generative-ai-for-beginners vs llms-from-scratch
Two of the top projects in Learning and cookbooks, side by side: who leads on each score part and who each one suits, then setup, license, activity and what each review found.
generative-ai-for-beginners
21-lesson course on building generative AI apps in Python and TypeScript
llms-from-scratch
Code for the book on building a GPT-style LLM in PyTorch
Verdict
generative-ai-for-beginners ranks higher, 80 to 74. Of the 4 score parts we measure for both, generative-ai-for-beginners leads on 3, llms-from-scratch on none and 1 is even.
- Adoptiongenerative-ai-for-beginners leads by 8 points, 96 to 88.
- FreshnessEven at 100.
- Maintenancegenerative-ai-for-beginners leads by 4 points, 62 to 58: close to even.
- Agent-readygenerative-ai-for-beginners leads by 30 points, 30 to 0: a wide gap.
Pick generative-ai-for-beginners if you are
Developers new to building LLM apps who know basic Python or TypeScript
- Build lessons ship code in both Python and TypeScript where possible
- Covers RAG, function calling, agents, fine-tuning and security in one sequence
Pick llms-from-scratch if you are
Python developers learning how LLMs work by coding one in PyTorch
- Main chapter code runs on a conventional laptop and uses a GPU automatically if present
- Plain PyTorch with no external LLM libraries, so every component is readable
| What we compare | generative-ai-for-beginners | llms-from-scratch |
|---|---|---|
| Score parts, out of 100 | ||
| Adoption | 96, widely used | 88, widely used |
| Freshness | 100, active | 100, active |
| Maintenance | 62, fair | 58, fair |
| Easy to run | Not scored for this kind | Not scored for this kind |
| Agent-ready | 30, minimal | 0, none |
| Facts from GitHub and the README | ||
| Stars | 121.3k | 106.5k |
| License | MIT (permissive) | custom license (read the license) |
| Last commit | Oct 2026 | Oct 2026 |
| Last release | None published | None published |
| Language | Jupyter Notebook | Jupyter Notebook |
| Docker | No | No |
| GPU | Not needed | Optional |
| arm64 or Apple Silicon | Not stated | Not stated |
generative-ai-for-beginners
A Microsoft Cloud Advocates course of 21 lessons, each a README with a short video and code samples, split into "Learn" concept lessons and "Build" lessons. Topics include prompt engineering, text and chat apps, embeddings search, image generation, function calling, RAG, agents, fine-tuning, and small, Mistral and Meta models. Code targets Azure OpenAI, OpenAI API, Microsoft Foundry Models or Foundry Local.
Who it is for: Developers new to building LLM apps who know basic Python or TypeScript
Strengths
- Build lessons ship code in both Python and TypeScript where possible
- Covers RAG, function calling, agents, fine-tuning and security in one sequence
- Lessons are independent, so you can start at any number
- MIT license; translations into 50+ languages
Weaknesses
- Samples assume Azure OpenAI, OpenAI API or Foundry access, which may need paid accounts
- Several lessons list video as coming soon
- Cloning includes 50+ translations, so sparse checkout is needed for a small download
- GitHub Models lessons are tied to a service retiring end of July 2026
- no GPU
- Needs Azure OpenAI Service, OpenAI API, Microsoft Foundry Models, Foundry Local, GitHub account
- Models: Azure OpenAI, OpenAI, Microsoft Foundry Models, Foundry Local, Hugging Face open models
llms-from-scratch
Official code for the Manning book "Build a Large Language Model (From Scratch)". Notebooks and scripts implement tokenization, attention, a GPT model, pretraining, classification finetuning, instruction finetuning and LoRA in plain PyTorch, without external LLM libraries. Bonus folders add from-scratch Llama 3.2, Qwen3, Gemma 3, Olmo 3, KV cache, MoE and DPO.
Who it is for: Python developers learning how LLMs work by coding one in PyTorch
Strengths
- Main chapter code runs on a conventional laptop and uses a GPU automatically if present
- Plain PyTorch with no external LLM libraries, so every component is readable
- Bonus material covers KV cache, GQA, MoE, DPO and several model architectures
- Exercise solutions, a companion video course and CI tests on Linux, Windows and macOS
Weaknesses
- Tied to a print book; contributions extending main chapter code are not accepted
- Produces a small educational model, not a production-grade LLM
- License is reported as NOASSERTION; the README states no license terms
- Video course and quiz PDF are hosted by the publisher, not in the repo
- GPU optional
- Needs PyTorch, Jupyter, Ollama (optional, ch07 evaluation)
- Models: GPT-2, Llama 3.2, Qwen3, Gemma 3, Olmo 3
More in Learning and cookbooks
Building with these?
We help teams pick an open-source AI stack and run it in production. Client work never changes a rank.