Show HN: DAC – open-source dashboard as code tool for agents and humans
TL;DR Highlight
DAC builds open-source dashboards defined as code—using YAML and TSX—and allows AI agents to automatically generate and modify them.
Who Should Read
Data engineers and backend developers seeking to automate dashboard creation with AI agents or integrate dashboards into Git-based review workflows.
Core Mechanics
- DAC is an open-source tool for defining dashboards as YAML or TSX (TypeScript JSX) files, enabling version control, review, and automation.
- An embedded AI agent (based on Codex) modifies dashboards in real-time via chat, and outputs YAML/TSX files for human review via pull requests.
- DAC includes a built-in Semantic Layer: defining metrics and dimensions once in the `semantic/` directory allows widgets to reference them, and DAC automatically generates SQL.
- DAC supports major databases including Postgres, MySQL, Snowflake, BigQuery, Redshift, and Databricks, executing queries via the Bruin CLI (which must be installed).
- Dashboards can be built using TSX, allowing JSX-like component writing with support for dynamic charts, tabs, loops, and conditionals—offering more complex layout options than simple YAML.
- Installation is possible with a single-line curl script, offering stable and edge (main branch latest) versions.
- DAC is open-source under the AGPL-3.0 license, with a pre-defined file structure for AI agent integration including `.agents/skills`, `AGENTS.md`, and `CLAUDE.md`.
Evidence
- "Feedback suggested DAC’s framing as a ‘dashboard for agents’ differentiates it from existing dashboard-as-code tools, as standardizing and making agent-generated results reviewable is a key challenge. Concerns were raised about YAML scalability, with some finding 1000+ line YAML files difficult to manage, while others defended YAML with proper tooling and conventions. The Semantic Layer and validation features were highlighted as key differentiators, with suggestions to prioritize them in documentation. Users requested comparisons to similar open-source tools like Evidence, Lightdash, and Vega-Lite, and shared experiences of Claude working well with Vega-Lite."
How to Apply
- "If you want to automate dashboard generation with AI agents but need team review, DAC enables agents to generate YAML/TSX files that can be reviewed and merged via Git pull requests. Teams using Snowflake or BigQuery with duplicated SQL for metrics can define them once in DAC’s Semantic Layer (`semantic/`) to reduce redundancy. If you already use Bruin for data pipelines, DAC can quickly leverage existing Bruin connections. For complex dashboard layouts, switch to TSX to manage them as React components with loops, conditionals, and dynamic tabs."
Code Example
# YAML example
name: Sales Overview
connection: warehouse
rows:
- widgets:
- name: Revenue
type: metric
sql: SELECT SUM(amount) AS value FROM sales
column: value
prefix: "$"
col: 4
---
// TSX example
export default (
<Dashboard name="Simple Dashboard" connection="my_db">
<Row>
<Metric
name="Total Revenue"
col={4}
sql="SELECT SUM(amount) AS value FROM sales"
column="value"
prefix="$"
format="number"
/>
</Row>
</Dashboard>
)
---
# Install (stable)
curl -fsSL https://raw.githubusercontent.com/bruin-data/dac/main/install.sh | bash
# Install (edge, latest main branch)
curl -fsSL https://raw.githubusercontent.com/bruin-data/dac/main/install.sh | bash -s -- --channel edgeTerminology
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