Developer publishes cost breakdown of using Gemini to train replacement model
1 Sep 17 9:17 AM · 9d ago · 1 article · 2 posts · 9 comments · 3 sources · development 1 of 1
Peter Vijeh published a detailed technical writeup showing how he used Gemini 3.1 Pro to label 4,290 Reddit comments for $9 (at $0.0021 per comment), then trained an open-source named-entity recognition model (GLiNER) on those labels to replace expensive ongoing API calls.
“Gemini labeled 4,290 comments for $9, or $0.0021 a comment. That means the trained model pays for itself at roughly comment 4,291, as long as later comments are about the same length and it runs on a GPU I already own.”
Peter VijehPeter Vijeh Developer/engineer
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first by HN Frontpage, 9d ago
What people said 9 voices · verbatim
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Unfortunately, advertisers are getting smarter and using bots to praise their own products on Reddit. Thanks to training on genuine comments, some models are very good at sounding like a human commenter, and can easily generate a comment history with diverse interests to appear human, making them basically undetectable. So it seems like this…
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The thinking people who would find this interesting and read this are probably more than capable of understanding this and critical enough to expect that. Conversation over the title is distraction of what's important. Just stick to keeping original source title and let people vote and down vote if they don't like it. That's what votes are for.
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Isn't it just learning to map specific words, from the "knife world", to the correct class? If so, a simple dictionary would fit. What I think is a better way to validate is to split train/validation by words used presented in NER classes (like, it should be able to find new brands never seen before). It is a interesting problem.
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The other day I wanted to gather Reddit comments about a solar panel vendor. Claude doesn't have access to I had Gemini do some "deep research". When I fed the verbose report back to Claude it basically said it was a bunch of "hallucinated bullshit".
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I found it much more useful to go to a knife shop and handle a whole bunch of knives for myself. They’re all pretty similar besides material, so not much signal you’re going to be able to glean from people arguing on reddit.
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I think this is the way. An LLM is an expensive general purpose tool and for repeatable tasks, after it's clarified the process flow, it builds cheaper special purpose tools for each step
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I was hoping he tricked Gemini into running the training on the cluster that Gemini itself is running on. That would be novel!
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Request subtopic be changed to “I used Gemini to design a tool to replace specific uses of Gemini.”
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It’s unreadable but then again it says it on top, but it really is so why post it
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