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+[[!meta title="MatplotLLM, iterative natural language data visualization in org-babel"]]
+[[!meta copyright="Copyright &copy; 2023 Abhinav Tushar"]]
+[[!inline pages="internal(2023/info/matplotllm-nav)" raw="yes"]]
+
+<!-- Initially generated with emacsconf-publish-talk-page and then left alone for manual editing -->
+<!-- You can manually edit this file to update the abstract, add links, etc. --->
+
+
+# MatplotLLM, iterative natural language data visualization in org-babel
+Abhinav Tushar (he/him) - abhinav@lepisma.xyz, https://lepisma.xyz, @lepisma@mathstodon.xyz, <mailto:abhinav@lepisma.xyz>
+
+[[!inline pages="internal(2023/info/matplotllm-before)" raw="yes"]]
+
+Large Language Models (LLMs) have improved in capabilities to an extent
+where a lot of manual workflows can be automated by just providing
+natural language instructions.
+
+On such manual work is to create custom visualizations. I have found the
+process to be really tedious if you want to make something non-standard
+with common tools like matplotlib or d3. These frameworks provide low
+level abstractions that you can then use to make your own
+visualizations.
+
+Earlier to make a new custom visualization, I would open two windows in
+Emacs, one for code, other for the generated image. In this talk, I will
+show how a powerful LLM could lead to a much more natural interface
+where I only need to work with text instructions and feedback on the
+currently generated plot. The system isn't perfect, but it shows us how
+the future or such work could look like.
+
+The package is called MatplotLLM and lives here
+<https://github.com/lepisma/matplotllm>
+
+About the speaker:
+
+I am a Programmer and Machine Learning Engineer who has been in love
+with Emacs' extendability from the moment I pressed M-x. Since then, I
+have been doing as many things inside Emacs as I can. In this talk, I
+will cover a recent attempt at automating one of my workflows inside
+Emacs.
+
+# Discussion
+
+## Questions and answers
+
+-   Q: What is the license of <https://github.com/lepisma/matplotllm>
+    project ? Sjo
+    -   A: GPLv3 or later. Sorry, I didn't put this in the repository,
+        You can refer to
+        <https://github.com/lepisma/matplotllm/blob/main/matplotllm.el#L18C12-L29>
+        though.
+-   Q: Sometimes LLMs hallucinate. Can we trust the graph that it
+    produces?
+    -   A: Not always, but the chances of hallucinations impacting
+        'generated code' that causes a harmful but not identifiable
+        hallucinations are a little lower. Usually hallucination in code
+        show up as very visible bug so you can always do a retry. But I
+        haven't done a thorough analysis here yet.
+- Q: What are your thoughts on the carbon footprint of LLM useage?
+  - (not the speaker): to add a bit more to power usage of LLMs, it is not inherent that the models must take many megawatts to train and run. work is happening and seems promising to decrease power usage
+## Notes
+
+-   Repository link <https://github.com/lepisma/matplotllm> . A
+    connected blog post here
+    <https://lepisma.xyz/2023/08/20/matplotllm:-an-llm-assisted-data-visualization-framework/index.html>
+
+
+
+[[!inline pages="internal(2023/info/matplotllm-after)" raw="yes"]]
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+
+