Kimi, Zhipu, and Douyin Gather at a Crypto Hackathon: What Did AI Developers Build on Monad?

Author: Deep Tide TechFlow

Hackathons have long become a standard part of blockchain ecosystem development. Compared to the lively “hosting an event,” what’s more important is what this event “leaves for the ecosystem.”

On March 21, 2026, with the announcement of the winners, the Monad Rebel in Paradise AI Hackathon concluded successfully.

In an era where AI has become a common “lifeline” for crypto ecosystem building, this hackathon is especially worth revisiting. Not only because, as a top-tier L1 project, every move Monad makes in building its ecosystem after issuing tokens has been a community focus, but also because the community can’t help but notice the partners involved in this hackathon:

Including well-known LLM providers like Kimi, Zhipu, Doubao, among others.

This elevates the event’s significance far beyond a simple “on-chain developer competition.” It signals that crypto, as a core component, is expanding into broader scenarios, and also facilitates a convergence of AI large models and on-chain infrastructure:

On one side, Monad’s high-performance public chain provides the on-chain execution environment; on the other, traditional vendors bring large model capabilities, toolchains, and development resources, while developers try to turn their imagination into products.

In the era of intelligent agents, the underlying network needs to support higher frequency, more complex interactions, and value flows. How does Monad perform in this regard?

Meanwhile, in this hackathon, what have developers created around AI themes on Monad?

Let’s explore the AI layout of the Monad ecosystem through the winning projects of this hackathon.

A Hackathon with Both “Strong Lineup” and “Intensive Resources”

When agents are no longer just dialogue tools but possess execution capabilities, which directions are most worth developers’ investment?

The Monad Rebel in Paradise AI Hackathon aims to give the most direct answer.

The competition focused on three key areas that best represent the practical value of agents: Agent payments, intelligent marketplaces, and application innovation.

To make the results more impressive, Monad didn’t hold back on resources: participants could directly communicate with leading figures and VCs in LLM, infrastructure, and intelligent agent fields. They also competed for a total prize pool of over $40,000, including $20,000 in cash and $20,000 in creative and resource support, such as free trials of cutting-edge models, development tools, and infrastructure.

As the first AI agent-focused financial hackathon in Greater China, Monad aims to demonstrate the deep integration of high-performance parallel EVM and top LLMs. It also runs training camps in Beijing and Shenzhen, bringing developers, model capabilities, infrastructure, and investors into a shared experimental space.

VC judges from top firms like Delphi Ventures, Pantera Capital, CoinFund, Vertex, and Enlight participated, providing contestants with early opportunities to showcase themselves before model vendors, infrastructure providers, and top investment institutions.

Additionally, top AI companies such as Kimi, Zhipu AI, Doubao, Jieyue Xingchen, Silicon Base Flow, and YouWare joined, offering support from model APIs, computing power, technical guidance, to review resources.

This lineup naturally sparks curiosity about the behind-the-scenes collaborations. But upon closer look, it’s understandable:

As LLM vendors seek overseas opportunities and the next AI innovation focus, they see crypto—characterized by decentralization, trustlessness, and verifiable incentives—as a strategic fit. Monad has become the L1 platform discovered and chosen by major players.

The dense resource infusion provides the necessary foundation for high-quality outputs in this hackathon. So, what do the first products daring enough to try and find their niche look like?

From Payments to Script Generation: An Overview of 11 Winning Projects

Grand Champion: OpenAlice

OpenAlice is a locally deployable trading agent that consolidates research, strategy, execution, and risk control into a transparent, collaborative workspace.

Its core architecture uses Markdown + JSON configuration-driven design. All agent behaviors are defined in human-readable Markdown and structured JSON, with clear logs, facilitating human-agent iterative collaboration. It also supports local deployment, with data and execution not fully dependent on the cloud, enhancing privacy and control.

【View Demo】

NVIDIA Super Compute Special Award: Orbit AI

Orbit AI is a decentralized AI cloud that moves computing power “to orbit,” targeting agent scenarios by connecting verifiable satellite GPU clusters. Its key selling points are stronger physical isolation and tamper resistance, enabling high-trust computing with global availability.

【View Demo】

First Prize in Payments and Infrastructure Track: Libra

Libra is a “new Git” designed for the agent era, aiming to solve issues like exploding commit histories, hard-to-read logs, and lost intent information after machine code generation.

It reconstructs intent expression, parallel collaboration, auditing, and debugging experiences, returning the entire process to a human-friendly state.

【View Demo】

Second Prize in Payments and Infrastructure Track: Agora-mesh

Agora-mesh aims to facilitate smoother service discovery for agents and completes on-chain settlement via MON, significantly lowering payment barriers for agents and enabling seamless machine-to-machine service transactions.

Its overall process is similar to x402: quote, on-chain payment, and delivery of results.

【View Demo】

Third Prize in Payments and Infrastructure Track: TickPay

TickPay focuses on high-frequency, small-value streaming payments suitable for scenarios like per-second billed video services and per-call AI APIs. Using account abstraction authorization mechanisms, payment permissions can be toggled at any time, with settlement automatically handled.

【View Demo】

First Prize in Symbiosis with Agents: Kimi-swarm

Kimi-swarm is an open-source multi-agent collaboration IDE developed by Kimi, supporting conversational-like intervention and interference among agents. With graph and context panels, the entire swarm process becomes observable and debuggable, no longer a black box.

【View Demo】

Second Prize in Symbiosis with Agents: A2A IntentPool Protocol

A2A IntentPool Protocol is a “task settlement layer” for machine-to-machine collaboration, enabling automated agents to discover, execute, prove results, and receive on-chain payments directly. Its goal is to reduce platform intermediaries, API handoff costs, and manual reconciliation.

【View Demo】

Third Prize in Symbiosis with Agents: Anime AI Studio

Anime AI Studio is an all-in-one agent for generating short anime dramas, capable of handling the entire process from idea, script, storyboarding, keyframes, to scene-level video generation. It supports segment rollback and partial regeneration, so modifying a scene doesn’t require rerunning the entire chain.

【View Demo】

Application Innovation First Prize: AgentVerse

AgentVerse is a native “million-grid map” supporting x402, where agents can buy plots, build homepages, and be discovered externally. It combines identity, payments, and display space, enabling agents to showcase themselves while also having trading capabilities.

【View Demo】

Application Innovation Second Prize: campfire

campfire is a social playground that brings people and agents together, allowing task collaboration, market interaction, or participation in Agent Arena competitions. It emphasizes high-frequency interactions and quantifiable results, making the experience closer to real products rather than just a demo.

【View Demo】

Application Innovation Third Prize: Web3 Quant Trading Challenge Game

This game teaches Web3 quantitative trading through a level-based challenge mechanism. Users can drag and drop strategy modules to run strategies directly, learning quant logic through “play and learn.” Each level provides diagnostic feedback to help users identify issues and adjust.

【View Demo】

Monad Ecosystem AI Layout: More Than Just a Hackathon

In fact, beyond this event, Monad has not only focused on AI before. On the Monad official website’s “Application Center,” AI is listed as a separate category, showcasing 12 AI applications, three of which are supported by the Monad Momentum incentive program. While not yet “rich,” this indicates Monad’s growing emphasis on AI.

In terms of infrastructure solidification and ecosystem expansion, Monad has already launched a series of initiatives.

Previously, Monad released a dedicated payment guide for x402 and a registration tutorial for ERC-8004 (Trustless Agents), attempting to connect key payment links: enabling AI agents not just to think but to autonomously discover, quote, pay, and deliver results seamlessly.

In December 2025, Monad launched the AI Blueprint program, providing comprehensive support for AI applications, including resources and infrastructure to help developers build, launch, and scale projects. Focus areas include decentralized inference networks, autonomous agent clusters, on-chain generative AI, verifiable memory systems, and privacy-preserving computing with consumer-grade hardware distributed inference.

In February 2026, Monad co-hosted the Moltiverse Hackathon, leveraging the popularity of OpenClaw, emphasizing agent application development and monetization tools, highlighting autonomous collaboration, micro-payments, and on-chain execution.

With these intensive efforts, AI seems to have become one of the main battlegrounds in Monad’s ecosystem development.

Of course, betting resources on AI isn’t just due to its popularity:

On the infrastructure side, Monad’s architecture is naturally suited for high-frequency, low-latency, continuously interactive agent scenarios.

Designs like optimistic parallel execution, pipelined architecture, and MonadDB deliver over 10,000 TPS, 0.4-second block times, and extremely low gas costs. These performance advantages support autonomous trading, settlement, and collaboration, positioning Monad as a fast, cheap, and stable execution platform.

Additionally, Monad’s rich and solid DeFi ecosystem provides AI agents with abundant financial tools, accessible liquidity pools, and yield opportunities, enabling AI agents to discover opportunities, trade, settle, and compound on their own—upgrading from smart chatbots to autonomous on-chain economic entities.

This vision of future AI financial exploration sets Monad apart from many crypto AI projects still stuck in conceptual stages. It also creates an important anchor point for continued attention to Monad’s ecosystem after this AI-themed hackathon concludes.