no-magic-ai/no-magic resource

Health v2 · maintenance only

Adoption not part of the score

1410 stars · 108 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

A curated collection of 48 single-file, zero-dependency Python implementations of core AI algorithms, from GPT and LSTM to LoRA, quantization, and MCTS. Each script is a complete runnable program that trains a model from scratch, designed as an educational resource for understanding what frameworks like PyTorch do underneath.

Use cases

• learn how transformers and GPT work from scratch
• understand what model.fit() does under the hood
• implement BPE tokenization without libraries
• study LoRA, DPO, and RLHF alignment algorithms
• learn quantization and KV-cache inference optimization
• understand RAG and vector search internals
• study agent algorithms like MCTS and ReAct
• teach deep learning concepts with runnable code

When to choose

• you want to deeply understand AI algorithms rather than just call APIs
• you prefer dependency-free, runnable single-file examples
• you want a structured curriculum through modern ML concepts
• you're preparing for ML interviews or teaching

When to avoid

• you need production-ready, performant model training
• you want GPU-accelerated training with ecosystem support
• you need a framework to build applications on top of

Facets

learning-resource · maturity active

machine-learning deep-learning llm-training agent-framework rag nlp machine-learning deep-learning large-language-models artificial-intelligence education tutorials python cross-platform cli educational zero-dependencies single-file algorithms-from-scratch transformers reinforcement-learning quantization microgpt ai-agents

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