this post was submitted on 03 Aug 2025
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Proton’s Lumo AI chatbot: not end-to-end encrypted, not open source (pivot-to-ai.com)
submitted 3 days ago by Pro@programming.dev to c/technology@lemmy.world
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[–] WhyJiffie@sh.itjust.works 9 points 2 days ago (2 children)

Why does an email service need a chatbot, even for business?

they are not only an email service, for quite some time now

There are about a bajillion of these, and one could host the same thing inside docker in like 10 minutes.

sure, with a thousand or two dollars worth of equipment and then computer knowledge. Anyone could do it really. but even if not, why don't they just rawdog deepseek? I don't get it either

...On the other hand, it has no access to email I think?

that's right. you can upload files though, or select some from your proton drive, and can do web search.

[–] brucethemoose@lemmy.world 8 points 2 days ago* (last edited 2 days ago)

sure, with a thousand or two dollars worth of equipment and then computer knowledge. Anyone could do it really. but even if not, why don’t they just rawdog deepseek? I don’t get it either

What I mean is there are about 1000 different places to get 32B class models via Open Web UI with privacy guarantees.

With mail, vpn, (and some of their other services?) they have a great software stack and cross integration to differentiate them, but this is literally a carbon copy of any Open Web UI service… There is nothing different other than the color scheme and system prompt.

I’m not trying to sound condescending, but it really feels like a cloned “me too,” with the only value being the Proton brand and customer trust.

[–] DarkDarkHouse@lemmy.sdf.org 1 points 2 days ago (1 children)

I guess the sell is easy access to Proton Drive for RAG here?

[–] WhyJiffie@sh.itjust.works 3 points 2 days ago (1 children)

what is RAG?

[–] DarkDarkHouse@lemmy.sdf.org 3 points 2 days ago

Retrieval-augmented generation (RAG) is a technique that enables large language models (LLMs) to retrieve and incorporate new information. With RAG, LLMs do not respond to user queries until they refer to a specified set of documents. These documents supplement information from the LLM's pre-existing training data. This allows LLMs to use domain-specific and/or updated information that is not available in the training data. For example, this helps LLM-based chatbots access internal company data or generate responses based on authoritative sources.

From Retrieval-augmented generation.

Specifically here, I imagine the idea is to talk to the chatbot about what's in your documents.