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Изворен канал @pythonotes · Post #238 · 28 апр.

В PyCharm есть окно со статистикой продуктивности (Help / Productivity Guide), где отображена интересная информация об использовании IDE. В частности, можно узнать сколько нажатий на кнопки вам сэкономил автокомплит. У меня 231К за 14 месяцев ! Не плохо конечно, но клавиатура всё равно стёрлась))) 🙌⌨️😵 #offtop

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@githubtrending · Post #15523 · 25.02.2026 г., 12:30

#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