Add presentation materials for technical talk on transformer training

Five standalone HTML visuals covering the RSA pipeline, training pipeline,
gate decomposition, dataset anatomy, and model tier sizing. Slide deck brief
describing a 35-slide two-act deck for a data science audience. Handoff note
documenting what was created this session.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

HANDOFF-2026-04-21-presentation.md

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+# Handoff: Presentation Materials — 2026-04-21
+
+## Context
+
+This session was primarily infrastructure and communication work, not pipeline or
+training work. The purpose was to prepare presentation materials for a technical
+audience (data science background) covering the actual transformer training approach
+in whetstone_RSA.
+
+The session also established a new workstation (`spindoctor`) as a Gitea client
+for this repo. Gitea SSH runs on port 2222, not 22. An SSH config entry for
+`borgswarm` with `Port 2222` is in place at `~/.ssh/config` on spindoctor.
+
+---
+
+## What Was Created
+
+### presentations/slide_deck_brief.md
+
+Full briefing document for a slide deck agent. Describes a 35-slide deck in two acts:
+
+- **Act I (slides 1–17):** Refers to the existing `rsa_tiny_models_talk.md` as-is.
+  No changes to that file.
+
+- **Act II (slides 18–35):** New material covering the actual training work.
+  Each slide is described with: title, content, visual reference, speaker notes.
+
+The brief references each HTML visual by filename and includes honest framing of the
+weak baseline results and their structural cause.
+
+### presentations/visual_rsa_pipeline.html
+
+The core RSA pattern as a four-box pipeline:
+
+```
+Fuzzy input → RSA Gate → Typed Contract → Deterministic Execution
+```
+
+Includes two worked examples: a developer tools query and a whetstone_DSL
+prereq gate decision. Dark theme, standalone HTML.
+
+### presentations/visual_training_pipeline.html
+
+Six-step pipeline from run artifacts to trained specialist:
+
+1. whetstone_DSL run corpus (1000 runs)
+2. Gate extraction (`extract_gate_rows.py`)
+3. Gate decomposition (1 gate → 2 binary classifiers)
+4. TSV generation (`gen_prereq_op_rsa_data.py`)
+5. Fabricate training (~213K params, 4000 steps, GPU desktop)
+6. Specialist checkpoint (~800KB)
+
+Includes the corpus caveat (17 unique templates) as an inline note.
+
+### presentations/visual_gate_decomposition.html
+
+Shows how `prereq_op_selector` decomposed into two independent binary classifiers
+after discovering `needs_validate_intake` is always True across all 1297 accepted rows.
+
+Displays:
+- Gate A: `needs_resolve_dependencies` — 76.9% positive, 69.4% baseline accuracy
+- Gate B: `needs_architect_review` — 19.8% positive, 75.0% baseline accuracy
+- Accuracy bars with honest majority-classifier context
+- Three bottom notes: why two models, what the corpus lacks, next step pointer
+
+### presentations/visual_dataset_anatomy.html
+
+Dataset statistics for the prereq_op_selector extraction:
+
+- Top-line numbers: 1000 runs, 1297 accepted rows, 24 schema-drift rejected, ~17 unique templates
+- Class balance bar chart for both binary gates
+- Corpus limitation callout (why 1297 rows ≠ 1297 diverse examples)
+- Sample rows in Fabricate TSV format (`label <TAB> hop <TAB> text`)
+
+### presentations/visual_model_tiers.html
+
+The entropy → model tier hypothesis as a five-tier diagram:
+
+| Tier | Description | Size |
+|---|---|---|
+| 0 | Deterministic rules | 0 params |
+| 1 | Tiny specialist (Fabricate) | ~213K params / ~800KB |
+| 2 | Medium specialist (planned) | ~10× tier 1 |
+| 3 | Large specialist (planned) | ~16× tier 1 |
+| 4 | SLM / LLM (escalation only) | billions |
+
+Includes an entropy axis, the current sizing hypothesis as pseudocode, and the
+"check schema before moving up a tier" caveat.
+
+---
+
+## What Was Not Changed
+
+- `rsa_tiny_models_talk.md` — untouched. Act I of the deck uses it as-is.
+- All `docs/`, `src/`, `include/`, `semantic/`, `tools/`, `schemas/` — untouched.
+- All sprint documents — untouched.
+
+---
+
+## Known State
+
+The Fabricate training project has been deleted. The trained specialist checkpoints
+(`/mnt/storage/fabricate_runs/`) are on the GPU desktop, not accessible from
+spindoctor. The training results cited in the visuals come from
+`HANDOFF-2026-04-12-session2.md`.
+
+The HTML visuals are standalone — no external dependencies, no build step required.
+Open in any browser.