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