by billyp-rva on 3/18/25, 12:09 PM with 68 comments
by diggan on 3/20/25, 4:27 PM
Instead of doing what the author is doing here, and sending messages back and forward, leading to a longer and longer conversation, where each messages leads to worse and worse quality replies, until the LLM seems like a dumb rock, rewrite your initial message with everything that went wrong/was misunderstood, and aim to have whatever you want solved in the first message, and you'll get a lot higher quality answers. If the LLM misunderstood, don't reply "No, what I mean was..." but instead rewrite the first message so it's clearer.
This is at least true for all ChatGPT, Claude and DeepSeek models, YMMV with other models.
by LASR on 3/20/25, 7:48 AM
Then we injected the generated mermaid diagrams back into subsequent requests. Reasoning performance improves for a whole variety of applications.
by graphviz on 3/20/25, 12:02 PM
Sketching backed by automated cleanup can be good for entering small diagrams. There used to be an iOS app based on graphviz: http://instaviz.com
Constraint-based interactive layout may be underinvested, as a consequence of too many disappointments and false starts in the 1980s.
LLMs seem ill-suited to solving the optimization of combinatorial and geometric constraints and objectives required for good diagram layout. Overall, one has to admire the directness and simplicity of mermaid. Also, it would be great to someday see a practical tool with the quality and generality of the ultra-compact grid layout prototype from the Monash group, https://ialab.it.monash.edu/~dwyer/papers/gridlayout2015.pdf (2015!!)
by vunderba on 3/20/25, 5:50 AM
by 30minAdayHN on 3/20/25, 4:49 PM
I thought that LLMs are great at compressing information and thought of putting it to good use by compressing a large codebase into a single diagram. Since entire codebase doesn't fit in the context window, I built a recursive LLM tool that calls itself.
It takes two params: * current diagram state, * new files it needs to expand the diagram.
The seed set would be an empty diagram and an entry point to source code. And I also extended it to complexity analysis.
It worked magically well. Here are couple of diagrams it generated: * https://gist.github.com/priyankc/27eb786e50e41c32d332390a42e... * https://gist.github.com/priyankc/0ca04f09a32f6d91c6b42bd8b18...
If you are interested in trying out, I've blogged here: https://updates.priyank.ch/projects/2025/03/12/complexity-an...
by stared on 3/20/25, 9:53 AM
Make sure it is allowed to think before doing (not necessarily in a dedicated thinking mode, it can be a regular prompt to design a graph before implementing it; make sure to add in a prompt who the graph is for (e.g. "a clean graph, suitable for a blog post for technical audience").
by McNutty on 3/20/25, 5:45 PM
I do like the idea of another commenter here who takes a photo of their whiteboard and instructs the AI tool to turn it into a structured diagram. That seems to be well within reach of these tools.
by larodi on 3/20/25, 1:47 PM
It also really depends on the printing.
by RKFADU_UOFCCLEL on 3/20/25, 3:44 PM
by victorbjorklund on 3/20/25, 9:48 AM
They have icons for common things like cloud things.
by cadamsdotcom on 3/20/25, 6:09 AM
Interesting perspective but it’s a bit incomplete without a comparison of various models and how they perform.
Kind of like Simon Willison’s now-famous “pelican on a bicycle” test, these diagrams might be done better by some models than others.
Second, this presents a static picture of things, but AI moves really fast! It’d also be great to understand how this capability is improving over time.
by submeta on 3/20/25, 6:52 AM
I also experimented with bpmn markup (xml). Realized there are already repos on GitHub creating bpmn diagrams from prompt.
You can also ask llms to create svg.
by trash_cat on 3/20/25, 11:58 AM
by giberson on 3/21/25, 2:50 PM
I'm mainly speaking to the ability to read IaC code ([probably of any library but at LEAST in my case] cdk, pulumi, terraform, cloudformation, serverless) and be able to infer architectural flow from it. It's really not conducive to that use case.
I could also, kidding/not kidding, be speaking to the range of abilities for "mid" and "senior" developers to know and convey such flows in diagrams.
But really my point is this feels like more validation that AI doesn't provide increased ability, it provides existing (and demonstrated) ability faster with less formalized context. The "less formalized context" is what distinguishes it from programs/code.
by ndr_ on 3/20/25, 2:57 PM
Rather than relying on end-user products like ChatGPT or Claude.ai, this article is based on the „pure“ model offerings via API and frontends that build on these. While the Ilograph blog ponders „AI’s ability to create generic diagrams“, I‘d conclude: do it, but avoid the „open“ models and low-cost offerings.
by enoeht on 3/20/25, 1:54 PM
by james-bcn on 3/20/25, 10:01 AM
Simon Willison has shown that current models aren't very good at creating an SVG of a pelican on a bicycle, but drawing a box diagram in SVG is a much simpler task.
by peter_retief on 3/20/25, 8:14 AM
by notTooFarGone on 3/20/25, 8:24 AM
It was a well defined domain so I guess the training data argument doesn't fit for stuff that is within a "natural" domain like graphs. LLMs can infer the behavior based on naming quite well.
by mulmboy on 3/20/25, 8:11 AM
It's disingenuous to conclude that AI is no good at diagramming after using an impotent prompt AND refusing to iterate with it. A human would do no better with the same instructions, LLMs aren't magic.
This is the same as my previous comment https://news.ycombinator.com/item?id=42524125
by melagonster on 3/21/25, 2:00 AM
by WesleyLivesay on 3/20/25, 12:09 PM
by jbverschoor on 3/20/25, 1:13 PM