Nice, I recently did a similar but much simpler thing and open-sourced it under MIT, maybe some bits and pieced will be useful https://github.com/eatmydata-org/eatmydata
For example, MIT-licensed sqlite vector search extension.
Overall, I have a orchestrator - sql coder - js coder - dashboards, all without backend, running locally in the browser. It's mostly tested on small analysis and question answering with Gemini Flash Lite, and the overall target was speed from question to answer, including data sharing and waiting.
Fast response. I can upload Excel/csv and iterate under 10 seconds from question to result. Doing same thing in Claude with 10x less data takes 5 minutes.
I hear you on fast responses. One of the frustrations I've had using BI / data tools in the past was not being able to get local performance... which led to me exporting data to spreadsheets or local code. We're taking this to heart for BitBoard as well.
I do exactly this (and more since my role is much broader and so is my approach) as a fractional head of product, data, and operations for multiple companies all in healthcare (fast growing self-funded to series D/IPOing soon). I saw your initial launch and felt validated by you all working on it, and now I’m further validated by the pivot. I have more work than I can handle, so I’m happy to share tips. You can find me via a bit of googling my HN handle or just adding a dot com to the end.
First, I love this concept and I think your demo is great! Collaboration with existing harnesses makes a ton of sense. Just had a conversation with some folks in the non-tech world raving about using Claude.
A few questions:
- How do you think about competing with ChatGPT Canvas or Anthropic's artifacts, when these are shareable, native experiences in their products where users already work?
- Is a "dashboard" limited to analytics or are you trying to expand it to include written reports?
Since teams are connecting MCPs like Granola, Slack, I imagine BitBoard would facilitate sharing demos, PRDs/briefs, or customer reports. This seems like a natural expansion and trivial functionally, so I'm wondering if that's part of the sell now or something you're looking at expanding into as you grow.
Thanks! Non-OP BitBoard cofounder here. Would love to hear your thoughts when you get a chance to check it out.
> How do you think about competing with ChatGPT Canvas or Anthropic's artifacts, when these are shareable, native experiences in their products where users already work?
The flexibility is amazing for static content and playing around with visuals, the experience is just more like a whiteboard than a dashboard. It's hard to do both well in the same place. For reporting I want live connections, consistent logic, the ability to trace provenance, and a more opinionated starting point for the UI.
We started with an extremely flexible surface but there are just a ton of things you don't want to leave up to the agent to implement and we gradually layered those in. It's no fun having to prompt the agent to expose a "view source" affordance, "run" button, or working data labels. But it's a lot of fun building whatever visualization you want and generating a dashboard without a billion clicks in some SQL-abstraction UI.
> Is a "dashboard" limited to analytics or are you trying to expand it to include written reports?
We weakly support written reports today (technically possible with markdown blocks in dashboards for commentary) but will do more to support them in the future for exactly the reasons you called out.
We actually built a more notebook-like artifact for this but cut it to focus on dashboards since they seemed to be a bigger pain point for users. One-off reports can be hit or miss with a chat or coding agent today but static reporting is at least supported with some effort. Live reporting with connection infrastructure, provenance, etc. is much harder to pull together.
Looks cool! It's a lot of work to get a full data stack set up and people are losing interest in stitching the pieces (ETL, warehouse, BI) together.
> Agents made bad inferences because they had no context on the business
We've been working on this since before the chatgpt launch.
We started with a semantic layer since there were already good open source options and LLMs at the time were good at writing the JSON (remember function calling?) to run a semantic query.
But as LLMs have gotten smarter and people wanted to do more data work in agents, we found we needed something more flexible, so we built an "Ontology" that lets you store all the terms you use in your company and connect them to the data points (e.g. tables, columns, metrics) that matter.
Highly rec going after a specific vertical - healthcare might be the right spot given your experience. Why did you use DuckDB instead of CockroachDB/Snowflake?
Our outreach is vertical-specific, and healthcare is indeed on the list! But what we learned working a vertical is that the primitives underneath (shared queries, permissions, caching, refresh semantics) repeat across industries.
We use DuckDB internally because we like its ergonomics - it's flexible, runs well in memory, manages a lot of file structures under the hood, but we do work with Snowflake (and Databricks and other warehouses) as well.
For example, MIT-licensed sqlite vector search extension.
Overall, I have a orchestrator - sql coder - js coder - dashboards, all without backend, running locally in the browser. It's mostly tested on small analysis and question answering with Gemini Flash Lite, and the overall target was speed from question to answer, including data sharing and waiting.
A few questions:
- How do you think about competing with ChatGPT Canvas or Anthropic's artifacts, when these are shareable, native experiences in their products where users already work?
- Is a "dashboard" limited to analytics or are you trying to expand it to include written reports?
Since teams are connecting MCPs like Granola, Slack, I imagine BitBoard would facilitate sharing demos, PRDs/briefs, or customer reports. This seems like a natural expansion and trivial functionally, so I'm wondering if that's part of the sell now or something you're looking at expanding into as you grow.
> How do you think about competing with ChatGPT Canvas or Anthropic's artifacts, when these are shareable, native experiences in their products where users already work?
The flexibility is amazing for static content and playing around with visuals, the experience is just more like a whiteboard than a dashboard. It's hard to do both well in the same place. For reporting I want live connections, consistent logic, the ability to trace provenance, and a more opinionated starting point for the UI.
We started with an extremely flexible surface but there are just a ton of things you don't want to leave up to the agent to implement and we gradually layered those in. It's no fun having to prompt the agent to expose a "view source" affordance, "run" button, or working data labels. But it's a lot of fun building whatever visualization you want and generating a dashboard without a billion clicks in some SQL-abstraction UI.
> Is a "dashboard" limited to analytics or are you trying to expand it to include written reports?
We weakly support written reports today (technically possible with markdown blocks in dashboards for commentary) but will do more to support them in the future for exactly the reasons you called out.
We actually built a more notebook-like artifact for this but cut it to focus on dashboards since they seemed to be a bigger pain point for users. One-off reports can be hit or miss with a chat or coding agent today but static reporting is at least supported with some effort. Live reporting with connection infrastructure, provenance, etc. is much harder to pull together.
> Agents made bad inferences because they had no context on the business
We've been working on this since before the chatgpt launch.
We started with a semantic layer since there were already good open source options and LLMs at the time were good at writing the JSON (remember function calling?) to run a semantic query.
But as LLMs have gotten smarter and people wanted to do more data work in agents, we found we needed something more flexible, so we built an "Ontology" that lets you store all the terms you use in your company and connect them to the data points (e.g. tables, columns, metrics) that matter.
https://www.definite.app/blog/ontology-ai-analytics
We use DuckDB internally because we like its ergonomics - it's flexible, runs well in memory, manages a lot of file structures under the hood, but we do work with Snowflake (and Databricks and other warehouses) as well.