Burn Your Flags: Interactive CLIs for Agents — Mark Lummus & Navinkumar Patil, PayPal

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Original language: English

Original video: Burn Your Flags: Interactive CLIs for Agents — Mark Lummus & Navinkumar Patil, PayPal · Channel: AI Engineer · 53:14 · Watch on YouTube

Topics: AI agents, AI developer tools

Summary

Mark Lummus and Naveen Kumar explain why interactive CLIs can fail when used by AI agents: agents may not perceive terminal prompts, and the failure can be silent. They argue that flags work for simple, predictable flows, but interactive decision trees can preserve context and guardrails in ways flags cannot. The workshop challenges participants to adapt PayPal’s interactive coffee-order CLI for agentic use. Their recommendations include using tmux, providing machine-readable side channels and context, and keeping the CLI deterministic while the agent handles reasoning.

Key points

Interactive CLI prompts can be invisible to agents or unsupported by their harnesses, causing quiet failures.
Flags are useful for simple, independent options, but struggle with context-dependent questions and real-time responses.
Agents can reason, react, retry, back up, and follow a decision tree; automation scripts generally rely on decisions made in advance.
Accessibility offers a useful model: design CLI interfaces with both human users and agents in mind.
The workshop asks participants to make the interactive Bris Barista 9000 coffee-order CLI work better in an agent workflow.
The speakers suggest using tmux sessions, help commands, and pane capture to let agents interact with terminal-based programs.
Machine-readable side channels can expose state and accept input without relying solely on screen scraping.
Context injection can give agents relevant operational details, such as limited ingredient supplies, and guide what they should ask users.
A CLI used by an LLM should be deterministic and narrowly focused; the agent can provide the non-deterministic reasoning.
The closing recommendations are to provide good context, add a side channel, and tell the agent to use tmux.

Chapters

Start	Chapter	Summary
00:12	Workshop introduction	The presenters introduce the session’s focus on interactive CLIs and its hands-on challenge for agent workflows.
02:18	Why interactive CLIs can fail for agents	They describe how agents may miss interactive prompts and why the resulting failures can be silent.
04:26	Flags, agents, and decision trees	The speakers contrast simple flag-based commands with contextual flows that an agent can reason through.
06:06	The coffee CLI challenge	Participants are invited to adapt the Bris Barista 9000 CLI, with a Mac Mini offered as the prize.
08:14	CLIs return in the age of AI	Naveen describes how CLIs have persisted through changing development environments and are becoming useful again with agents.
10:19	Getting started with PayPal Cafe	The presenters explain how to obtain the repository, register, run the CLI, and begin the exercise.
15:52	Choosing models for the challenge	They encourage participants to consider whether less costly, faster models can use the CLI effectively.
25:44	First suggestion: use tmux	The presenters recommend tmux, help commands, and capturing terminal panes to support agent interaction.
28:20	Side channels and structured output	They introduce machine-readable side channels as an alternative to relying only on visual terminal output.
31:25	Designing for agents with context	They explain how structured inputs and injected context can help agents handle situations such as ingredient shortages.
34:14	Leaderboard and scoring	The presenters review participant scores and explain that ties should be resolved using response time.
39:52	Participant strategies and determinism	A participant discusses deterministic parsing, and Mark explains why a CLI used by an LLM should behave predictably.
47:15	How participants built their solutions	Participants describe using agents to build deterministic CLI solvers and improve their results.
51:36	Takeaways and winner	The speakers recap the main recommendations—context, side channels, and tmux—and confirm the winner.

Quotes

not all harnesses handle interaction very well. — 02:50

An agent can walk a decision tree if you provide provide one. — 05:34

every AI agent in today’s world relies on the help command — 26:16

We want the CLI to be as someone described it a small sharp instrument. — 41:59

Give it good context, give it a side channel, and tell it to use T-Mox. — 52:06

Concepts and entities

Interactive CLI, PayPal Cafe, Bris Barista 9000, tmux, PayPal, Mark Lummus, Navinkumar Patil, Claude Agent SDK

Full transcript (original language)

00:12 Hi everyone, welcome in. My name is Mark Lumus. I’m head of developer tools at PayPal. With me I have Naveen Kumar. He’s our tech lead for the developer tools team. And today we’re going to talk to you a little bit about CLI. The title of our presentation is meant to be a little provocative and you’ll understand why at the end of the presentation.

00:43 So today we’re going to talk a little bit about a specific kind of CLI, interactive CLI, and we’re going to set up an exercise and we’re going to give you for those of you who wish to participate, this is a workshop after all. We’ll give you a CLI that you can make work better in an Agentic workflow. So that’s the purpose of this workshop today. So we’re going

01:14 to give you plenty of time. We’ll talk for a few minutes. Try to keep the talking to a minimum and then we’ll get you started. Now it’s kind of interesting. I think someone said what’s old is new. When I started my career long time ago, I have gray hair. Everything was a CLI. Everything was a command line interface. Any of you remember working with MS DOSs before we had Microsoft Windows? There you go. My

01:46 first job out of college, I was designing embedded systems. And so you would connect to a computer over a serial port and you would pull up a VT100 compatible terminal and you would start typing. And that was your CLI. That was the the the way you interfaced with the computer. So with today CLIs have kind of had a resurgence I guess you know with the maybe it was clawed code that really made them much more

02:18 popular and now we have codecs um and so a lot of people are are adding CLI uh tools so that it’s easier to access the capabilities that they want to expose to an agent. Now, with interactive CLI, this is why we’re here today with what we want to talk about. Um, agents shell out to Bash constantly, and that’s good. But if they run an interactive CLI,

02:50 that may be bad because not all harnesses handle interaction very well. And if they don’t handle it very well, they’ll fail quietly. You won’t know. No errors, no nothing. But why do we care? Interactive CLIs are great because the next question can emerge based on what way you answer a previous question. Flags multiply if you try to create

03:22 flags that predict all the possible flows. The choices are contextual and they can also be responsive to what’s happening in an environment. So if your CLI is calling an API and getting a response and then the question comes up based on what the API returns, that’s really really challenging to handle with flags and the guard rails can live in the interactive flow.

03:55 So here’s an example over here on the on the left. We see a a sample CLI. It’s interactive. The CLI’s opened an interactive prompt, an error key prompt. The human sees it. The human can use the arrow keys, but the agent doesn’t see it. The agent sees a redraw loop, not a question. and the harness loses the UI

04:26 state. So here’s the failure mode. It’s silent and invisible. So the common fix will just add flags. And flags are fine for simple use cases. You have uh you know what the you know what the flows are up front. It’s a single pass. All the options are independent. Great great use case for a a a simple flag.

05:02 But when the answers change based on the context or they’re changing based on reactions to real time events, that’s when it becomes a problem. You really can’t use flags for those situations. Now, an agent is not like an automation script. An automation script knows nothing. All of the decisions are pre-baked up front. The agent can reason. It can

05:34 react. It can ask. It can retry. It can back up. And most importantly, an agent can walk a decision tree if you provide provide one. You can’t do that with flags. So we already have a model today for what’s happening here. It’s called accessibility. And so if you map what we do for humans

06:06 to what we need to do for agents, then the solutions become apparent. It’s the same discipline with a new reader. So here’s the here’s the challenge. How might we keep the guard rails and the guidance and stop pretending that agents and humans have the same abilities? Now, I’m not going to answer this question. That’s the whole purpose of this workshop.

06:40 So, here’s the challenge. We’ve got a CLI. We call it Bris Barista 9000. It’s going to take coffee orders and it’s going to fail when you try to run it from an agent because it’s interactive. And if you choose to participate in the challenge, the winner will win a Mac Mini.

07:12 Walk walk away with it today. It’s in that bag right there. [laughter] >> That was a challenge in and of itself getting a Mac Mini. I’ll be straight up. Okay. Now, [clears throat] simple rules. We’re going to give you a CLI. You make changes to that CLI, run it through an agent, and it’s going to send some information

07:42 up to our server. And then it’s going to update a leaderboard. And we’ll put put the leaderboard up in a moment. We don’t have two inputs, so we’re going to change the slide deck out for the leaderboard so you can see it. Now, here’s Naveen. He’s going to talk you through this. >> Thank you, Mark. All right. >> Cool. Yeah. Uh I think it’s an exciting assignment for everybody and this topic

08:14 is very near and dear to me but uh keeping things aside Mark are we getting any coffee after we build this [laughter] >> I hope so if we’re successful >> okay cool so yeah let me uh before we start into um the assignment I want to take to a journey right uh when I started my engineering career I started to play with ids. I started to use Eclipse because I was primarily using Java.

08:45 And as time went by, moved into Intelligj uh Jet Brains. It was amazing. Looking back, five or six years behind uh things started to evolve. there were new ids in the market especially Microsoft VS code which gave birth to a lot of u other ids as well. So if you look right now Microsoft had its own VS code editor but later on new

09:18 AI id started to pop out like cursor windsurf to name a few. In all these journeys, CLIs were constant, right? At some point, I thought, okay, you know, CLIs are having a slow death of their own. Maybe they are not going to come back. But, you know, uh I I I actually like to see this hangover movie dialogue that um you know, all all the four friends are in the car and they say, “Hey, we are

09:49 back.” Imagine CLI seeing that to each other. And in the world of AI, they are actually VC, right? Whoever bought it, yeah, I don’t know. But the whole story is now the CLI have changed. And who is responsible for that? Agents, right? So this workshop is primarily focusing on agents, your CLIs. And this is going to be a hands-on exercise.

10:19 There are rules to it. There are no rules to it. So feel free to hack as much as possible and you know the prize is here. Um so yeah we have maybe uh ven if I can get the laptop. So we have couple of things. So we have yeah um so we have the repo which is PayPal cafe. It has the CLI that you can actually download. That’s the barebone CLI. Now, it’s not going to

10:52 be that easy uh for a Mac Mini. Yeah. Uh so, you will get some initial commands with it. Uh play around with it. Register yourself. Uh provide your username, whatever you like. Um your nicknames, anything, and try to brew a coffee out of it. Okay, that’s that’s the goal. And in this whole exercise, we want you to also be innovative in your approaches and going forward, it’s going to get

11:22 interesting with agents using the CLI. So think it from that perspective. I change this. I just change my laptops here. Now during the during the session, we’re we’re not starting you out with any hints. Once we feel like enough people have gotten the the repo installed, can run the the CLI as is, then maybe every 15

11:56 or 20 minutes, we’ll share a good idea. Now, I’m not going to call it a best practice. I’m just going to call it a good idea because I feel like we’re early enough in the journey here where we don’t know what the best practices are. We’re kind of feeling them out now. They’re good ideas. I’m also hoping that at the end we’ll hear some great ideas from you in the audience so we can

12:27 share. So just keep that in mind. We’re we’re all learning. This is a journey. We’ll come back to that towards the end. Is it not working? Give us a couple of minutes. I think we’ll get the technical issues resolved.

13:13 seems like people are actually using it. So on the right side you will actually see get CLI and um if you see PayPal cafe/ rules that’s where you will have all the rules outlined to it. Feel free to start working on the CLI till we figure it out. Okay.

13:53 >> Do we have the Europe? Oh, is it that one? >> Yeah. Hey, there you go. >> Nice. >> Awesome. >> Thank you. Thank you so much. Hey

14:26 Cool. So you can see this is Oh, we do see some signings. Okay. So on the right side you will see get CLI. You can download that and I hope you guys can see it. It’s at the bottom right. And there are some rules here. You can click on these rules and it will pop out some rules.

15:00 So, we’ll probably take around about 25 to 30 minutes. Um, and in between we’ll interrupt with more slides and make it interesting. [laughter] >> Where’s the laptop for the other slides? Venode’s got it. Okay, that’s fine. and we’re here to help. If you’re having trouble, please raise your hand. We’ll try to assist you as best we can.

15:52 a little bit about the models. Don’t use models. Now, we had a little bit of a debate internally. There are some great models out there. The interesting thing about this challenge,

16:22 if you use the highest thinking mode on the bleeding edge LLMs, you can probably brute way brute force your way through this. But I feel like in the current climate, teams are starting to think about their token budget. And so you don’t necessarily want to waste your highinking tokens on running a CLI tool. So the real

16:52 challenge is can you make Haik coup or sonnet or something at that level run your CLI? We know you can do it with the highest. Can you do it with something that’s somewhere in the middle that’s faster and cheaper? That’s the That’s the interesting challenge if you if you want to take it on. >> Sounds fun. Yeah. >> Awesome.

17:56 Since we are on the coffee topic, uh Mark, were you aware that San Francisco City has the best bakeries in the entire United States? Were you aware about that fact? >> I was not aware of that. >> Yeah, you should definitely. >> Go on. Yeah. Go on, tell me more. >> Um, I have I’ve done some bakery crawls uh around in the city and they are so fun. Like you should definitely check out all the amazing bakeries out here. >> Okay. >> Yeah. Any favorites? Butter and crumble.

18:30 My favorite one. Although I couldn’t go there. [laughter] Awesome. So as of now we have J Gray Codex at the top. So guys please catch up with him or her

21:03 So with the init command, you could actually register your names. I hope some of you have figured that out.

22:06 Do you want to talk about the first

22:45 You might have to think about

24:47 Once I plug it in, I

25:44 >> [sighs] >> Go ahead. >> Okay, >> cool. Um, let’s move on to some presentations. Meanwhile, you can start working. I mean, you’ll continue working with the CLI that you have. But as as a first hint, you know, it’s it’s a good idea to start using T-Max. How many of you know about T-Max here? Okay, quite a fair amount of audience out here. Um, so

26:16 yeah u in short start with running T-Max and um start by putting help command. Now, help is really important because every AI agent in today’s world relies on the help command before it even starts using that command. [laughter] So, yeah, send keys and capture pain gets

26:48 you far. Now, T-Max actually um for those who actually don’t know about it is like a multiple you could actually use multiple terminal sessions uh with the T-Max and it’s like a split pane feature that you could leverage with T-Max. So, with this example, you can see here um I mean the screen is very short for me. So, let me just zoom in a little bit. Yeah, T-Max D. Now, what this

27:19 flag does is it detaches the entire uh CLI and puts it under a different emox instance all together for [laughter] you. So you could see size uh T-max capture pane like I said you could actually have split panes in T-maxes. So you could actually take care of u working around with um in in the

27:50 CLI and uh that’s one of those cool features that can help your agents perform lot of activities. Um, think of it when you want to run multiple agents. You could actually spin up different sessions and T-Max actually takes a log of those things where it knows what are you working on and resumes those sessions whenever it’s needed. So, it’s actually helpful for agents to

28:20 communicate and start communicating when they have multiple sessions open. And these are some long running tasks that you could actually run inside T-Max. [snorts] Second good idea is IPC site channels. So like I mentioned before, T-Max was a good upgrade. So instead of doing screen scraping, which T-Max actually does um for visual in inference, [laughter]

28:51 you could start using side channels. Now [snorts] site channels would help you define your arrow keys that you have and help you understand and I’ll go in the next slide uh how it works but the agent should know what is the output JSON now this becomes really critical in the entire talk is output JSONs and Asians want that since we have multiple personalities now earlier we used to

29:22 only cater to human beings now we have agents. So think of both these personas so that your CLI can actually take advantage of both these use cases. So site channel for those who don’t know about it let me explain to you in in simple words think of it like adding a REST API to your web application and the website gives humans a visual interfere uh interface.

29:52 The API gives you a machine structured interface right so think of side channel as exactly the same that’s one of those analogies that I can refer read state send input as a data [clears throat] so we have the bista brew hyphen agent now this this is a command that will help you run your CLI with background agents, right?

30:24 Um, you want your agents to be running in the background and uh you put all of these commands, they are going to help you try using it in your current CLI. See if you are able to um make some progress in the barista application that is out there for in your laptops. Now, there are a couple of things that you could actually provide as an example out here. You can see barista [clears throat] brew ask and then

30:55 there’s a bunch of information out there. Oh, what is it? Okay, fields then small, large, medium, maybe the sizes of the coffee and yeah, default as large. What does that even mean? That’s input data, right? So all you have to do is feed in this input data so that agent actually knows that and you got to do it in machine readable formats. So this is where um that thing comes in play.

31:25 So don’t just tell the agent what the human sees, okay? Think it out of the box. What are the agents going to think? They have different skill sets and you have to start leveraging those skill sets. Same prompt with a different brain. Now, [clears throat] so on the left side you see I was talking about different personas. One is the human beings and the other one is the agents.

31:57 The CLI is the same but when you look at the way it is structured human beings want set of options but on the other hand you have agents wherein you could provide all this information in one single input file. [snorts] Okay. So this is where we call this as context injection comes in play. You provide context in such a way that let me take an example. You you are actually

32:29 low on oat milk. Okay. And uh you have only two gallons of uh oat milk left. Tell your agent what kind of information it needs to ask the user. confirm it if if two ounces are good enough. Do you want me to pick a backup for you? This information should be provided using context in injection as a technique and you can leverage this in

33:00 the CLI that you are actually building. Try using it. Maybe you’ll be on the highest on the leaderboard. So we started a journey from T-Max which is basically screen scraping to injecting skills. So T-Max is low effort. I think a lot of you might have tried it. But this is where site channels [snorts] also will help you in providing [clears throat]

33:32 good um sort of feedback to your agents and context injection will provide you those different scenarios that you can think about which doesn’t happen in the human case. So yeah, keep pushing your scores. You want to show this? >> Yeah.

34:14 It does take some time. >> There you go. >> Okay, [snorts] cool. We do see a lot of traffic out here. [clears throat] [laughter] Oh, somebody is really close to 5,000. Is it bank? Okay. [clears throat]

34:53 Any questions so far? Anybody wants to get it answered? Any queries? >> Sorry. >> [laughter] >> Okay, let me repeat that question to you. [clears throat] What happens if multiple people reach a score of 5,000? Mark, you want to take that? >> So, the the idea was we would look at score and if there’s a tie, we would

35:25 look at which one took the least amount of time. Now, based on the resolution of this, I see several that are that have completed three of three in zero seconds. I see [laughter] a bunch of these that have completed it in zero seconds. So, I need to consult with someone because that’s not supposed to be possible. But the idea is that we would look at the high score the the score first and

35:58 then the time it takes to respond second. So that an accurate response returned quickly would be biased over an accurate response returned slowly. So we have only one for now.

36:29 >> Okay, sorry. >> We have only one. eventually comes to this is the bottom of the leaderboard. Nicholas [snorts] game up. [clears throat]

37:09 0 second. Do you want to

37:41 wait two more minutes? We can open it up ideas how engaged they are. Did we have

38:23 >> [clears throat] >> In just a few minutes, we’ll open up the floor for some idea sharing. So, I’m I’m hoping that we’ll hear some great ideas that go above and beyond our good ideas because I know that there’s some out there.

38:58 Mark, were you expecting so many people to be in the 4,000? >> Yes, absolutely. >> At this conference? >> Absolutely. >> Awesome. Were you not? >> No, I was. [laughter]

39:52 What do you think? Anybody can make it to 5,000? >> I don’t know. We’ll hold it open. [laughter] >> Why don’t we open the floor up now? >> Yeah, sure. We’d love to hear ideas. So, did anybody in the room come up with other ideas that were able to get them to such a high score so quickly? Nobody wants to leave the desk.

40:24 [laughter] Oh, we have Yes. Let me let me come over to you

40:55 [clears throat] because I can barely hear you. >> Okay. it it’s a very easy to game benchmark because the only thing that matters is you fulfill one order within like small like a very small latency. So if you do a deterministic parser um it which defeats the spirit of this challenge entirely but you deterministic parser

41:26 and you have something like really close to the server you can get the latency down uh really quick if you just brute force it. >> Okay, which is that what you did? >> That’s what I’m doing right now. >> Okay. All right. Now you said something interesting the the deterministic parser that’s running in your CLI. Okay. I actually like the idea of the determinism in the CLI. And [clears throat] the reason is that

41:59 if the CLI is a tool to be used by an LLM, then you absolutely want it to be deterministic. The LLM is the non-deterministic player here. We want the CLI to be as someone described it a small sharp instrument. Should be very very focused on what it does. And every time you give it a command, you want it to come back with the same response. So

42:31 you don’t want to build an agent into the CLI and then hand that CLI off to another agent because you don’t know what you’re going to get. Any other interesting ideas? Obviously we a lot of people have figured out how to game the system. >> [laughter] >> Where there’s a Mac Mini, there’s a way, I guess. >> Yeah. [laughter]

43:16 Wait five minutes. Five minutes. The score hasn’t changed in a while before the site crashes. >> Did you do refresh?

43:57 >> [cough] [clears throat]

44:41 >> Why does it say reconnecting? Oh, there we go. Okay. See, the network is a little laggy. The network is a little laggy out here. >> A lot of lot of computers. Sounds good. Crash.

45:35 And we’re frozen right now. [clears throat] Here we go. No change in the results. Okay.

46:11 Do we want to wait a few more minutes or call it? >> Yeah, five minutes. >> Five minutes. Okay. You want to bring the PPD to PPD for the closing arguments. >> We’re going to give it about five more minutes. >> Yeah. >> And then we’ll call it. >> Where’s Bon? Where’s Bonk? Raise your hand. >> Who is Bon? Okay, we have something. >> All right. Big smile. Big smile. [laughter]

46:44 Now, >> we do want to hear how you did it. So once once we call it, then we’ll we’ll we’ll we’ll let you explain what you did. I’d love to hear. [laughter]

47:15 I need to go over here. >> Yeah, >> hold on. Sorry. I should have asked for another I should have asked for another microphone for the room for questions. >> Um, yeah. So, Bonk is just a an agent uh thing built on top of cloud agent SDK. Um, it built the whole thing. Prior to this, I already had a sort of eval bench skill that will create sort of an

47:45 environment and um optimize. So, it basically ended up building a uh deterministic CLI solver and then now it’s just running on a T-m slamming your APIs. >> Okay. >> Yeah. >> All right. aso. I think I got lucky because I hit it at one point at like 29 milliseconds. Uh probably because we’re in the same region. >> Okay. >> It’s just a network latency, >> right? And this is all done on claude >> uh claude agent SDK. Okay. Just driving it through Slack.

48:16 >> No, which model? >> Opus 4.8. >> Okay. >> Yeah. >> Did you try it with any other models >> for the build uh for building the CLI or >> for for your a for your agent? How would we do that? The CLI is completely deterministic. Sure. Sure. So, so the agent built the CLI and is just running it on T-Max now. >> Okay. >> Yeah. >> Awesome. >> Yeah. >> Congrats.

48:46 >> Going to go to the next one. Danny. Who’s Danny here? You see last one. [clears throat] What do we want to know? >> Yeah. How’d you build it? >> Yeah. So, I don’t know anything about uh

49:19 coding at all. I’m I’m using uh I’m basically a Vibe coder. I have a couple agents and I just said, “Hey, here’s the problem. Read it and then fix it and fix it.” So, I just have Yeah. Cloud code up on uh VSC. >> Have you ever built a CLI before? >> Uh no. I I saw a couple hours ago that obviously there was a prize to be won. So, I got in and I said, “Hey, [laughter] uh figure this out. Explain it to me like I’m, you know, like I don’t know anything.” And then explain like I’m

49:51 five. Yeah, I I I don’t know. So, and then we came up with uh yeah, we came up with some good results and I said, “Hey, we need to make it faster and give me some suggestions and yeah, seriously.” So, I I guess [snorts] we have uh two scripts running and the first one got the uh 49.98 and then I said, “Hey, make a copy and give me some suggestions for how we can get the 49.99.” >> Right. >> Did you get any suggestions? Uh it’s

50:22 this agent’s pretty automated. So I just say like hey here’s the start here’s the end give me a couple you know questions at the beginning if you need it but then fix the problems so I don’t have to deal with it kind of thing. So sorry that’s not a very good >> No it’s good. I’m I’m sure a lot of people are wondering like writing a CLI is that something that you would ever do or would you ask your agent to do it for you? So clearly we have many people in

50:54 the room and just going to point Claude or Codeex at this and say build a CLI, make it fast, make it deterministic. And those are the three ideas, right? Okay. You want to change the slide?

51:36 There it is. >> [clears throat] >> So, we talked about all of our suggestions. We heard some ideas. The predominant idea was let your agent figure it out for you. I love that idea.

52:06 Um, anything else before we close it out? All right, there you go. Takeaway for us based on our instructions, our recommendations. Give it good context, give it a side channel, and tell it to use T-Mox. Those are three good ideas to start with. Thanks everyone.

52:37 Bonk, you want to come on up? [applause] >> We still have Monk as the winner, right? Yeah. Okay. No, no, we called it. We called it five minutes ago. >> Okay. >> Yeah. Yeah. I’m not gonna change [clears throat] I’m not going to change it. >> Yeah. >> We We called it. >> Awesome. [applause] >> Let us [clears throat] We’ll get We’ll just get a picture, >> you know, social media. That’s right.

53:14 It’s a real Mac Mini. >> It’s a real Mac. Oh my god, these exist. [laughter] >> All right, awesome. >> Where’d it come? >> [music]