⚡ TencentCloud/TencentDB-Agent-Memory is making waves. Here's the full picture.

🔗 https://github.com/TencentCloud/TencentDB-Agent-Memory
📝 TencentDB Agent Memory is a team-level memory hub for AI Agents — turning conversations, docs, and code into four reusable memory assets (Chat Memory, Skill, LLM-Wiki, Code-Graph) that are governed, shared, and equipped across agents and frameworks.
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The TencentDB Agent Memory is a powerful tool designed to enhance the memory capabilities of AI agents. Its primary purpose is to help agents learn from workflows, retain task context, and reuse past experiences, thereby sparing humans from repetitive tasks. The key features of this tool include symbolic short-term memory and layered long-term memory, which enable agents to condense heavy tool logs into compact symbols and distill fragmented conversations into structured personas and scenes.

From a technical standpoint, the tool employs a heterogeneous storage strategy and progressive disclosure, allowing for robust full-text retrieval and high information density. The Mermaid symbol graph is used to encode task state transitions, providing a concise and readable format for both humans and LLMs.

To get started with the TencentDB Agent Memory, users can follow the Quick Start guide, which provides instructions for installing the plugin, enabling zero-config, and applying runtime patches. The tool supports both OpenClaw and Hermes agents, making it a versatile solution for various deployment scenarios.

The target audience for this tool includes developers, researchers, and professionals working with AI agents and seeking to improve their memory and performance. With the TencentDB Agent Memory, users can unlock the full potential of their AI agents and focus on high-level tasks that require human judgment and creativity.

In summary, the TencentDB Agent Memory is a game-changing tool that revolutionizes the way AI agents learn, remember, and interact with their environment. By leveraging its powerful features and capabilities, users can supercharge their AI agents and achieve unprecedented levels of productivity and efficiency: let the agents remember, so humans can innovate.

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🧠 Channel: https://t.iss.one/GithubRe

💡 zhaoxuya520/reverse-skill just hit the trending charts — here's why it matters.

🔗 https://github.com/zhaoxuya520/reverse-skill
📝 Reverse Engineering / Authorized Penetration Testing / Security Research Skill Router Pack AI-powered routing + On-demand toolchain bootstrapping + Self-evolving knowledge base Supports Claude Code, Kiro, Cursor, Cline, and other AI coding clients 逆向/渗透/安全技能路由包 - AI 自动路由 + 按需自举工具链 + 自动进化经验库 | 支持 Claude Code / Kiro / Cursor / Cline 等代码 AI 客户端
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The reverse-skill GitHub repository is a comprehensive cybersecurity skills router designed to navigate the complexities of reverse engineering, penetration testing, and security analysis. It provides a structured approach to tackling various scenarios, including APK and binary analysis, frontend JS encryption, and CTF challenges. The repository is built with a range of technologies, including Python, Node.js, PowerShell, and Bash, and integrates tools like IDA Pro, radare2, and Ghidra.

To get started, users can clone the repository and follow the Getting Started guide, which includes installing prerequisites like Java and Node.js. The repository layout is well-organized, with clear documentation and a MASTER-ROUTING.md file that serves as a primary fast ladder for navigating the various skills and tools.

The repository is primarily licensed under the MIT License, with some submodules and third-party dependencies subject to their respective licenses. The project is open to contributions, and users can fork the repository, create a feature branch, and open a pull request to submit changes.

Overall, the reverse-skill repository is a valuable resource for cybersecurity professionals and enthusiasts looking to improve their skills and stay up-to-date with the latest tools and techniques. With its structured approach and comprehensive documentation, it's an ideal starting point for those looking to dive into the world of reverse engineering and security analysis.
Takeaway: reverse-skill is your one-stop-shop for mastering cybersecurity skills.

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🧠 Channel: https://t.iss.one/GithubRe

📌 Spotted on GitHub Trending: firecrawl/pdf-inspector — let's break it down.

🔗 https://github.com/firecrawl/pdf-inspector
📝 Fast Rust library for PDF inspection, classification, and text extraction. Intelligently detects scanned vs text-based PDFs to enable smart routing decisions.
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Pdf-Inspector is a fast Rust library for classifying and extracting text from PDFs. It detects whether a PDF is text-based or scanned, extracts text with position awareness, and converts it to clean Markdown - all without using OCR. The library has bindings for Python, Node.js, and browser WebAssembly, making it versatile and easy to integrate into various projects.

The key features of pdf-inspector include:
- Smart classification of PDFs into text-based, scanned, image-based, or mixed types
- Position-aware text extraction with font information and X/Y coordinates
- Conversion of extracted text to Markdown format
- Table detection using rectangle-based and heuristic methods
- Support for CID fonts and multi-column layouts

To use pdf-inspector, you can install it via pip or npm and then import it into your project. The library provides a simple API for processing PDFs and extracting text. For example, in Python:

import pdf_inspector

result = pdf_inspector.process_pdf("document.pdf")
print(result.pdf_type)  # prints the type of the PDF
print(result.markdown)  # prints the extracted text in Markdown format

pdf-inspector is designed for use cases where speed and accuracy are crucial, such as in large-scale PDF processing pipelines. By using pdf-inspector, you can save cost and latency by extracting text from PDFs locally, without relying on OCR services.

In summary, pdf-inspector is a powerful and flexible library for working with PDFs, and its fast and accurate text extraction capabilities make it an ideal choice for a wide range of applications - Process your PDFs faster and smarter with pdf-inspector!

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🧠 Channel: https://t.iss.one/GithubRe

🔍 Deep-diving into uber/ADR — fresh off the trending list.

🔗 https://github.com/uber/ADR
📝 ADR secures enterprise AI agents through observability, security benchmarking, and threat detection. Deployed at Uber.
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The uber/ADR repository on GitHub is home to ADR, an enterprise security system designed to protect AI agents from potential threats. The primary purpose of ADR is to secure employee-facing and customer-facing AI agents through four key capabilities: observing agent activity, evaluating defenses, detecting threats, and preventing unsafe actions.

The repository includes the open-source ADR Sensor, ADR-Bench, and ADR Detector components. The ADR Sensor collects and normalizes agent telemetry, while ADR-Bench tests agent security under realistic enterprise conditions. The ADR Detector uses a two-tier architecture to detect risky agent behavior efficiently.

To get started with ADR Detection, users can clone the repository, navigate to the Detection folder, and follow the provided instructions. The default detector is adr, but users can also opt for a keyless smoke test using the llamafirewall detector.

From a technical standpoint, ADR is notable for its ability to capture agent intent, tool use, and execution traces across multiple AI coding tools and platforms. The system's architecture is designed to provide high-recall triage and deeper agentic reasoning for suspicious sessions.

The target audience for ADR includes enterprise security teams, AI researchers, and developers interested in securing AI agents. With its robust capabilities and open-source availability, ADR has the potential to make a significant impact on the security of AI systems.

In a nutshell, ADR is a game-changer for securing AI agents - and the best part is, it's open-source and ready to be leveraged by the community!

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🧠 Channel: https://t.iss.one/GithubRe

🔍 Deep-diving into obra/superpowers — fresh off the trending list.

🔗 https://github.com/obra/superpowers
📝 An agentic skills framework & software development methodology that works.
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Superpowers is a software development methodology that empowers coding agents with a set of composable skills. It's designed to work with various coding agents, including Claude Code, Antigravity, Codex App, and more.

To get started, you can install Superpowers as a plugin for your chosen coding agent. For example, in Claude Code, you can install it from the official plugin marketplace using the following bash command:

/plugin install superpowers@claude-plugins-official

Once installed, Superpowers guides your agent through a development workflow that includes brainstorming, writing plans, and subagent-driven development. It emphasizes test-driven development, systematic debugging, and collaboration.

The Superpowers methodology is based on principles like test-driven development, systematic over ad-hoc, and complexity reduction. It's designed to help developers create high-quality code efficiently.

With Superpowers, you can streamline your coding process and focus on building amazing projects. So, what are you waiting for? Give your coding agent Superpowers today and take your development to the next level!

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🧠 Channel: https://t.iss.one/GithubRe

🔥 microsoft/generative-ai-for-beginners is trending — and it deserves your attention.

🔗 https://github.com/microsoft/generative-ai-for-beginners
📝 21 Lessons, Get Started Building with Generative AI
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The Microsoft/generative-ai-for-beginners GitHub repository is a comprehensive course designed for beginners to learn about Generative AI. It includes 21 lessons that cover various topics, from the basics of Generative AI to advanced concepts like prompt engineering. The course provides a step-by-step guide on how to build Generative AI applications using Python and TypeScript.

To get started, you'll need to have a basic understanding of Python or TypeScript, as well as a GitHub account to fork the repository. The course also supports multiple services, including Azure OpenAI Service, Microsoft Foundry Models, and OpenAI API.

One of the key features of this repository is its multi-language support, with translations available in over 50 languages. The course is designed to be flexible, allowing you to start with any lesson and learn at your own pace.

Whether you're a beginner or an experienced developer, this course provides a wealth of information and resources to help you get started with Generative AI. So, dive in and start learning - your next Generative AI project is just a step away!

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🧠 Channel: https://t.iss.one/GithubRe

🚀 Meet cypress-io/cypress: a gem from today's GitHub trending list.

🔗 https://github.com/cypress-io/cypress
📝 Fast, easy and reliable testing for anything that runs in a browser.
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Cypress is a game-changing testing framework that makes it easy to test web applications. Its fast and reliable nature allows developers to write end-to-end tests with ease. The key features of Cypress include automatic waiting, network traffic control, and retries. To get started, simply install Cypress using npm install cypress --save-dev or yarn add cypress --dev, then write your tests in JavaScript.

Cypress is built with modular architecture and supports both JavaScript and TypeScript. It's perfect for developers and QA engineers who want to ensure their web applications work flawlessly.

With Cypress, you can run tests in parallel, debug tests, and even use a dashboard to view test results. The Cypress community is active, with many resources available for learning and troubleshooting.

Ready to take your testing to the next level? Try Cypress today and experience the power of fast, easy, and reliable testing - your code will thank you!

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🧠 Channel: https://t.iss.one/GithubRe

⚡ lyogavin/airllm is making waves. Here's the full picture.

🔗 https://github.com/lyogavin/airllm
📝 AirLLM 70B inference with single 4GB GPU
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AirLLM is a revolutionary open-source solution that enables large language models to run on limited GPU memory, without sacrificing performance. With AirLLM, you can deploy massive models like 405B Llama 3.1 on a mere 8GB GPU or DeepSeek-V3 (671B) on ~12GB. The secret lies in its ability to stream one expert at a time, rather than loading the entire layer.

To get started, simply install the airllm package using pip install airllm and initialize the model with the Hugging Face repo ID or local path. You can also enable model compression for up to 3x inference speedup.

AirLLM supports a wide range of models, including ChatGLM, QWen, and Baichuan, and is compatible with MacOS and Linux. The community-driven project is constantly evolving, with new features and model support being added regularly.

Join the AirLLM community today and experience the power of large language models on limited hardware - Run big models, not big bills!

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🧠 Channel: https://t.iss.one/GithubRe