Based on our record, Jupyter should be more popular than .NET. 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.
Editing PDF files programmatically is a common requirement in enterprise applications — whether you're modifying invoices, generating reports, or enabling users to fill and save forms. The .NET ecosystem lacks native support for advanced PDF editing, which makes third-party libraries crucial. - Source: dev.to / about 1 month ago
Dotnet (.NET 9 is used for this article) and C# decompiler. - Source: dev.to / 3 months ago
C# was developed by Microsoft in the early 2000s as part of its .NET initiative, led by Anders Hejlsberg. Originally designed as an alternative to Java, C# evolved into a powerful language for Windows applications, backend services, game development (via Unity), and cloud computing. The introduction of .NET Core made C# fully cross-platform, allowing it to run on Windows, Linux, and macOS. - Source: dev.to / 4 months ago
This blog post details how to implement social authentication and provide users with several social login options and how we can handle the users' data obtained as a result of these logins in our application. In this blog post, we’ll look at how we can integrate Google and Facebook login authentications. We will see how this can be implemented from the server side of an application using .NET 6; Microsoft's own... - Source: dev.to / 10 months ago
We never quite lost it, just the startup SV culture lost sight of them, https://www.embarcadero.com/products/delphi https://dotnet.microsoft.com/en-us/ https://www.outsystems.com/ Or in the game industry, https://docs.unrealengine.com/5.2/en-US/blueprints-visual-scripting-in-unreal-engine/. - Source: Hacker News / almost 2 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 / 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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