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278 points itzlambda | 2 comments | | HN request time: 0.428s | source
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lsy ◴[] No.44608975[source]
If you have a decent understanding of how LLMs work (you put in basically every piece of text you can find, get a statistical machine that models text really well, then use contractors to train it to model text in conversational form), then you probably don't need to consume a big diet of ongoing output from PR people, bloggers, thought leaders, and internet rationalists. That seems likely to get you going down some millenarian path that's not helpful.

Despite the feeling that it's a fast-moving field, most of the differences in actual models over the last years are in degree and not kind, and the majority of ongoing work is in tooling and integrations, which you can probably keep up with as it seems useful for your work. Remembering that it's a model of text and is ungrounded goes a long way to discerning what kinds of work it's useful for (where verification of output is either straightforward or unnecessary), and what kinds of work it's not useful for.

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1. Melonololoti ◴[] No.44613946[source]
You still need to learn the names of models, understand their use cases, concepts like MoE, then you have different architectures like diffusion vs transformers, agents etc.

And then you have GenAI like flux and all the open source projects.

I think it's beneficial to get all of that and then keeping an eye on it to catch the moment when it becomes relevant for you and not being surprised and too late.

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2. gammalost ◴[] No.44614075[source]
> You still need to learn the names of models, understand their use cases, concepts like MoE, then you have different architectures like diffusion vs transformers, agents etc

Why? When you think you might need something just search for it. There are too many models with incremental improvements