Codeium is particularly recommended for software developers, coding enthusiasts, and teams looking to boost productivity and reduce the time spent on coding and debugging. It is suitable for beginners who need guidance, as well as experienced developers looking for efficiency enhancements.
Based on our record, Jupyter should be more popular than Codeium. 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.
Visit the official website at https://codeium.com Download the extension for your preferred code editor. Follow installation instructions to enable AI completions. Begin coding with AI-assisted suggestions immediately. - Source: dev.to / 21 days ago
Codeium: Supports multiple editors and privacy-conscious teams. - Source: dev.to / about 1 month ago
The only workflow change was switching IDEs, from VSCode/Copilot to Windsurf/Cascade, as it was depicted as a more performant AI regarding the app context. - Source: dev.to / 3 months ago
What started as a slow Friday turned into a productive coding session using Windsurf, Codeiums AI-powered IDE, to create a block thumbnail generator for Umbraco block editors. - Source: dev.to / 3 months ago
Built on VS Code (sensing a theme here?), Windsurf offers a sophisticated AI-powered development environment with its Supercomplete and Cascade features. Developed by Codeium, it focuses on real-time workspace understanding and intelligent code assistance. - 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 / 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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