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I'm reiterating - I think a better interface for LLMs on desktop is an interactive notebook, not chat. I'm trying to build a better intuition for PCA and strongly feel an interactive notebook where I'm chatting with LLM + also modifying code/plotting figures is a much better interaction. Bonus...

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Paras Chopra

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58,064 görüntüleme • 9 ay önce •via X (Twitter)

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AI coding just arrived in Jupyter notebooks - and bhupesh ganger (Jupyter co-founder) and I will show you how to use it. Coding by hand is becoming obsolete. The latest Jupyter AI - built by the Jupyter team and showcased at JupyterCon this week - brings AI assistance directly into notebooks. Most AI coding assistants struggle with Jupyter notebooks. Jupyter AI was designed specifically for them. This is the first course to teach it. In this course, Brian and I teach you to: - Generate and debug code directly in notebook cells through an integrated chat interface - Provide the right context (like API docs) to help AI write accurate code - Use Jupyter AI's unique notebook features: drag cells to chat, generate cells from chat, attach context for the LLM We've integrated Jupyter AI directly into the DeepLearningAI platform, so you can start using it immediately. Since Jupyter AI is open source, you can also install and run it locally afterward. Whether you're experienced with notebooks or learning them for the first time, this course will prepare you for AI-assisted notebook development. Start using Jupyter AI (free):
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AI coding just arrived in Jupyter notebooks - and bhupesh ganger (Jupyter co-founder) and I will show you how to use it. Coding by hand is becoming obsolete. The latest Jupyter AI - built by the Jupyter team and showcased at JupyterCon this week - brings AI assistance directly into notebooks. Most AI coding assistants struggle with Jupyter notebooks. Jupyter AI was designed specifically for them. This is the first course to teach it. In this course, Brian and I teach you to: - Generate and debug code directly in notebook cells through an integrated chat interface - Provide the right context (like API docs) to help AI write accurate code - Use Jupyter AI's unique notebook features: drag cells to chat, generate cells from chat, attach context for the LLM We've integrated Jupyter AI directly into the DeepLearningAI platform, so you can start using it immediately. Since Jupyter AI is open source, you can also install and run it locally afterward. Whether you're experienced with notebooks or learning them for the first time, this course will prepare you for AI-assisted notebook development. Start using Jupyter AI (free):

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We’re excited to release an interactive guide highlighting the definitive set of principles for building AI agents 🔥 Based on the popular 12-Factor agents repo by dex. We packaged the principles into an interactive website and Colab notebook with working code examples, so that you can incorporate these principles into your agent application in minutes. As a refresher - some of the principles include getting structured outputs from tools, state management, checkpointing, human-in-the-loop, error handling, composing smaller agents into bigger ones, and more. This is amazing work by Laurie Voss and should be required reading for anyone looking to get started building agents. Website: Notebook:

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🔈Welcome to Nim.Trade AI Notebook demo, the ultimate interface for AI agent creators. See how the Nimble SDK integrates seamlessly for an unparalleled developer experience! ⭐To apply for access to Nimble's AI Notebook, please fill out the form:

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If your hero section doesnt have an interactive ASCII shader, did you even really make an ASCII Animation app? This is my first time making a landing page. Any of my super talented site designer friends want to roast so I can make it better?
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🤓 I just published my notebook on the BlackBasta chat log leak! It’s a dynamic marimo notebook you can run in your browser—no setup needed. Be patient, though—it takes a few seconds to load as the notebook is heavy. Here is what I did 👇 ➡️ Generated diagrams to vizualise the data and extracted some stats. ➡️ I added a dynamic table with all extracted IOCs (not curated) so you can browse and filter them directly from your browser. ➡️ Then I built a hybrid RAG using FAISS (semantic search) + BM25 (keyword search) + ensemble retriever. ➡️ The retrieved data is shown with the LLM’s response for verification. ➡️ I also included context relevancy checks to reduce hallucinations. Everything is documented in the notebook! No API key needed—I provided mine (it will be disabled once the limit is reached). ✨ I used GPT-4o Mini as it is less expensive, but I got much better results with o1, so you may want to use the code locally with your own api key. 👉 Note: This is a personal work and it is not affiliated with my employer. If you like it, share it & let me know what you think! 😊 👉 Notebook:
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“I'm not here to make friends. I'm here to make America an independent country and if you are in the way of that, I'm calling you out.”
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