Latent Space / episode intelligence
Speakers in the public record
Claim mix
Evidence policy
Every row below preserves an exact excerpt. Identified speakers are linked; unresolved voices are labeled and excluded from people counts.
Claim ledger
15 published records
01 / belief
“I think there's a lot of people that have only been exposed to Copilot so far, which is one use case, just complete what I'm writing.”
02 / belief
“I think like different models are better on different properties, for example, how obedient you are to instruction, how good you are to prompt forcing, like to format forcing.”
03 / belief
“Like if we noticed, if we think one of the tests, for example, is failing because of a bug.”
04 / prediction
“I expect that it'll bring me back something like relevance and level nine it actually prints the cocktail for me I taste it and it's good. So, so I think like how I see it is like we need to have data sets similar to before and make sure that we not fine tuning the model the same way we test it.”
05 / belief
“Very interesting tech-wise, philosophy wise, et cetera, that that's like, I think need to be explored more.”
06 / evaluation
“I think the Singapore startup scene has not done as well as the Israeli startup scene.”
07 / preference
“For different tasks, we, we use different models and I think like this is for individuals, for developers to check, try to sync, like the test that now you are working on, what is most important for you to get, you want the semantic understanding, that's most important?”
08 / commitment
“Our vision is like for Tesla zero emission or something like that, for us it's zero bugs. We started with building an IDE extension either in VS Code or in JetBrains.”
09 / evaluation
“I think I talked about it on the podcast before, but like the switch from syntax to like semantics, like developers used to be focused on the syntax and not the meaning of what they're writing.”
10 / evaluation
“Generally speaking, like there's a lot of peer-to-peer transactions going on, like payments and, and China with QR codes. So basically if for example 5% of the scanning does not work and with our scanner we [00:01:30] reduce it to 4%, that's a lot of money.”
11 / preference
“Like sometimes like VCs prefer to [01:00:00] put money on a, on an entrepreneur that failed in his first startup and actually succeeded because now that person is knowledgeable, what it mean to be, to fail and very hungry to, to succeed.”
12 / recommendation
“Try it for specific use cases and see what's easy to do. And then if your purpose is just like incorporating stuff and that's what you wanna do and then do it, but don't like, tell everyone you're gonna do it before you do it, because you might find that it's actually really hard and there's a lot of problems.”
13 / evaluation
“I love that you, you mentioned that because if you go to CS undergrad you take so many courses in development, but none of them probably in testing, and it's so important.”
14 / evaluation
“Like even as a manager, I think I was like top one percentile being transparent in Alibaba. It wasn't five out of five, which is a good thing because that's extreme, but it was a good, but it also could be a bad, some people would claim it's a bad thing.”
15 / prediction
“It's not according to the specification. So I think like one more thing that AI could really help is help to match, like if there is some natural language description of the code, we can match it.”