React Native might be a bit more popular than Jupyter. We know about 232 links to it since March 2021 and only 216 links to Jupyter. 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.
React Native: Assez faile à prendre en main si on maitrise React. - Source: dev.to / 10 days ago
React skills work for React Native development - Although React Native is a separate framework designed specifically for building mobile applications, many of the skills a developer gains working with the React framework are applicable here as well. - Source: dev.to / about 1 month ago
React Native (Official Documentation) allows you to create apps for both iOS and Android with a single codebase, while TypeScript adds type safety to your JavaScript, reducing bugs and improving code quality. - Source: dev.to / 4 months ago
React Native is the powerhouse for cross-platform mobile development. Write once, run everywhere, get native performance when you need it, enjoy hot reloading for rapid development, tap into a huge ecosystem of libraries and tools, and integrate with native modules when you need platform-specific features. - Source: dev.to / 4 months ago
React Native is a powerful framework for building cross-platform mobile applications using JavaScript and React. Whether you're a seasoned developer or a beginner, starting a new React Native project can be both exciting and challenging. This guide will walk you through the essential steps, tools, and best practices to set up your React Native project efficiently and effectively. - Source: dev.to / 4 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 1 month 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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