Learning track 01
LLM Foundations
Build a mechanical model of tokens, neural networks, generation, prompts, and inference.
- Guides
- 5
- Sections
- 37
- Cards
- 132
Suggested sequence
Follow the track or enter where the problem begins.
Every guide is self-contained. The sequence only preserves prerequisite order when one topic depends on another.
- 01Vol. 07
From a Neuron to an LLM
The route from linear regression to transformer: classical ML models and their limit, biological neuron and perceptron, deep networks and representations, recurrence and its problems, attention as a breakthrough, transformer as architecture and LLM as a result of scale.
- Sections
- 8
- Cards
- 23
- 02Vol. 15
LLMs in Plain English
Not the history of architectures or GPU micro-optimizations, but practical mechanics without academic fog: how text is cut into tokens, how transformer blocks recalculate the meaning, how the encoder and decoder differ, how the model learns and why it responds one token at a time.
- Sections
- 7
- Cards
- 23
- 03Vol. 05
LLM Internals: From Token to Logit to Answer
The full path from the input request to the next token: tokenization, embeddings, positional encoding, self-attention, MLP, normalization, KV-cache, logits, softmax and sampling parameters - parsed atomically.
- Sections
- 7
- Cards
- 28
- 04Vol. 14
LLM Internals: KV Cache & Generation
An in-depth analysis of the mechanics of inference: how exactly KV-cache works and why O(n²) without it, prefill vs decode phases, GPU memory arithmetic, GQA/MQA/PagedAttention, prompt caching at the API level (Anthropic + OpenAI), sampling from logits to token, speculative decoding, and the main case - “mother washed the frame” through LLM step by step.
- Sections
- 8
- Cards
- 24
- 05Vol. 23
What a Prompt Is and Why a Question Becomes an Answer
The main paradox of LLM is this: if the model is just predicting the next token, why does it answer the question meaningfully, and not just continue our text? This volume analyzes the prompt as the model's actual input, chat mode as a special data format, instruction tuning as a shift in behavior towards the “assistant”, and the connection of prompt with the model weights.
- Sections
- 7
- Cards
- 34