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I&#x27;ll be here for the next 6 hours. As usual, there are lots of possible topics and I&#x27;ll be guided by whatever you&#x27;re interested in. Please remember that I can&#x27;t provide legal advice on specific cases because I won&#x27;t have access to all the facts. Please try to stick to a factual discussion in your questions and comments and I&#x27;ll try to do the same in my answers!Previous threads we&#x27;ve done: <a href="https:&#x2F;&#x2F;news.ycombinator.com&#x2F;submitted?id=proberts">https:&#x2F;&#x2F;news.ycombinator.com&#x2F;submitted?id=proberts</a>.

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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&#x27;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&#x27;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&#x27;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&#x2F;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&#x27;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: <a href="https:&#x2F;&#x2F;github.com&#x2F;bruin-data&#x2F;dac" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;bruin-data&#x2F;dac</a>I would love to hear what can be improved here, please let me know your thoughts.

Show HN: DAC – open-source dashboard as code tool for agents and humans

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Hi HN,I’ve been working on mljar-supervised (open-source AutoML for tabular data) for a few years. Recently I built a desktop app around it called MLJAR Studio.The idea is simple: you talk to your data in natural language, the AI generates Python code, executes it locally, and the whole conversation becomes a reproducible notebook (*.ipynb file). So instead of just chatting with data, you end up with something you can inspect, modify, and rerun.What MLJAR Studio does:- Sets up a local Python environment automatically, runs on Mac, Windows, and Linux- Installs missing packages during the conversation- Built-in AutoML for tabular data (classification, regression, multiclass)- Works with standard Python libraries (pandas, matplotlib, etc.)- Works with any data file: CSV, Excel, Stata, Parquet ...- Connects to PostgreSQL, MySQL, SQL Server, Snowflake, Databricks, and Supabase.For AI: use Ollama locally (zero data egress), bring your own OpenAI key, or use MLJAR AI add-on.I built this because I wanted something between Jupyter Notebook (flexible but manual) and AI tools that generate code but don’t preserve the workflow. Most tools I tried either hide too much or don’t give reproducible results and are cloud basedDemos:- 60-second demo: <a href="https:&#x2F;&#x2F;youtu.be&#x2F;BjxpZYRiY4c" rel="nofollow">https:&#x2F;&#x2F;youtu.be&#x2F;BjxpZYRiY4c</a>- Full 3-minute analysis: <a href="https:&#x2F;&#x2F;youtu.be&#x2F;1DHMMxaNJxI" rel="nofollow">https:&#x2F;&#x2F;youtu.be&#x2F;1DHMMxaNJxI</a>Pricing is $199 one-time, with a 7-day trial.Curious if this is useful for others doing real data work, or if I’m solving my own problem here.Happy to answer questions.

Show HN: Mljar Studio – local AI data analyst that saves analysis as notebooks