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Source channel @githubtrending · Post #15583 · Mar 24

#python#ai_prompts#ai_skill#bluesky#claude#claude_code#clawhub#deep_research#hackernews#instagram#openclaw#polymarket#recency#reddit#research#social_media#tiktok#trends#twitter#web_search#youtube /last30days is a Claude Code skill that scans Reddit, X, Bluesky, YouTube, TikTok, Instagram, Hacker News, Polymarket, and web for your topic's top discussions, upvotes, bets, and videos from the last 30 days, then synthesizes a cited briefing with ready-to-use prompts. New v2.9.5 adds Bluesky, "X vs Y" comparisons, and auto-saves to build your research library. Install easily via `/plugin install last30days@last30days-skill`. You stay ahead on AI trends, tools, and techniques with real community insights in minutes, skipping hours of manual searching. https://github.com/mvanhorn/last30days-skill

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@githubtrending · Post #15523 · 02/25/2026, 12:30 PM

#typescript#agent#agentic#agentic_framework#agentic_workflow#ai#ai_agents#bytedance#deep_research#harness#langchain#langgraph#langmanus#llm#multi_agent#nodejs#podcast#python#superagent#typescript DeerFlow 2.0 is an open-source super agent harness that orchestrates multiple sub-agents, memory systems, and sandboxed execution environments to accomplish complex tasks. Built on LangGraph and LangChain, it combines research, coding, and content creation capabilities with extensible skills and tools. The platform features isolated Docker containers for safe execution, long-term memory that learns your preferences, and the ability to spawn sub-agents that work in parallel on different task angles. You benefit from dramatically reduced research and automation time—tasks that typically take hours complete in minutes—while maintaining full transparency and control over agent decisions through human-in-the-loop collaboration. Whether you need deep research reports, data analysis, slide decks, or custom workflows, DeerFlow handles multi-step complexity without requiring extensive coding knowledge. https://github.com/bytedance/deer-flow