TechBricks · AI Engineering, taught by scientists
Deconstruct to reconstruct. Concept-first courses in machine learning, deep learning, and AI engineering from scientists with 15+ years across academia and industry, so you build systems you actually understand.
ML · Deep Learning · LLMs · agents · evals · production · cloud + local · lifetime updates
Take it apart. Put it back together. Now build a different one.
Why we exist
You finish a tutorial and can't explain what you just ran.
You finish a course and can't debug your own code.
You finish a bootcamp and still feel like AI is magic.
We built TechBricks to fix that; by teaching the way scientists actually learn.
How learning actually works
Real understanding isn't memorized. It's rebuilt.
Every concept we teach gets broken into its smallest moving parts; and then you put it back together yourself. That moment of reconstruction is the moment the idea becomes yours.
Bricks. Walls. Castles. Deconstruct. Reconstruct. Own it.
Deconstruct to understand.
Break ideas into pieces small enough to actually grasp.
Deconstruct to retain.
Small, well-formed bricks stick. Walls of slides don't.
Deconstruct to connect.
See how every brick relates to every other.
Deconstruct to transfer.
The same bricks build many different castles.
Deconstruct to reconstruct.
The moment you can rebuild it from scratch; it's yours.
The TechBricks method
Every course we build follows the same architecture; borrowed from how research labs train new scientists, not from how content factories ship tutorials.
Bricks
Deconstructed concepts
Single concepts, taught in isolation. One idea. One short explanation. One tiny lab.
Example
"What is self-attention, really?"
Walls
Reconstructed mini-labs
Multi-concept labs that combine bricks. Three or four ideas, working together.
Example
A multi-head attention layer, built from scratch.
Castles
Engineered projects
Full projects you ship and own. The whole picture, reconstructed by you.
Example
A mini-Transformer that generates text.
The two courses launching first
Two courses, same method, designed to flow into each other. ML and deep learning bricks first, then full LLM-engineering castles.
The on-ramp. Modern LLM systems rebuilt from first principles; tokens, prompts, RAG, agents, evaluation, and production.
The flagship. Where Fundamentals stops, Professional starts; advanced RAG, fine-tuning, RAGAS, multimodal, LLMOps, cloud, and safety.
The track, not just one course
We're building a coherent curriculum, not a content library. Every course slots into the same brick-walls-castles model; so everything you learn keeps compounding.
Modern LLM systems rebuilt from first principles; tokens, prompts, RAG, agents, evaluation, and production.
Advanced RAG, fine-tuning (LoRA / DPO), RAGAS evaluation, multimodal, LLMOps, cloud deployment, AI safety.
Regression, trees, gradient boosting, kernels; the math, the intuition, the deployments.
Neural networks from neurons to transformers. Backprop, optimizers, CNNs, RNNs, attention; built by hand.
Planner-executor loops, tool design, memory, multi-agent workflows, observability.
From CNNs to ViTs to multimodal LLMs; built brick by brick from first principles.
SFT, LoRA, DPO, reward modeling; when to do it, and how to do it without burning compute.
Prompt injection, jailbreaks, model + data exfil, defenses for production systems.
FAQ
Working comfort with high-school algebra and a willingness to look at small matrices is enough. We re-derive every piece of math we use, with code alongside.
No. Every lab runs on a laptop. Heavier examples can use a free Colab tier or a local Ollama model; your choice.
Both. You'll learn the OpenAI SDK first because it teaches the right concepts, and then map the same ideas onto a local Ollama model so you're never locked in.
Concept-first. Framework-light. Built from scratch before any abstraction. We refuse to copy notebooks. If you can't rebuild a thing, you don't move on.
You'll get something better: nine portfolio projects you actually built and can defend in an interview.
If you're new to ML / DL / LLMs, start with AI Engineering Fundamentals; it rebuilds machine learning, deep learning, and the LLM on-ramp from first principles. If you already ship ML / LLM systems, jump straight to AI Engineering Professional.
Both AI Engineering Fundamentals and AI Engineering Professional are coming soon, with the rest of the roadmap following. Drop your email in the Brick Letter and you'll be the first to know.