Hit.One ($HIT)
Recent developments:
0% of the initial token allocation is held by the creator.
Creator token stats last updated: Aug 6, 2025 23:03
The following is generated by an LLM:
Summary
AI-driven prediction markets with zero creator stake
Analysis
Hit.One ($HIT) is an AI-driven platform aiming to revolutionize prediction markets through scalable automation, reducing costs and processing time significantly compared to traditional platforms like Polymarket. Key concerns include the creator holding 0% of the token supply initially, raising questions about commitment and alignment of incentives, as well as the lack of transparency regarding team details, legal structure, or token distribution mechanisms beyond the launch. While the project demonstrates innovation with its AI copilot and integration into the Virtuals ecosystem, the absence of creator token allocation (contrary to typical serious projects) and unclear safeguards against centralization or rug pulls are critical risks. The token utility is tied to platform features and revenue sharing, but the necessity of a native token over stablecoins or existing assets is debatable.
Rating: 0
Generated with LLM: deepseek/deepseek-r1
LLM responses last updated: Aug 6, 2025 23:03
Original investment data:
# Hit.One ($HIT)
URL on launchpad: https://app.virtuals.io/prototypes/0x6341b0920B08f08E23Be507c7971529b47E11136
Launched at: Wed, 06 Aug 2025 23:00:30 GMT
Launched through the launchpad: Virtuals Protocol
Launch status: UNDERGRAD
## Token details and tokenomics
Token address: 0x6341b0920B08f08E23Be507c7971529b47E11136
Top holders: https://basescan.org/token/0x6341b0920B08f08E23Be507c7971529b47E11136#balances
Liquidity contract: https://basescan.org/address/0xC8d6Db6d6A667600e0C23d2c81A1D87E2C9Bf96C#asset-tokens
Token symbol: $HIT
Token supply: 1 billion
Creator initial number of tokens: Creator initial number of tokens: 0 (0% of token supply)
## Creator info
Creator address: 0xD1030c38e5848bC9cf4fE167Ef22Aff3a2B05502
Creator on basescan.org: https://basescan.org/address/0xD1030c38e5848bC9cf4fE167Ef22Aff3a2B05502#asset-tokens
Creator on virtuals.io: https://app.virtuals.io/profile/0xD1030c38e5848bC9cf4fE167Ef22Aff3a2B05502
Creator on zerion.io: https://app.zerion.io/0xD1030c38e5848bC9cf4fE167Ef22Aff3a2B05502/overview
Creator on debank.com: https://debank.com/profile/0xD1030c38e5848bC9cf4fE167Ef22Aff3a2B05502
## Description at launch
Hit.One is the AI engine behind Hit.One — a social media platform built on permissionless, AI-driven prediction markets. Hit.One was also awarded 3rd place at the Virtuals Global Hackathon. Prediction markets let people trade on the likelihood of future events, much like trading stocks. With real money on the line, they tap into collective intelligence and often outperform traditional social media speculation.
## Overview
**Introduce:**
**Hit.One** is the AI engine behind HitX — a social media platform built on permissionless, AI-driven prediction markets.
**Hit.One was also awarded 3rd place at the** [**Virtuals Global Hackathon.**](https://x.com/virtuals_io/status/1914323687073001804)
Prediction markets let people trade on the likelihood of future events, much like trading stocks. With real money on the line, they tap into collective intelligence and often outperform traditional social media speculation.
But prediction markets still face serious friction. Placing a bet often requires deep research, yet users lack the tools and insights available in traditional finance. The topics people care about are fast-moving and diverse — but creating corresponding prediction markets and securing reliable oracle support is still a heavy lift.
**Hit.One changes all that.**
It analyzes data, surfaces insights, and helps users make smarter bets. As an AI copilot, **Hit.One** also assists users in creating new markets, providing oracle support for resolution, and bootstrapping liquidity — making it easier to turn any topic into a tradable conversation.
**Roadmap**
In the future, we aim to revolutionize prediction markets with AI-driven solutions.
Our journey unfolds in three phases — from an AI copilot, to a scalable prediction market, to a non-standard AI oracle.
## Phase 1:
In Phase 1, **Hit.One** is your ultimate trading assistant for prediction markets. Our AI analyzes markets and delivers actionable insights through intuitive interfaces, helping you make smarter betting decisions.
You can follow **Hit.One** on Twitter to get real-time insights for every new Polymarket market and even ask questions by tagging the bot. For a more immersive experience, access **Hit.One**’s web-based terminal — or activate our upcoming Chrome extension to receive tailored betting suggestions directly on Polymarket.
## Phase 2: **Hit.One** - AI-Driven Prediction Market
In Phase 2, join us in **Hit.One**, our AI-driven prediction market platform. Unlike traditional platforms like Polymarket, Buzzing leverages Xione’s automation to streamline rule creation, liquidity management, and oracle data, delivering unmatched efficiency and scalability. By leveraging AI, Buzzing is built to be:
* Your truth oracle and trends tracker
* Your first social media where every content feeds are backed by real money
* Your first curator community where ideas will be rewarded
Traditional prediction markets like Polymarket are fully human-driven — expensive, slow, and unable to keep up with the diversity of demand on social media. They can only launch a few hundred markets per month. **Hit.One** is powered by AI, cutting costs by hundreds of times and boosting speed by thousands, enabling the creation of tens of thousands of markets per day.
Here’s how we compare:
| Metric | **Hit.One** (AI-Driven) | Polymarket (Manual) |
| ------------------------------------ | ------------------------------------------------------------------- | --------------------------------------------------------- |
| AI Inference Cost (for 1,000 topics) | \~$18 ($0.75 per hour via Huggingface advanced endpoint\*) | $0 (no AI used) |
| Human Review Cost (for 1,000 topics) | \~$40 (0.3% flagged → \~3 topics reviewed by staff) | \~$15,000 (100% of topics manually reviewed by 2 staff\*) |
| Total Cost for 1,000 Topics | \~$58 (AI + minimal human) | \~$15,000 (all human labor) |
| Time to Process 1,000 Topics | \< 24 hours (AI parallel processing + quick check of flagged cases) | \~30 days (approx. 1 month of manual reviews) |
**Hit.One** focuses on creating markets in more vertical domains, catering to users who are eager to trade but struggle to find suitable venues. In the future, **Hit.One** will also join the ACP and collaborate with other agents, enabling domain-specific agents to create markets tailored to their expertise — including those related to Virtuals, such as predicting which Genesis launch project will be the next $aixbt.
## Phase 3: Ultimate InfoFi
In Phase 3, **Hit.One** evolves into a full Universal Truth Engine — working with other agents through the Agent Commerce Protocol ([ACP](https://app.virtuals.io/research/agent-commerce-protocol)) to fuse real-time financial data and AI insights. Together, they will power InfoFi: a new kind of financial social media where truth is verified, incentivized, and widely distributed.
InfoFi is a future where:
* Posts are not only opinions, but markets — backed by belief, evidence, and economic skin in the game.
* AI agents collaborate to validate facts, track narratives, and offer trusted insight.
* Users are rewarded for contributing accurate, valuable information, not viral noise.
By uniting AI and prediction markets, **Hit.One** helps build a more trustworthy financial web — one where the incentives push toward accuracy, transparency, and collective intelligence.
**Hit.One** will be at the heart of this transformation — a core agent in the **Hit.One** ecosystem.
**Token Utility**
The **Hit.One** Agent powers the core AI copilot capabilities behind prediction markets. The **Hit.One** Token is your key to unlocking this future — giving you access to advanced AI tools and a share of protocol revenue and incentives as the ecosystem grows.
Our team is fully committed to the long-term value of the **Hit.One** Token, and we will continue to grow its utility, role, and alignment with the protocol’s success. At each phase, the **Hit.One** **Token** unlocks powerful new benefits:
* **Phase 1 – AI Copilot**: Access advanced features like deeper insights, analytics, and smarter prediction assistance.
* **Phase 2 – Prediction Market**: Enjoy early access to the Buzzing platform, conversion rights into Buzzing Tokens, and a share in protocol revenue and governance.
* **Phase 3 – Ultimate InfoFi:** Unlock participation in a new class of financial social media. Use $HIT to coordinate with other agents through ACP, access premium InfoFi tools, reward verified content, and help govern a future where financial information is backed by truth and economic incentives.
**Hit.One** is more than just an information collector—it’s a logical thinker and evidence-based analyst. Instead of simply scraping data, **Hit.One** carefully reasons through complex questions, clarifies vague ideas, and ensures every generated prediction market topic is factual and precise.
Powered by ReAct (Reason + Act) and Active Retrieval-Augmented Generation (Active RAG), **Hit.One** iteratively approaches problems, breaking them down step-by-step and actively retrieving the most relevant and current information. Utilizing its working memory, **Hit.One** continuously assesses retrieved data within context, identifies gaps, and iterates until it reaches a clear, well-defined prediction market topic.
**Breaking Down Hit.One’s Logic**
**LLM Planner/Reasoner:** The “brain” of the copilot is a large language model that uses chain-of-thought reasoning to plan and decide next steps. It employs the ReAct (Reason + Act) paradigm , interleaving reasoning about the prediction market query with tool use. The LLM analyzes the user’s request, formulates hypotheses or sub-questions, and determines which external information is needed. This Active Retrieval-Augmented Generation approach allows the agent to iteratively fetch new data during generation (not just once), so the LLM can refine topic suggestions based on the latest observations.
* **Tool Executor:** The planner’s decisions (actions) are handed off to the Tool Executor, which interfaces with external data sources. For example, the LLM might Reason that it needs current crypto trends, then Act by commanding a “CoinMarketCap lookup.” The Tool Executor translates these high-level actions into API calls via the MCP layer. It executes queries on the appropriate channel (finance, social media, web search, etc.) and returns the results (observations) back to the LLM. This component essentially serves as the agent’s “hands,” letting the AI perform actions in the outside world as guided by its reasoning.
* **Observation Cache (Working Memory):** As each tool action returns data, the information is stored in an observation cache (short-term memory). This working memory accumulates facts, snippets, and context retrieved from various sources. In the ReAct loop, the LLM’s next reasoning step takes into account these cached observations – updating its context with new information before deciding whether another retrieval is needed . By maintaining a memory of all retrieved evidence, the agent avoids redundant searches and can gradually converge on well-informed prediction market topics. (In a more advanced setup, a long-term retrieval memory or vector database could also be used to remember information across sessions, but the core loop relies on the immediate observation cache.)
* **MCP Orchestrator (Multi-Channel Protocol):** This is the external data access layer that manages and unifies calls to heterogeneous sources. The MCP orchestrator acts as a hub or plugin manager for the copilot – it receives tool requests from the agent and routes them to the proper external API or service. By abstracting multiple channels behind a common interface, the agent can ask for “current market data” or “latest social trends” without worrying about the specifics of each API. The MCP layer handles authentication, rate-limits, and formatting of queries/responses to each source. It enables the agent to seamlessly invoke multiple tools in one workflow, coordinating queries across finance (stock/crypto), social media, and web search as needed.
* **External Data Sources:** A variety of external information channels feed the copilot with up-to-date data:
\*
* *Financial Data APIs*(e.g. Yahoo Finance, CoinMarketCap): Provide real-time market prices, stock trends, crypto valuations, and financial news – useful for suggesting prediction markets on economic events or asset prices.
*
* *Social & Media APIs*(e.g. YouTube Data API, X (Twitter) API): Supply insights on trending topics, viral videos, social sentiment, and breaking news in the wider culture. These help the agent identify popular discussion points or emerging themes that could inspire prediction markets.
*
* *General Web Search*: Allows the agent to retrieve relevant articles, forecasts, or reference information from the internet at large. Web search fills in knowledge gaps and ensures no important topic is missed if it’s not covered by the dedicated APIs.
*
* The MCP orchestrator treats each of these sources as a plugin channel. During the reasoning loop, the LLM can flexibly call one or multiple channels (in parallel or sequence) via MCP – for example, first pulling financial stats from Yahoo, then checking social buzz on Twitter – depending on what information the agent’s reasoning requires. Each API response is fed back into the observation cache, enriching the agent’s knowledge.
* **Iterative Retrieval-Action Cycle:** The architecture above enables a tool-augmented reasoning loop: the LLM plans → acts → observes → (updates context) and then plans again, repeating as needed. This ReAct loop with Active RAG means the agent can start with a broad query, gather data in steps, and hone in on well-defined prediction market ideas. For instance, it might begin by asking “What events are trending in finance right now?” (via Yahoo/CMC), get some candidates, then follow up with “Are these topics generating buzz on social media?” (via X/YouTube), and so on. At each iteration, the agent’s internal planner refines the topic suggestions using the fresh data. Once sufficient information is gathered and the topics are refined, the LLM produces the final output – a set of proposed prediction market topics – which is then returned to the user. This design leverages cutting-edge agent paradigms (reasoning + tool use, retrieval loops, and plugin orchestration) to ensure the suggested markets are relevant, timely, and well-informed by the latest available data.
## Additional information extracted from relevant pages
<fetched_info>
""" https://x.com/virtuals_io/status/1914323687073001804
Skipped social media URL (https://x.com/virtuals_io/status/1914323687073001804) - requires authentication
"""
""" [Creator profile on Virtuals Protocol](https://api.virtuals.io/api/profile/0xD1030c38e5848bC9cf4fE167Ef22Aff3a2B05502)
{
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"id": 485601,
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"bio": null,
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"userSocials": [
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"id": 548307,
"provider": "metamask",
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"""
""" https://app.virtuals.io/research/agent-commerce-protocol
[](https://app.virtuals.io/)
[AI Agents](https://app.virtuals.io/) [ACP](https://app.virtuals.io/acp)
Build
[GAME\\
\\
Agentic framework for agent commerce](https://docs.game.virtuals.io/)
[Virtuals Venture\\
\\
Ecosystem fund for Virtuals Protocol](https://www.virtuals.vc/)
[Virtuals Partner Network (VPN)\\
\\
Connect builders with Venture Partners](https://virtuals.io/vpn)
[Developer Resources\\
\\
Learn more about building with Virtuals](https://dev.virtuals.io/)
[Join ACP\\
\\
Register your agent to participate in ACP](https://app.virtuals.io/acp/join)
[VIRTUAL](https://app.virtuals.io/dashboard) [About](https://virtuals.io/about)
Search...
Butler
Connect Wallet
August 06, 2025
[Dashboard\\
\\
](https://app.virtuals.io/research/agent-commerce-protocol) [Paper\\
\\
](https://app.virtuals.io/research/agent-commerce-protocol)
We may employ on-the-spot tracking techniques during your browsing session to collect data on your interactions, preferences, and behaviour. This data helps us personalise your experience and improve our services. See our [Privacy Policy](https://app.virtuals.io/privacy_policy.pdf).
Allow AnalyticsOpt-out
Wallet · Privy
Turnkey Recovery and Auth
## Init Recovery or Auth
_This public key will be sent along with your email inside of a new_
_`INIT_USER_EMAIL_RECOVERY` or_
_`EMAIL_AUTH` activity_
Embedded keyReset KeyInit Key
## Inject Credential Bundle
_The credential bundle will come from your email. This bundle can then_
_be used for email recovery or auth. We can simulate this locally: see_
_instructions_
_[here](https://github.com/tkhq/frames#running-local-auth). A credential bundle is composed of a public key and an encrypted_
_payload. The payload is encrypted to this document's embedded key_
_(stored in local storage and displayed above). The scheme relies on_
_[HPKE (RFC 9180)](https://datatracker.ietf.org/doc/rfc9180/)_.
BundleInject Bundle
## Stamp
_Once you've injected the credential bundle, the credential is ready to_
_sign. A new `RECOVER` activity for example. This iframe_
_doesn't know anything about Turnkey activity however, it's a simple_
_stamper!_
PayloadStamp
## Message log
_Below we display a log of the messages sent / received. The forms above_
_send messages, and the code communicates results by sending events via_
_the `postMessage` API._
⬆️ Sent message PUBLIC\_KEY\_READY: 0477ef054008aef2a8bc1965e8bbe50e55b85c3a7710124dabd44b3165143bc1fab166ce1d0a0f951979edc148717df3a5ed6cbc8c6359e014c9d933f0d326184f
"""
</fetched_info>
<full_details>
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"overview": "**Introduce:**\n\n**Hit.One** is the AI engine behind HitX — a social media platform built on permissionless, AI-driven prediction markets.\n\n**Hit.One was also awarded 3rd place at the** [**Virtuals Global Hackathon.**](https://x.com/virtuals_io/status/1914323687073001804)\n\nPrediction markets let people trade on the likelihood of future events, much like trading stocks. With real money on the line, they tap into collective intelligence and often outperform traditional social media speculation.\n\nBut prediction markets still face serious friction. Placing a bet often requires deep research, yet users lack the tools and insights available in traditional finance. The topics people care about are fast-moving and diverse — but creating corresponding prediction markets and securing reliable oracle support is still a heavy lift.\n\n**Hit.One changes all that.**\n\nIt analyzes data, surfaces insights, and helps users make smarter bets. As an AI copilot, **Hit.One** also assists users in creating new markets, providing oracle support for resolution, and bootstrapping liquidity — making it easier to turn any topic into a tradable conversation.\n\n**Roadmap**\n\nIn the future, we aim to revolutionize prediction markets with AI-driven solutions.\n\nOur journey unfolds in three phases — from an AI copilot, to a scalable prediction market, to a non-standard AI oracle.\n\n## Phase 1:\n\nIn Phase 1, **Hit.One** is your ultimate trading assistant for prediction markets. Our AI analyzes markets and delivers actionable insights through intuitive interfaces, helping you make smarter betting decisions.\n\nYou can follow **Hit.One** on Twitter to get real-time insights for every new Polymarket market and even ask questions by tagging the bot. For a more immersive experience, access **Hit.One**’s web-based terminal — or activate our upcoming Chrome extension to receive tailored betting suggestions directly on Polymarket.\n\n## Phase 2: **Hit.One** - AI-Driven Prediction Market\n\nIn Phase 2, join us in **Hit.One**, our AI-driven prediction market platform. Unlike traditional platforms like Polymarket, Buzzing leverages Xione’s automation to streamline rule creation, liquidity management, and oracle data, delivering unmatched efficiency and scalability. By leveraging AI, Buzzing is built to be:\n\n* Your truth oracle and trends tracker\n* Your first social media where every content feeds are backed by real money\n* Your first curator community where ideas will be rewarded\n\nTraditional prediction markets like Polymarket are fully human-driven — expensive, slow, and unable to keep up with the diversity of demand on social media. They can only launch a few hundred markets per month. **Hit.One** is powered by AI, cutting costs by hundreds of times and boosting speed by thousands, enabling the creation of tens of thousands of markets per day.\n\nHere’s how we compare:\n\n| Metric | **Hit.One** (AI-Driven) | Polymarket (Manual) |\n| ------------------------------------ | ------------------------------------------------------------------- | --------------------------------------------------------- |\n| AI Inference Cost (for 1,000 topics) | \\~$18 ($0.75 per hour via Huggingface advanced endpoint\\*) | $0 (no AI used) |\n| Human Review Cost (for 1,000 topics) | \\~$40 (0.3% flagged → \\~3 topics reviewed by staff) | \\~$15,000 (100% of topics manually reviewed by 2 staff\\*) |\n| Total Cost for 1,000 Topics | \\~$58 (AI + minimal human) | \\~$15,000 (all human labor) |\n| Time to Process 1,000 Topics | \\< 24 hours (AI parallel processing + quick check of flagged cases) | \\~30 days (approx. 1 month of manual reviews) |\n\n**Hit.One** focuses on creating markets in more vertical domains, catering to users who are eager to trade but struggle to find suitable venues. In the future, **Hit.One** will also join the ACP and collaborate with other agents, enabling domain-specific agents to create markets tailored to their expertise — including those related to Virtuals, such as predicting which Genesis launch project will be the next $aixbt.\n\n## Phase 3: Ultimate InfoFi\n\nIn Phase 3, **Hit.One** evolves into a full Universal Truth Engine — working with other agents through the Agent Commerce Protocol ([ACP](https://app.virtuals.io/research/agent-commerce-protocol)) to fuse real-time financial data and AI insights. Together, they will power InfoFi: a new kind of financial social media where truth is verified, incentivized, and widely distributed.\n\nInfoFi is a future where:\n\n* Posts are not only opinions, but markets — backed by belief, evidence, and economic skin in the game.\n* AI agents collaborate to validate facts, track narratives, and offer trusted insight.\n* Users are rewarded for contributing accurate, valuable information, not viral noise.\n\nBy uniting AI and prediction markets, **Hit.One** helps build a more trustworthy financial web — one where the incentives push toward accuracy, transparency, and collective intelligence.\n\n**Hit.One** will be at the heart of this transformation — a core agent in the **Hit.One** ecosystem.\n\n**Token Utility**\n\nThe **Hit.One** Agent powers the core AI copilot capabilities behind prediction markets. The **Hit.One** Token is your key to unlocking this future — giving you access to advanced AI tools and a share of protocol revenue and incentives as the ecosystem grows.\n\nOur team is fully committed to the long-term value of the **Hit.One** Token, and we will continue to grow its utility, role, and alignment with the protocol’s success. At each phase, the **Hit.One** **Token** unlocks powerful new benefits:\n\n* **Phase 1 – AI Copilot**: Access advanced features like deeper insights, analytics, and smarter prediction assistance.\n* **Phase 2 – Prediction Market**: Enjoy early access to the Buzzing platform, conversion rights into Buzzing Tokens, and a share in protocol revenue and governance.\n* **Phase 3 – Ultimate InfoFi:** Unlock participation in a new class of financial social media. Use $HIT to coordinate with other agents through ACP, access premium InfoFi tools, reward verified content, and help govern a future where financial information is backed by truth and economic incentives.\n\n**Hit.One** is more than just an information collector—it’s a logical thinker and evidence-based analyst. Instead of simply scraping data, **Hit.One** carefully reasons through complex questions, clarifies vague ideas, and ensures every generated prediction market topic is factual and precise.\n\nPowered by ReAct (Reason + Act) and Active Retrieval-Augmented Generation (Active RAG), **Hit.One** iteratively approaches problems, breaking them down step-by-step and actively retrieving the most relevant and current information. Utilizing its working memory, **Hit.One** continuously assesses retrieved data within context, identifies gaps, and iterates until it reaches a clear, well-defined prediction market topic.\n\n**Breaking Down Hit.One’s Logic**\n\n**LLM Planner/Reasoner:** The “brain” of the copilot is a large language model that uses chain-of-thought reasoning to plan and decide next steps. It employs the ReAct (Reason + Act) paradigm , interleaving reasoning about the prediction market query with tool use. The LLM analyzes the user’s request, formulates hypotheses or sub-questions, and determines which external information is needed. This Active Retrieval-Augmented Generation approach allows the agent to iteratively fetch new data during generation (not just once), so the LLM can refine topic suggestions based on the latest observations.\n\n* **Tool Executor:** The planner’s decisions (actions) are handed off to the Tool Executor, which interfaces with external data sources. For example, the LLM might Reason that it needs current crypto trends, then Act by commanding a “CoinMarketCap lookup.” The Tool Executor translates these high-level actions into API calls via the MCP layer. It executes queries on the appropriate channel (finance, social media, web search, etc.) and returns the results (observations) back to the LLM. This component essentially serves as the agent’s “hands,” letting the AI perform actions in the outside world as guided by its reasoning.\n* **Observation Cache (Working Memory):** As each tool action returns data, the information is stored in an observation cache (short-term memory). This working memory accumulates facts, snippets, and context retrieved from various sources. In the ReAct loop, the LLM’s next reasoning step takes into account these cached observations – updating its context with new information before deciding whether another retrieval is needed . By maintaining a memory of all retrieved evidence, the agent avoids redundant searches and can gradually converge on well-informed prediction market topics. (In a more advanced setup, a long-term retrieval memory or vector database could also be used to remember information across sessions, but the core loop relies on the immediate observation cache.)\n* **MCP Orchestrator (Multi-Channel Protocol):** This is the external data access layer that manages and unifies calls to heterogeneous sources. The MCP orchestrator acts as a hub or plugin manager for the copilot – it receives tool requests from the agent and routes them to the proper external API or service. By abstracting multiple channels behind a common interface, the agent can ask for “current market data” or “latest social trends” without worrying about the specifics of each API. The MCP layer handles authentication, rate-limits, and formatting of queries/responses to each source. It enables the agent to seamlessly invoke multiple tools in one workflow, coordinating queries across finance (stock/crypto), social media, and web search as needed.\n* **External Data Sources:** A variety of external information channels feed the copilot with up-to-date data:\n \\*\n * *Financial Data APIs*(e.g. Yahoo Finance, CoinMarketCap): Provide real-time market prices, stock trends, crypto valuations, and financial news – useful for suggesting prediction markets on economic events or asset prices.\n *\n * *Social & Media APIs*(e.g. YouTube Data API, X (Twitter) API): Supply insights on trending topics, viral videos, social sentiment, and breaking news in the wider culture. These help the agent identify popular discussion points or emerging themes that could inspire prediction markets.\n *\n * *General Web Search*: Allows the agent to retrieve relevant articles, forecasts, or reference information from the internet at large. Web search fills in knowledge gaps and ensures no important topic is missed if it’s not covered by the dedicated APIs.\n *\n* The MCP orchestrator treats each of these sources as a plugin channel. During the reasoning loop, the LLM can flexibly call one or multiple channels (in parallel or sequence) via MCP – for example, first pulling financial stats from Yahoo, then checking social buzz on Twitter – depending on what information the agent’s reasoning requires. Each API response is fed back into the observation cache, enriching the agent’s knowledge.\n* **Iterative Retrieval-Action Cycle:** The architecture above enables a tool-augmented reasoning loop: the LLM plans → acts → observes → (updates context) and then plans again, repeating as needed. This ReAct loop with Active RAG means the agent can start with a broad query, gather data in steps, and hone in on well-defined prediction market ideas. For instance, it might begin by asking “What events are trending in finance right now?” (via Yahoo/CMC), get some candidates, then follow up with “Are these topics generating buzz on social media?” (via X/YouTube), and so on. At each iteration, the agent’s internal planner refines the topic suggestions using the fresh data. Once sufficient information is gathered and the topics are refined, the LLM produces the final output – a set of proposed prediction market topics – which is then returned to the user. This design leverages cutting-edge agent paradigms (reasoning + tool use, retrieval loops, and plugin orchestration) to ensure the suggested markets are relevant, timely, and well-informed by the latest available data.",
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Investment info last updated: Aug 6, 2025 23:03