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Gemini Executive Synthesis

DAC (Dashboard-As-Code) is an open-source tool and specification for defining dashboards using version-controllable text (YAML) and code (JSX). It includes a Go implementation, supports 10+ SQL backends, validation, and a built-in semantic layer.

Technical Positioning
Positions itself as a solution for automating dashboard creation for AI agents, addressing the limitations of UI-driven BI tools and the complexity of agents building dashboards from scratch. It emphasizes version control, reviewability, dynamic capabilities, static analysis, and standardized deployment.
SaaS Insight & Market Implications
The proliferation of AI agents exposes critical infrastructure gaps in traditional BI. Current UI-driven dashboard tools are incompatible with agentic workflows, hindering automation, reviewability, and standardization. Agents attempting to build dashboards from scratch face challenges like backend implementation, lack of semantic layers, and inconsistent visualization standards, essentially reinventing BI tools. DAC addresses this by treating dashboards as code, enabling version control, static analysis, and programmatic generation via YAML and JSX. This approach streamlines agent-driven data visualization, reduces token costs, and ensures consistency. The market demands "as-code" paradigms for all infrastructure, and data visualization is no exception. This shift is crucial for scalable, auditable, and automated data operations in an agent-first enterprise environment.
Proprietary Technical Taxonomy
open-source dashboard as code agents UI-driven React app backend query execution semantic layer

Raw Developer Origin & Technical Request

Source Icon Hacker News May 2, 2026
Show HN: DAC – open-source dashboard as code tool for agents and humans

Hi all, this is Burak.When agents became a reality one of the first things I wanted to do was to automate building dashboards. The first, and the most obvious, wall that I ran into was that a lot of the tools were just driven by UI. This meant that without the agents handling browser UIs and whatnot, it wasn't possible to have the agents do that. In addition, it would be impossible to review any of the changes the agent would make.The first instinct there is to get your agent to build a React app for the dashboard. This works beautifully for the happy path, but I quickly ran into other issues there:
- every dashboard turns out to be different
- have to implement a backend to centralize the query execution
- there is no centralized mechanism to control the rules and standards around visualizations
- there is no way to get a semantic layer working with the dashboards easilyIn the end, agents ended up reinventing the wheel for every new dashboard, even under the same project. Building a standardized, local project for these turned out to be building a BI tool from scratch.After trying these out, I asked myself: what if the dashboards were built for agents as the primary user?A product like that would need to have a couple of features:
- First of all, everything needs to be driven by version-controllable text. YAML is fine.
- Changes to the dashboards should be easy to review and understand by humans.
- Agents are great at writing code, it'd be great if this were driven by code to have dynamic stuff: JSX would be great.
- Static analysis being a first-class citizen: validate dashboards before deploying. Agents can check their work too.
- A standardized way of deploying these based on a couple of files in a folder: operationally very simple.
- Built-in semantic layer to standardize metrics.That's what I ended up building: dac (Dashboard-As-Code) is an open-source tool and a spec to define dashboards, well, as code. It contains an implementation in Go that can be deployed as a single binary anywhere. The dashboards are defined in YAML and JSX, YAML for static stuff, JSX for dynamic dashboards. You can run queries at load time to define conditional charts, generate tabs on the fly per customer, or list charts for each A/B test you are running.I built it in Go because I do love Go, and I think it is the greatest language at the moment to work with AI agents.dac runs as a single binary, you can get started with a `dac init` command and it'll automatically create some sample dashboards for you based on duckdb. It supports 10+ SQL backends, with more to come. It supports validation, custom themes and whatnot.You can see it here: github.com/bruin-data/dacI would love to hear what can be improved here, please let me know your thoughts.

Developer Debate & Comments

xixixao • May 2, 2026
Why do ppl think building something through yaml is ever a good idea??(I know why: for a platform it’s simpler to parse a yaml than to run code, but it’s almost never a good idea for anything that needs to scale in complexity)
Huzzi • May 2, 2026
[flagged]
MSaiRam10 • May 2, 2026
Semantic layer + validation is the interesting part imo, everything else is table stakes. would lead with that
hasyimibhar • May 2, 2026
Why not use Vega-Lite[0]? It’s my go-to data viz DSL with Claude.[0] https://vega.github.io/vega-lite/
MuffinFlavored • May 2, 2026
Might want to add how this compares to other products in the space.Some that come to mind that are potentially tangentially related/similar:https://github.com/evidence-dev/evidence
SomeHacker44 • May 2, 2026
The blurb about this is repeated several times but it is unclear to me what it actually does.
crefiz • May 2, 2026
I reckon this is a simplification of existing BIAC tools (eg, https://github.com/lightdash/lightdash)
ajaystream • May 2, 2026
[dead]
5-0 • May 2, 2026
DaC might be more distinguishable from DAC, although the context obviously also helps readers telling them apart.Yours sincerely, came here for another DAC
lexh • May 2, 2026
Consider adding that snazzy gif in the README to the docs landing page. I went straight to the docs and then hunted for a screenshot to no avail.

Frequently Asked Questions

Market intelligence mapped to DAC (Dashboard-As-Code) is an open-source tool and specification for defining dashboards using version-controllable text (YAML) and code (JSX). It includes a Go implementation, supports 10+ SQL backends, validation, and a built-in semantic layer..

How is DAC (Dashboard-As-Code) is an open-source tool and specification for defining dashboards using version-controllable text (YAML) and code (JSX). It includes a Go implementation, supports 10+ SQL backends, validation, and a built-in semantic layer. positioned in the market?
Based on our AI analysis of the original developer request, its primary technical positioning is: Positions itself as a solution for automating dashboard creation for AI agents, addressing the limitations of UI-driven BI tools and the complexity of agents building dashboards from scratch. It emphasizes version control, reviewability, dynamic capabilities, static analysis, and standardized deployment.
What is the general sentiment around DAC (Dashboard-As-Code) is an open-source tool and specification for defining dashboards using version-controllable text (YAML) and code (JSX). It includes a Go implementation, supports 10+ SQL backends, validation, and a built-in semantic layer.?
Yes, we have tracked 15 direct responses and active debates regarding this specific topic originating from Hacker News.
What architecture is tied to DAC (Dashboard-As-Code) is an open-source tool and specification for defining dashboards using version-controllable text (YAML) and code (JSX). It includes a Go implementation, supports 10+ SQL backends, validation, and a built-in semantic layer.?
Our proprietary extraction maps DAC (Dashboard-As-Code) is an open-source tool and specification for defining dashboards using version-controllable text (YAML) and code (JSX). It includes a Go implementation, supports 10+ SQL backends, validation, and a built-in semantic layer. to adjacent architectural concepts including open-source, dashboard as code, agents, UI-driven.
Are there startups building around DAC (Dashboard-As-Code) is an open-source tool and specification for defining dashboards using version-controllable text (YAML) and code (JSX). It includes a Go implementation, supports 10+ SQL backends, validation, and a built-in semantic layer.?
Yes, market intelligence reveals commercial overlap. A product named 'theORQL' focuses directly on this: Cursor for frontend. Build and debug in Chrome and VS Code.

Engagement Signals

66
Upvotes
15
Comments

Cross-Market Term Frequency

Quantifies the cross-market adoption of foundational terms like agents and Go by tracking occurrence frequency across active SaaS architectures and enterprise developer debates.