approach from the beginning rather than teaching through side effects. The course covers standard introductory topics like variables, functions, loops, and OOP, but distinguishes itself by requiring no Python setup since all exercises run in the browser using Pyodide-powered widgets. Projects are completed via Google CoLab notebooks. The functions-based methodology, inspired by UC Berkeley's CS61A curriculum, helps students learn abstraction early but may be challenging for complete beginners. The course includes custom web components built with Lit framework and uses localStorage for progress tracking without requiring user accounts.

  8. 8
    Article
    Avatar of hnHacker News·1y

    zasper-io/zasper: Supercharged IDE for Data Science

    Zasper is a high-performance IDE designed to support massive concurrency with a minimal memory footprint. It utilizes significantly fewer resources than JupyterLab and can be run on local machines without requiring cloud support. Zasper is available as both an Electron App and a Web App. It primarily targets data scientists and AI engineers, aiming to enhance efficiency and support custom data applications.

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    Video
    Avatar of denoDeno·1y

    The easiest way to work with LLMs

    Discover the simplest approach to integrating large language models (LLMs) into applications using Deno and Jupyter Notebooks. Learn how to install Ollama for local LLMs, use the DeepSeek R1 model, and leverage the LangChain library for consistent interaction with LLMs. Additionally, explore the creation of a Retrieval Augmented Generation (RAG) agent for AI chatbots with domain-specific knowledge, and see a step-by-step process of building and deploying such an AI agent effectively.

  10. 10
    Article
    Avatar of devblogsDevBlogs·46w

    Microsoft for Python Developers Blog

    Microsoft released July 2025 updates for Python, Pylance, and Jupyter extensions in VS Code. Key changes include the Python Environments extension being bundled as an optional dependency with enhanced environment management features like one-click setup and automatic terminal activation. PyREPL has been disabled for Python 3.13+ due to indentation and cursor issues. Additional improvements include better Poetry support, automatic git-ignore for .venv folders, improved environment deletion, and experimental MCP tools in Pylance.

  11. 11
    Article
    Avatar of deep_learningDeep Learning·46w

    12 Hands-On Notebooks for Modern Document Parsing with AI & OCR

    A curated collection of 12 Jupyter notebooks demonstrates and compares various OCR and document parsing tools including PaddleOCR, Florence 2, Gemini 2.5 Pro, Pix2Text, Docling, and LLaMA3 Vision. The notebooks provide hands-on examples for extracting text, tables, and charts from documents, helping developers evaluate different tools for applications like invoice automation and PDF parsing.

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