Jonathan KirkwoodandClaude Fable 5
32d5d7f373
- Dashboard renders deck reports and scorecards as formatted pages (self-contained markdown renderer inlined in index.html — headings, styled tables, evidence blockquotes; no CDN, air-gap friendly) - At-a-glance strip on every deck report: composite with delta vs the prior deck, quant/qual/penalty mix, KPI hit/miss chips, BDEF best/weakest category, red-flag count - Generated DECK_REPORT.md now leads with a concise "At a glance" summary table (scorecard.py); jobs.py passes the previous deck record so the delta appears in the file too; dashboard hides the duplicate section since the strip covers it - .gitignore: .venv/ Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
144 lines
7.9 KiB
Markdown
144 lines
7.9 KiB
Markdown
# Boardroom Map — private board-deck grading on your DGX Sparks
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Boardroom Map is a StartOS service (`.s9pk`) that **grades portfolio-company
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board decks with a panel of local LLMs** on your NVIDIA DGX Sparks. Drop each
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company's deck into `inbox/<company-slug>/`; the panel grades it against the
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**BDEF v1.1 framework** (Girdley + Munger/Buffett), an optional local
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**adjudicator** reconciles the panel, and a deterministic Python scorer computes
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a 0–100 composite that lands on the company's **running scorecard ledger**. A
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web dashboard shows per-company trends. There is **no frontier model and no
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cloud API key** — in the default air-gapped mode the decks and their grades
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never leave your hardware.
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It is a sibling of [Chambers](../chambers) and reuses the same control-plane
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pattern (a GPU-free orchestrator on StartOS driving the Sparks over SSH), but
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swaps the free-form document-review panel for a **deterministic deck-grading
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pipeline** with pinned KPI targets and per-company ledgers.
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## The scoring model
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`composite (0–100) = quant 60 + qual 40 − red flags (capped at 15)`
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- **Quantitative 60:** profitability KPI attainment **30** (heaviest slice),
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other measurable KPIs **20**, **forecast integrity 10** — deck N's actuals are
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chained against deck N−1's stated targets, so moved goalposts cost points.
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KPI credit is linear above a floor ratio (default 0.5 → zero credit below).
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- **Qualitative 40:** eight BDEF categories (A–H) × 5 points, scored by the
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panel with evidence quotes; thin evidence scales down.
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- **Red flags:** up to **−15**; silently dropped KPIs are auto-flagged (capped),
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and flags raised by a single grader are damped by 0.5.
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Every knob lives in config (`weights`, per-company `pinnedTargets` and
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`kpiAliases`) so the model can be retuned without a rebuild.
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## Architecture
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```
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StartOS box (control plane, no GPU) DGX Spark(s)
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┌────────────────────────────────────┐ ┌───────────────────────────────┐
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│ FastAPI dashboard + job runner │ SSH │ per-job Docker network │
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│ • inbox/<company-slug>/ (decks) │ ───────▶│ (──internal in airgapped) │
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│ • extract text (PDF/PPTX/DOCX/…) │ rsync │ ┌─────────┐ ┌────────────┐ │
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│ • plan model "waves" │ ───────▶│ │ vLLM(s) │◀─│ LiteLLM │ │
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│ • extractor → graders → adjudicator│ │ └─────────┘ │ router │ │
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│ • deterministic composite scorer │◀─────── │ ┌──────────────┐ ▲ │ │
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│ • per-company ledgers + scorecards │ rsync │ │ grader ×N │──┘ │ │
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└────────────────────────────────────┘ │ │ (read-only, │ │ │
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│ │ sandboxed) │ │ │
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│ └──────────────┘ │ │
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└───────────────────────────────┘
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```
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- **Graders** are one-shot, read-only, hardened containers (non-root,
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`--cap-drop ALL`, read-only rootfs, no docker socket). In air-gapped mode they
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sit on an `--internal` network and can reach only the model proxy.
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- **Waves:** the job runner serves models in waves bounded by
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`maxConcurrentModels`, so a panel can span more models than fit in GPU memory
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at once.
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- **Air-gap modes:** `airgapped` (default — graders reach only the on-Spark
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model proxy, zero egress, models pre-pulled) or `local_services` (graders may
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reach LAN services like SearXNG and the second Spark — has egress unless
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firewalled).
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- **Confidentiality:** decks are extracted to text on the StartOS box; only
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text crosses to the Sparks, and it is wiped from the Sparks after the job.
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Scorecards and ledgers live only on the StartOS box.
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## Setup order
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Configure Sparks → Test Spark Connection → Configure Models → Configure Graders
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→ Configure Grading (rubric, air-gap, weights) → Configure Companies (slugs,
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KPI aliases, pinned targets — especially profitability thresholds) → drop decks
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into `inbox/<company-slug>/` → **Grade Decks** → watch the dashboard.
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Two operational notes:
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- **Deck filenames must contain the period with the year** — e.g.
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`Board Deck - Q4 2025.pdf` parses; a bare `Q4` does not. Periods are
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canonicalized (`2025-Q4`, `2026-H1`, `2026-05`, `FY2026`) and drive the
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forecast-target chaining between consecutive decks.
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- If a Spark already runs resident vLLM containers, list their names in
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**Configure Grading → `preJobStopContainers`** — they are docker-stopped on
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the head Spark at job start to free GPU memory (and deliberately *not*
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restarted afterward; re-warm them from whatever job owns them).
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## Repo layout
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```
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startos/ StartOS package definition (TypeScript / start-sdk)
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manifest/ main.ts interfaces.ts versions/ file-models/ actions/
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orchestrator/ The control-plane app (Python)
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app.py FastAPI dashboard + JSON API
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jobs.py the job runner (extract → serve waves → grade → adjudicate → score)
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serving.py vLLM + LiteLLM router on the Sparks, in waves
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graders.py launch the grading panel
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adjudicator.py the local lead grader
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extraction.py PDF/PPTX/DOCX/TXT/MD → text (on the StartOS box)
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preflight.py probe models before launching graders
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spark_client.py SSH/rsync helpers
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bdef.md the baked-in BDEF v1.1 rubric
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bm_config.py config defaults (mirrors startos/file-models/config.ts)
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sandbox/ grader image (built ON the Spark, not packed in the s9pk)
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grader_agent.py grader.Dockerfile build.sh
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```
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## Build
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Local build with `start-cli` (on a Mac: colima + start-cli in a VM — see the
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s9pk-build-on-mac recipe). The vLLM and grader images are built **on the
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Sparks**, not packed into the `.s9pk`:
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- `boardroom-vllm:latest` — the vLLM serving image with `ENTRYPOINT []`
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(serving.py passes the full `vllm serve …` command as the container CMD).
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- `boardroom-grader:latest` — from `sandbox/build.sh`.
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```
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npm ci && npm run check && npm run build # type-check + bundle
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make # pack the .s9pk (needs start-cli)
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```
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Canonical repo: `https://gitea.ten31.ai/Ten31AI/boardroom-map`.
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## Status
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**v0.1.6 — live in production.** Deployed on a StartOS box driving a DGX Spark
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in single-spark air-gapped mode (gemma-4-31B panel: munger-lens /
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girdley-operator / buffett-owner). First full grading run completed
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2026-07-29: a three-deck company history graded end-to-end into a running
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ledger with quarter-over-quarter forecast chaining.
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Version history: v0.1.1 fixed config persistence (absolute `/media/startos/…`
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paths — relative paths resolved into an ephemeral cwd); v0.1.2 added
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`preJobStopContainers`; v0.1.3 hardened preflight (authed model probes,
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poll-until-loaded, crash fast-fail); v0.1.4 fixed air-gapped serving
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(`HF_HUB_OFFLINE` — the `--internal` network has no DNS) and raised grader
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timeouts for ~3.6 tok/s local generation; v0.1.5 fixed the dashboard viewer
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(stays open across background refresh) and added report/JSON/scorecard
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downloads; v0.1.6 made reports human-friendly — rendered markdown in the
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dashboard, an at-a-glance strip per deck report, and a concise "At a glance"
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summary table atop every generated DECK_REPORT.md.
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Known optimization not yet done: the wave is torn down per deck, so the 31B
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reloads from disk (~6 min) between decks even when the model set is unchanged.
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HF model pre-pull on the head Spark is required for air-gapped runs.
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