Learning coverage map — every subsystem has a home
Learning coverage map — every subsystem has a home
This map is the guarantee that the learning corpus covers everything in the
project. The ground-up curriculum.md is the spine (10
modules, first principles to self-improving factory). This page is the net:
every shipped capability in PROJECT_STATUS.md maps to a learning anchor here,
so no subsystem is a black box with no explainer.
How to read it: the Anchor column is where to go to learn that thing. Where a concept has a definitive external source, the anchor stays thin and links out (per the repo’s “lean on external sources” rule) rather than re-explaining.
The spine teaches you to read the whole system; this map is the index that proves nothing was left unread.
1. Foundations — the ground-up spine
The 10-module arc. This is the “learn it in order” path; everything below builds on it.
| Module | Topic | Anchor |
|---|---|---|
| 1 | Functions, data, parameters | session-01-neural-net-basics.md |
| 2 | Loss and gradient descent | session-02-gradient-descent.md |
| 3 | Vectors, matrices, tensors | session-09-tensors.md |
| 4 | Non-linear nets + backprop | session-03-non-linearities.md |
| 5 | ML paradigms and scaling | session-04-ml-paradigms.md, session-05-scaling.md |
| 6 | Tokenization, embeddings, LM | session-06-tokenization-embeddings.md |
| 7 | Attention and transformer blocks | session-10-attention.md |
| 8 | Training mechanics | session-08-training-mechanics.md |
| 9 | Post-training: SFT, LoRA, preference tuning | session-07-behavior-learning.md |
| 10 | Evals, rewards, self-improvement | session-11-evals-rewards.md |
Reference layer under the spine:
llm-mechanics-fundamentals.md (RoPE, GQA, MoE,
attention variants) and
essential-vs-optimization.md (which math
defines the model vs which only makes it fast).
2. Post-training methods and internals
Everything under PROJECT_STATUS.md → “Training and post-training.”
| Capability | Anchor |
|---|---|
| SFT | ../training/sft.md, Module 9 |
| Pretraining | ../training/pretrain.md, Session 8 |
| LoRA / DoRA / QLoRA | ../lora_guide.md, ../peft_variants.md, ../factory/lora-geometry.md, Session 9 |
| Encoder-decoder (seq2seq) adapters, copy bias, edit-aware loss | encoder-decoder-adapters.md, ../factory/autocorrect-adapter-recipe.md |
| DPO / SimPO / preference tuning | ../training/dpo.md, Module 9 + 10 |
| Distillation | ../distillation.md, diversity-driven-small-model-reasoning.md |
| Evolution strategies (ES) | ../evolution_strategies.md, castform-rl-finetune.md |
| RLVR / ReST / GRPO | advanced-llm-training.md, ../GRPO_CLARIFY.md |
| Optimizers, schedules, stability | ../optimizers.md, ../galore_and_stability.md, Session 8 |
| NEFTune, z-loss, WSD, LLRD, seq packing, grad checkpointing | ../training_guide.md, ../gradient_checkpointing_results.md, Session 8 |
| Precision (bf16 / fp8 / mixed) | ../precision.md, advanced-llm-training.md |
| Method-vs-recipe discipline | ../techniques/method-vs-recipe.md |
3. Data pipeline
PROJECT_STATUS.md → “Data.”
| Capability | Anchor |
|---|---|
| Dataset registry / inventory | ../dataset-inventory.md, ../data_inventory.md |
| HF integration | ../hf_datasets_integration.md |
| GitHub fetcher | ../github_data_integration.md |
| Magpie / synthesis | advanced-llm-training.md (data curation), small-model-tool-calling-playbook.md |
| Tokenizer training, extractor data | session-06-tokenization-embeddings.md, ../tool_call_extractor.md |
| Traces → data, corrections → data | ../recipes/from-traces.md |
| Quality filter, dedupe, reasoning-classify | ../factory/post-training-factory.md, castform-rl-finetune.md |
4. Evaluation
PROJECT_STATUS.md → “Evals.” Module 10 is the concept spine; these are the
surfaces.
| Capability | Anchor |
|---|---|
| Eval protocol, frozen baselines, gates | ../factory/eval-protocol.md, Module 10 |
| Eval methodology / broken-eval lessons | eval-methodology-2026-06-08.md, eval-matrix-2026-06-08.md |
| BFCL / tool-calling eval | tool-calling-frontier-parity.md, small-model-tool-calling-playbook.md |
| SQL execution / exact / slices / candidate-choice | ../techniques/sql-technique-backlog.md, Module 10 |
| lm-eval / HumanEval / MTEB / MILU / tau-bench | ../lm_eval_integration.md, advanced-ml-systems-eval.md |
| Router / routed-specialist eval, eval-gate, planner eval | ../recipes/eval-gate.md, ../recipes/eval_planner.md, ../planner-lock-2026-06-19.md |
| LLM-as-judge, perplexity, contamination | advanced-ml-systems-eval.md, Module 10 |
| Leaderboard / reporting | ../leaderboard.md, ../factory/reports.md |
5. Runtime, serving, and agents
PROJECT_STATUS.md → “Runtime/serving.”
| Capability | Anchor |
|---|---|
| Inference/serving architecture, batching, roofline | advanced-llm-inference.md |
| KV cache + paging | ../kv_cache_optimization.md, advanced-llm-inference.md |
| OpenAI/Ollama-compatible serve, Continue provider | ../agent_runtime.md, ../continue_provider.md |
| Agent loop, tool dispatch | model-vs-agent.md, ../async_tool_dispatch.md, agent-context-hierarchy.md |
| Constrained JSON / FSM generation | ../constrained_generation.md |
| Speculative decoding / MTP | ../speculative_heads.md, ../mtp.md, advanced-llm-inference.md |
| Cost routing / escalation / cascade (AutoMix, ScaleDown) | ../recipes/automix.md, ../recipes/b25-scaledown.md, agent-context-hierarchy.md |
| Streaming / long context (StreamingLLM, KIVI) | ../streaming_llm_kivi.md |
6. Quantization, packaging, and inference optimization
PROJECT_STATUS.md → “Packaging/runtime.”
| Capability | Anchor |
|---|---|
| Quantization (GGUF / AWQ / GPTQ / HQQ), quant theory | ../quantization_expansion.md, advanced-llm-inference.md |
| Export to MLX / safetensors / CoreML | ../recipes/mlx-export.md, ../factory/packaging.md |
| merge / bake-lora | ../factory/lora-geometry.md, ../lora_guide.md |
| Pruning | ../pruning.md |
| Specialist packaging / model cards | ../factory/packaging.md, ../factory/public-artifacts.md |
7. Attention and kernel internals
The math oracle is python_ref/model.py; Session 10
is the concept.
| Capability | Anchor |
|---|---|
| Attention math, transformer block | session-10-attention.md |
| FlashAttention-2 forward/backward | ../fa2_forward_notes.md, ../fa2_backward_notes.md |
| Online softmax | ../online_softmax_in_attention.md |
| MoE / expert routing | ../moe.md, llm-mechanics-fundamentals.md |
8. Browser / WASM / WebGPU track (completed, parked)
PROJECT_STATUS.md → “Completed/parked learning tracks.”
| Capability | Anchor |
|---|---|
WebGPU execution model (read before the .wgsl files) |
webgpu-execution-model.md |
| Browser GPT training, WASM SIMD, OPFS | ../browser_notes.md |
| BPE-in-browser scoring | ../bpe_browser_scoring.md |
| Numerics gates / precision drift | ../precision.md, ../determinism.md |
9. Native Mac / MLX / ANE / Apple Foundation Models
| Capability | Anchor |
|---|---|
| Mac-local mastery map (living agenda) | mac-mastery-map.md |
| Apple on-device Foundation Models — where they fit | apple-on-device-foundation-models.md, app-intents-comparison.md |
| ANE / CoreML research (negative results) | ane-research/ |
| Native runtime architecture | ../../native-mac/ARCHITECTURE.md |
10. Interpretability (completed, parked)
| Capability | Anchor |
|---|---|
| Attention heatmap + logit lens (browser) | ../interpretability.md, Session 10 |
| SAE, ROME, MEMIT, tuned/logit lens, activation patching | ../interpretability.md, advanced-ml-systems-eval.md |
11. Multimodal (VLM) — research/parked
| Capability | Anchor |
|---|---|
| Qwen3-VL mRoPE + DeepStack, vision-language attention | qwen3-vl-mrope-deepstack.md |
12. The factory loop and methodology
The thing all of the above serves.
| Capability | Anchor |
|---|---|
| Factory overview + run schema | ../factory/overview.md, ../factory/run-schema.md |
| Batch post-training / rollout plan | ../factory/batch-posttraining.md |
| Case-study reports, public artifacts | ../factory/case-study-template.md, ../factory/public-artifacts.md |
| Attempt history (worked/failed/regressed) | ../attempt-ledger.md |
| Owner learning sequence tied to factory work | ../learning-pipeline.md, ../learning-progress.md |
13. Strategy and external knowledge
| Capability | Anchor |
|---|---|
| Competitive landscape, Mac-first whitespace | competitive-landscape.md |
| Reviewed external products / stolen techniques | ../external-products-reviewed.md, castform-rl-finetune.md |
| Speech & systems interview-grade topics | speech-and-systems-topics.md |
| Papers / reading list | external-references.md, ../CITATIONS.md |
| Running questions and tangents | journal.md |
Coverage Guarantee
Every shipped capability in PROJECT_STATUS.md → “Features (shipped)” and
“Completed/parked learning tracks” appears in exactly one section above with a
learning anchor. The factory-loop framing is the checklist:
target -> data -> post-training -> eval -> package -> report- target / methodology → §12
- data → §3
- post-training → §2, §7 (internals), §9 (Mac runtime), §11 (VLM)
- eval → §4, Module 10
- package → §6
- report → §4, §12
- serve/run the result → §5
- understand the result → §10 (interpretability), §1 (foundations)
Maintenance rule
This map is guarded by
../../scripts/check_learning_roadmap.py
(run via bash evals/learning-roadmap-smoke.sh). When a new subsystem ships
in PROJECT_STATUS.md, add a row here pointing to its best explainer before
calling the feature done — a capability with no learning anchor is an
undocumented black box. When an existing doc is the definitive explainer, point
to it (DRY); only write a new learn doc when nothing adequate exists.