--- title: "do-not-answer vs ai-engineering-hub" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/libr-ai-do-not-answer-vs-patchy631-ai-engineering-hub" tools: ["libr-ai-do-not-answer", "patchy631-ai-engineering-hub"] --- # do-not-answer vs ai-engineering-hub *GraphCanon updated Jul 11, 2026* ## Verdict Pick do-not-answer when license: do-not-answer is Apache-2.0, ai-engineering-hub is MIT; pick ai-engineering-hub when license: ai-engineering-hub is MIT, do-not-answer is Apache-2.0. [do-not-answer](https://github.com/Libr-AI/do-not-answer) reports 334 GitHub stars, 29 forks, and 0 open issues, last pushed Jun 7, 2024. [ai-engineering-hub](https://join.dailydoseofds.com) has 36k stars, 6.0k forks, and 119 open issues, last pushed Jun 8, 2026. Figures are from public GitHub metadata via [do-not-answer's repository](https://github.com/Libr-AI/do-not-answer) and [ai-engineering-hub's repository](https://github.com/patchy631/ai-engineering-hub). | | [do-not-answer](/tools/libr-ai-do-not-answer.md) | [ai-engineering-hub](/tools/patchy631-ai-engineering-hub.md) | | --- | --- | --- | | Tagline | Do-Not-Answer: A Dataset for Evaluating Safeguards in LLMs | Tutorials on LLMs, RAGs, and real-world AI agent applications | | Stars | 334 | 36,439 | | Forks | 29 | 6,039 | | Open issues | 0 | 119 | | Language | Jupyter Notebook | Jupyter Notebook | | Adopt for | - | A collection of in-depth tutorials aiming to cover a wide range from beginner to advanced concepts in AI, including large language models (LLMs), Retrieval-Augmented Generation (RAG) systems and practical applications of | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | MIT License | | Categories | Evaluation & Observability, LLM Frameworks | AI Agents, LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [do-not-answer](/tools/libr-ai-do-not-answer.md) | [ai-engineering-hub](/tools/patchy631-ai-engineering-hub.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Steady (60%) | | Days since push | 764d | 32d | | Open issues (now) | 0 | 119 | | Owner type | Organization | User | | Security scan | No lockfile | No MCP manifest | | Full report | [trust report](/tools/libr-ai-do-not-answer/trust.md) | [trust report](/tools/patchy631-ai-engineering-hub/trust.md) | ## Decision facts: ai-engineering-hub - **Requirements:** The tutorials and projects use Jupyter Notebooks which require Python and a compatible local environment or cloud-based Jupyter services. - **Adopt for:** A collection of in-depth tutorials aiming to cover a wide range from beginner to advanced concepts in AI, including large language models (LLMs), Retrieval-Augmented Generation (RAG) systems and practical applications of - **License detail:** MIT License ## Choose when ### Choose do-not-answer if… - License: do-not-answer is Apache-2.0, ai-engineering-hub is MIT. - Tags unique to do-not-answer: jupyter notebook. - Also covers Evaluation & Observability. ### Choose ai-engineering-hub if… - License: ai-engineering-hub is MIT, do-not-answer is Apache-2.0. - Requirements: The tutorials and projects use Jupyter Notebooks which require Python and a compatible local environment or cloud-based Jupyter services.. - Tags unique to ai-engineering-hub: agents, ai, llms, machine-learning. - Also covers AI Agents. - When you are looking for comprehensive learning paths ranging from complete beginners to advanced experts. ## When NOT to use do-not-answer - Last GitHub push was 764 days ago (dormant maintenance, Jun 7, 2024). Validate activity before betting a new project on do-not-answer. - Evaluation & Observability: Defer heavyweight eval infra only until you have real traffic - never skip it once users depend on answers. - LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. ## When NOT to use ai-engineering-hub - If your team already has significant proficiency in AI engineering and advanced LLM frameworks, as the content starts from zero knowledge up. - When you specifically need industry-standard proprietary tools or heavily specialized niche applications that go beyond foundational learning covered by this hub. - In scenarios where immediate advanced project results are required; ai-engineering-hub focuses on education through step-by-step tutorials rather than providing ready-made solutions with minimal setup ## Common questions ### What is the difference between do-not-answer and ai-engineering-hub? do-not-answer: Do-Not-Answer: A Dataset for Evaluating Safeguards in LLMs. ai-engineering-hub: Tutorials on LLMs, RAGs, and real-world AI agent applications. See the comparison table for live GitHub stats and shared categories. ### When should I choose do-not-answer over ai-engineering-hub? Choose do-not-answer over ai-engineering-hub when License: do-not-answer is Apache-2.0, ai-engineering-hub is MIT; Tags unique to do-not-answer: jupyter notebook; Also covers Evaluation & Observability. ### When should I choose ai-engineering-hub over do-not-answer? Choose ai-engineering-hub over do-not-answer when License: ai-engineering-hub is MIT, do-not-answer is Apache-2.0; Requirements: The tutorials and projects use Jupyter Notebooks which require Python and a compatible local environment or cloud-based Jupyter services.; Tags unique to ai-engineering-hub: agents, ai, llms, machine-learning; Also covers AI Agents; When you are looking for comprehensive learning paths ranging from complete beginners to advanced experts. ### When should I avoid do-not-answer? Last GitHub push was 764 days ago (dormant maintenance, Jun 7, 2024). Validate activity before betting a new project on do-not-answer. Evaluation & Observability: Defer heavyweight eval infra only until you have real traffic - never skip it once users depend on answers. LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. ### When should I avoid ai-engineering-hub? If your team already has significant proficiency in AI engineering and advanced LLM frameworks, as the content starts from zero knowledge up. When you specifically need industry-standard proprietary tools or heavily specialized niche applications that go beyond foundational learning covered by this hub. In scenarios where immediate advanced project results are required; ai-engineering-hub focuses on education through step-by-step tutorials rather than providing ready-made solutions with minimal setup ### Is do-not-answer or ai-engineering-hub more popular on GitHub? ai-engineering-hub has more GitHub stars (36,439 vs 334). Stars measure visibility, not whether either tool fits your constraints. ### Are do-not-answer and ai-engineering-hub open source? Yes - both are open-source projects on GitHub (do-not-answer: Apache-2.0, ai-engineering-hub: MIT). ### Where can I find alternatives to do-not-answer or ai-engineering-hub? GraphCanon lists graph-backed alternatives at [do-not-answer alternatives](/tools/libr-ai-do-not-answer/alternatives) and [ai-engineering-hub alternatives](/tools/patchy631-ai-engineering-hub/alternatives) ([do-not-answer markdown twin](/tools/libr-ai-do-not-answer/alternatives.md), [ai-engineering-hub markdown twin](/tools/patchy631-ai-engineering-hub/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/libr-ai-do-not-answer-vs-patchy631-ai-engineering-hub.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, do-not-answer or ai-engineering-hub? do-not-answer: Dormant. ai-engineering-hub: Steady. 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 do-not-answer and ai-engineering-hub? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [do-not-answer trust report](/tools/libr-ai-do-not-answer/trust); [ai-engineering-hub trust report](/tools/patchy631-ai-engineering-hub/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=libr-ai-do-not-answer`](/api/graphcanon/graph?tool=libr-ai-do-not-answer) - 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/_