Haha I copied it from that page, hilliarious!

I'll keep it haha

It stores it in browser local storage using IndexDB, if you have access to years of documents, it might take up to 10+gb of persistant storage.

gb's of storage, potentially 10+ for very large datasets.

Minutes, not days. Very big data sets might take 30+ minutes (or even a couple of hours), but usefulness starts in the first few minutes (because of the priority algorithm)

Actually a few hundred documents is really no biggy, my current benchmarks is in the range of <250ms (instant feeling) for hundreds of thousands of paragraphs.

I'm testing this on a large knowledge base.

Same bundle of weights but is being run by some rust code that is compiled down to WebAssembly.

It will be very soon https://github.com/haystackoss/haystack

some rust code that compiles to WASM loads LLM from memory, and uses custom transformer.py like rust alternative we wrote.

Forgive me if I don't understand, but I don't think there's a problem with multiple companies using the same common noun as the base for their domain.

Let the best product be remembered for the name.

Certainly not, presuming it doesn't escalate to the level of trademark/service mark infringement (to be fair, IANAL). Just a risk consideration...your product, your call.

But I think there's value in at least recognizing that the namespace is quite crowded given the collisions that two interweb randoms were able to identify in short order.

Not really, gethaystack searches your tabs

Their beta seems so

Thanks

I’m using a fine-tuned t5-small model, I fune-tuned it for two tasks, question answering from a paragraph, and highlighting relevant text of search results.

I’m planning on releasing an open source version of this.

But also have paid features that managers would like to use.