💡 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://xn--r1a.website/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://xn--r1a.website/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://xn--r1a.website/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://xn--r1a.website/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://xn--r1a.website/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://xn--r1a.website/GithubRe