📱Artificial intelligence
📱Natural Language Processing (NLP) on Amazon Bedrock

💡 13 Practical Steps For Creating an AI Agent

💡 9 AI Skills to Master in 2026

It’s the infrastructure behind how smart businesses run today.

The gap between users and experts is closing fast.
But the gap between curiosity and capability is getting wider.

The difference comes down to skill, not just tools.

These are the nine that matter most in 2026.
Each one compounds the rest and turns AI from novelty into leverage.

1⃣ Prompt Engineering to ask better questions and get sharper answers
🔢 AI Workflow Automation to connect apps and remove repetitive work
🔢 AI Agents to build systems that act without human input
🔢 Retrieval-Augmented Generation (RAG) to give models access to your own data
🔢 Fine-Tuning and Custom GPTs to train models for your goals and tone
🔢 Multimodal AI to mix text, image, and audio in one workflow
🔢 AI Video Generation to turn ideas into content without editing tools
🔢 AI Tool Stacking to link platforms into a single automated system
🔢 LLM Evaluation and Management to measure accuracy, cost, and performance

🔅 Advanced LLMs with Retrieval Augmented Generation (RAG): Practical Projects for AI Applications

📝 Discover the core concepts of successful AI applications using LLMs to achieve high levels of performance and accuracy.

🌐 Author: Guy Ernest
🔰 Level: Advanced
⏰ Duration: 1h 47m

📋 Topics: Retrieval-Augmented Generation, Large Language Models, Artificial Intelligence

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📱Artificial intelligence
📱Advanced LLMs with Retrieval Augmented Generation (RAG): Practical Projects for AI Applications

💡 20 Concepts In LLMs

🔅 AI Projects with Python, TensorFlow, and NLTK

📝 Supercharge your technical know-how and start building AI projects using Python, TensorFlow, and NLTK.

🌐 Author: Dhhyey Desai
🔰 Level: Intermediate
⏰ Duration: 24m

📋 Topics: TensorFlow, Artificial Intelligence, NLTK

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📱Artificial intelligence
📱AI Projects with Python, TensorFlow, and NLTK

🚀 TrajectoryCrafter (Moving-Camera Diffusion) is a new tool from Tencent that offers a new approach to redirecting camera trajectories in monochrome videos.

How the model works:
🌟 Initialization :
starts with an existing camera trajectory or even pure noise. This sets the initial state that the model will gradually improve.

The model uses two types of input data simultaneously: rendered point clouds (3D representations of scenes) and source videos.

🌟 Diffusion process:
The model learns to “clean up” random noise step by step, turning it into a sequence of trajectories. At each step, iterative refinement occurs — the model predicts what a more realistic trajectory should look like, based on given conditions (e.g., smoothness of motion, and consistency of the scene).

Instead of using only videos taken from different angles, the authors created a training set by combining extensive monocular videos (with a regular camera) with limited but high-quality multi-view videos. This strategy is achieved using what is called “double reprojection”, which helps the model better adapt to different scenes.

🌟 Generating the final trajectory:
After a series of iterations, when the noise is removed, a new camera trajectory is generated that meets the given conditions and has high quality visual dynamics.

Installation :
git clone --recursive https://github.com/TrajectoryCrafter/TrajectoryCrafter.git
cd TrajectoryCrafter

🖥 Github
🟡 Article
🟡 Project
🟡 Demo
🟡 Video

🔅 AI for Beginners: Inside Large Language Models

⏲ 3 hours 📁 326 Lessons

📔 Understand how LLMs actually work under the hood from scratch with practical and fun lessons. No prior knowledge required!

🎙 Taught by: Scott Kerr

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🔰 Aiopandas is a lightweight patch for Pandas that adds native async support for the most popular data processing methods: map, apply, applymap, aggregate and transform.

Allows you to pass async functions to these methods without any problems. The library will automatically run them asynchronously, controlling the number of tasks executed simultaneously using the max_parallel parameter.

✨ Key features:

▪️ Easy integration: Use as a replacement for standard Pandas functions, but now with full support for async functions.
▪️ Controlled parallelism: Automatically execute your coroutines asynchronously, with the ability to limit the maximum number of parallel tasks (max_parallel). Ideal for managing the load on external services!
▪️ Flexible error handling: Built-in options for managing runtime errors: raise, ignore, or log.
▪️ Progress Indication: Built-in tqdm support for visually tracking the progress of long operations in real time.

🌐 Github : https://github.com/telekinesis-inc/aiopandas