The Ultimate Local AI Coding Guide (2026 Is Already Here) скачать в хорошем качестве

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

⚡ Master AI and become a high-paid AI Engineer: https://aiengineer.community/join 🎁 Free course to build any local AI system: https://zenvanriel.nl/ai-roadmap/ 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