The Exchanges

Every argument clarity score on this site is built from rows on this page. Each question and answer was assessed with names hidden, the host's own answers included, on four things from 1 to 5: directness (does it answer the question asked), coherence (do the ideas follow), precision (concrete details and clear references), compression (says a lot per word). The weighted mix (30/30/25/15) is the exchange score. A person's published score averages their exchange scores on raw tape only, at least 8 of them, shrunk toward the cohort mean. Full method →

Stephen Slattery no published score: only 2 usable exchanges on raw tape, and a fair score needs 8+ record → ← everyone

Every exchange below was scored with names hidden, four dimensions each from 1 to 5. An exchange's score is 0.30·directness + 0.30·coherence + 0.25·precision + 0.15·compression. The published score averages the raw tape exchange scores and shrinks small samples toward the cohort mean, so five great answers can't beat twenty good ones. Produced feed rows count only toward coarse estimates, never toward a full score.

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2exchanges match
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Answered raw tape D 5 · C 5 · P 5 · Cm 4 4.85

Q standard FDIC coverage through their partner banks, and even high yield from day one. With same day and even same hour liquidity, access your funds anytime. Companies like Scale AI, DoorDash, Service Titan, HIMSS, Anthropic, Flexport, Robinhood, and Plaid trust and use Brex. Start today at brex.com slash sorcery That's B-R-E-X dot com slash sorcery. And Steven, you're one of the earliest employees, so what got you hooked into it?

A Yeah, when I was getting ready to leave Andrel, really the two main things for me, one was a very small, extremely high caliber team, and I had a pretty high bar coming from Andrel, and then before that, SpaceX, and meeting the initial folks at Nominal, it was, it was evident from those conversations that this team was above and beyond even those groups I had worked with at Andrel and at SpaceX, so that was extremely exciting. Checked that box immediately. Um, and obviously I knew Cameron as well from our Anduril days. And then I was looking for a product that I just really believed in, um, and really could passionately develop and sell and, and be a part of that process. And I wasn't actually at the time very picky about hardware versus software or, um, you know, whether it was SAS or something else, but got the product pitch, saw the demo and immediately tied it full circle back to my sort of career path, which was starting at more of a prime, Sierra Space, a small group within that organization, having to build a lot of this software tooling myself, um, and doing it also in air-gapped environments for secure testing, and really there was just no buy option. You know, I had to go and build it with some teammates, um, over multiple years. Going to SpaceX, they've invested millions in their tool chain, everything from ERP to fleet management. Um, to data analysis, obviously. An…

AI assessment note: “two main things for me, one was a very small, extremely high caliber team”

Answered raw tape D 5 · C 5 · P 4 · Cm 4 4.60

Q To go further into where nominal software fits in, could you just explain out the end-to-end hardware development process?

A Yeah. So starting on the hardware development process side, Generally, our, our customers are going to fall into one of two buckets, and sometimes if they have multiple products, they're in both. So on the one hand, you have the zero to one development program, and you're starting from pure simulation data to prototyping to initial qualification testing. Um, qualification testing can take months, often takes years. Um, it's a very arduous process. And then into production testing, which is kind of, you know, things are rolling off the line and we're just making sure that everything works. Our software product overall is focused on this entire spectrum of test cases, right? So we have customers that use nominal only for simulation data and customers using it all the way on production manufacturing lines and out to their fleet. And all of this data is relevant to pull into a single consolidated database, be able to manage it, present it back to engineers, to technicians, to VPs in an easily consumable way. And then the second piece is In the hardware development process, right, you need to scale one to n, and that means bringing up a production line, but to Cameron's point about everything is a test asset, you also need to be aggregating all the data from your fleet back into the same telemetry management and analysis tooling that you're using for development to be able to close …

AI assessment note: “you're starting from pure simulation data to prototyping to initial qualification testing”

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