thingsilearned

Really well put. I hope this article can finally put to bed this very frustrating dismissal.

Many debates about AI are complicated and hard to resolve, but this one isn’t: all of these stories are clearly false. Some evidence:

The AI experts who aren’t affiliated with big companies are just as concerned as the ones who are.

The AI company leaders giving these warnings were giving the exact same warnings before they had AI companies - and in many cases, before they worked in the industry, or before the industry existed.

If these corporate plots were real, they would be incredibly dumb.

We should point blumify also at all HN commenters, and I'd expect this to be an opinion that'd change in the next few years :)

Oh to be clear

> dct validate <file.yml>

Or any of the other commands that read the file will quickly fail with any syntax or SQL issues. It’s not just an open schema.

Yup! Big plans for deep semantic layer interaction

Thank you Eoin!

Exactly and that's why dbt Charts is a spec in YAML vs a python library. It ensures that the logic stays in SQL where its easy to test and trace.

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.

I've worked in data for 15 years and I've never heard this argument. I love it :)!

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.

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.

Yup, that's exactly the idea.

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.