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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.

I have tried using Flint vs asking the AI to generate a Vega lite spec directly, and in my opinion Flint was not as nice of a solution.

Flint is fine for doing predetermined chat types, with very low customization. But I found using an agent or sub agent to create the Vega spec directly allowed for a lot more flexibility, and ultimately that means higher quality visualizations (stuff like adding points for min and max on a timeseries, or adding a callout marker for a date where some event happened).

That being said, with Vega lite you have to validate your chart specs, provide specific guidance, and play whack a mole with Vega bugs/idiosyncrasies. So Flint is more reliable if you don’t want to dedicate a whole skill to making charts and want to get running quickly.

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.

02 Jul 2026 04:28:21 UTC

Microsoft/Flint-Chart

https://github.com/microsoft/flint-chart

https://news.ycombinator.com/item?id=48756577

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08 Jul 2026 17:46:12 UTC

Show HN: Microsoft releases Flint, a visualization language for AI agents

https://microsoft.github.io/flint-chart/#/

https://news.ycombinator.com/item?id=48834924

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DSLs for AI doesn't really make sense- they were trained on existing graphics libraries and are pretty good at them.

Maybe a long term play would be putting this out and creating a "graphics bench" to entice the labs to overfit on your DSL but that seems like a lot of work

What is wrong with "please make plot XYZ in plotly?"

An earlier larger discussion 22 days ago on this https://news.ycombinator.com/item?id=48834924

When does it get too abstract? What’s wrong with plotly? Or plotly express? How is yet another JSON spec era-anything?

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.

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?

Maybe they’re not selling this well. Maybe they’re so close to the AI research that this seems like an obviously good idea.

But there’s not one word of why this is good for LLMs, or how they tested/measured that.

My gut would tell me that a new solution put up against all the vega lite specs it’s already be trained on would be a hard thing to win.

This needs a side-by-side config comparison with something like echarts config schema format. I really don't see the point.

I'm 99% sure the verbosity required in the system prompt to teach non-M$ models this new ever-so-slightly-different-but-not-obviously-necessary chart def abstraction format, and the iterations required to get it right, will outweigh any supposed efficiency gains resulting from using it.

Just stating the obvious.

What is the point of this. I already can get an llm to draw charts using plotly, matplotlib, echarts, etc all the time. There will be always a better way but what does this buy us?

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.

If it's made for LLMs, the spec should be yaml not json. Way more token efficient

Every time I needed to do charts at work I had a conundrum. Libraries are aplenty but basically only plotly does everything, but it’s not the prettiest. I’d much rather developers contributed new features into existing libraries.

I've seen this play out plenty of times

Yeah this is not needed and every single day it is needed even less.

"A Visualization Language for the AI Era", as a tagline, sounds weird.

I had the best success with popular and versatile packages like matplotlib and ggplot2 - even 1.5 ago (vide https://quesma.com/blog/which-chart-would-you-swipe-right/).

Now, frankly, my go-to visualization package is React, sometimes with a pinch of D3.js (e.g. https://p.migdal.pl/tree-of-tree/).

What a waste of time over at Microsoft Research

So a stringly typed JSON-based DSL without a linter or LSP?

Does it do flowcharts and arch diagrams? Doesn’t seem like it.

Can it respond to my several open github tickets?

Why is this for the AI era"?

My conspiracy theory is that this was not related to AI in any capacity, but it was tacked on because ai

The linked website has only two mentions of AI, and one of them is the title, the approach isn't AI-centric in any meaningful sense either

unfortunately JSON is doomed to fail in "the AI era"

LLMs are surprisingly bad at generating JSON.

idk ~ it's either a bit thin or i'd expect something more substantial "for the AI era"

... as the kids would say: "weak sauce"

... and it's just a tool to create charts not a "visualization language"

you could probably vibeslop a pipeline from some ad-hoc DSL to excel or pandas or R and get a better integration with your business context

Give it a year and Microsoft will have abandoned it for some other shiny buzzword rich agentic slop ...

If "AI" was actually as capable as its salesmen claim, why can't it just use whatever existing library is out there already?

"for the ai era" == slop era

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