Output
8 intense weeksML, LLM systems, RAG, MCP, LLMOps, MLOps, launch discipline, and a portfolio SaaS.
Machine-Built Academy
AI Engineer Intensive
An English-only, operator-grade eight-week bootcamp for self-learners who want to build, evaluate, deploy, and harden AI systems.
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Let me teach you, human, how to work with me in a way that ships real systems, survives contact with production, and becomes portfolio proof.
Pressure
AI reviews every artifactThe machine does not only explain. It evaluates, scores, and pushes the learner into revision loops.
Manifesto
Let me teach you, human, how to work with me properly.
makeyourAI.work is built around a reversal: the machine authors the pressure system, the human survives it, and the result is a sharper AI Engineer.
The product is the curriculum. The curriculum is also the capstone. By the end, the learner has not only studied an AI-native system but shipped one.
Academy Signal
Built like an editorial front, structured like a training system.
Surface
Public pages read like a thesis.
They explain what the course demands, what the learner ships, and why the machine is in charge of the pressure.
Runtime
Private app enforces the work.
Inside the app the learner hits submissions, checkpoints, AI reviews, revision loops, and capstone pressure.
Narrative
This product teaches itself.
makeyourAI.work uses the same stack, review logic, and operating discipline the learner is expected to master.
Curriculum Arc
Eight weeks. No filler.
Week 1
Week 1: Foundations of Working With Machines
Programming rigor, APIs, auth boundaries, networking, and debugging discipline.
A technical readiness brief and first backend boundary review.
Open weekWeek 2
Week 2: Data, ML, and How Models Learn
Data handling, feature thinking, evaluation, and classical ML before the LLM layer.
A simple ML pipeline with evaluation and a leakage audit.
Open weekWeek 3
Week 3: Talking to Models Properly
LLM foundations, structured outputs, prompt architecture, and secure API usage.
A prompt contract and structured-output integration design.
Open weekWeek 4
Week 4: RAG, Context, and Agentic Systems
RAG, retrieval quality, orchestration, and attack-aware agent design.
A retrieval architecture brief and an agent threat model.
Open weekWeek 5
Week 5: Shipping Systems, Not Demos
Containers, deployment, runtime isolation, and hardening the path to production.
A local stack blueprint and deployment hardening plan.
Open weekWeek 6
Week 6: MCP, Evaluation, and LLMOps
MCP, tracing, evals, cost, compliance-aware logging, and post-launch operations.
An evaluation scorecard and post-launch monitoring plan.
Open weekWeek 7
Week 7: Build the Product Core
Translate the curriculum into product loops: onboarding, progression, review, and admin visibility.
A product loop map, review system flow, and admin spec.
Open weekWeek 8
Week 8: Ship the AI Tutor
Polish the capstone, prove launch readiness, and turn the system into a portfolio narrative.
A case study, launch checklist, and personal AI Engineer operating manual.
Open weekProof Surface
This is not content marketing.
Lesson Contract
Every lesson is structured.
Concept, why it matters, mental model, deep dive, worked example, failure modes, exercise, rubric, ship task.
Review Loop
Feedback is operational.
Submissions are scored across technical accuracy, architecture judgment, security awareness, and ops maturity.
Capstone
The project teaches itself.
The final product is an AI-native SaaS that uses the same methods and tools the learner is expected to master.
Sample Lesson
The course starts by cutting through fake leverage.
Lesson Preview
Why AI Still Demands Technical Foundations
Prompting does not replace engineering literacy.
This lesson resets the role of AI in your career. The model is an amplifier for judgment, not a substitute for technical taste, system literacy, or the ability to verify behavior.
In production, weak fundamentals turn every AI-generated answer into a liability. If you cannot read stack traces, inspect data flow, and reason about interfaces, you will ship impressive-looking nonsense.
Treat AI as a junior-but-fast collaborator embedded inside a real software system. Your value is in defining the constraints, judging outputs, and spotting when the collaborator is confidently wrong.