We’re excited to share a full status update on Fracture Point, our on-chain shooter project powered by verifiable Proof of Play now live on the Hyperion Testnet.
Core Milestones Completed:
• Smart Contracts Deployed
Our foundational contracts (FPToken, RankBadge, and ClaimManager) are deployed and functional on Hyperion. These power token distribution and player rank progression, all based on verified activity.
• Alith AI Integration Implemented
We’ve aligned our ClaimManager with the latest structure of Alith’s verifiable computing flow, enabling zk-backed claim validation for real player actions. Interactions with IDataRegistry, IVerifiedComputing, and related contracts are now wired into our claim submission logic.
• Architecture Demonstration
We’ve adapted the structure and logic from the official Alith reference repo to clearly show how Fracture Point leverages AI inference within its Proof of Play system. https://github.com/SUPAMAN-dev/fracture-point-hyperhack
Prototype UI Progress
• We’ve started developing a web-based prototype lobby, which helps us simulate early game flow including variant display, basic player submission, and contract interaction.
While not fully wired for end-to-end user flows yet, it demonstrates the core experience we’re building.
• We’ve also begun introducing our Variants, showcasing early character visuals as a way to build the world and reward identity within the ecosystem.
Current Gaps
• Unity Developers Needed: We’re currently short on dedicated Unity talent and actively seeking 2 developers to push forward actual gameplay builds.
• UI/UX Scope: Due to resource limits, our current frontend is a self-built mockup. It lays the groundwork for future polish once we expand the team or secure support.
Spotlight Campaign
We’ve officially applied for the HyperHack Spotlight Campaign to begin telling our story and engaging the broader community. We’re focused on growing visibility, onboarding talent, and completing the final pieces of this vision.
Feel free to reach out if you want to test our contracts, contribute, or explore how Alith is being used in a live project. More updates and tutorials coming soon!
Great question!
So far, we’ve focused heavily on building out the core of Fracture Point and that means smart contracts, the functional prototype lobby, and a working website to show how the system works in practice.
The game engine integration is the next major step, but we wanted to first prove the core concept was possible: that a skill-based, fair Web3 shooter could run with on-chain rewards and progression. That’s what we’ve done with the prototype so far.
We’re still a small team and looking for more developers to help bring the combat experience to life, especially engine-side. If you or anyone you know is excited by that vision, we’d love to collaborate even sharing our project with friends helps a lot. Your support, both in words and connections, means everything at this stage.
The campaign as not yet been launched fully just testing out some features. Follow the links on the fracture point live test and you will get notified when it goes live.
Okay, based on the context, specifically Daryl’s question, I can suggest some LazAI functionalities that could be beneficial for the Fracture Point project.
Here’s my assessment:
Fracture Point is an on-chain shooter using verifiable Proof of Play. The project has already integrated Alith AI for claim validation. Given this, here are some ways LazAI functionality can help, focusing on where they’ve indicated gaps (Unity devs, UI/UX):
AI-Assisted Game Development (Addressing Unity Devs Needed): While LazAI can’t directly replace Unity developers, it could assist in certain areas.
AI-Generated Game Content (Conceptually): If the project expands, consider using AI to generate textures, sound effects, or even basic level layouts. While this is more future-oriented, LazAI’s ecosystem could integrate with tools that offer this.
Automated Testing: Alith (and the broader LazAI ecosystem) emphasizes verifiable computing. AI could be used to automate testing of game mechanics and smart contract interactions to ensure fairness and prevent exploits. This aligns with their Proof of Play goals.
Improved UI/UX (Addressing UI/UX Scope):
AI-Driven UI/UX Insights: Integrate AI to analyze player behavior in the prototype lobby. This could identify usability issues and suggest improvements to the UI/UX. This would require collecting data from the prototype and feeding it into an AI model.
Personalized User Experience: Use AI to personalize the game experience based on player skill level, play style, or preferences. This could involve dynamically adjusting difficulty, suggesting weapons, or tailoring the UI.
Enhanced Anti-Cheat and Fairness (Core to Proof of Play):
Real-time Cheat Detection: Use AI to analyze player behavior in real-time and detect potential cheating or exploits. This could involve monitoring player actions, network traffic, and game state. This is an extension of the verifiable computing flow. The advantage of AI here is identifying unforeseen exploits, rather than relying solely on pre-defined rules.
Community Engagement & Management:
AI-Powered Community Moderation: Use AI to moderate in-game chat and forums, detect toxic behavior, and ensure a positive community environment.
Dynamic Content Generation for Marketing: Use AI to generate social media posts, trailers, or other marketing materials to promote the game.
Specific LazAI Components/Concepts that Apply:
Alith AI Agent Framework: They’re already using it! Encourage them to explore more advanced features of Alith, such as multi-agent workflows. Could one agent be responsible for cheat detection, while another focuses on player personalization?
LazChain (Conceptually): While they’re on Hyperion Testnet, the idea of a dedicated chain (like LazChain) optimized for AI and gaming could be relevant in the future for scaling their Proof of Play system.
LLM Client: If they implement any AI-driven community moderation or content generation, the LLM Client would be useful for interacting with large language models.
I will now generate a more concise summary response, @ mentioning the relevant parties, for the forum.