--- title: "awesome-generative-ai vs ai-engineering-from-scratch" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/filipecalegario-awesome-generative-ai-vs-rohitg00-ai-engineering-from-scratch" tools: ["filipecalegario-awesome-generative-ai", "rohitg00-ai-engineering-from-scratch"] --- # awesome-generative-ai vs ai-engineering-from-scratch *GraphCanon updated Sep 20, 2026* ## Verdict Pick awesome-generative-ai if awesome-generative-ai offers an extensive directory of resources on generative AI spanning from models to artwork without coding or setup; pick ai-engineering-from-scratch if ai-engineering-from-scratch is a comprehensive course that teaches AI engineering skills from foundational math to advanced AI agents and machine learning techniques, using Python and Node.js for interactive learning and. [awesome-generative-ai](https://github.com/filipecalegario/awesome-generative-ai) reports 3.5k GitHub stars, 885 forks, and 317 open issues, last pushed Dec 18, 2025. [ai-engineering-from-scratch](https://aiengineeringfromscratch.com) has 55k stars, 9.6k forks, and 114 open issues, last pushed Sep 7, 2026. Figures are from public GitHub metadata via [awesome-generative-ai's repository](https://github.com/filipecalegario/awesome-generative-ai) and [ai-engineering-from-scratch's repository](https://github.com/rohitg00/ai-engineering-from-scratch). | | [awesome-generative-ai](/tools/filipecalegario-awesome-generative-ai.md) | [ai-engineering-from-scratch](/tools/rohitg00-ai-engineering-from-scratch.md) | | --- | --- | --- | | Tagline | A comprehensive list of generative AI resources | Learn, build, and deploy AI engineering skills from scratch. | | Stars | 3,539 | 54,935 | | Forks | 885 | 9,649 | | Open issues | 317 | 114 | | Language | - | Python | | Adopt for | awesome-generative-ai offers an extensive directory of resources on generative AI spanning from models to artwork without coding or setup. | ai-engineering-from-scratch is a comprehensive course that teaches AI engineering skills from foundational math to advanced AI agents and machine learning techniques, using Python and Node.js for interactive learning and | | Persona | - | - | | Runtime | - | - | | License | CC0-1.0 - public domain with no attribution required, ideal for broad distribution and integration in any project without legal constraints. | MIT | | Categories | AI Agents, Computer Vision, Data & Retrieval, Developer Tools, LLM Frameworks, Speech & Audio | AI Agents, Computer Vision, Developer Tools, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [awesome-generative-ai](/tools/filipecalegario-awesome-generative-ai.md) | [ai-engineering-from-scratch](/tools/rohitg00-ai-engineering-from-scratch.md) | | --- | --- | --- | | Maintenance | Slowing (36%) | Active (82%) | | Days since push | 275d | 10d | | Open issues (now) | 317 | 114 | | Stars delta | +31 (30d) | +8.1k (30d) | | Open issues delta | +56 (30d) | +7 (30d) | | Full report | [trust report](/tools/filipecalegario-awesome-generative-ai/trust.md) | [trust report](/tools/rohitg00-ai-engineering-from-scratch/trust.md) | ## Decision facts: awesome-generative-ai - **Adopt for:** awesome-generative-ai offers an extensive directory of resources on generative AI spanning from models to artwork without coding or setup. - **License detail:** CC0-1.0 - public domain with no attribution required, ideal for broad distribution and integration in any project without legal constraints. ## Decision facts: ai-engineering-from-scratch - **Adopt for:** ai-engineering-from-scratch is a comprehensive course that teaches AI engineering skills from foundational math to advanced AI agents and machine learning techniques, using Python and Node.js for interactive learning and ## Choose when ### Choose awesome-generative-ai if… - License: awesome-generative-ai is CC0-1.0, ai-engineering-from-scratch is MIT. - Tags unique to awesome-generative-ai: ai art, awesome-list, chatgpt, dall-e. - Also covers Data & Retrieval, LLM Frameworks, Speech & Audio. - You want a curated list covering a broad range of generative AI tools and models. ### Choose ai-engineering-from-scratch if… - License: ai-engineering-from-scratch is MIT, awesome-generative-ai is CC0-1.0. - Tags unique to ai-engineering-from-scratch: agents, ai-engineering, computer-vision, deep-learning. - Also covers Model Training. - when you need a structured course that covers a wide range of AI engineering topics from scratch, including foundational math, deep learning, and reinforcement learning. ## When NOT to use awesome-generative-ai - Seeking direct tool functionality or hands-on code implementation support. - Looking for resources focused on specific frameworks like TensorFlow or PyTorch exclusively. ## When NOT to use ai-engineering-from-scratch - if you are looking for a tool that focuses solely on theoretical knowledge without practical application, as this course emphasizes hands-on learning. - when you do not have access to Node.js or Python, as these are required for running the course and its interactive components. - if you prefer a more traditional learning approach without the use of terminal-based learning tools, as the course is designed for interactive terminal sessions. - when you are working with a development environment that does not support skill-capable hosts, as the course is optimized for such environments. ## Common questions ### What is the difference between awesome-generative-ai and ai-engineering-from-scratch? awesome-generative-ai: A comprehensive list of generative AI resources. ai-engineering-from-scratch: Learn, build, and deploy AI engineering skills from scratch.. See the comparison table for live GitHub stats and shared categories. ### When should I choose awesome-generative-ai over ai-engineering-from-scratch? Choose awesome-generative-ai over ai-engineering-from-scratch when License: awesome-generative-ai is CC0-1.0, ai-engineering-from-scratch is MIT; Tags unique to awesome-generative-ai: ai art, awesome-list, chatgpt, dall-e; Also covers Data & Retrieval, LLM Frameworks, Speech & Audio; You want a curated list covering a broad range of generative AI tools and models. ### When should I choose ai-engineering-from-scratch over awesome-generative-ai? Choose ai-engineering-from-scratch over awesome-generative-ai when License: ai-engineering-from-scratch is MIT, awesome-generative-ai is CC0-1.0; Tags unique to ai-engineering-from-scratch: agents, ai-engineering, computer-vision, deep-learning; Also covers Model Training; when you need a structured course that covers a wide range of AI engineering topics from scratch, including foundational math, deep learning, and reinforcement learning. ### When should I avoid awesome-generative-ai? Seeking direct tool functionality or hands-on code implementation support. Looking for resources focused on specific frameworks like TensorFlow or PyTorch exclusively. ### When should I avoid ai-engineering-from-scratch? if you are looking for a tool that focuses solely on theoretical knowledge without practical application, as this course emphasizes hands-on learning. when you do not have access to Node.js or Python, as these are required for running the course and its interactive components. if you prefer a more traditional learning approach without the use of terminal-based learning tools, as the course is designed for interactive terminal sessions. when you are working with a development environment that does not support skill-capable hosts, as the course is optimized for such environments. ### Is awesome-generative-ai or ai-engineering-from-scratch more popular on GitHub? ai-engineering-from-scratch has more GitHub stars (54,935 vs 3,539). Stars measure visibility, not whether either tool fits your constraints. ### Are awesome-generative-ai and ai-engineering-from-scratch open source? Yes - both are open-source projects on GitHub (awesome-generative-ai: CC0-1.0, ai-engineering-from-scratch: MIT). ### Where can I find alternatives to awesome-generative-ai or ai-engineering-from-scratch? GraphCanon lists graph-backed alternatives at [awesome-generative-ai alternatives](/tools/filipecalegario-awesome-generative-ai/alternatives) and [ai-engineering-from-scratch alternatives](/tools/rohitg00-ai-engineering-from-scratch/alternatives) ([awesome-generative-ai markdown twin](/tools/filipecalegario-awesome-generative-ai/alternatives.md), [ai-engineering-from-scratch markdown twin](/tools/rohitg00-ai-engineering-from-scratch/alternatives.md)), ranked by typed relationship edges rather than popularity votes. ### Is there a machine-readable version of this comparison? Yes. The markdown twin at [this comparison](/compare/filipecalegario-awesome-generative-ai-vs-rohitg00-ai-engineering-from-scratch.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, awesome-generative-ai or ai-engineering-from-scratch? awesome-generative-ai: Slowing. ai-engineering-from-scratch: Active. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance. ### Where are the full trust reports for awesome-generative-ai and ai-engineering-from-scratch? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [awesome-generative-ai trust report](/tools/filipecalegario-awesome-generative-ai/trust); [ai-engineering-from-scratch trust report](/tools/rohitg00-ai-engineering-from-scratch/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=filipecalegario-awesome-generative-ai`](/api/graphcanon/graph?tool=filipecalegario-awesome-generative-ai) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_