Typst
Quarto
Obsidian.md
R Markdown
Medium
Productivity Power Tools
Docusaurus
Mathpix
Jupyter
Looker
Databricks
Google BigQuery
Presto DB
Rakam
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JupyterBased on our record, Jupyter should be more popular than Typst. It has been mentiond 216 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
The masochism of latex is becoming increasingly irrelevant with every typst [1] release. No going back once you experience realtime rendering of your document, and support in VS Code is stellar IMO. [1] http://typst.app. - Source: Hacker News / about 2 months ago
I've never used Quarto, but I might give it a go someday. I currently have a convoluted workflow for generating math-heavy documents that involves generating equations using SymPy in a notebook, accumulating them in a string, and ultimately dumping the string into a Markdown. I would love to simplify this sooner rather than later. I'm also keeping an eye on https://typst.app/ and hoping for a sane alternative to... - Source: Hacker News / 3 months ago
We could be using html based DSLs and powerful importable components instead of special characters. Monaco (VSCode editor framework) allows frontend devs to make special DSL editors with autocomplete for both desktop and web. Between Spectacle and Typst approaches, I would choose Spectacle. I read the 2003 book The art of Unix programming where the author praises plain text config and says hand editing xml is a... - Source: Hacker News / 3 months ago
> No way I'll use LaTeX for all my writing, and anything Markdown-based just won't cut it formatting-wise. Have a look at Typst[0]. It's a lot easier to use than LaTeX, while still offering full formatting and layout. Or you could give macOS a go. UNIX with proper desktop versions of the Office apps. ;) [0]: https://typst.app/. - Source: Hacker News / 3 months ago
I wish journals would start accepting Typst[0] files. It is definitely the format of the next decade in my opinion. It's both open source and highly performant. Sadly existing legacy structures prevent it from gaining the critical mass needed for it to thrive just yet. [0] https://typst.app/. - Source: Hacker News / 3 months ago
Showcase and share: Easily embed UIs in Jupyter Notebook, Google Colab or share them on Hugging Face using a public link. - Source: dev.to / about 2 months ago
LangChain wasn’t designed in isolation — it was built in the data pipeline world, where every data engineer’s tool of choice was Jupyter Notebooks. Jupyter was an innovative tool, making pipeline programming easy to experiment with, iterate on, and debug. It was a perfect fit for machine learning workflows, where you preprocess data, train models, analyze outputs, and fine-tune parameters — all in a structured,... - Source: dev.to / 3 months ago
Leverage versatile resources to prototype and refine your ideas, such as Jupyter Notebooks for rapid iterations, Google Colabs for cloud-based experimentation, OpenAI’s API Playground for testing and fine-tuning prompts, and Anthropic's Prompt Engineering Library for inspiration and guidance on advanced prompting techniques. For frontend experimentation, tools like v0 are invaluable, providing a seamless way to... - Source: dev.to / 4 months ago
Lately I've been working on Langgraph4J which is a Java implementation of the more famous Langgraph.js which is a Javascript library used to create agent and multi-agent workflows by Langchain. Interesting note is that [Langchain.js] uses Javascript Jupyter notebooks powered by a DENO Jupiter Kernel to implement and document How-Tos. So, I faced a dilemma on how to use (or possibly simulate) the same approach in... - Source: dev.to / 8 months ago
One of the most convenient ways to play with datasets is to utilize Jupyter. If you are not familiar with this tool, do not worry. I will show how to use it to solve our problem. For local experiments, I like to use DataSpell by JetBrains, but there are services available online and for free. One of the most well-known services among data scientists is Kaggle. However, their notebooks don't allow you to make... - Source: dev.to / 11 months ago
Quarto - Open-source scientific and technical publishing system built on Pandoc.
Looker - Looker makes it easy for analysts to create and curate custom data experiences—so everyone in the business can explore the data that matters to them, in the context that makes it truly meaningful.
Obsidian.md - A second brain, for you, forever. Obsidian is a powerful knowledge base that works on top of a local folder of plain text Markdown files.
Databricks - Databricks provides a Unified Analytics Platform that accelerates innovation by unifying data science, engineering and business.What is Apache Spark?
R Markdown - Dynamic Documents for R
Google BigQuery - A fully managed data warehouse for large-scale data analytics.
