Microsoft launched the best course on Generative AI!
The Free 21 lesson course is available on #Github and will teach you everything you need to know to start building #GenerativeAI applications.
Enroll: https://github.com/microsoft/generative-ai-for-beginners
https://github.com/microsoft/generative-ai-for-beginners
https://t.me/CodeProgrammer🩷
The Free 21 lesson course is available on #Github and will teach you everything you need to know to start building #GenerativeAI applications.
Enroll: https://github.com/microsoft/generative-ai-for-beginners
https://github.com/microsoft/generative-ai-for-beginners
https://t.me/CodeProgrammer
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🤖🧠 AI Projects : A Comprehensive Showcase of Machine Learning, Deep Learning and Generative AI
🗓️ 27 Oct 2025
📚 AI News & Trends
Artificial Intelligence (AI) is transforming industries across the globe, driving innovation through automation, data-driven insights and intelligent decision-making. Whether it’s predicting house prices, detecting diseases or building conversational chatbots, AI is at the core of modern digital solutions. The AI Project Gallery by Hema Kalyan Murapaka is an exceptional GitHub repository that curates a wide ...
#AI #MachineLearning #DeepLearning #GenerativeAI #ArtificialIntelligence #GitHub
🗓️ 27 Oct 2025
📚 AI News & Trends
Artificial Intelligence (AI) is transforming industries across the globe, driving innovation through automation, data-driven insights and intelligent decision-making. Whether it’s predicting house prices, detecting diseases or building conversational chatbots, AI is at the core of modern digital solutions. The AI Project Gallery by Hema Kalyan Murapaka is an exceptional GitHub repository that curates a wide ...
#AI #MachineLearning #DeepLearning #GenerativeAI #ArtificialIntelligence #GitHub
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Perfect for those who want to level up from
print('Hello') to advanced projects.1. 30-Days-Of-Python — a 30-day Python challenge covering the basics of the language.
2. Python Basics — simple and clear Python basics for beginners.
3. Learn Python — a topic-based guide with examples and code.
4. Python Guide — best practices, tools, and advanced topics.
5. Learn Python 3 — an easy-to-understand guide to Python 3 with practice.
6. Python Programming Exercises — 100+ Python exercises.
7. Coding Problems — algorithmic problems, perfect for interview prep.
8. Project-Based-Learning — learn Python through real projects.
9. Projects — ideas for practical projects and skill improvement.
10. 100-Days-Of-ML-Code — a step-by-step guide to Machine Learning in Python.
11. TheAlgorithms/Python — a huge collection of algorithms in Python.
12. Amazing-Python-Scripts — useful scripts from automation to advanced utilities.
13. Geekcomputers/Python — a collection of practical scripts: networking, files, automation.
14. Materials — code, exercises, and projects from Real Python.
15. Awesome Python — a top list of the best frameworks and libraries.
16. 30-Seconds-of-Python — short snippets for quick solutions.
17. Python Reference — life hacks, tutorials, and useful scripts.
#python #doc #github #soft
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YOLO Training Template
Manual data labeling has become significantly more convenient. Now the process looks like in the usual labeling systems - you just outline the object with a frame and a bounding box is immediately created.
The platform allows:
• to upload your own dataset
• to label manually or auto-label via DINOv3
• to enrich the data if desired
• to train a #YOLO model on your own data
• to run inference immediately
• to export to ONNX or NCNN, which ensures compatibility with edge hardware and smartphones
All of this is available for free and can already be tested on #GitHub.
Repo:
https://github.com/computer-vision-with-marco/yolo-training-template
👍 Top Channels on Telegram 🌟
Manual data labeling has become significantly more convenient. Now the process looks like in the usual labeling systems - you just outline the object with a frame and a bounding box is immediately created.
The platform allows:
• to upload your own dataset
• to label manually or auto-label via DINOv3
• to enrich the data if desired
• to train a #YOLO model on your own data
• to run inference immediately
• to export to ONNX or NCNN, which ensures compatibility with edge hardware and smartphones
All of this is available for free and can already be tested on #GitHub.
Repo:
https://github.com/computer-vision-with-marco/yolo-training-template
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GitHub has launched its learning platform: all #courses and certificates in one place.
#Git, #GitHub, #MCP, using #AI, #VSCode, and much more.
And most of the content is #free: → https://learn.github.com
👉 @codeprogrammer
#Git, #GitHub, #MCP, using #AI, #VSCode, and much more.
And most of the content is #free: → https://learn.github.com
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Design patterns are proven solutions to common problems in development. If you've ever found yourself constantly writing the same thing when creating objects or struggling with managing different types of objects, then the factory pattern might be exactly what you need.
In this tutorial:
https://www.freecodecamp.org/news/how-to-use-the-factory-pattern-in-python-a-practical-guide/
you'll learn what a factory is, why it's useful, and how to implement it in #Python. We'll gather practical examples that will show when and how to apply this pattern in real tasks.
The code can be found on #GitHub
https://github.com/balapriyac/python-basics/tree/main/design-patterns/factory
https://t.me/CodeProgrammer
In this tutorial:
https://www.freecodecamp.org/news/how-to-use-the-factory-pattern-in-python-a-practical-guide/
you'll learn what a factory is, why it's useful, and how to implement it in #Python. We'll gather practical examples that will show when and how to apply this pattern in real tasks.
The code can be found on #GitHub
https://github.com/balapriyac/python-basics/tree/main/design-patterns/factory
https://t.me/CodeProgrammer
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🗂 Building our own mini-Skynet — a collection of 10 powerful AI repositories from big tech companies
1. Generative AI for Beginners and AI Agents for Beginners
Microsoft provides a detailed explanation of generative AI and agent architecture: from theory to practice.
2. LLMs from Scratch
Step-by-step assembly of your own GPT to understand how LLMs are structured "under the hood".
3. OpenAI Cookbook
An official set of examples for working with APIs, RAG systems, and integrating AI into production from OpenAI.
4. Segment Anything and Stable Diffusion
Classic tools for computer vision and image generation from Meta and the CompVis research team.
5. Python 100 Days and Python Data Science Handbook
A powerful resource for Python and data analysis.
6. LLM App Templates and ML for Beginners
Ready-made app templates with LLMs and a structured course on classic machine learning.
If you want to delve deeply into AI or start building your own projects — this is an excellent starting kit.
tags: #github #LLM #AI #ML
➡️ https://t.me/CodeProgrammer
1. Generative AI for Beginners and AI Agents for Beginners
Microsoft provides a detailed explanation of generative AI and agent architecture: from theory to practice.
2. LLMs from Scratch
Step-by-step assembly of your own GPT to understand how LLMs are structured "under the hood".
3. OpenAI Cookbook
An official set of examples for working with APIs, RAG systems, and integrating AI into production from OpenAI.
4. Segment Anything and Stable Diffusion
Classic tools for computer vision and image generation from Meta and the CompVis research team.
5. Python 100 Days and Python Data Science Handbook
A powerful resource for Python and data analysis.
6. LLM App Templates and ML for Beginners
Ready-made app templates with LLMs and a structured course on classic machine learning.
If you want to delve deeply into AI or start building your own projects — this is an excellent starting kit.
tags: #github #LLM #AI #ML
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SVFR — a full-fledged framework for restoring faces in videos.
It can:
Essentially, the model takes old or damaged videos and makes them "as if they were shot yesterday". And it's free and open-source.
1. Create an environment
conda create -n svfr python=3.9 -y
conda activate svfr
2. Install PyTorch (for your CUDA)
pip install torch==2.2.2 torchvision==0.17.2 torchaudio==2.2.2
3. Install dependencies
pip install -r requirements.txt
4. Download models
conda install git-lfs
git lfs install
git clone https://huggingface.co/stabilityai/stable-video-diffusion-img2vid-xt models/stable-video-diffusion-img2vid-xt
5. Start processing videos
python infer.py \
--config config/infer.yaml \
--task_ids 0 \
--input_path input.mp4 \
--output_dir results/ \
--crop_face_region
Where task_ids:
*
0 — face enhancement*
1 — colorization*
2 — redrawing damageAn ideal tool if:
#python #soft #github
https://t.me/CodeProgrammer
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📰 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://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://t.me/CodeProgrammer
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