- 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.
- 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.
- Appreciate the validation. Would be great to connect and exchange notes - will reach out directly.
- > 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!
- We hear you on getting pulled into tarpit problems, and on the pattern you're describing leading to them. The core product motivation we're excited about is letting humans and their agents act on data together, but we do think that requires thoughtful tooling to exist before that becomes desirable (more to come here). Our newer customers tend to be a little more technology forward, which helps us focus on the product we're offering them rather than internal politics or process issues.
- 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.
- There are a lot of cool and useful things in there. What are you most excited about?
- 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.
- Totally. One thing that all major AI vendors are not doing currently is merging server AI with edge devices.
For example, there is no way neither in Claude nor in ChatGPT to run your own WASM or JS or whatever AI produces directly in user's browser context as a tool/skill - there is no call site for that. The only option is remote server-side.
My whole idea was that AI can perfectly write SQL and dashboard code knowing only the shape of your data and not it's contents. With direct upload to vendor now we're forced to share the contents.
- I suspect stronger edge performance will come as a side-effect of local inference. Your point on edge tool calls is interesting and I'll think about that. Features like offline mode could be a great motivating reason. Re knowing the shape vs not the internals - I'm mixed here. It feels like there's always a sampling period where you have to look at contents in order to understand what you want. But edge AI (like antirez's work running DeepSeek on Mac) will let you have both. I'm excited for that future!
- Why would an LLM want to look into the contents, what for?
We have low-cardinality data and yes this is safe to share and required to build an actual query.
Then we have high-cardinality and possibly PII - there’s absolutely no reason to share that data, there’s nothing for LLM to analyse there. Also semantic index (vector search) will find relevant records much faster and more accurately that any chain-of-thoughts just with an LLM-authored search fn call.
Further there are continuous numerical values and there’s not much LLM needs to see in there either. We can say, for example, if you look at data distributions when building your analysis, it can drive your analysis logic, but another point of view here is taht it creates unnecessary bias instead.
- On re-read I think I might have overreached in my reply. I think having local LLMs being able run tool loops to _transform_ data, rather than just summary or analysis, will become 1/ great for non-technical users, 2/ fast.
- 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.
- 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.
- How are you connecting to various data sources?
- We're offering secure connections to sources like SQL DBs, warehouses, file stores, and MCP/API sources like PostHog or Salesforce. Customers can choose to set up credentials in our key store. We also support directly dropping data into BitBoard (where we sync it to object storage).
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49 points by arcb 1 day ago | 23 comments