Scaffold: fork of Chambers architecture, renamed to Boardroom Map

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
2026-07-06 13:10:25 -05:00
commit 1dde915540
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+import { setupManifest } from '@start9labs/start-sdk'
+
+/**
+ * Boardroom Map manifest.
+ *
+ * Like the LLaMA-Factory and Nightshift services, this is a CONTROL PLANE — it
+ * does not run any GPU workload itself. It is a small web UI + job runner that
+ * SSHes into one or two NVIDIA DGX Sparks to:
+ *   1. serve a panel of local LLMs with vLLM (loaded in waves to fit GPU memory),
+ *   2. extract text from documents you drop in (PDF/DOCX/TXT/MD — done on the
+ *      StartOS box), ship it to the Sparks, and launch a panel of sandboxed
+ *      "reviewer" containers (each a model + a persona) that read the documents
+ *      and write a report,
+ *   3. optionally run a local "lead reviewer" that synthesizes the panel's
+ *      reports into one consolidated report.
+ *
+ * There is NO frontier model and NO cloud API key. In the default `airgapped`
+ * network mode the reviewer containers can reach ONLY the on-Spark model proxy —
+ * the documents and their reviews never touch the internet.
+ *
+ * NOTE: s9pk.mk extracts the package identifier from the single-quoted value on
+ * the line below, so keep that field on one line and avoid stray quotes above it.
+ */
+export const manifest = setupManifest({
+  id: 'boardroom-map',
+  title: 'Boardroom Map',
+  license: 'Apache-2.0',
+  packageRepo: 'https://github.com/ten31/boardroom-map',
+  upstreamRepo: 'https://github.com/ten31/boardroom-map',
+  marketingUrl: 'https://github.com/ten31/boardroom-map',
+  donationUrl: null,
+  description: {
+    short: 'A private panel of local LLMs that reviews your confidential documents on your DGX Sparks',
+    long:
+      'Boardroom Map lets you drop confidential documents in and convene a panel of ' +
+      'local LLMs running on your NVIDIA DGX Sparks to review them. You choose ' +
+      'which models and which personas (lenses) sit on the panel and how many ' +
+      'reviews to run. Each reviewer reads the documents and writes a report; an ' +
+      'optional local lead reviewer synthesizes them into one consolidated ' +
+      'report. There is no frontier model and no cloud key: in the default ' +
+      'air-gapped mode the reviewers reach only the on-Spark model endpoint, so ' +
+      'your documents and their reviews never leave your hardware. No GPU is ' +
+      'needed on the StartOS host.',
+  },
+  // Arch-agnostic orchestrator. Docker build paths are relative to the PROJECT
+  // ROOT (where the Makefile runs), matching the Start9 convention.
+  images: {
+    main: {
+      source: {
+        dockerBuild: {
+          dockerfile: './orchestrator.Dockerfile',
+          workdir: '.',
+        },
+      },
+      arch: ['x86_64', 'aarch64'],
+      // The orchestrator only SSHes out + extracts document text on CPU; it never
+      // touches a local GPU.
+      nvidiaContainer: false,
+    },
+  },
+  volumes: ['main'],
+  dependencies: {},
+  hardwareRequirements: {
+    ram: 2048,
+  },
+  alerts: {
+    install:
+      'Boardroom Map drives work on REMOTE machines (your DGX Sparks) over SSH; ' +
+      'nothing serves or runs on your StartOS server. After install: ' +
+      '(1) "Configure Sparks" for SSH access, (2) "Configure Models" for the ' +
+      'local models to serve, (3) "Configure Reviewers" for the panel + personas, ' +
+      '(4) "Configure Review" for the rubric and air-gap mode. Then drop ' +
+      'documents in the inbox and run "Run Review".',
+  },
+})