I find it absolutely mindblowing to witness the rate at which Anthropic can ship new features. Only a year ago I couldn't wait to see some sort of Github integration and then it appeared only a week later. Seriously impressive stuff.
It was inevitable until the point all apps will disappear and AI will be the entry point for all work. You can see how anything required appear based on a single request. After which world models and other forms of interaction that are more dynamic will make sense and we'll need something that's not a screen.
I feel like this is a feature which improves the perceived confidence of the LLM but doesn't do much for correctness of other outputs, i.e. an exacerbation of the "confidently incorrect" criticism.
Wow, I asked it to build me a simple diagram explaining agile development and it did an amazing job. Wow it felt magical to watch that diagram slowly animating to life.
Like a much prettier version of Mermaid.
Kudos, Anthropic. Geez, this is so nice.
Now I'm going to ask it to draw a diagram of a pelican riding a bicycle, why not?
This is pretty neat and I am experimenting with it now, but hasn't ChatGPT had capability to create graphs and interact with data for a while? "ChatGPT advanced data analysis" for example. I'm asking in good faith as maybe some of you have been using that and can compare the two and give an informed opinion.
I usually use a lot of other tools for data analysis or write code with Claude code or another LLM to do data analysis and visualization.
Interesting. So if I'm reading this correctly, this is distinctly different than the artifacts that Claude creates? If that's the case, why create it inline as opposed to an artifact? Any time I get a visual, I tend to find them so useful that I _want_ them to be an artifact that I can export and share.
I tried the periodic table in their examples using sonnet 4.6 on the $20/mo plan. After a few minutes Claude told me it reached the max message length and bailed. I pressed continue and eventually it generated the table, but it wasn't inline, it was a jsx artifact, and I've now hit my daily usage limit.
Since the underlying tool seems to be named something like "widget," I found I can nudge it into this embedded interactive output instead of artifacts by saying, "show me a widget that..."
Unable to reproduce the recipe image in the iOS app. It first gave a normal text answer. Then when referencing this blog post it produced a wonky HTML artifact.
I asked it to do some portfolio analysis for me and it created BEAUTIFUL, tabbed, interactive charts UNPROMPTED. This is kind of magical. The charts were not just beautiful, but actually super useful in understanding the data faster. I honestly could not have produced those in a week if you asked me to.
Likewise, it created a couple visualizations for me that weren't requested but were very useful. That's a nice surprise. My takeaway was essentially the same in that I'd take much longer to make something comparable. I'll take advantage of this quite a bit, I think. I spend a lot of time visualizing data
Reliability has been the real bottleneck for multi-agent setups in production. The hard part isnt getting one agent to do something clever once - its making repeated runs observable and bounded when tools fail halfway through. Idempotency checks, explicit handoff state, and human review gates have mattered more for us than adding another model or another agent role.
The artifact output model is more useful than it looks at first. We use Claude in a multi-agent pipeline and discovered that structured artifact outputs reduce parse errors significantly compared to freeform text responses -- the model seems to reason differently when it knows the output will be rendered. Curious whether Anthropic sees similar quality improvements in tool-use tasks when the output has a concrete format constraint.
When I ask chatgpt to create a mermaid diagram for me it regularly will add new lines to certain labels that will break the parse. If you then feed the parse error back to it the second version is always correct And it seems to exactly know the problem. There are some other examples where it will almost always get it wrong the first time but right if nudged to correct itself. I wonder what the underlying cause is
Chat --> Notebook: Jupyter is so much more functional than slack for communicating real work product!
Next up: exporting or sharing selections from the chat as a document or interactive page. If they allow share with non-subscribers, subscriptions could hockey stick -- particularly if the document/page included prompts necessary to replicate (or modify and adapt).
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[ 2.7 ms ] story [ 54.9 ms ] thread[0]: https://github.com/vercel-labs/json-render
[1]: https://github.com/thesysdev/openui
Like a much prettier version of Mermaid.
Kudos, Anthropic. Geez, this is so nice.
Now I'm going to ask it to draw a diagram of a pelican riding a bicycle, why not?
Great for summarizing a multi-step process and quick to render with simple tools.
I usually use a lot of other tools for data analysis or write code with Claude code or another LLM to do data analysis and visualization.
article about the ChatGPT charts and graphs https://www.zdnet.com/article/how-to-use-chatgpt-to-make-cha...
(Literally nobody needs an image of a cake when asking for a cake recipe)
https://petergpt.github.io/bullshit-benchmark/viewer/index.v...
Next up: exporting or sharing selections from the chat as a document or interactive page. If they allow share with non-subscribers, subscriptions could hockey stick -- particularly if the document/page included prompts necessary to replicate (or modify and adapt).