Comment by arcb
2 days ago
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.
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