AI Engineer Europe 2026
Scaling Agents on Kubernetes with acpx and ACP
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Scaling Agents on Kubernetes with acpx and ACP
Direct agent connections turn chat channels into development workspaces; structured workflows and Kubernetes extend that pattern to disposable agents for enterprise tasks.
From a talk by Onur Solmaz
Before you start: Familiarity with coding agents, pull requests, CI, and basic Kubernetes concepts will help you follow the workflow and deployment examples.
Getting instructions to the right agent
How do you use your preferred coding agent from Discord without another agent rewriting your instructions along the way? That integration problem sits inside a longer history of building agent harnesses. Onur Solmaz started with a JupyterLab extension over an early Codex model, then repeatedly rebuilt it into the harness used at his startup. After following Peter Steinberger’s Claude Code is My Computer, he installed OpenClaw in his company’s cluster, where coworkers could interact with it through Discord. Enterprise requirements led him to add Microsoft Teams integration and become an OpenClaw maintainer. His focus expanded to interoperability and orchestration, with security still a work in progress.
Chat-driven development initially involved a telephone game. Solmaz preferred the Codex harness for complex coding work, but from Discord he was asking Opus to tell Codex what to do. When he inspected the resulting Codex session, his instructions had been paraphrased. The work sometimes succeeded, but the extra interpretation was an uncontrolled transformation at a boundary where wording matters. His assessment of Opus concerned the version he was using then; he explicitly notes that the agents had since improved.
Direct channel binding made Discord function more like an IDE. Solmaz reports working with one to five agents across separate channels, with another channel reserved for testing OpenClaw’s ACP support. This made it practical to start and follow side projects while away from his desk. He built acpx through Discord itself: a channel connects to Codex through the Agent Client Protocol, or through the Codex App Server integration he credits to fellow maintainer Harold.
The PDF example exposes what channel binding does not solve. Before flying to London, Solmaz asked Codex to turn the ACP documentation into a PDF. Codex could create the file, but it did not understand the surrounding chat harness or how to deliver an attachment into Discord. He therefore asked it to save the PDF in a temporary location, then moved to another channel and asked the agent there to send it. Reaching an agent and delivering its artifacts are separate integration responsibilities.
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One client interface, multiple agent harnesses
ACP addresses the boundary between an agent and its client. The motivation is visible in editors: if each coding agent builds a separate plugin for each editor, much of the integration work repeats. Zed’s approach is to give those integrations a common interface, so clients do not have to reproduce the same machinery for every agent.
| Interface | Boundary it addresses |
|---|---|
| MCP | Tools made available to a model |
| ACP | Interaction between a client and an agent |
| Agent-to-agent protocols | Communication between agents |
These categories do not prevent an agent from acting as an ACP client. An interface designed for a human-facing editor or chat client can also let one agent invoke another. Solmaz leaves room for supporting other protocols as they mature; ACP was his practical starting point because Zed already had the Codex and Claude Code adapters he needed. His comparison with Google concerns those adapters at the time of his choice, not Google’s participation in ACP.
OpenClaw’s command-line extension pattern suggested the next step: expose ACP through a CLI. That became acpx, allowing an agent with command execution to call another agent through the same standardized client boundary. It began as a way to invoke agents and expanded toward broader ACP tooling. The CLI is the accessible entry point; the protocol is what separates the caller from the particular harness being called.
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A bad patch can still be useful feedback
Solmaz reports more than 60,000 total OpenClaw pull requests and roughly 300–500 opened per day on average at the time of the talk. These are his estimates of incoming work, not automated processing throughput. With many stakeholders requesting changes, the difficulty is not merely reviewing patches quickly: it is absorbing their needs without allowing every local fix to dictate the architecture.
The review starts by asking what a PR actually does, then whether it is the best available fix. An AI-generated description may not answer either question. A user can encounter a problem, ask their agent to fix it, and submit the result through GitHub without doing the reasoning a maintainer needs before merging. Further conversation with a coding agent can help unpack the implementation and alternatives.
Separate the evidence of a problem from the proposed implementation. A patch may be unmergeable while still identifying broken behavior that matters to a user. Categorizing that signal preserves it for future work. Solmaz shows Codex sessions making these judgments; after repeating the same questions and follow-ups, the procedure begins to look programmable. The next automation target is the maintainer’s repeated interaction with the agent.
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Turn review conversations into executable procedures
The PR workflow breaks the work into explicit stages:
- Identify the intent behind the incoming PR.
- Judge whether the implementation fits that intent.
- Resolve conflicts and address issues raised by review.
- Repair failing CI before presenting the result to a maintainer.
The goal is to remove mechanical work from the maintainer’s queue where possible. It does not make every incoming patch suitable for merging.
Repeated review and refactor loops have a bounded role here. Solmaz argues that an unattended loop can uncover shallow bugs and make superficial repairs without being entrusted with architectural design. When the required change becomes a fundamental refactor, the workflow should relay it to a human. Conflict resolution and other routine operations become parts of an executable standard operating procedure.
In acpx, this takes the form of an n8n-like workflow engine driving a Codex session. The demonstration is a replay of one PR: the agent reproduces a bug, assesses the refactor, and enters a review loop that produces no further findings. Programmatic steps surround the agent’s work. The crucial connection is structured JSON output: a judgment becomes data that a workflow can inspect and use to choose its next step.
A small TypeScript routing function illustrates that boundary. Here the review result proposes a next action; it does not itself execute a repair or approve a merge.
typescript
type ReviewResult = {
refactor: "none" | "superficial" | "fundamental";
findings: string[];
};
type NextStep = "human-review" | "repair" | "continue";
function nextStep(review: ReviewResult): NextStep {
if (review.refactor === "fundamental") return "human-review";
if (review.refactor === "superficial" || review.findings.length > 0) {
return "repair";
}
return "continue";
}
const review: ReviewResult = {
refactor: "none",
findings: [],
};
const proposedNextStep = nextStep(review);
This is the useful division of labor: the agent supplies a judgment, while ordinary code handles the branch. PR review is the demonstration, but the engine is presented as usable for other workflows built from agent turns and programmatic steps.
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Provision an agent for each task
Once workflows can run without constant personal intervention, the scale of enterprise use may diverge from personal use. Solmaz expects organizations to consume substantially more inference, and sees commercial potential in that difference. His proposed operating model is on-demand, disposable agents: start an agent for a task, let it work, and avoid forcing every task through one persistent personal assistant.
Chat identity becomes an obstacle. In the integrations he describes, connecting an OpenClaw app to Slack, Teams, or Discord gives users one app identity. Giving another agent its own name, avatar, and separately addressable presence can require creating another app and manifest. Repeating that provisioning manually does not fit an agent-per-task workflow.
The slide’s multiple named agents illustrate the desired experience, not a working chat-platform feature: Solmaz used ChatGPT to generate the concept. Because his desired provisioning model was not supported, his implementation moves task-agent conversations into another UI. Those agents create and edit files, so the system must also keep their working state synchronized. The interface problem therefore leads directly to an infrastructure problem.
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A shared concierge, separate task environments
The proposed infrastructure has several distinct responsibilities:
- Orchestration: Kubernetes supplies the substrate for running task agents.
- Agent execution: OpenClaw, Codex, or Claude Code supplies the harness; ACP provides a common interaction boundary.
- Working data: Repository read/write access lets agents act on code, while synchronization keeps files and state available where needed.
Solmaz mentions GitHub, rsync, and Dropbox-like synchronization as possible ingredients. He does not specify a single implemented synchronization algorithm in this part of the talk.
The orchestrator he introduces comes from his TextCortex work, outside OpenClaw. A Go operator handles the provisioning complexity. Consider his example of a shared Slack concierge serving 100 employees: one conversational entry point can become a bottleneck, so a new task should be able to get a separate agent. The concierge creates that agent and returns a website link where the user can continue. Solmaz acknowledges Cognition and Devin as precedents for this category of developer-agent experience.
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Dispatch debugging work from Slack into Spritz
The project is TextCortex Spritz, and the demonstrated use case is error reporting. A user asks in Slack about new bugs after a production release, then asks for an agent to investigate. Slack is the starting point; the new agent’s conversation moves into a separate web interface because Solmaz cannot provision the desired agent presence directly inside Slack.
The web UI is an open-source React app hosted in the same cluster where the Helm charts are deployed. Opening the task starts a conversation in that interface, and the agent begins working on the problem. The selected frame shows an error report and a debugging response with root-cause, fix, and verification details. The experience resembles Codex Web or Devin, but the task runs in a full Kubernetes pod.
Solmaz acknowledges that this can be wasteful. His reason for choosing it is the flexibility of giving an agent a full computer-like environment, a capability he sees demonstrated by OpenClaw. He tentatively mentions OpenHands and Firecracker while acknowledging limited familiarity with virtualization options; that is not a settled comparison of runtime architectures. His concrete claim is narrower: he has a working product running on Kubernetes.
The intended deployments include internal web access to Codex and separate agents for individual tasks. Solmaz also offers setup help to open-source projects receiving hundreds of issues per day; that is an adoption target, not a demonstrated processing rate. The product handles the surrounding Slack integration, agent lifecycle, and user interaction, while ACP abstracts access to the agent so the harness can be switched. The final distinction matters: this orchestration and deployment demonstration is his TextCortex work, separate from his contributions to OpenClaw.
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Resources
From the talk
CLI for persistent ACP agent sessions, structured output, and executable workflows, with installation and usage examples.
Self-hosted Kubernetes control plane for disposable agent instances, with Helm deployment, an ACP gateway, and a web UI. Currently marked alpha.
Introduction to the protocol connecting coding agents with editors and other clients.
Peter Steinberger's account of using Claude Code for development and everyday computer tasks.
Further reading
- Codex is Live in ZedArticle
Zed's announcement of its Codex ACP integration and open-source adapter, alongside existing Claude Code support.
- OpenHands V0 runtime optionsDocumentation
Legacy runtime documentation covering Docker, local execution, remote environments, and third-party providers.
Read the complete timestamped transcript
- 0:00
[upbeat music] Welcome.
- 0:16
Um, so the talk is building an ACP on, uh, at OpenClaw. It's, uh, also about other things like how to, uh, put open source agents on open source agent frameworks on Kubernetes and stuff.
- 0:32
So I'm, I'm-- I hope, uh, t- I will h- today share i-in a nice way what I've been working on in the last two months. Um,
- 0:41
a little bit about me, very brief. My-- Ah. A little bit about me. I've been building harnesses since a few months before ChatGPT came out. I built a JupyterLab extension over o-OG Codex model, like DaVinci Code 2, um, back in the days, that eventually...
- 0:58
So I'm currently working for a startup, and that, that initial coding harness turned into this current harness over time, like a ship of Theseus. It got ripped apart and put back together so many times.
- 1:12
And, uh, yeah, I'm a founding engineer there. I've been, uh, i-in, in the industry since three and a half, four years. I'm using OpenClaw since Clawbot, uh, s- first dropped in Discord.
- 1:24
I went in there. I've been following Peter since he wrote, uh, "Claude Code is my computer." I was like, "This guy's crazy" [chuckles]
- 1:34
because, uh, Claude forced on it. Like, I wouldn't give my machine to it, but he, he vent- he ventured forth, and he's paved the way for us. And when I saw, uh, Clawbot in Discord, my mind was blown.
- 1:46
The next day, I installed it, uh, at the company, uh, at our, at our, like, uh, cluster. Uh, it was basically talking to people on Discord, and also everybody else's minds were blown.
- 1:58
And we are like, we have, uh, we're used to selling the enterprise for a few, uh, years now. So that's why I started by adding an MS Teams integration in case it might be useful at some point, and I became a maintainer a-along the way.
- 2:12
I was there when it was, uh, renamed, uh, two times. Um, and today I, I'm-- my focus is on agent interoperability and orchestration. Um, like one of my goals is accelerate enterprise adoption of OpenClaw and adjacent software and also address, you know, OpenClaw is not secure.
- 2:30
Well, this will be secure or... Peter talked a lot about that earlier, so I don't have anything else to add on top of it, like it's work in progress.
- 2:47
Sorry. I'll use this. So, uh, it started by, uh, I, I started on developer workflows right away, so I created a PR, uh, and then call it Discord-driven development.
- 3:01
Well, Telegram-driven development suits better because it's TDD, so... Um, and then, uh, s-
- 3:09
right after I set up personally and I realized, you know, Opus is not so reliable for complex-- Uh, it wasn't back in the days. It's now better. It's-- Agents are improving.
- 3:20
But Codex was main-- my main harness, and I wanted to use Codex in Discord. But it was basic-- I was telling-- I was playing a telephone game. I was telling Opus to tell Codex to do something.
- 3:30
God knows what it's saying, you know, because wording matters when prompting. But it was working somehow, and I would go, uh, [chuckles] and look at the Codex session, and then it paraphrased kind of what I was saying.
- 3:42
But eventually, you know, uh, I got some stuff done, but I knew this could be done much easier. Um,
- 3:51
and today I'm, uh, running a full ID on Discord. Like, uh, you know, we got parallel workloads. You have like one to f- like five channels. At any point, I'm working with one to five agents.
- 4:01
Um, so you see Codex one to five, and then Claw is for testing the OpenClaw's ACP feature. And then, yeah, uh, basically, that's how my ch-- that's how my channels look like.
- 4:12
And it's very good for coding on the go because I, I'm a guy who's addicted to side projects and, uh, I, I like to just... You know, uh, AI is making thing a l-- making things a lot easier to do these sort of things.
- 4:27
Like in parallel, you have an inspiration, you execute on a weekend, and you just get it ready, you ship it. acpx, which I'm gonna talk about, is o- is like similar.
- 4:35
I built it through, uh, Discord. And, uh, what you do is, uh, you bind the channel, uh, Discord channel to Codex through ACP. You can also use Codex App Server protocol.
- 4:46
Harold, another maintainer, uh, developed it. And here is me using it before I'm flying to London to create an PDF about, like, convert the docs of ACP into P-PDF.
- 4:57
And then I have to say, put it in temp because OpenClaw-- Codex doesn't know about the harness, and it cannot send me in Discord. So, uh, then I go to another channel and tell it to send it to me on that channel.
- 5:09
So we are developers and our tools, we don't have time to polish them very well, but we know the,
- 5:16
like, advantages and disadvantages. What is ACP? So it's not, uh, most people say, "Is it like MCP?" MCP for-- is for giving tools to the model. ACP is for, uh, like standardizing agent-to-client interaction.
- 5:32
It's, uh, shout out to Zed. I actually forgot to put Zed logo. Um, Zed is building a new editor in Rust, more efficient, like l-lower memory usage, not Electron.
- 5:44
So, uh, I'm using Zed also since like, uh, last fall. And, uh, you know, uh, if you use Codex on VS Code or Claude Code, they are all building different plugins.
- 5:54
It's not exactly-- It's like so much wasted work. If only you could just standardize them under one interface and you just build it once and then you, you ship it.
- 6:02
And that is what the i-- the, the idea-- that's the idea they had. It's also more-
- 6:07
It's, it, it's much less duplicate w- duplicated work. Um, there are competing standards, um, so agent, like agent to-- uh, Agent Protocol, that's for agent talking to agent. ACP, Agent Client Protocol, is for a- uh, human talking to agent, but agent can use the human one to talk to other agents as well.
- 6:29
I-in the long run, as, as these protocols get adopted, we will support all of them. We will w-weigh the, you know, the advantages, and then we will use them somehow.
- 6:40
But when I need... So I chose ACP because when I need it the most, I needed adapters for Codex and Claude Code, and only Zed had built them, and Google didn't, uh, have them back, back, back at the time, so that's why I chose it.
- 6:55
I, um, that-- So I-- When you're adding any functionality to OpenClaw, you do, uh, you use-- do it through a CLI. Um,
- 7:06
so I said, "Okay, let's create a CLI for ACP. Let's an agent, an agent call any other agent over the command line." So that's, that's how it started, uh,
- 7:15
and it's slowly turning into a Swiss Army knife for ACP, as I will show in a bit.
- 7:26
So in, uh, at OpenClaw, you have fire hoses. Uh, we have sixty KPRs, uh, over sixty KPRs total. Three hundred to five hundred, uh, per day on average, uh, are open.
- 7:38
And basically overnight, people woke up and decided they want-- like they like OpenClaw, so we have tens of thousands of stakeholders who want to add features to OpenClaw. And the biggest challenge of the project currently is how do you, uh, absorb all the needs and wants of these people, and how do you balance them?
- 7:58
How do you... Like, you can't please everyone. Well, how do you create an elegant system without, uh, creating AI slop, uh, that can cater everyone's needs? And
- 8:12
Peter's workflow, uh, gave me an idea, so this is also something I do. You go and you ask your clanker, "What is this?" So PR comes your way, and like
- 8:22
most of the time, it's AI-generated description. You're like, "What is this?" Uh, if the human put thought into it, great, but like, yeah, you need to ask what it's doing.
- 8:32
You ask what-- "Is this the best possible fix?" Most of the time, it's no. And then you either continue dis-- like, he, he wrote it like you, you do some back and forth with the agent.
- 8:44
The reason is people just, uh, run into an issue with their OpenClaw, and it, it can use GitHub. And then they just say, uh, "Please fix," and then they just send some slop your way.
- 8:57
You know, this is-- you can't merge it, but you can also fully discard it. You need to take this data point. You-- It's like crucial feedback from the user.
- 9:04
Uh, so you need to put it, categorize it, put it in a bin, you know. It, it tells you when some part of the code is broken, and Vincent also talked about that a bit.
- 9:15
Uh, and here is, uh, his, his Codex session doing that. On one side, there's the one that says it's, it's a good fix. Uh, and on this other side, it's just saying it's not bad.
- 9:27
And yeah, this is so mechanical, and you, you-- once you s- do this over and over, you realize you're repeating something. If only you could program something to automate it, so you're automating the automator.
- 9:39
Um, so I created-- I started, uh, to create like workflows, uh, in, in an abstract way. So, uh, item comes, PR, and then you find the intent. You're judging implementation.
- 9:51
You're like, uh, looking into con-- if a-- if there's conflicts, uh, i-if reviews gives you like some issues that need to be addressed. So you need to make the CI pass if it's not passing already.
- 10:04
Most people just don't care about that. All this mechanical work, by the time the main-- the PR ends up in front of you, should be resolved ideally and can be resolved, and that's what I'm working on.
- 10:18
Ah, in the workflow, we have the shameful, uh, RLHF review refactor loops. Uh, I, I'm a believer that, you know, when people say like give the--
- 10:30
Just, just run AI-- uh, running an agent in a loop doesn't-- not necessarily have to be, uh, uh, something that will create slop. As long as you're not making it design something but you're making it uncover shallow bugs that can be easily fixed, should be fine.
- 10:46
So in the w- abstract workflow that I created, um, which is actually code, like pro-- like turned into a program, you can, uh, tell it to, uh, do superficial refactors, and then you can tell if it's-- it needs a fundamental refactor, relay it to the human.
- 11:03
Um, resolving conflicts also doesn't need like no-- It was hard back in the days. I don't think anyone is like resolving conflicts by hand by now. Um, so we are basically creating, uh, standard operating procedures for agents.
- 11:19
That's a fancy word for workflow. Um, so that's what I built into acpx. Uh, it's a, an A10-like workflow engine, uh, but it's driving a Codex session you can see on the right.
- 11:31
Let me show you in action. So this was one PR.
- 11:41
It's loading. Uh, let's speed it up a little bit. So there's some programmatic parts. So this is just replaying, uh, what it's doing.
- 11:51
Uh, it's reproducing the bug. It's judging refactor. It's, it's reviewing now, doing a review loop. And then review didn't, uh, bring anything. It, it, it's, it's what I do, uh, but I make it output like JSON structured data, so I can put it in like an A-- an A10-like workflow.
- 12:08
And I will talk... You know, this is a general workflow eng- engine. Um- You can use it on other things as well.
- 12:18
Um, like you need to apply agents generously on problems. So I see it as like an ointment, uh, that you apply generously on any problem that can be solved with agents.
- 12:33
You need to take yourself out of the loop and solve it with agents. Um, and personal agents, I think, uh, are on a d-- Like, there, there's a spectrum enterprise and personal agent.
- 12:45
And normally you see on the, the work you use at the computer and then the PC-- The, the, the PC you use at work and the PC you use at, at, at, uh, home used to be relatively similar, but that will not be the case with agents because at, at work you will be using, consuming a lot
- 13:03
more inference. Uh, and that means in enterprise there will be a lot more money to be made. So that's why I'm a bit also excited about enterprise agents and OpenClaw's, uh, potential.
- 13:15
That's why, uh, I, I believe in, uh, on-demand disposable agents. If you use OpenClaw on Slack or Teams or Discord, it's just one instance. You create an app. The problem is you can't really talk to multiple instances of this.
- 13:31
Um, like you create a, a connection, you create a Slack app to connect another agent and another name and another, uh, profile picture, you need to create another app, and you have to create an app manifest.
- 13:45
And it's, it's, it's something that shouldn't be managed manually by clicking and the platforms like chat apps don't have this standard yet where you can, uh, mult-- do multi-agent provisioning, like cosmetically create different agents.
- 14:00
This is what-- This is on, on the screen you see I asked ChatGPT to gen-generate the idea. You run agents, uh, and then the name can be generated by un-an underlying app, and you can talk to them separately.
- 14:12
This is not supported, and this must be supported for this, uh, vision to work. Until it's supported, I'm using it on another UI because, like we are all gonna start one, uh, agent per task and there will be, uh, they will work on these tasks and they will be creating files, editing files, and it will all be
- 14:34
synchronized. It will be, it will be, uh, tad bit different than what you're used to with your, uh, personal agent. And to do-- to have that you need to have Ku-- like, uh, a few key components.
- 14:46
You need to have Kubernetes. You need to, uh, have an agent harness. OpenClaw could be one of them. Codex, Claude Code could be one of them. Could be ACP.
- 14:55
It could be, uh, GitHub. Uh, you give read, write access and you do like state data synchronization. Like you, maybe you do something that's use Rsync, some-something like whatev-whatever algorithm Dropbox is using.
- 15:10
And there are some projects that are, uh, taking this on. I've been working on this. Uh, this is outside of OpenClaw. This is my day job and, uh, it's an open source, uh, orchestrator.
- 15:22
Uh, it's a Go Operator that's, uh, basically handles the complicated parts. You know, there's a user experience. You wanna create a concierge on Slack, like concierge agent, and you're talking to it, but you get bottleneck because you're a hundred employees on Slack, so you need to, for, for some other task you may, you may need to create
- 15:43
a new one and then it creates and gives you like a website link. Um, I'm gonna skip the, like shout-outs to Cognition and Devin, um, because, uh, yeah, they invented the category, but I'm running low on time.
- 15:58
So the repo is at TextCortex Spritz. I'm just gonna, uh, demo it for our use case. We use it on error reporting, uh, currently. So if you're on Slack, you know, uh, you can ask it to dispatch an agent to debug it.
- 16:12
You know, you're like, "I'm, uh, asking any new bugs after prod release," and it's c-saying something and then you ask it, uh, to create an agent. Uh, if I could put that agent into Slack, I would, but I can't do that, so I have to put it in another UI.
- 16:29
And this is an open source project. You can take this, uh, UI's also, uh, like a React app hosted in the cluster that you're deploying this, these Helm charts to.
- 16:40
And, uh, yeah, you, you-- it starts the conversation there. It starts working on the, uh, problem. This is like Codex Web or Devin or anything, but it's actually using like a [clears throat] full Kubernetes pod.
- 16:55
Wasteful but I think it's the better abstraction because OpenClaw showed the power. You know, when you give a full computer to an agent, it's a lot more powerful. Um, and I, I believe that as well.
- 17:08
Um, I, uh, like, I think, uh, OpenHands uses Firecracker so I'm also, uh, not so, uh, well-versed on all the different virtualization frameworks. Uh,
- 17:21
so it's-- I'm also learning along the way but I have a working product that's, uh, running on Kubernetes.
- 17:28
Uh, and you can use this, uh, product, uh, you can-- If you're interested in deploying internally and, uh, using Codex on the web, uh, and then just spin things off if you're like, I don't know, a backend, uh...
- 17:43
If, if you're an open source project first of all, I can help you set this up. If you have a system like inlet of just hundred of, hundreds of issues per day, I can help you process it.
- 17:53
Um, this is, um, that does all the wiring around Slack and like keeping those agents al-- uh, like on and the user experience and then the, the interoperability. Like you're not locked in, in any agent.
- 18:09
You can switch. It's all abstract the way Bill-- uh, with ACP. Yeah. That was my talk. Uh, thank you for listening. Some social links in case you wanna- [audience clapping]
- 18:20
-uh, get in contact with me. I just wanna make c-clear like of my, uh, the OpenClaw, uh, side and, uh, TextCortex side. Um, so the, the last part was, uh, TextCo- about the work I do at TextCortex,
- 18:35
uh, just to give a disclaimer. Um, thank you for listening. I guess- [audience clapping]