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Discussion (18 Comments)Read Original on HackerNews
"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/
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.
Sure the data is correct, but it's obviously way easier for me to read it as a properly rendered table. Then you want to sort, group, aggregate the data. Visualize it many different ways if you will :)
Who knows, with AI this may be the future. Visualizations were created to help us understand data. Florence Nightingale published them "to affect thro' the Eyes what we fail to convey to the public through their word-proof ears."
Often a formula better represents a situation. But I'm not sure how much impact they'd have in spreading that understanding outside of a formula-minded audience.
> 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.
>"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
https://github.com/nzoschke/gistdeck
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.
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.