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.
baetylus
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.
rancar2
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.
dennis16384
> but customers kept pulling us toward their data analysis problems
I hear this all the time, I still don’t think it’s a good justification to build a BI tool, but I hope this time it is different.
Product looks cool! I’m hopeful that agents do actually unlock business analytics and we can move on from the BI concept
Edit: a rough explanation of why you get pulled towards data problems is that they are intractable symptoms of upstream process issues. Customer sees a capable startup and co-opts them into trying to solve their tarpit problems. Happens all the time!
sails
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?
spmartin823
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.
comments (10)
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.
baetylus
rancar2
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.
dennis16384
I hear this all the time, I still don’t think it’s a good justification to build a BI tool, but I hope this time it is different.
Product looks cool! I’m hopeful that agents do actually unlock business analytics and we can move on from the BI concept
Edit: a rough explanation of why you get pulled towards data problems is that they are intractable symptoms of upstream process issues. Customer sees a capable startup and co-opts them into trying to solve their tarpit problems. Happens all the time!
sails
spmartin823
> 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
mritchie712
straydusk
rohand7
htrp
BoorishBears