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Source channel @githubtrending · Post #15523 · Feb 25

#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

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Google Facts™ [ ️@googlefactss🌎]

@googlefactss · Post #40776 · 03/11/2026, 11:01 PM

During World War II, engineers studied planes that returned from missions. They first thought the areas with the most bullet holes needed armor. Statistician Abraham Wald realized this was survivorship bias. Survivorship bias happens when you focus only on survivors and ignore failures.The undamaged areas on returning planes were actually the critical spots. Planes hit there did not survive. He recommended reinforcing those undamaged areas. ✈️📊🛡️ [Read more] @googlefactss #SurvivorshipBias#WWII#AbrahamWald#Planes#Statistics#History