For context I created a video search engine last year, I shut it down and put the data online. You can read about it here: https://www.bendangelo.me/2024/07/16/failed-attempt-at-creating-a-video-search-engine/

I put that project on hold because of scaling issues, anyway I’m back with an other idea. I’ve been frustrated with how AI slop is ruining the internet and recently it’s been hitting Youitube pretty hard with AI videos. I’m brainstorming a tool for people to selfhost:

Self-hosted crawler: Pick which sites/videos to index (blogs, forums, YT channels, etc.). AI chat interface: Ask questions like, “Show me Rust tutorials from 2023” or “Summarize recent posts about homelab backups.” Optional sharing: Pool indexes with trusted friends/communities.

Why? No Google/YouTube spam—only content you choose. Works offline (archive forums, videos, docs). Local AI (Mistral) or cloud (paid) for smarter searches.

Would this be useful to you? What sites would you crawl? Any killer features I’m missing?

Prototype in progress—just testing interest!

  • T156@lemmy.world
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    4 days ago

    I can’t imagine self hosting an LLM-based search engine would be too viable. The hardware demands, even for a relatively small quantised model, are considerable. Doubly so if you don’t have a GPU to accelerate with.

    • CameronDev@programming.dev
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      3 days ago

      Yeah, absolutely. And running a GPU 24/7 to occasionally search is just a waste of power. I’m not convinced that google and bings AI search makes financial sense either, Google dropped live search (where the results updated as you typed realtime) because it was too expensive, how does LLM search end up cheaper than live search?!

      Edit: This is the live search thing: https://searchengineland.com/test-google-updating-search-results-as-you-type-49116 ~~Annoyingly hard to find, and I can’t find the articles on its cancellation, but from memory it was related to expense. ~~

      Edit2: Google Instant Search, and the death was blamed on mobile, and wanting to unify the mobile/desktop experience. I do vaguely remember expense being an unofficial/rumored reason, but I can’t back that up.

      • Jakeroxs@sh.itjust.works
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        3 days ago

        You realize the gpu site idle when not actively being used right?

        It’d be cheaper if you host it locally, essentially just your normal electricity bill which is the entire point of what op is saying lol.

        • CameronDev@programming.dev
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          3 days ago

          Idle is low power, not zero power. And it won’t be idle when its scraping and parsing the sites, so depending on how much scraping its doing, it could be significant non-idle energy usage.

          • Jakeroxs@sh.itjust.works
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            3 days ago

            The gpu is already running because it’s in the device, by this logic I shouldn’t have a GPU in my homelab until I want to use it for something, rip jellyfin and immich I guess.

            I get the impression you don’t really understand how local LLMs work, you likely wouldn’t need a very large model to run basic scraping, just would depend on what OP has in mind really or what kind of schedule it runs on. You should consider the difference between a mega corps server farm compared to some rando using this locally on consumer hardware. (which seems to be the intent from OP)

            • CameronDev@programming.dev
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              3 days ago

              I didn’t say you can’t have a GPU, but to me, its wasteful. I keep my jellyfin server off when not in use, and use WoL to start it when its needed.

              I have played with local LLMs, and the models I used were unimpressive, but without knowing what the OP has in mind, we cant know how much power it will use. If it just spins up the GPU once a day for 20 minutes, probably okay, you won’t even notice it. But anyone like me who doesn’t already have a GPU in their lab will probably notice it quite clearly on their power bill.

              A megacorps server farm is huge, but its also amortised over millions of users, they probably don’t need 1-1 GPU to customers, so the efficiency isnt necessarily bad. (Although at the moment, given megacorps are tripping over themselves to throw compute at LLM training, this may not be true)