Einblick Prompt AI for JupyterLab
A generative AI pair programmer optimized for data science
What it does
This Jupyter extension is engineered specifically to solve data problems with Python. Get to answers faster by utilizing generative AI to clean and explore data, create visualizations, train ML models, and much more.
Does a similar job
all alternatives →- AMAdding Mistral Codestral and GPT-4o to Jupyter Notebooks2024 · github.com · ▲269
Hey HN! We’ve forked Jupyter Lab and added AI code generation features that feel native and have all the context about your notebook. You can see a demo video (2 min) here: https://www.tella.tv/video/clxt7ei4v00rr09i5gt1laop6/view Try a hosted version here: https://pretzelai.app Jupyter is by far the most used Data Science tool. Despite its popularity, it still lacks good code-generation extensions. The flagship AI extension jupyter-ai lags far behind in features and UX compared to modern AI code generation and understanding tools (like…
- TAThread – AI-powered Jupyter Notebook built using React2024 · github.com · ▲162
Hey HN, we're building Thread (https://thread.dev/) an open-source Jupyter Notebook that has a bunch of AI features built in. The easiest way to think of Thread is if the chat interface of OpenAI code interpreter was fused into a Jupyter Notebook development environment where you could still edit code or re-run cells. To check it out, you can see a video demo here: https://www.youtube.com/watch?v=Jq1_eoO6w-c We initially got the idea when building Vizly (https://vizly.fyi/) a tool that lets non-technical users ask questions from their data. While…
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I trained a 125M-parameter transformer to autocomplete piano performances in real time (~108 notes/sec on an iPhone 15). The idea is basically GitHub Copilot or Tabnine, except instead of prompting it with code, you prompt it by playing a few notes on a MIDI piano. The model then continues what you played, entirely on-device. The app is free if anyone wants to try it. Happy to answer questions about the model, training, Core ML, or the many things that didn't work.
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Hey HN, Henry from Cactus here! We previously released Cactus Needle, a 14MB agentic LLM for tool call, device use, and structured extraction for phones, wearables, smart homes, small robots and microcontrollers. We got really great feedback here, and have now incorporated the suggestions to release Needle 2. The whole model is a single 14MB binary that runs a full session in 28MB of RAM; 45m parameters at 2bit compression. Needle hits 500 tokens/sec decode speed on a Raspberry Pi 5, sits between 400-1,500 tokens/sec on VR devices like Meta Quest 3S and Apple Vision Pro, and ranges…
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