Why Your AI Agents Don’t Work with Dex Horthy of HumanLayer | In-Context Cooking

Look at that. Look at that sauce. Fully incorporated. I'm sure the model labs love that that's the answer. This is getting chaotic, dude. This is fun. Yeah, I wanna, I want, I want the verdict here.

Hey guys, welcome to In-Context Cooking, a show where we take one dish, taste it, and try to recreate it with minimal help. My name's Alan, and today we have a very special guest, the man behind Context Engineering, Dex Horthy, founder and CEO of HumanLayer.

Welcome, Dex.

Dude, I'm so stoked to be here.

Yeah. I mean, I always start off with this. So on a scale of one to 10, how would you rate yourself as a cook? One being bad, 10 being amazing.

I would put myself at, like, seven or eight. I don't cook that much anymore.

Okay.

And I do it 'cause it's kind of, like, fun. I li-... The idea of just following a process which is very deterministic, uh, but also kind of chaotic is, is, uh, is a lot of fun, so.

Yeah, we like that. Chaotic, but with, um, certainty. Looking at these ingredients we have before us, do you have any guesses of the dish we're gonna try and make?

I mean, clearly we're doing some sort of noodle. I see some bok choy.

Okay. Yep.

Um, I'm... But, uh, yeah, I, I, I don't know a lot about noodle dishes, so, uh, I'm excited to see what we got.

Yeah. So the dish we'll be trying today is Dan Dan noodles.

Oh, nice. I love Dan Dan noodles.

Yeah. We have a lot of protein here, some flavors. Very simple to make at home, but yeah, we'll taste it and then try to recreate ourselves.

Great.

Oh, that's fire. I'm gonna go for one more. That's awesome.

Are you also good with spice or are you kind of-

Oh, I love spice.

Okay, great, 'cause we do have some chili oil here. I've read that you've started coding since you were 17, and you had an internship at NASA for the Jet Propulsion lab.

Yeah.

So, like, how does a high school kid end up getting an internship at this very prestigious lab?

Uh, so they do have, they had this high school internship program where it was like-

Mm-hmm

... they would take for a summer. It was, like, summer after my junior year.

Yeah.

And they just, like, put us up in dorms at a local, like, university that was kind of empty. And so there's... The reason why people wanna go explore the South Pole of the moon is 'cause there's, like, craters there that are so deep-

Mm

... that there is, like, frozen ice there. The moon's very dry, but in, in these craters there's, there's, uh, water that has never been hit by sunlight. And so it's there for, it, like, has been frozen there since the formation of the moon, since it got, like, ejected from the Earth.

Oh, wow.

And so it's like, "Oh, we really wanna go see what's in that ice and collect samples and study it." Problem is, very, very deep craters.

Yeah.

And so, like, most of the rovers can't do it.

Wow.

And so we had to, like, we were basically, like, building some, like, pathfinding algorithms of, like, okay, given a rover and its current capabilities, and, like, what's the steepest hill it can go up? Like, I wanna go from here to here.

And I was 17. I didn't have a CS degree. Like-

Mm-hmm

... so we built a very naive implementation of Dijkstra's algorithm- ... to, like, find the shortest path according to some, like, constraints of what the rover could do.

Great. Yeah. I mean, that's a great start. So we can now look at some of the spices so we have it in our context before we go off to cook.

Here we go.

I think we're ready to go. Are you ready? Do you feel comfortable and we get started?

Let's ship it, dude. Let's go.

Dex, are you ready?

All right. Tell me what we're doing here, baby.

Okay. Let's start with the meat first. So-

Okay

... you should probably see some-

Pork?

Yeah, ground pork. And so ta- let's take one of the bowls.

Yep.

Um, and just mix some of... Put the pork in there. Shoot.

Okay.

Uh, I don't know how much pork you have. I'm gonna try to put not too much to crowd it and then

yeah, I believe we use-

Switching bowls here

... the Shaoxing wine. Okay. Let's cut up the garlic.

How much garlic you going for?

Um, honestly, probably, like, three cloves. Two, three cloves.

Three cloves?

Yeah.

All right, cool. I'm doing six.

Okay. How did you get into context engineering? I feel like, you know-

Oh

... coining the term, now it's like-

Incredible. Ac- according to Swix, there are- There are many people who, who, uh-

Jamie Sorkin

... Gemini thinks it was Andrej Karpathy.

Okay. I mean, that's a very valid guess.

Yeah. And then I, uh, and then it had a crash-out about Andrej Karpathy. I literally, I have a screenshot, it just, like, says the word An- Andrej Karpathy like 30 times in a row 'cause something gets, uh-

Okay

... yeah, you know. The standard-

Yeah

... Gemini crash-out. Uh, yeah, so basically I was building, like, dev tools for AI engineers.

Okay.

And, uh, what happened was I, like, talked to all the best founders and engineers and founding engineers that would, like, take my call, and I was like, "Hey, we're making this thing that helps you build better agents." I won't go into, like, the specifics of it-

Yeah

... but it was built of, like, I had this assumption about how, uh, people build agents.

Mm-hmm.

And, uh, so I built tools for, to, like, map onto that way of building agents, right?

Mm-hmm.

And the problem was, was that when I talked to all these people who were shipping, like, real AI to the enterprise-

Yeah

... like, reliable systems, it was like, I think, I think I saw this, uh... It was, like, an old tweet from, uh, from, like, Swix, actually. It was this idea of, like, the way the top 1% build is so different from the bottom 99%.

Mm-hmm.

You have all your, like, indie hackers and, like, open source frameworks that are very, very popular.

Yeah.

And, like, everyone uses, and that's what you see in public.

Yeah.

And then you go see how real people are building stuff and getting reliability, and it's completely different.

Oh, okay.

And so, like-

Why do you think there's such a discrepancy?

I think there's a difference between, like, people who wanna build, like, reliable software and people who wanna build a cool demo.

Okay, yeah.

I think that's the core of it.

The incentives are different.

The incentives are different. Yeah.

Okay.

The incentives of, like, okay, if this is right 80% of the time, that's fine. It'll look good on the demo.

Yeah.

But if you wanna go sell something to a real company and, like, charge them-

An enterprise

... you know, 100K a year-

Yeah

... it's gotta be much more, uh-

Bulletproof and not as-

Yeah.

Okay, that makes sense.

So I learned all this stuff and I was like, damn, I got a little bit, like, distracted by the, like, public hype machine.

Mm-hmm.

And so I said, "Okay, cool." Like, I don't want anyone else to go through this. So I wrote down everything that I learned and I called it, among other... Like, there was 12 chapters. There was only one of them was really about context engineering.

Yeah.

But that was the one that stuck.

And then you went through Y Combinator, right? In 2024.

Uh, yes.

Okay.

Yeah. So this is, this happened, we did Y Combinator in fall of 2024, and this whole, like, 12-factor agents context engineering journey happened in, like... Kind of like Q1, I wrote about it in Q1 of 2025-

Okay

... and then it kind of like went viral in April.

Yeah, 'cause you had a couple AI engineering talks that, um, even recently, right, in New York. No Vibes Allowed, was it the title? That-

Yeah

... you know, had, had some good reception. One thing that was very commonly said, even I think Swix had a talk about, was about slop. And I'm kind of curious, like-

More slop

... how do you define slop?

I mean, it's kind of fun. The word has kind of evolved.

Yeah.

And like, you know, it used to be like that's just AI slop, but now-

Okay

... you can say slop about anything, I think.

Yeah.

Of just like any like low effort or like contrived or derivative thing that is like someone didn't put a lot of effort into.

Yeah.

It's obviously most common when someone just like uses AI to write a 10-page document that they didn't read and is like full of garbage.

Yeah.

But, um... All right, so all the gar- garlic's going in the meat?

Yes.

Okay.

All the garlic, you just put it in there. We'll let the meat sit a little bit, and then we'll work on the sauce.

All right.

So, you should have a bowl in front of you. We can start off with a paste. I believe this is the... Yeah. Sesame paste-

The sesame paste

... you smelled earlier.

Right?

Yeah. Add like a spoonful.

Small spoon or big spoon?

Big spoon.

Okay.

Maybe like a s- big spoonful and a half.

Aren't you always big spooning?

That's a crazy statement.

Wow, Swix.

Okay. Um, yeah, so sesame paste.

Can we, can we quiet down in the back there, please?

Okay. Um, we can add some sugar.

Okay. Look at that. Look at that sauce.

Okay.

Fully incorporated.

Chili paste. Now, do like a spoonful or two, depending on how spicy you like it. I'm doing like two.

We're gonna go big here.

Yeah.

I didn't get my four cloves of garlic in, so...

Okay. Let's move on to... We're on a time crunch. Let's move on to cooking the beef. Not the beef, the pork. So, turn on your induction stove.

Yep.

You with me? Okay.

We're on.

I saw that you were at Replicate, was it, for seven years?

Replicate was dope. It was, um... We built... I started as an engineer, and we built a platform for, uh, how do we help... Uh, if you have like a SaaS application-

Yeah

... uh, like, you know, people subscribe, they wanna use it, and then you wanna sell it to like a big enterprise, like a bank or something.

Mm-hmm.

Um, they're pretty, like strict about data controls.

Okay.

And so instead of like using your SaaS, they'll almost always ask you to like send the app into their data center, into their AWS cloud.

Gotcha.

And that's a very hard and like expensive thing to build as like an engineering team.

Yeah.

And so we built a lot of tools to help solve that problem.

Okay. Would you say there was a lot of learnings and, you know, experiences that you transfer even today with HumanLayer?

Um, yeah. So I, I was an engineer there for like two years, and then I like... I kind of always knew I wanted to be a founder.

What made you say that? Were there just moments that-

There was just like... I just like... I don't know. I had a... I... One of the guys I did all my CS classes with in college, he would, um... Are you going veg in first?

You put in the meat.

Oh, the meat in. Okay. Yeah, yeah, yeah.

Just the meat.

Yeah.

The veg we're saving for later.

Okay. Like I said, I kind of like always wanted to be a founder.

Mm-hmm.

And so I wanted to do all the other things. So this opportunity came out to like basically do a more like salesy side of the job.

Gotcha.

Uh, and I like jumped at it. I was just like, "Yeah, I wanna learn sales, and I wanna learn product, and I wanna learn all..." So like I ran... I basically became the first customer-facing engineer.

Okay.

Or s- uh, yeah, basically. So, uh, and we had all these deals. We had like parted ways with our head of sales, so he had a bunch of deals on the like radar-

Yeah

... for like, "Hey, we're gonna close."

Yeah.

And then like they'd just been stuck for like nine months.

Okay.

And I went and I met with all of them, and we closed like... I think we closed like 12 deals in three months.

Oh, wow. 12 deals in three months.

And the CEO was losing his . He's like, "Holy . Like the investors are taking my calls again, and we just made the quarter for the first time in a while."

Yeah.

Uh, and it r- it was very exciting. He's like, "Dex, I know you wanna get back to coding, but like can you go hire three more people and like turn this into a team? 'Cause whatever, whatever you're doing of like going and onboarding into our, our stuff, it is, it is working."

Gotcha.

So that's how I got, that's how I got pulled out of like writing code all day and into like helping other people use this like Kubernetes Terraform like deployment s- deployment system.

Gotcha. And even today, like would you say your work is kinda distributed or less engineering, more salesy?

Uh, I still write a lot of code.

Okay.

Uh, I, I brought on, uh, like a technical co-founder over the summer.

Yeah.

Uh, and he's, he's awesome. Uh, he is a much better engineer than me. So I let him write most of the code.

Gotcha.

We like to joke. It was like, uh, yeah, we have the early... Oh, I was talking to some people last night, and I was like, "Yeah, we have an early product. It's got a couple bugs." And he's like...

And someone's like, "Yeah, all software has bugs." I'm like, "Well, this isn't really bugs. It's more like the features that Kyle let Dexter ship instead of building them himself."

Mm-hmm.

Yeah, so I'm, I'm probably, uh, you know, I'm spending most of my time, I'm on the like, uh, Dan Dan noodle CEO track- ... where I'm just hanging out making noodles with cool people now. And like we do a lot of workshops with customers.

Yeah.

Like what we're doing is like there's a product that helps you do it-

Mm-hmm

... but there's also just like a lot of like whiteboarding that goes into helping people like ship more code with AI-

Gotcha

... which is like we're focused on helping people ship like... How, how do we get coding agents to solve like hard problems in complex code bases?

Yeah.

And so that is, uh, you can automate a lot of that with a product, but there's still like, just like trainings and stuff. So I, I run those, and I own those, and like I travel around and like sit with our customers and help them like level up their engineering teams, in addition to like giving them a product that kind of like when I leave, they have kind of like guardrails and guidelines for how to do it.

Gotcha. And are your customers mainly like more upstream, bigger enterprises, or are they also like startups, a bit smaller mix?

Uh, we're doing a little bit of both.

Okay.

You could argue that we should pick one and focus a little bit.

Yeah.

Um, but yeah, at the moment it's really, it's a mix of like, uh, you know- 10 to 20 person engineering teams at, like, a bunch of, bunch of random, like, YC startups.

Yeah.

And then, like, a mix of, like, you know, 1,000 engineer, like, publicly traded companies that, like, have to figure out how to, like, standardize and adopt AI across their whole company.

Yeah, okay, that makes sense. Your meat should also probably be cooked by this point. Um, you should take it off and turn off the stove when it's got a color, like a golden brown color.

I need a little more color on this.

Okay.

I'm not, I'm not happy with it yet, but-

Yeah. But what-

... keep, keep, keep feeding me instructions, I will, uh, I will queue the messages.

Okay, yeah, so once you have that just put it into your main plating bowl and then we'll-

The main what?

Plating bowl, the big white bowl.

Okay.

Show the camera the plating bowl.

Yeah, it should be behind.

Yeah, see this? This is not, this is not colored enough. We're gonna get a little more color on here.

We could talk more about your favorite, you know, context engineering. How have you seen the craft of context engineering change over the past, I guess, year? We had much smaller context, uh, windows, uh, but then now we have a lot larger ones, and so probably the needs change.

Have there been, like, big things that you've noticed?

Well, so I think there's two competing things here, right?

Yeah.

There's this idea of, like, as the models get better you don't have to do as much context engineering to get the same quality of results.

Mm-hmm.

Um, like I saw a lot, like, a lot of my... People who used to be, like, my favorite engineers that I looked up to, and I still look up to them, but they were just like I thought about them a lot more in, like, 2015.

Yeah.

People who were, like, OG engineers on Docker and people like Mitchell Hashimoto. Um, suddenly there's this arrive of, like, all of the OG, like, DevOps people when Opus 4.5 came out they're like, "Oh, this is good enough. This is a big change."

Mm-hmm.

And I saw the things that they were shipping and I was like, okay, but I know a bunch of engineers who got really good at context engineering, and they were getting the same results from Opus 4.0 and Opus 4.1.

Gotcha.

And so, like, even as the models get smarter, I wonder, like, those people who were getting, like, really good results back in the summer, what are they doing now with Opus 4.5? What have they unlocked now that it's not being like, "Oh my God, Opus 4.5 is AGI"?

It's like, Opus 4.5 is AGI, uh, for people who didn't want to put in the time to learn how to do it.

Mm-hmm, mm-hmm.

You know what I mean?

Yeah. So would you say a lot of these models are kind of capped by the users or an enterprise's ability to kind of properly context engineer?

Um-

Or do you think we'll kind of get to a point where there won't really need to be thinking about what goes into the context of your model with such huge context windows?

So there was a thing I said in my first AI engineer talk-

Mm

... that was based on, like, an episode where, like, the latent space guys, they interviewed, uh, the NotebookLM team.

Yeah.

And, uh, I think it was Usama said this thing that was like, basically, like, there will always be a thing that the model can, like, only kind of get right reliably. Like, you find a thing that's right on the boundary of the model's capabilities, and you figure out how to get it right over, and over, and over again.

Yeah.

And, like, that's how you build incredible AI experiences.

Gotcha.

And I think that's still true, it's just, like, that, that, that frontier of what's possible is constantly shifting outward.

Mm-hmm.

And so, like, it's still, it's still reasonable to know how to do these things.

Yeah.

Um, because sometimes, some- at some point you might hit a problem that the AI can't solve, and then you have to go figure out how to solve it reliably anyways.

Yeah. No, that's fair. You mentioned, I think in the most recent AI engineering talk about, was it the dead zone? Where it's like after you've-

Oh, the dumb zone.

Yeah. With 40% right of context-

Bring me the dumb zone right here.

The dumb zone.

I'm the dumb... Dude, I, I know the dumb zone 'cause I've spent a lot of my life in the dumb zone. Let's put it, let's just, let's just be clear here.

So I guess for, like, people who want to do simpler tasks or more advanced tasks, are there, like, heuristics that you advise either, like, through your work with HumanLayer or just in general on when they should kind of think about taking more time to engineer it versus just, you know, putting everything in context and not caring about, like, over fitting this whole space?

Yeah, I mean, I, I love that question. Um, I think a thing, I tweeted about this a, a couple of weeks ago-

Mm-hmm

... is, like, the idea with the dumb zone is also, like, it's kind of meant to be a rule of thumb. Like-

Gotcha

... there are people I know who have been doing AI engineering for a long time who regularly push all the way to the end of the context window-

Mm-hmm

... because they kind of, like, know what they're doing and what they want and, like, sometimes a good trajectory is worth... Like, you'll actually keep getting better results 'cause your trajectory is really good.

Yeah.

So it's like if you don't know what you're doing and you don't really know what the AI model is capable of, and you don't have a lot of experience, like, you know, training wheels is like when you get to 40% start thinking about wrapping it up or like-

Mm-hmm

... like, doing a, like, you know, intentional steered compaction to where you want to be.

Yeah, compaction is-

But, like, depending on the model and what you're doing, and if it's really simple, like, I will regularly blow out to, like, 60, 70% of the context window used. Um, but it's like-

Mm-hmm

... if you don't know and you haven't developed the intuition, then, like, I tell people, like, shoot for 40% if you're learning.

Gotcha. And so, like, how does someone even, like, get familiar and build this intuition? Like, if somebody came up to you and says they want to take this more seriously and don't really know where to start, and even when it comes to compaction, like, how to even assess, like, accuracy, or relevance, or what to even consider, and how to even judge whether compaction is useful, um, what would you advise them?

Would you tell them to take specific steps or, you know?

Uh, yeah, talk to Claude for 70 hours a week. That's, that's how you do it. You gotta build intuition. Uh-

Just use AI as much as possible.

Use as much AI as possible, make mista- I mean, like, the thing that people actually ask me is, like, we developed this framework called, like, research, plan, implement.

Yeah.

And I get this question a lot of, like, what is, like, how do I know when to use research, plan, implement versus, like, just talk to Claude, right?

Mm-hmm.

There's some issues that are really small and, like, it doesn't really warrant the whole, like, process of like, okay, we're making a one-line change. The only way you learn how hard the problem is for AI is, like, you're gonna do too much sometimes.

You're gonna be like, "Oh, that was way too much attention," and now, like, I, I wasted a bunch of time on a thing that I could've just talked to Claude on.

Yeah.

And then you'll learn and be like, "Okay, next time I'm gonna go to the other side." And sometimes you'll, like, go talk to Claude and Claude just, like, flails and can't fix it 'cause it's too big of a problem-

Yeah

... or the context wasn't curated well enough. And so it's like the only way to know how much context engineering to use on a given problem, I think, is- Um, you just have to get some reps.

Yeah.

I don't, I don't have a better answer than that. I'm, I'm sure the model labs love that that's the answer.

Mm-hmm.

Uh, but, uh, yeah, that's, that's my biggest advice, is like put in the reps and figure out what's possible, and like try to push the boundaries.

Gotcha. Oh yeah, that's helpful. Definitely just using a lot more reps, kind of like if you're going to the gym and trying to work out a muscle group.

Yeah, the, uh, what do we, what do we call it? Like, the mind-model connection.

Yeah, the mind- ... mind-model connection. I guess like on that, do you have predictions going into like '26, '27, even the next, you know, couple of years about how this industry will change that are top of mind?

Uh, I mean, I think I s- I mentioned this before is like the software engineering role is gonna keep evolving. I don't believe the like software engineering is dead and there will be more, no more, no more coders.

I think the, the way I would describe it is like the role of the software engineer will change from like write working code to like produce working code.

Mm.

Or like cause working code to be produced.

Gotcha.

And one of my biggest pet peeves is the people who run around Twitter talking about how much like Claude code they spent and how many tokens they did and how parallel their workflows are-

Yeah

... and don't talk about like, "Yeah, I shipped a reliable product to people that like it, and like maybe even are paying for it." But it's like stop. We need to talk less about how much like code we produ- like how much code we produce and how much we actu- Like Guillermo Rauch talks about this.

He's like-

Yeah

... "There's a difference between coding and shipping."

Mm.

Coding is like making the software work and maybe making it work on your la- on your workstation. Shipping is like getting it into prod, fixing the things that are broken, maintaining it over time, and like continuing to make it better.

Gotcha.

And like that part is not quite solved by AI yet.

Yeah.

Maybe we'll get closer, but like I'm always looking... I mean, our company's called HumanLayer-

Mm-hmm

... 'cause we used to do human-in-the-loop.

Mm.

But now it's a little bit more like I think about it as like what is the high leverage things for humans to do?

Yeah.

And like what is the things that we can like leverage AI for, and it's kind of a dance. There's like if you're making code, there's like parts of it that AI just can't do right now, and there's parts of it that it can do really well.

Gotcha. Yeah, so to complement, sounds like quality over quantity, especially with all the, you know, tweets about-

Yes

... multiple Claude code instances and just shipping things.

Well, it's like we're working with like big enterprises in like fintech and like other like spaces where it's like

we can't afford to get it wrong.

Yeah.

There's no ship fast and break things.

The stakes are much higher. Mm-hmm.

I mean, though, like it's great for all the indie hackers and vibe coders that they're shipping a bunch of Claude code stuff.

Yeah.

But it's like how do we make this stuff accessible and make it so that like real production-grade products can get access to the same gains?

Okay. I'm in the assembling stage right now. I'll give you like another minute.

I didn't realize we were on the clock, dude. All right.

Yeah. But no rush, of course, Dex.

And then how are you, how are you straining these noodles out?

I'm just taking a fork and just transferring it over.

Oh, no. I tried to set this on 7 and it is still on 10. All right, we're gonna take this off. This is getting chaotic, dude. This is fun. Let's go.

All right, whatever.

All right, we're gonna just do a little sauce riff here.

Great. All right.

Final plating.

My plating is not gonna look as good as yours- ... but we're gonna figure it out.

Yeah. Taste is all that matters.

This is why I love cooking for myself- ... 'cause I like, I don't wanna have to care what it looks like. I'll eat, I'll eat the ugliest slop in the world if it's delicious.

All right. Food's all plated?

No.

Nope?

Let me just wipe this off. Let's get that fork out of there, and we are ready to rock.

Great. Let's rock.

Let me turn this off.

Smell.

It's beautiful. Look at that .

Great. How was that, Dex? Did you have fun?

Uh, it was a little chaotic, but it was a good time. I'm excited to try these things.

Yeah, it was definitely a rush during the end, but we both finished, so both of our Dan Dan noodles.

All right. Let's have a look.

Let's see. Okay. Wow, yours is a lot prettier than mine.

I, yeah, I-

Well, I would say least

... at the last minute I decided I was gonna try to, try to present it a little bit.

We should give it a try. Here's a fork for you.

Let's give it a taste. Are you gonna, are you gonna try mine or we're gonna-

Yeah, let's, let's try yours first.

Okay.

Then we can try mine after. Here. Cheers.

All right. Cheers.

Mm. The bark chu was done very well.

Really?

Yeah. It's like-

If you had given me... If you let me take my time I probably would've overcooked it.

Mm-hmm.

I think I used too much, I threw in some paste at the end. I feel like I used too much-

Oh, okay

... of the, the sesame paste.

Yeah, I think yours is definitely a little spicier, but let's see how mine... Mine's definitely a little more thick.

Let's give this a shot.

Great. Cheers.

Mm.

Damn. Okay. I really like the like... Something about this one is like it's more well-rounded. It just like the flavors all come together in a nice way.

Yeah, I think this is like-

This one feels a little sharper.

Yeah. Let's try the rougher.

Yeah, I'm definitely like a follow the recipe once guy.

Yeah.

And then-

I also usually just-

... and then, and then, and then riff it myself. 'Cause like now I'm like I made this, so I was like, okay, I can think of like four things I would change next time.

Yeah. Cheers.

I feel like this-

Mm

... one just has less flavor than both of them.

Yeah.

It's just a little more-

I, I agree

... like mild.

Like I, I think this is like very like safe and-

Yeah

... you know, not as-

If I was making food for other people I would make that and then I would put the chili oil on the side-

Yeah, yeah

... and be like make it, make it, make it, uh, make it more fun if you want to kind of thing.

Yeah. Add a little extra spice in there.

So this one's mine, this one is Alan's, and that's the reference one.

Yeah.

Any call-outs? Um, where can people find you?

Uh, I'm on, I'm on Twitter @dexhorthy. Uh, you can get my weird, uh, unfiltered brain trace as I go through the world of building agentic IDEs and playing with coding agents and keeping up with all the new things that all the labs are shipping.

Yeah.

Uh, and then we are, we just rebuilt our product from scratch. It's an agentic IDE. It's-

Exciting

... uh, coming out soon, and, uh, you can go sign up at humanlayer.dev, and, uh, we'll shoot you a note when it's ready.

Yeah. Great. Well, back to our judges. Are you guys ready to say?

Yeah, I want to, I want, I want the verdict here.

So I give Dex the slight win here. It, s- they, they all used the same ingredients-

That's cool

... so it's like-

Yeah

... you're, you're gonna be very comparable. But I just like the texture and the flavor. And also I, I really like the, the extra protein. Like you just like went all out with like the flavor. I love-

I took a bunch of noodles out. I was like I want, I want more pork.

Yeah.

I want the ratio. I want protein.

Yeah. The, the ratio is just very different, and I like that.

Okay.

Um, I, I have to agree. Dex's is more meatier.

Aw.

Yeah.

And-

Damn, that's the-

Sorry, Alan

... is that-

Let's go, Alan. Oh, yeah.

Yeah. Win, win any cooking competition with this one weird trick. Just make more meat on the plate.