Malloy is an alternative to dbt, they have a similar product called Malloyyo [1], and Publisher [3], you can also the chat with HN data demo they have [4].
DBTCharts says you can serve charts locally, but it seems they want you to use their hosting service in production. Whereas, Malloyyo / Publisher are free to use anywhere. If you like college football checkout how I visualize drive data in [2], made with Malloyyo.
"Unbundling BI" is absolutely where things are headed now that more people have agents, coding agents, agent computers to help with work.
I'm seeing this all up and down knowledge work tools. I've started treating email as a BI problem -- ETL it from Gmail and and create many different views into it and reports from it.
>"Unbundling BI" is absolutely where things are headed now that more people have agents, coding agents, agent computers to help with work.
Agreed. Coupled with bento[0] for slide decks, I think this type of project is a very welcome development, helping us move away from walled gardens and proprietary software suites.
Hi HN, I'm Dave the founder of Chartio (YC'10 now Atlassian Analytics), announcing today dbt Charts, an open source YAML dialect and tool for declaring and rendering dashboards.
When making dashboards with claude or other agents, a lot of free-form artifacts are created that makes it hard to audit and scale. dbt Charts is a simple YAML dialect that declares and renders a chart (think markdown but for dashboards). Along with dbt its Apache 2.0 and launched today.
We hope this language + AI help make the BI space more open with dashboards as concise auditable code. Would love any thoughts and feedback.
This is obviously a space we are very much interested in, so it is definitely nice to see approaches that attack the same problem. I haven't played with dbtcharts in depth yet but it looks very similar to DaC in principle, and also in the actual spec. I believe the industry definitely needs solutions like this to help get out of the legacy BI tools as Bİ is one of the biggest bottlenecks for AI adoption in large orgs.
Excited to see further competition in the space, nice launch!
Neat, but… I’ve built up skills for my agents to just generate SVG charts and embed them in the chat timelines. A library helps, sure, but you can get surprisingly good results that cover 80% of the timeline and bar chart visualizations without any libraries, and that goes for diagrams as well (mine generate .drawio XML)
Interesting, but I'm not sure how it's different from Observable Framework ( https://observablehq.github.io/framework/ ) for dashboards as code. I like that one because it's Markdown and JS instead of a language defined in YAML + templates
The use case that immediately came to mind for me was to use dbt charts as a rendering method for an MCP App.
You don't really want to give a LLM client code execution environment like Observable Framework does. dbt charts appear to validate the entire yaml input including the SQL being sent to the data source.
So I'm getting: a data vis layer that can be dynamically defined and rendered via LLM/MCP client without the headache of sandboxing a JS Runtime environment.
I like the observable framework too - very flexible, you can nail very specific visualization details by dropping down to JS/html.
Maybe this is targeted to pure SQL folks that can get lost in UI details.
PS: one issue that I have with observable framework is authentication and authorization , it requires some system to be built on top of it to handle authn.
It's neat but pretends to be more innovative than it actually is. BI has already been decoupled from everything else. People use AI to generate Excel and Power BI reports. YAML/XML/JSON - that doesn't matter. AI can generate whatever you instruct it to do.
So yes, a nice and logical development of dbt, but hardly as innovative as the blog post wants to sound. Nevertheless, I think it's a good idea that will be popular in certain circles. Hiring a professional data designer is a good idea - data visualization is very easy to get wrong.
I think I agree, but I'm not sure where Dave is claiming this is a paradigm shift in data visualization?
One of the key claims is actually that it's based on _old_ established visualization patterns from before BI tools were popular, and therefore results in consistently nice charts. The other key claim is just that it's simple and maintainable.
Sometimes tools & products can be useful as just well executed points in the known design space. You can already do all this with AI, but it's perhaps a little bit less nice and less maintainable.
AI can definitely generate whatever you tell it to do, but what to do with that artifact afterwards? If you want it to be reusable it needs to be a well-defined structured protocol/language. What good is a YAML describing a chart if all I can do is to give it back to the agent and say "read this and create a chart from it", the result will be vastly different from the chart you got the first time.
very cool! i'd been thinking about this idea and found vega-lite to be an interesting take as well [0].
i'll compare and look at folding this into setoku for app generation [1]. right now apps are just html blobs your claude authors + a mechanism for populating them with live data. definitely hard to audit but very flexible for operators to claude together internal apps. anyway, the charts look decent but really depend on the model that's making them and don't really follow any sort of style guide (example: https://demo.setoku.com/apps/a7a1240ae0bc202c5eefa1cc). Your lib could bring some consistency and make global styling possible.
Let's say (because it's true) I've been programming for close to 40 years but in completely different domains, such that the concept of "dashboard" means almost nothing to me. Could someone explain what this project actually is, or what "BI" is or why I would want it? I mean, I assume that "BI" is the thing that "PowerBI" implements, but other than that I couldn't tell you anything.
Say you have an inventory - list of cloud database instances from all engineering teams in the org.
Is this list growing over time or shrinking or remains the mostly the same?
If the list is growing, is it growing evenly across all teams , or some teams stand out? Can I narrow down to a few teams and compare?
What about overall health of the inventory - how many instances list security or billing optimization findings, how many instances are on the version that would be out of support soon and would need to be upgraded?
A dashboard can answer all those questions immediately and make issues visible, as opposed to a long list in a form of a spreadsheet.
I'm not going to pretend to be an expert at it, but BI tools let you interrogate your data. This can be used for endless use cases for basically any department in an organization. But for instance, if you're a sales leader you might have some questions about all of the sales and marketing data in your company to focus your team's efforts for the next quarter. When you figure out which questions are actually returning useful findings, you then can take a set of these and build a dashboard which lets you easily re-examine those particular questions and their results with always up-to-date (or up-to-date-ish) data and can easily share it with the team.
well BI is I believe business intelligence, which is sort of like military intelligence, the set of practices and tools that have evolved around the need to present data to business people in such a way that they can make sensible decisions regarding their business.
on edit: So a dashboard is just one of those things were you see all the things you can do or interact with assembled together and from which you can navigate into any particular tool or view of data. Probably there would be meaningful data views on top. Like Number of Purchases versus people who started a purchase. And you could then drill down into the data that this chart represented to see how long purchases took, repeat customers, when people leave process or purchase etc. etc.
The dashboard is the entry point for how you will navigate your business data.
Congrats on the launch! As someone who as led BI teams for many years, this looks great.. but - and you know this too, I’m sure - visualizations are only a small piece of the BI tool value prop. There’s governance, access controls, interactivity, and connectivity to semantic layers. The examples here show raw SQL but in reality we’d want something more abstract - names of dimensions and measures would probably be part of it.
So, are those other pieces going to be part of dbtTran Cloud?
hey -- Anders here, I've been helping the dbt Charts team. I loved this list you've enumerated here
> There’s governance, access controls, interactivity, and connectivity to semantic layers
I'd add to that list as well: KPIs, dropdowns, text boxes, theming, shared data sources.
If you're familiar with Vega-Lite [1], I like to explain dbt Charts as the Vega-Lite but for BI, in that it is a language that specifies all of the components of BI.
> are those other pieces going to be part of dbtTran Cloud?
Certainly there are some features that are more conducive to being offered as a managed service. However, we've built this enterprise BI tool backwards in that we've started less with commercialization in mind, but rather laying the groundwork in the language and OSS project, so that we need not reinvent the wheel over and over again.
I'm hand-waving here about a future that doens't yet exist. But in theory dbt Charts could be extended in the front end to allow for not just SQL or SL metric support, but any query language. Same for the backend, we dbt Charts can render to png and html today, but other formats are also possible!
This looks really slick! I was building something similar, so visuals are re-useable artifacts that external services know how to render, where we ask agents in whatever the interface is, slack, team's other agentic harnesses etc. and the agents receive the spec from the service, in this case dbt charts. if there was a unified spec that was agreed upon these third-party harnesses and apps would all speak the same charting language which would be super cool. I haven't read through it in detail but how does this differ from something like https://github.com/vega/vega-lite
We actually use vega-lite under the hood. vega-lite is vast tool set to build charts with static data. dbt Charts adds a whole layer on top of that, which is dashboards with repeatable SQL queries. Also dbt Charts comes with a lot of opinionated chart style ideas. So vega-lite is a toolbox for chart assembly, and we use that toolbox to build dashboards.
On the standardized charting language, that would be the dream. All agents giving the same spec when they want to build a chart or dashboard, and then having different renderers of that. That is a steep goal. Such a language would need to be simple but still extremely versatile. I'm not sure that combination exists yet. Our language is simple and fairly versatile, but not as versatile as vega-lite itself, or JavaScript code even. Maybe we can evolve in that direction though
repeat after me: yaml is not a programming language. (boo, hn strips emojis, i should know that)
very disappointed that their domain specific language is just yaml. conditionals and variable binding become insane war crimes when yaml comes to town. you have a friggin llm, do better
and when we computer folks see the word language its implied that its a computing on computers language not a friggin data format.
edit: the yaml to avoid complexity that should live in the sql side can back fire, some folks I was working with last fall were evaluating if a yaml based OLAP tool would work for them, and there were some pretty gnarly gotchas from a yaml based approach. Secondarily, theres a real case to be made that having the data linkages not visible in the charting layer means that groups of related plots with different axes wont have the right data linkage without forcing a lot more ETL for what should be a quick plot if the data already fits in memory.
Fair hit on "language". I keep forgetting YAML stands for "YAML Ain't Markup Language". What we built is a declarative spec written in YAML, and that's deliberate. Think of it as HTML for boards rather than a programming language. The tradeoff is readability and structure versus flexibility.
That lack of flexibility is actually a feature for one of the bigger problems we're solving: lineage, or knowing where a number came from. If the chart layer can't transform data, the logic stays in SQL, where you can version, test and audit it.
It's also a choice about who this is for. The data community already works in SQL, Jinja and YAML every day, so there's no new language to learn.
Man, I didn't think anyone would out yaml helm, but here we are not just templating invisibly scoped files (yaml), but also SQL within them?
Looking forward to the yaml confusion when helm/dbt both see their default "charts/" directory in the same repo, or the dbt chart is set as a config map so it can be updated without rolling a new version of the full app... helm_argo is already a nightmare
rant aside, this does look super useful, and I do have CUE to help with the yamhell, but I may still prefer js/ts options so I can dynamically change the chart (like user clicking a dropdown for a different set of data). Sounds cool until the "dynamic" part of the chart shows it's limitations in crafting your ideal UX
I'll definitely be taking this for a spin, gets at that unbundling and "I need a quick chart" situations
A beacon of hope between all the vibe coded JS slop charts and dashboards! I love that this creates artifacts that are readable, maintainable and reproduce the same dashboard consistently (deterministically!), just with fresh data.
Everyone wants to come up with a clever One Spec to Rule Them All for generative UI. My bet is that the bitter lesson still bites. Models will continue getting faster and more error free at single-shot writing things from scratch with primitive libraries, and the flexibility that allows will make all of this for naught.
So this is a fairly domain specific (dashboards) spec and we intentionally avoid getting too generic.
The raw HTML/SVG or base libraries approach may well win out, but it does make it quite hard or impossible for humans to follow along and verify for instance where the numbers on a chart came from.
I think in a future where AI's doing all that verifying (or we just trust it), the AI might still prefer to use a DSL like ours because the abstraction maintains consistency, lowers maintenance, and saves a lot of tokens.
But the most helpful bits of a structured DSL are for sure still for humans. The structured format ensures things are readable and testable. Ours also enables a generative UI, which for now at least is still a much faster way to make visual edits while working with an AI, vs always through it.
I'm an idiot really... based on the title I though it was a chart solution to establish a discussion, with... actual people. I was starting to imagine a shared pointer, a way to interact with a notebook, a la Observable, with the data and a staging environment for each participant, etc. Nope, it's about agents.
Malloy is an alternative to dbt, they have a similar product called Malloyyo [1], and Publisher [3], you can also the chat with HN data demo they have [4].
DBTCharts says you can serve charts locally, but it seems they want you to use their hosting service in production. Whereas, Malloyyo / Publisher are free to use anywhere. If you like college football checkout how I visualize drive data in [2], made with Malloyyo.
[1] - https://github.com/malloydata/malloyyo [2] - https://mrtimo.github.io/cfb-games/games-2026.html?%24SEASON... [3] - https://github.com/malloydata/publisher [4] - https://community.credibledata.com/hackernews
This looks great.
"Unbundling BI" is absolutely where things are headed now that more people have agents, coding agents, agent computers to help with work.
I'm seeing this all up and down knowledge work tools. I've started treating email as a BI problem -- ETL it from Gmail and and create many different views into it and reports from it.
I just wrote up some thoughts on that here: https://housecat.com/blog/making-gmail-data-fast-for-humans-...
A natural followup is how to better visualize this data in chat. The DBT table component looks like it could help https://docs.dbtcharts.com/charts/tables/
Wow https://github.com/dbt-labs/dbt-charts has a great chat user / agent coding experience:
> Make charts of this with dbt Charts. Start with: uv tool install dbt-charts && dct skills intro
>
> build a dashboard of my hiring inboxes: list of candidate name / email / locale, application quality, response age
The one-shot dashboard is surprisingly good.
>This looks great.
>"Unbundling BI" is absolutely where things are headed now that more people have agents, coding agents, agent computers to help with work.
Agreed. Coupled with bento[0] for slide decks, I think this type of project is a very welcome development, helping us move away from walled gardens and proprietary software suites.
[0] https://github.com/nyblnet/bento
Why visualize data? Graphs and charts always felt like the "show your work" in math class. It is forced synesthesia.
The rocket either lands or doesn't, safely, as expected. Green and Red lights are abstracted data plots.
The context should be the focus.
"Rocket must land at less than 0.2/mps" and pump the data into that context filter, more - reduce velocity, less - green light.
There is also an interesting approach recently published: VACP: Visual Analytics Context Protocol https://vacp.dev/
Hi HN, I'm Dave the founder of Chartio (YC'10 now Atlassian Analytics), announcing today dbt Charts, an open source YAML dialect and tool for declaring and rendering dashboards.
When making dashboards with claude or other agents, a lot of free-form artifacts are created that makes it hard to audit and scale. dbt Charts is a simple YAML dialect that declares and renders a chart (think markdown but for dashboards). Along with dbt its Apache 2.0 and launched today.
We hope this language + AI help make the BI space more open with dashboards as concise auditable code. Would love any thoughts and feedback.
Congrats on the launch Dave! I was wondering who was going to launch an attempt at industry standard here. Hope it gains widespread adoption.
Hi there, Burak here, creator of DaC and Bruin: https://github.com/bruin-data/dac
This is obviously a space we are very much interested in, so it is definitely nice to see approaches that attack the same problem. I haven't played with dbtcharts in depth yet but it looks very similar to DaC in principle, and also in the actual spec. I believe the industry definitely needs solutions like this to help get out of the legacy BI tools as Bİ is one of the biggest bottlenecks for AI adoption in large orgs.
Excited to see further competition in the space, nice launch!
Neat, but… I’ve built up skills for my agents to just generate SVG charts and embed them in the chat timelines. A library helps, sure, but you can get surprisingly good results that cover 80% of the timeline and bar chart visualizations without any libraries, and that goes for diagrams as well (mine generate .drawio XML)
Interesting, but I'm not sure how it's different from Observable Framework ( https://observablehq.github.io/framework/ ) for dashboards as code. I like that one because it's Markdown and JS instead of a language defined in YAML + templates
The use case that immediately came to mind for me was to use dbt charts as a rendering method for an MCP App.
You don't really want to give a LLM client code execution environment like Observable Framework does. dbt charts appear to validate the entire yaml input including the SQL being sent to the data source.
So I'm getting: a data vis layer that can be dynamically defined and rendered via LLM/MCP client without the headache of sandboxing a JS Runtime environment.
I like the observable framework too - very flexible, you can nail very specific visualization details by dropping down to JS/html.
Maybe this is targeted to pure SQL folks that can get lost in UI details.
PS: one issue that I have with observable framework is authentication and authorization , it requires some system to be built on top of it to handle authn.
It's neat but pretends to be more innovative than it actually is. BI has already been decoupled from everything else. People use AI to generate Excel and Power BI reports. YAML/XML/JSON - that doesn't matter. AI can generate whatever you instruct it to do.
So yes, a nice and logical development of dbt, but hardly as innovative as the blog post wants to sound. Nevertheless, I think it's a good idea that will be popular in certain circles. Hiring a professional data designer is a good idea - data visualization is very easy to get wrong.
I think I agree, but I'm not sure where Dave is claiming this is a paradigm shift in data visualization?
One of the key claims is actually that it's based on _old_ established visualization patterns from before BI tools were popular, and therefore results in consistently nice charts. The other key claim is just that it's simple and maintainable.
Sometimes tools & products can be useful as just well executed points in the known design space. You can already do all this with AI, but it's perhaps a little bit less nice and less maintainable.
[I work at dbt/Fivetran]
AI can definitely generate whatever you tell it to do, but what to do with that artifact afterwards? If you want it to be reusable it needs to be a well-defined structured protocol/language. What good is a YAML describing a chart if all I can do is to give it back to the agent and say "read this and create a chart from it", the result will be vastly different from the chart you got the first time.
very cool! i'd been thinking about this idea and found vega-lite to be an interesting take as well [0].
i'll compare and look at folding this into setoku for app generation [1]. right now apps are just html blobs your claude authors + a mechanism for populating them with live data. definitely hard to audit but very flexible for operators to claude together internal apps. anyway, the charts look decent but really depend on the model that's making them and don't really follow any sort of style guide (example: https://demo.setoku.com/apps/a7a1240ae0bc202c5eefa1cc). Your lib could bring some consistency and make global styling possible.
[0]: https://vega.github.io/vega-lite/
[1]: https://setoku.com
Let's say (because it's true) I've been programming for close to 40 years but in completely different domains, such that the concept of "dashboard" means almost nothing to me. Could someone explain what this project actually is, or what "BI" is or why I would want it? I mean, I assume that "BI" is the thing that "PowerBI" implements, but other than that I couldn't tell you anything.
Say you have an inventory - list of cloud database instances from all engineering teams in the org.
Is this list growing over time or shrinking or remains the mostly the same?
If the list is growing, is it growing evenly across all teams , or some teams stand out? Can I narrow down to a few teams and compare?
What about overall health of the inventory - how many instances list security or billing optimization findings, how many instances are on the version that would be out of support soon and would need to be upgraded?
A dashboard can answer all those questions immediately and make issues visible, as opposed to a long list in a form of a spreadsheet.
I'm not going to pretend to be an expert at it, but BI tools let you interrogate your data. This can be used for endless use cases for basically any department in an organization. But for instance, if you're a sales leader you might have some questions about all of the sales and marketing data in your company to focus your team's efforts for the next quarter. When you figure out which questions are actually returning useful findings, you then can take a set of these and build a dashboard which lets you easily re-examine those particular questions and their results with always up-to-date (or up-to-date-ish) data and can easily share it with the team.
well BI is I believe business intelligence, which is sort of like military intelligence, the set of practices and tools that have evolved around the need to present data to business people in such a way that they can make sensible decisions regarding their business.
on edit: So a dashboard is just one of those things were you see all the things you can do or interact with assembled together and from which you can navigate into any particular tool or view of data. Probably there would be meaningful data views on top. Like Number of Purchases versus people who started a purchase. And you could then drill down into the data that this chart represented to see how long purchases took, repeat customers, when people leave process or purchase etc. etc.
The dashboard is the entry point for how you will navigate your business data.
You can specify Apache Superset Dashboards, Charts, ... using yaml, for example:
https://github.com/preset-io/headless-bi-blog-post-examples/...
But this is mostly just a dump of internal Superset data structures.
Congrats on the launch! As someone who as led BI teams for many years, this looks great.. but - and you know this too, I’m sure - visualizations are only a small piece of the BI tool value prop. There’s governance, access controls, interactivity, and connectivity to semantic layers. The examples here show raw SQL but in reality we’d want something more abstract - names of dimensions and measures would probably be part of it.
So, are those other pieces going to be part of dbtTran Cloud?
hey -- Anders here, I've been helping the dbt Charts team. I loved this list you've enumerated here
> There’s governance, access controls, interactivity, and connectivity to semantic layers
I'd add to that list as well: KPIs, dropdowns, text boxes, theming, shared data sources.
If you're familiar with Vega-Lite [1], I like to explain dbt Charts as the Vega-Lite but for BI, in that it is a language that specifies all of the components of BI.
> are those other pieces going to be part of dbtTran Cloud?
Certainly there are some features that are more conducive to being offered as a managed service. However, we've built this enterprise BI tool backwards in that we've started less with commercialization in mind, but rather laying the groundwork in the language and OSS project, so that we need not reinvent the wheel over and over again.
I'm hand-waving here about a future that doens't yet exist. But in theory dbt Charts could be extended in the front end to allow for not just SQL or SL metric support, but any query language. Same for the backend, we dbt Charts can render to png and html today, but other formats are also possible!
[1]: https://vega.github.io/vega-lite/
Yup! Big plans for deep semantic layer interaction
Okay nice chart, but can I click in and filter into the data? Visualisations are not what a BI tool does; it's just the tip of the iceberg.
This looks really slick! I was building something similar, so visuals are re-useable artifacts that external services know how to render, where we ask agents in whatever the interface is, slack, team's other agentic harnesses etc. and the agents receive the spec from the service, in this case dbt charts. if there was a unified spec that was agreed upon these third-party harnesses and apps would all speak the same charting language which would be super cool. I haven't read through it in detail but how does this differ from something like https://github.com/vega/vega-lite
We actually use vega-lite under the hood. vega-lite is vast tool set to build charts with static data. dbt Charts adds a whole layer on top of that, which is dashboards with repeatable SQL queries. Also dbt Charts comes with a lot of opinionated chart style ideas. So vega-lite is a toolbox for chart assembly, and we use that toolbox to build dashboards.
On the standardized charting language, that would be the dream. All agents giving the same spec when they want to build a chart or dashboard, and then having different renderers of that. That is a steep goal. Such a language would need to be simple but still extremely versatile. I'm not sure that combination exists yet. Our language is simple and fairly versatile, but not as versatile as vega-lite itself, or JavaScript code even. Maybe we can evolve in that direction though
Nice to see something built for AI that's supporting both openness and interpretablilty.
dbt/Fivetran announces that they will unbundle BI by bundling it with their ELT solution. Ironic to say the least.
repeat after me: yaml is not a programming language. (boo, hn strips emojis, i should know that)
very disappointed that their domain specific language is just yaml. conditionals and variable binding become insane war crimes when yaml comes to town. you have a friggin llm, do better
and when we computer folks see the word language its implied that its a computing on computers language not a friggin data format.
edit: the yaml to avoid complexity that should live in the sql side can back fire, some folks I was working with last fall were evaluating if a yaml based OLAP tool would work for them, and there were some pretty gnarly gotchas from a yaml based approach. Secondarily, theres a real case to be made that having the data linkages not visible in the charting layer means that groups of related plots with different axes wont have the right data linkage without forcing a lot more ETL for what should be a quick plot if the data already fits in memory.
what would you have chosen for your DSL? Ideally something not from scratch
Fair hit on "language". I keep forgetting YAML stands for "YAML Ain't Markup Language". What we built is a declarative spec written in YAML, and that's deliberate. Think of it as HTML for boards rather than a programming language. The tradeoff is readability and structure versus flexibility.
That lack of flexibility is actually a feature for one of the bigger problems we're solving: lineage, or knowing where a number came from. If the chart layer can't transform data, the logic stays in SQL, where you can version, test and audit it.
It's also a choice about who this is for. The data community already works in SQL, Jinja and YAML every day, so there's no new language to learn.
Is this associated with dbtlabs?
Yes this is a dbt product - will be in the dbt cli soon, we shipped it initially as a separate tool while it’s in preview.
How is this distinct from something like Mosaic (https://idl.uw.edu/mosaic/spec/)?
Man, I didn't think anyone would out yaml helm, but here we are not just templating invisibly scoped files (yaml), but also SQL within them?
Looking forward to the yaml confusion when helm/dbt both see their default "charts/" directory in the same repo, or the dbt chart is set as a config map so it can be updated without rolling a new version of the full app... helm_argo is already a nightmare
rant aside, this does look super useful, and I do have CUE to help with the yamhell, but I may still prefer js/ts options so I can dynamically change the chart (like user clicking a dropdown for a different set of data). Sounds cool until the "dynamic" part of the chart shows it's limitations in crafting your ideal UX
I'll definitely be taking this for a spin, gets at that unbundling and "I need a quick chart" situations
how does this compare w/ evidence (https://evidence.dev/)?
All docs for evidence.dev are AI-hallucinated …YMMV
A beacon of hope between all the vibe coded JS slop charts and dashboards! I love that this creates artifacts that are readable, maintainable and reproduce the same dashboard consistently (deterministically!), just with fresh data.
Yup, that's exactly the idea.
This looks great can’t wait to try it out
Everyone wants to come up with a clever One Spec to Rule Them All for generative UI. My bet is that the bitter lesson still bites. Models will continue getting faster and more error free at single-shot writing things from scratch with primitive libraries, and the flexibility that allows will make all of this for naught.
So this is a fairly domain specific (dashboards) spec and we intentionally avoid getting too generic.
The raw HTML/SVG or base libraries approach may well win out, but it does make it quite hard or impossible for humans to follow along and verify for instance where the numbers on a chart came from.
I think in a future where AI's doing all that verifying (or we just trust it), the AI might still prefer to use a DSL like ours because the abstraction maintains consistency, lowers maintenance, and saves a lot of tokens.
But the most helpful bits of a structured DSL are for sure still for humans. The structured format ensures things are readable and testable. Ours also enables a generative UI, which for now at least is still a much faster way to make visual edits while working with an AI, vs always through it.
thats intresting
I'm an idiot really... based on the title I though it was a chart solution to establish a discussion, with... actual people. I was starting to imagine a shared pointer, a way to interact with a notebook, a la Observable, with the data and a staging environment for each participant, etc. Nope, it's about agents.
Gosh I'm so "behind" it's painful. /s