DeepSeek, every mention

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Verbatim, from the transcripts: the passages where DeepSeek comes up

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▶ 10:41 Shayle Kann I mean, well, let me say, first of all, remember DeepSeek? 3 times in the scene

▶ 15:59 Sam Smith-Eppsteiner So I think folks are getting creative and will continue to do so in terms of how exactly you build this, and it, and it's going to be different based on the problem, but also I think we're going to see a lot of evolution here, and my hope…

▶ 3:59 Shayle Kann And then DeepSeek comes along, and for a moment,
▶ 17:51 John Koomey And so you saw the announcement of Deep Seek a few weeks ago, and that was an example.
▶ 18:19 Shayle Kann To the extent that there was conventional wisdom, conventional wisdom in this space, um, it was that, prior to DeepSeq, right, for the, for the past year or two, it was, 4 times in the scene
▶ 26:45 Shayle Kann So, but then deep seek is its own thing, right? 2 times in the scene

▶ 36:00 Shayle Kann Has there been, so we're, we're recording this, what, like three weeks past the public market meltdown of DeepSeek. 4 times in the scene

▶ 17:44 Shayle Kann So we're going to do slides one on one, one Oh five, and then one 86, where we get to talk about deep seek, which is the topic du jour of this week.
▶ 21:09 Shayle Kann And they are DeepSeek. 6 times in the scene
▶ 23:05 Nat Bullard And deep seek is getting similar results of V three with six hundred and seventy one billion parameters, but fewer than 2.8 million training hours.
▶ 39:55 Nat Bullard You know, it will be fascinating in the context of the deep seek conversation we were having to see if that doesn't pull backward at some point.

▶ 3:53 Shayle Kann We talk about DeepSeek, which is obviously topical this week, if you are listening when we release this thing.

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