This is just json right? One issue with llms right now it's that if you give them a json specification, they're not always amazing at following it. I think it makes sense to have an agent tool that takes llm json, a file name and a spec path and only writes out the file if it conforms.
Even in the Era AI, GGPlot's API is still the best charting API. The name "Grammar of Graphics" isn't just marketing, they literally sought to write a god damn grammar to was capable of expressing all possible qualitative graphics.
I actually stumbled upon this book when I was trying to look up how draftsmen (with pens and pencils on paper) did qualitative graphics as I found they had a lot of charm as opposed to modern charting libraries. It's something I noticed when looking through a bunch of historical RBA (Reserve bank of Australia) annual reports, the 1960-1980 charts had a lot of character, but then you go into the early 2000s and its a stale chart from excel.
Anyways ggplot doesn't really recapture the magic of those older charts, but it seems use quite a few of those as a baseline for how to communicate information. Like in figure 20.1 they talk about efforts to replicate older inforgraphics that showed Napoleon’s March on Russia, this graphic here (I think the example in the book is a bit nicer than the one in this blogpost IMO)
On top of the charts just look nicer than anything you could produce with pyplot (and any API built on top of it) as pyplot seems to be have some really limited raster based rendering or something and the text handling is incredibly limited, I've never had this issue in ggplot.
I feel like most software engineers aren't exposed to because it exists in the R ecosystem which is more so data scientist, econometricians, statisticians and other quantitative data professions, but it definitely one of the nicer APIs and I wish more people in the node and python ecosystem copied their homework. I see vega's full name is something to do with grammars, but idk it's for the same reason.
It's cool...but is it needed? I'm thinking of Apache echarts and any of many other mature charting libraries. Kinda seems like reinventing the wheel; thing gets released, thing gets more and more new features bolted onto it, eventually someone offers another thing that's basically the original thing with some slightly different design and syntax opinions...
Switch backends to use their native strengths: ECharts for hierarchical sunbursts, Plotly for statistical and analytical traces, or Excel for editable charts embedded in a workbook.
Or just find a charting library that you like and actually get to know what it can do, vs mixing and matching presets from different libraries but never tweaking them.
So this is one interface that can render to multiple charting backends?
If AI is writing the "Flint", why not just have it write the backend code instead? I'm not sure why I would want pluggable charting backends.
I can see an argument for providing simpler APIs for LLMs, though, so that it can be more token efficient for example.
unfortunately JSON is doomed to fail in "the AI era"
LLMs are surprisingly bad at generating JSON.
Why is that?
This is just json right? One issue with llms right now it's that if you give them a json specification, they're not always amazing at following it. I think it makes sense to have an agent tool that takes llm json, a file name and a spec path and only writes out the file if it conforms.
Even in the Era AI, GGPlot's API is still the best charting API. The name "Grammar of Graphics" isn't just marketing, they literally sought to write a god damn grammar to was capable of expressing all possible qualitative graphics.
They even wrote a book about how they went about it (not that it speaks to the quality of the API) https://link.springer.com/book/10.1007/0-387-28695-0
I actually stumbled upon this book when I was trying to look up how draftsmen (with pens and pencils on paper) did qualitative graphics as I found they had a lot of charm as opposed to modern charting libraries. It's something I noticed when looking through a bunch of historical RBA (Reserve bank of Australia) annual reports, the 1960-1980 charts had a lot of character, but then you go into the early 2000s and its a stale chart from excel.
Anyways ggplot doesn't really recapture the magic of those older charts, but it seems use quite a few of those as a baseline for how to communicate information. Like in figure 20.1 they talk about efforts to replicate older inforgraphics that showed Napoleon’s March on Russia, this graphic here (I think the example in the book is a bit nicer than the one in this blogpost IMO)
https://www.andrewheiss.com/blog/2017/08/10/exploring-minard...
On top of the charts just look nicer than anything you could produce with pyplot (and any API built on top of it) as pyplot seems to be have some really limited raster based rendering or something and the text handling is incredibly limited, I've never had this issue in ggplot.
I feel like most software engineers aren't exposed to because it exists in the R ecosystem which is more so data scientist, econometricians, statisticians and other quantitative data professions, but it definitely one of the nicer APIs and I wish more people in the node and python ecosystem copied their homework. I see vega's full name is something to do with grammars, but idk it's for the same reason.
Why is this "for the AI era"? Isn't it kind of not for the AI era since an LLM can create a gnarly python or js chart of whatever you want in seconds?
Nowadays you either create something with or for AI.
Like you created you own framework for wen apps before.
"For the AI era" is the necessary buzzword
An earlier larger discussion 22 days ago on this https://news.ycombinator.com/item?id=48834924
It's cool...but is it needed? I'm thinking of Apache echarts and any of many other mature charting libraries. Kinda seems like reinventing the wheel; thing gets released, thing gets more and more new features bolted onto it, eventually someone offers another thing that's basically the original thing with some slightly different design and syntax opinions...
Switch backends to use their native strengths: ECharts for hierarchical sunbursts, Plotly for statistical and analytical traces, or Excel for editable charts embedded in a workbook.
Or just find a charting library that you like and actually get to know what it can do, vs mixing and matching presets from different libraries but never tweaking them.
When does it get too abstract? What’s wrong with plotly? Or plotly express? How is yet another JSON spec era-anything?
Yeah this is not needed and every single day it is needed even less.
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Literally the first backend they mention is Vega-Lite.