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| author | Sacha Chua <sacha@sachachua.com> | 2023-09-22 09:13:43 -0400 |
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| committer | Sacha Chua <sacha@sachachua.com> | 2023-09-22 09:13:43 -0400 |
| commit | c871fbb83d91e34c22fcc809d543e5be6aef9114 (patch) | |
| tree | 80adbdc341308e31394e7db19f2816f7556feb3e /2023/talks/matplotllm.md | |
| parent | 9721177466e21a970e10ae6900fd7d40a9ee79c1 (diff) | |
| download | emacsconf-wiki-c871fbb83d91e34c22fcc809d543e5be6aef9114.tar.xz emacsconf-wiki-c871fbb83d91e34c22fcc809d543e5be6aef9114.zip | |
add cubing, gc, matplotllm, parallel, repl, solo
Diffstat (limited to '2023/talks/matplotllm.md')
| -rw-r--r-- | 2023/talks/matplotllm.md | 40 |
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diff --git a/2023/talks/matplotllm.md b/2023/talks/matplotllm.md new file mode 100644 index 00000000..453eb114 --- /dev/null +++ b/2023/talks/matplotllm.md @@ -0,0 +1,40 @@ +[[!meta title="MatplotLLM, iterative natural language data visualization in org-babel"]] +[[!meta copyright="Copyright © 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> + + + +[[!inline pages="internal(2023/info/matplotllm-after)" raw="yes"]] + +[[!inline pages="internal(2023/info/matplotllm-nav)" raw="yes"]] + + |