--- title: "awesome-list-of-awesomes vs ai-engineering-from-scratch" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/nachimak28-awesome-list-of-awesomes-vs-rohitg00-ai-engineering-from-scratch" tools: ["nachimak28-awesome-list-of-awesomes", "rohitg00-ai-engineering-from-scratch"] --- # awesome-list-of-awesomes vs ai-engineering-from-scratch *GraphCanon updated Jul 11, 2026* ## Verdict Pick awesome-list-of-awesomes when tags unique to awesome-list-of-awesomes: data-science, ai, dl, cv; pick ai-engineering-from-scratch when pricing: 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. [awesome-list-of-awesomes](https://github.com/Nachimak28/awesome-list-of-awesomes) reports 345 GitHub stars, 48 forks, and 1 open issues, last pushed Nov 13, 2023. [ai-engineering-from-scratch](https://aiengineeringfromscratch.com) has 38k stars, 6.3k forks, and 96 open issues, last pushed Jun 25, 2026. Figures are from public GitHub metadata via [awesome-list-of-awesomes's repository](https://github.com/Nachimak28/awesome-list-of-awesomes) and [ai-engineering-from-scratch's repository](https://github.com/rohitg00/ai-engineering-from-scratch). | | [awesome-list-of-awesomes](/tools/nachimak28-awesome-list-of-awesomes.md) | [ai-engineering-from-scratch](/tools/rohitg00-ai-engineering-from-scratch.md) | | --- | --- | --- | | Tagline | A curated list of all the Awesome --Topic Name-- lists I've found till date relevant to Data lifecycle, ML and DL. | Learn it. Build it. Ship it for others. | | Stars | 345 | 37,922 | | Forks | 48 | 6,329 | | Open issues | 1 | 96 | | Language | - | Python | | Adopt for | - | Specifically designed for individuals looking to build a comprehensive understanding of AI tools and frameworks from the ground up. | | Persona | - | - | | Runtime | - | - | | License | MIT | MIT | | Categories | Model Training, Computer Vision | LLM Frameworks, AI Agents, Computer Vision, Developer Tools | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [awesome-list-of-awesomes](/tools/nachimak28-awesome-list-of-awesomes.md) | [ai-engineering-from-scratch](/tools/rohitg00-ai-engineering-from-scratch.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Active (82%) | | Days since push | 971d | 15d | | Open issues (now) | 1 | 96 | | Security scan | No lockfile | No MCP manifest | | Full report | [trust report](/tools/nachimak28-awesome-list-of-awesomes/trust.md) | [trust report](/tools/rohitg00-ai-engineering-from-scratch/trust.md) | ## Decision facts: ai-engineering-from-scratch - **Pricing:** 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. Consult official or up - **Adopt for:** Specifically designed for individuals looking to build a comprehensive understanding of AI tools and frameworks from the ground up. ## Choose when ### Choose awesome-list-of-awesomes if… - Tags unique to awesome-list-of-awesomes: data-science, ai, dl, cv. - Also covers Model Training. - Leaner open-issue backlog (1). ### Choose ai-engineering-from-scratch if… - Pricing: 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. - Tags unique to ai-engineering-from-scratch: ai-engineering, agents, llm, from-scratch. - Also covers LLM Frameworks, AI Agents, Developer Tools. - When you want to start with foundational knowledge and learn the intricacies behind AI systems. ## When NOT to use awesome-list-of-awesomes - Last GitHub push was 972 days ago (dormant maintenance, Nov 13, 2023). Validate activity before betting a new project on awesome-list-of-awesomes. - Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. ## When NOT to use ai-engineering-from-scratch - 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. ## Common questions ### What is the difference between awesome-list-of-awesomes and ai-engineering-from-scratch? awesome-list-of-awesomes: A curated list of all the Awesome --Topic Name-- lists I've found till date relevant to Data lifecycle, ML and DL.. ai-engineering-from-scratch: Learn it. Build it. Ship it for others.. See the comparison table for live GitHub stats and shared categories. ### When should I choose awesome-list-of-awesomes over ai-engineering-from-scratch? Choose awesome-list-of-awesomes over ai-engineering-from-scratch when Tags unique to awesome-list-of-awesomes: data-science, ai, dl, cv; Also covers Model Training; Leaner open-issue backlog (1). ### When should I choose ai-engineering-from-scratch over awesome-list-of-awesomes? Choose ai-engineering-from-scratch over awesome-list-of-awesomes when Pricing: 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; Tags unique to ai-engineering-from-scratch: ai-engineering, agents, llm, from-scratch; Also covers LLM Frameworks, AI Agents, Developer Tools; When you want to start with foundational knowledge and learn the intricacies behind AI systems. ### When should I avoid awesome-list-of-awesomes? Last GitHub push was 972 days ago (dormant maintenance, Nov 13, 2023). Validate activity before betting a new project on awesome-list-of-awesomes. Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. ### When should I avoid ai-engineering-from-scratch? 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. ### Is awesome-list-of-awesomes or ai-engineering-from-scratch more popular on GitHub? ai-engineering-from-scratch has more GitHub stars (37,922 vs 345). Stars measure visibility, not whether either tool fits your constraints. ### Are awesome-list-of-awesomes and ai-engineering-from-scratch open source? Yes - both are open-source projects on GitHub (awesome-list-of-awesomes: MIT, ai-engineering-from-scratch: MIT). ### Where can I find alternatives to awesome-list-of-awesomes or ai-engineering-from-scratch? GraphCanon lists graph-backed alternatives at [awesome-list-of-awesomes alternatives](/tools/nachimak28-awesome-list-of-awesomes/alternatives) and [ai-engineering-from-scratch alternatives](/tools/rohitg00-ai-engineering-from-scratch/alternatives) ([awesome-list-of-awesomes markdown twin](/tools/nachimak28-awesome-list-of-awesomes/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/nachimak28-awesome-list-of-awesomes-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-list-of-awesomes or ai-engineering-from-scratch? awesome-list-of-awesomes: Dormant. 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-list-of-awesomes and ai-engineering-from-scratch? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [awesome-list-of-awesomes trust report](/tools/nachimak28-awesome-list-of-awesomes/trust); [ai-engineering-from-scratch trust report](/tools/rohitg00-ai-engineering-from-scratch/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=nachimak28-awesome-list-of-awesomes`](/api/graphcanon/graph?tool=nachimak28-awesome-list-of-awesomes) - 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/_