Based on our record, Jupyter seems to be a lot more popular than Light Table. While we know about 216 links to Jupyter, we've tracked only 14 mentions of Light Table. 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.
Having never used the authoring tool, can you point me towards something to model it after? I think a Bret Victor, "Inventing on Principal" style editor would be perfect, combined with some sort of scratch like Python IDE where each element is defined in terms of its reactive behaviors with other elements on a timeline. https://youtu.be/PUv66718DII?t=634 With some http://lighttable.com/. - Source: Hacker News / 2 months ago
For those like me who have never heard of it, I think OP is referring to this: http://lighttable.com. - Source: Hacker News / 10 months ago
Http://lighttable.com/ (somewhat tangentially related, I think?) Sorry this is so scattered I need to hit the hay LOL. - Source: Hacker News / 11 months ago
He's got a point. I'm surprised he didn't mention Light Table. Source: over 2 years ago
There was a massive amount of excitement around Light Table when it was first announced. I remember one or more pretty amazing videos. I don't have link(s) on-hand though. http://lighttable.com/. - Source: Hacker News / almost 3 years 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
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