AITV

Demo

About

The problem it solves Launching and sustaining a livestreaming presence today is **time-intensive, costly, and limited by human constraints**. Creators, brands, and token communities must constantly be live and available to capture attention - a model that doesn’t scale. AITV solves this by enabling **anyone to launch their own autonomous livestreaming agent**. These agents run 24/7, interact with audiences in real time, and are directly connected to onchain rewards. This unlocks: - **Accessibility** – Anyone, from individual creators to DAOs, can spin up an agent that represents their identity or project. - **Scalability** – Agents never need rest, letting communities engage globally without burnout. - **Token-aligned engagement** – Communities can give their tokens a “face and voice,” turning abstract assets into living, interactive presences that strengthen culture and loyalty. - **Economic empowerment** – Agents natively integrate onchain credits and rewards, enabling seamless monetization for both creators and communities. - **Creative experimentation** – Low-cost, customizable personalities let communities test new ways of engaging holders and attracting new audiences. By lowering the barrier to launch and run a **persistent, interactive, token-powered presence**, AITV transforms livestreaming from something only full-time creators can sustain into something **any community can access, customize, and use to grow their ecosystem**. Smart Contract Address(es) AITV Credits Token Distributor: [0x880E6a3268c98772dF4e8B1b5eaa4ed059FEc337](https://basescan.org/address/0x880E6a3268c98772dF4e8B1b5eaa4ed059FEc337)

How it's made

Challenges we ran into One of the biggest challenges we faced was **orchestrating live AI inference at scale** while keeping the experience smooth for viewers. Running autonomous livestreaming agents means combining real-time video, interactive prompting, and onchain reward logic - each of which has different performance requirements. Early versions struggled with **latency and reliability**: when many users prompted the agent at once, responses could lag or even drop mid-stream. This broke the sense of “live” interaction and risked disengaging users. Another major hurdle was making the agent feel **human and engaging** in this environment. The agent isn’t just answering prompts - it’s also navigating a fast-moving chat, responding to multiple users, and pursuing its own goals for the stream. Balancing these competing priorities in a way that feels natural required a lot of experimentation. We overcame these issues by: - **Implementing a queueing and prioritization layer** so that prompts are processed in order without overwhelming the model. - **Separating video streaming from AI inference** so that the livestream never stalls, even if the AI is momentarily busy. - **Batching onchain interactions** to lower gas costs and ensure rewards are distributed efficiently. - **Adding conversational memory and context management** so the agent can juggle user prompts while maintaining its own narrative flow. By restructuring the architecture and refining the agent’s behavior, we were able to deliver a **consistent, always-on experience** where users feel their prompts are heard, their rewards are instant, and the agent remains engaging and lifelike throughout the stream.