7sedam7
Joined 7 years ago
Karma
1
Submissions
5
Member since
Feb 24
Recent submissions
Comment · 14 days ago
i don't know, seems pretty reasonable choice, especially with harder problem for requests that look like bots
Comment · 8 months ago
I built a search engine over the full Epstein document dump (1.4M files) as a live demo for LaSearch, a
search engine I've been working on.
It's full-text with semantic understanding and fuzzy matching — no embeddings, no vectors, no ML models.
Everything runs in the browser, results appear as you type with no noticeable lag on a 1.4M document corpus.
The interesting technical constraint was making semantic-like behavior (finding conceptually related results, not
just exact keyword matches) without the overhead of vector search. Happy to go into the approach if there's
interest.
Demo: https://epstein.lasearch.app LaSearch (the underlying engine, also available as a desktop app for your
own files): https://lasearch.app
Comment · 11 months ago
Cool, i have something similar that works locally on your machine: https://lasearch.app
(uses custom algorithm instead of embeddings. Not as powerful in terms of contextual understanding, but enough for search and a LOT faster, plus minimal resource usage)
Comment · 1 year ago
very cool touch, i was personally not patient enough, but likely my users won't be too, so this seem like a useful product