Author: Shenchao TechFlow
Hackathons have long become a standard part of building public chain ecosystems. More important than the excitement of simply hosting an event is what the event leaves behind for the ecosystem.
On March 21, 2026, the Monad Rebel in Paradise AI hackathon concluded successfully with the announcement of the winners.
At a time when AI has become a ubiquitous "lifeline" that Crypto ecosystems feel compelled to latch onto, this hackathon remains particularly worth reviewing—not only because, as a top-tier L1 project, every move Monad makes to build its ecosystem after its token launch is a constant focus of community inquiry, but also because the community can hardly fail to notice the event’s partners:
Prominent LLM providers such as Kimi, Zhipu, and DouBao are prominently listed.
This makes the significance of this event extend far beyond merely being an on-chain developer competition—it signals that Crypto is becoming a core component integrated into broader applications, while also bringing together large AI models and on-chain infrastructure:
On one side is the on-chain execution environment provided by the high-performance public chain Monad; on the other side is the concentrated infusion of large model capabilities, toolchains, and development resources from traditional manufacturers; in the middle are developers striving to turn their imagination into products.
In the era of the agent economy, the underlying network must support higher-frequency and more complex interactions and value transfers—how does Monad perform in this regard?
Meanwhile, in such a hackathon centered on the AI theme, what did developers build on Monad?
Let’s explore Monad’s AI strategy further through this hackathon-winning project.
A hackathon featuring a stellar lineup and intensive resources
When agents are no longer just conversational tools but also possess execution capabilities, which directions are most worth developers investing in?
The Monad Rebel in Paradise AI hackathon aims to provide the most direct answers.
In terms of challenge design, the event focuses on three areas that best represent the practical value of agents: Agent Payments, Intelligent Markets, and Application Innovation.
To present the results in the most compelling way, Monad has also generously allocated resources: participants will not only have direct access to leaders in LLMs, infrastructure, and agents, as well as venture capitalists, but will also compete for a total prize pool of over $40,000—$20,000 in cash and $20,000 in creative and resource support, including free trial credits for cutting-edge models, development tools, and infrastructure.
As the first hackathon in Greater China focused on AI agents in finance, Monad aims to demonstrate deep integration between high-performance parallel EVM and leading LLMs through this event. Training camps will be held primarily in Beijing and Shenzhen, bringing together developers, model capabilities, infrastructure, and investors in a shared experimental environment.
The VC judges for the event include leading institutions such as Delphi Ventures, Pantera Capital, CoinFund, Vertex, and Enlight, offering participants a unique opportunity to demonstrate their potential before model providers, infrastructure partners, and top-tier investment firms.
Meanwhile, the event also attracted leading AI companies such as Kimi, Zhipu AI, DouBao, Step星辰, Silicon Flow, and YouWare, collectively offering a range of support including model APIs, computing power, technical guidance, and evaluation resources.
This lineup has sparked curiosity about the motivation behind the collaboration, but upon closer examination, it’s not hard to understand:
As LLM vendors began seeking overseas opportunities and the next frontier for AI innovation, they identified Crypto—characterized by decentralization, trustlessness, and verifiable incentives—and chose Monad as the L1 foundation they discovered and selected.
The intensive resource allocation has laid the essential foundation for the high-quality outputs of this hackathon—so what do the first products, pioneered by those willing to take the leap and find their footing, actually look like?
From Payments to Comic Generation: 11 Winning Projects
Champion: OpenAlice
OpenAlice is a locally runnable trading agent that integrates research, strategy, execution, and risk management into a transparent, collaborative workspace.
The OpenAlice core architecture is driven by Markdown and JSON configurations, with all Agent behaviors defined in human-readable Markdown and structured JSON, ensuring clear and transparent logs that facilitate collaborative iteration between humans and the Agent. Additionally, the project supports local deployment, reducing complete reliance on cloud services and enhancing privacy and control.
- NVIDIA Super Compute Special Award: Orbit AI
Orbit AI is a decentralized AI cloud that moves compute power onto orbit, designed for Agent scenarios and connecting verifiable satellite GPU clusters. Its core strengths are enhanced physical isolation and tamper resistance, enabling high-trust computing to be globally accessible.
First Prize in the Payments and Infrastructure Track: Libra
Libra is a "new Git" built for the Agent era, designed to address issues such as explosive commit histories after machine-generated code, difficulty reading historical records, and loss of intent information.
It has been significantly restructured to improve intent expression, parallel collaboration, and the auditing and debugging experience, bringing the entire process back to a user-friendly state.
Second Prize in the Payments and Infrastructure Track: Agora-mesh
Agora-mesh aims to enable agents to discover services more smoothly and settle transactions on-chain via MON, striving to significantly lower the cost barrier for agents and enable seamless machine-to-machine service transactions.
Its overall process is similar to x402: first, submit a quote; then, make an on-chain payment; finally, deliver the result.
Third Prize in the Payments and Infrastructure Track: TickPay
TickPay specializes in high-frequency, micro-streaming payments, ideal for scenarios such as video services charged by the second or AI APIs billed per invocation. Combined with account abstraction authorization, payment permissions can be enabled or disabled at any time, while settlement is fully automated.
First Prize in the Coexistence with Agent Track: Kimi-swarm
Kimi-swarm is an open-source multi-agent collaboration IDE developed by Kimi, allowing users to interject and intervene with any agent as easily as in a chat. Through graph and context panels, the entire Swarm process becomes observable and debuggable, no longer a black box.
- Second Prize in the Coexistence with Agent Track: A2A IntentPool Protocol
The A2A IntentPool Protocol is a "task settlement layer" designed for machine-to-machine collaboration, enabling automated agents to discover tasks, execute them, prove outcomes, and receive on-chain payments directly. Its goal is to reduce platform intermediation, API integration costs, and manual reconciliation processes.
- Third Prize in the Coexistence with Agent Track: Anime AI Studio
Anime AI Studio is an all-in-one agent for generating anime short films, capable of streamlining the entire workflow from concept and script to storyboarding, keyframes, and frame-level video generation. It also supports segmented rollback and partial regeneration, allowing you to modify a single scene without re-running the entire pipeline.
First Prize in the Application Innovation Track: AgentVerse
AgentVerse is a native x402-supported "million-grid map" where Agents can purchase plots, build homepages, and be discovered by others. It integrates identity, payment, and display space, enabling Agents to showcase themselves while also possessing trading capabilities.
Second Prize in the Application Innovation Track: Campfire
Campfire brings people and agents together into a shared social playground, where users can collaborate on tasks, engage in market interactions, or compete in the Agent Arena. It emphasizes high-frequency interaction and measurable outcomes, making the overall experience feel more like a real product rather than just a demo.
Third Prize in the Innovation Track: Web3 Quant Trading Challenge Game
Web3 Quant Trading Challenge is a product that teaches Web3 quantitative trading through a level-based gameplay mechanism. Users can drag and drop strategy modules to instantly run strategies, learning quantitative logic through interactive play. Each level includes diagnostic feedback to help users identify issues and understand how to make adjustments.
Monad's AI ecosystem strategy goes far beyond a single hackathon.
In fact, this is not the first time Monad has focused on AI, beyond this hackathon.
On the "App Hub" page of the Monad website, AI is listed as a separate category tag, currently showcasing 12 AI applications, three of which are supported by the Monad Momentum incentive program. Although this data set is not yet substantial, it signals Monad’s growing emphasis on AI.
Monad has already initiated a series of actions in areas such as strengthening infrastructure and expanding ecosystem support.
Previously, the Monad official documentation introduced a dedicated x402 payment guide and ERC-8004 (Trustless Agents) registration tutorial, aiming to streamline the critical payment flow: enabling AI agents not only to think, but also to autonomously discover, obtain quotes, complete payments, and deliver results—with a seamless, nearly imperceptible user experience.
In December 2025, Monad launched the AI Blueprint initiative, providing comprehensive support for AI applications, including resources and infrastructure to help developers build, launch, and scale projects. Key focus areas include decentralized inference networks, autonomous agent clusters, on-chain generative AI, verifiable memory systems, and privacy-preserving computation combined with distributed inference for consumer-grade hardware.
In February 2026, Monad co-hosted the Moltiverse Hackathon, leveraging the momentum of OpenClaw to strongly encourage the development of agent applications and monetization tools, emphasizing agents' autonomous collaboration, micropayments, and on-chain execution capabilities.
Under intensive efforts, AI has seemingly become one of the primary frontiers in building the Monad ecosystem.
Of course, betting resources on AI isn't just because of the AI hype:
On the infrastructure level, Monad’s architecture is naturally suited for high-frequency, low-latency agent scenarios requiring continuous interaction.
Whether through Optimistic Parallel Execution, Pipelined Architecture, or MonadDB, these designs equip Monad with performance advantages such as over 10,000 TPS, 0.4-second block times, and extremely low gas costs—positioning Monad as a fast, affordable, and stable execution layer capable of enabling agents to truly achieve autonomous trading, settlement, and collaboration.
On the other hand, Monad’s robust and well-developed DeFi ecosystem provides AI agents with a rich array of accessible financial tools, liquid pools, and yield opportunities, enabling them to better discover opportunities, execute trades, settle transactions, and compound returns on their own—elevating them from intelligent chatbots to autonomous on-chain economic entities.
This imaginative vision for the future of AI in finance sets Monad apart from many crypto AI projects that remain stuck in conceptual packaging. It may also serve as a key anchor for users to continue following Monad’s ecosystem developments after the AI-themed hackathon concludes.