The Ultimate Local AI Coding Guide (2026 Is Already Here)

⚡ Master AI with me 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-...
Demo Auction Repository: https://github.com/AI-Engineer-Skool/...

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:   / zen-van-riel