Back End Development and APIs Certification freeCodeCamp org Part 1-5 NPM Install

Learn how to use Deno in this complete course. Deno is a Node.js alternative created by the same person who created Node.js. In this tutorial course, you will learn how to build real apps with Deno. You will also learn the basics of the Typescript. You will see how to use Deno to build a survey app with a REST API using MongoDB.

💻 Code: https://github.com/thecodeholi....c/freecodecamp-deno-
💻 Survey application code: https://github.com/thecodeholic/deno-survey

🎥 Course from The Codeholic. Check out his channel: https://www.youtube.com/channe....l/UC_UMEcP_kF0z4E6Kb
🐦 The Codeholic on Twitter: https://twitter.com/TheCodeholic

🔗 Official Deno website: https://deno.land/

⭐️ Course Contents ⭐️
Introduction
---------------------
⌨️ (00:00:00) Introduction
⌨️ (00:02:02) Course overview
⌨️ (00:04:38) Course Project
⌨️ (00:05:51) What is Deno
⌨️ (00:08:19) Course project (Survey app) demo
⌨️ (00:11:54) Install and Getting started
⌨️ (00:14:34) Write "Hello World"
⌨️ (00:15:50) Main Features

TypeScript
---------------------
⌨️ (00:17:22) What is TypeScript
⌨️ (00:24:04) TypeScript Types
⌨️ (00:37:40) Interfaces
⌨️ (00:43:46) Classes
⌨️ (00:48:03) Generics
⌨️ (00:56:31) Enums

Main features
---------------------
⌨️ (01:01:23) Import from URL
⌨️ (01:02:23) ES6 Modules import syntax
⌨️ (01:03:01) Top Level await
⌨️ (01:04:18) Browser API
⌨️ (01:06:20) Security
⌨️ (01:16:31) Single Executable file

⌨️ (01:18:55) Terminal commands
⌨️ (01:23:52) Standard Library and 3rd party modules
⌨️ (01:26:31) Create basic HTTP server
⌨️ (01:32:20) Working with file system
⌨️ (01:41:55) Using npm packages
⌨️ (01:45:40) Answers on common questions

Project - Survey app
----------------------
⌨️ (01:57:02) Install oak framework and setup basic server
⌨️ (02:03:41) Install denon for automatic server restart on file change
⌨️ (02:06:03) Easily manage versions and dependencies
⌨️ (02:08:06) Organize routes
⌨️ (02:09:36) Create AuthController and configure Login & Register routes
⌨️ (02:13:54) Setup connection with MongoDB
⌨️ (02:19:40) Create User model and implement registration
⌨️ (02:40:42) Implement login
⌨️ (02:54:16) Install dontenv and create .env file

Survey CRUD
----------------------
⌨️ (02:59:35) Configure routes for Survey CRUD and Create SurveyController
⌨️ (03:04:52) Create Survey model and implement Survey creation
⌨️ (03:11:22) Implement get surveys
⌨️ (03:20:33) Create BaseModel and inherit Survey and User models from there
⌨️ (03:26:12) Implement to get single survey
⌨️ (03:31:29) Implement survey update
⌨️ (03:39:22) Printing application errors
⌨️ (03:40:26) Fixing problem of "this" binding
⌨️ (03:44:27) Implement survey deletion
⌨️ (03:48:52) Create authMiddleware and add authentication to Survey CRUD

⌨️ (04:07:38) Implement CRUD of questions

Create Website
----------------------
⌨️ (04:44:15) Create SiteController and configure routes
⌨️ (04:47:38) Install template engine and create views
⌨️ (04:51:48) Render all surveys
⌨️ (04:55:32) Configure oak to handle static files (CSS, Javascript, Images)
⌨️ (05:06:05) Write basic styles
⌨️ (05:07:31) Create and use partial view files
⌨️ (05:09:30) Implement to view single survey
⌨️ (05:33:58) Get form submitted data and validation
⌨️ (05:49:07) Implement displaying validation errors in form
⌨️ (06:01:45) Implement to save survey answers
⌨️ (06:14:49) Create a real survey using our survey app



--



Learn to code for free and get a developer job: https://www.freecodecamp.org

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Lex Fridman Podcast full episode: https://www.youtube.com/watch?v=L_Guz73e6fw
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GUEST BIO:
Sam Altman is the CEO of OpenAI, the company behind GPT-4, ChatGPT, DALL-E, Codex, and many other state-of-the-art AI technologies.

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NEWSLETTER ✉️ http://DylanCurious.com
PATREON 💰 https://patreon.com/DylanCurious (Monthly Video Call)

In today's episode, we're charting the course of Artificial General Intelligence (AGI) as of June 2023, guided by the timeline presented by AI expert Dr. Alan D. Thompson.

A critical turning point came in 2017, with the birth of the attention mechanism by Google researchers, paving the way for the creation of Transformers. These are the foundation of powerful AI models like GPT, and according to Dr. Thompson, we're about halfway to achieving AGI from here.

The period between 2017 and 2023 has been rich with major AI advancements, each one a stepping stone on our path to AGI. These include our deeper understanding of GPT-3's capabilities, innovative applications of AI in microchip design, and the emergence of multitasking models like DeepMind's Gato.

By 2020, we had made roughly 30% progress towards AGI, nudging up to 31% by 2021. Significant leaps, like the unveiling of GPT-3's remarkable abilities and the use of AI in hardware design, were clear indicators of this progress. In May 2022, the introduction of DeepMind's Gato model propelled us to 39% completion, thanks to its impressive capabilities and the demonstration of human-like general intelligence.

As 2022 came to a close, progress continued with developments in autonomous AI systems and AI-assisted human tasks. Into 2023, we saw AGI estimates rise to 41%, largely due to Microsoft's innovative use of ChatGPT in operating robotic components. The introduction of Google's PaLM-E 562B, capable of planning its own future based on visual and language data, took AGI estimates up to 42%.

The launch of the even more powerful GPT-4 model and the integration of AI into robotics have substantially fast-tracked our journey towards AGI. Stay tuned to "Curious Future" for more updates on the fascinating world of AI, and don't forget to subscribe for the latest in AI, technology, and innovation.

CURIOUS FUTURE: @dylan_curious
↪ https://www.youtube.com/channe....l/UCpdyFxSktWo3W6kMY

CURIOUS PODCAST: @dontsweatitpod
↪ https://www.youtube.com/channe....l/UChChPrDzfifYB9Si1

CURIOUS FRIENDS: @vegasfriends
↪ https://www.youtube.com/channe....l/UCJ2Q3smwCLmbEDDA3

00:00 - Artificial General Intelligence, 50% Complete: Fact or Fiction?
00:42 - Did Google’s Invention of the Transformers Take Us 20% of the Way to AGI?
01:23 - 30% to AGI in 2020? Unraveling the AI Progress Debate
02:24 - AI Designing AI: The 31% Milestone Towards AGI in 2021
03:14 - DeepMind’s Gato Model: The Super Multitasker Pushing AGI to 39%?
03:48 - We Are 39% Of The Way To AGI: Unraveling Antropic’s Human Reinforcement Method
04:55 - Microsoft’s ChatGPT Pushed Us 41% Closer To AGI
05:49 - AGI at 42%: After Google’s PaLM-E 562B’s Shows Adaptive Abilities
07:19 - AGI at 48%: What Does this Mean for AI’s Future?
08:43 - Humans at 94%, AI at 90.9% | AGI Progress Is Moving Fast
09:37 - 2 Milestone To Look For Before AGI Arrives On Earth

SOURCES:
https://lifearchitect.ai/agi/
https://ai.googleblog.com/2017..../08/transformer-nove
https://www.youtube.com/watch?v=HrV19SjKUss&t=175s&ab_channel=MachineLearningStreetTalk
@MachineLearningStreetTalk
https://www.theverge.com/2021/....6/10/22527476/google
https://www.nature.com/articles/s41586-021-03544-w
https://lifearchitect.ai/the-sky-is-on-fire/
https://www.youtube.com/watch?v=6fWEHrXN9zo&list=PLqJbCeNOfEK-o63ACEKEbwE6-XpEXXS_I&ab_channel=DrAlanD.Thompson
https://en.wikipedia.org/wiki/Gato_(DeepMind)
https://www.deepmind.com/publi....cations/a-generalist
@DrAlanDThompson
https://developer.nvidia.com/b....log/designing-arithm
https://www.microsoft.com/en-u....s/research/group/aut
https://www.youtube.com/watch?v=wLOChUtdqoA&ab_channel=MicrosoftAutonomousSystems%26RoboticsResearch
@msftautonomoussys
https://palm-e.github.io/
https://arxiv.org/abs/2303.12712
https://www.youtube.com/watch?v=XyCKe3rrYik&ab_channel=STrucBot
https://www.fastcompany.com/90....889271/boston-dynami
https://tidybot.cs.princeton.edu/
https://www.youtube.com/watch?v=Vq_DcZ_xc_E&ab_channel=AgilityRobotics
@Structon
@AgilityRobotics

WATCH THE FULL VIDEO ⤵
https://www.youtube.com/watch?v=xyzY3cj7tTA

#ai #artificialintelligence #tech #agi #gpt3 #gpt4 #deepmind #chatgpt #robotics #google #microsoft #machinelearning #autonomousai #palm-e562b #gato #transformers #futuretech #innovation #aitechnology #aitimeline #curiousfuture #alanthompson #artificialgeneralintelligence #aidesign #aipredictions #aiadvancements #aiupdates

Siempre quise saber por qué las películas de cine se veían mejor que las de video. Las imágenes electrónicas en movimiento tienen una larga historia, pero fueron inventadas con un propósito diferente. 📺 SUSCRÍBETE para ver todos nuestros videos:http://bit.ly/Suscribirse_Veritasium_en_español

Quiénes de ustedes se han criado viendo un tubo de rayos de catódicos. Los televisores eran aparatos que podían llegar a ser muy grandes y pesados. Pero la tecnología avanzó y cada vez es más difícil encontrarlos.

Este es un video que he querido hacer desde hace mucho tiempo, sobre qué hace que un video se vea como un video y, hasta hace 10 años más o menos, no se veía tan atractivo como las películas de cine. Crecí con las dos tecnologías en paralelo (cintas de cine y de video) y siempre me parecieron dos formas de lograr los mismos fines: grabar y reproducir imágenes en movimiento.
Pero sus historias son bastante distintas. El cine siempre fue una forma de capturar imágenes en movimiento para su posterior reproducción.
El video comenzó como una forma de transferir imágenes de un lugar a otro instantáneamente. Esto se remonta a la primera máquina de fax, la televisión mecánica, la televisión en directo y, finalmente, las cintas de video.
Esta historia se centra en las primeras décadas del vídeo y no en los más recientes cambios a las cámaras de chip y el almacenamiento en estado sólido. Tal vez esa sea una historia para otro día.

Muchas gracias a:

Richard Diehl, Video Labguy https://www.youtube.com/user/videolabguy
https://www.labguysworld.com

Branch Education por sus animaciones impresionantes
https://ve42.co/BranchEd

Minutephysics por las animaciones de la TV mecánica
https://www.youtube.com/minutephysics

Mark Schubin
Ingeniero y divulgador, becario de por vida del SMPTE
https://www.smpte.org


Fuentes y referencias adicionales:

The Dawn of Tape: Transmission Device as Preservation Medium
https://ve42.co/dawnoftape

What Sparked Video Research in 1877? The Overlooked Role of the Siemens Artificial Eye
https://ve42.co/sparkvideo

Video Preservation Website:
https://cool.culturalheritage.....org/videopreservatio
Image Orthicon Tube:
http://interiorcommunicationse....lectrician.tpub.com/
Film vs Digital
https://stephenfollows.com/film-vs-digital/
Eyes of a Generation:
http://eyesofageneration.com

Television in the US:
https://www.youtube.com/redirect?event=video_description&v=rjDX5ItsOnQ&redir_token=659XFg6LdOuhcg9MgOOwI7cHP3d8MTU5MDAwODI5MkAxNTg5OTIxODky&q=http%3A%2F%2Fwww3.northern.edu%2Fwild%2Fth100%2Ftv.htm
http://www.classictvinfo.com

Música de https://www.epidemicsound.com "Seaweed" "Capture a Picture 1" "Colorful Animation 4"

Video en Inglés del Canal @veritasium :
https://www.youtube.com/watch?v=rjDX5ItsOnQ
------------------------------------------------------------------------
🕹 Este canal de Youtube es administrado por: https://www.unilingo.tv/
❓Comentarios o sugerencias de traducción: [email protected]
------------------------------------------------------------------------
#veritasiumenespañol

Intellipaat Training courses: https://intellipaat.com/
Intellipaat is a global online professional training provider. We are offering some of the most updated, industry-designed certification training programs which includes courses in Big Data, Data Science, Artificial Intelligence and 150 other top trending technologies.
We help professionals make the right career decisions, choose the trainers with over a decade of industry experience, provide extensive hands-on projects, rigorously evaluate learner progress and offer industry-recognized certifications. We also assist corporate clients to upskill their workforce and keep them in sync with the changing technology and digital landscape.

#ChatbotusingPythonNLPandData Science #BuildYourOwnChatbot #chatbots #Chatbot #intellipaat

Following topics are covered in this session:
00:00:00 - Introduction to Chatbot using Python, NLP, and Data Science
00:02:08 - Brief on ChatBots
00:05:44 - Need of ChatBots
00:10:12 - Types of ChatBots
00:12:35 - Rule Based vs Self Learning ChatBots
00:17:01 - How a ChatBot Works?
00:19:08 - Creating Rule Based ChatBots
00:45:56 - ChatBot using Python, & ChatBot using Python & NLTK
00:50:39 - Steps Involved in Building ChatBot with Python and NLTK
00:54:09 - Tokenization, Stemming, Lemmatization
00:56:17 - Creating NLTK ChatBot in Python


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#DataAnalyticsCourse #DataAnalystfullCourse #DataAnalysisTraining #Intellipaat

In this comprehensive data analysis full course, you will learn everything you need to know to become a data analyst, from the basics of data analysis to the most advanced techniques. This course is designed for beginners with no prior experience in data analysis.

We will begin this Data Analytics Course with an Introduction to Data Analytics, and then we will move on to Excel Dashboards. In addition to this, we will also learn about Fill Series, Referencing, Stat Functions, Text Functions, VLOOKUP, HLOOKUP, Data Validation, Pivots, Charts, Statistics, Probability, Probability Distributions, and Hypothesis Testing. Lastly, we will design projects related to data analytics like COVID-19, Netflix, and Census Income Projects, and prepare for interviews with some data analytics Interview Questions.

✅What do you learn in the data analytics course?
Data analysts are typically expected to be proficient in software like Excel and, in some cases, querying and programming languages like SQL, R, SAS, and Python. Analysts need to be comfortable using such tools and languages to carry out data mining, statistical analysis, database management and reporting.

✅ Is data analytics a good career?
Data analysts are in great demand across all industries and there is a good career path for those who want to move up the professional ladder. Many top organizations require data analysts to help them to make better use of their data or provide the right insights that will help them in their business activities.

✅Is the data analytics course easy to learn?
A: Learning data analytics can be challenging, especially if you're new to programming, statistics, and data manipulation. However, with dedication, the right resources, and a strategic approach, it's possible to overcome the challenges and become proficient in this field.

➡️ About the Course
Enroll in this advanced certification by IITM Pravartak (The Technology Innovation Hub of IIT Madras) and Intellipaat and learn under the expert guidance of the IIT Madras faculty. The program is designed by industry experts to help you master AWS, cloud computing, VPC, etc., through real-time hands-on projects.

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☑️BI Professionals
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☑️Project Managers
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☑️Information Architects
☑️Freshers and Undergraduates can apply for the course

✅ Key Features
👉🏼50+ Live sessions across 7 months
👉🏼218 Hrs Self-paced Videos
👉🏼200 Hrs Project & Exercises
👉🏼Learn from IIT Madras Faculty and Industry Practitioners
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👉🏼Resume Preparation and LinkedIn Profile Review
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What’s Covered in This Program?
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✅KNIME
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✅Predictive Modeling
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✅Data Science Execution Strategy
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For course details please visit : https://ineuron.ai/course/gene....rative-ai-community-

Generative AI Foundational Community Course Part 1 !!!!

Part 2 coming soon…

#OpenAI #Langchain #Streamlit #MCQGenerator

Connect with us on : +91 8071176111

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Stay tuned, stay connected, and let's master GenerativeAI together

#GenerativeAI #AIcourse #AIeducation #MachineLearning #DeepLearning #AIFoundations #TechnologyEducation #ArtificialIntelligence #AItraining #learningseries

Timeline : -
0:00 Introduction
6:10 What is Generative AI ?
1:43:57 Open AI
3:35:00 LangChain for Generative Ai
5:10:27 Hugging Face API & Langchain
7:04:10 Memory in Langchain
9:00:08 LLM Generative AI Project using OpenAI & Langchain

🔥Machine Learning Training with Python: https://www.edureka.co/machine....-learning-certificat
This Edureka video on "Deploy an ML Model using Amazon Sagemaker" discusses what is Amazon Sagemaker and how you can build, train and deploy your Machine Learning Models in Amazon Sagemaker.

🔹Check Edureka's complete DevOps playlist here: http://goo.gl/O2vo13
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About the Course

AWS Architect Certification Training from Edureka is designed to provide in depth knowledge about Amazon AWS architectural principles and its components. The sessions will be conducted by Industry practitioners who will train you to leverage AWS services to make the AWS cloud infrastructure scalable, reliable, and highly available. This course is completely aligned to AWS Architect Certification - Associate Level exam conducted by Amazon Web Services.

During this AWS Architect Online training, you'll learn:
1. AWS Architecture and different models of Cloud Computing
2. Compute Services: Amazon EC2, Auto Scaling and Load Balancing, AWS Lambda, Elastic Beanstalk
3. Amazon Storage Services : EBS, S3 AWS, Glacier, CloudFront, Snowball, Storage Gateway
4. Database Services: RDS, DynamoDB, ElastiCache, RedShift
5. Security and Identity Services: IAM, KMS
6. Networking Services: Amazon VPC, Route 53, Direct Connect
7. Management Tools: CloudTrail, CloudWatch, CloudFormation, OpsWorks, Trusty Advisor
8. Application Services: SES, SNS, SQS

Course Objectives

On completion of the AWS Architect Certification training, learner will be able to:
1. Design and deploy scalable, highly available, and fault tolerant systems on AWS
2. Understand lift and shift of an existing on-premises application to AWS
3. Ingress and egress of data to and from AWS
4. Identifying appropriate use of AWS architectural best practices
5. Estimating AWS costs and identifying cost control mechanisms

Pre-requisites

There are no specific prerequisites for this course. Any professional who has an understanding of IT Service Management can join this training. There is no programming knowledge needed and no prior AWS experience required.

If you are looking for live online training, write back to us at [email protected] or call us at US: + 18338555775 (Toll-Free) or India: +91 9606058406 for more information.

🔥Edureka Tensorflow Training: https://www.edureka.co/ai-deep....-learning-with-tenso
This Edureka LSTM Explained video will help you in understanding why we need Recurrent Neural Networks (RNN) and what exactly it is. It also explains few issues with training a Recurrent Neural Network and how to overcome those challenges using LSTMs.

🔹Check our complete Deep Learning With TensorFlow playlist here: https://goo.gl/cck4hE
🔹Check our complete Deep Learning With TensorFlow Blog Series: http://bit.ly/2sqmP4s

00:00:00 Agenda
00:02:37 What is Natural Language Processing
00:04:00 Ways to Process Text Data
00:05:46 Recurrent Neural Networks
00:12:26 Long short term Memory
00:17:06 Long short term memory use cases
00:18:06 Real time applications of LSTM
00:18:09 Name Entity Recognition
00:20:45 Sentiment Analysis
00:22:40 Machine Translation
00:24:32 Hands-on

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🔵 Artificial and Machine Learning PGD: https://bit.ly/3AylL0q


How it Works?

1. This is 21 hrs of Online Live Instructor-led course. Weekend class: 7 sessions of 3 hours each.
2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course.
3. At the end of the training you will have to undergo a 2-hour LIVE Practical Exam based on which we will provide you a Grade and a Verifiable Certificate!

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About the Course
Edureka's Deep learning with Tensorflow course will help you to learn the basic concepts of TensorFlow, the main functions, operations and the execution pipeline. Starting with a simple “Hello Word” example, throughout the course you will be able to see how TensorFlow can be used in curve fitting, regression, classification and minimization of error functions. This concept is then explored in the Deep Learning world. You will evaluate the common, and not so common, deep neural networks and see how these can be exploited in the real world with complex raw data using TensorFlow. In addition, you will learn how to apply TensorFlow for backpropagation to tune the weights and biases while the Neural Networks are being trained. Finally, the course covers different types of Deep Architectures, such as Convolutional Networks, Recurrent Networks and Autoencoders.

Delve into neural networks, implement Deep Learning algorithms, and explore layers of data abstraction with the help of this Deep Learning with TensorFlow course.


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Who should go for this course?

The following professionals can go for this course:

1. Developers aspiring to be a 'Data Scientist'

2. Analytics Managers who are leading a team of analysts

3. Business Analysts who want to understand Deep Learning (ML) Techniques

4. Information Architects who want to gain expertise in Predictive Analytics

5. Professionals who want to captivate and analyze Big Data

6. Analysts wanting to understand Data Science methodologies

However, Deep learning is not just focused to one particular industry or skill set, it can be used by anyone to enhance their portfolio.

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Why Learn Deep Learning With TensorFlow?
TensorFlow is one of the best libraries to implement Deep Learning. TensorFlow is a software library for numerical computation of mathematical expressions, using data flow graphs. Nodes in the graph represent mathematical operations, while the edges represent the multidimensional data arrays (tensors) that flow between them. It was created by Google and tailored for Machine Learning. In fact, it is being widely used to develop solutions with Deep Learning.


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

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Code link: https://github.com/krishnaik06..../Agentic-LanggraphCr
LangGraph is built for developers who want to build powerful, adaptable AI agents. Developers choose LangGraph for:

Reliability and controllability. Steer agent actions with moderation checks and human-in-the-loop approvals. LangGraph persists context for long-running workflows, keeping your agents on course.
Low-level and extensible. Build custom agents with fully descriptive, low-level primitives free from rigid abstractions that limit customization. Design scalable multi-agent systems, with each agent serving a specific role tailored to your use case.
First-class streaming support. With token-by-token streaming and streaming of intermediate steps, LangGraph gives users clear visibility into agent reasoning and actions as they unfold in real time.
Learn LangGraph basics¶
To get acquainted with LangGraph's key concepts and features, complete the following LangGraph basics tutorials series:

Build a basic chatbot
Add tools
Add memory
Add human-in-the-loop controls
Customize state
Time travel

Timestamp:

00:00:00 Introduction And Agenda
00:03:37 Langgraph Projects Structure
00:11:15 Building Blocks Of LAnggraph
00:23:47 Building a Basic Chatbot
00:49:05 Building Chatbot With Tools
01:18:37 ReACT Agent Architecture
01:26:22 Adding Memory In Langgraph
01:35:00 Streaming In Langgraph
01:44:58 Human Feedback In the loop
01:52:42 MCP Server Scratch Implementation