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This is pretty crazy, literally built something almost exactly like this for a project I'm working on (a local-first AI agent that does work on folders while you sleep). Basically going from JSON "Lego blocks" to full reports (including charting, though a subset of what Flint offers). And with post-generation validation and retry steps. Functions extremely well and the result is a very clear (and consitent) human-rea…
The charts are very nice, and I think the visualisation layer for LLMs is a very interesting problem. I’ve been building https://smalldocs.org for this exact reason. It’s an office suite for AI agents - but my main use case is giving a cli based LLM the canvas to express itself - charts, mermaid diagrams, etc. I’ve extended it a bit further to be a format for all types of work so the agent can embed slides and spread…
Earlier quoted context omitted.
In fact, Json as a common language for human in visualization has been around for a while! The benefit of declarative grammar is that users can effective manipulate specs through UI (drag and drop, clicks). Btw, Flint is intentionally designed to allow agent skip low-level params like scale, axe, zero, step size etc (which are extremely crucial for "GOOD-looking") and they are dynamically optimized by the compiler. S…
> Json as a common language for human in visualization has been around for a while Plant, Mermaid, Graphviz are all declarative textual representations designed for human authoring, JSON is made for tools. Its not a criticism just a statement that if interop across agent and human was intended this is not the simplest option.
N of only a few of us working on an analytics agent, I don't think we've been finding this to be the case. We've been impressed with just how good LLMs (even smaller open weight models) are at using Python and R for visualization. Often any shortcomings go away if we iterate a bit to about ambiguity. Are there any threads of research that could better support this claim or highlight where issues might be?
The charts are very nice, and I think the visualisation layer for LLMs is a very interesting problem. I’ve been building https://smalldocs.org for this exact reason. It’s an office suite for AI agents - but my main use case is giving a cli based LLM the canvas to express itself - charts, mermaid diagrams, etc. I’ve extended it a bit further to be a format for all types of work so the agent can embed slides and spread…
interesting how you don't discuss literally anything about the project actually posted and spam your thing. Not pointing you out, seen many other comments like this on HN but always felt a bit weird about them
A deterministic layer like a compiler or generator of code with some kind of IR that the LLM generates and feeds it with.
I feel we will be seeing this more and more in the near future.
There’s an emerging pattern in agentic systems and this project is a great example. A deterministic layer like a compiler or generator of code with some kind of IR that the LLM generates and feeds it with. I feel we will be seeing this more and more in the near future.
> simple chart specs can be reliable, but generated charts are often of low quality due to reliance on system defaults; - complex chart specs with explicit details can produce good-looking charts, but they are verbose and agents can struggle with reliability N of only a few of us working on an analytics agent, I don't think we've been finding this to be the case. We've been impressed with just how good LLMs (even sma…
Or at least, maybe that's the idea?
IME, Claude and ChatGPT do just fine generating ggplot models, but extensive customization can get a bit hairy.