§ 001 / THE LAB COURSE
The Local AI Engineering Lab
invite-only betaEight build-first modules. Every open model runs on hardware you control, and every module ends in a benchmark you publish.
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§ 002 / HOW THIS TEACHES
After the eight builds you can
- Train a language model from a blank file and read its loss curve like an instrument
- Fine-tune an open model on your own data with LoRA and prove the lift with an eval
- Build retrieval, agent, and reasoning pipelines that run on hardware you control
- Harden an agent with memory, injection defense, and runtime budget guards
- Publish benchmark-backed build logs that double as a portfolio
Build first
Every section starts with something to make, and theory arrives when the build needs it. Struggling with a problem before the explanation measurably improves transfer.
Check yourself
Sections end with retrieval questions and every module ends with a closed-book rebuild. Self-testing is the strongest replicated result in the learning literature.
Spaced by design
Each module reuses the previous module's artifact, so old material gets recalled weeks later exactly when forgetting would set in.
Prove it in public
Every module ends in a published benchmark. Explaining your build to strangers is the Feynman step, and the artifact is your proof-of-build.
§ 003 / CURRICULUM
- FOUNDATIONS24m read · 120m build
LLM Playground on Local Hardware
Build a language model from nothing and learn why every piece exists
Proof: A decoding-strategy benchmark table, quality vs tokens/sec on your card.
- ADAPTATION12m read · 105m build
Fine-Tune Llama 3.1 8B into a Support Bot
LoRA on one consumer GPU, measured before and after
Proof: A before/after eval table plus the VRAM and wall-clock cost of the tune.
- RETRIEVAL11m read · 100m build
Build a Local RAG Pipeline
Chunk, embed, retrieve, and guard, all on your box
Proof: A retrieval-quality table comparing three chunking strategies.
- AGENTS11m read · 110m build
Build an Ask-the-Web Agent
Routing, reflection, and tools over MCP, priced against the API
Proof: A local-vs-API cost curve for the same agent trace.
- REASONING11m read · 105m build
Add a Deep Research Capability
Spend inference-time compute on a local reasoning model, and measure it
Proof: An inference-time-scaling curve, answer quality vs thinking tokens.
- GENERATION12m read · 95m build
Ship a Local Diffusion Image Service
Text-to-image behind a small API, benchmarked across quant levels
Proof: A VRAM/latency/quality grid across quant levels for the same prompt.
- OBSERVABILITY13m read · 115m build
Harden the Agent - Memory, Guards, and Observability
Make Module 4's agent safe to leave running
Proof: A red-team log, injection attacks attempted vs which guard caught them.
- CAPSTONE11m read · 120m build
Capstone - Ship One Production-Shaped Local Agent
Compose the seven parts into one system a stranger can run
Proof: A public repo plus a lab report with at least one real benchmark table.
§ 004 / COMPUTE
You do not need to buy anything to take this course. 4 ways to run the work, no card required:
Your browser
freeEvery module ships a rebuild exercise that runs right on the page (Python compiled to WebAssembly). Nothing to install, nothing uploaded.
Your own GPU
hardware you already ownThe lab's home turf. One consumer card (8GB+ VRAM) runs every module; the writeups are tuned for local rigs first.
NVIDIA free endpoints
free creditsNo GPU? build.nvidia.com serves Llama and DeepSeek-R1 behind a free OpenAI-compatible API. The agent and reasoning modules (4, 5, 7) run against it with a one-line base-url change.
Colab free tier
free T4 GPUThe training modules (1 and 2) fit Colab's free T4 when you don't have a local card. Same code, slower card, still yours to run.