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19 results for “optimization”
“The startup dilemma: optimize for revenue or product data?”
what would you wanna see on the go to market side? Because I feel like I maybe lately missed the mark. What are you optimizing for? Are you optimizing for revenue? Are you optimizing for usage and dat
24:16 / 24:52
tactical advice24:16 – 24:52
“China's AI advantage isn't tech—it's being forced to optimize”
...that optimization is happening largely because of necessity, because of a scarcity of, the fastest infrastructure that they have available to them. Yeah. So,
15:51 / 16:29
contrarian take15:51 – 16:29
...before the optimization starts, the optimization surface or objective becomes more complicated and so it's harder to find good minima. So there is also a complicated interplay I think between the optimization process and and these constraints you put
29:55 / 31:03
from transcript29:55 – 31:03
“You can spend ages optimizing photos and descriptions, but if people aren't in the market for a house, it doesn't fucking matter.”
...stuff's optimization in in my head. Still important. But yeah.
12:31 / 13:22
Practical Framework12:31 – 13:22
...optimization starts, the optimization surface or objective becomes more complicated and so it's harder to find good minima. So there is also a complicated interplay I think between the optimization process and and these constraints you put in your ne
29:14 / 30:25
from transcript29:14 – 30:25
“I was a litigator and it was my job to fight. And now I'm a diplomat, so it is my job to be diplomatic. And in that spirit, I would like to sound a gong of optimism.”
President Trump and Secretary Rubio did me the honor of this appointment, I was a litigator and it was my job to fight. And now I'm a diplomat, so it is my job to be diplomatic. And in that spirit, I
28:27 / 28:55
Origin Story28:27 – 28:55
“How data reveals the optimal operational processes by ditching industry best practices”
the selection of the best practices of operational processes across The UK with not just selecting the best one of the variety, but also quite often selecting none of the existing and reviewing all of
23:40 / 24:15
framework23:40 – 24:15
“This AI connector optimizes for goodwill instead of traditional metrics”
Gordy is when you think about any unsupervised learning system, you're trying to optimize an objective function or a cost function. For Gordy, that is goodwill. Right? How much goodwill can I actually
19:49 / 20:26
framework19:49 – 20:26
“We are optimizing for diversity in terms of our cap table. It's our first institutional round and we know our next one is going to be a much bigger round and we want to concentrate that in one or two maximal three investors.”
We are not even inviting them at this point just because we see that this is the last round in which we want to bring folks in, in the sense of like having multiple investors that can help with our gr
22:26 / 22:55
Business Mechanics22:26 – 22:55
“I spend 80% of my time planning, only 20% coding”
very early on, I started leveraging chat mode. And to this day, I can say I spent 80% of my time in planning and chatting and only 20% in executing the plan, actually. Right? I'm optimizing for the ri
13:19 / 13:51
tactical advice13:19 – 13:51
“Why predicting AI job displacement is impossible but optimism matters”
So people are trying to estimate which jobs are going to get redundant and which jobs are going to be created. It's just impossible to know these things. I think you will need to feed through. There w
24:14 / 24:40
prediction24:14 – 24:40
“How AI analyzes live shopping data to optimize performance in real-time”
Yeah. It's it's interesting. We're using AI in a couple of very interesting, I think, probably different ways. The most obvious is to more deeply understand our data and drive actionable insights from
13:01 / 13:37
tactical advice13:01 – 13:37
And they will they will put the right guardrails to, to not overfit. But down the line, if they look at it too much, they will overfit a little bit because they will find, like, data sets that are correlated or they will just ill climb the thing, in
36:45 / 37:49
from transcript36:45 – 37:49
even though we don't think that these academic benchmarks matter all that all that much, then we just need to optimize for them anyways because we our marketing team needs to show certain progress on certain standard evaluations that every other comp
14:59 / 16:00
from transcript14:59 – 16:00
and and you use the same model instead of a bigger model with more parameters. You use the same model kind of recurrently. You you make it think for a while, and then and then you you optimize that sort of the thinking trace with, with reinforcement
34:20 / 35:36
from transcript34:20 – 35:36
to optimize for that machine legibility piece, optimize for insight, optimize for relevance, especially versus in the past, it was more about hooking people in, capturing attention in flashy ways. What we're seeing already is case of high volume hype
9:01 / 10:01
from transcript9:01 – 10:01
...optimization? Or is this a short term thing where the biggest gains will be more on the algorithmic high level side of, like, post training? Is is this, like, a short term leap because they figured out, like, a hack because constraints, necessity is
48:39 / 49:55
from transcript48:39 – 49:55
task. You know, being able to crisply specify what it is you want is gonna be really important. Yeah. My joke is, you know, good prompting is in, indistinguishable from sufficiently advanced executive communication. Like, it's like writing an interna
1:15:48 / 1:16:55
from transcript1:15:48 – 1:16:55
specify what software you want the agent to write for you. You'd better be pretty darn careful of and how you specify that because that's going to dictate the quality of the output, right? Like if you, if you don't cover that it needs to handle this
1:15:01 / 1:16:30
from transcript1:15:01 – 1:16:30
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