LLMs often don't do well on tasks that require composition into smaller subtasks. In this case there is a chain of relations that depend on the previous result.
426 karma · joined February 15, 2022
Oh Sweet summer child.
On the other hand, salaries in the EU are significantly lower and it is a lot easier to find engineers.
Even as a person who dislikes the overregulation it is not totally clear to me that the net status of the labour market is what makes entrepeneurship as impossible as you claim, despite the obvious negative effect of overregulation.
If that was the main problem I would expect a "Silicon Valley" of Europe to form in Denmark or one of the other smaller states that have relatively flexible labour markets, but we are not really seeing that, as they are still hampered by a lack of funding.
Instead we see that the biggest startup hubs are in Berlin and Paris, probably more related to network effects and national (financial) market size then to particularly attractive regulation, for which those places are not known.
The lack of a unified (financial) service sector and hence a very weak and fragmented private capital market (-> VC ecosystem) is a bigger problem. Most funding is from risk-averse banks and slow-moving governments.
Many EU based startups move to silicon valley for access to financing.
> LLMs only reliably know what you just told them, don't rely on training data
This depends a lot on the model but i've been using 4o for all kinds of information retrieval and found it to be generally reliable. At least not much worse than the general internet. You can ask it for sources and of course you should not use it as an authority, but it can often be a very good way to quickly find out a fact. You do need to developed a feeling for the kind of thing it will know realiably and the kind of thing that will cause it to start halucinating (a bit like some of your co-workers).
> LLMs cannot write for you
I disagree, LLMs can write small blocks of text very well. But there is an art to using it. Don't try to create too much at once. I often find it works better then I give less input. If you list a bunch of things it needs to include, it tends the result reads like a student trying to include all the buzzwords.
> LLMs can help a human perform tasks, they cannot replace a human
I don't think anybody claims otherwise for current public models.
> Have the LLM do as little as possible
You need to learn what LLMs do well, and then use it for that. The idea that it is most efficient to program everything by hand as much as possible does not match my experience. Writing boilerplate code of under 50 lines of code or so is something current models already do very well and very quickly. I usually just try to generate it and if it does not work I write it by hand.
Finally, LLMs now take video and audio. We use Gemini to write meeting notes from Google meet and they tend to be very high quality, a lot better then what a random person taking notes usually produces. So the models are not text-only.
Meanwhile Italy is allowed to buy chips with 1.5% defense spending.
It doesn't make sense to think that defense spending is the criterion used to select the countries.