OpenClaw Core Contributors: After the Lobster craze fades, who should Agents listen to?

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After the viral success of OpenClaw, the project faces community governance challenges. Vincent Koc points out that the core of an Agent lies not in model intelligence, but in action boundaries and memory management. (Context: After OpenClaw went viral: How did an open-source crayfish shake up the US stock market?) (Background: A review of the OpenClaw and Moltbook incident: From AI social narratives to the outlook for the Agent economy) OpenClaw is the most unignorable presence in the open-source world of 2026. Created by Austrian engineer Peter Steinberger at the end of 2025, this personal AI Agent project reached the highest number of stars for an executable software in GitHub history within three months. The founder was personally recruited by Sam Altman to OpenAI, and the project was subsequently handed over to a foundation for independent operation. The community activity ClawCon, which grew around it, started with its first event in San Francisco and expanded to New York, Miami, Austin, Madrid, and Tokyo, drawing thousands of attendees in every city. In May, ClawCon made its China debut in Shanghai. Beating conducted exclusive interviews with two key figures on-site: Vincent Koc and Michael Galpert. Vincent Koc is the second-largest contributor to OpenClaw’s global codebase, second only to Peter himself. He is also a Lead AI Research Engineer at Comet ML, an MIT lecturer, and submitted 20% of OpenClaw’s early core security patches. Michael Galpert is the founder and global organizer of ClawCon. A serial entrepreneur, he co-founded the image editing tool Aviary, which was acquired by Adobe in 2014. He later served as Product Director for Epic Games’ *Fortnite* and now runs the AI product studio Contains Inc. He turned ClawCon from an impromptu gathering in a San Francisco living room into a personal AI community brand spanning over a dozen cities worldwide. When we interviewed Vincent and Michael, the peak of OpenClaw’s hype had already passed. This, however, is a better time to talk about OpenClaw. When the hype is at its peak, a project is always pushed forward by numbers: GitHub stars, PRs, attendance, community buzz, and media coverage. Each number acts like a spotlight, making people look bright, but also making it hard to see clearly. Only when the lights dim slightly do the real questions emerge: Why did it suddenly strike a chord with so many people? Can it transform from a fleeting trend into a daily tool? When an AI is no longer just chatting, but starts sending messages, editing files, and running tasks for people, who should it actually listen to? The scene at ClawCon Shanghai was still hot. An open-source AI project reaching hundreds of thousands of GitHub stars in just a few months; the event was part of muShanghai’s 28-day nomadic tech community, which press releases claimed gathered 800 global builders. Many Chinese developers attended, concerned with Feishu, WeChat, WeCom, DingTalk, local files, and automation scripts—interested in how to integrate OpenClaw into their work and lives. Conventionally, this is the time to talk about passion, speed, and the influx of developers, preferably accompanied by a steep growth curve. But after Vincent took the stage, he didn't frame it as a pretty growth story. He started with a problem: OpenClaw had received 10,000 PRs. This number would normally be perfect for a celebration. The biggest fear for an open-source project is having no users, no issues, no code submissions, and no one willing to sacrifice their weekend. But OpenClaw faced a situation where everyone wanted to shove their own ideas into it. Some wanted to integrate Feishu, others WeChat and DingTalk. Some wanted it to read local files, run automation scripts, write code, and orchestrate data; others wanted it to run trading strategies or operate a content account 24/7. Previously, open-source projects had a natural barrier: to submit code, you had to at least read the documentation, understand some architecture, run tests, and know what you were changing. Now, that barrier has been thinned by AI programming tools. Those who don't understand architecture can still have models write code, run tests, and submit patches. An idea that would have stayed in one's head can now be packaged into a submission that looks like it works. Impulses that would have been naturally blocked by skill barriers in the past are now all landing on the maintainers' desks. The same applies to security submissions. Vincent mentioned on-site that for a period, they were receiving over 100 security vulnerability reports daily, each requiring classification and inspection. Real vulnerabilities were patched quickly, but a large portion were generated directly

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