Github Top Repositories
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📌 Spotted on GitHub Trending: microsoft/AI-For-Beginners — let's break it down.
🔗 https://github.com/microsoft/AI-For-Beginners
📝 12 Weeks, 24 Lessons, AI for All!
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The AI-For-Beginners GitHub repository provides a comprehensive 12-week curriculum for learning Artificial Intelligence. This beginner-friendly course covers key topics such as symbolic AI, neural networks, and deep learning, with practical lessons, quizzes, and labs using popular frameworks like
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🧠 Channel: https://t.iss.one/GithubRe
🔗 https://github.com/microsoft/AI-For-Beginners
📝 12 Weeks, 24 Lessons, AI for All!
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The AI-For-Beginners GitHub repository provides a comprehensive 12-week curriculum for learning Artificial Intelligence. This beginner-friendly course covers key topics such as symbolic AI, neural networks, and deep learning, with practical lessons, quizzes, and labs using popular frameworks like
TensorFlow and PyTorch. The curriculum is translated into over 50 languages, making it accessible to a global audience. To get started, you can clone the repository locally or use the automated translations. The course is suitable for beginners, and no prior experience in AI is required. You can join the community on Discord to connect with other learners and instructors. With this curriculum, you'll gain a solid understanding of AI concepts and be able to apply them in real-world projects. Start your AI journey today and become proficient in building intelligent systems with this free and open-source resource!──────────────────────────────
🧠 Channel: https://t.iss.one/GithubRe
Github Top Repositories
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💡 microsoft/generative-ai-for-beginners just hit the trending charts — here's why it matters.
🔗 https://github.com/microsoft/generative-ai-for-beginners
📝 21 Lessons, Get Started Building with Generative AI
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Get started with Generative AI using the microsoft/generative-ai-for-beginners GitHub repository. This comprehensive course offers 21 lessons to teach you the fundamentals of building Generative AI applications.
The course covers topics such as Introduction to Generative AI and LLMs, Exploring and comparing different LLMs, Using Generative AI Responsibly, and Understanding Prompt Engineering Fundamentals. You'll learn through a combination of
To get started, you'll need basic knowledge of Python or TypeScript and a Github account to fork the repository. You can use either Azure OpenAI Service, Microsoft Foundry Models, OpenAI API, or Foundry Local to run the code.
The course is multi-language supported with translations available in over 50 languages. Join the Microsoft Foundry Discord server to meet other learners and get support.
In summary, this course is perfect for beginners and experienced developers alike, providing a comprehensive introduction to Generative AI and hands-on experience with building applications. Dive in and start building your Generative AI skills today!
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🧠 Channel: https://t.iss.one/GithubRe
🔗 https://github.com/microsoft/generative-ai-for-beginners
📝 21 Lessons, Get Started Building with Generative AI
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Get started with Generative AI using the microsoft/generative-ai-for-beginners GitHub repository. This comprehensive course offers 21 lessons to teach you the fundamentals of building Generative AI applications.
The course covers topics such as Introduction to Generative AI and LLMs, Exploring and comparing different LLMs, Using Generative AI Responsibly, and Understanding Prompt Engineering Fundamentals. You'll learn through a combination of
video introductions, written lessons, and Python and TypeScript code samples.To get started, you'll need basic knowledge of Python or TypeScript and a Github account to fork the repository. You can use either Azure OpenAI Service, Microsoft Foundry Models, OpenAI API, or Foundry Local to run the code.
The course is multi-language supported with translations available in over 50 languages. Join the Microsoft Foundry Discord server to meet other learners and get support.
In summary, this course is perfect for beginners and experienced developers alike, providing a comprehensive introduction to Generative AI and hands-on experience with building applications. Dive in and start building your Generative AI skills today!
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🧠 Channel: https://t.iss.one/GithubRe
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Github Top Repositories
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🚀 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
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🧠 Channel: https://t.iss.one/GithubRe
🔗 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.──────────────────────────────
🧠 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:
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
🔗 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
Github Top Repositories
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🌟 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
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
🔗 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