AI Agents Learning resources

 📹 Videos:

1. LLM Introduction: https://www.youtube.com/watch?v=zjkBMFhNj_g

2. LLMs from Scratch: https://www.youtube.com/watch?v=9vM4p9NN0Ts

3. Agentic AI Overview (Stanford): https://www.youtube.com/watch?v=kJLiOGle3Lw

4. Building and Evaluating Agents: https://www.youtube.com/watch?v=d5EltXhbcfA

5. Building Effective Agents: https://www.youtube.com/watch?v=D7_ipDqhtwk

6. Building Agents with MCP: https://www.youtube.com/watch?v=kQmXtrmQ5Zg

7. Building an Agent from Scratch: https://www.youtube.com/watch?v=xzXdLRUyjUg

8. Philo Agents: https://www.youtube.com/playlist?list=PLacQJwuclt_sV-tfZmpT1Ov6jldHl30NR

🗂️ Repos

1. GenAI Agents: https://github.com/nirdiamant/GenAI_Agents

2. Microsoft's AI Agents for Beginners: https://github.com/microsoft/ai-agents-for-beginners

3. Prompt Engineering Guide: https://lnkd.in/gJjGbxQr

4. Hands-On Large Language Models: https://lnkd.in/dxaVF86w

5. AI Agents for Beginners: https://github.com/microsoft/ai-agents-for-beginners

6. GenAI Agentshttps://lnkd.in/dEt72MEy

7. Made with ML: https://lnkd.in/d2dMACMj

8. Hands-On AI Engineering:https://github.com/Sumanth077/Hands-On-AI-Engineering

9. Awesome Generative AI Guide: https://lnkd.in/dJ8gxp3a

10. Designing Machine Learning Systems: https://lnkd.in/dEx8sQJK

11. Machine Learning for Beginners from Microsoft: https://lnkd.in/dBj3BAEY

12. LLM Course: https://github.com/mlabonne/llm-course

🗺️ Guides

1. Google's Agent Whitepaper: https://lnkd.in/gFvCfbSN

2. Google's Agent Companion: https://lnkd.in/gfmCrgAH

3. Building Effective Agents by Anthropic: https://lnkd.in/gRWKANS4.

4. Claude Code Best Agentic Coding practices: https://lnkd.in/gs99zyCf

5. OpenAI's Practical Guide to Building Agents: https://lnkd.in/guRfXsFK

📚Books:

1. Understanding Deep Learning: https://udlbook.github.io/udlbook/

2. Building an LLM from Scratch: https://lnkd.in/g2YGbnWS

3. The LLM Engineering Handbook: https://lnkd.in/gWUT2EXe

4. AI Agents: The Definitive Guide - Nicole Koenigstein:  https://lnkd.in/dJ9wFNMD

5. Building Applications with AI Agents - Michael Albada: https://lnkd.in/dSs8srk5

6. AI Agents with MCP - Kyle Stratis: https://lnkd.in/dR22bEiZ

7. AI Engineering: https://www.oreilly.com/library/view/ai-engineering/9781098166298/

📜 Papers

1. ReAct: https://lnkd.in/gRBH3ZRq

Credit : Rakesh Gohel on LinkedIn

Free Google courses for Generative AI

 Google just launched a free learning path for Generative AI.

If you're in tech, business, or just curious, this is worth a look.

𝟏. 𝐈𝐧𝐭𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧 𝐭𝐨 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐯𝐞 𝐀𝐈:

👉 https://lnkd.in/g7eqXQ7H

↳ Explain how generative AI works

↳ Describe generative AI model types

↳ Describe generative AI applications

𝟐. 𝐈𝐧𝐭𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧 𝐭𝐨 𝐋𝐋𝐌:

👉 https://lnkd.in/gijvaHyZ

↳ Define large language models (LLMs)

↳ Describe LLM use cases

↳ Explain prompt tuning

↳ Describe Google’s generative AI development tools

𝟑. 𝐈𝐧𝐭𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧 𝐭𝐨 𝐑𝐞𝐬𝐩𝐨𝐧𝐬𝐢𝐛𝐥𝐞 𝐀𝐈:

👉 https://lnkd.in/gWSjT4Zq

↳ Identify the need for a responsible AI practice within an organization

↳ Recognize that decisions made at all stages of a project make an impact in Responsible AI

↳ Recognize that organizations can design an AI infrastructure to fit their own business needs and values

♻️ Repost this to help your network get started 

I asked Perplexity Pro which is the best laptop for ai work & macbook is not in the list !!

 Suggest the best laptop for ai model training that can be bought in Bangalore

Top Laptops for AI Model Training (2025, Bangalore)

Model Name	GPU/CPU	RAM	Key Features & Reasons to Buy	Approx. Price (INR)	Availability
ASUS ROG Zephyrus G14	AMD Ryzen 9/7, RTX 40XX series	16/32GB	Compact, powerful CPU/GPU for deep learning; reliable ventilation	1,60,000–2,10,000	Major electronics retailers[1][2]
HP Victus 15-fb3025AX	Ryzen 7, NVIDIA RTX 4050+	16/32GB	Good for large datasets and image/video tasks, gaming build for high performance	1,40,000–1,60,000	HP, Amazon, local stores[3][4]
Holoware AI Laptop	Intel Core Ultra 5/7, Intel Arc GPU	16/32GB DDR5	India’s dedicated AI laptop, optimized for ML workloads, DDR5 RAM, efficient cooling	~1,80,000+	Direct via Holoware, Bangalore[5]
HP Omen/Acer Nitro/ASUS TUF series	Ryzen 7/9, RTX 4070/4080 (NVIDIA)	16–32GB	RTX 4070+ GPUs, high TGP, suited for prolonged ML/deep learning tasks	1,70,000–2,00,000+	Amazon sale, HP/ASUS stores[2][4]
Lenovo IdeaPad Pro 5 (AI model)	Intel Core Ultra 9, Integrated AI GPU	32GB DDR5	OLED screen, advanced AI, general ML/data science (integrated GPU, less CUDA)	1,30,000–1,60,000	Lenovo exclusive/play stores[4]

---

Expert Tips for Buying an AI/ML Laptop

• GPU is critical: For local training, prefer NVIDIA RTX series (ideally 4050 or above) for CUDA compatibility with TensorFlow/PyTorch[1][2][6].
• RAM: Minimum 16GB; 32GB preferred for large models and datasets.
• Storage: NVMe SSD (512GB–1TB) for speedy data access.
• Cooling: Machines like ROG Zephyrus, HP Omen, Holoware AI offer solid cooling to prevent performance throttling during long model training[3][5].
• Linux Compatibility: Check if your chosen machine runs Ubuntu well, as many ML frameworks work best on Linux[1].
• Sales: August 2025 Amazon festivals offer major discounts on AI/GPU laptops. Visit Bangalore’s electronics markets for local deals and warranty support[4].

---

Where to Buy in Bangalore

• Physical Stores: Pai International, Croma, authorized HP/ASUS/Lenovo stores.
• Online: Amazon India (watch for sales periods like the Great Freedom Festival), dedicated sites like Holoware for India-specific AI laptops[5][4][7][8].
• Refurbished Options: EazyPC offers checked, high-spec used models at a lower price, suitable for budget-conscious AI practitioners[1].

---

Standout Recommendation

For maximum AI training power, the ASUS ROG Zephyrus G14 (with NVIDIA RTX 4060 or 4070, Ryzen 9/7, and at least 32GB RAM) is one of the best available in Bangalore for deep learning, model prototyping, and heavy ML workloads. If you want a specialized option, consider the Holoware AI laptop—designed and supported locally in India for AI workloads (though with Intel Arc GPUs, so check framework compatibility)[1][2][5][4].

---

Always confirm exact specs and CUDA compatibility if you primarily work in TensorFlow or PyTorch, and ensure the laptop fits your specific dataset and framework needs before purchase[1][2][5][9][4][6].

Sources
[1] Best Laptop For Artificial Intelligence Students 2025 https://eazypc.in/best-laptop-for-artificial-intelligence-students-in-2025/
[2] Best Laptops for AI & Machine Learning (2025) https://www.youtube.com/watch?v=_2Eie6LayZc
[3] Best HP Laptops for AI Development India 2025 https://www.hp.com/in-en/shop/tech-takes/post/best-workstations-for-ai-development
[4] AI powered laptops price drop in Amazon Great Freedom Festival … https://www.hindustantimes.com/technology/ai-powered-laptops-price-drop-in-amazon-great-freedom-festival-sale-with-up-to-48-off-on-ryzen-ai-and-core-ultra-models-101754454762242.html
[5] Top Laptop for Artificial Intelligence in India https://holoware.co/best-laptop-for-artificial-intelligence-in-india-holoware-guide/
[6] Best Laptop for Data Science in 2025: Top 15 Picks https://www.upgrad.com/blog/top-10-best-data-science-laptops/
[7] AI Laptops in India 2025 – Best AI Laptop Brands & Buying Guide https://www.paiinternational.in/blog-details/all-about-ai-laptops-what-they-are-how-they-work-w
[8] Top 5 AI Laptops in India in 2025 - April https://www.paiinternational.in/blog-details/revolutionize-your-work-play-top-5-ai-laptops-in-i
[9] Best Laptop for Machine Learning in 2025 https://www.edureka.co/blog/best-laptop-for-machine-learning/
[10] Best Affordable AI Laptops for Gaming & Studies | HP® Tech Takes https://www.hp.com/in-en/shop/tech-takes/post/affordable-ai-laptops-gaming-school-balance

why is apple MacBook not in this list

Apple MacBook models, including the MacBook Pro with Apple Silicon (M1, M2, and M3 chips), are not typically recommended for intensive AI model training—particularly for tasks using popular frameworks like TensorFlow and PyTorch—due to several key reasons relevant in 2025:

- **Lack of CUDA Support:** Most AI and machine learning libraries (especially TensorFlow and PyTorch) use NVIDIA’s CUDA platform for GPU acceleration. Apple's M-series chips use integrated graphics, which are not CUDA-compatible, meaning you can't leverage powerful GPU acceleration for many deep learning tasks as you would on an NVIDIA RTX-equipped laptop[1][2][3].

- **Limited eGPU Expansion:** Apple Silicon Macs do not natively support external graphics cards (eGPUs) for ML, unlike earlier Intel-based Macs.

- **Framework Optimization:** While Apple has made progress with Metal and CoreML optimizations, the overall ML ecosystem (especially for research and large-scale model training) still relies heavily on CUDA and NVIDIA GPUs for maximum performance with large datasets and advanced architectures[1][4][5].

- **Native Support Challenges:** Some advanced features in deep learning libraries, along with many third-party tools and dependencies, either do not work optimally or are not fully supported on Apple Silicon, especially for newest AI research tools.

- **Price-to-Performance:** For a similar budget, Windows laptops (with NVIDIA GPUs) usually offer much better raw performance for AI/ML model training compared to Apple MacBooks[1][4][3][5].

Apple MacBooks can be excellent for software development, classical machine learning (CPU-based), and on-the-go prototyping, but for **heavy AI model training**, a laptop with a high-end NVIDIA GPU remains the standard recommendation in India and globally, which is why MacBook is not included in the top picks for this specific requirement[1][4][2][3][5].

Citations:

[1] Best Laptop For Artificial Intelligence Students 2025 https://eazypc.in/best-laptop-for-artificial-intelligence-students-in-2025/

[2] Top Laptop for Artificial Intelligence in India https://holoware.co/best-laptop-for-artificial-intelligence-in-india-holoware-guide/

[3] Best Laptop for Machine Learning in 2025 https://www.edureka.co/blog/best-laptop-for-machine-learning/

[4] Best Laptops for AI & Machine Learning (2025) https://www.youtube.com/watch?v=_2Eie6LayZc

[5] Best Laptop for Data Science in 2025: Top 15 Picks https://www.upgrad.com/blog/top-10-best-data-science-laptops/

AI tools for Software Engineers

 

Google I/O 2025 summary

 Google just dropped their biggest Al updates ever during Google I/O 2025. 

Here are 13 new Al updates you can't miss:

1. Gemini Live. You can now turn on your camera, point at anything, and talk to Gemini about it in real time
2. Imagen. Google's best image model yet
3. Veo 3. The first video model with native sound generation
4. Deep Research
5. Project Astra. A JARVIS-like research prototype exploring the capabilities of a universal Al assistant
6. Google Flow. Al filmmaking tool for creators
7. Agent Mode. A new feature in the Gemini app that lets you state a goal, and Gemini will handle the steps to achieve it
8. Google Jules. Jules is an Al-powered coding assistant that can read your code, write tests, fix bugs, and update dependencies
9. Al Mode in Search. Al Mode transforms Google Search into a conversational assistant
10. Real-time speech translation in Google Meet
11. Google Beam. An Al-first video communication platform that turns 2D video streams into realistic 3D experiences
12. Gemma 3n. A new open-source Al model optimized for mobile devices
13. Try-On. Google's Virtual Try-On feature lets you upload a photo of yourself to see how clothes would look on you

What are your thoughts on this?

Free MCP model context protocol course

Worth investing time on learning the Model context protocol using a free course provided by huggingface.

 https://huggingface.co/learn/mcp-course/unit0/introduction

MCP vs RAG (Model Context Protocol vs Retrieval Augmented Generation)

RAG (Retrieval-Augmented Generation) focuses on enhancing AI responses by retrieving external data, while MCP (Model Context Protocol) standardizes how AI interacts with various data sources and tools.

Overview of RAG

Definition: RAG is an AI architecture that improves the accuracy and relevance of responses generated by large language models (LLMs) by pulling in up-to-date information from external sources, such as databases or APIs, before generating a reply.

2
Functionality: When a user submits a query, RAG retrieves relevant content from connected data sources and appends this information to the input prompt, enriching the model's context with real-world relevance. This helps reduce inaccuracies and hallucinations in AI responses by grounding them in verifiable sources.

2
Use Cases: RAG is particularly useful in scenarios where real-time data is crucial, such as customer support, news aggregation, and any application requiring current information.

3 Sources
Overview of MCP

Definition: MCP is a framework developed by Anthropic that standardizes how AI applications interact with various external data sources and tools, aiming to simplify integration and enhance functionality.

2
Functionality: MCP allows LLMs to fetch data from multiple sources, such as databases or APIs, without needing to create custom solutions for each interaction. It operates on a client-server model, where the MCP server manages the logic for data retrieval and the MCP client interfaces with the LLM.

2
Use Cases: MCP is designed for applications that require seamless integration of various tools and data sources, making it suitable for complex workflows and environments where multiple data interactions are necessary.

3 Sources
Key Differences

Scope: RAG is a specific method focused on improving the accuracy of LLM outputs by grounding them in external knowledge, while MCP is a broader protocol that standardizes interactions between AI and various data systems.

1
Data Retrieval: RAG retrieves external data each time a query is made, whereas MCP allows LLMs to access contextual memory and external data more efficiently, reducing the need for repeated data retrieval.

2
Integration: RAG requires specific setups for each data source, while MCP provides a universal framework that simplifies the integration of multiple data sources and tools into AI applications.

3 Sources
Conclusion
Both RAG and MCP play significant roles in enhancing AI capabilities, but they serve different purposes. RAG is ideal for applications needing real-time data retrieval to improve response accuracy, while MCP offers a standardized approach for integrating various tools and data sources, making it easier to build complex AI systems. Understanding these differences is crucial for developers and organizations looking to leverage AI effectively in their applications.

How to deply Ollama & open web-ui on your laptop

How to deploy Ollama

 Installation:

• Download Ollama: Get the Ollama package from the GitHub repository. 
• Install Dependencies: Ensure you have any required dependencies, including libraries for your specific model. 
Verify Installation: Use ollama --version to confirm Ollama is installed correctly. 

2. Model Deployment and Usage:

Pull the Model: Use the ollama pull <model_name> command to download the desired model. 
Run the Model: Use ollama run <model_name> to initiate the model's execution. 
Interacting with the Model: Ollama provides an API at http://localhost:11434/api/generate for interacting with the model. 
Optional: Web UI: Explore Open WebUI for a user-friendly interface to manage and interact with models. 
Optional: Custom Applications: Build custom applications using libraries like FastAPI and Gradio to integrate Ollama models into your workflows. 

How to deploy open-webui

Open WebUI is an extensible, feature-rich, and user-friendly self-hosted AI platform designed to operate entirely offline. It supports various LLM runners like Ollama and OpenAI-compatible APIs, with built-in inference engine for RAG, making it a powerful AI deployment solution.

How to Install 🚀

Installation via Python pip 🐍

Open WebUI can be installed using pip, the Python package installer. Before proceeding, ensure you're using Python 3.11 to avoid compatibility issues.

1. Install Open WebUI: Open your terminal and run the following command to install Open WebUI:

   pip install open-webui

2. Running Open WebUI: After installation, you can start Open WebUI by executing:

   open-webui serve

This will start the Open WebUI server, which you can access at http://localhost:8080

To upgrade the Open-webui components

pip install open-webui --upgrade