Start a project
Your work, in a real repository,
with written feedback
Sign in with GitHub and we set up your first project for free in a private repository under the TechMonster organization. When you are ready for a full specialization track, request it and an advisor reviews the fit with you.
24 tracks · 1018 hand-authored projects · 197 video lessons · free start: C & Systems Programming (Zero to the Kernel)
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Two ways in
Free first project. Coached tracks on request.
FREE · AUTOMATIC
Free first project
- Sign in with GitHub — your login is verified, nothing to type.
- A private repository is set up for you automatically, with the brief and the project assessment criteria inside.
- Our reviewer reads your commits and opens feedback as GitHub issues.
- No payment, no card, no approval needed.
How the repository works
- ·It is created inside the TechMonster organization on GitHub and you are invited to it — it is not created under your own account.
- ·It is private: you, and the people reviewing your work, can see it.
- ·Your commits are made under your own GitHub identity, and `git clone` gives you a complete copy, history included, that is yours to keep.
COACHED · ADVISOR-APPROVED
Specialization tracks
- Pick a track from the catalog and send a request with a short note.
- An advisor reviews the fit. Terms, fees and payment details are set out in a written program agreement, and the track starts only after you have signed it.
- On approval your track repository is provisioned automatically — your free project continues inside it.
- Coaching, AI-assisted code review and interview preparation follow the Career Coaching Program.
Catalog
Every track, straight from the curriculum
This list is generated from the same curriculum the repositories are built from. When a track is authored, it appears here.
AI-Chip & ML-Systems Engineering (GPU Kernels to Compilers)
Build the layer that makes AI fast on hardware — the layer of the AI-infrastructure stack that sits closest to the hardware.
Related roles: GPU Kernel Engineer, ML Systems / Inference Engineer …
6 phases · 48 projects
AI / ML with Python
Build ML engineering skills through projects you can show and explain: data foundations (NumPy, pandas, viz, SQL), classical machine learning with honest evaluation, deep learning (CNNs, RNNs, transf…
Related roles: ML Engineer, Data Scientist …
6 phases · 49 projects
AI-Native / LLM Application Engineering
Build production LLM applications the way modern teams actually do: structured outputs, RAG, agents, tool use, evals, and MCP.
Related roles: AI Engineer, LLM Application Developer …
4 phases · 17 projects
Backend / API Engineering
Build production backends: REST and GraphQL APIs, relational and cached data, auth, background jobs, testing, and system design.
Related roles: Backend Engineer, API Engineer …
4 phases · 19 projects
Build Your Own Small Language Model
Build the whole stack that sits underneath a language model API, on the laptop you already own.
Related roles: Machine Learning Engineer (training and inference), LLM Infrastructure / Inference Engineer …
7 phases · 17 projects · 17 videos
C & Systems Programming (Zero to the Kernel)
Free startLearn C the way the systems world actually uses it — as the portable assembly that the Linux kernel, device drivers, embedded firmware, databases, and network stacks are written in.
Related roles: Systems / Kernel Engineer, Embedded / Firmware Engineer …
6 phases · 48 projects · 48 videos
Honest expectations
What this is, and is not
The program provides coaching, skill building and job-search guidance. It does not guarantee employment, interviews, salaries or any specific outcome. A request is not an acceptance: an advisor decides, and program terms and fees are confirmed in writing before you commit to anything.
Read the Program terms and the Privacy Policy for what we store about program accounts.
Questions people ask
Questions people ask about the program
Is the first project really free?
- Yes. After you sign in with GitHub, the first project is created for you in a private repository under the TechMonster GitHub organization, with no advisor approval and nothing to pay.
Do I need a GitHub account?
- Yes. Sign-in is through GitHub because every project lives in your own repository and reviews are posted there. The GitHub login you sign in with is the one that receives the repository invitation.
What is a specialization track?
- A track is a sequence of hand-authored projects, grouped into phases, for one target role. You request a track from the catalog; an advisor reviews the fit and confirms terms with you before the track repository is set up.
How are fees handled?
- Program terms, fees and payment details are confirmed with you in writing before you enrol in a paid track. Nothing is charged for the free first project.
Does the program guarantee a job?
- No. The program provides coaching, skill building and job-search guidance. It does not guarantee employment, interviews, salaries or any specific outcome.