TGTGInsighttelegram intelligenceLIVE / telegram public index
← GitHub Trends

TGINSIGHT SIMILAR POSTS

Find similar content

Source channel @githubtrending · Post #14672 · May 5

#typescript#app#cap#coss#loom#mac#nextjs#nextjs14#open_source#oss#react#record#screen_capture#screen_recorder#screenshot#solidjs#tauri#tauri_app#typescript#vite Cap is a free, open-source tool that helps you record and share videos quickly. It's similar to Loom but gives you more control over your recordings. You can use it on both macOS and Windows, making it easy to work with different devices. Cap allows you to store your videos locally or in the cloud, which means you can access them from anywhere. This tool is great for sharing information with teams or clients securely and efficiently. https://github.com/CapSoftware/Cap

Results

1 similar post found

Search: #superagent

当前筛选 #superagent清除筛选
GitHub Trends

@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