# 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.