service / likes
AWS
“I love AWS for like the stuff it's built, you know, like historically in order for me to like, you know, what it enables me to build, but like AWS is always like struggle with developer experience.”
Public evidence record
Erik Bernhardsson
Published podcast speaker
- Claims
- 21
- Episodes
- 1
- Shows
- 1
- Named items
- 1
Books, apps, and tools
The evidenced stack.
Claim ledger
What Erik said.
5 transcript-backed records
01 / evaluation
I do think one like massive serendipitous thing that happened like halfway, you know, a year and a half into like the, you know, building model was like Gen AI started exploding and the IO pattern of Gen AI is like fits the serverless model like so well, because it's like, you know, you send this tiny piece of information, like a prompt, right, or something like that.
“I do think one like massive serendipitous thing that happened like halfway, you know, a year and a half into like the, you know, building model was like Gen AI started exploding and the IO pattern of Gen AI is like fits the serverless model like so well, because it's like, you know, you send this tiny piece of information, like a prompt, right, or something like that.”
- Speaker
- Erik Bernhardsson
- Publisher
- Latent Space
02 / evaluation
We think the value in Modal comes from all these, you know, the other use cases, the more custom stuff, like fine tuning and complex, you know, guided output, like type stuff.
“We think the value in Modal comes from all these, you know, the other use cases, the more custom stuff, like fine tuning and complex, you know, guided output, like type stuff.”
- Speaker
- Erik Bernhardsson
- Publisher
- Latent Space
03 / evaluation
And so I think there's a lot of different reasons why the technology wasn't well suited for back end. And I think the attitude at that time is often like, you know, like you had friction between the data team and the platform team, like, well, it works for the back end stuff, you know, why don't you just like, you know, make it work.
“And so I think there's a lot of different reasons why the technology wasn't well suited for back end. And I think the attitude at that time is often like, you know, like you had friction between the data team and the platform team, like, well, it works for the back end stuff, you know, why don't you just like, you know, make it work.”
- Speaker
- Erik Bernhardsson
- Publisher
- Latent Space
04 / evaluation
And you know, all the different data engineers and machine learning engineers end up kind of struggling with the same things. So I started thinking about like, how do I build a new data stack, which is kind of a megalomaniac project, like, because you kind of want to like throw out everything and start over.
“And you know, all the different data engineers and machine learning engineers end up kind of struggling with the same things. So I started thinking about like, how do I build a new data stack, which is kind of a megalomaniac project, like, because you kind of want to like throw out everything and start over.”
- Speaker
- Erik Bernhardsson
- Publisher
- Latent Space
05 / evaluation
I think when you get to like training, like very large foundational models, that's a use case we don't support super well, because that's very high IO, you know, you need to have like infinite band and all these things.
“I think when you get to like training, like very large foundational models, that's a use case we don't support super well, because that's very high IO, you know, you need to have like infinite band and all these things.”
- Speaker
- Erik Bernhardsson
- Publisher
- Latent Space