* feat(site): interactive training-foundations figures in 5 lessons
Add five theme-aware interactive widgets to lesson-figures.js, embedded
via the existing ```figure fence:
- gradient-descent (P1.08 optimization): drag learning rate, watch the
descent path converge or diverge past lr > 1
- softmax-temperature (P3.04 activations): divide logits by T, reshape
the distribution from argmax to uniform
- bias-variance (P2.10): slide model complexity across the U-shaped
test-error curve, see the sweet spot move
- l2-regularization (P3.07): raise lambda, watch every weight shrink
- lr-schedule (P3.09): compare warmup, cosine, step, exponential decay
Validated headless: all five mount with no console errors, sliders and
selects drive re-render, both light and dark themes render correctly.
* feat(site): interactive LLM-internals figures in 5 lessons
Batch 2, building on the same widget system:
- sampling-decoder (P10.04 mini-gpt): temperature then top-k then top-p
filtering over the logits, survivors renormalized
- scaling-laws (P7.13): Chinchilla loss from params and tokens, with the
20-tokens-per-parameter compute-optimal rule
- quantization (P10.11): bits per weight against model size and the
precision lost at fp16/int8/int4/int2
- rope-explorer (P7.04): rotary frequencies across position and dimension,
base controls wavelength and usable context
- lora-params (P11.08): rank against the 2r/d trainable fraction
Validated headless: all five mount with no console errors, sliders and
selects drive re-render, both light and dark render correctly.
* feat(site): interactive evaluation and representation figures in 5 lessons
Batch 3, same widget system:
- precision-recall-threshold (P2.09 model-evaluation): slide the cutoff
across two class distributions, watch precision/recall/F1 trade
- cross-entropy-loss (P3.05 loss-functions): -log(p_true), the price of
being confident and wrong
- cosine-similarity (P11.04 embeddings): the angle between two vectors is
the similarity, magnitude drops out
- tokenizer-tradeoff (P10.01 tokenizers): vocab size against tokens-per-word
and the embedding table cost
- rag-chunking (P11.06 rag): chunk size, overlap, and top-k against chunk
count and context tokens per query
Validated headless: all five mount with no console errors, math checks out
(thr 0.8 -> P 1.00/R 0.11, -ln(0.05)=2.996, cos 90 deg = 0, 224 chunks),
sliders drive re-render, both light and dark render correctly.
* feat(site): interactive figure system — 74 new widgets across 11 phases
Expand the lesson-figure system from a handful of widgets into a curriculum-wide
library. Refactor lesson-figures.js to expose a shared LF toolkit (el, svgEl,
slider, select, fmtInt, clamp, lerp, raf, register) and split widgets into eight
per-phase module files that plug in via LF.register.
New module files (3,682 LOC) and the concepts they make draggable:
- figures-math.js (P1, 11): vector projection, matrix transform + determinant,
eigenvectors, derivative tangent, chain rule, gaussian, bayes update,
entropy/KL, PCA axes, fourier synthesis, convex vs nonconvex
- figures-ml.js (P2, 10): regression fit/MSE, logistic boundary, SVM margin,
kNN smoothness, k-means steps, tree depth, feature scaling, naive bayes,
class imbalance, k-fold CV
- figures-dl.js (P3, 9): perceptron boundary, MLP forward pass, vanishing
gradients, optimizer trajectories, weight-init variance, dropout, batchnorm,
learning curves, gradient clipping
- figures-vision-speech.js (P4/P6, 8): convolution kernel, pooling, receptive
field, conv output size, CNN params, spectrogram window, mel scale, aliasing
- figures-transformers.js (P5/P7, 9): attention heatmap, multihead split, causal
mask, sqrt(d_k) scaling, word2vec arithmetic, BPE merges, GQA sharing,
residual stream, flash-attention memory
- figures-genai-rl.js (P8/P9, 9): diffusion denoise, noise schedule, VAE latent,
GAN minimax, Q-learning gridworld, value iteration, epsilon-greedy, discount
horizon, policy-gradient ascent
- figures-llms-systems.js (P10/P12/P13, 9): beam search, speculative decoding,
MoE routing, context window, perplexity, continuous batching, ViT patches,
multimodal fusion, MCP round trip
- figures-agents-alignment.js (P11/P14/P16/P18, 9): agent loop, ReAct trace,
tool routing, swarm message scaling, supervisor tree, RLHF reward-KL,
DPO margin, context budget, guardrail gates
Each widget embedded in its lesson via the figure fence (74 lessons). All
theme-aware through CSS vars, vanilla ES5, no dependencies.
Validated headless: all 90 registered figures (16 prior + 74) mount with zero
console errors in a master harness; rich SVG visualizations (attention heatmap,
gridworld policy, convolution feature map, swarm graphs) render correctly in
both light and dark.
* feat(site): 44 more interactive figures — NLP, LLM internals, infra, autonomy
Wave 2 extends the figure system into the phases that were still bare,
plus deeper coverage of the large NLP and LLM phases. Five new module
files (2,219 LOC), each plugging into the shared LF toolkit:
- figures-math2.js (P1, 9): SVD low-rank reconstruction, tensor broadcasting,
log-sum-exp stability, Lp unit balls, monte-carlo pi, system conditioning,
random-walk diffusion, roots of unity, graph degree
- figures-nlp2.js (P5, 8): BoW/TF-IDF, RNN unroll, LSTM gates, seq2seq
alignment, edit distance, n-gram backoff, BIO tagging, sentiment logits
- figures-llms2.js (P10, 9): RMSNorm vs LayerNorm, SwiGLU, RLHF pipeline,
DPO loss, paged KV cache, expert capacity, sliding-window attention,
differential attention, weight tying
- figures-infra.js (P17, 9): data/tensor/pipeline parallelism, ZeRO sharding,
GPU memory breakdown, throughput-latency, autoscaling, cost-per-token,
roofline
- figures-frontier.js (P15/P19, 9): task decomposition, reflection loop,
memory consolidation, world-model rollout, autonomy oversight, pass@k,
eval-harness matrix, canary rollout, trace spans
Embedded in 44 lessons via the figure fence. Validated headless: all 134
registered figures (16 core + 118 module) mount with zero console errors in
a full harness; pipeline-bubble, SVD energy, and trace-span visualizations
render correctly in light and dark.
* fix(site): address review findings on figure widgets
- sampling-decoder: formula now reads 'cumulative >= p' (nucleus keeps the
smallest set covering p, matching the implementation)
- supervisor-hierarchy: drop the dead capped-total accumulator; show the exact
geometric total and note when the diagram caps a level at 64 so the number
and the drawn nodes stay consistent; handle b=1 (total = depth + 1) instead
of the closed form that is undefined at b=1
- image-patch-tokens: use ceil(size/patch) so non-divisible sizes count the
partial patch row; formula shows the ceil and meta notes the padded size
- debugging-neural-networks: normalize the one-off Type 'Practice' to 'Build'
Verified in browser: all three widgets render with the corrected text/math,
no console errors.
Skipped: the 'figure fence is not an approved language tag' findings. lesson.html
keys on codeLang === 'figure' to emit the widget mount point; the fence body is
the figure id. Renaming the fence to the figure id would stop it rendering.
There is no fence-language allowlist for these lesson docs.
Rohit Ghumare
cb55ea0cc6