Comparison
do-not-answer vs llm-course
Verdict
Pick do-not-answer when tags unique to do-not-answer: jupyter notebook; pick llm-course when requirements: Course materials are available in Colab notebooks; access requires a Google account.
Markdown twin · do-not-answer alternatives · llm-course alternatives
GraphCanon updated today
vs
Trust & integrity
| Signal | do-not-answer | llm-course |
|---|---|---|
| Maintenance | Dormant (764d since push) As of today · github_public_v1 | Slowing (155d since push) As of today · github_public_v1 |
| Provenance | Not a fork · Organization account As of today · github_public_v1 | Not a fork · Personal account As of today · github_public_v1 |
| Security (OSV) | No lockfile As of today · none | No lockfile As of today · none |
Tagline
- do-not-answer
- Do-Not-Answer: A Dataset for Evaluating Safeguards in LLMs
- llm-course
- Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks.
Stars
- do-not-answer
- 334
- llm-course
- 81k
Forks
- do-not-answer
- 29
- llm-course
- 9.4k
Open issues
- do-not-answer
- 0
- llm-course
- 84
Language
- do-not-answer
- Jupyter Notebook
- llm-course
- -
Adopt for
- do-not-answer
- -
- llm-course
- The llm-course provides a comprehensive guided course on Large Language Models (LLMs), divided into three parts: LLM Fundamentals, The LLM Scientist, and The LLM Engineer. It includes resources such as Colab notebooks to
Persona
- do-not-answer
- -
- llm-course
- -
Runtime
- do-not-answer
- -
- llm-course
- -
License
- do-not-answer
- Apache-2.0
- llm-course
- Apache-2.0
Last pushed
- do-not-answer
- Jun 7, 2024
- llm-course
- Feb 5, 2026
Categories
- do-not-answer
- LLM Frameworks, Evaluation & Observability
- llm-course
- LLM Frameworks, Model Training, Inference & Serving, Evaluation & Observability
Trust and health
Maintenance
- do-not-answer
- Dormant (18%)
- llm-course
- Slowing (36%)
Days since push
- do-not-answer
- 764d
- llm-course
- 155d
Open issues (now)
- do-not-answer
- 0
- llm-course
- 84
Owner type
- do-not-answer
- Organization
- llm-course
- User
Full report
- do-not-answer
- Trust report
- llm-course
- Trust report
Choose do-not-answer if…
- Tags unique to do-not-answer: jupyter notebook.
- Leaner open-issue backlog (0).
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.
- LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves.
- Evaluation & Observability: Defer heavyweight eval infra only until you have real traffic - never skip it once users depend on answers.
Choose llm-course if…
- Requirements: Course materials are available in Colab notebooks; access requires a Google account.
- Tags unique to llm-course: colab-notebooks, machine-learning, course, large-language-models.
- Also covers Model Training, Inference & Serving.
- - When you want a comprehensive roadmap for understanding large language models including fundamental knowledge
When NOT to use llm-course
- - If you only require a quick introduction to LLMs without deep dive into core components
- - When you prefer working directly with commercial platforms that provide complete services rather than following detailed steps on building and deploying models yourself through this course's open,DI
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (Libr-AI/do-not-answer) · observed Jul 11, 2026
- GitHub forks (Libr-AI/do-not-answer) · observed Jul 11, 2026
- Last push (Libr-AI/do-not-answer) · observed Jun 7, 2024
- License file (Apache-2.0) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (mlabonne/llm-course) · observed Jul 11, 2026
- GitHub forks (mlabonne/llm-course) · observed Jul 11, 2026
- Last push (mlabonne/llm-course) · observed Feb 5, 2026
- License file (Apache-2.0) · observed Jul 11, 2026
- Decision facts (enrichment) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: do-not-answer 334 · llm-course 81k (synced Jul 11, 2026).
Common questions
- What is the difference between do-not-answer and llm-course?
- do-not-answer: Do-Not-Answer: A Dataset for Evaluating Safeguards in LLMs. llm-course: Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks.. See the comparison table for live GitHub stats and shared categories.
- When should I choose do-not-answer over llm-course?
- Choose do-not-answer over llm-course when Tags unique to do-not-answer: jupyter notebook; Leaner open-issue backlog (0).
- When should I choose llm-course over do-not-answer?
- Choose llm-course over do-not-answer when Requirements: Course materials are available in Colab notebooks; access requires a Google account; Tags unique to llm-course: colab-notebooks, machine-learning, course, large-language-models; Also covers Model Training, Inference & Serving; - When you want a comprehensive roadmap for understanding large language models including fundamental knowledge.
- 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. LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. Evaluation & Observability: Defer heavyweight eval infra only until you have real traffic - never skip it once users depend on answers.
- When should I avoid llm-course?
- - If you only require a quick introduction to LLMs without deep dive into core components - When you prefer working directly with commercial platforms that provide complete services rather than following detailed steps on building and deploying models yourself through this course's open,DI
- Is do-not-answer or llm-course more popular on GitHub?
- llm-course has more GitHub stars (80,839 vs 334). Stars measure visibility, not whether either tool fits your constraints.
- Are do-not-answer and llm-course open source?
- Yes - both are open-source projects on GitHub (do-not-answer: Apache-2.0, llm-course: Apache-2.0).
- Where can I find alternatives to do-not-answer or llm-course?
- GraphCanon lists graph-backed alternatives at do-not-answer alternatives and llm-course alternatives (do-not-answer markdown twin, llm-course markdown twin), 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 mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.
- Which is better maintained, do-not-answer or llm-course?
- do-not-answer: Dormant. llm-course: Slowing. 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 llm-course?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: do-not-answer trust report; llm-course trust report.