Based on our record, Socket.io should be more popular than Jupyter. It has been mentiond 734 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.
In line 32 we have the socket.io editaData event which handles data editing in the server. When the user clicks edit in the client, the server searches for the data using the findIndex method. If it exists it updates the data in the crudData array then it broadcasts the edited data to the client. - Source: dev.to / 4 months ago
Tools like Socket.IO and WebSockets significantly simplify the implementation of real-time communication between client and server. - Source: dev.to / 4 months ago
To capture the test execution status, I wrote a custom karma reporter(a good resource) with which I was able to emit the test execution status back to the vscode extension. I am using socket.io to do this communication. - Source: dev.to / 5 months ago
Building such experiences is already possible, using libraries such as socket.io and React Together. This blog post explains how to easily add real-time collaboration to an existing React app, using React Together. - Source: dev.to / 5 months ago
Complexity: WebSockets require you to handle connection lifecycle events, such as errors and reconnections. While the code example I provided could suffice for simple use cases, more complex use cases might arise, like automatic reconnection and queueing messages sent by the client when the connection wasn't open. For that, you can either extend this code or use an external library like react-use-websocket for a... - Source: dev.to / 7 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 / 3 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 / 4 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 / 5 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 / 9 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 / 12 months ago
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