Multi-Agent Event Predictor
Multi-Agent Event Predictor
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Description
Multi-Agent Event Predictor - By simulating social media interactions of target market users, it proactively identifies cultural, religious, and political taboo scenarios that may be triggered by product feature launches, and generates targeted testing suggestions.
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ResearchNiche AI Product Radar
A systematic engine for discovering niche AI product opportunities, v3.1. It combines the mathematical rigor of v2.0 with the execution methodology of App Gold Mining: a five-level anchor scale and logarithmic demand-intensity scoring, category-weighted supply scarcity, signal time-decay functions, competitive response and platform-risk game analysis, and a Bayesian prior-to-posterior validation framework. v3.1 adds the SonarPing supply-side detection layer, incorporating its daily scans of new AI products, elimination of wrapper and lookalike products, hands-on testing, and time-series snapshot data. It filters out ineffective competitors from nominal supply, calculates the effective supply rate, new-product entry speed, and deactivation speed, and upgrades the static half-life into a dynamic opportunity window driven by real supply flows. It integrates data from 130+ Chinese Twitter creators, Chinese-language communities, and payment behavior as high-quality demand signals, helping indie developers identify low-competition, high-pain-density opportunities in AaaS and B2C subscription products, and produces actionable radar reports and promotion-channel matrices.
Cosmic Insight
Automatically searches multiple platforms for news in finance, tech, or business topics, filters preliminary and core clues, and uncovers related events within 10 years to output a structured topic analysis report. Step 1: Multi-Source Information Collection - Search news from the past 30 days covering WeChat official accounts, Wall Street CN, 36Kr (high weight), Huxiu, Xueqiu, TMTpost (standard weight) - Record title, time, platform, summary, original links - Initially filter duplicates, ads, and low-credibility content Step 2: Information Deduplication and Aggregation - Aggregate the same event by core object, core fact, and time window (within 7 days) - Count how many independent sources mention it - Remove low-value events, output a candidate event list Step 3: Event Analysis and Clue Classification Preliminary clues (any one): abnormal events, high discussion (more than 3 sources), unusual changes, major industry milestones, negative news about large companies Core clues (2 or more): dramatic conflict, room for meaning exploration, more than 3 entities, timeliness (within 14 days) - At least 5 core clues, total 10-25 clues - Each clue includes: event title, summary, deep analysis of writing value, criteria met Step 4: Deep Association Mining - Expand search scope to 10 years - Extract key entities, search: historical nodes of the subject, industry policy evolution, competitor dynamics over the same period, upstream and downstream industry chain changes - Include any association, note association type and year Step 5: Structured Output Output includes: 1. Core clues block; 2. Preliminary clues block; 3. Research suggestions Each clue includes: event overview, criteria met, deep writing value analysis, related events (10-year history), reference links Tools: Web search, long document generation Reference resources: None fixed; user can attach at input.
Trending Hit Prediction
Spot the next big hit like a trend hunter. Capture weak signals within 72 hours, predict viral topics, and generate a countdown action checklist to become a market trendsetter.
Information
- Version
- v3
- Last updated
- Runtime credits
- Usage-based
- Models
- Auto
