👉 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.

🎁 Get my FREE local AI projects: https://zenvanriel.com/open-source
⚡ Master AI and become a high-paid AI Engineer: https://aiengineer.community/join

Video references:
- LM Studio: https://lmstudio.ai/
- Continue (VS Code Extension): https://docs.continue.dev/
- Kilo Code: https://kilocode.ai/
- Claude Code Router: https://github.com/musistudio/claude-code-router
- Demo Auction Repository: https://github.com/AI-Engineer....-Skool/ai-coding-auc

The future of AI coding is local - no cloud dependencies, no API costs, and full control over your development environment.
Most engineers are completely dependent on cloud APIs, but 2026 is the year where running your own AI coding agents becomes essential.
The problem? Most local AI tutorials show toy examples that collapse with real codebases.
This masterclass shows you how to build a local AI coding setup that actually works for production projects.

What You'll Learn:
- Why VRAM is your usual bottleneck and how to calculate what models you can run
- The context window limitation that breaks 90% of local AI coding attempts
- How to select models that balance parameters, speed, and memory constraints
- Real performance testing: 20B vs 32B parameter models with actual repositories (not todo apps)
- Setting up LM Studio, Continue, Kilo Code, and even Claude Code with local models
- Advanced optimizations (Flash Attention, KCache quantization) to maximize your context window
- Why Mac devices with unified memory might be your best budget option

Timestamps:
0:00 Why local AI coding is your advantage
1:12 AI GPU explanation (VRAM & Speed)
2:46 Selecting models in LM Studio
6:30 Loading in open source GPT model
11:15 Generating Python code locally
16:07 Loading a bigger 32B model
19:33 Comparing 32B with 20B
21:30 Exposing LLM API to Continue
22:58 Installing Kilo Code
25:04 Solving Context Window Limits
27:59 Checking AI Agent edit
29:28 Using local models with Claude Code Router
32:48 Running Claude Code Router
35:11 When to combine Local & Cloud AI

Why did I create this video?
Most local AI coding tutorials are dangerously optimistic - they show tiny demo apps and claim "look how fast it is!"
But the moment you work with a real codebase, everything breaks. Context windows overflow, GPUs choke on memory, and token generation crawls to a halt
This video shows real AI Coding use cases and how to set it all up.

Connect with me:
https://www.linkedin.com/in/zen-van-riel
https://www.skool.com/ai-engineer

Sponsorships & Business Inquiries: business@aiengineer.community

Hey everyone,

Recently in AnythingLLM Desktop, we merged in AI Agents. AI Agents are basically LLMs that do something instead of just replying. We support both tool-call-enabled models like OpenAI but have even now have a no-code way to bring AI agents to every open-source LLMs like with Ollama or LMStudio.

Now, with no code required, you can take any LLM and get automatic web scraping, web-browsing, chart generation, RAG memory, and summarization all autonomously and running locally.

If the future of AI is agents, AnythingLLM is where it is going to happen!.

Download AnythingLLM: https://useanything.com/download
Star on Github: https://github.com/Mintplex-Labs/anything-llm

Chapters:
0:00 Introduction to adding agents to Ollama
0:45 What is Ollama?
1:08 What is LLM Quantization?
1:28 What is an AI Agent?
2:54 How to pick the right LLM on Ollama
5:11 Pulling Ollama models and running the server
5:45 Downloading AnythingLLM Desktop
6:17 AnythingLLM - Initial setup
7:21 Sending our first chat - no RAG
8:22 Uploading a document privately
8:43 Sending a chat again but with RAG
9:10 How to add agent capabilities to Ollama
10:45 Add live web-searching to Ollama LLMs (Free)
11:41 Using agents in AnythingLLM demonstration
13:24 Agent document summarization and long-term memory
14:35 Why you should use AnythingLLM x Ollama
15:00 Star on Github, please!
15:06 Thank you

🛠️ Build your own AI Hub!! Run OpenWebUI on your own VPS with Hostinger (code networkchuck10): https://hostinger.com/networkchuckvps


AI is getting expensive…but it doesn’t have to be. I found a way to access all the major AI models– ChatGPT, Claude, Gemini, even Grok – without paying for multiple expensive subscriptions. Not only do I get unlimited access to the newest models, but I can also share it with my entire team, my wife, my kids– all while keeping full control over what they can access. Better security, more privacy, and a ton of features… this might be the best way to use AI.




RESOURCES:

🔧 Self-host OpenWebUI (On-Prem Installation Guide): https://youtu.be/JJ_0-pAOIEk
🌐 Turn OpenWebUI into a website (Domain + SSL Setup): https://youtu.be/BdH_yR_J3FQ


TIMESTAMPS:

0:00 - Intro
1:11 - The Plan (What is OpenWebUI?)
2:20 - The Cloud Option
5:02 - Install OpenWebUI
6:18 - Connecting ChatGPT API
9:06 - How Much Does This Cost?
13:06 - Using LiteLLM to do MORE








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fast and reliable unifi in the cloud: https://hostifi.com/?via=chuck


How to use AI without paying for subscriptions
Best AI alternatives to ChatGPT
Self-hosted AI tools you can use today
Stop paying for AI—here’s a better way
How to access all AI models for free
OpenWebUI tutorial: Run all AI models in one place
The best way to host AI yourself
How to set up OpenWebUI for AI access
Free ChatGPT alternative: Run AI yourself
Self-host AI and take control of your data
AI is too expensive—here’s how to fix that
How to use ChatGPT, Claude, and Gemini for free
The best self-hosted AI dashboard
How to host your own AI assistant
Secure and private AI hosting with OpenWebUI








#AI #OpenWebUI #SelfHostedAI

#NVIDIAPartner Learn how NVIDIA GeForce RTX PCs are powering AI: https://nvda.ws/3ZiqosD

AI Agents and locally run LLMs are revolutionizing workflows, offering unmatched privacy, speed, and cost efficiency. In this video, we explore how tools like AnythingLLM and NVIDIA GeForce RTX GPUs enable individuals to harness the next evolution of AI right on their personal machines. @NVIDIADeveloper @TimCarambat #AIonRTX

Business Inquiries:
contact@kgunderman.com

Timestamps:
----------------------------------------------------------------------------
Intro: (0:00)
Why People Are Running AI Locally: (0:50)
Why You Need a Good GPU To Run AI and AI Agents Locally: (1:31)
What are AI Agents?: (2:57)
How To Run AI Agents Locally: (4:13)
AI Agent Tutorial: (5:34)
Why AI Agents Are So Powerful: (10:40)
Outro: (12:50)

👉 Get access to our Fully Local SOTA RAG system and learn how to customize it, in our community https://www.theaiautomators.com/?utm_source=youtube&utm_medium=video&utm_campaign=tutorial&utm_content=sota-local-rag

Every document you send to ChatGPT or Claude is a potential security liability.

Legal contracts. Medical records. Financial statements. Client data. HR documents.

The cloud-based AI tools we rely on every day are brilliant—but they're also third-party services with their own data policies, training practices, and potential breach vulnerabilities.

For enterprises handling sensitive information, "trust me" isn't good enough.

In this tutorial, I'll show you how to build a production-grade multimodal RAG system that never leaves your infrastructure. Zero external API calls. Complete data sovereignty.

🔗 Get Started:
Our Forked Self Hosted AI Starter Kit: https://github.com/theaiautoma....tors/self-hosted-ai-

🎯 What You'll Learn:
✅ Complete air-gapped RAG architecture
✅ Processing PDFs, images, tables, and audio locally
✅ IBM Docling for zero-hallucination document extraction
✅ Ollama + N8N + Docker local stack
✅ GPU requirements for production deployment
✅ Network deployment for team access
✅ Standard vs VLM document processing pipelines
✅ Maintaining semantic structure without cloud APIs
✅ Real-world hardware cost breakdowns

🔗 Links:
Docling Documentation: https://www.docling.ai/
Ollama Vision Models: https://ollama.com/
Qdrant: https://qdrant.tech/

⏱️ Timestamps:
00:00 Local AI + Docling
07:58 n8n + Docker
11:58 Setup Local AI Starter Kit
16:30 Building the RAG Ingestion
34:39 Building the AI Agent
46:56 Creating the Agent Frontend
50:17 Deploying to Local Network

💬 Questions or Comments?
What's preventing you from deploying local AI in your organization? Hardware costs? Complexity? Performance concerns? Let me know below!

Check out the full video here:
https://youtu.be/XvbVePuP7NY?si=T3PZ_UB50MmL_tVO

#SmartHome #HomeAssistant #Rhasspy

New video Out - How to Create Expert AI Agent for FREE - NO Coding

https://youtu.be/6Sq6xD8UqT4?si=LmolNehXbBd_XMmg

Course Details:

🚀 All-in-One Masterclass is LIVE! 🔥

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Facing any Technical issues or need any further details?
WhatsApp / Call me at : 6304836880
-----------------------------
🚀 Want to build your own AI Agent without writing a single line of code? In this step-by-step tutorial, I’ll show you exactly how to create your first AI Agent using N8n – an easy-to-use, no-code AI platform.
Whether you’re a beginner, business owner, content creator, or student, this video is perfect for you. By the end, you’ll know how to:
✅ Set up your N8n account
✅ Create your first AI Agent from scratch
✅ Customize it without coding or technical skills
✅ Start using your AI Agent for automation, productivity, and business
🔥 No coding. No prior technical knowledge. Just drag, drop, and create powerful AI Agents in minutes!
👉 Don’t forget to Like 👍, Subscribe 🔔, and Share this video with anyone interested in AI tools.
--------------
Tools used:
n8n - https://n8n.io
serpapi - https://serpapi.com
openai workflow - https://platform.openai.com/docs/overview

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#ai #aiagents #aiagent #freeaitools #n8n #NoCode #FreeAI #AITutorial #ArtificialIntelligence #Automation #ProductivityTools #AIForBeginners #NoCodingRequired #AIForBusiness #CreateAIAgent #15MinuteAI #AIWithoutCoding #ai #aiagents

Ever wish ChatGPT would just answer your question — no refusals, no filters, and no data going to the cloud?
In this video, I’ll show you how to jailbreak ChatGPT (safely) by running your own private, uncensored AI model completely offline using LM Studio and Ollama.

You’ll learn:
• How large language models (LLMs) actually work — base models, system prompts, and fine-tuning
• What “jailbreaking” means and how people remove built-in restrictions
• How to download and run open-source models like GPT-OSS and Qwen locally
• How to edit system prompts to create custom personalities (Tony Robbins, RoastBot, etc.)
• Where chat data is stored on your Mac — and how to delete it for true privacy
• The difference between LM Studio and Ollama, and when to use each

🔗 Resources Mentioned
• LM Studio – https://lmstudio.ai

• Ollama – https://ollama.ai

• GPT-OSS 20B Model – https://huggingface.co/placeholder

• Qwen 3 Model – https://huggingface.co/placeholder

⚠️ Disclaimer: All examples shown are for research and educational purposes only. Do not use or distribute jailbroken models for harmful or illegal activity.

If you enjoy deep dives like this, subscribe for more AI tools, jailbreak experiments, and founder-friendly tech breakdowns.

Full courses + unlimited support: https://www.skool.com/ai-autom....ation-society-plus/a
All my FREE resources: https://www.skool.com/ai-autom....ation-society/about?
Apply for my YT podcast: https://podcast.nateherk.com/apply
Work with me: https://uppitai.com/

My Tools💻
FREE MONTH voice to text: https://get.glaido.com/nate
Code NATEHERK for 10% off VPS (annual plan): https://www.hostinger.com/vps/claude-code-hosting



Here are 4 AI Agents I Sold in 2025 that made me $23,000. In this video, I'll be showing you the 4 ai agents I sold in 2025 that made me $21,000 as a complete beginner. I'll be breaking down, what the 4 ai agents were, how I sold them, and how you can do it yourself with your own AI agents.


TIMESTAMPS
0:00 - Intro
0:26 - AI Agent 1
2:57 - AI Agent 2
4:58 - AI Agent 3
7:33 - AI Agent 4
9:09 - How to do it yourself


Sponsorship Inquiries:
📧 nate@smoothmedia.co

Connect with me:
https://www.linkedin.com/in/nateherkelman/
https://x.com/nateherk
https://www.instagram.com/nateherk/

Gear I Used:
Camera: Razer Kiyo Pro
Microphone: Blue Yeti USB

Building your own Private AI server is one of the most fun and exciting projects you can try out in your home lab environment. It is also a great way to learn more about AI, LLM models, and GPU passthrough configuration. Self-hosting and configuring your own AI environment is not as difficult as it might sound. Let's take a look at Proxmox, Ollama, and OpenWebUI, as well as Docker Desktop, GPU passthrough a my best practices and tips and tricks. I also show you what projects I am using Private AI servers for. Check it out!

GPU passthrough to LXC containers in Proxmox: https://www.virtualizationhowt....o.com/2025/05/how-to

Run Ollama with NVIDIA GPU in Proxmox VMs and LXC containers: https://www.virtualizationhowt....o.com/2025/05/run-ol

My 2025 Proxmox Build (affiliate links)
Minisforum BD795M – https://geni.us/bd795m
RackChoice 2U Micro ATX Compact – https://geni.us/rackchoice2u
Cooler Master MWE Gold 850 V2 – https://geni.us/coolermaster850
Crucial 128 GB 5600MT/sec RAM – https://geni.us/crucial128gbramkit
Noctua NH-L9i-17xx, Premium Low-Profile CPU Cooler – https://amzn.to/4hAKIwG
Crucial 96GB kit of DDR5 SODIMM memory kit – https://geni.us/noctuanhl9i17xx
Intel X520-DA2 10 GbE network adapter – https://geni.us/intelx520
Kingston 240 GB drive for boot – https://geni.us/kingston240
Samsung EVO 990 Pro 2TB – https://geni.us/samsung990pro2tb
MX-4 Thermal paste – https://geni.us/mx4thermalpaste
Case fans: https://geni.us/arcticp8

Check out the VHT forums to get your questions answered: https://www.virtualizationhowto.com/community/

Join the coolest home lab community here!
https://www.skool.com/homelabe....xplorers/about?ref=2

★ Subscribe to the channel: https://www.youtube.com/channe....l/UCrxcWtpd1IGHG9RbD
★ My blog: https://www.virtualizationhowto.com
★ Twitter: https://twitter.com/vspinmaster
★ LinkedIn: https://www.linkedin.com/in/brandon-lee-vht/
★ Github: https://github.com/brandonleegit
★ Facebook: https://www.facebook.com/peopl....e/VirtualizationHowt
★ Discord: https://discord.gg/Zb46NV6mB3
★ Pinterest: https://www.pinterest.com/brandonleevht/

Introduction to Private AI in the home lab - 0:00
Why self-host GPT for private AI - 0:54
Prerequisites to running private AI at home - 1:27
Can you run this on something old? 1:55
Docker to play around with AI - 2:12
Ollama overview and what it does - 2:33
OpenWebUI chatGPT style interface to interact with Ollama - 2:44
Using Docker and overview of the setup - 3:06
Docker run or Compose can be used - 3:49
Looking at Docker Desktop - 4:11
Command to spin up ollama in Docker Desktop - 4:36
Spinning the OpenWebUI container from the docker command line - 5:55
Overview of what happens after we spin up both containers - 7:06
Showing how to browse out to the OpenWebUI - 7:40
First steps and adding models to your Ollama instance - 7:47
Talking about better performance with LLMs with GPUs - 8:17
Enabling the NVIDIA container toolkit and when needed - 8:43
Overview of GPU passthrough with Proxmox - 9:00
Options for running GPU passthrough in Proxmox (LXC containers, and Virtual Machines) - 10:00
Steps for GPU passthrough overview - 10:50
IOMMU requirement - 11:05
Checking IOMMU - 11:20
Grepping for the device - 11:56
Claiming the IDs for GPU passthrough in Proxmox - 12:53
Blacklisting the drivers for the host - 13:02
Seeing the RTX video card recognized in the VM running in Proxmox - 13:29
Adding a PCI Device in your virtual machine - 13:51
Passthrough challenges in Proxmox - 15:00
Performance of GPU - 15:41
Quick tips and best practices of using this in the home lab - 16:20
Wrapping up self-hosting private AI in your home - 17:00

Get your own VPS and set up OpenClaw: https://hostinger.com/ncopenclaw Use code NETWORKCHUCK!

OpenClaw has 308K GitHub stars — more than React, more than the Linux kernel. But what IS it actually? I set it up on a VPS, gave it a Telegram bot, and watched it build a news briefing and server monitoring dashboard in minutes — something that took an entire n8n workflow video to do before. But I also kinda hate it. In this video, I cut through the hype and give you my honest take as someone who built the same thing with Claude Code before OpenClaw existed.

In this video, you'll learn how to install OpenClaw on a Linux VPS in under 5 minutes, connect it to Telegram, build an AI news briefing agent and IT monitoring dashboard, understand the four pillars of OpenClaw (AI models, channels, memory, and tools), configure security with the built-in security audit, set up tool profiles and red lines, and decide if OpenClaw is right for you. Whether you're an AI enthusiast drowning in hype or a sysadmin curious about AI agents, this covers everything from one-line install to hardened security config.


RESOURCES / LINKS:
🎓 NetworkChuck Academy (OpenClaw Course):https://ntck-ac.co/oc1
💻 GitHub Setup Guide (all commands): https://github.com/theNetworkChuck/openclaw-setup
🌐 OpenClaw: https://openclaw.ai
🛠️ ClawHub (Skills Directory): https://clawhub.com
🛠️ Hostinger VPS: https://hostinger.com/ncopenclaw
📺 n8n Automation Video: https://youtube.com/watch?v=ONgECvZNI3o
☕ NetworkChuck Coffee: https://networkchuck.coffee


TIMESTAMPS:
0:00 - OpenClaw stressed me out (308K GitHub stars)
1:44 - Setting up OpenClaw in 5 minutes on a VPS
4:22 - Connecting Telegram and hatching your agent
7:30 - Project 1: AI news briefing (one sentence vs entire n8n workflow)
8:14 - Project 2: AI IT engineer monitoring your own server
9:34 - What IS OpenClaw actually? (gateway + 4 pillars)
15:07 - Tools, cron jobs, and heartbeats
17:08 - ClawHub skills, browser, and sub-agents
19:27 - Why everyone freaked out (my honest take)
20:55 - Securing your OpenClaw instance
30:01 - My verdict: how I actually use OpenClaw


**Sponsored by Hostinger



SUPPORT NETWORKCHUCK:
☕☕ COFFEE and MERCH: https://ntck.co/coffee


READY TO LEARN??
🔥🔥Join the NetworkChuck Academy!: https://ntck.co/NCAcademy
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FOLLOW ME EVERYWHERE:
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Some links in this description are affiliate links. If you buy through them, I may earn a small commission at no extra cost to you.


#openclaw #aiagents #networkchuck

Access your devices securely from anywhere with TwinGate: https://ntck.co/dgxspark


NVIDIA just sent me their smallest AI supercomputer that fits in the palm of my hand. This is the NVIDIA DGX Spark, an AI powerhouse with a Grace Blackwell Superchip that can run up to 200 billion parameter models. In this video, I pin it against my insane dual RTX 4090 AI Server “Terry” to see if this new category of computing can really compete.


**Correction:** The DGX Spark uses the GB10 Grace Blackwell Superchip (not GP10).


RESOURCES / LINKS:

⚡ Nvidia DGX Spark (launches Oct 15) : https://nvda.ws/4nFFtPT

🍏 AI Supercomputer Video (5 Mac Studios): https://youtu.be/Ju0ndy2kwlw?si=hiWzpU3jIOhHf7iv

🗣️ Cloning my voice into an AI Assitant: https://youtu.be/3fg7Ht0DSnE?si=ePo1JjrC6gnChWPb





🔥🔥Join the NetworkChuck Academy!: https://ntck.co/NCAcademy



**Sponsored by Twingate







SUPPORT NETWORKCHUCK
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STUDY WITH ME on Twitch: https://bit.ly/nc_twitch

READY TO LEARN??
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-Get your CCNA: https://bit.ly/nc-ccna

FOLLOW ME EVERYWHERE
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Twitter: https://twitter.com/networkchuck
Facebook: https://www.facebook.com/NetworkChuck/
Join the Discord server: http://bit.ly/nc-discord




AFFILIATES & REFERRALS
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(GEAR I USE...STUFF I RECOMMEND)
My network gear: https://geni.us/L6wyIUj
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“NVIDIA DGX Spark Review: The AI Supercomputer That Fits in Your Hand”


“Can NVIDIA’s DGX Spark Beat My Dual 4090 AI Server?”


“NVIDIA DGX Spark vs. RTX 4090: AI Battle Test”


“Is the $4,000 NVIDIA DGX Spark Worth It?”


“Running 200B Parameter Models on a Handheld AI Server”


“Grace Blackwell Superchip Explained: NVIDIA’s Tiny Powerhouse”


“Local AI Just Changed Forever: DGX Spark Review”


“How I Built a Mini AI Server That Trains Models Locally”


“Speculative Decoding on the DGX Spark: What It Actually Does”


“DGX Spark vs. Terry: Local AI Showdown”


“Running Llama 3 and DeepSeek on NVIDIA’s DGX Spark”


“The Most Powerful Mini AI Computer Yet?”


“Fine-Tuning Models Locally with NVIDIA DGX Spark”


“Can You Train AI Models at Home with This Device?”


“NVIDIA’s Tiny AI Server for Developers (Full Breakdown)”


“The Future of Local AI: Hands-On with DGX Spark”


“Running FP4 Models on NVIDIA’s New DGX Spark”


“NVIDIA DGX Spark Review: A Developer’s Dream Machine?”


“How Fast Is the DGX Spark? Real Benchmark Results”


“The Palm-Sized AI Supercomputer from NVIDIA”



#NVIDIA #DGXSpark #AIserver

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Have you been told you must pick one local-AI tool and stick with it? Both LM Studio and AnythingLLM promise ChatGPT-level power without the cloud. But what they don’t tell you might cost you your privacy **and** your productivity.

**In this investigation, I'll reveal:**
❌ The polished interface of LM Studio is hiding a closed-source paradox you weren’t warned about
⚠️ The free-download promise of AnythingLLM comes with complexity and extra configuration many tutorials skip
💸 That “free” label hides trade-offs: beginner ease vs advanced features, transparency vs convenience
🚩 The biggest red flags users on Reddit flagged when running local LLMs… and what nobody points out
🔍 How combining LM Studio + AnythingLLM gives you a hybrid setup most power users discovered — and why that matters
💬 Real user complaints from the field: missing features, lack of verification, setup frustrations

You’ll want to watch this before you install either tool — the best setup might be **both**, not one vs the other.

Seen ads or tutorials saying you *must* choose LM Studio *or* AnythingLLM? Comment below with what you heard — and I’ll respond.

👍 If this saved you money, subscribe to Savage Reviews for more brutal product truth.

*Disclosures & Disclaimer*

🧠 Opinions: This video reflects my own opinions and research. It is for educational and informational purposes only. Do your own research before buying anything.

🚫 No sponsorship: This video is not sponsored. I did not receive compensation, products, or direction from the brand or seller.

🔍 Accuracy: I strive for accuracy, but I cannot guarantee that all information is complete, current, or error-free. Pricing and availability can change at any time.

🔗 Affiliate links: Some links are affiliate links. If you purchase through them, I may earn a small commission at no extra cost to you. This helps support the channel and more honest reviews. As an Amazon Associate, I earn from qualifying purchases.

©️ Fair use & copyright: Clips and images may be used for commentary, criticism, news reporting, teaching, and research under Section 107 of the U.S. Copyright Act (fair use). If you own rights to material used here and believe it was not used appropriately, contact me and I will credit or remove it.

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#LMStudio #LMStudioReview #LMStudioScam #LMStudio2025 #LMStudioExposed #ScamAlert #HonestReview #ProductReview #LocalLLMTool #ConsumerWarning #SavageReviews #ScamExposed #BuyerBeware #AIReview #TruthRevealed

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If you're not running LLMs locally, then you're missing out. ChatGPT and other hosted solutions are great, but if you care about speed, privacy and cost, then you'll want to learn how to run them on your own machine. In this video, I'll show you two methods of running LLMs locally from a developer perspective.

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

🎞 Video Resources 🎞
Download Ollama: https://ollama.com/download
Ollama Library: https://ollama.com/library
Ollama GitHub: https://github.com/ollama/ollama
Docker Model Runner Full Video: https://www.youtube.com/watch?v=GOgfQxDPaDw&t=24

⏳ Timestamps ⏳
00:00 | Overview
00:38 | Method 1 - Ollama
04:29 | Ollama from Code
08:27 | Method 2 - Docker Model Runner
12:32 | Docker Model Runner from Code

Hashtags
#Ollama #Docker #LLM

UAE Media License Number: 3635141

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🤖 I Built a Real Life JARVIS AI Assistant (Iron Man Style)
If you are like me, you have always dreamed of having your own Jarvis-like artificial intelligence assistant. For years, we've watched Tony Stark command his tech in Iron Man and The Avengers, wishing we had that level of automation in our daily lives.

In this video, I stop wishing and start building. I’m diving deep into the code to create a functional, real-life JARVIS.

I have worked on projects in the past showcasing the potential of AI agents, but I also ran into their limitations. Today, we are breaking past those barriers. This video explores the AI future, diving into how machine learning and current AI models (like Gemini) are evolving to make Agentic AI a reality.

Whether you are interested in Python for beginners, curious about the automation debate, or just a huge Marvel fan who wants to see how to build AI agents, this project is for you.

🚀 What You'll Learn in This Video:
The Evolution of AI: How we moved from simple chatbots to capable AI Agents.

The Build: A look at the Python coding and libraries required to build a voice-activated assistant.

Agentic AI Explained: Understanding how modern AI can take action, not just answer questions.

Limitations vs. Potential: A realistic look at where AI automation stands today and where it is going.

Design & Interface: Touching on the CAD design and visual elements that bring the Iron Man aesthetic to life.

🛠️ Tools & Tech Used:
Python: The core language for this project.

Gemini: Leveraging advanced LLMs for reasoning.

Machine Learning: For adaptive responses.

AI Automation: Scripting tasks for the assistant to perform.

📢 Connect & Learn More:
📱 Connect with Me:
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Comment Below: What is the first command you would give your own JARVIS?

Get the Code: https://github.com/nazirlouis/ada_v2