Comparison
open-r1 vs ai-engineering-from-scratch
Verdict
Pick open-r1 if open-R1 is an open-source effort to replicate DeepSeek-R1's models and training pipelines involving model distillation, RL pipeline replication, and multi-stage training; pick ai-engineering-from-scratch if ai-engineering-from-scratch is a comprehensive course that teaches AI engineering skills from foundational math to advanced AI agents and machine learning techniques, using Python and Node.js for interactive learning and.
Markdown twin · open-r1 alternatives · ai-engineering-from-scratch alternatives
GraphCanon updated Sep 20, 2026
9views this month
vs
Trust & integrity
| Signal | open-r1 | ai-engineering-from-scratch |
|---|---|---|
| Maintenance | Slowing (156d since push) As of Sep 6, 2026 · github_public_v1 | Active (10d since push) As of Sep 18, 2026 · github_public_v1 |
| Provenance | Not a fork · Organization account As of Sep 6, 2026 · github_public_v1 | Not a fork · Personal account As of Sep 18, 2026 · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of Jul 11, 2026 · osv@v1 | Published findings As of Sep 18, 2026 · osv@v1 |
| deps.dev advisories | Not queried deps.dev@v1 | Not queried deps.dev@v1 |
| OpenSSF Scorecard | Not queried openssf-scorecard@v1 | Not queried openssf-scorecard@v1 |
Tagline
- open-r1
- Fully open reproduction of DeepSeek-R1
- ai-engineering-from-scratch
- Learn, build, and deploy AI engineering skills from scratch.
Stars
- open-r1
- 26k
- ai-engineering-from-scratch
- 55k
Forks
- open-r1
- 2.4k
- ai-engineering-from-scratch
- 9.6k
Open issues
- open-r1
- 340
- ai-engineering-from-scratch
- 114
Language
- open-r1
- Python
- ai-engineering-from-scratch
- Python
Adopt for
- open-r1
- Open-R1 is an open-source effort to replicate DeepSeek-R1's models and training pipelines involving model distillation, RL pipeline replication, and multi-stage training.
- ai-engineering-from-scratch
- ai-engineering-from-scratch is a comprehensive course that teaches AI engineering skills from foundational math to advanced AI agents and machine learning techniques, using Python and Node.js for interactive learning and
Persona
- open-r1
- -
- ai-engineering-from-scratch
- -
Runtime
- open-r1
- -
- ai-engineering-from-scratch
- -
License
- open-r1
- The project is licensed under Apache-2.0, providing a permissive license that allows for free use, modification, and distribution.
- ai-engineering-from-scratch
- MIT
Last pushed
- open-r1
- Apr 2, 2026
- ai-engineering-from-scratch
- Sep 7, 2026
Categories
- open-r1
- Inference & Serving, Model Training
- ai-engineering-from-scratch
- AI Agents, Computer Vision, Developer Tools, Model Training
Trust and health
Maintenance
- open-r1
- Slowing (36%)
- ai-engineering-from-scratch
- Active (82%)
Days since push
- open-r1
- 156d
- ai-engineering-from-scratch
- 10d
Open issues (now)
- open-r1
- 340
- ai-engineering-from-scratch
- 114
Stars delta
- open-r1
- +26 (30d)
- ai-engineering-from-scratch
- +8.1k (30d)
Open issues delta
- open-r1
- 0 (30d)
- ai-engineering-from-scratch
- +7 (30d)
Owner type
- open-r1
- Organization
- ai-engineering-from-scratch
- User
OSV dependency advisories
- open-r1
- No lockfile (source not queried)
- ai-engineering-from-scratch
- Published findings
Full report
- open-r1
- Trust report
- ai-engineering-from-scratch
- Trust report
Shared compatibility
- Python · open-r1: Python runtime · ai-engineering-from-scratch: Python runtime
Choose open-r1 if…
- License: open-r1 is Apache-2.0, ai-engineering-from-scratch is MIT.
- Requirements: Min 8 GB RAM; Installation requires CUDA version 12.4 and PyTorch v2.6.0, with specific dependencies like vLLM and FlashAttention that are critical..
- Tags unique to open-r1: cuda, deepseek-r1, flashattention, model distillation.
- Also covers Inference & Serving.
- Use Open-R1 when you need a detailed understanding of how DeepSeek-R1 operates, considering the project closely mirrors its architecture and processes.
When NOT to use open-r1
- Avoid Open-R1 if your hardware does not support CUDA 12.4 or cannot run PyTorch `v2.6.0`, as this may lead to errors.
- Do not use it if the need for rapid experimentation outweighs the value of detailed replication, since the multi-stage training and datasets curation process can be time-consuming.
Choose ai-engineering-from-scratch if…
- License: ai-engineering-from-scratch is MIT, open-r1 is Apache-2.0.
- Tags unique to ai-engineering-from-scratch: agents, ai-engineering, computer-vision, deep-learning.
- Also covers AI Agents, Computer Vision, Developer Tools.
- when you need a structured course that covers a wide range of AI engineering topics from scratch, including foundational math, deep learning, and reinforcement learning.
When NOT to use ai-engineering-from-scratch
- if you are looking for a tool that focuses solely on theoretical knowledge without practical application, as this course emphasizes hands-on learning.
- when you do not have access to Node.js or Python, as these are required for running the course and its interactive components.
- if you prefer a more traditional learning approach without the use of terminal-based learning tools, as the course is designed for interactive terminal sessions.
- when you are working with a development environment that does not support skill-capable hosts, as the course is optimized for such environments.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (huggingface/open-r1) · observed Sep 20, 2026
- GitHub forks (huggingface/open-r1) · observed Sep 20, 2026
- Last push (huggingface/open-r1) · observed Apr 2, 2026
- License file (Apache-2.0) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (rohitg00/ai-engineering-from-scratch) · observed Sep 20, 2026
- GitHub forks (rohitg00/ai-engineering-from-scratch) · observed Sep 20, 2026
- Last push (rohitg00/ai-engineering-from-scratch) · observed Sep 7, 2026
- License file (MIT) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Sep 18, 2026
- Trust scan (lockfile / OSV) · observed Sep 18, 2026
GitHub stars on cards: open-r1 26k · ai-engineering-from-scratch 55k (synced Sep 20, 2026).
Common questions
- What is the difference between open-r1 and ai-engineering-from-scratch?
- open-r1: Fully open reproduction of DeepSeek-R1. ai-engineering-from-scratch: Learn, build, and deploy AI engineering skills from scratch.. See the comparison table for live GitHub stats and shared categories.
- When should I choose open-r1 over ai-engineering-from-scratch?
- Choose open-r1 over ai-engineering-from-scratch when License: open-r1 is Apache-2.0, ai-engineering-from-scratch is MIT; Requirements: Min 8 GB RAM; Installation requires CUDA version 12.4 and PyTorch v2.6.0, with specific dependencies like vLLM and FlashAttention that are critical.; Tags unique to open-r1: cuda, deepseek-r1, flashattention, model distillation; Also covers Inference & Serving; Use Open-R1 when you need a detailed understanding of how DeepSeek-R1 operates, considering the project closely mirrors its architecture and processes.
- When should I choose ai-engineering-from-scratch over open-r1?
- Choose ai-engineering-from-scratch over open-r1 when License: ai-engineering-from-scratch is MIT, open-r1 is Apache-2.0; Tags unique to ai-engineering-from-scratch: agents, ai-engineering, computer-vision, deep-learning; Also covers AI Agents, Computer Vision, Developer Tools; when you need a structured course that covers a wide range of AI engineering topics from scratch, including foundational math, deep learning, and reinforcement learning.
- When should I avoid open-r1?
- Avoid Open-R1 if your hardware does not support CUDA 12.4 or cannot run PyTorch
v2.6.0, as this may lead to errors. Do not use it if the need for rapid experimentation outweighs the value of detailed replication, since the multi-stage training and datasets curation process can be time-consuming. - When should I avoid ai-engineering-from-scratch?
- if you are looking for a tool that focuses solely on theoretical knowledge without practical application, as this course emphasizes hands-on learning. when you do not have access to Node.js or Python, as these are required for running the course and its interactive components. if you prefer a more traditional learning approach without the use of terminal-based learning tools, as the course is designed for interactive terminal sessions. when you are working with a development environment that does not support skill-capable hosts, as the course is optimized for such environments.
- Is open-r1 or ai-engineering-from-scratch more popular on GitHub?
- ai-engineering-from-scratch has more GitHub stars (54,935 vs 26,449). Stars measure visibility, not whether either tool fits your constraints.
- Are open-r1 and ai-engineering-from-scratch open source?
- Yes - both are open-source projects on GitHub (open-r1: Apache-2.0, ai-engineering-from-scratch: MIT).
- Where can I find alternatives to open-r1 or ai-engineering-from-scratch?
- GraphCanon lists graph-backed alternatives at open-r1 alternatives and ai-engineering-from-scratch alternatives (open-r1 markdown twin, ai-engineering-from-scratch 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, open-r1 or ai-engineering-from-scratch?
- open-r1: Slowing. 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 open-r1 and ai-engineering-from-scratch?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: open-r1 trust report; ai-engineering-from-scratch trust report.