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    Open-source Deep Research

    5 deep research workflows

    Use MCP-native channel access to combine global, technical, and Chinese sources without binding research to a single LLM.

    01

    Weekly arxiv LLM digest

    Search arxiv papers, GitHub trending repos, and Reddit r/MachineLearning discussions. Summarize citations, implementation links, and open questions.

    Try this prompt

    Find this week's notable LLM papers across arxiv, GitHub trending, and Reddit r/MachineLearning. Return a cited digest with code links.
    02

    Competitive analysis

    Search GitHub, Hacker News, Reddit, and Twitter for a product name. Compare adoption signals, complaints, roadmap hints, and adjacent projects.

    Try this prompt

    Research {product} across GitHub, Hacker News, Reddit, and Twitter. Summarize positioning, adoption signals, and risks with citations.
    03

    Chinese RFC research

    Search WeChat, Zhihu, 36Kr, and Bilibili for Chinese-language technical opinions. Extract consensus, objections, and dated source links.

    Try this prompt

    Search WeChat, Zhihu, 36Kr, and Bilibili for discussion around {topic}. Return a bilingual RFC brief with cited claims.
    04

    GitHub similar-projects discovery

    Search GitHub, dev.to, and Stack Overflow for projects with similar keywords. Rank repos by maintenance, usage, and implementation clarity.

    Try this prompt

    Find similar open-source projects for {idea} across GitHub, dev.to, and Stack Overflow. Rank them and explain tradeoffs.
    05

    Reddit + Hacker News sentiment summary

    Search Reddit, Hacker News, and Twitter to measure how technical users describe a release. Separate sentiment from evidence.

    Try this prompt

    Summarize sentiment for {release} across Reddit, Hacker News, and Twitter. Include representative citations and recurring objections.