GNES – a cloud-native semantic search system based on deep neural network | Hacker News Reader

GNES – a cloud-native semantic search system based on deep neural network

github.com

59 pointsnorthfoxz11 comments

The project looks impressive, but it doesn't seem clear to me at a first glance what does it do or the advantage it has over other solutions.

Names sounds like an NES emulator. Maybe one using GTK+.

man... people are fussy nowadays... i had the same exact thought of an NES emulator. what's wrong with this generation of hacker news people?!

Yeah, please give it a different name, OP. It looks like you've put a good amount of work into the project itself;

However, as a normal tech user looking at various things, I would be confused by looking at the name too. Please give it a unique name - find one that isn't already on GH, PyPi or what not.

TL;DR: Brand identity = Unique name

The fact that any technology runs Dockerised or within K8 can’t be its principal selling point (above what the tech itself does) imo.

This ‘looks’ cool but I had to read a lot and still had to make some guesses about what it does.

How about a comparison of some real world use cases? For example, could I use this to store and query some structural information that might come from an hOcr or ALTO formatted ocr output? Can OCR even be plugged in? Can I intelligently index the diff of multiple versions of a file and query that?

I have no clear idea ‘cos the team behind it are so focussed in their literature on the technical detail.

What is "cloud-native semantic search"? What does it do?

I'm not sure either, but: cloud=someone else's computer, cloud-native=living on someone else's computer, semantic=meaning of things, semantic-search=Find the meaning of something. Maybe it can explain what it self is.

It’s called Generic Neural “Elastic Search” - but does it use “Elasticsearch?”

Sorry if it’s a dumb question, poking around I don’t see where it interacts with Elasticsearch (maybe it doesn’t)

My guess: I think they mean the searches are elastic (i.e. F fuzzy). This is the same meaning that Elasticsearch the product emphasises through its product Name. There is crossover in functionality

Seems like elastic here is a reference to the scaling and not to ElasticSearch. It searches by comparing vectors encoded by a neural net

The name wins buzzword bingo.

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