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It walks through the core stages — tokenization, POS tagging, dependency parsing, embeddings — and visualizes how meaning gets fragmented and simulated along the way.

Built with Streamlit, spaCy, BERT, and Plotly. It’s fast, interactive, and aimed at anyone curious about how LLMs turn your sentence into structured data.

Would love thoughts and feedback from the HN crowd — especially devs, linguists, or anyone working with or thinking about NLP systems.

GitHub: https://github.com/jdspiral/meaning-machine Live Demo: https://meaning-machine.streamlit.app

georgewsinger•3h ago

Is this really how SOTA LLMs parse our queries? To what extent is this a simplified representation of what they really "see"?

jdspiral•1h ago

Yes, tokenization and embeddings are exactly how LLMs process input—they break text into tokens and map them to vectors. POS tags and SVOs aren't part of the model pipeline but help visualize structures the models learn implicitly.

andai•2h ago

See also: explainer post: https://theperformanceage.com/p/how-language-models-see-you

sherdil2022•2h ago

Great job! Do you have any plans to visualize/explain how machine translation - between human languages - works?

Dwedit•1h ago

Send tokens to model, model goes brrrr, get output tokens back.

jdspiral•1h ago

Thanks! Yes — that’s on the roadmap, along with some other cool visualizations I’m working on. Machine translation is definitely something I want to work on: showing how models align meaning across languages using shared embeddings and attention patterns. I’d love to make that interactive too.

sherdil2022•59m ago

I would love to get involved with that (I speak a handful of himan languages). Let me know if you are looking for collaborators.