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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>
+
+
+
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