Laravel might be a bit more popular than Jupyter. We know about 240 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.
Laravel is a PHP web framework used by developers around the world. In March 2024, they launched Laravel Cloud with a full lineup of updates. All dropped at once. - Source: dev.to / about 2 months ago
Laravel is a full-stack PHP framework that can be integrated with powerful front-end frameworks such as React or Vue.js. With Laravel, you’ll be creating websites and apps that are scalable and capable of growth. - Source: dev.to / 18 days ago
If you're planning to convert a backend server to an AI agent using function calling, avoid languages and frameworks that require developers to manually write JSON schemas, such as PHP Laravel and Java Spring RestDocs. - Source: dev.to / 21 days ago
Using a full-stack framework with batteries-included, such as Wasp for JavaScript (React, Node.js, Prisma) or Laravel for PHP, takes the complexity out of piecing the different parts of the stack together. Since these frameworks are opinionated, they've chosen a set of tools that work well together, and the have the added benefit of doing a lot of work under-the-hood. In the end, the AI can focus on just the... - Source: dev.to / 24 days ago
Laravel installed (version 8+ recommended). - Source: dev.to / about 1 month 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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