Nice! Excited tools that makes using microservices easier.
Question tho, when will you guys have python support? I’m a ml researcher here and can you tell that most of my work is now pipelines between different services, e.g. Chaining multiple LLM services. Big bottleneck is if one service returns an error and crashes the full chain.
Big fan of this work nevertheless. Just think you have alpha on the table
We are currently gathering feedback on which SDKs to prioritize next. Python has been asked for a couple of times already. Once we decide on the next SDK, we'll let you know.
I'm really confused. When looking at artificial intelligence it seems that actually we have dominating corporate labs which through integrated services (such as compute and data), totally dominate academia.
It's coming to the point where researchers that want to make a difference are actively choosing corporate research labs over academic ones.
This headline doesn't jive with my experience having worked with lots of folks in corporate R&D at big tech. After looking at the data in the paper I think it's far too soon to talk about the "death of corporate research labs".
> These examples are backed by systematic evidence...The figure also shows that the absolute amount of research in industry, after increasing over the 1980s, barely grew over the 20 year period between 1990 to 2010. Other data show the same decline
That seems like a cherry-pick. In that very same figure, you can see that 2015 is about 300% of 1990.
So from 1990 to 2015 corporate research spending grew 5% on average annually. I'm not sure how you could refer to that as "dying". Of course, the paper doesn't use that term, just the parent blog post.
I have no sense of non-tech research labs, and maybe if you exclude those corporate research labs are indeed dying, but the headline is click baity.
> I'm really confused. When looking at artificial intelligence it seems that actually we have dominating corporate labs which through integrated services (such as compute and data), totally dominate academia.
But this is just one unique case - where access to a lot of data and a lot of compute makes a huge difference.
I'm not sure that it translates to many other fields in CS.
Corporate research is still going to somewhat be at the whimsy of what's bleeding edge AND has some applicable value; the idea of it just is a little different with things like the 10% or 20% "constructive free-time" model. Each engineer in FAANG-like companies just get a 10-20% salary allotment instead of the equivalent expense of researcher headcount. The idea of "constructive free time" for work is just the "agile" iteration of the research lab.
Question tho, when will you guys have python support? I’m a ml researcher here and can you tell that most of my work is now pipelines between different services, e.g. Chaining multiple LLM services. Big bottleneck is if one service returns an error and crashes the full chain.
Big fan of this work nevertheless. Just think you have alpha on the table