Don't study for a tech career. Build your way into one.
Four of the most in-demand tech roles, each a roadmap you learn by building. You get short time boxes, real company briefs and honest rules for using AI, and every quest you finish reveals something new.
Pick a future. Try it before you commit.
Each role page shows a day in that life, an honest take on the job market, and a 2-minute taste test of real decisions people in that role make. You don't need to choose yet. Open all four.
Full-Stack Applied AI Engineer
You become the person who turns an idea into a live AI product by Friday.
- AI copilots inside real products
- Chat over company documents (RAG)
- Agents that take actions through tools
AI / ML Engineer
You become the person who makes machines learn, and proves they learned the right thing.
- Models that classify, rank, predict and generate
- Fine-tuned LLMs for specific tasks
- Search and recommendation systems
Data Scientist
You become the person in the room who knows what's actually true.
- Root-cause investigations that change decisions
- A/B tests and experiment analysis
- Predictive models (churn, demand, risk)
Data Engineer
You become the person whose pipelines everyone else runs on.
- Pipelines that move and clean data on schedule
- Data warehouses and models that analysts love
- Streaming systems for real-time data
Five stages, from first line of code to people using your work
Every role follows the same arc. Each stage unlocks a real capability you can put on your resume.
Sprint Zero
Language, core ideas and framework in 5 days. Just enough to start building.
5 daysLearn by building
Small builds that each teach one real concept by doing it.
4–6 weeksCompany-style builds
Real briefs: problem, goal, definition of done. Working out the how is up to you.
~4 weeksArchitect mode
You write the spec, AI agents build it, and you review like a tech lead.
~1.5 weeksMeaningful project
Something real people use. This is your story in interviews.
4–6 weeksLearn the way working engineers actually learn
Build first, study on demand
You won't remember a framework from a course. You'll remember the bug that cost you two hours. Read just enough to start, then learn each detail the moment a build needs it.
Time-box everything
Each quest has a time box. If you go over by 2×, stop, ship what you have, and note what was hard. Finishing matters more than perfection at this stage.
Read, don't binge-watch
Docs are faster than videos, and you can search them. When a video is linked, it's short and meant to be watched in one sitting.
Every quest ends in proof
A repo, a URL, a chart, a memo. Log it in your journal and by the end you'll have a portfolio without ever 'making a portfolio'.
AI is your tutor first, and your team later
Used well, AI makes you learn faster. Used badly, you end up with a portfolio you can't explain. Here are the rules. The AI Playbook covers how to architect and prompt once you're past the basics.
Is your project idea worth building?
An 8-question test that filters out tutorial clones before you lose a month to one.
Get hiredWhen, where and how to apply
LinkedIn habits, DSA targets that matter, and when you're ready to start applying.
JournalYour proof of work
Every quest you finish becomes an entry. Export it as a portfolio or a LinkedIn post.