Stop learning AI from static diagrams: a curated visual learning roadmap

X user @techNmak published a thread advocating learning AI through interactive tools rather than static diagrams: watching a Transformer process text, seeing neural networks train in real time, tracing diffusion steps, and inspecting features inside real LLMs.

Confirmed

• Beginner tools: Georgia Tech Polo Club's Transformer Explainer lets you feed arbitrary text into GPT-2 (124M params) and watch tokenization and every subsequent computation; TensorFlow Playground exposes datasets, layers, neurons, activations, learning rate and regularization, making underfitting/overfitting visible.
• Classic illustrated guides: Jay Alammar's The Illustrated Transformer remains one of the clearest explanations covering embeddings, Q/K/V, multi-head attention and autoregressive generation; Maarten Grootendorst's A Visual Guide to Mixture of Experts uses 50+ visualizations to unpack experts and routing.
• Advanced concepts: Abhik Sarkar's 26 interactive articles cover RoPE, KV cache, FlashAttention, MQA, GQA and sliding-window attention; Sebastian Raschka's guide visually contrasts MHA, MQA, GQA and MLA and explains why shared key/value heads shrink KV cache memory; Brendan Bycroft's LLM Visualization zooms from model level down to tensor-level operations.
• Systems & inference: Modular's LLM Inference Handbook covers prefill, decode, KV cache, batching, scheduling, quantization, prefix caching and speculative decoding.
• Research-grade: Google PAIR's AI Explorables include understanding LLM hidden representations with sparse autoencoders (SAE), the Patchscopes introspection framework, and grokking; Distill's archive remains a trove with How to Use t-SNE Effectively, Feature Visualization, Activation Atlas and The Grand Tour.
• Math foundations: 3Blue1Brown teaches linear algebra, neural networks, gradient descent, backpropagation and attention via geometric intuition.

Why it matters

LLM concepts (KV cache, attention variants, MoE, inference optimization) evolve fast and are inherently dynamic, which static diagrams fail to convey. The thread closes with a full roadmap—math from 3Blue1Brown to Seeing Theory, ML from TensorFlow Playground to Distill, then Transformer/LLM topics up to interpretability tools like Neuronpedia—offering learners a layered path from basic math to research-grade interpretability.

2026-09-04 ~ 2026-09-04 · 13 related posts

• [source] Stop learning AI from static diagrams: a thread of the best interactive visual resources — techNmak · 2026-09-04
• [source] Transformer Explainer lets you watch your text flow through GPT-2, token by token — techNmak · 2026-09-04
• Two interactive visualizations take you from GPT-2 tokenization down to tensor-level LLM internals — techNmak · 2026-09-04
• 3Blue1Brown and LLM Visualization: the best ways to build geometric intuition for AI — techNmak · 2026-09-04
• Jay Alammar's Illustrated Transformer remains the cleanest visual explanation of the architecture — techNmak · 2026-09-04
• TensorFlow Playground makes overfitting and model capacity visible as you tweak layers live — techNmak · 2026-09-04
• Google PAIR's AI Explorables: research-grade interactive essays from SAEs to differential privacy — techNmak · 2026-09-04
• Distill is no longer publishing, but its archive remains a goldmine: t-SNE, feature visualization, GNNs — techNmak · 2026-09-04
• 26 interactive visual explainers demystify RoPE, KV cache, FlashAttention and attention sinks — techNmak · 2026-09-04
• Sebastian Raschka's visual guide makes MHA, MQA, GQA and MLA attention variants finally click — techNmak · 2026-09-04
• A visual guide with 50+ diagrams demystifies Mixture of Experts in modern LLMs — techNmak · 2026-09-04
• Modular's LLM Inference Handbook covers prefill, KV cache, batching, quantization and speculative decoding — techNmak · 2026-09-04
• [source] A visual AI learning roadmap: 3Blue1Brown, Transformer Explainer, Neuronpedia and more — techNmak · 2026-09-04