{"data":{"slug":"rohitg00-ai-engineering-from-scratch","name":"ai-engineering-from-scratch","tagline":"Learn it. Build it. Ship it for others.","github_url":"https://github.com/rohitg00/ai-engineering-from-scratch","owner":"rohitg00","repo":"ai-engineering-from-scratch","owner_avatar_url":"https://avatars.githubusercontent.com/u/48523873?v=4","primary_language":"Python","stars":46862,"forks":8195,"topics":["agents","ai","ai-agents","ai-engineering","computer-vision","course","deep-learning","from-scratch","generative-ai","llm","machine-learning","mcp","nlp","python","reinforcement-learning","rust","swarm-intelligence","transformers","tutorial","typescript"],"archived":false,"github_pushed_at":"2026-08-10T07:05:52+00:00","maintenance_label":"Very active","stars_delta_30d":8301,"url":"https://www.graphcanon.com/tools/rohitg00-ai-engineering-from-scratch","markdown_url":"https://www.graphcanon.com/tools/rohitg00-ai-engineering-from-scratch.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/rohitg00-ai-engineering-from-scratch","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=rohitg00-ai-engineering-from-scratch","description":"Learn it. Build it. Ship it for others.","homepage_url":"https://aiengineeringfromscratch.com","license":"MIT","open_issues":107,"watchers":287,"ai_summary":"A comprehensive course and set of projects focused on building AI systems from scratch using various languages like Python, Rust, and TypeScript. Covers a wide range of topics including deep learning, computer vision, NLP, reinforcement learning, and more.","readme_excerpt":"## Getting started\n\nThree ways in. Pick one.\n\n**Option A — learn in your terminal *(recommended)*.** Install the learning\nskills into any agent and let the course drive itself:\n\n```bash\nnpx skills add rohitg00/ai-engineering-from-scratch\n```\n\n```text\n/start-learning     # interview + placement quiz -> personalized plan in LEARNING.md\n/learn              # next lesson, taught interactively: concept -> math -> code -> quiz\n/course-guide rag   # \"which lessons teach X?\" -> exact lessons + links\n```\n\nLessons stream from this repo as you go — no clone needed. Progress lives in\n`LEARNING.md` in your project, so every session resumes where you left off.\n\n**Option B — read.** Open any completed lesson on\n[aiengineeringfromscratch.com](https://aiengineeringfromscratch.com) or expand a phase under\n[Contents](#contents). No setup, no cloning.\n\n**Option C — clone and run.**\n\n```bash\ngit clone https://github.com/rohitg00/ai-engineering-from-scratch.git\ncd ai-engineering-from-scratch\npython phases/01-math-foundations/01-linear-algebra-intuition/code/vectors.py\n```\n\nCloning also auto-loads the learning skills in Claude Code, and gives every\nlesson's code to `/learn` for real execution instead of read-along.\n\n---\n\n### Install course skills into your agent\n\nTwo skill sets, two installers:\n\n**The learning skills** (`/start-learning`, `/learn`, `/course-guide`,\n`/claude-certification`, `/find-your-level`, `/check-understanding`) live under [`skills/`](skills/)\nand install into any agent with one command — no clone, no Python:\n\n```bash\nnpx skills add rohitg00/ai-engineering-from-scratch\n```\n\n`skills` writes to whichever directory your agent picks up: `.claude/skills/`,\n`.cursor/skills/`, `.codex/skills/`, OpenClaw's skills folder, Hermes's bundle\npath, or any SKILL.md-aware tool. One command, every agent.\n\n**The lesson artifacts.** The repo ships 388 skills and 99 prompts under\n`phases/**/outputs/`; install them via `scripts/install_skills.py`. Requires\ncloning the repo. Supports tag filters, dry-runs, and per-agent layouts:\n\n```bash\npython3 scripts/install_skills.py <target>                                 # every skill, default --layout skills (nested)\npython3 scripts/install_skills.py <target> --layout skills                 # same as above, explicit\npython3 scripts/install_skills.py <target> --type all                      # skills + prompts + agents\npython3 scripts/install_skills.py <target> --phase 14                      # one phase only\npython3 scripts/install_skills.py <target> --tag rag                       # filter by tag\npython3 scripts/install_skills.py <target> --layout flat                   # flat files\npython3 scripts/install_skills.py <target> --dry-run                       # preview without writing\npython3 scripts/install_skills.py <target> --force                         # overwrite existing files\n```\n\n`<target>` is the skills directory for your agent (examples:\n`~/.claude/skills/`, `~/.cursor/skills/`, `~/.config/openclaw/skills/`,\n`.skills/`, or any path your agent reads).\n\nBy default the script refuses to overwrite an existing destination and exits\nwith code 1 after listing every colliding path. Use `--dry-run` to preview\ncollisions or `--force` to overwrite. Every non-dry-run run writes a\n`manifest.json` in the target with the full inventory grouped by type and\nphase. Pick the layout your agent reads:\n\n| `--layout`  | Path written |\n|---|---|\n| `skills`    | `<target>/<name>/SKILL.md` (nested convention, supported by Claude / Cursor / Codex / OpenClaw / Hermes) |\n| `by-phase`  | `<target>/phase-NN/<name>.md` |\n| `flat`      | `<target>/<name>.md` |\n\n---\n\n## License\n\nMIT. Use it however you want — fork it, teach it, sell it, ship it. Attribution appreciated,\nnot required.\n\nMaintained by [Rohit Ghumare](https://github.com/rohitg00) and the community.\n\n<sub>\n  <a href=\"https://x.com/ghumare64\">@ghumare64</a> &nbsp;·&nbsp;\n  <a href=\"https://aiengineeringfromscratch.com\">aiengineeringfromscratch.com</a> &nbsp;·&nbsp;\n  <a hr","github_created_at":"2026-03-18T18:38:02+00:00","created_at":"2026-07-07T17:31:38.909936+00:00","updated_at":"2026-08-16T12:01:48.682891+00:00","categories":[{"slug":"ai-agents","name":"AI Agents","url":"https://www.graphcanon.com/categories/ai-agents","markdown_url":"https://www.graphcanon.com/categories/ai-agents.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/ai-agents"},{"slug":"computer-vision","name":"Computer Vision","url":"https://www.graphcanon.com/categories/computer-vision","markdown_url":"https://www.graphcanon.com/categories/computer-vision.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/computer-vision"},{"slug":"developer-tools","name":"Developer Tools","url":"https://www.graphcanon.com/categories/developer-tools","markdown_url":"https://www.graphcanon.com/categories/developer-tools.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/developer-tools"},{"slug":"llm-frameworks","name":"LLM Frameworks","url":"https://www.graphcanon.com/categories/llm-frameworks","markdown_url":"https://www.graphcanon.com/categories/llm-frameworks.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/llm-frameworks"}],"tags":[{"slug":"agents","name":"agents"},{"slug":"ai-engineering","name":"ai-engineering"},{"slug":"computer-vision","name":"computer-vision"},{"slug":"deep-learning","name":"deep-learning"},{"slug":"from-scratch","name":"from-scratch"},{"slug":"generative-ai","name":"generative-ai"},{"slug":"llm","name":"llm"},{"slug":"machine-learning","name":"machine-learning"}],"trust":{"provenance":{"is_fork":false,"github_id":1185590488,"owner_type":"User","methodology":"github_public_v1","parent_repo":null,"near_duplicate_slugs":[]},"computed_at":"2026-08-16T12:01:47.930Z","maintenance":{"label":"Very active","score":96,"methodology":"github_public_v1","releases_90d":2,"days_since_push":6,"last_release_at":"2026-08-10T07:05:52Z","stars_delta_30d":8301,"open_issues_delta_30d":9},"security_summary":{"status":"findings","scanner":"osv@v1","low_count":113,"high_count":0,"last_scan_at":"2026-08-02T04:00:35.533Z","medium_count":0,"scan_profile":"deps","critical_count":0}},"capability_facts":{"scan":{"source":"repo_scan","observed_at":"2026-08-16T12:01:48.377Z"},"languages":{"value":["python"],"source":"github.language","observed_at":"2026-08-16T12:01:48.377Z"},"license_spdx":{"value":"MIT","source":"github.license","observed_at":"2026-08-16T12:01:48.377Z"}},"decision_facts":{"hosting":null,"pricing":{"model":"freemium","summary":"The `ai-engineering-from-scratch` repository is free and open-source under an MIT license, but for full access to additional resources or support, a paid option may be provided. Consult official or up"},"requirements":null,"constraints":{"pricing_model":"freemium"},"when_to_use":["When you want to start with foundational knowledge and learn the intricacies behind AI systems.","If your goal is comprehensive coverage across multiple domains including deep learning, computer vision, NLP, and reinforcement learning using multiple languages like Python, Rust, and TypeScript."],"when_not_to_use":["If you are looking for a quick setup or ready-to-go solution without diving into the foundational understanding.","When your project requires immediate practical application with less emphasis on self-implemented solutions from scratch."],"source":"enrich:decision_facts","observed_at":"2026-07-11T12:42:19.005Z"},"constraint_facets":{"pricing_model":"freemium"},"decision_summary":[{"label":"Pricing","value":"freemium - The `ai-engineering-from-scratch` repository is free and open-source under an MIT license, but for full access to additional resources or support, a paid option may be provided. 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