Created on 13th June 2025
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Creators can’t verify ownership of content, and often don’t earn proportionally to their impact.
Learners have no on-chain proof of participation, credentials, or contributions.
There's no universal token economy to reward student progress, tournament wins, or knowledge-sharing efforts.
Existing platforms limit the transferability of digital credentials or assets.
BrainInk solves this by using the blockchain (Base chain) to tokenize achievements, learning activity, and content ownership — enabling direct monetization, verification, and transferability of educational assets.
BrainInk integrates real-time messaging directly with AI (K.A.N.A.), group study, and tournament updates — creating a central hub for all academic and social interactions.
In Summary
BrainInk reimagines learning for the digital-native generation — decentralized, social, gamified, and fair.
It solves the problem of:
Unrewarded learning
Unverifiable digital achievements
Unmonetized content creation
Scattered communication in academic spaces
All by building an onchain educational ecosystem with AI-powered collaboration at the center.
Real-time AI + Messaging Integration
Challenge:
We wanted users to seamlessly chat with our AI assistant (K.A.N.A.) within the same interface as their friend and group conversations. Integrating real-time messaging with an always-on AI, while ensuring low latency, context memory, and multimodal file support, proved complex.
How We Solved It:
We decoupled AI message parsing from standard chat WebSocket streams.
Introduced a queuing system and fallback responses when latency spiked.
Used streaming token responses (à la OpenAI's chat streaming) to keep it feeling real-time.
Created isolated AI agents per conversation thread with scoped memory.
How We Solved It:
We implemented an embedded wallet system using a social login layer (Firebase + third-party custodial service) for users who don't want full wallet control.
For advanced users, we added optional MetaMask/WalletConnect flows.
Built a test token faucet and sandbox mode so users could learn the system risk-free.
How We Solved It:
Cached search results by subject and region to reduce duplicate requests.
Preloaded high-frequency academic topics.
Built a local proxy server that handles retries and exponential backoff.
How We Solved It:
Created strict module boundaries using a mono-repo structure.
Used feature flags to allow partial deployment of dependent systems.
Wrote shared logic as external libraries consumed by both teams, ensuring autonomy but compatibility.
Technologies used
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