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 →

Brett Adcock no published score: only 4 usable exchanges on raw tape, and a fair score needs 8+ · coarse estimate ≈4.5/5 from 4 raw tape exchanges 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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Answered raw tape D 5 · C 5 · P 5 · Cm 4 4.85

Q Can I actually, um, just describe the two public demos so far and like why they're important?

A Yeah, we've been doing, um, kind of like two divergent set of demonstrations to the world. The first is we, we do plan as a business to start launching into more kind of industrial solutions, like more like the corporate labor market, uh, you know, manufacturing, supply chain, logistics, those type of areas. We think that'll allow us to, to build the AI data engine quicker because we're shipping robots faster and we'll, it'll help us build manufacturing volumes quicker, which will help cost. Those are like the reasons why we're doing it. There's another market that we're extremely excited about, which is in the home. And the home is a really messy place. It's very unstructured. Everything's different. There's like a higher variance of failures where we're like, you know, if we drop like the number one dad cup at home or the number one mom cup, like no, not great. We drop a bin in like a warehouse, like who cares? You know what I mean? It's a little bit different scenario. Also safety is impacted. There's pricing compression as we move into the consumer world. There's just a bunch of stuff that happens. Um, so, so we've done a few demos so far. First is we've done like bin moving, very traditional industrial solutions roles where we're taking bins from palace into conveyor systems. We're doing that fully autonomous end to end on our robots now, all bipedal. Um, and the second is…

AI assessment note: “First is we've done like bin moving... and the second is we're doing”

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

Q to take on the dull and dangerous jobs that humans shouldn't be doing. In this clip, we talk to Brett about how he runs a team with Velocity to drive hardware, software, and AI into reality. Big question, but can you describe, like, if you want to run a hardware project, a hardware and software project like this, with this complexity at velocity, like how do you manage product development?

A From like a thesis perspective, I, I strongly believe in like an iterative design approach. We really don't believe on spending a lot of time, like just, just doing research and analyzing. We spend a lot of time on just testing, building the testing here. And, um, that helps us really shake out all the problems. It helps us learn, helps us recursively add it into a continuum of product that's coming down, uh, coming out. And, um, so first that's our strategy. We, um, we want to be continuously updating the hardware and software forever. It'll, I don't think it will ever be good enough for us. Um, So we have a whole process built around building a robot from a, like a basically hardware and software design that we run here. We first set out with understanding who are the customers? Like, what does the robot need to do? From there, we, uh, we basically set requirements like, okay, we need the robot to lift this much pounds. It needs to run this long. It needs to charge here, the safety requirements so that it can't Battery can't burn down the building. There's a bunch of stuff we have to, um, the environment on IP rating has to be done on level actuators. There's just a bunch of requirements that come from there. From there, we look at those requirements and we, we do engineering design and we have basically Like three big phases. Uh, we have a conceptual and preliminary and crit…

AI assessment note: “we have basically Like three big phases. Uh, we have a conceptual and preliminary”

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

Q Big question, but can you describe, like, if you want to run a hardware project, a hardware and software project like this, With this complexity at velocity, like, how do you manage product development?

A From, like, a thesis perspective, I, I strongly believe in, like, an iterative design approach. We really don't believe on spending a lot of time, like, just, just doing research and analyzing. We spend a lot of time on just testing, building the testing, uh, here. And, um, that helps us really shake out all the problems. It helps us learn. It helps us recursively add it into a continuum of product that's coming down, uh, coming out. And, um, so first that's our strategy. We, um, we want to be continuously updating the hardware and software forever. It'll, I don't think it will ever be good enough for us. Um, So we have a whole process built around building a robot from a, like a basically hardware and software design that we run here. We first set out with understanding who are the customers, like what does a robot need to do? From there, we, uh, we basically set requirements like, okay, we need the robot to lift this much pounds. It needs to run this long. It needs to charge here, the safety requirements so that it can't Battery can't burn down the building. There's a bunch of stuff we have to, um, the environment on IP rating has to be done on level actuators. There's just a bunch of requirements that come from there. From there, we look at those requirements and we, we do engineering design and we have basically Like three big phases. Uh, we have a conceptual and preliminar…

AI assessment note: “we have a conceptual and preliminary and critical design review that we do here”

Answered raw tape D 4 · C 4 · P 4 · Cm 3 3.85

Q So eyes wide open, like, how do you end up?

A There's no mature supply chain for what we're doing, and there is no other option. We have this philosophy that we have at the, we do like a nine o'clock stand up every day on the, um, like every morning, like rain or shine. And during, like, bringing up processes where we're bringing up, like, new robots and stuff, it's a mess. Like, the robots, like, never really work well on the first time. Everything breaks because we're, like, getting all the systems to start working together. There are things on software and hardware that haven't communicated before. There's just nothing, um, available that we could go buy that would satisfy our needs. So we've been forced to go build. And Like design, and then, you know, in a lot of cases, we even manufacture.

AI assessment note: “There's just nothing, um, available that we could go buy... So we've been forced to go build.”

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