🔍 Deep-diving into TencentCloud/TencentDB-Agent-Memory — fresh off the trending list.

🔗 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, allowing them to learn from workflows, retain task context, and reuse past experiences. At its core, it features symbolic short-term memory and layered long-term memory, which work together to ensure that agents not only remember more but also reason better.

The symbolic short-term memory offloads heavy tool logs into compact Mermaid symbols, reducing token usage and improving task success. Meanwhile, the layered long-term memory distills fragmented conversations into structured personas and scenes, instead of relying on flat vector piles.

When integrated with OpenClaw, it can cut token usage by up to 61.38% and improve the pass rate by 51.52%. The system is designed with heterogeneous storage and progressive disclosure in mind, allowing for a dual-layer storage strategy that balances robust full-text retrieval with high information density and white-box inspection.

To get started, users can follow the Quick Start guide, which provides steps for installing the plugin, enabling it, and configuring it for use with OpenClaw or Hermes. The plugin is suitable for developers and researchers looking to enhance the memory capabilities of their AI agents.

In a nutshell, TencentDB Agent Memory is all about empowering agents to remember what should be remembered, so people can focus on judgment, creation, and work that truly matters - let the machines handle the memorization, and humans handle the innovation.

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🧠 Channel: https://t.iss.one/GithubRe

🌟 mvanhorn/last30days-skill caught my eye on GitHub Trending today.

🔗 https://github.com/mvanhorn/last30days-skill
📝 AI agent skill that researches any topic across Reddit, X, YouTube, HN, Polymarket, and the web - then synthesizes a grounded summary
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Introduction to last30days-skill: This GitHub repository is home to a game-changing AI agent-led search engine that scores results based on upvotes, likes, and real money, not editors.

The key features of this tool include its ability to search multiple platforms like Reddit, X, YouTube, TikTok, and GitHub in parallel, and provide a synthesized brief of the most relevant information. It's zero-config, meaning you can start using it right away, and it works immediately with Reddit, HN, Polymarket, and GitHub.

To use this tool, you can install it using Claude Code, Codex, Cursor, Copilot, Gemini CLI, or any other Agent Skills host. There are also more install options available, including a manual setup.

From a technical perspective, this tool is built to bridge the gap between multiple disconnected platforms, using an AI agent to search and score information from various sources. It's a constantly evolving project, with new features and updates being added regularly, such as the recent integration with OpenAI Codex, arXiv, Techmeme, and Digg.

This tool is perfect for anyone looking to stay up-to-date with the latest information on a particular topic or person. Whether you're a researcher, a journalist, or simply someone who wants to stay informed, last30days-skill is an invaluable resource.

In short, last30days-skill is a powerful search engine that uses AI to provide you with the most relevant and up-to-date information on any topic, and its best feature is that it synthesizes information from multiple sources into one brief, making it easy to stay informed in a fast-changing world.
So, try it out and experience the power of AI-led search for yourself: last30days-skill is the ultimate tool for anyone who wants to stay ahead of the curve!

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🧠 Channel: https://t.iss.one/GithubRe

⚡ NomaDamas/k-skill is making waves. Here's the full picture.

🔗 https://github.com/NomaDamas/k-skill
📝 한국인을 위한 스킬 모음집 - 에이전트를 한국인으로
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k-skill is a collection of AI agent skills that can automate various tasks for Koreans, from booking train tickets to searching for real estate information. Key features include support for multiple coding agents like Claude Code and Codex, and the ability to download and use the skills without needing an additional client API layer. Node.js 18+ and npx are required for installation.

To use, you can install all skills with npx --yes skills add NomaDamas/k-skill --all -g or install specific skills like npx --yes skills add NomaDamas/k-skill --skill srt-booking -g. The skills cater to a wide range of tasks, including transportation, real estate, education, finance, and more.

The audience for this repository includes anyone looking to automate tasks in Korea, from individuals to businesses. With k-skill, you can simplify your life by letting AI handle the mundane tasks. Here's a sneak peek at what you can do: query Korean stock information, search for real estate listings, or even find the cheapest gas station near you. This toolkit is perfect for those looking to streamline their daily routines.

In a nutshell, k-skill is your one-stop-shop for automating tasks in Korea - it's like having your own personal AI assistant!

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🧠 Channel: https://t.iss.one/GithubRe

🌟 HarbourMasters/Lighthouse caught my eye on GitHub Trending today.

🔗 https://github.com/HarbourMasters/Lighthouse
📝 No description.
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The HarbourMasters/Lighthouse GitHub repository is a project focused on decompiling and recompiling the classic game Banjo-Kazooie. The purpose of this project is to provide a comprehensive understanding of the game's inner workings and to allow for modifications and improvements.

Key features of the repository include the ability to build the game using different baserom versions, such as us.v10, us.v11, jp, and pal, as well as the option to use Docker for building on various platforms, including Linux and macOS.

Usage involves installing dependencies, adding the baserom file, and running the build command using make. The repository also provides instructions for building on cloud platforms using GitLab CI.

From a technical standpoint, the project uses a combination of bash scripts, python, and Rust to manage the building process. The repository is well-organized, with clear instructions and a detailed README file.

The target audience for this repository appears to be developers and gamers interested in game development, reverse engineering, and modding.

In summary, HarbourMasters/Lighthouse is a valuable resource for anyone looking to dive into the world of game decompilation and modification - and with great power comes great banjos.

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🧠 Channel: https://t.iss.one/GithubRe

💡 antirez/ds4 just hit the trending charts — here's why it matters.

🔗 https://github.com/antirez/ds4
📝 DeepSeek 4 Flash and PRO local inference engine for Metal, CUDA and ROCm
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DwarfStar is a native inference engine optimized for DeepSeek V4 Flash and other select models, offering high performance on various hardware backends, including Metal, NVIDIA CUDA, and ROCm. Key features include SSD streaming for smaller machines, multi-GPU support, and experimental speculative decoding. The project is self-contained and focused on a narrow set of models, allowing for efficient and specialized implementation.

You can use ds4 to run capable models on consumer hardware, create a multi-user LLM server with older CUDA cards, or leverage pipeline parallelism to combine systems and run larger models. The project is designed for high-end personal machines, leveraging compressed KV caches and fast local SSDs to make long contexts practical.

Technical highlights include the use of asymmetrical quantization, where only routed MoE experts are quantized, and the implementation of DSpark speculative decoding, which can accelerate generation speed. The engine is optimized for DeepSeek V4 Flash and GLM 5.2 models, with support for various quantization formats and tensor layouts.

Audience: The project is geared towards developers and users who want to run high-performance models on their local machines or servers, particularly those interested in DeepSeek V4 Flash and GLM 5.2.

One-liner takeaway: With DwarfStar, you can unlock the full potential of your hardware and run high-performance models like DeepSeek V4 Flash at incredible speeds, making it an exciting project for anyone interested in AI and machine learning.

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🧠 Channel: https://t.iss.one/GithubRe

📌 Spotted on GitHub Trending: esengine/DeepSeek-Reasonix — let's break it down.

🔗 https://github.com/esengine/DeepSeek-Reasonix
📝 DeepSeek-native AI coding agent for your terminal. Engineered around prefix-cache stability — leave it running.
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Reasonix is an AI coding agent that runs in your terminal, engineered around prefix-cache stability to keep token costs low. It's designed to work with DeepSeek and provides a range of features, including cache stability, filesystem tools, and shell tools. To get started, you can install Reasonix globally using npm install -g reasonix or run it once without installing globally using npx reasonix code.

The project has a bilingual Discord community with channels for setup help, workflow showcases, feature ideas, and contributor-only PR coordination.

Reasonix has a simple and intuitive usage, with a range of subcommands, including reasonix code, reasonix chat, and reasonix run. The reasonix code subcommand launches the coding agent, while the reasonix chat subcommand launches a plain chat mode.

Some of the key technical highlights of Reasonix include its prefix-cache stability, which ensures that token costs stay low across long sessions, and its cacheable bytes, which provide a cost-effective way to interact with the AI model.

Reasonix is suitable for a range of audiences, including developers, researchers, and anyone interested in AI-powered coding tools.

One-liner takeaway: Reasonix is the ultimate AI-powered coding companion that helps you code smarter, not harder, with its innovative prefix-cache stability and extensive feature set.

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🧠 Channel: https://t.iss.one/GithubRe

📌 Spotted on GitHub Trending: lyogavin/airllm — let's break it down.

🔗 https://github.com/lyogavin/airllm
📝 AirLLM 70B inference with single 4GB GPU
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AirLLM is a cutting-edge solution that enables large language models to run on limited GPU resources without sacrificing performance. Its key features include inference memory reduction, allowing 70B models to run on a single 4GB GPU card, and support for various models, such as Llama 3.1, DeepSeek-V3, and Kimi K3.

To use AirLLM, simply install the package with pip install airllm and initialize the model with AutoModel.from_pretrained("model_id"). The library also supports model compression for up to 3x inference speedup and prefetching to overlap model loading and computation.

AirLLM is geared towards data scientists and researchers working with large language models, providing an efficient and scalable solution for inference and deployment. With its extensive support for popular models and continuous updates, AirLLM is an essential tool for anyone looking to push the boundaries of language modeling.

One-liner takeaway: AirLLM revolutionizes large language model deployment by drastically reducing memory requirements, making it possible to run massive models like Kimi K3 on a single 4GB GPU card!

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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 complex security tasks with ease. Its primary purpose is to route AI agents, such as Claude Code or Codex CLI, to the right methodology and tools for a given task, ensuring a repeatable workflow.

Key features of reverse-skill include:

* MASTER-ROUTING.md: A PRIMARY fast ladder for routing tasks to the right skills
* routing.md: A task-to-skill routing matrix for various scenarios
* skills/: A directory containing various skills, including APK reverse, binary reverse, and more

To get started with reverse-skill, you'll need to:

* Clone the repository using git clone https://github.com/zhaoxuya520/reverse-skill.git
* Refresh the tool index using the provided scripts for your platform (Windows, Linux, or macOS)

The repository is built with a range of technologies, including Python, Node.js, PowerShell, and Bash, and integrates various tools like IDA Pro, radare2, and Ghidra.

Audience: The reverse-skill repository is designed for cybersecurity professionals, AI agents, and anyone interested in navigating complex security tasks with ease.

In summary, the reverse-skill repository is a powerful tool for cybersecurity professionals, providing a comprehensive framework for navigating complex security tasks with ease. With its PRIMARY fast ladder, task-to-skill routing matrix, and extensive skills directory, reverse-skill is an indispensable resource for anyone looking to streamline their cybersecurity workflows: Automate your cybersecurity tasks with reverse-skill and take your skills to the next level!

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🧠 Channel: https://t.iss.one/GithubRe

🌟 firecrawl/pdf-inspector caught my eye on GitHub Trending today.

🔗 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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The pdf-inspector library is a fast and lightweight Rust-based solution for PDF classification and text extraction. Its key features include smart classification, text extraction, and Markdown conversion, all of which can be performed without the need for OCR services.

This library can be used in various programming languages, including Python, Node.js, and Rust, and can also be run in browser WebAssembly environments. The pdf-inspector has been benchmarked against other popular libraries, and its results show that it outperforms them in terms of speed and accuracy.

The library is designed to handle various types of PDFs, including those with mixed content, tables, and images. It also supports CID font decoding and multi-column layout detection, making it a robust solution for PDF processing needs.

Overall, pdf-inspector is a powerful tool for anyone looking to work with PDFs, and its ease of use, speed, and accuracy make it an ideal choice for a wide range of applications.
One-liner takeaway: pdf-inspector is the ultimate PDF processing library that saves you time and resources by skipping expensive OCR services for text-based PDFs.

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🧠 Channel: https://t.iss.one/GithubRe

📌 Spotted on GitHub Trending: esengine/DeepSeek-Reasonix — let's break it down.

🔗 https://github.com/esengine/DeepSeek-Reasonix
📝 DeepSeek-native AI coding agent for your terminal. Engineered around prefix-cache stability — leave it running.
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Reasonix is a DeepSeek-native AI coding agent designed for terminal use, engineered around prefix-cache stability for low token costs. This legacy TypeScript line is in maintenance mode, with active development moved to the Go rewrite in the main-v2 branch.

Key features include cache stability, DeepSeek API integration, and filesystem tools. To use Reasonix, simply install it globally with npm install -g reasonix and run reasonix code my-project to get started.

Technical highlights include a cache-first loop and byte-stable prefix-cache mechanic. The target audience is developers and users who want a thinking partner with MCP attached but no disk access.

With a strong focus on community involvement, Reasonix has a bilingual Discord for setup help, workflow showcases, feature ideas, and contributor-only PR coordination.

In short, Reasonix is your AI-powered coding sidekick for the terminal, designed to keep token costs low and productivity high.

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🧠 Channel: https://t.iss.one/GithubRe