--- title: "do-not-answer vs ai-engineering-from-scratch" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/libr-ai-do-not-answer-vs-rohitg00-ai-engineering-from-scratch" tools: ["libr-ai-do-not-answer", "rohitg00-ai-engineering-from-scratch"] --- # do-not-answer vs ai-engineering-from-scratch *GraphCanon updated Jul 11, 2026* ## Verdict Pick do-not-answer when do-not-answer is primarily Jupyter Notebook; ai-engineering-from-scratch is Python; pick ai-engineering-from-scratch when ai-engineering-from-scratch is primarily Python; do-not-answer is Jupyter Notebook. [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-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 [do-not-answer's repository](https://github.com/Libr-AI/do-not-answer) and [ai-engineering-from-scratch's repository](https://github.com/rohitg00/ai-engineering-from-scratch). | | [do-not-answer](/tools/libr-ai-do-not-answer.md) | [ai-engineering-from-scratch](/tools/rohitg00-ai-engineering-from-scratch.md) | | --- | --- | --- | | Tagline | Do-Not-Answer: A Dataset for Evaluating Safeguards in LLMs | Learn it. Build it. Ship it for others. | | Stars | 334 | 37,922 | | Forks | 29 | 6,329 | | Open issues | 0 | 96 | | Language | Jupyter Notebook | 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 | Evaluation & Observability, LLM Frameworks | AI Agents, Computer Vision, Developer Tools, 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-from-scratch](/tools/rohitg00-ai-engineering-from-scratch.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Active (82%) | | Days since push | 764d | 15d | | Open issues (now) | 0 | 96 | | 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/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 do-not-answer if… - do-not-answer is primarily Jupyter Notebook; ai-engineering-from-scratch is Python. - License: do-not-answer is Apache-2.0, ai-engineering-from-scratch is MIT. - Tags unique to do-not-answer: jupyter notebook. - Also covers Evaluation & Observability. ### Choose ai-engineering-from-scratch if… - ai-engineering-from-scratch is primarily Python; do-not-answer is Jupyter Notebook. - License: ai-engineering-from-scratch is MIT, do-not-answer 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: agents, ai-engineering, computer-vision, deep-learning. - Also covers AI Agents, Computer Vision, Developer Tools. - When you want to start with foundational knowledge and learn the intricacies behind AI systems. ## 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-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 do-not-answer and ai-engineering-from-scratch? do-not-answer: Do-Not-Answer: A Dataset for Evaluating Safeguards in LLMs. 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 do-not-answer over ai-engineering-from-scratch? Choose do-not-answer over ai-engineering-from-scratch when do-not-answer is primarily Jupyter Notebook; ai-engineering-from-scratch is Python; License: do-not-answer is Apache-2.0, ai-engineering-from-scratch is MIT; Tags unique to do-not-answer: jupyter notebook; Also covers Evaluation & Observability. ### When should I choose ai-engineering-from-scratch over do-not-answer? Choose ai-engineering-from-scratch over do-not-answer when ai-engineering-from-scratch is primarily Python; do-not-answer is Jupyter Notebook; License: ai-engineering-from-scratch is MIT, do-not-answer 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: agents, ai-engineering, computer-vision, deep-learning; Also covers AI Agents, Computer Vision, Developer Tools; When you want to start with foundational knowledge and learn the intricacies behind AI systems. ### 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-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 do-not-answer or ai-engineering-from-scratch more popular on GitHub? ai-engineering-from-scratch has more GitHub stars (37,922 vs 334). Stars measure visibility, not whether either tool fits your constraints. ### Are do-not-answer and ai-engineering-from-scratch open source? Yes - both are open-source projects on GitHub (do-not-answer: Apache-2.0, ai-engineering-from-scratch: MIT). ### Where can I find alternatives to do-not-answer or ai-engineering-from-scratch? GraphCanon lists graph-backed alternatives at [do-not-answer alternatives](/tools/libr-ai-do-not-answer/alternatives) and [ai-engineering-from-scratch alternatives](/tools/rohitg00-ai-engineering-from-scratch/alternatives) ([do-not-answer markdown twin](/tools/libr-ai-do-not-answer/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/libr-ai-do-not-answer-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, do-not-answer or ai-engineering-from-scratch? do-not-answer: 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 do-not-answer and ai-engineering-from-scratch? 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-from-scratch trust report](/tools/rohitg00-ai-engineering-from-scratch/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/_