Encountering the Founder of OpenClaw at a Hackathon: What Else Can These Lobsters Do?

Author｜jk

In March 2026, the UK AI Agent Hackathon 2026, initiated by the Imperial College London Blockchain Association, was held in London. Centered around the OpenClaw technical framework, this hackathon attracted over 1,200 registered participants. On Demo Day, it set a record with 5,000 concurrent online viewers and even topped the global trending list on platform X.

It was considered by many participants as the “world’s first University OpenClaw Hackathon”. Peter Steinberger, the father of OpenClaw, personally flew to London for this event.

Which Projects Were the Most Interesting?

On March 7th, teams from various universities showcased the prototype products they had built within a week, covering a vast landscape from agriculture to biosecurity, and from urban governance to DeFi protection. Here are 6 projects worth focusing on:

AgroMind: Satellite Data + AI Agent, Making Agricultural Risk Hedging a Reality

AgroMind integrates satellite crop monitoring, meteorological data, and market signals to build a predictive and automated hedging system for agricultural supply chain risks. Its core scenario is an automated hedging workflow.

The information gap in agricultural supply chains has always been about money. Sharp fluctuations in commodity prices often stem from climate risks buried months earlier in a certain production region, and the market only reacts when the news breaks. AgroMind aims to fill this gap. It combines satellite crop monitoring, weather data, and market signals. When satellite imagery shows early signs of drought stress in a soybean-producing region in Brazil, before any official report is released, the system is already running. It checks the user’s inventory and current market volatility, drafts hedging proposals, and, if conditions are suitable, directly places orders on commodity exchanges. It’s less of an AI tool and more of an analyst watching the markets from satellite imagery for you, except it doesn’t sleep.

ClawBio: The Hugging Face for Bioinformatics

Bioinformatics has a long-standing problem: top-tier analysis tools and knowledge are essentially locked within a few universities and a handful of pharmaceutical companies, inaccessible to ordinary researchers. What ClawBio wants to do is quite easy to understand by analogy: replicate what Hugging Face does for AI models in the field of bioinformatics. It is an open repository of verified, reproducible bio-skills that any Agent can directly call upon, including toxin screening and hazardous biological function identification. One scenario is particularly interesting: a user takes a photo of a drug package, the Agent calls upon ClawBio’s skills to query a local genomic archive, and returns a personalized medication dosage card within seconds. All data is processed locally, with nothing uploaded to any server. This “Local-First” approach is especially sensitive in healthcare scenarios and crucial for privacy protection.

BioSentinel: End-to-End Automation from Pathogen Identification to Drug Candidates

BioSentinel’s ambition is even greater. Starting from global public health data, the system continuously scrapes information from sources like WHO, CDC, and CIDRAP. Once an emerging threat is identified, it automatically locates the pathogen’s target protein, then calls upon computational biology tools like RFdiffusion and ProteinMPNN to design potentially effective therapeutic binding molecule candidates. Each candidate molecule is screened against toxin databases before proceeding to the next step, ensuring nothing dangerous is inadvertently created. The entire workflow can be driven via a chat interface. Researchers don’t need to run commands one by one; they just articulate their needs, and the Agent schedules the tools itself. This significantly lowers the barrier in computational biology.

“London Nervous System”: From Smart City to “Thinking City”

The starting point of this project is simple: London generates massive amounts of sensor data daily—traffic, air quality, infrastructure status—but this data is largely siloed. No one knows the city’s true state at any given moment.

The project team used OpenClaw to simultaneously connect to real-time traffic flow, air quality sensors, and financial market data monitoring. If air quality suddenly drops in a district, the system doesn’t just log it in the background; it proactively pushes low-pollution route suggestions to nearby schools and commuters. If a streetlight or sensor fails somewhere, the system’s response is much faster than waiting for manual reporting. The team’s long-term goal is to open this framework to local governments, integrating it with existing city systems rather than building from scratch.

Highstreet AI: Creating “Digital Employees” for London’s Street Shops

Most AI products are designed with tech companies in mind, not the small seafood restaurant on Kingston Street. Highstreet AI aims to bridge this gap.

It targets small and medium-sized enterprises that receive orders via email, WhatsApp messages, and phone calls daily but have no IT systems. Highstreet’s solution is to deploy a group of collaborative Agents: one is responsible for understanding incoming requests, another checks real-time inventory, another drafts invoices and payment links, and finally presents the owner with an “approve” button on a dashboard.

The human only needs to perform that final confirmation step. Highstreet claims that this system can save a shop owner over 10 hours per week without requiring any technical knowledge.

AlphaMind AI: Bringing Institutional-Grade Investment Logic to Retail Investors

There’s a deep chasm between retail investors and institutional investors, not entirely due to capital differences, but more due to analytical capabilities and response speed.

AlphaMind is a product designed to fill this gap. Users can compare their portfolios with public holdings like Warren Buffett’s, but the system doesn’t just show a comparison chart. It uses OpenClaw’s Agents to analyze asset concentration risks across multiple brokerages and exchanges and then automatically executes rebalancing operations. Its positioning is: past tools tell you what happened; AlphaMind tells you why and then handles it for you.

“Lobster Godfather” Peter Steinberger Attends in Person

In November, Austrian developer Peter Steinberger released a project called “Clawdbot.” You could send it messages via Telegram or WhatsApp, and it would help manage your calendar, handle emails, run scripts, and even browse the web. No one expected this project to sweep through the global AI community in just two months. OpenClaw went viral in late January 2026. On February 14th, Steinberger announced he was joining OpenAI to advance the development of next-generation personal AI Agents, while the OpenClaw project was transferred to an independent open-source foundation for continued operation. This developer, who had just become a central figure in the AI world, came to London for this hackathon.

This trip to London almost didn’t happen. The organizers revealed that Peter discovered visa issues just before departure, causing “the whole team to basically panic.” It was only resolved two days before the event started. After sorting the visa, he even rescheduled his flight to ensure he could participate in all agenda items as planned. When he first walked into the Imperial College classroom, he just kept his head down, focusing on his phone, diligently taking notes and preparing his speech, showing no airs of an “AI influencer.”

Peter at this hackathon

At the subsequent Sequoia Capital party, a developer who couldn’t get a ticket stood outside the venue in the London rain. Peter noticed and, without hesitation, went over to chat with him. When asked grand questions like “How will the explosion of Agents change the future of foundational large models?”, his answer was straightforward and honest: “I don’t know. I’m better at using the tools at hand to build interesting things.” The speech was originally scheduled for 30 minutes, but the atmosphere was so good, and audience questions kept coming. Peter stayed for over two hours. The organizers later said, “This meant a lot to us. To be fair, we owe him an apology.”

When Peter left London, he left behind a statement: “You don’t go looking for meaning; you go create meaning.” Perhaps this is the very sentence everyone who wants to make a difference in the AI era needs to hear.

OpenClaw × Web3: Huge Potential, But Security is the Biggest Constraint

Steinberger himself isn’t fond of the mật mã space, but the submission list for this hackathon contrasted sharply with his personal stance. On the DoraHacks project page, several directions where Web3 could be concretely implemented emerged.

• Agent Identity and Sovereignty was the most frequently mentioned theme. clawOS is built on the Nostr protocol, with each Agent holding an independent identity and wallet, not relying on any platform; Cortex.OS attempts to solve the black-box problem of AI in Web3, making every step of an Agent’s decision traceable on-chain.
• Directly Managing Money is another direction. Trading Narwhal and Vibe4Trading are both betting on Agents evolving from assisting with market watching to directly executing trades, even though the OpenClaw architecture itself isn’t particularly friendly to private keys.
• Governance and Public Oversight also spawned several interesting projects: WatchDog uses 6 autonomous Agents to continuously scan UK government contracts for anomalies; CivicLift enables citizens to interact with local governments through Agents; GreenClaw is building a multi-Agent collaborative Urban Security Operations Center.

However, from start to finish, security remains the most difficult hurdle for OpenClaw to overcome in entering Web3. Agents can access your files, APIs, and systems, but nothing monitors what they are actually doing. In scenarios involving real assets, adopting OpenClaw still requires caution.

Bài viết này được lấy từ internet: Encountering the Founder of OpenClaw at a Hackathon: What Else Can These Lobsters Do?

Related: Lobster’s Key 11 Questions: The Most Accessible Breakdown of OpenClaw’s Principles

Compiled by | Odaily Suzz OpenClaw is incredibly popular. Amidst the nationwide AI learning frenzy, many novice users who have never touched AI (or even the internet) are FOMO-learning, installing, and trying it out. You’ve probably seen many practical tutorials, but this video trending on YouTube these days is, without a doubt, the most accessible explanation of AI Agent principles I’ve ever seen. Using humans as a metaphor, it explains in “language even a grandmother can understand” the questions we naturally wonder about: how AI memory forms, why it’s so expensive, how tool calling is implemented and its process, the necessity and boundaries of spawning sub-agents, the design for proactive work, and most importantly, safe usage. Some of you might already be showing off your OpenClaw’s intelligence to friends while…