🚀 Meet donnemartin/system-design-primer: a gem from today's GitHub trending list.

🔗 https://github.com/donnemartin/system-design-primer
📝 Learn how to design large-scale systems. Prep for the system design interview. Includes Anki flashcards.
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The System Design Primer is a comprehensive GitHub repository designed to help you learn how to build systems at scale. Its primary purpose is to provide an organized collection of resources, including system design topics, interview questions, and study guides, to aid in becoming a better engineer.

The repository offers key features such as Anki flashcard decks for retaining key system design concepts, interactive coding challenges, and a vast array of system design topics, including performance, scalability, latency, and availability. To use this repository, you can start by reviewing the system design topics, practicing with interview questions, and utilizing the provided resources to improve your understanding of system design.

From a technical standpoint, the repository covers a wide range of topics, including load balancers, reverse proxies, databases, caching, and security. It also provides information on communication protocols, such as TCP and UDP, and design patterns, such as microservices and service discovery.

The repository is suitable for a broad audience, including software engineers, system architects, and anyone interested in learning about system design. Whether you're preparing for a system design interview or simply looking to improve your skills, the System Design Primer is an invaluable resource.

In summary, the System Design Primer is a treasure trove of system design knowledge, and its contributions are welcome from the open-source community. So, dive in and explore the repository to take your system design skills to the next level: designing scalable systems is not just about handling traffic, it's about creating a better user experience.

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🧠 Channel: https://t.iss.one/GithubRe

📌 Spotted on GitHub Trending: antirez/ds4 — let's break it down.

🔗 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 also supporting GLM 5.2 and DeepSeek V4 PRO on high-memory machines. It's designed to be self-contained and narrow in scope, not a general-purpose GGUF runner. The engine includes tools and data for GGUF, imatrix, quality, and speed.

The project supports multiple backends: Metal on Macs, NVIDIA CUDA including multi-GPU systems, and ROCm on Strix Halo systems.
It's capable of running on consumer hardware, like MacBooks, and can also be used to turn servers into multi-user LLM servers with good results.

Some of the key features include:
- Running capable models on consumer hardware
- Turning old servers into multi-user LLM servers
- Supporting pipeline parallelism to glue multiple systems together
- Experimental DSpark speculative decoding for faster generation

The project is still in the beta stage and is very fast-changing, so instabilities are possible.
The code is developed with strong assistance from AI, including GPT 5.5, 5.6, and Claude Fable, and is not suitable for those who are not comfortable with AI-developed code.

In summary: DwarfStar is an optimized inference engine for select models, with a focus on speed and efficiency, making it a great tool for those looking to run capable models on consumer hardware - you can now run a super-smart AI model on your MacBook!

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🧠 Channel: https://t.iss.one/GithubRe

🌟 shiyu-coder/Kronos caught my eye on GitHub Trending today.

🔗 https://github.com/shiyu-coder/Kronos
📝 Kronos: A Foundation Model for the Language of Financial Markets
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Kronos is the first open-source foundation model for financial candlesticks, trained on data from over 45 global exchanges. It's a decoder-only model, designed to handle the unique characteristics of financial data. Key features include a novel two-stage framework, with a specialized tokenizer and a large autoregressive Transformer.

To get started, you can install the dependencies with pip install -r requirements.txt, then load a pre-trained model and tokenizer from the Hugging Face Hub. The KronosPredictor class simplifies forecasting, handling data preprocessing, normalization, and prediction.

Technical highlights include a family of pre-trained models with varying capacities, a live demo for visualizing forecasting results, and a model zoo with readily accessible models. The model is designed for diverse quantitative tasks, such as forecasting and backtesting.

The target audience includes quantitative analysts, traders, and researchers interested in applying AI to financial markets. With Kronos, you can unlock new insights and make more informed investment decisions.
Kronos is a game-changer: forecast your financial future with precision and ease!

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🧠 Channel: https://t.iss.one/GithubRe

🔥 Panniantong/Agent-Reach is trending — and it deserves your attention.

🔗 https://github.com/Panniantong/Agent-Reach
📝 Give your AI agent eyes to see the entire internet. Read & search Twitter, Reddit, YouTube, GitHub, Bilibili, XiaoHongShu — one CLI, zero API fees.
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Agent Reach 是一个能力层，让你的 AI Agent 能够访问互联网。它可以帮助你的 Agent 读取网页、搜索推特、看 YouTube 视频、刷小红书 等。只需告诉你的 Agent "帮我安装 Agent Reach"，它就会自己完成安装和配置。

主要特点：

* 零配置：安装后无需任何配置，即可使用
* 支持多平台：包括网页、YouTube、Twitter、Reddit、Facebook、Instagram 等
* 安全：Cookie 和 Token 只存储在本地，且支持安全模式
* 开源：完全开源，欢迎社区贡献

使用方法：

1. 告诉你的 Agent "帮我安装 Agent Reach"
2. 等待安装和配置完成
3. 即可使用 Agent Reach 的功能

注意：需要登录的平台（如 Twitter、小红书）需要配置 Cookie 或登录态。

One-liner takeaway: Agent Reach gives your AI Agent internet superpowers with zero configuration and top-notch security.

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🧠 Channel: https://t.iss.one/GithubRe