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Launch HN: BitBoard (YC P25) – Analytics Workspace for Agents

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Re: Launch HN: BitBoard (YC P25) – Analytics Workspace for Agents

#22

Post by arcb »

BoorishBears wrote: post #21

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).

Re: Launch HN: BitBoard (YC P25) – Analytics Workspace for Agents

#23

Post by dennis16384 »

arcb wrote: post #20
dennis16384 wrote: post #19

Earlier quoted context omitted.

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 knowi…

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 anti…

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.

Re: Launch HN: BitBoard (YC P25) – Analytics Workspace for Agents

#24

Post by sails »

> 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!

Re: Launch HN: BitBoard (YC P25) – Analytics Workspace for Agents

#25

Post by arcb »

sails wrote: post #24

> 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 sy…

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.

Re: Launch HN: BitBoard (YC P25) – Analytics Workspace for Agents

#26

Post by arcb »

dennis16384 wrote: post #23
arcb wrote: post #20

Earlier quoted context omitted.

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 anti…

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 j…

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.
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