How-to & Style
Local AI models/LLMs are the future. Here's how they work and how to set them up on ANY device.
FULL local LLM bootcamp in the Vibe Coding Academy: https://vibecodingacademy.dev
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OpenClaw:
https://openclaw.ai/
Hugging Face:
https://huggingface.co
Timestamps:
0:00 Intro
0:50 What are local models
6:16 Which computer you need
11:30 Which local models to use
13:54 Local models demos
In this video, I show you how to run a fully unrestricted and private, offline LLM directly on your own machine. If you are a cybersecurity professional, Red Teamer, or Pentester, you know the pain of getting the "I cannot help with that" error when working on legitimate security simulations or code analysis. I also show you how to set up a full private AI with RAG!
https://docs.privategpt.dev/ov....erview/welcome/intro
https://dev.to/docteurrs/insta....lling-privategpt-on-
*00:00* - Introduction to Private AI and Setup Guide
*00:56* - Understanding AI Models and Exploring Hugging Face
*01:24* - Installing Ollama for Local AI Models
*02:00* - Running Your First Local AI Model
*04:15* - Understanding what is RAG in AI
*05:05* - Setting Up Windows Subsystem for Linux (WSL) for AI
*05:24* - Private AI Setup and Installation
*10:04* - Fine-Tuning AI with Your Own Data
*10:37* - Setting Up Your Own Private GPT with RAG
Setting up Private AI on your computer
Offline AI models like ChatGPT
Enhancing job performance with Private AI
Fine-tuning AI models for specific needs
Running AI without internet
Privacy concerns with AI technologies
Surviving a zombie apocalypse with AI
Connecting knowledge bases to Private GPT
Retrieval Augmented Generation (RAG) with AI
Installing WSL for AI projects
Running LLMs on personal devices
VMware deep learning VMs
Customizing AI with VMware and Nvidia
Private GPT project setup
Leveraging GPUs for AI processing
Consulting databases with AI for accurate answers
Running local private AI in companies
Guide to private AI
Future of technology with private and fine-tuned AI
Here is the actual uncensored prompt explaining it: https://www.linkedin.com/posts/maddyhivemind_been-messing-around-with-local-uncensored-activity-7418024051081797632-myHZ?utm_source=share&utm_medium=member_desktop&rcm=ACoAADXr8m0B6M1r3WOYZgX9cWPwo96f9XA-XPk
Video Idea: https://www.youtube.com/@UC9x0AN7BWHpCDHSm9NiJFJQ
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Local AI finally got good. Google's new Gemma 4 runs on a MacBook (and even on a phone) and performs about as well as the best frontier model from a year ago. Free & Offline!
In this video, I break down why local AI suddenly matters, walk through Gemma 4's real specs (not the cherry-picked charts), and test it on my M4 Pro MacBook using OpenCode, OpenClaw, and the Pi coding agent. I also rented an 80GB GPU on RunPod to try the biggest version of Gemma 4 on a more complex coding task - my own YouTube Copilot app.
If you've been wondering if it's finally time to take local AI more seriously - this video is worth a watch!
⏱ Timestamps
00:00 - How we got here
01:30 - Gemma 4
03:30 - Testing (OpenCode / OpenClaw / Pi Agent)
08:00 - Future Takeaway
🔗 Tools mentioned:
Ollama — https://ollama.com
LM Studio — https://lmstudio.ai
Google AI Edge Gallery (iOS / Android)
OpenCode, OpenClaw, Pi coding agent
RunPod (for renting GPUs)
💬 Got a more powerful machine? Running your own local setup? Drop your experience in the comments - I'd love to hear what's working for you.
👍 If this helped, smash that like button and subscribe for more hands-on AI experiments.
#LocalAI #Gemma4 #Ollama #OpenSourceAI #LLM #AI #GoogleGemma
OpenClaw can be run for free forever using local ai models through Ollama. Models like qwen 3.5 can be setup to connect directly to Open Claw, so you won't be charged per token use like with Clade or ChatGPT.
In this video I'll show you how to setup Local Models, how to connect them to OpenClaw, and how to setup things like MCP to reduce the cost of doing large context queries or API calls.
🔶 Install MCP Tools via Zapier:
https://bit.ly/4ccnm0G
🦙 Install Ollama Setup
https://ollama.com/
The model I used for openclaw here was Qwen 3.5 however there are many other models you can use from Google, Meta, etc.
#openclaw #ollama #ai
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To run openclaw local models, you need to install openclaw local llm setup. This lets openclaw llm charge you only what electricity you use for your mahcine!
Llama.cpp Web UI + GGUF Setup Walkthrough and Ollama comparisons.
Check out ChatLLM: https://chatllm.abacus.ai/ltf
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Join this channel to get access to perks:
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⏱️ Chapters
00:00 – Local LLMs, many stacks
01:05 – Building from source
04:15 – Picking a GGUF model
07:56 – New Llama.cpp Web UI
09:06 – Ollama UI & speed check
10:44 – Ollama’s concurrency limit
11:48 – Llama.cpp parallel chats
#llm #llamacpp #macbook
CLI tools and MCP servers are different avenues in which agents can interact with external tools. See what option works best for your projects!
Subscribe to Google for Developers → https://goo.gle/developers
Speaker: Jasmyne Roberts
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Run Claude Code completely offline with Ollama — no usage limits, no data leaving your machine. Full setup walkthrough plus the local models that actually work.
00:00 Intro
00:39 What Claude Code Does
01:28 Why Pair It With Ollama
02:09 Install & Connect (Step-by-Step)
03:25 Critical Context Window Tip
04:14 Best Local & Cloud Models
04:58 Real Use Cases
05:38 Hidden /loop Automation Feature
06:04 Setup Tips to Avoid Frustration
06:50 Final Thoughts
Ready to become a certified watsonx AI Assistant Engineer? Register now and use code IBMTechYT20 for 20% off of your exam → https://ibm.biz/Bdpsiy
Learn more about Large Language Models (LLMs) here → https://ibm.biz/BdpsiS
Your laptop, your AI. 💻 Cedric Clyburn explains what Llama.cpp is and how this powerful inference engine enables local LLMs with full data privacy. Discover model quantization, RAG, and how to optimize AI for small devices.
AI news moves fast. Sign up for a monthly newsletter for AI updates from IBM → https://ibm.biz/Bdpsim
#llm #llama #inference #localai
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Community with All Resources 📦: http://ailabspro.io/
Video code: V43
Browser agents are broken. Not because the models are bad, but because the entire internet was built for human eyes, not machines. WebMCP changes the approach entirely: instead of making agents better at reading websites, it makes websites better at talking to agents.
We tested it with Claude Code, built both the declarative and imperative implementations, and ran into real limitations along the way.
Here's what actually works, what doesn't, and what Google is quietly setting up with this "open standard."
Build meeting bots and desktop recording apps in hours - https://www.recall.ai/fireship gets you $100 in free credits
In today's we'll look at 7 open source AI projects you've never heard of that will help you whip your agents into shape and build highly effective slop pipelines.
#coding #programming #ai
🔖 Topics Covered
- Agency Agents
- PropmtFoo
- MicroFish
- NanoChat
- Impeccable
- Heretic
- OpenViking
Want more Fireship?
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🧠 Courses: https://fireship.dev
In this video, we break down the key differences between Large Language Models (LLMs) and AI Agents.
Large Language Models like ChatGPT generate text-based responses based on training data. AI agents, on the other hand, use Large Language Models along with additional tools and capabilities to take autonomous actions - making them more versatile for achieving specific objectives.
#LLMs #AIAgents #AI #MachineLearning #AI #ArtificialIntelligence #ChatGPT #KaneAI #Claude #LamaAI#GitHubCopilot
𝐊𝐧𝐨𝐰 𝐦𝐨𝐫𝐞:
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Learn more about AI Agent Use Cases here → https://ibm.biz/Bdexeb
AI agents can reason, plan, and act autonomously to achieve complex goals. 🤖 Martin Keen highlights 10 use cases, including IoT in agriculture, RAG for content creation, and multi-agent disaster response. Explore how AI agents are transforming industries and solving real-world problems. 🚀
AI news moves fast. Sign up for a monthly newsletter for AI updates from IBM → https://ibm.biz/Bdexep
#aiagents #iot #aiusecases #multiagentsystems
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
👉 In this video, I will show you how to set up and use AnythingLLM on your Android phone to run a fully local AI agent that works offline without any cloud dependency. This powerful mobile app brings the complete AnythingLLM experience to your pocket with on-device small language models, web scraping tools, document processing, and cross-app integrations like calendar and email management.
💬 $15 Wispr Flow AI Credit: https://wisprflow.ai/r/JOHN220
🔍 $10 Discount Perplexity: https://perplexity.ai/pro?referral_code=VA2J7SUD
🎁 200 Recraft AI Credit: https://www.recraft.ai/invite/F4F9qQGFxM
🔊 ElevenLabs TTS https://try.elevenlabs.io/1xnn0lhp3htg
👨💻 500 Windsurf AI Credit: https://windsurf.com/refer?ref....erral_code=664caa22f
🎙 Microphone: https://amzn.to/4jDkUC2
🦾 Boom arm: https://amzn.to/40MzgHx
💻 Web hosting: https://asapguide.com/hosting
❤️ Subscribe: https://www.youtube.com/@asapg....uide?sub_confirmatio
🧠 AI Tools I Used: https://asapguide.com/ai-tools/
I built a fully functional, talking BMO from Adventure Time using a Raspberry Pi 5 and local AI models.
In this video, I break down the hardware I used, designing and assembling a 3D printed enclosure, and using Ollama to build a battery-powered, local, voice activated AI agent.
0:00 Intro
2:25 Hardware breakdown
4:10 Creating the enclosure
7:15 Software deep-dive
15:49 Assembling BMO
18:55 Full AI Agent demo
21:54 Closing thoughts
🔧 Hardware:
- Raspberry Pi 5 (16GB)
- Raspberry Pi Camera Module v2
- Pimoroni NVMe Base Duo
- Geekworm X1203 5V UPS shield
- Freenove 5 Inch Touchscreen Monitor
- Adafruit Feather 32u4 Basic Proto
- Mini USB Microphone
- Mini External USB 2.0 Speaker
- 6mm momentary switches x 7
- 5x2mm round magnets x 16
- 3.7V lithium ion battery
🔧 The Tech Stack:
- LLM & VLM (Brain): Gemma 3 + Moondream 2 (via Ollama)
- STT (Ears): Open Wake Word + Whisper
- TTS (Voice): Piper TTS
🔧 STL & PCB files: https://www.printables.com/mod....el/1582055-bmo-from-
🔧 GitHub repository: https://github.com/brenpoly/be-more-agent
🛡️ A Note on Safety (Please Read):
I have done my best to ensure this code is safe and clean. However, you should never blindly trust public code. As much as you may like this project, I'm still a stranger from the internet with one viral video 😅. Stay curious, but stay safe!
🔒 Good Security Practice:
- Always scan files before running them.
- Read through the Python scripts (.py files) to understand what they are doing before you execute them.
- If you are new to this, ask a friend (or an AI!) to help you review the code first.