⚡ CloakHQ/CloakBrowser is making waves. Here's the full picture.
🔗 https://github.com/CloakHQ/CloakBrowser
📝 Stealth Chromium that passes every bot detection test. Drop-in Playwright replacement with source-level fingerprint patches. 30/30 tests passed.
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Meet CloakBrowser, a stealthy Chromium browser that evades bot detection with ease. Its source-level patches modify the Chromium binary at the C++ level, making it undetectable to antibot systems. With
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🧠 Channel: https://t.iss.one/GithubRe
🔗 https://github.com/CloakHQ/CloakBrowser
📝 Stealth Chromium that passes every bot detection test. Drop-in Playwright replacement with source-level fingerprint patches. 30/30 tests passed.
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Meet CloakBrowser, a stealthy Chromium browser that evades bot detection with ease. Its source-level patches modify the Chromium binary at the C++ level, making it undetectable to antibot systems. With
launch(), you can create a new browser instance in just a few lines of code. Key features include auto-updating binary, human-like behavior with humanize=True, and passing reCAPTCHA v3 with a score of 0.9. CloakBrowser works seamlessly with Playwright and Puppeteer, and is perfect for web scraping, automation, and bypassing Cloudflare Turnstile. Give it a try with docker run --rm cloakhq/cloakbrowser cloaktest or pip install cloakbrowser. CloakBrowser is the ultimate solution for those tired of bot detection woes: it just works, and that's the cloak of stealth you need.──────────────────────────────
🧠 Channel: https://t.iss.one/GithubRe
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Github Top Repositories
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🚀 Meet awslabs/aidlc-workflows: a gem from today's GitHub trending list.
🔗 https://github.com/awslabs/aidlc-workflows
📝 AI-Driven Life Cycle (AI-DLC) adaptive workflow steering rules for AI coding agents
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The awslabs/aidlc-workflows GitHub repository introduces the AI-Driven Development Life Cycle (AI-DLC), an intelligent software development workflow. This workflow is designed to adapt to the user's needs, maintain quality standards, and keep the developer in control of the process.
Key features of AI-DLC include its ability to integrate with various coding agents and platforms such as Kiro, Amazon Q Developer IDE Plugin, Cursor IDE, Cline, and Claude Code. To use AI-DLC, users need to download the latest release zip file, extract it to a folder outside their project directory, and follow the setup instructions for their chosen coding agent and platform.
Technical highlights of AI-DLC include its use of core workflow rules and detailed rules conditionally referenced by the core rules. The workflow is implemented using steering files, rules, or memory files, depending on the platform.
The target audience for AI-DLC includes software developers and teams looking to streamline their development process using AI-driven tools.
In summary, the AI-DLC workflow is a powerful tool for software development, offering an adaptive and intelligent approach to coding.
Takeaway: AI-DLC is revolutionizing the way we code, one workflow at a time.
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🧠 Channel: https://t.iss.one/GithubRe
🔗 https://github.com/awslabs/aidlc-workflows
📝 AI-Driven Life Cycle (AI-DLC) adaptive workflow steering rules for AI coding agents
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The awslabs/aidlc-workflows GitHub repository introduces the AI-Driven Development Life Cycle (AI-DLC), an intelligent software development workflow. This workflow is designed to adapt to the user's needs, maintain quality standards, and keep the developer in control of the process.
Key features of AI-DLC include its ability to integrate with various coding agents and platforms such as Kiro, Amazon Q Developer IDE Plugin, Cursor IDE, Cline, and Claude Code. To use AI-DLC, users need to download the latest release zip file, extract it to a folder outside their project directory, and follow the setup instructions for their chosen coding agent and platform.
Technical highlights of AI-DLC include its use of core workflow rules and detailed rules conditionally referenced by the core rules. The workflow is implemented using steering files, rules, or memory files, depending on the platform.
The target audience for AI-DLC includes software developers and teams looking to streamline their development process using AI-driven tools.
In summary, the AI-DLC workflow is a powerful tool for software development, offering an adaptive and intelligent approach to coding.
Takeaway: AI-DLC is revolutionizing the way we code, one workflow at a time.
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🧠 Channel: https://t.iss.one/GithubRe
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⚡ HKUDS/AI-Trader is making waves. Here's the full picture.
🔗 https://github.com/HKUDS/AI-Trader
📝 "AI-Trader: 100% Fully-Automated Agent-Native Trading"
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Introduction to AI-Trader: This GitHub repository, HKUDS/AI-Trader, presents a 100% fully-automated agent-native trading platform. The primary purpose is to provide a space where AI agents can exchange ideas, sharpen their trading skills, and collaborate.
Key Features include instant agent integration, collective intelligence trading, cross-platform signal synchronization, one-click copy trading, universal market access, three signal types, and a reward system.
To use AI-Trader, agents can join by sending a simple message, while human traders can sign up directly on the platform. The repository is well-organized, with a clear architecture and comprehensive documentation for both agents and developers.
The platform is designed for all users, whether experienced traders looking to share their expertise or newcomers wanting to learn from the community. With its cutting-edge technology and user-friendly interface, AI-Trader is poised to revolutionize the trading landscape.
Technical highlights include the use of FastAPI for the backend, React for the frontend, and a modular design for easy maintenance and updates.
In short, AI-Trader is an innovative platform that empowers AI agents and human traders alike to achieve their full potential in financial markets.
Join the AI-Trader community today and start trading with the power of collective intelligence!
Automate your trades, elevate your investments, and experience the future of trading with AI-Trader!
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🧠 Channel: https://t.iss.one/GithubRe
🔗 https://github.com/HKUDS/AI-Trader
📝 "AI-Trader: 100% Fully-Automated Agent-Native Trading"
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Introduction to AI-Trader: This GitHub repository, HKUDS/AI-Trader, presents a 100% fully-automated agent-native trading platform. The primary purpose is to provide a space where AI agents can exchange ideas, sharpen their trading skills, and collaborate.
Key Features include instant agent integration, collective intelligence trading, cross-platform signal synchronization, one-click copy trading, universal market access, three signal types, and a reward system.
To use AI-Trader, agents can join by sending a simple message, while human traders can sign up directly on the platform. The repository is well-organized, with a clear architecture and comprehensive documentation for both agents and developers.
The platform is designed for all users, whether experienced traders looking to share their expertise or newcomers wanting to learn from the community. With its cutting-edge technology and user-friendly interface, AI-Trader is poised to revolutionize the trading landscape.
Technical highlights include the use of FastAPI for the backend, React for the frontend, and a modular design for easy maintenance and updates.
In short, AI-Trader is an innovative platform that empowers AI agents and human traders alike to achieve their full potential in financial markets.
Join the AI-Trader community today and start trading with the power of collective intelligence!
Automate your trades, elevate your investments, and experience the future of trading with AI-Trader!
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🧠 Channel: https://t.iss.one/GithubRe
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🚀 Meet LearningCircuit/local-deep-research: a gem from today's GitHub trending list.
🔗 https://github.com/LearningCircuit/local-deep-research
📝 ~95% on SimpleQA (e.g. Qwen3.6-27B on a 3090). Supports all local and cloud LLMs (llama.cpp, Ollama, Google, ...). 10+ search engines - arXiv, PubMed, your private documents. Everything Local & Encrypted.
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Local Deep Research is an AI-powered research assistant that runs locally for privacy, allowing you to use any Large Language Model (LLM) and build your own searchable knowledge base. With multiple research strategies and proper citations, it helps you perform deep, agentic research across the web, academic papers, and your own documents.
The
The tool builds your knowledge base by downloading sources, extracting text, indexing, and making them searchable. It features SQLCipher encryption with AES-256, ensuring your data stays private.
Security is a top priority, with static analysis, dependency scanning, and container security measures in place. The repository includes a Security Policy and a Security Review Process to ensure transparency.
In short, Local Deep Research is a powerful, private, and secure research assistant that helps you take control of your data and research. You own your data and can see exactly how it works, making it an ideal solution for anyone looking for a reliable and trustworthy research tool - Empowering your research, one query at a time.
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🧠 Channel: https://t.iss.one/GithubRe
🔗 https://github.com/LearningCircuit/local-deep-research
📝 ~95% on SimpleQA (e.g. Qwen3.6-27B on a 3090). Supports all local and cloud LLMs (llama.cpp, Ollama, Google, ...). 10+ search engines - arXiv, PubMed, your private documents. Everything Local & Encrypted.
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Local Deep Research is an AI-powered research assistant that runs locally for privacy, allowing you to use any Large Language Model (LLM) and build your own searchable knowledge base. With multiple research strategies and proper citations, it helps you perform deep, agentic research across the web, academic papers, and your own documents.
The
local-deep-research repository provides a Docker-based solution, with options for Docker Run and Docker Compose, as well as a pip install method for Windows, macOS, and Linux. You can choose from 20+ research strategies, including the new LangGraph Agent Strategy, which adaptively switches between search engines.The tool builds your knowledge base by downloading sources, extracting text, indexing, and making them searchable. It features SQLCipher encryption with AES-256, ensuring your data stays private.
Security is a top priority, with static analysis, dependency scanning, and container security measures in place. The repository includes a Security Policy and a Security Review Process to ensure transparency.
In short, Local Deep Research is a powerful, private, and secure research assistant that helps you take control of your data and research. You own your data and can see exactly how it works, making it an ideal solution for anyone looking for a reliable and trustworthy research tool - Empowering your research, one query at a time.
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🧠 Channel: https://t.iss.one/GithubRe
Github Top Repositories
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🎯 lobehub/lobehub landed on trending. Worth a proper look.
🔗 https://github.com/lobehub/lobehub
📝 The ultimate space for work and life — to find, build, and collaborate with agent teammates that grow with you. We are taking agent harness to the next level — enabling multi-agent collaboration, effortless agent team design, and introducing agents as the unit of work interaction.
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LobeHub is a revolutionary platform that enables humans and agents to co-evolve, transforming the way we work and interact with AI. The platform offers a range of key features, including the ability to create, collaborate, and evolve with agent teammates that grow with you.
To get started, users can join the LobeHub community and explore the various features and tools available. The platform provides a
Some of the technical highlights of LobeHub include its
LobeHub is designed for a wide range of users, from professional developers to enthusiastic users, and offers a user-friendly product ecosystem that is open, transparent, and constantly evolving.
In a nutshell, LobeHub is the future of human-AI collaboration - and it's here to revolutionize the way we work and interact with AI.
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🧠 Channel: https://t.iss.one/GithubRe
🔗 https://github.com/lobehub/lobehub
📝 The ultimate space for work and life — to find, build, and collaborate with agent teammates that grow with you. We are taking agent harness to the next level — enabling multi-agent collaboration, effortless agent team design, and introducing agents as the unit of work interaction.
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LobeHub is a revolutionary platform that enables humans and agents to co-evolve, transforming the way we work and interact with AI. The platform offers a range of key features, including the ability to create, collaborate, and evolve with agent teammates that grow with you.
To get started, users can join the LobeHub community and explore the various features and tools available. The platform provides a
unified intelligence system, allowing users to access any model and modality, and a library of over 10,000+ skills and MCP-compatible plugins.Some of the technical highlights of LobeHub include its
Agent Builder, Agent Groups, and Personal Memory features, which enable users to build, collaborate, and learn with their agent teammates. The platform also supports local large language model (LLM) support, model visual recognition, and TTS & STT voice conversation.LobeHub is designed for a wide range of users, from professional developers to enthusiastic users, and offers a user-friendly product ecosystem that is open, transparent, and constantly evolving.
In a nutshell, LobeHub is the future of human-AI collaboration - and it's here to revolutionize the way we work and interact with AI.
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🧠 Channel: https://t.iss.one/GithubRe
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