Join My Community to Level Up ➡ https://www.skool.com/earlyaidopters/about

🚀 Gumroad Link to Assets in the Video: https://markkashef.gumroad.com..../l/agenticdesignpatt

Work With Us: https://www.promptadvisers.com/

🎬 Core Video Description

What if you could learn the 20 agentic design patterns the pros actually use—without wading through a 400-page manual? In this practical 63-minute breakdown, I translate a Google engineer’s book into plain English and show you exactly how to apply each pattern in real workflows. You’ll see where each architecture shines, the tradeoffs that matter (cost, latency, failure modes), and quick ways to combine patterns for robust systems—so you can ship reliable agents faster and avoid over-engineering rabbit holes. Expect concise TL;DRs, labeled visuals, and a free repo packed with diagrams, ASCII flows, and Mermaid files to help you implement immediately.

[Main Topic]: A practical, plain-English guide to 20 agentic design patterns

[Key Benefits or Outcomes]: Understand when/why to use each pattern, reduce hallucinations and cost, add safety/quality gates, route work across models/agents, and ship production-ready automations with fewer retries and rollbacks

[Tools or Techniques Covered]: Prompt chaining, routing, parallelization, reflection loops, tool use, planning/orchestration, multi-agent collaboration, memory management, learning/feedback, goal tracking, exception handling, human-in-the-loop, RAG, inter-agent comms, resource-aware model routing, reasoning strategies (CoT/ToT, debate), evaluation & monitoring, guardrails/safety, prioritization, exploration/discovery

⏳ TIMESTAMPS:
00:00 – Intro: Why agentic patterns separate pros from beginners
00:36 – What you’ll get: TL;DRs, visuals, free resources
00:54 – Pattern 1: Prompt Chaining (assembly-line steps & validations)
05:42 – Pattern 2: Routing (smart triage to specialist agents)
09:30 – Pattern 3: Parallelization (split, normalize, merge)
13:16 – Pattern 4: Reflection (critic → revise → pass)
15:51 – Pattern 5: Tool Use (discover, authorize, execute, fallback)
18:19 – Pattern 6: Planning (milestones, dependencies, constraints)
20:49 – Pattern 7: Multi-Agent Collaboration (manager + roles + shared memory)
23:45 – Pattern 8: Memory Management (short/episodic/long-term, retrieval)
26:42 – Pattern 9: Learning & Adaptation (feedback → prompts/policies/tests)
29:17 – Pattern 10: Goal Setting & Monitoring (KPIs, drift, course-correct)
31:34 – Pattern 11: Exception Handling & Recovery (classify, backoff, fallbacks)
34:11 – Pattern 12: Human-in-the-Loop (review cues & approvals)
36:01 – Pattern 13: Retrieval (RAG): parse, chunk, embed, rerank
38:14 – Pattern 14: Inter-Agent Communication (protocols, IDs, expiry)
43:08 – Pattern 15: Resource-Aware Optimization (route by cost/complexity)
46:35 – Pattern 16: Reasoning Techniques (CoT, ToT, self-consistency, debate)
49:57 – Pattern 17: Evaluation & Monitoring (golden sets, SLAs, drift)
52:44 – Pattern 18: Guardrails & Safety (PII, injection, sandboxing)
56:04 – Pattern 19: Prioritization (value×effort×urgency×risk, re-order)
59:29 – Pattern 20: Exploration & Discovery (map space, cluster, probe)
62:17 – Free Repo & Diagrams (ASCII + Mermaid)
63:08 – Final CTA: Share, comment, and join the community

#AgenticAI #AIAgents #PromptEngineering #RAG #LLMEngineering #Automation #MCP #AIDesignPatterns #Evaluation #Guardrails #Routing #Parallelization #Reflection #AIforBusiness #WorkflowAutomation

Complete Agentic AI Course - AI Agents, RAG, Embeddings, Architectures, Framework, VectorDB & Memory #aiagents #agenticai #aiforbeginners #learnai #ai #2026

Join Our Discord Server :- https://discord.gg/2abyjxr4v6

🤖 Learn Agentic AI — AI Agents Explained (A Complete Powerful Course to Master It)

This is the most comprehensive Agentic AI course you'll find — completely FREE. We go from the absolute ground up — AI basics, LLMs, the Agent loop — all the way to Vector Databases, RAG, MCP, Multi-Agent Systems, and real architectures being used in production RIGHT NOW in 2026.

Whether you're a total beginner or someone who's used AI tools but never truly understood them — this video is built for you.

📌 WHAT YOU'LL LEARN IN THIS VIDEO:

✅ What AI actually is — and the 3 eras that brought us here
✅ How Transformers & the Attention Mechanism changed everything (2017 → now)
✅ How LLMs generate text — tokens, temperature, and context windows explained
✅ The EXACT difference between a Chatbot and a true AI Agent
✅ The Core Agent Loop (Perceive → Think → Act → Observe) broken down simply
✅ The ReAct Pattern — how agents reason before every single action
✅ Tools & Function Calling — how agents interact with the real world
✅ Memory systems — Sensory, Working, Episodic, and Semantic memory
✅ RAG (Retrieval-Augmented Generation) — the most important AI dev concept right now
✅ Vector Databases — Pinecone, Weaviate, Qdrant, Chroma & when to use each
✅ Embeddings & Semantic Search — the math made simple
✅ MCP (Model Context Protocol) — the "USB port" for AI, explained clearly
✅ 6 Agentic Architectures — ReAct, Chain-of-Thought, Plan-and-Execute, Tree of Thoughts, Reflexion & LATS
✅ Multi-Agent Systems — Orchestrator-Worker, Parallel, Debate Pattern & more
✅ Frameworks — LangChain, LlamaIndex, AutoGen, CrewAI
✅ Advanced patterns — Self-Modifying Agents, Stochastic Consensus, Iceberg Technique, 60-30-10 Cost Rule
✅ Safety & Guardrails — Prompt Injection, Scope Creep, Human-in-the-Loop & more
✅ Real-world use cases across Enterprise, Healthcare, Finance, Education & Dev
✅ A step-by-step 10-week roadmap to go from zero to building production agents

💡 Key Insight:

A chatbot reacts. An AI Agent plans, decides, acts, and adapts — all on its own. Understanding that difference is worth everything in today's AI landscape.

Related Topics :-

Agentic AI, AI agents explained, how AI agents work, what is an AI agent, AI agents 2026, learn AI agents, build AI agents, agentic AI course for beginners, AI agents for beginners, master AI agents, complete agentic AI course, AI Fundamentals, LLMs explained, AI agent loops, AI memory systems, AI tools, RAG explained, vector databases explained, Embeddings explained, MCP explained, AI architecture patterns, agentic architecture, AI frameworks and advanced patterns, multi-agent systems, AI safety, AI guardrails, tejas ai, tejas_aiz, ai 2026, 2026 etc.

- Tejas AI

🔥Professional Certificate Program in Agentic AI & Multi-Agent Systems -https://www.simplilearn.com/agentic-ai-professional-certificate-course?utm_campaign=kRIz-j5IltQ&utm_medium=DescriptionFirstFold&utm_source=Youtube


Following are the topics covered in this tutorial on Agentic AI Course 2026 :

00:00:05 - Introduction to Agentic AI and Multi-Agent Systems
00:01:35 - Quiz Question
00:01:55 - What Is Agentic AI And How It Works
00:02:00 - Generative AI, LLMs, RAG, And AI Workflows
00:02:39 - Practical Projects And Hands-On Learning Approach
00:03:11 - Tools And Frameworks For Building AI Systems
00:03:37 - Career Opportunities And Who Should Learn Agentic AI
00:04:03 - Learning Support, Mentorship, And Career Assistance
00:04:23 - Latest Trends In Generative AI And Agentic AI
00:04:39 - Outro

This video on Agentic AI and Multi-Agent Systems by Simplilearn explains how modern AI systems are moving beyond simple question-answering tools to intelligent systems that can plan tasks, use tools, and complete workflows. In this video, you will learn the fundamentals of Agentic AI, AI agents, multi-agent systems, LLMs, RAG, and workflow automation. The session covers how AI agents are designed, how different AI components work together, and how technologies like LangChain, Crew AI, AutoGen, MCP, and AI automation tools are used. You will also explore practical applications, real-world projects, and enterprise use cases of agentic workflows. This video is useful for developers, product managers, technology professionals, AI enthusiasts, and beginners who want to understand the future of AI systems. By the end of this video, you will understand how intelligent AI workflows are created and how skills in Agentic AI can support future career opportunities.



Related Videos:
✅ 1. https://youtu.be/2R-niMsB0QY?si=uoG8eqArZkZ8M88i
✅ 2. https://youtu.be/2MHjpOHQNNQ?si=hBzT0uI-B6ESuEU4
✅ 3. https://youtu.be/uFTrtMw8cIM?si=2GB6EM9HxTSQGVUL
✅ 4. https://www.youtube.com/live/s....TzbTqjsmIw?si=7VwvBU
✅ 5. https://youtu.be/8ykgB9lKA3M?si=UtUcxTb5grCmo7Fp

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

Host Your AI Agent with Hostinger 👉 https://www.hostg.xyz/SHJwM

In this video, I explain how AI agents work, then show how to deploy Hermes on a Hostinger VPS, connect it to OpenRouter and Telegram, give it scheduled jobs, use persistent memory and learned skills, and keep the agent secure and inexpensive while it runs around the clock.

Grab The AI Agent Build Kit 👉 https://youri-van-hofwegen.kit.com/35ac6b24d1

for inquiries: roboverse at youripartnerships.com

#Roboverse

GStack is an open-source toolkit built by YC President & CEO Garry Tan that turns Claude Code into an AI engineering team — with skills for office hours, design, code review, QA, and browser testing.

Use it with Claude Code or Codex or Cursor. It's free and open source: https://github.com/garrytan/gstack

In this video, Garry walks through how GStack works, starting with Office Hours, a skill modeled after real YC partner sessions that pressure-tests your idea before you write a line of code. He demos it live, going from idea through adversarial review, design mockups, and automated QA in a single session.

00:00 – AI Just Changed Coding Forever
00:09 – From YC to Building With AI
01:07 – Why AI Coding Feels So Different
02:45 – Turning AI Into a Real Team (GStack)
03:45 – Let’s Build an App Live
05:23 – The Question That Kills Most Ideas
07:13 – This Idea Just Got Way Bigger
08:38 – The “Feels Illegal” AI Hack
10:50 – Upgrading the Idea in Real Time
12:44 – Breaking + Fixing the Plan
14:25 – AI Designs the App
16:59 – The Full System Explained
18:00 – Running Multiple AI Engineers
20:00 – Shipping 10x Faster
21:20 – The Only Thing That Matters Now

Apply to Y Combinator: https://www.ycombinator.com/apply
Work at a startup: https://www.ycombinator.com/jobs

https://www.conductor.build/

Get started with Snyk's MCP today: https://snyk.plug.dev/ZlOH7qR
---

Opencode is different, because it's built for YOU. It's not affiliated with one provider or another, and let's you use its internal router to make sure you get the latest and greatest, always.

✅ Build a Second Brain With Neovim in Under 90 Minutes: https://learn.dotb.sh/courses/second-brain-neovim

✅ Zero To KNOWING Kubernetes in Under 90 Minutes:
https://learn.dotb.sh/courses/k8s-from-scratch
❗Use `devopstoolbox20` at checkout for 20% off!

⌨️ The keyboard on this video is the Dygma Defy: http://dygma.com/DEVOPSTOOLBOX
🎹 Keycaps on my Defy are made by 3DKeyCaps: https://3dkeycap.com/?ref=vxdqqmmo
⚡ Tech I use: https://kit.co/omerxx/my-battle-station

Deploy your site using here.now completely for free, copy the prompt for your agent here! https://here.now/r/twt

In this complete guide, I walk you through the best local agentic coding workflow using LM Studio and VS Code - no API keys, no cloud, no cost per token.You'll learn how to install and configure LM Studio, choose and run the right local models for coding, and connect everything to VS Code so you can use AI-powered agents directly in your editor. This is the exact setup I use for local AI coding that actually works.

Want to make real money with coding? I share high-signal insights on careers, monetization, and leverage in my free newsletter. Join here and get my guide How to Make Money With Coding instantly: https://techwithtim.net/newsletter

🚀 Tools I Use
Get 10% off with code techwithtim
Openclaw setup: https://www.hostinger.com/techwithtim
VPS setup: https://www.hostinger.com/techwithtim10
Wispr Flow (Best AI Dictation): https://ref.wisprflow.ai/TechWithTim-jun26

🎞 Video Resources 🎞
Presentation in This Video: https://clever-mirage-d82v.here.now/
LM Studio Download: https://lmstudio.ai/
VSCode Download: https://code.visualstudio.com/

⏳ Timestamps ⏳
00:00 | Overview
00:46 | What are Local Models
02:30 | VRAM & Computer Hardware
05:18 | Memory & Model Speed
06:39 | How to Pick a Model
11:10 | Trying It Live
12:23 | LM Studio Setup & Model Download
22:15 | Using Local Models in VS Code
28:43 | Autocomplete Model Setup

Hashtags
#LMStudio #AICoding #AIDevTools

UAE Media License Number: 3635141

Learn more about AI in the SDLC here → https://ibm.biz/~naNTyKNWO

AI promises speed, but where are the real gains? Cedric Clyburn breaks down why productivity stalls across the software development lifecycle despite faster coding. Learn how redesigning SDLC workflows with AI agents improves outcomes, testing, and delivery.

AI news moves fast. Sign up for a monthly newsletter for AI updates from IBM → https://ibm.biz/~nBt0HFgxR

#aifordevelopers #sdlc #aicodingtools #softwaredevelopment

AI Agents are everywhere from automated emails to running deep research, striving to leverage the full power of LLMs. And for many of us, it boils down to one question: To code or not to code? Is the future of AI in the hands of seasoned developers building agents with LangGraph, ADK, Agno? Or with everyone else, using drag & drop in a no-code platform like n8n, Zapier, Make?

In this video, I will discuss which approach wins.

Build your first no code AI agent: https://youtu.be/Jekzc6BM5_w
LangGraph tutorial: https://youtu.be/CnXdddeZ4tQ
CrewAI tutorial: https://youtu.be/G42J2MSKyc8


Do you want to learn technology from me? Check https://resources.codebasics.io/6kp3C8 for my affordable video courses.

Need help building software or data analytics/AI solutions? My company https://www.atliq.com/ can help. Click on the Contact button on that website.

🎥 Codebasics Hindi channel: https://www.youtube.com/channe....l/UCTmFBhuhMibVoSfYo

#️⃣ Social Media #️⃣

🧑‍🤝‍🧑 Discord for Community Support: https://discord.gg/r42Kbuk
📸 Codebasics' Instagram: https://www.instagram.com/codebasicshub/
📝 Codebasics' Linkedin : https://www.linkedin.com/company/codebasics/

------

📝 Dhaval's Linkedin : https://www.linkedin.com/in/dhavalsays/
📝 Hem's Linkedin: https://www.linkedin.com/in/hemvad/

📽️ Hem's Instagram for daily tips: https://www.instagram.com/hemvadivel/
📸 Dhaval's Personal Instagram: https://www.instagram.com/dhavalsays/

🔗 Patreon: https://www.patreon.com/codeba....sics?fan_landing=tru

Learn more about AI Code-Generation Software here → https://ibm.biz/BdpBwX

Is AI-assisted coding the future? 🤔 Cedric Clyburn explores spec-driven development, a game-changing approach that combines LLMs with software development best practices. Learn how it differs from vibe coding, integrates SDLC principles, and improves coding workflows with requirements-driven precision. 🚀

AI news moves fast. Sign up for a monthly newsletter for AI updates from IBM → https://ibm.biz/BdpBwH

#aicoding #llms #aiassisted

Game development veteran, creator of libGDX, and 17-year open-source contributor Mario Zechner tells the story of how he ended up building pi, his own minimal, opinionated terminal coding agent.

It started in April 2025 when Peter Steinberger and Armin Ronacher (Flask, Sentry) dragged him into an overnight AI hackathon. Within weeks, Mario was hooked on Claude Code — until he wasn't. There was feature bloat, hidden context injection that changed daily, the infamous terminal flicker, and zero extensibility for power users.

He then surveyed the alternatives — Codex CLI, Amp, OpenCode... Eventually, he came across Terminus — an agent that gives the model nothing but a tmux session and raw keystrokes. If that's enough for the model to perform, what are all those extra features actually doing?

Mario's thesis: we're still in the "messing around and finding out" stage, and coding agents need to become more malleable so developers can experiment faster.

Pi is his answer: four tools (read, write, edit, bash), the shortest system prompt of any major agent, tree-structured sessions, full cost tracking, hot-reloading TypeScript extensions, and nothing injected behind your back. No MCP, no sub-agents, no plan mode — but all of it buildable in minutes through extensions.

The community has already shipped pi-annotate (visual frontend feedback), pi-messenger (a multi-agent chatroom), and someone even got Doom running. On TerminalBench, pi with Claude Opus 4.5 landed right behind Terminus — before it even had compaction.

🔗 LINKS & RESOURCES
pi coding agent: https://pi.dev
Mario Zechner: https://mariozechner.at
Peter Steinberger / OpenClaw: https://github.com/steipete
Armin Ronacher: https://lucumr.pocoo.org
Claude Code: https://docs.anthropic.com/en/docs/claude-code
Aider: https://aider.chat
OpenCode: https://github.com/anthropics/opencode
Amp (Sourcegraph): https://sourcegraph.com/amp
TerminalBench: https://terminalbench.com
Ghostty: https://ghostty.org
Vouch: https://github.com/mitchellh/vouch
libGDX: https://libgdx.com

AI Engineer London is a community meetup for engineers and founders building with AI, covering everything from agent frameworks and RAG pipelines to LLMs in production. Each event features technical talks, live demos, and hands-on networking. This talk was recorded at AI Engineer London #10, hosted by Tessl, in collaboration with AI Engineer London.

AI ENGINEER LONDON
📅 Events: https://lu.ma/aiengineerlondon
💼 LinkedIn: https://linkedin.com/company/a....i-engineer-london-me

📚 MASTRA RESOURCES
Mastra: https://mastra.ai
Learn Mastra in the world's first MCP-Based Course: https://mastra.ai/course
Principles of Building AI Agents (Book): https://mastra.ai/books/principles-of-building-ai-agents
Patterns for Building AI Agents (New Book): https://mastra.ai/books/patterns-of-building-ai-agents

MASTRA?
Mastra is an open-source TypeScript framework designed for building and shipping AI-powered applications and agents with minimal friction. It supports the full lifecycle of agent development—from prototype to production. You can integrate it with frontend and backend stacks (e.g., React, Next.js, Node) or run agents as standalone services. If you're a JavaScript or TypeScript developer looking to build an agentic or AI-powered product without starting from first principles, Mastra provides the scaffolding, tools, and integrations to accelerate that process.

📑 CHAPTERS
00:00 Intro
02:17 The history of coding agents: ChatGPT → Copilot → Aider → Claude Code
04:52 What Claude Code got right — and where it became a spaceship
06:04 Claude Code Drawbacks
09:39 Claude Code Alternatives
11:38 OpenCode's compaction problem and prompt cache busting
12:51 Why LSP feedback mid-edit is a terrible idea
14:26 OpenCode's architecture issues and security vulnerability
16:06 TerminalBench and Terminus
18:13 Mario's Two Theses
19:08 Introducing pi — strip everything, build a minimal extensible core
20:01 The system prompt
21:18 What's not in pi — and what you build instead
22:40 Extensions: custom tools, custom UI, hot reloading
24:00 Community extensions
24:59 Tree-structured sessions, cost tracking, HTML export
25:33 TerminalBench results
25:54 Open source under siege and human verification

Setting up a local AI model can be intimidating, which is why I created this video. In this video I will show you everything you need to know about setting up local AI models from the basics of how local AI models work, to how to optimize local AI models for your hardware, and even how to use these local AI models in real world agentic environments.


📚 Materials/References:

LM Studio: https://lmstudio.ai/
Hugging Face: https://huggingface.co/
Pi Coding Agent: https://pi.dev/


🌎 Find Me Here:

My Blog: https://blog.webdevsimplified.com/
My Courses: https://courses.webdevsimplified.com/
Patreon: https://www.patreon.com/WebDevSimplified
Twitter: https://twitter.com/DevSimplified
Discord: https://discord.gg/7StTjnR
GitHub: https://github.com/WebDevSimplified
CodePen: https://codepen.io/WebDevSimplified


⏱️ Timestamps:

00:00 - Introduction
01:08 - How Local AI Works
05:33 - Picking Models
11:46 - Configuring Your Model
21:57 - Using Local Models In Your IDE
34:17 - Using Local Models With Copilot
38:17 - Using Local Models With Pi
40:55 - Comparing Local Models to Anthropic Models


#LocalAI #WDS #AgenticCoding

Warp is free to try but for a limited time, you can try Warp Pro free for 7 days with 2,500 AI credits - no card required. Use my link https://go.warp.dev/forrestytcow

Everyone seems to be using AI to code now, and in this video, I'm showing you how to actually code with AI - not vibe code. Because there's a huge difference between software developers who strategically guide AI, and vibe coders who just let it take the wheel.

0:00 Everyone’s Coding with AI
0:32 Stop Repeating Yourself
1:45 Prompting is Important
4:21 The Software Development Workflow
6:42 Pair Programming
7:57 Implement Multiple Agents
9:25 git worktree
9:57 Extra Stuff AI Can Help With
11:21 Pull the Plug
11:58 Always Remember This

This video is sponsored by Warp.

If you're a developer, sign up to my free newsletter Dev Notes 👉 https://www.devnotesdaily.com/

If you're a student, checkout my Notion template Studious: https://notionstudent.com

Don't know why you'd want to follow me on other socials. I don't even post. But here you go.
🐱‍🚀 GitHub: https://github.com/forrestknight
🐦 Twitter: https://www.twitter.com/forrestpknight
💼 LinkedIn: https://www.linkedin.com/in/forrestpknight
📸 Instagram: https://www.instagram.com/forrestpknight

Learn how to use AI tools to become more productive as a developer.

You will master AI pair programming and agentic terminal workflows using top-tier tools like GitHub Copilot, Anthropic's Claude Code, and the Gemini CLI. The course also covers open-source automation with OpenClaw, teaching you how to set up a highly customizable, locally hosted AI assistant for your development environment. Finally, you will learn how to maintain high code quality and streamline your team's workflow by integrating CodeRabbit for automated, AI-driven pull request analysis.

Some of this course is based on this article from Mrugesh Mohapatra: https://www.freecodecamp.org/n....ews/how-to-become-an

CodeRabbit provided a grant to make this course possible.
Try out CodeRabbit: https://coderabbit.link/fcc

Contents
- 0:00:00 Introduction to AI-Assisted Development
- 0:00:42 Core Fundamentals: Tokens, Context Windows, and Hallucinations
- 0:05:48 When to Use AI vs. When to Code Manually
- 0:06:57 Setting Up GitHub Copilot in VS Code
- 0:09:42 Copilot Pricing and Plans
- 0:10:18 First Steps: Ghost Text and Code Completions
- 0:13:40 Pro Tip: The Neighboring Tabs Trick
- 0:15:52 Practice Exercise: Building a To-Do App
- 0:17:15 Interaction Modes: Ask, Edit, and Agent Mode
- 0:21:44 Agent Mode: Building a Full REST API Autonomously
- 0:24:52 Repository Customization with Instruction Files
- 0:26:34 Using Participants and Slash Commands
- 0:28:34 Automated Code Reviews with CodeRabbit
- 0:30:57 Setting Up CodeRabbit for GitHub Repositories
- 0:32:03 Simulating Real-World PR Reviews and Security Fixes
- 0:37:33 Chatting with AI Directly in Pull Requests
- 0:41:12 Configuring CodeRabbit Behaviors (.yaml)
- 0:41:58 Local Reviews via the CodeRabbit CLI
- 0:48:13 Powerful Terminal AI: Claude Code vs. Gemini CLI
- 0:49:12 Claude Code: Reasoning, Thinking Modes, and Fixes
- 0:53:02 Gemini CLI: Million-Token Context and Multimodal Features
- 0:57:01 OpenClaw: Your Open-Source Personal AI Assistant
- 1:01:01 Automating Tasks with Cron Jobs and Desktop Actions
- 1:03:33 Orchestrating Your AI Workflow
- 1:08:27 Model Context Protocol (MCP): Giving AI Real-World Tools
- 1:12:40 AI Code Quality and Security Essentials
- 1:15:03 The Formula for Better Prompt Engineering
- 1:16:13 Course Recap and Final Resources

Try out Junie: https://jb.gg/JunieAI-coding

AI is alright if you have a brain. AI can give good sloppy.

This video is sponsored by JetBrains.

// USEFUL AI RESOURCES //
MCPs: https://mcpmarket.com/server
AGENTS.MD: https://agents.md/#examples
Claude prompting best practices: https://platform.claude.com/do....cs/en/build-with-cla
OpenAI prompting best practices: https://help.openai.com/en/art....icles/6654000-best-p

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