4 ms·
My take is that Altman recognizes LLM winter is coming and is trying to entrench.
by tomrod 2y ago
My take is that Altman recognizes LLM winter is coming and is trying to entrench.
- chinathrow 2y agoLooking at ChatGPT or Claude coding output, it's already here.
- criticalfault 2y agoBad? I just tried Gemini and it was useless.
- andrewinardeer 2y agoGoogle ought to hang its head in utter disgrace over the putrid swill they have the audacity to peddle under the Gemini label. Their laughably overzealous nanny-state censorship, paired with a model so appallingly inept it would embarrass a chatbot from the 90s, makes it nothing short of highway robbery that this digital dumpster fire is permitted to masquerade as a product fit for public consumption. The sheer gall of Google to foist this steaming pile of silicon refuse onto unsuspecting users borders on fraudulent.
- mnk47 2y agoStarting to wonder why this is so common in LLM discussions at HN. Someone says "X is the model that really impressive. Y is good too." Then someone responds "What?! I just used Z and it was terrible!" I see this at least once in practically every AI thread
- fzzzy 2y agoYou're the one that chose to try Gemini for some reason.
- dartos 2y agoI don’t think we’re gonna see a winter. LLMs are here to stay. Natural language interfaces are great. Embeddings are incredibly useful. They just won’t be the hottest thing since smartphones.
- ForHackernews 2y agoThey're useful in some situations, but extremely expensive to operate. It's unclear if they'll be profitable in the near future. OpenAI seems to be claiming they need an extra $XXX billion in investment before they can...?
- eastbound 2y agoIt’s a glorified grammar corrector?
- CharlieDigital 2y agoNot really. I think actually the best use case for LLMs is "explainer". When combined with RAG, it's fantastic at taking a complex corpus of information and distilling it down into more digestible summaries.
- bot347851834 2y agoCan you share an example of a use case you have in mind of this "explainer + RAG" combo you just described? I think that RAG and RAG-based tooling around LLMs is gonna be the clear way forward for most companies with a properly constructed knowledge base but I wonder what you mean by "explainer"?. Are you talking about asking an LLM something like "in which way did the teams working on project X deal with Y problem?" and then having it breaking it down for you? Or is there something more to it?
- nebula8804 2y agoI'm not the OP but I got some fun ones that I think are what you are asking? I would also love to hear others interesting ideas/findings. 1. I got this medical provider that has a webapp that downloads graphql data(basically json) to the frontend and shows some of the data to the template as a result while hiding the rest. Furthermore, I see that they hide even more info after I pay the bill. I download all the data, combine it with other historical data that I have downloaded and dumped it into the LLM. It spits out interesting insights about my health history, ways in which I have been unusually charged by my insurance, and the speed at which the company operates based on all the historical data showing time between appointment and the bill adjusted for the time of year. It then formats everything into an open format that is easy for me to self host. (HTML + JS tables). Its a tiny way to wrestle back control from the company until they wise up. 2. Companies are increasingly allowing customers to receive a "backup" of all the data they have on them(Thanks EU and California). For example Burger King/Wendys allow this. What do they give you when you request data? A zip file filled with just a bunch of crud from their internal system. No worries: Dump it into the LLM and it tells you everything that the company knows about you in an easy to understand format (Bullet points in this case). You know when the company managed to track you, how much they "remember", how much money they got out of you, your behaviors, etc.
- piuantiderp 2y agoA cash out