Public evidence record
Published podcast speaker
Claim ledger
32 transcript-backed records
01 / belief
“I think that's maybe a little bit overplayed a little bit just because maybe here's my argument for that.”
02 / belief
“Are you just going to pull them down?" Because I think one of the things that's true about companies is it's like this massive group project.”
03 / observation
“The kitchen sink of features worked for a lot of companies, but each individual company only wanted 10% of the features. But the problem is, each individual company was not capable of maintaining a piece of software or building the custom piece of software for 10% of the features, but that has now changed entirely.”
04 / belief
“You just want to give people the flexibility to build where they can build. And I think the IDE and building locally has been a thing that people have done for decades, so probably it's not going to go away in the next couple of years.”
05 / belief
“I think the best thing to do is just get your hands dirty with all of these products. And I think one of the most obvious things that's going to happen is, in the next year, there will be a tremendous amount of alpha for anyone that is able to take maximum advantage of these tools.”
06 / belief
“Exactly. So for us, I think that's what we more care about. We have a culture where it's very collaborative.”
07 / belief
“We are okay with people using the tools because I think one of the worst things is like, if someone comes here and doesn't like using these tools, we believe there are massive productivity improvements.”
08 / prediction
“So I think look, we are definitely seeing over 30, maybe close to 40% productivity improvements. But I think for the vision that we're solving for, even if I were to say the company in the long tail had 200 engineers, it'd probably be too low still at that point.”
09 / evaluation
“I think one of the things that's maybe a little bit undervalued is this kind of agency piece. And I think about this a lot, which is, you have a lot of people that could go through college and go through school and they're basically told exactly what to do on a P-set.”
10 / evaluation
“They know when to go all the way down. And I think sometimes you see people that talk about roles and they irrationally feel like, "Oh, because I'm this role, I'm not allowed to touch this.”
11 / recommendation
“Code is one of the most important pieces of IP for the company. So I think just if you were to look at it from a big company perspective, there are many reasons why over the years of just building an enterprise product, we've handled a lot of complexities that large companies want to see.”
12 / prediction
“Just because of distribution, resources and capital, they're going to commoditize every space. So I think in some ways this amount of just understanding that, hey, re-evaluate your hypotheses and get into an uncomfortable space way more frequently is something I need to remind myself even to this day.”
13 / preference
“We try to go with two pizza teams, teams that are fairly small just because I think the problem is when a team gets too big, the person leading the team is no longer able to get in the weeds of the technology itself.”
14 / recommendation
“If there's a very large directory, don't go out and make it refactor the entire directory because then if it's wrong, it's going to basically it destroy 20 files. And I think from there, one of the key pieces I think that comes from the users that use the product is they sort of learn what the hills and valleys of the product are.”
15 / evaluation
“At the time, we were free cash flow positive. But I think what we felt was once these generative models started to get very good, we sort of felt a lot of what we built was not as valuable.”
16 / commitment
“There was a crazy stat that I think people would find surprising, but we've saved over half a million dollars of SaaS products we were going to buy because our go-to-market team has now built apps instead of buying them.”
17 / evaluation
“Our growth is like around, you know, four to 5% day over day right now. So all of our growth right now is sort of like word of mouth, and that's fundamentally because like the product is actually one of those products where.”
18 / belief
“I think that was the main I, it's not that these teams were like not good at what they were doing, it's just that they were trying to solve a completely separate problem.”
19 / belief
“The reality is the model is like probably at a state right now where it can do crazy enough things. Uh, and I think great apps will, will come out of this.”
20 / belief
“I know that this is like a big niche, but I think the fact that Strava is such a big group of people and like swyx is a big group of people, seems to suggest that I think a lot of people would be willing to pay for something like this.”
21 / belief
“What I think is always gonna happen is if you find a cheap way to embed context, people are gonna figure out a way to, to put as much as possible in because L LM so far have been like virtually stateless.”
22 / belief
“You want a base model that's still really good and that's probably trained on normal text, like a lot of different content. But I think probably one thing that old school machine learning, even though I'm like the kind of person that says a lot of old school machine learning is just gonna die, is that training on a high quality data set for your workload is, is always gonna yield better results and more, more predictable results.”
23 / evaluation
“One of the key issues that we noticed is GPUs are extremely hard to manage fundamentally because they work differently than CPUs.”
24 / recommendation
“about it? So I think one thing that has proven to be true over the last year and a half is companies, for the most part, should not be trying to figure out what the optimal ML architecture is or training architecture is.”
25 / evaluation
“We saw that there was a gap there where a lot of people probably weren't developing high intensive L L M applications because of cost, because of the inability to train models the way they want to.”
26 / belief
“I think one of the things that we genuinely believe as a startup is most startups can't really even do one thing properly.”
27 / evaluation
“When I was there, I like shipped a change and then Cora had like millions of daily actives and then it looked like it was good, and then a week later it was just like way worse.”
28 / evaluation
“A lot of them are, are kind of like weight and, you know, just generate a lot of stuff and see what happens because one is clearly more compute intensive than the other Basically.”
29 / preference
“We ended up basically using a lot of open, I guess, permissively licensed code, uh, in the public internet, mainly because I think also the pile is, is fairly a small subset.”
30 / prediction
“I think that's like a really cool thing and I'm sure at some point we will be able to get there.”
31 / evaluation
“I mean, there's like a lot of maybe issues with the current version of the transformers, which is like the way attention works, the attention layers work, the amount of computers quadratic in the context sense, because you're like doing like an n squared operation on the attention blocks basically.”
32 / commitment
“I will just say for us, we will probably just build this in-house to start with, and that's because I think the technology probably isn't there.”