Agentic AI Coding
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| 2 |  Do you want to understand the methods used to train LLMs?
The training of large language models (LLMs) is based on various approaches that help models understand and generate text.
Each method shapes the learning process in its own way - from predicting the next word to classifying entire sentences or labeling entities.
Here are 4 common methods of training LLMs in simple language 👇
1. Causal Language Modeling
Predicts the next word in a sequence based on the previous ones. Helps the model master the natural flow of speech and the structure of sentences.
Analogy: how to finish a sentence for another person by guessing the next word.
2. Masked Language Modeling
Learns by guessing the missing words in a sentence based on the surrounding context. Improves the overall understanding of language.
Analogy: how to solve tasks with missing words.
3. Text Classification Modeling
Determines the general class of a sentence (for example, tone or topic) by comparing predictions with actual labels.
Analogy: how to sort letters into folders "Work", "Personal", or "Promotions".
4. Token Classification Modeling
Assigns labels to each word or subword - for example, highlights names, places, or dates in the text.
Analogy: how to highlight words with different colors - names in blue, places in green, dates in yellow.
These methods form the basis of modern LLMs, and each of them plays a role in making AI smarter and more useful. | 417 |
| 3 | 📱Artificial intelligence
📱The AI-Driven Supply Chain Manager | 325 |
| 4 |  🔅 The AI-Driven Supply Chain Manager
📝 Learn practical generative AI prompts to help you enhance the efficiency, accuracy, and sustainability of your supply chain.
🌐 Author: Daniel Stanton
🔰 Level: Beginner + Intermediate
⏰ Duration: 41m
📋 Topics: Supply Chain Management, Artificial Intelligence for Business
🔗 Join Artificial intelligence for more courses | 320 |
| 5 |  📱 TorchCode — a PyTorch training tool for preparing for ML interviews
40 tasks for implementing operators and architectures that are actually asked in interviews. Automatic checking, hints, and reference solutions — all in the browser without installation.
If you're preparing for an ML interview, it's useful to go through at least half of them.
🔗 Link: https://github.com/duoan/TorchCode | 271 |
| 6 | ✅ Top Artificial Intelligence Concepts You Should Know 🤖🧠
🔹 1. Natural Language Processing (NLP)
Use Case: Chatbots, language translation
→ Enables machines to understand and generate human language.
🔹 2. Computer Vision
Use Case: Face recognition, self-driving cars
→ Allows machines to "see" and interpret visual data.
🔹 3. Machine Learning (ML)
Use Case: Predictive analytics, spam filtering
→ AI learns patterns from data to make decisions without explicit programming.
🔹 4. Deep Learning
Use Case: Voice assistants, image recognition
→ A type of ML using neural networks with many layers for complex tasks.
🔹 5. Reinforcement Learning
Use Case: Game AI, robotics
→ AI learns by interacting with the environment and receiving feedback.
🔹 6. Generative AI
Use Case: Text, image, and music generation
→ Models like ChatGPT or DALL·E create human-like content.
🔹 7. Expert Systems
Use Case: Medical diagnosis, legal advice
→ AI systems that mimic decision-making of human experts.
🔹 8. Speech Recognition
Use Case: Voice search, virtual assistants
→ Converts spoken language into text.
🔹 9. AI Ethics
Use Case: Bias detection, fair AI systems
→ Ensures responsible and transparent AI usage.
🔹 10. Robotic Process Automation (RPA)
Use Case: Automating repetitive office tasks
→ Uses AI to handle rule-based digital tasks efficiently.
💡 Learn these concepts to understand how AI is transforming industries!
💬 Tap ❤️ for more! | 223 |
| 7 |  +1Colorizing old black-and-white videos and "bringing faces to life" for FREE
SVFR — a full-fledged framework for restoring faces in videos.
It can:
💬 BFR — improve blurry faces.
💬 Colorization — colorize black-and-white videos.
💬 Inpainting — redraw damaged areas.
💬 and combine all of this in one pass.
Essentially, the model takes old or damaged videos and makes them "as if they were shot yesterday". And it's free and open-source.
⚙️ Installation locally:
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 damage
An ideal tool if:
🟢you're restoring archival videos;
🟢you're creating historical content;
🟢you're working with neural networks and video effects;
🟢you want a wow result without paid services.
▶️ Demo on Hugging Face
🌐 GitHub/Instructions | 211 |
| 8 | 📱Artificial intelligence
📱Responsible AI Framework for Your Enterprise AI Product | 223 |
| 9 |  🔅 Responsible AI Framework for Your Enterprise AI Product
📝 Master responsible AI with the Five Rings framework. Learn how to build ethical, secure, and transparent AI products that align with human values and business success.
🌐 Author: Alina Zhang
🔰 Level: Intermediate
⏰ Duration: 45m
📋 Topics: Responsible AI, Artificial Intelligence
🔗 Join Artificial intelligence for more courses | 248 |
| 10 | 📕 Learn AI for Free, Straight From the Top Companies
Build real skills. Learn from the source. Grow your AI future. 🚀
1⃣ Anthropic
Learn Claude, LLMs and AI safety from the creators of Claude.
🔗 https://anthropic.skilljar.com
🔢 Google
Explore AI courses, tools and resources from Google.
🔗 https://grow.google/ai
🔢 AI Tools
Explore AI courses, tools and resources from AI Tools.
🔗 https://t.me/openai_mastery
🔢 Meta
Learn about Llama, AI research and developer resources.
🔗 https://ai.meta.com/resources
🔢 NVIDIA
Get started with GPU computing, CUDA and AI development.
🔗 https://developer.nvidia.com/cuda
🔢 Microsoft
Free AI training, certifications and learning paths.
🔗 https://learn.microsoft.com/en-us/training
🔢 OpenAI
Learn about ChatGPT, LLMs and AI fundamentals.
🔗 https://academy.openai.com
🔢 IBM
Build AI skills with practical courses and labs.
🔗 https://skillsbuild.org
🔢 AWS
Learn cloud, AI and machine learning with hands-on labs.
🔗 https://skillbuilder.aws
1⃣0⃣ DeepLearning.AI
Learn from Andrew Ng and get hands-on with AI.
🔗 https://deeplearning.ai
1⃣1⃣ Hugging Face
Explore models, datasets and learn AI together.
🔗 https://huggingface.co/learn
✨ Learn • Build • Stay Ahead
💡 AI is for everyone. | 418 |
| 11 |  2. Claude Cowork Challenge | 319 |
| 12 |  1. Introduction to Claude Cowork | 324 |
| 13 |  🔅 Claude Cowork 7-Day Challenge: Find Practical AI Use Cases That Deliver
📝 Learn how to use Claude Cowork to safely work with real files, documents, and everyday tasks through a short, daily challenge format.
🔗 Join AI Coding for more courses | 371 |
| 14 |  🧿 24 Things to Install in Claude | 355 |
| 15 |  🧿 Claude Code
And this is not a superficial overview, but a thorough analysis of the tool for those who want to really improve their vibe coding and work with AI agents.
The course is led by Lydia Hallie from Anthropic. 👩💻
📋 Content:
• Basics of Claude Code
• Skills
• Hooks
• Sub-agents
• MCP
• Plugins
• Agent SDK
• Advanced work scenarios | 4 677 |
| 16 | 1. agent-desktop
Desktop automation. Read the system’s accessibility tree and judge which button, menu, or input field to interact with next.
https://github.com/lahfir/agent-desktop
2. typesafe-mario
Have Jev play Super Mario. No screenshots — just read the structured state from the emulator’s RAM, then decide whether to run, jump, or dodge.
https://github.com/fhshaik/typesafe-mario
3. jev-drone
Use Jev to control a drone. The underlying flight-control system handles stability and safety; Jev makes higher-level tactical judgments such as climbing, braking, and navigating obstacles.
https://github.com/RomanSlack/jev-drone
4. OneVOneJev
1v1 FPS in the browser. At every decision tick, judge movement, view angle, aiming, firing, and jumping.
https://github.com/emrickgarrett/OneVOneJev
5. jev-trader
AI trading decisions on the Monad testnet. Jev evaluates the next trade based on market conditions such as spreads and trade direction.
https://github.com/jarrodwatts/jev-trader
6. Prism
Jev doesn’t directly place orders. It evaluates states such as toxic flow, market pressure, mean reversion, etc., then hands the judgment back to the original strategy.
https://github.com/irfndi/prism-llm
7. neo4jev
Put Jev inside a knowledge graph. At each node, judge which edge is the most worthwhile to follow next, then continue the traversal.
https://github.com/jexp/neo4jev
8. jev-curate
Use Jev to screen training data. For JSONL / Parquet datasets, first judge quality, relevance, and risk, then decide which records should enter the next training round.
https://github.com/AkashPriyadarshee/jev-curate
9. Canny
Prevents Coding Agents from stubbornly claiming they’re done. Look at tool outputs, code diffs, and test results, then judge whether the completion claim is actually reliable.
https://github.com/qkal/Canny
10. killmyidea
Input a startup idea, and Jev evaluates it from multiple angles before producing one of three outcomes: KILL, FIX, or SHIP.
https://github.com/monteduro/killmyidea
Copy these complete Jev blueprints — then read the full Jev setup below ↓ ↓ | 461 |
| 17 |  Jev has been exploding in popularity recently. 🚀
If you already have access to the Jev API but aren’t sure how to start experimenting with it, just copy this checklist: | 1 424 |
| 18 | PDFs @ai_codin.zip | 630 |
| 19 |  06 - Microsoft 365 Copilot Pro Course Wrap Up | 619 |
| 20 |  05 - Copilot in PowerPoint | 588 |
