A 20-course path to building AI locally

Understand every AI concept — from neural networks to production deployment

Build neural networks from scratch and deploy production AI systems

Build chatbots, search engines, and text analysis pipelines

Create image classifiers, object detectors, and face recognition systems

Build AI that answers from your private documents — zero data leaves your machine

Design AI agents that use tools, plan, and solve complex problems autonomously

Build systems that understand text, images, and audio together

Deploy, monitor, and scale ML models like a senior ML engineer

Make AI 10x cheaper to run in production with inference optimization

Train AI from human feedback — the technique behind ChatGPT

Build, secure, and ship Model Context Protocol servers. Tools, resources, transport, observability, and production deployment patterns.

Speech-to-text, text-to-speech, realtime LLM orchestration, voice agents with tools, observability, and telephony deployment.

End-to-end AI app craft. Prompting, embeddings, tools, evals, guardrails, performance, deployment, and CI/CD for AI systems.

Threat modeling, prompt injection, agent risks, data security, red teaming, monitoring, and compliance for production AI.

Decision rights, trust calibration, oversight, workflow design, and team adoption for working alongside AI systems.

Real production code for deploying LLMs locally. Quantization, KV cache, vLLM, multi-GPU, edge devices, OpenAI-compatible servers. Full GitHub repo included.

The discipline behind every great AI system. Collection, cleaning, deduplication at scale, synthetic data, instruction tuning, preference data, evals, contamination defense. Full GitHub repo with production code.

Build repo-aware coding agents, engineering copilots, PR-review systems, and safe CI-aware automation — production-grade, beginner to advanced.

Decide when to fine-tune vs distill, engineer datasets, run SFT / distillation / preference optimization, evaluate, and deploy for cost, latency, and reliability.

Generate images on your own GPU — free, private, and uncensored. Install ComfyUI, master SDXL and FLUX, control output with ControlNet and LoRA, inpaint and upscale to high resolution, optimize for your VRAM, and turn it into income. No Midjourney or DALL·E subscription.

A complete, practical digital marketing course — branding, websites, SEO, content, social, email, paid ads, analytics, advanced strategy, and AI marketing. 115 chapters across 12 modules.

The buyer's guide to running AI locally. How VRAM sets your ceiling, the 8/12/16/24GB ladder, NVIDIA vs AMD vs Apple vs NPU, real builds from $500 to multi-GPU, the used-market playbook, and a capstone to spec your own machine. Updated for the 2026 hardware market — decide before you spend a dollar.

The complete operator's course for the tool most people run local models with. Install it properly on any OS, decode system requirements, read the model library, write Modelfiles, fix GPU offload, tune speed, serve it safely to other machines, and diagnose the 20 errors that actually happen. Ends with a documented, tuned multi-model install of your own.

Replace your speech-AI subscription with your own machine. The local TTS landscape, your first voice in 20 minutes, voice cloning that doesn't sound robotic, Whisper and its faster cousins, transcription with speaker names, an end-to-end audiobook pipeline, subtitles and dubbing, a local TTS API — and a clear read on which licences let you sell what you make.

Stop guessing which model to run. Verdict-first picks for 8, 12, 16 and 24GB+ cards and for each job you actually do, then the reasoning underneath: the three numbers that decide everything, quant formats, when a 7B beats a 70B, real usable context, sampling settings, honest speed measurement, how to read a leaderboard without being fooled, and how to build an eval from your own work.