--- title: "awesome-automl-papers vs ai-engineering-from-scratch" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/hibayesian-awesome-automl-papers-vs-rohitg00-ai-engineering-from-scratch" tools: ["hibayesian-awesome-automl-papers", "rohitg00-ai-engineering-from-scratch"] --- # awesome-automl-papers vs ai-engineering-from-scratch *GraphCanon updated Jul 11, 2026* ## Verdict Pick awesome-automl-papers when license: awesome-automl-papers is Apache-2.0, ai-engineering-from-scratch is MIT; pick ai-engineering-from-scratch when license: ai-engineering-from-scratch is MIT, awesome-automl-papers is Apache-2.0. [awesome-automl-papers](https://github.com/hibayesian/awesome-automl-papers) reports 4.2k GitHub stars, 680 forks, and 2 open issues, last pushed Jun 11, 2024. [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-automl-papers's repository](https://github.com/hibayesian/awesome-automl-papers) and [ai-engineering-from-scratch's repository](https://github.com/rohitg00/ai-engineering-from-scratch). | | [awesome-automl-papers](/tools/hibayesian-awesome-automl-papers.md) | [ai-engineering-from-scratch](/tools/rohitg00-ai-engineering-from-scratch.md) | | --- | --- | --- | | Tagline | A curated list of automated machine learning papers, articles, tutorials, slides and projects | Learn it. Build it. Ship it for others. | | Stars | 4,152 | 37,922 | | Forks | 680 | 6,329 | | Open issues | 2 | 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 | Apache-2.0 | MIT | | Categories | Vector Databases, Computer Vision | LLM Frameworks, AI Agents, Developer Tools, Computer Vision | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [awesome-automl-papers](/tools/hibayesian-awesome-automl-papers.md) | [ai-engineering-from-scratch](/tools/rohitg00-ai-engineering-from-scratch.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Active (82%) | | Days since push | 760d | 15d | | Open issues (now) | 2 | 96 | | Security scan | No lockfile | No MCP manifest | | Full report | [trust report](/tools/hibayesian-awesome-automl-papers/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-automl-papers if… - License: awesome-automl-papers is Apache-2.0, ai-engineering-from-scratch is MIT. - Tags unique to awesome-automl-papers: automl, neural-architecture-search, automated-feature-engineering, hyperparameter-optimization. - Also covers Vector Databases. ### Choose ai-engineering-from-scratch if… - License: ai-engineering-from-scratch is MIT, awesome-automl-papers is Apache-2.0. - 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: deep-learning, ai-engineering, agents, llm. - 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-automl-papers - Last GitHub push was 760 days ago (dormant maintenance, Jun 11, 2024). Validate activity before betting a new project on awesome-automl-papers. - Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate. ## 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-automl-papers and ai-engineering-from-scratch? awesome-automl-papers: A curated list of automated machine learning papers, articles, tutorials, slides and projects. 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-automl-papers over ai-engineering-from-scratch? Choose awesome-automl-papers over ai-engineering-from-scratch when License: awesome-automl-papers is Apache-2.0, ai-engineering-from-scratch is MIT; Tags unique to awesome-automl-papers: automl, neural-architecture-search, automated-feature-engineering, hyperparameter-optimization; Also covers Vector Databases. ### When should I choose ai-engineering-from-scratch over awesome-automl-papers? Choose ai-engineering-from-scratch over awesome-automl-papers when License: ai-engineering-from-scratch is MIT, awesome-automl-papers is Apache-2.0; 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: deep-learning, ai-engineering, agents, llm; 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-automl-papers? Last GitHub push was 760 days ago (dormant maintenance, Jun 11, 2024). Validate activity before betting a new project on awesome-automl-papers. Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate. ### 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-automl-papers or ai-engineering-from-scratch more popular on GitHub? ai-engineering-from-scratch has more GitHub stars (37,922 vs 4,152). Stars measure visibility, not whether either tool fits your constraints. ### Are awesome-automl-papers and ai-engineering-from-scratch open source? Yes - both are open-source projects on GitHub (awesome-automl-papers: Apache-2.0, ai-engineering-from-scratch: MIT). ### Where can I find alternatives to awesome-automl-papers or ai-engineering-from-scratch? GraphCanon lists graph-backed alternatives at [awesome-automl-papers alternatives](/tools/hibayesian-awesome-automl-papers/alternatives) and [ai-engineering-from-scratch alternatives](/tools/rohitg00-ai-engineering-from-scratch/alternatives) ([awesome-automl-papers markdown twin](/tools/hibayesian-awesome-automl-papers/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/hibayesian-awesome-automl-papers-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-automl-papers or ai-engineering-from-scratch? awesome-automl-papers: 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-automl-papers and ai-engineering-from-scratch? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [awesome-automl-papers trust report](/tools/hibayesian-awesome-automl-papers/trust); [ai-engineering-from-scratch trust report](/tools/rohitg00-ai-engineering-from-scratch/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=hibayesian-awesome-automl-papers`](/api/graphcanon/graph?tool=hibayesian-awesome-automl-papers) - 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/_