# jupyter notebooks Published articles for jupyter notebooks. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## From COBOL to Copilot: 30 Years of Data, BI, and AI with David Langer DevFeed: [From COBOL to Copilot: 30 Years of Data, BI, and AI with David Langer]() Original publisher: [Read original article]() Author: Daniel Beach Published: 2026-07-01T13:43:11Z Content type: article Language: en Sources: [Data Engineering Central]() Topics: [Artificial Intelligence](), [cobol](), [Data Science](), [mainframes](), [Software Engineering](), [AI Strategy](), [self-service](), [dashboards](), [jupyter notebooks]() Tags: [adoption](), [ai](), [business-intelligence](), [cobol](), [data-science](), [enterprise](), [jupyter-notebooks](), [mainframes](), [programming](), [self-service](), [semantic]() ### AI overview A podcast conversation with Dave Langer about nearly three decades spanning COBOL, enterprise architecture, business intelligence, analytics, data science, machine learning, and AI. It discusses persistent data-industry problems, self-service analytics, dimensional modeling, AI adoption, semantic layers, governance, and career advice for data professionals. ### Source excerpt What happens when someone who started programming on a Commodore 64 watches AI reshape the entire data industry? ## Why I Stopped Using nbdev DevFeed: [Why I Stopped Using nbdev]() Original publisher: [Read original article]() Author: Hamel Husain Published: 2026-01-18T08:00:00Z Content type: opinion Language: en Sources: [Hamel Husain]() Topics: [Artificial Intelligence](), [ai-coding](), [jupyter](), [Python](), [Development]() Tags: [ai](), [ai-coding](), [developer](), [jupyter](), [jupyter-notebooks](), [nbdev](), [python](), [software](), [tools]() ### AI overview The author explains why they stopped using nbdev, a literate-programming development environment built around Jupyter notebooks. They argue that AI coding tools work less effectively with nbdev's notebook-to-library workflow, while AI can now help understand and maintain documentation separately from code. The author concludes that disciplined development and documentation depend more on developer effort than on the tooling environment. ### Source excerpt Programmers love to proclaim they've found the best tool. Paul Graham called Lisp his "secret weapon." DHH described Ruby as "a magical glove that just fit my brain perfectly." Pieter Levels ships million-dollar products with vanilla PHP and jQuery. These declarations aren't about the languages themselves. They're about developers finding tools that fit how they think. When the environment clicks, you move fast. I had that experience with nbdev, a development environment for literate programming that I helped build and maintain1. I created hundreds of projects with it and was one of its biggest proponents. Today, I no longer use it. AI coding tools changed the trade-offs. Fighting the AI The beauty of nbdev is its workflow. You write code, documentation and tests in one source of truth: Jupyter notebooks. Afterwards, these notebooks are transpiled into a Python library and documentation website. This workflow is idiosyncratic. AI coding tools, trained on vast amounts of conventional source code, get confused. They struggle to differentiate between editing the notebook and editing the final source code. It feels like fighting the AI instead of working with it. I write software to solve problems, not to write code. I want to work in an environment where AI has the highest chance of success. With nbdev, I was swimming upstream. Some argue that AI tools encourage lazy thinking: that without guardrails, developers skip the hard work of breaking problems into steps. But thinking step-by-step is a human skill. Notebooks don't force you to write clean code. AI tools don't force you to think carefully. Discipline comes from the developer, not the environment. Tools Don't Matter As Much As I Thought A central promise of literate programming is better documentation. By keeping code and docs in one place, you reduce the chance they become stale. Strangely, many nbdev projects lacked sufficient documentation for my taste. Sometimes, this helped me learn a codebase by contributin ## Jupyter Notebooks on Heroku with Persistent Storage DevFeed: [Jupyter Notebooks on Heroku with Persistent Storage]() Original publisher: [Read original article]() Author: Anush DSouza Published: 2025-04-02T06:54:00Z Content type: release Language: en Sources: [Heroku]() Topics: [Heroku](), [jupyter](), [Disk image](), [PostgreSQL](), [Security](), [Retrieval Augmented Generation (RAG)]() Tags: [ai](), [cloud](), [cloud-infrastructure](), [data-analytics](), [developer-tools](), [heroku](), [jupyter](), [jupyter-notebooks](), [managed-inference-and-agents](), [news](), [open-source](), [pgvector](), [postgresql](), [python](), [rag](), [security](), [storage]() ### AI overview Heroku introduces Heroku-Jupyter, an open-source solution for running Jupyter Notebooks with persistent PostgreSQL storage, deployment support, password protection, and customizable environments. The article also describes using Heroku-Jupyter with pgvector and Heroku Managed Inference and Agents for retrieval-augmented generation applications. ### Source excerpt We're excited to introduce Heroku-Jupyter, an open-source, production-ready solution for running Jupyter Notebooks on Heroku with persistent storage, seamless deployment, and built-in security. Whether you're a data scientist, educator, or developer, you can now spin up a cloud-based Jupyter environment in minutes. Why Jupyter on Heroku? Jupyter Notebooks provide an interactive computing environment ideal for [...] The post Jupyter Notebooks on Heroku with Persistent Storage appeared first on Heroku. ## Bringing Interactive Computing to Zed: Introducing REPL Support DevFeed: [Bringing Interactive Computing to Zed: Introducing REPL Support]() Original publisher: [Read original article]() Author: Kyle Kelley Published: 2024-07-24T00:00:00Z Content type: release Language: en Sources: [Zed Industries - Blog]() Topics: [Development](), [jupyter](), [kernels](), [Python](), [Deno](), [R](), [Markdown]() Tags: [analysis](), [code](), [development](), [jupyter](), [jupyter-notebooks](), [kernel](), [kernels](), [language](), [markdown](), [python](), [release](), [typescript]() ### AI overview Zed introduces a built-in REPL that lets users run code line by line, block by block, or from a selection, visualize data, and iterate within the editor. The feature integrates REPL-style interaction with Jupyter-like output and supports installed kernels for languages including Python, TypeScript through Deno, and R. ### Source excerpt Run code, visualize data, and iterate right in your editor with Zed's new REPL support. ## AI-assisted programming can speed development while increasing abstraction and technical debt DevFeed: [AI-assisted programming can speed development while increasing abstraction and technical debt]() Original publisher: [Read original article]() Author: Adam Published: 2023-06-16T00:00:00Z Content type: opinion Language: en Sources: [Shostack & Friends Blog]() Topics: [Artificial Intelligence](), [GitHub Copilot](), [ChatGPT](), [jupyter notebooks](), [Application Security](), [Python](), [pair\_programming](), [Programming]() Tags: [ai](), [appsec](), [chatgpt](), [copilot](), [jupyter-notebooks](), [pair-programming](), [programming](), [python]() ### AI overview The author reflects on using ChatGPT, GitHub Copilot, pair programming, and Jupyter Notebooks while learning Python and producing graphs. The experience accelerated development and debugging, but raised concerns about code quality, application security, abstraction, and the faster accumulation of technical debt. ### Source excerpt AI will be the high interest credit card of 2023 I haven't done a lot of work in Python, and I've never used it to produce graphs. But after an hour of pair programming and then using Chatgpt and Github Copilot got me quite far in writing a set of Jupyter Notebooks, and dramatically shrunk the effort to use and debug a new tool. I wanted to record some thoughts on the experience, and what it means for programming and for application security. ## Анонсируем версию Datalore Enterprise для команд Data Science DevFeed: [Анонсируем версию Datalore Enterprise для команд Data Science]() Original publisher: [Read original article]() Author: alenaguzharina (JetBrains) Published: 2021-07-06T15:00:01Z Content type: release Language: ru Sources: [JetBrains RU]() Topics: [Data Science](), [jupyter](), [Python](), [Amazon S3]() Tags: [aws](), [aws-s3](), [data-science](), [datalore](), [jetbrains](), [jupyter](), [jupyter-notebooks](), [jupyterhub](), [jupyterlab](), [python](), [tag-055aee430837](), [tag-752bfc3f8269](), [tag-baf5012a40ff]() ### AI overview JetBrains announces Datalore Enterprise, a version of Datalore designed for Data Science teams. It adds team-oriented capabilities to Jupyter notebooks, including PyCharm code assistance, persistent data storage, authentication, configurable environments, compute-resource management, versioning, real-time collaboration, and AWS S3 integration. ### Source excerpt Привет, Хабр! Jupyter-ноутбуки хоть и вызывают споры, однако все равно являются самым часто используемым инструментом для решения задач Data Science. Ноутбуки просты в личном использовании. Но если вы когда-либо пробовали организовать командную работу с ними, то наверняка сталкивались со сложностями. Именно поэтому мы в JetBrains решили выпустить специальную версию Datalore Enterprise для команд Data Science! Читать далее ## From YAGNI to YDNIY DevFeed: [From YAGNI to YDNIY]() Original publisher: [Read original article]() Author: Itamar Turner-Trauring Published: 2020-09-18T00:00:00Z Content type: tutorial Language: en Sources: [Itamar Turner-Trauring]() Topics: [Python](), [Conda](), [jupyter](), [Linux](), [macOS](), [pip](), [data](), [C++]() Tags: [c-plus-plus](), [conda](), [jupyter](), [jupyter-notebooks](), [linux](), [macos](), [python](), [releases]() ### AI overview The article introduces YDNIY, or "You Don't Need It Yet," as a complement to YAGNI. It argues that features that are necessary but not release blockers can be delayed when a product remains useful without them. The author illustrates this with the staged development of the Python memory profiler Fil across Linux, macOS, Jupyter, Conda, and multiple memory-allocation APIs. ### Source excerpt How do you ship a product on schedule? One useful approach is applying the You Ain't Gonna Need It principle, or YAGNI for short: leave out all the things that seem nice-to-have, but you have no proof you actually need. But beyond the things you don't need, there are still plenty of features you pretty clearly do need... but are not blockers on releasing your product. So beyond YAGNI, there's also YDNIY: You Don't Need It Yet. Let's see an example of this principle in practice, visualize the principle as a flowchart, and then compare it to another popular acronymed concept, the Minimum Viable Product. A real world example: shipping a new memory profiler In March 2020 I shipped the initial release of a new memory profiler for Python, Fil. Here's how it changed over time in terms of features, from May to August 2020: 0.3.0, initial release: Installable via pip packaging tool, runs only on Linux, only profiles complete program runs. 0.3.3: Support for an additional memory allocation API. 0.4.0: Support for out-of-memory situations. 0.5.0: macOS support. 0.6.0: Support for mmap() allocation API. 0.7.0: Support for C++ memory allocation API. 0.9.0: Much faster and lower overhead in some use cases, added support for yet another memory allocation API. 0.10.0: Support for running inside Jupyter notebooks, and native support for installing via the Conda packaging tool. All of the features I added in later releases were clearly necessary from the start; YAGNI did not apply. Lots of people use macOS, the target audience of data scientists and scientists often use Conda and Jupyter, all those memory allocation APIs are used in the real world, and so on. But even a tool that only runs complete programs on Linux, and only tracks the most popular memory allocation APIs, is still useful to some people. If I had waited until all those features were implemented to ship an initial release, all the people who used the profiler during the first four months of its existence would have had ## When Jupyter Notebooks Won't Import Libraries DevFeed: [When Jupyter Notebooks Won't Import Libraries]() Original publisher: [Read original article]() Author: Fuzzygroup Published: 2020-01-03T00:00:00Z Content type: tutorial Language: en Sources: [Scott Johnson]() Topics: [Jupyter Notebook](), [Python](), [Data Science]() Tags: [data-science](), [execution](), [flow](), [installation](), [jupyter](), [jupyter-notebooks](), [kernel](), [python]() ### AI overview An account of difficulties using Jupyter Notebooks with Python virtual environments. It discusses external data, dependency conflicts, attempts to change the Jupyter kernel, and the decision to convert the notebook into a Python script that can run in a virtual environment. ### Source excerpt I don't claim to be an expert when it comes to Python. At best I'm an apprentice striving to be a journey man. One of the interesting tools in the Python / Data Science ecosystem is the Jupyter Notebook which gives a cell based representation of code, visualizations, documentation and execution flow and allows you to package things up for distribution i.e. hand your work, in a complete fashion, from say Data Scientist 1 ("Rebekah") to Data Scientist 2 ("Dawn"). This is a laudable goal and one that I theoretically agree with. Note: Jupyter Notebooks do NOT include data so that's still external to the notebook, something that can easily bite you (as it is currently biting me). I know a lot of pythonistas simply install libraries to their local machine and just have a collection of tools that the throw at problems. This, however, is a terrible practice due to code deprecation, version conflicts, etc. I say this with authority because I've been through this in the Ruby world before we all regularly started using Ruby virtual environments / Ruby version managers like RBENV / RVM which manage dependencies on a per project basis. Knowing this, my first practice with Python, is to always create a virtual environment, generally using Virtual ENV (VENV). So, when I set out today to use a Jupyter Notebook, my first approach was to make this work with a Jupyter Notebook. And, alas, I haven't been so lucky as to make this work cleanly with a Python virtual environment like VENV. Here are some of the things I tried: Installing jupyter into the virtual env and running from bin/jupyter notebook Following Angelo Basille's Directions Following Janakiev's Directions Changing the Kernel If you're curious about how to use Python Virtual Environments, I wrote a solid tutorial back in September that I used to get a full installation of Tensor Flow up and running. I've referred back to this over and over, each time I needed a Python Virtual Environment, so I know it works. I'm sure there i ## Commas in big numbers everywhere: An OpenType adventure DevFeed: [Commas in big numbers everywhere: An OpenType adventure]() Original publisher: [Read original article]() Author: Tristan Hume Published: 2019-10-14T00:00:00Z Content type: article Language: en Sources: [Jane Street]() Topics: [Font](), [data](), [jupyter](), [Terminal](), [browser]() Tags: [browser](), [documentation](), [efficiency](), [feature](), [fonts](), [jupyter](), [jupyter-notebooks](), [numbers](), [terminal]() ### AI overview This article describes Numderline, a font that uses OpenType shaping features to insert commas into numbers and emphasize digit grouping. It explains the basics of font shaping, including glyph substitutions and positioning, and discusses how reverse substitution enables grouping digits from the end of a number. ### Source excerpt My job involves a lot of staring at large numbers, mostly latencies in nanoseconds, and picking out magnitudes like microseconds. I noticed myself constantly counting digits in my text editor, in my terminal, and in Jupyter notebooks in my browser.