In this video, I walk through my complete workflow for tackling large coding projects using Claude Code's plan mode. I demonstrate how to start with a rough dictated prompt, use plan mode to explore the codebase and generate clarifying questions, and break complex work into multi-phase plans that can span multiple context windows. I show my custom rules configuration that keeps plans concise and adds unresolved questions, how to monitor context usage throughout implementation, and my strategy of storing plans as GitHub issues to preserve them across context resets. This approach combines upfront planning with aggressive auto-accept during implementation phases, allowing AI to handle substantial features while maintaining control and code quality. I share practical tips including my favorite concision rule, the benefits of multi-phase planning, and how to effectively manage context windows for large projects.

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📢 Access top AI models and creators like Anthropic’s Claude, OpenAI’s GPT, Meta’s Llama, DeepSeek, Moonshot AI’s Kimi, plus image generation with Black Forest Labs (Flux) and Recraft — all in one place with Mammouth starting at just $10/month: https://mammouth.ai

Anthropic is back with massive upgrades to Claude Code! From Claude Code Security to Claude Engineer and built-in git worktree support, this update takes AI-assisted development to the next level.

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[Must Watch]:
Gemini 3.1 Pro Is Google's Greatest Model Ever! Most Powerful AI EVER! (Fully Tested): https://youtu.be/_uQKI-NOCFg
Claude Code + Figma MCP Is The Greatest AI Design System I've Ever Used!: https://youtu.be/mBJNfze9H0I
Claude Sonnet 4.6: The Best AI Coding Model Ever! 1M Context, Cheap, & More! (Fully Tested): https://youtu.be/enoBTzLziEs

📌 LINKS & RESOURCES
Claude Code Desktop: https://claude.com/download
Claude Code Secruity: https://www.anthropic.com/news..../claude-code-securit
Waitlist: https://claude.com/contact-sales/security
Boris Thread On Worktrees: https://x.com/bcherny/status/2025007393290272904

In this video, we dive into:
Claude Code Desktop Preview – Spin up apps, iterate, and preview running servers in real time
PR & CI Monitoring – Auto-fix failures, auto-merge PRs, and keep working on your next task while Claude handles the last one
Session Mobility – Move CLI sessions to desktop, cloud, web, or mobile
Claude Code Security – Scan for vulnerabilities, suggest patches, and review fixes with human oversight
Git Worktree Support – Run multiple agents in parallel without interference

Anthropic is clearly reshaping software engineering, automating complex tasks, and helping developers work smarter, faster, and safer. Don’t miss this deep dive into everything new!

Tags/Keywords:
Claude Code, Claude Code Desktop, Claude Code Security, Claude Engineer, Anthropic AI, AI coding assistant, AI software engineer, PR monitoring, CI automation, frontend development AI, git worktree, session mobility, code review AI, AI dev tools, Opus 4.6, AI cybersecurity, code vulnerability scanner

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#ClaudeCode #AnthropicAI #AICoding #SoftwareEngineering #CodeSecurity #AIEngineer #GitWorktree #CI #DevTools #AIProductivity

Episode Summary: MIT professor Andrew W. Lo tackles AI-assisted financial advising, healthcare, and the effect of human behavior on financial markets.

Episode Description: In this the first of two pilot episodes of Chalk Radio with VIDEO, Professor Andrew Lo, who teaches finance at MIT’s Sloan School of Management, knows that many people find financial matters perplexing and scary. Lots of us don’t have a good head for numbers, and besides, how can one get advice and make sound decisions when it’s taboo to discuss one’s finances at all? That’s where a financial advisor is useful–someone who understands the concepts, can crunch the numbers, and has a fiduciary responsibility to look out for your best interests. For many people, hiring a financial advisor might be a financial impossibility, but Prof. Lo and his colleagues are working to develop an AI financial advisor that not only gives ordinary people access to sound financial advice, but acts with real fiduciary responsibility. Large language models can’t do this yet, he says, but the technology is developing fast. Other topics he touches on in this episode include the outsized influence of finance on drug development and global decarbonization and the equally outsized influence of teachers on their students–he names many who changed his own life, from his third-grade teacher in Queens to his professors at college and graduate school.

Subscribe to Chalk Radio on podcast platforms ➜ https://chalk-radio.simplecast.com

Relevant Resources:
MIT OpenCourseWare (https://ocw.mit.edu)
The OCW Educator portal (https://ocw.mit.edu/educator)
Professor Lo’s faculty page (https://mitsloan.mit.edu/facul....ty/directory/andrew-
15.401 Finance Theory I on MIT OpenCourseWare (https://ocw.mit.edu/courses/15....-401-finance-theory-
15.481x Adaptive Markets: Financial Market Dynamics and Human Behavior on MIT Open Learning Library (https://openlearninglibrary.mi....t.edu/courses/course
15.482x Healthcare Finance on MIT Open Learning Library (https://openlearninglibrary.mi....t.edu/courses/course
Music in this episode by Blue Dot Sessions (https://www.sessions.blue/)

Connect with Us:
If you have a suggestion for a new episode or have used OCW to change your life or those of others, tell us your story. We’d love to hear from you!

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0:01 Intro
0:51 Why Finance Matters
3:02 Inflation, and practical finance applications to mitigate rising costs
5:19 Can ChatGPT reliably plan someone's retirement?
7:54 How to deal with AI hallucinations
9:14 Financial planning - why you need to start early!
9:58 Finances - a taboo topic?
11:07 AI Finance tools and ethics
12:31 Will AI take people's jobs?
13:18 Finance for positive impact on people & healthcare - Andrew's origin story
16:47 How Finance could help Climate
18:48 'It all comes down to money'
19:22 How human behavior affects Finance
19:47 How humans react to a market crash
21:12 Andrew's Adaptive Markets Hypothesis
22:02 How can we counteract irrational human tendencies?
24:00 How Andrew makes finance accessible through his teaching
24:55 Andrew's education and identifying different types of intelligence
28:31 Andrew's learning disorder and how teachers helped him manage it
35:08 Andrew's meaningful memento
38:17 Conclusion


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Credits:
Sarah Hansen, host and producer
Brett Paci, producer
Jackson Maher, producer
Dave Lishansky, producer
Peter Chipman, show notes

We're going to build a variational autoencoder capable of generating novel images after being trained on a collection of images. We'll be using handwritten digit images as training data. Then we'll both generate new digits and plot out the learned embeddings. And I introduce Bayesian theory for the first time in this series :)

Code for this video:
https://github.com/llSourcell/....how_to_generate_imag

Mike's Winning Code:
https://github.com/xkortex/how...._to_win_slot_machine

SG's Runner up Code:
https://github.com/esha-sg/Int....ro-DeepLearning-Sira

Please subscribe! And like. And comment. That's what keeps me going.

2 things
-The embedding visualization at the end would be more spread out if i trained it for more epochs (50 is recommended) but i just used 5.
-The code in the video doesn't fully implement the reparameterization trick (to save space) but check the GitHub repo for details on that.

More Learning resources:
https://jaan.io/what-is-variat....ional-autoencoder-va
http://kvfrans.com/variational....-autoencoders-explai
http://blog.fastforwardlabs.co....m/2016/08/12/introdu
http://blog.fastforwardlabs.co....m/2016/08/22/under-t
http://blog.evjang.com/2016/11..../tutorial-categorica
https://jmetzen.github.io/2015-11-27/vae.html

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MIT Introduction to Deep Learning 6.S191: Lecture 4
Deep Generative Modeling
Lecturer: Ava Amini
** New 2025 Edition **

For all lectures, slides, and lab materials: http://introtodeeplearning.com​

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Xiaoqing Ge, Senior Innovation Engineer at Baker Hughes, discusses the following:
• Overview of computer vision
• How deep learning works in computer vision?
• Traditional methods vs deep learning
• Survey of Baker Hughes applications
• Looking ahead

Build your own functional AI coding agent from the ground up using Python and the free Gemini Flash API. This project-based tutorial provides a deep understanding of how powerful AI tools work by guiding you through the creation of an agentic loop powered by tool calling. You will implement the core abilities for your agent to interact with and modify a codebase, including reading files, writing to files, and executing code to get feedback.

Lane Wagner created this course.

Check out the interactive version of the course on boot.dev: https://www.boot.dev/courses/build-ai-agent-python

❤️ Support for this channel comes from our friends at Scrimba – the coding platform that's reinvented interactive learning: https://scrimba.com/freecodecamp

⭐️ Contents ⭐️
- 00:00:00 Introduction
- 00:01:14 Why Build an AI Agent?
- 00:01:49 Course Overview & What We're Building
- 00:02:25 How to Follow Along
- 00:03:47 What is an AI Agent? (Agentic Loops & Tool Calling)
- 00:06:03 The Agent's Four Tools
- 00:07:58 Prerequisites & Project Goals
- 00:10:08 Demo: Agentic vs. One-Shot Responses
- 00:13:07 Python Project Setup with UV
- 00:15:44 Getting Started with the Gemini API
- 00:19:21 Making Your First API Call
- 00:24:44 Accepting Command-Line Arguments
- 00:27:46 Managing Conversation History
- 00:30:39 Adding a Verbose Flag for Debugging
- 00:33:35 Setting Up the Project for Our Agent (Calculator App)
- 00:36:23 Building Tool #1: Get Files Info
- 00:49:39 Building Tool #2: Get File Content
- 00:58:24 Building Tool #3: Write File
- 01:05:26 Security Note: Dangers of Running AI-Generated Code
- 01:07:30 Building Tool #4: Run Python File
- 01:18:00 Understanding the System Prompt
- 01:33:10 How Tool Calling Works: Declaring Functions for the LLM
- 01:41:38 Adding All Function Declarations
- 01:49:19 Implementing the Function Calling Logic
- 01:57:30 Creating the Agentic Loop
- 02:07:11 Final Demo: Agent Fixes a Bug Autonomously
- 02:13:44 Conclusion & Next Steps

🎉 Thanks to our Champion and Sponsor supporters:
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Build an AI agent that learns alongside you

In this free, end‑to‑end course, you’ll build a powerful language‑learning AI agent from scratch using Python, LangGraph, OpenAI, Ollama, and MCP. Over this hands‑on tutorial, we’ll create a ReAct‑style agent that sources vocabulary, performs accurate translations, and automatically generates Anki flashcards.

You’ll learn how to:
• Build a ReAct agent using LangGraph
• Connect agents to external tools with MCP
• Combine proprietary and local LLMs (OpenAI & Ollama)
• Design real‑world AI agent workflows

No NLP background required — we explain both the how and the why. By the end of this course, you’ll go from an empty Python project to a fully functional AI assistant you can adapt for your own AI projects.

Download PyCharm for free, the only Python IDE you need to build data models and AI agents: https://jb.gg/ai-agents-course

The full code for this tutorial, as well as the resources used, can be found in this GitHub repo: https://github.com/t-redactyl/....language-learning-ag
You can connect with Jodie, as well as see more of her work, at http://t-redactyl.io

Resources:
https://github.com/eymenefealt....un/all-words-in-all-
https://www.kaggle.com/datasets
https://www.kaggle.com/datasets?tags=13204-NLP
https://archive.ics.uci.edu/datasets
https://huggingface.co/learn/agents-course/

Timestamps
00:00 - Course Intro: What We’ll Build, AI agent tech stack
00:43 - What You’ll Learn: Building an AI Language Learning Agent
01:41 – Who This AI Agent Tutorial Is For (Python Prerequisites)
02:42 – Instructor Introduction: NLP & Data Science Background
03:01 – Why Use PyCharm for AI Agents & Data Science

Environment & Project Setup
03:36 – Installing PyCharm and AI Assistant
05:45 – Creating a Python Project with Virtual Environments

Dataset Selection & Preparation
07:26 – Choosing a Multilingual Vocabulary Dataset
08:46 – Best NLP Datasets: Kaggle & UCI Repositories
10:02 – Cloning and Organizing NLP Datasets in PyCharm

Installing Python & AI Dependencies
13:06 – Installing NLP Libraries: pandas, SpaCy, wordfreq
15:09 – Installing LangChain, LangGraph & MCP Libraries

Data Exploration & Analysis
16:56 – Exploring NLP Data with Jupyter Notebooks
18:01 – Analyzing Vocabulary Size Across Languages
20:45 – Visualizing Word Counts with Pandas Charts
22:08 – Identifying Data Problems in Multilingual Word Lists
24:36 – Introduction to SpaCy for Natural Language Processing

Cleaning the Word Lists
29:02 – Inspecting and Debugging Raw Vocabulary Data
31:48 – Removing Noise: Basic Text Cleaning Techniques
33:06 – Lemmatizing Words with SpaCy Models
35:10 – Using Zipf’s Law Overview to Filter Rare Words
37:26 – Word Frequency Analysis with wordfreq and SpaCy
42:35 - Understand Word Frequencies with wordfreq

Final Dataset Creation
45:22 – Building a Complete NLP Cleaning Pipeline
49:01 – Validating Results with a Spanish Dataset
50:01 – Comparing Raw vs Cleaned NLP Data

ReAct Agent Basics
54:40 – From Clean Data to an AI Agent
55:45 – What Is an AI Agent? Core Concepts
56:19 – Thought-Action-Observation Loop Explained
58:11 – Types of AI Agents

Large Language Models (LLMs) Explained
1:00:56 – How Large Language Models Understand Language
1:01:01 – Word Embeddings & Word2Vec Explained
1:05:24 – Why Word Embeddings Fail Without Context
1:06:05 – Transformers & Self-Attention Explained
1:10:23 – GPT Models and Decoder-Only Architectures

Reasoning Models for AI Agents
1:11:52 – Why Reasoning Models Power AI Agents
1:12:08 – How Reasoning Models Are Trained (Chain-of-Thought)
1:17:04 – When Not to Use Reasoning Models

1:20:24 How to Build a ReAct Agent with LangGraph
1:21:31 Agent State, Memory & Tools Explained
1:23:34 Choosing Between GPT-4 and Open-Source Models
1:26:36 How to Manage OpenAI API Keys Securely

1:30:09 How to Build Custom Tools for LangGraph Agents
1:33:14 Auto-Generating Tool Docstrings with AI
1:35:01 Improving Agent Reliability with System Prompts

1:38:13 Building and Connecting a LangGraph Agent Graph
1:41:21 Running an AI Agent End-to-End
1:43:02 How to Debug AI Agents in PyCharm
1:44:37 Visualizing Agent Execution Graphs

1:47:36 How to Run AI Agents Locally with Ollama
1:49:24 Choosing the Best Open-Source Reasoning Model
1:51:04 Installing and Managing Ollama Models
1:53:19 GPT-4 vs Ollama: Model Comparison for Agents

1:58:12 Switching LangGraph Agents from OpenAI to Ollama
2:00:22 Testing a Fully Local AI Agent

2:11:31 Adding Difficulty-Aware Vocabulary Tools
2:14:01 Handling Ambiguous User Requests in AI Agents
2:19:56 Testing AI Agents with Natural Language Prompts

2:24:48 How to Translate Words Using an LLM Tool
2:27:34 Building a Translation Tool with Ollama
2:32:28 Parsing Structured Output from LLMs

2:37:02 Multi-Step Tool Use in ReAct Agents
2:39:34 Handling Errors and Non-Determinism in AI Agents

2:41:40 What Is MCP (Model Context Protocol)?
2:42:13 Connecting AI Agents to External Tools with MCP

🚀 Complete Agentic AI Course for Beginners in Telugu

Code files:
LangGraph code:
https://drive.google.com/file/....d/1l6N1T06ghWX-PpTSR

CrewAI code:
https://drive.google.com/file/....d/13QghlEBku_OCM5kYi

In this 3-hour full course, you will learn Agentic AI from scratch and understand how to build your own AI Agents step by step.

This course is explained in Simple Telugu and is perfect for beginners who want to learn modern AI Agent development.

What You Will Learn

✅ What is an AI Agent?
✅ AI Agent vs Chatbot
✅ How AI Agents think, plan, use tools and complete tasks
✅ LangGraph explained with practical workflow
✅ MCP — Model Context Protocol
✅ A2A — Agent-to-Agent Protocol
✅ CrewAI multi-agent systems
✅ OpenAI Agents SDK overview
✅ How to build AI Agents from scratch
✅ Real-world Agentic AI use cases

Topics Covered

🤖 AI Agents
🔷 LangGraph
🔗 MCP
🤝 A2A Protocol
👥 CrewAI
⚡ OpenAI Agents SDK
🧠 Agentic AI Architecture
💻 Practical AI Agent Projects

If you want to learn AI, Machine Learning, Generative AI and Agentic AI in Simple Telugu, subscribe to AI School of India.

Let’s make AI learning simple and practical 🚀

Hashtags

#AgenticAI #AIAgents #LangGraph #CrewAI #MCP #A2A #OpenAIAgentsSDK #GenerativeAI #ArtificialIntelligence #AIinTelugu #AISchoolOfIndia #MachineLearning #DataScience

🔴 𝐋𝐞𝐚𝐫𝐧 𝐓𝐫𝐞𝐧𝐝𝐢𝐧𝐠 𝐓𝐞𝐜𝐡𝐧𝐨𝐥𝐨𝐠𝐢𝐞𝐬 𝐅𝐨𝐫 𝐅𝐫𝐞𝐞! 𝐒𝐮𝐛𝐬𝐜𝐫𝐢𝐛𝐞 𝐭𝐨 𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐘𝐨𝐮𝐓𝐮𝐛𝐞 𝐂𝐡𝐚𝐧𝐧𝐞𝐥: https://edrk.in/DKQQ4Py
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This Edureka video on Build MERN Stack App from Scratch" will provide you with fundamental concepts of MongoDB, Express.js, React.js, and Node.js with hands-on.

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📌𝐂𝐚𝐬𝐭𝐛𝐨𝐱: https://castbox.fm/networks/505?country=IN
📌𝐌𝐞𝐞𝐭𝐮𝐩: https://www.meetup.com/edureka/
📌𝐂𝐨𝐦𝐦𝐮𝐧𝐢𝐭𝐲: https://www.edureka.co/community/

Please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll-free) for more information.

🔥Full Stack Java Developer Program (Discount Code - YTBE15) - https://www.simplilearn.com/java-full-stack-developer-certification?utm_campaign=mlyIXJiEJy8&utm_medium=DescriptionFirstFold&utm_source=Youtube
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🔥Caltech Coding Bootcamp (US Only) - https://www.simplilearn.com/coding-bootcamp?utm_campaign=mlyIXJiEJy8&utm_medium=DescriptionFirstFold&utm_source=Youtube

This video on Data Structures and Algorithms by Simplilearn introduces you to the fundamentals of programming, data structures, and algorithms. It begins with a foundational overview of data structures, helping you grasp the core concepts. The tutorial delves into essential algorithms like recursion, the Tower of Hanoi, the Knapsack problem, and Prim's Algorithm. You’ll explore various data structure types and learn how to select the most suitable one for specific scenarios. Additionally, it highlights the best AI coding tools to enhance efficiency and offers guidance on securing internships for hands-on experience. To reinforce your learning, the video presents the top 10 programming projects to improve your projects portfolio. By the end, you'll be well-prepared to tackle coding challenges and solve real-world problems with confidence.

This Data Structures and Algorithms Full Course will cover the following topics:

00:00:00 Introduction to Data Structures Full Course
05:26:27 Data Structures Tutorial For Beginners
05:38:31 Best AI Coding tools
05:50:06 What is an Algorithm
06:01:10 What is recursion
06:12:33 tower of Hanoi algorithm
06:36:45 Knapsack algorithm
07:05:34 Prims Algorithm
10:41:28 Data Structure tutorial
10:48:59 How to get Internships
11:02:09 Top 10 Programming Projects

✅ Subscribe to our Channel to learn more about the top Technologies: https://bit.ly/2VT4WtH

#datastructures #algorithm #datastructuresandalgorithms #queues #stack #linkedlist #cprogramming #cpp #programming #simplilearn #2025

➡️ About Post Graduate Program in Full Stack Web Development

This program will give you the foundation for building full stack web apps using the Java programming language. You'll begin with the basics of JavaScript, and then venture into some of the more advanced concepts like Angular, Spring Boot, Hibernate, JSPs, and MVC. Now is a perfect time to get started on your career as a full stack web developer!

Key Features
✅ Live sessions on the latest AI trends, such as generative AI, prompt engineering, explainable AI, and more
✅ Caltech CTME Post Graduate Certificate
✅ Attend Masterclasses from Caltech CTME instructors
✅ Online Convocation by Caltech CTME Program Director
✅ Receive up to 25 CEUs from Caltech CTME
✅ Caltech CTME Circle Membership
✅ Live virtual classes led by industry experts, hands-on projects and integrated labs
✅ 30+ in-demand tools and skills
✅ Capstone Project in 4 domains
✅ 20 lesson-end and 5 phase-end projects
✅ Build your own portfolio on GitHub

Skills Covered
✅ Agile
✅ JAVA
✅ Hibernate and JPA
✅ Spring Core 50
✅ DevOps
✅ HTML5 and CSS3
✅ AWS
✅ JavaScript ES6
✅ Servlets
✅ SOAP and REST
✅ JSP

Learning Path
✅ Induction for Caltech PGP Full Stack Web Development Program
✅ PG FSD: Planning and UI Design
✅ PG FSD: Backend and Database Development
✅ PG FSD: API End Points and Communication
✅ PG FSD: Integration and Deployment
✅ PG FSD: Full Stack Web Development Capstone Project
Electives:
✅ Caltech CTME Full Stack Development Master Class
✅ Generative AI Masterclass

Learn More: https://www.simplilearn.com/pgp-full-stack-web-development-certification-training-course?utm_campaign=mlyIXJiEJy8&utm_medium=Description&utm_source=Youtube

Aditya Music Telugu presenting to you all the latest compilations of these feel good songs will mesmerize you.

#telugusongs #teluguvideosongs #telugupopularsongs #telugusuperhitsongs #telugulyricalsongs #2023teluguhitsongs #jukebox #telugusongsjukebox #latesttelugusongs #newtrendingsongs

Stay updated with the latest videos, Subscribe on the link - http://bit.ly/adityamusic

00:00 - Mastaaru Mastaaru

02:55 - Ooru Palletooru

07:05 - Milky Beauty

10:10 - Hola Re Hola
13:47 - Lady Luck

16:36 - Kootha Ramp
20:50 - Rela Rela

24:47 - Neevuntey Chaalu

Movie : SIR
Song : Mastaaru Mastaaru
Singer : Shweta Mohan
Lyrics : 'Saraswati Putra' Ramajogayya Sastry
Music : GV Prakash Kumar

Movie : Balagam
Song : Ooru Palletooru
Singers : Mangli, Ram Miryala, Bheems Ceciroleo
Lyricist : Kasarla Shyam

Movie : Bholaa Shankar
Song : Milky Beauty
Singers : Mahati Swara Sagar , Vijay prakash, Sanjana Kalmanje
Lyrics : ‘Saraswathi Putra’ Ramajogayya Sastry
Music : Mahati Swara Sagar

Movie : Samajavaragamana
Song : Hola Re Hola
Lyrics : Shree Mani
Singer : J V Sudhanshu & Sony komanduri
Music : Gopi Sundar

Movie : Miss Shetty Mr Polishetty
Song : Lady Luck
Composed , Arranged & Rhythm Programmed by Radhan
Lyrics : 'Saraswati Putra' Ramajogayya Sastry
Vocals : Karthik

Movie : Bhaag Saale
Song : Kootha Ramp
Singer & Music : Kaala Bhairava
Lyrics : Krishna Kanth
Programmed & Mixed By : Siddharth S
Mastered By : Shadab Rayeen
Choreographer : Bhanu

Movie : Vimanam
Song : Rela Rela
Singer : Mangli
Music & Lyrics : Charan Arjun
Choreographer : Eshwar Penti

Movie : Michael
Song : Neevuntey chaalu
Music Director : Sam CS
Lyricist : Kalyana Chakravarthy Tripuraneni
Singer : Sid Sriram
Choreography : Satish Krishnan

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© 2023 Aditya Music India Pvt. Ltd.

2010s feel good mix ~throwback playlist

Help me to get 100.000 subs: https://www.youtube.com/channe....l/UCBepjMYiqtq_u4ZFO

All of the tracks used in my youtube playlists are copyrighted music, so if you see ads in my videos, it's because youtube places them automatically based on copyright-owners needs, I have no control over it, so sorry about that. My channel is NOT a monetized channel. My only goal is sharing nostalgic, road trip music for everyone!

#2010sthrowback #2010snostalgia #2010sfeelgood

Most Hated Songs in Human History

For what it's worth, I like a lot of these songs.

Spotify playlist: https://open.spotify.com/playl....ist/6WG3kURbUJRFruEv
TikTok: www.tiktok.com/@boogieheadmusic
Thumbnail: https://youtube.com/@MeghanTra....inor?si=qXS9aJHFgaub

#music #songs

My AI Toolkit: https://academy.jeffsu.org/ai-toolkit?utm_source=youtube&utm_medium=video&utm_campaign=177

Understanding AI Agents doesn't require a technical background. This video breaks down the evolution from basic LLMs like #ChatGPT to AI Workflows and finally to true #AI Agents through practical, real-world examples.

Learn the key differences between these technologies and discover how concepts like RAG and ReAct actually work in simple terms. Perfect for regular AI users who want to understand how these emerging technologies will impact their daily lives.

*TIMESTAMPS*
00:00 AI vs. AI Agents
01:04 Level 1: LLMs
02:17 Level 2: AI Workflows
05:26 Level 3: AI Agents
07:48 Real-world Example
09:10 Summary

*RESOURCES MENTIONED*
Helena Liu's AI Workflow Tutorial: https://youtu.be/H0YRniHh2tg
Andrew Ng's AI Agent Demo: https://youtu.be/KrRD7r7y7NY

*BE MY FRIEND:*
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*MY FAVORITE GEAR*
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*MY TOP 3 FAVORITE SOFTWARE*
❎ CleanShot X - https://geni.us/cleanshotx
✍️ Skillshare - https://geni.us/skillshare-jeff
💼 Teal - http://tealhq.co/jeffsu

#aiagents