Create app.py

.claude/settings.json

@@ -1,10 +0,0 @@
-{
-  "permissions": {
-    "allow": [
-      "Bash(xargs wc -l)",
-      "Bash(git add *)",
-      "Bash(git commit -m ' *)",
-      "Bash(git push *)"
-    ]
-  }
-}

.dockerignore

@@ -1,26 +0,0 @@
-.git
-.gitignore
-.github
-.venv
-venv
-env
-__pycache__
-*.pyc
-*.pyo
-*.egg-info
-.pytest_cache
-.mypy_cache
-.ruff_cache
-dist
-build
-*.egg
-.DS_Store
-.env
-.env.local
-tests/
-docs/screenshots/
-*.md
-!README.md
-.vscode
-.idea
-Makefile

.gitattributes

@@ -33,5 +33,3 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
-*.png filter=lfs diff=lfs merge=lfs -text
-hf_hackathon_screenrecording_v1.webm filter=lfs diff=lfs merge=lfs -text

.github/workflows/release.yml

@@ -1,193 +0,0 @@
-name: Release Build & Package
-
-on:
-  push:
-    tags:
-      - 'v*'
-  workflow_dispatch:
-    inputs:
-      variant:
-        description: 'Build variant'
-        required: true
-        default: 'both'
-        type: choice
-        options:
-          - slim
-          - full
-          - both
-
-concurrency:
-  group: release-${{ github.ref }}
-  cancel-in-progress: false
-
-jobs:
-  build-matrix:
-    runs-on: ${{ matrix.os }}
-    strategy:
-      fail-fast: false
-      matrix:
-        include:
-          # Windows
-          - os: windows-latest
-            artifact-type: exe
-            platform: windows
-          # Linux
-          - os: ubuntu-latest
-            artifact-type: appimage
-            platform: linux
-          # macOS
-          - os: macos-latest
-            artifact-type: dmg
-            platform: macos
-    
-    steps:
-      - name: Checkout code
-        uses: actions/checkout@v4
-
-      - name: Set up Python
-        uses: actions/setup-python@v4
-        with:
-          python-version: '3.12'
-
-      - name: Cache pip packages
-        uses: actions/cache@v3
-        with:
-          path: ~/.cache/pip
-          key: ${{ runner.os }}-pip-${{ hashFiles('**/requirements.txt') }}
-          restore-keys: |
-            ${{ runner.os }}-pip-
-
-      - name: Install dependencies
-        run: |
-          python -m pip install --upgrade pip setuptools wheel
-          pip install -r requirements.txt
-          pip install -r build/requirements-build.txt
-
-      - name: Build Windows EXE
-        if: matrix.platform == 'windows'
-        run: |
-          powershell -ExecutionPolicy Bypass -File build/windows/build.ps1 -Variant both -BuildInstaller $true
-          dir dist/ /s
-
-      - name: Build Linux packages
-        if: matrix.platform == 'linux'
-        run: |
-          chmod +x build/linux/build.sh
-          bash build/linux/build.sh both all
-          ls -lah dist/
-
-      - name: Build macOS app
-        if: matrix.platform == 'macos'
-        run: |
-          chmod +x build/macos/build.sh
-          bash build/macos/build.sh both
-          ls -lah dist/
-
-      - name: Upload artifacts
-        uses: actions/upload-artifact@v3
-        with:
-          name: hearthnet-${{ matrix.platform }}-${{ matrix.artifact-type }}
-          path: dist/
-          retention-days: 7
-
-  build-docker:
-    runs-on: ubuntu-latest
-    permissions:
-      contents: read
-      packages: write
-    
-    steps:
-      - name: Checkout code
-        uses: actions/checkout@v4
-
-      - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@v2
-
-      - name: Log in to GitHub Container Registry
-        uses: docker/login-action@v2
-        with:
-          registry: ghcr.io
-          username: ${{ github.actor }}
-          password: ${{ secrets.GITHUB_TOKEN }}
-
-      - name: Extract version
-        id: version
-        run: |
-          VERSION=$(grep '^version' pyproject.toml | head -1 | cut -d'"' -f2)
-          echo "version=$VERSION" >> $GITHUB_OUTPUT
-
-      - name: Build and push slim image
-        uses: docker/build-push-action@v4
-        with:
-          context: .
-          file: build/docker/Dockerfile.slim
-          push: true
-          tags: |
-            ghcr.io/${{ github.repository }}:${{ steps.version.outputs.version }}-slim
-            ghcr.io/${{ github.repository }}:latest-slim
-          labels: |
-            org.opencontainers.image.title=HearthNet (slim)
-            org.opencontainers.image.version=${{ steps.version.outputs.version }}
-
-      - name: Build and push full image
-        uses: docker/build-push-action@v4
-        with:
-          context: .
-          file: build/docker/Dockerfile.full
-          push: true
-          tags: |
-            ghcr.io/${{ github.repository }}:${{ steps.version.outputs.version }}-full
-            ghcr.io/${{ github.repository }}:latest-full
-          labels: |
-            org.opencontainers.image.title=HearthNet (full)
-            org.opencontainers.image.version=${{ steps.version.outputs.version }}
-
-  create-release:
-    needs: [build-matrix, build-docker]
-    runs-on: ubuntu-latest
-    if: startsWith(github.ref, 'refs/tags/')
-    
-    permissions:
-      contents: write
-
-    steps:
-      - name: Checkout code
-        uses: actions/checkout@v4
-
-      - name: Download all artifacts
-        uses: actions/download-artifact@v3
-        with:
-          path: all-artifacts
-
-      - name: Generate checksums
-        run: |
-          cd all-artifacts
-          for file in */*; do
-            sha256sum "$file" >> SHA256SUMS.txt
-          done
-          cat SHA256SUMS.txt
-
-      - name: Create release
-        uses: softprops/action-gh-release@v1
-        with:
-          files: |
-            all-artifacts/**/*
-            all-artifacts/SHA256SUMS.txt
-          body: |
-            ## HearthNet ${{ github.ref_name }} Release
-            
-            ### Download Options
-            - **Windows**: EXE (standalone) or MSI (installer)
-            - **Linux**: AppImage (portable) or native packages (snap/deb/rpm)
-            - **macOS**: DMG (drag-to-Applications)
-            - **Docker**: Pull from `ghcr.io/${{ github.repository }}`
-            
-            ### Installation
-            See [DEPLOYMENT.md](https://github.com/${{ github.repository }}/blob/main/docs/DEPLOYMENT.md) for detailed instructions.
-            
-            ### Checksums
-            Verify downloads with: `sha256sum -c SHA256SUMS.txt`
-          draft: false
-          prerelease: false
-        env:
-          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}

.github/workflows/test.yml

@@ -1,63 +0,0 @@
-name: Test Suite & Coverage
-on:
-  push:
-    branches: [ main, dev ]
-  pull_request:
-    branches: [ main, dev ]
-
-jobs:
-  test:
-    runs-on: ubuntu-latest
-    strategy:
-      matrix:
-        python-version: ['3.10', '3.11', '3.12']
-    
-    steps:
-    - uses: actions/checkout@v4
-    
-    - name: Set up Python ${{ matrix.python-version }}
-      uses: actions/setup-python@v4
-      with:
-        python-version: ${{ matrix.python-version }}
-        cache: 'pip'
-    
-    - name: Install dependencies
-      run: |
-        python -m pip install --upgrade pip
-        pip install -r requirements-dev.txt
-        pip install pytest pytest-cov pytest-asyncio pytest-benchmark
-    
-    - name: Run tests
-      run: |
-        python -m pytest tests/ -v --tb=short --cov=hearthnet --cov-report=xml --cov-report=html
-    
-    - name: Upload coverage to Codecov
-      uses: codecov/codecov-action@v3
-      with:
-        file: ./coverage.xml
-        flags: unittests
-        name: codecov-umbrella
-    
-    - name: Generate coverage badge
-      run: |
-        python -c "
-import xml.etree.ElementTree as ET
-tree = ET.parse('coverage.xml')
-root = tree.getroot()
-coverage = float(root.attrib.get('line-rate', 0)) * 100
-print(f'Coverage: {coverage:.1f}%')
-"
-    
-    - name: Comment PR with test results
-      if: github.event_name == 'pull_request'
-      uses: actions/github-script@v7
-      with:
-        script: |
-          const fs = require('fs');
-          const coverage = fs.readFileSync('./coverage.xml', 'utf8');
-          github.rest.issues.createComment({
-            issue_number: context.issue.number,
-            owner: context.repo.owner,
-            repo: context.repo.repo,
-            body: '✅ All tests passed!\n\n📊 [View Coverage Report](https://github.com/${{ github.repository }}/actions/runs/${{ github.run_id }})'
-          });

.playwright-mcp/console-2026-06-12T13-03-23-327Z.log

@@ -1,2 +0,0 @@
-[  756914ms] [ERROR] Access to script at 'http://127.0.0.1:8099/src/rag/rag.js' from origin 'http://127.0.0.1:7861' has been blocked by CORS policy: No 'Access-Control-Allow-Origin' header is present on the requested resource. @ http://127.0.0.1:7861/:0
-[  756914ms] [ERROR] Failed to load resource: net::ERR_FAILED @ http://127.0.0.1:8099/src/rag/rag.js:0

.playwright-mcp/console-2026-06-12T13-12-18-364Z.log

@@ -1,13 +0,0 @@
-[    1980ms] [ERROR] Failed to load resource: net::ERR_CONNECTION_REFUSED @ http://127.0.0.1:7860/theme.css?v=8ad6f9b14414574fe6c6d9b4362dcdd63dfdc66d8c34cbef0982888dfc44ff04:0
-[    1980ms] [ERROR] Unable to preload CSS for http://127.0.0.1:7860/theme.css?v=8ad6f9b14414574fe6c6d9b4362dcdd63dfdc66d8c34cbef0982888dfc44ff04 @ http://127.0.0.1:7860/assets/i18n-Cp0K4Pzb.js:12
-[    1995ms] [ERROR] Failed to load resource: net::ERR_CONNECTION_REFUSED @ http://127.0.0.1:7860/assets/Blocks-CXVCGwNW.css:0
-[    1995ms] Error: Unable to preload CSS for http://127.0.0.1:7860/assets/Blocks-CXVCGwNW.css
-    at HTMLLinkElement.<anonymous> (http://127.0.0.1:7860/assets/index-CTFdGFMX.js:2:1651)
-[    2059ms] [ERROR] Failed to load resource: net::ERR_CONNECTION_REFUSED @ http://127.0.0.1:7860/assets/de-BczWVYc-.js:0
-[    2059ms] [ERROR] Failed to load resource: net::ERR_CONNECTION_REFUSED @ http://127.0.0.1:7860/assets/Blocks-IWJfIfJH.js:0
-[    2059ms] Failed to fetch dynamically imported module: http://127.0.0.1:7860/assets/de-BczWVYc-.js
-[    2059ms] [ERROR] Failed to load resource: net::ERR_CONNECTION_REFUSED @ http://127.0.0.1:7860/assets/size-DEWon29f.js:0
-[    2059ms] [ERROR] Failed to load resource: net::ERR_CONNECTION_REFUSED @ http://127.0.0.1:7860/assets/state.svelte-C4lCeSea.js:0
-[    2059ms] Failed to fetch dynamically imported module: http://127.0.0.1:7860/assets/Blocks-IWJfIfJH.js
-[    3991ms] [ERROR] Failed to load resource: net::ERR_CONNECTION_REFUSED @ http://127.0.0.1:7860/static/img/logo_nosize.svg:0
-[    3991ms] [WARNING] Error while trying to use the following icon from the Manifest: http://127.0.0.1:7860/static/img/logo_nosize.svg (Download error or resource isn't a valid image) @ http://127.0.0.1:7860/:0

.playwright-mcp/console-2026-06-12T13-16-09-405Z.log

@@ -1,81 +0,0 @@
-[    1558ms] [ERROR] Failed to load resource: the server responded with a status of 404 (File not found) @ http://127.0.0.1:8099/favicon.ico:0
-[   12119ms] [ERROR] Access to fetch at 'https://api.allorigins.win/get?url=https%3A%2F%2Fnews.google.com%2Frss%2Fsearch%3Fq%3Dwhen%3A24h%2Ballinurl%3Areuters.com%26ceid%3DUS%3Aen%26hl%3Den-US%26gl%3DUS' from origin 'http://127.0.0.1:8099' has been blocked by CORS policy: No 'Access-Control-Allow-Origin' header is present on the requested resource. @ http://127.0.0.1:8099/index.html:0
-[   12119ms] [ERROR] Failed to load resource: net::ERR_FAILED @ https://api.allorigins.win/get?url=https%3A%2F%2Fnews.google.com%2Frss%2Fsearch%3Fq%3Dwhen%3A24h%2Ballinurl%3Areuters.com%26ceid%3DUS%3Aen%26hl%3Den-US%26gl%3DUS:0
-[   12416ms] [ERROR] Failed to load resource: the server responded with a status of 451 () @ https://r.jina.ai/https://news.google.com/rss/search?q=when:24h+allinurl:reuters.com&ceid=US:en&hl=en-US&gl=US:0
-[   17544ms] [ERROR] Access to fetch at 'https://api.allorigins.win/get?url=https%3A%2F%2Fnews.google.com%2Frss%2Fsearch%3Fq%3Dwhen%3A24h%2Ballinurl%3Areuters.com%26ceid%3DUS%3Aen%26hl%3Den-US%26gl%3DUS' from origin 'http://127.0.0.1:8099' has been blocked by CORS policy: No 'Access-Control-Allow-Origin' header is present on the requested resource. @ http://127.0.0.1:8099/index.html:0
-[   17544ms] [ERROR] Failed to load resource: net::ERR_FAILED @ https://api.allorigins.win/get?url=https%3A%2F%2Fnews.google.com%2Frss%2Fsearch%3Fq%3Dwhen%3A24h%2Ballinurl%3Areuters.com%26ceid%3DUS%3Aen%26hl%3Den-US%26gl%3DUS:0
-[   17758ms] [ERROR] Failed to load resource: the server responded with a status of 451 () @ https://r.jina.ai/https://news.google.com/rss/search?q=when:24h+allinurl:reuters.com&ceid=US:en&hl=en-US&gl=US:0
-[   30039ms] [ERROR] Access to fetch at 'https://api.allorigins.win/get?url=https%3A%2F%2Fnews.google.com%2Frss%2Fsearch%3Fq%3Dwhen%3A24h%2Ballinurl%3Aapnews.com%26ceid%3DUS%3Aen%26hl%3Den-US%26gl%3DUS' from origin 'http://127.0.0.1:8099' has been blocked by CORS policy: No 'Access-Control-Allow-Origin' header is present on the requested resource. @ http://127.0.0.1:8099/index.html:0
-[   30039ms] [ERROR] Failed to load resource: net::ERR_FAILED @ https://api.allorigins.win/get?url=https%3A%2F%2Fnews.google.com%2Frss%2Fsearch%3Fq%3Dwhen%3A24h%2Ballinurl%3Aapnews.com%26ceid%3DUS%3Aen%26hl%3Den-US%26gl%3DUS:0
-[   30537ms] [ERROR] Failed to load resource: the server responded with a status of 451 () @ https://r.jina.ai/https://news.google.com/rss/search?q=when:24h+allinurl:apnews.com&ceid=US:en&hl=en-US&gl=US:0
-[   42224ms] [ERROR] Access to fetch at 'https://api.allorigins.win/get?url=https%3A%2F%2Ffeeds.bbci.co.uk%2Fnews%2Fworld%2Frss.xml' from origin 'http://127.0.0.1:8099' has been blocked by CORS policy: No 'Access-Control-Allow-Origin' header is present on the requested resource. @ http://127.0.0.1:8099/index.html:0
-[   42224ms] [ERROR] Failed to load resource: net::ERR_FAILED @ https://api.allorigins.win/get?url=https%3A%2F%2Ffeeds.bbci.co.uk%2Fnews%2Fworld%2Frss.xml:0
-[   42225ms] [ERROR] Access to fetch at 'https://api.allorigins.win/get?url=https%3A%2F%2Ffeeds.bbci.co.uk%2Fnews%2Frss.xml' from origin 'http://127.0.0.1:8099' has been blocked by CORS policy: No 'Access-Control-Allow-Origin' header is present on the requested resource. @ http://127.0.0.1:8099/index.html:0
-[   42225ms] [ERROR] Failed to load resource: net::ERR_FAILED @ https://api.allorigins.win/get?url=https%3A%2F%2Ffeeds.bbci.co.uk%2Fnews%2Frss.xml:0
-[   42751ms] [ERROR] Access to fetch at 'https://api.allorigins.win/get?url=https%3A%2F%2Frss.dw.com%2Frdf%2Frss-en-top' from origin 'http://127.0.0.1:8099' has been blocked by CORS policy: No 'Access-Control-Allow-Origin' header is present on the requested resource. @ http://127.0.0.1:8099/index.html:0
-[   42751ms] [ERROR] Failed to load resource: net::ERR_FAILED @ https://api.allorigins.win/get?url=https%3A%2F%2Frss.dw.com%2Frdf%2Frss-en-top:0
-[   43250ms] [ERROR] Access to fetch at 'https://api.allorigins.win/get?url=https%3A%2F%2Fwww.france24.com%2Fen%2Frss' from origin 'http://127.0.0.1:8099' has been blocked by CORS policy: No 'Access-Control-Allow-Origin' header is present on the requested resource. @ http://127.0.0.1:8099/index.html:0
-[   43250ms] [ERROR] Failed to load resource: net::ERR_FAILED @ https://api.allorigins.win/get?url=https%3A%2F%2Fwww.france24.com%2Fen%2Frss:0
-[   45505ms] [ERROR] Access to fetch at 'https://api.allorigins.win/get?url=https%3A%2F%2Fwww.aljazeera.com%2Fxml%2Frss%2Fall.xml' from origin 'http://127.0.0.1:8099' has been blocked by CORS policy: No 'Access-Control-Allow-Origin' header is present on the requested resource. @ http://127.0.0.1:8099/index.html:0
-[   45505ms] [ERROR] Failed to load resource: net::ERR_FAILED @ https://api.allorigins.win/get?url=https%3A%2F%2Fwww.aljazeera.com%2Fxml%2Frss%2Fall.xml:0
-[   45717ms] [ERROR] Failed to load resource: the server responded with a status of 451 () @ https://r.jina.ai/https://www.aljazeera.com/xml/rss/all.xml:0
-[   49528ms] [ERROR] Access to fetch at 'https://api.allorigins.win/get?url=https%3A%2F%2Fnews.google.com%2Frss%2Fsearch%3Fq%3Dwhen%3A24h%2Ballinurl%3Aapnews.com%26ceid%3DUS%3Aen%26hl%3Den-US%26gl%3DUS' from origin 'http://127.0.0.1:8099' has been blocked by CORS policy: No 'Access-Control-Allow-Origin' header is present on the requested resource. @ http://127.0.0.1:8099/index.html:0
-[   49528ms] [ERROR] Failed to load resource: net::ERR_FAILED @ https://api.allorigins.win/get?url=https%3A%2F%2Fnews.google.com%2Frss%2Fsearch%3Fq%3Dwhen%3A24h%2Ballinurl%3Aapnews.com%26ceid%3DUS%3Aen%26hl%3Den-US%26gl%3DUS:0
-[   49773ms] [ERROR] Failed to load resource: the server responded with a status of 451 () @ https://r.jina.ai/https://news.google.com/rss/search?q=when:24h+allinurl:apnews.com&ceid=US:en&hl=en-US&gl=US:0
-[   55238ms] [ERROR] Access to fetch at 'https://api.allorigins.win/get?url=https%3A%2F%2Ffeeds.bbci.co.uk%2Fnews%2Fworld%2Frss.xml' from origin 'http://127.0.0.1:8099' has been blocked by CORS policy: No 'Access-Control-Allow-Origin' header is present on the requested resource. @ http://127.0.0.1:8099/index.html:0
-[   55238ms] [ERROR] Failed to load resource: net::ERR_FAILED @ https://api.allorigins.win/get?url=https%3A%2F%2Ffeeds.bbci.co.uk%2Fnews%2Fworld%2Frss.xml:0
-[   63318ms] [ERROR] Access to fetch at 'https://api.allorigins.win/get?url=https%3A%2F%2Fnews.ycombinator.com%2Frss' from origin 'http://127.0.0.1:8099' has been blocked by CORS policy: No 'Access-Control-Allow-Origin' header is present on the requested resource. @ http://127.0.0.1:8099/index.html:0
-[   63318ms] [ERROR] Failed to load resource: net::ERR_FAILED @ https://api.allorigins.win/get?url=https%3A%2F%2Fnews.ycombinator.com%2Frss:0
-[   64862ms] [ERROR] Access to fetch at 'https://api.allorigins.win/get?url=https%3A%2F%2Fwww.aljazeera.com%2Fxml%2Frss%2Fall.xml' from origin 'http://127.0.0.1:8099' has been blocked by CORS policy: No 'Access-Control-Allow-Origin' header is present on the requested resource. @ http://127.0.0.1:8099/index.html:0
-[   64862ms] [ERROR] Failed to load resource: net::ERR_FAILED @ https://api.allorigins.win/get?url=https%3A%2F%2Fwww.aljazeera.com%2Fxml%2Frss%2Fall.xml:0
-[   65075ms] [ERROR] Failed to load resource: the server responded with a status of 451 () @ https://r.jina.ai/https://www.aljazeera.com/xml/rss/all.xml:0
-[   65365ms] [ERROR] Access to fetch at 'https://api.allorigins.win/get?url=https%3A%2F%2Fthehackernews.com%2Frss.xml' from origin 'http://127.0.0.1:8099' has been blocked by CORS policy: No 'Access-Control-Allow-Origin' header is present on the requested resource. @ http://127.0.0.1:8099/index.html:0
-[   65365ms] [ERROR] Failed to load resource: net::ERR_FAILED @ https://api.allorigins.win/get?url=https%3A%2F%2Fthehackernews.com%2Frss.xml:0
-[   69461ms] [ERROR] Access to fetch at 'https://api.allorigins.win/get?url=https%3A%2F%2Frss.dw.com%2Frdf%2Frss-en-top' from origin 'http://127.0.0.1:8099' has been blocked by CORS policy: No 'Access-Control-Allow-Origin' header is present on the requested resource. @ http://127.0.0.1:8099/index.html:0
-[   69462ms] [ERROR] Failed to load resource: net::ERR_FAILED @ https://api.allorigins.win/get?url=https%3A%2F%2Frss.dw.com%2Frdf%2Frss-en-top:0
-[   71509ms] [ERROR] Access to fetch at 'https://api.allorigins.win/get?url=https%3A%2F%2Fkrebsonsecurity.com%2Ffeed%2F' from origin 'http://127.0.0.1:8099' has been blocked by CORS policy: No 'Access-Control-Allow-Origin' header is present on the requested resource. @ http://127.0.0.1:8099/index.html:0
-[   71509ms] [ERROR] Failed to load resource: net::ERR_FAILED @ https://api.allorigins.win/get?url=https%3A%2F%2Fkrebsonsecurity.com%2Ffeed%2F:0
-[   73032ms] [ERROR] Failed to load resource: the server responded with a status of 400 () @ https://api.allorigins.win/get?url=https%3A%2F%2Falerts.weather.gov%2Fcap%2Fus.php%3Fx%3D0:0
-[   73200ms] [ERROR] Failed to load resource: the server responded with a status of 400 () @ https://r.jina.ai/https://alerts.weather.gov/cap/us.php?x=0:0
-[   74176ms] [ERROR] Access to fetch at 'https://api.allorigins.win/get?url=https%3A%2F%2Fwww.bleepingcomputer.com%2Ffeed%2F' from origin 'http://127.0.0.1:8099' has been blocked by CORS policy: No 'Access-Control-Allow-Origin' header is present on the requested resource. @ http://127.0.0.1:8099/index.html:0
-[   74176ms] [ERROR] Failed to load resource: net::ERR_FAILED @ https://api.allorigins.win/get?url=https%3A%2F%2Fwww.bleepingcomputer.com%2Ffeed%2F:0
-[   74759ms] [ERROR] Failed to load resource: the server responded with a status of 451 () @ https://r.jina.ai/https://www.bleepingcomputer.com/feed/:0
-[   75095ms] [ERROR] Access to fetch at 'https://api.allorigins.win/get?url=https%3A%2F%2Fwww.france24.com%2Fen%2Frss' from origin 'http://127.0.0.1:8099' has been blocked by CORS policy: No 'Access-Control-Allow-Origin' header is present on the requested resource. @ http://127.0.0.1:8099/index.html:0
-[   75095ms] [ERROR] Failed to load resource: net::ERR_FAILED @ https://api.allorigins.win/get?url=https%3A%2F%2Fwww.france24.com%2Fen%2Frss:0
-[   76810ms] [ERROR] Access to fetch at 'https://api.allorigins.win/get?url=https%3A%2F%2Fwww.nasa.gov%2Frss%2Fdyn%2Fbreaking_news.rss' from origin 'http://127.0.0.1:8099' has been blocked by CORS policy: No 'Access-Control-Allow-Origin' header is present on the requested resource. @ http://127.0.0.1:8099/index.html:0
-[   76810ms] [ERROR] Failed to load resource: net::ERR_FAILED @ https://api.allorigins.win/get?url=https%3A%2F%2Fwww.nasa.gov%2Frss%2Fdyn%2Fbreaking_news.rss:0
-[   77217ms] [ERROR] Access to fetch at 'https://api.allorigins.win/get?url=https%3A%2F%2Fnews.google.com%2Frss%2Fsearch%3Fq%3Dwhen%3A24h%2Ballinurl%3Areuters.com%26ceid%3DUS%3Aen%26hl%3Den-US%26gl%3DUS' from origin 'http://127.0.0.1:8099' has been blocked by CORS policy: No 'Access-Control-Allow-Origin' header is present on the requested resource. @ http://127.0.0.1:8099/index.html:0
-[   77217ms] [ERROR] Failed to load resource: net::ERR_FAILED @ https://api.allorigins.win/get?url=https%3A%2F%2Fnews.google.com%2Frss%2Fsearch%3Fq%3Dwhen%3A24h%2Ballinurl%3Areuters.com%26ceid%3DUS%3Aen%26hl%3Den-US%26gl%3DUS:0
-[   77217ms] [ERROR] Access to fetch at 'https://api.allorigins.win/get?url=https%3A%2F%2Ffeeds.bbci.co.uk%2Fnews%2Frss.xml' from origin 'http://127.0.0.1:8099' has been blocked by CORS policy: No 'Access-Control-Allow-Origin' header is present on the requested resource. @ http://127.0.0.1:8099/index.html:0
-[   77217ms] [ERROR] Failed to load resource: net::ERR_FAILED @ https://api.allorigins.win/get?url=https%3A%2F%2Ffeeds.bbci.co.uk%2Fnews%2Frss.xml:0
-[   77538ms] [ERROR] Failed to load resource: the server responded with a status of 451 () @ https://r.jina.ai/https://news.google.com/rss/search?q=when:24h+allinurl:reuters.com&ceid=US:en&hl=en-US&gl=US:0
-[   78402ms] [ERROR] Access to fetch at 'https://api.allorigins.win/get?url=https%3A%2F%2Ffeeds.bbci.co.uk%2Fnews%2Fworld%2Frss.xml' from origin 'http://127.0.0.1:8099' has been blocked by CORS policy: No 'Access-Control-Allow-Origin' header is present on the requested resource. @ http://127.0.0.1:8099/index.html:0
-[   78402ms] [ERROR] Failed to load resource: net::ERR_FAILED @ https://api.allorigins.win/get?url=https%3A%2F%2Ffeeds.bbci.co.uk%2Fnews%2Fworld%2Frss.xml:0
-[   83766ms] [ERROR] Failed to load resource: net::ERR_QUIC_PROTOCOL_ERROR @ https://api.allorigins.win/get?url=https%3A%2F%2Fkrebsonsecurity.com%2Ffeed%2F:0
-[   84831ms] [ERROR] Access to fetch at 'https://api.allorigins.win/get?url=https%3A%2F%2Fnews.ycombinator.com%2Frss' from origin 'http://127.0.0.1:8099' has been blocked by CORS policy: No 'Access-Control-Allow-Origin' header is present on the requested resource. @ http://127.0.0.1:8099/index.html:0
-[   84831ms] [ERROR] Failed to load resource: net::ERR_FAILED @ https://api.allorigins.win/get?url=https%3A%2F%2Fnews.ycombinator.com%2Frss:0
-[   85281ms] [ERROR] Access to fetch at 'https://api.allorigins.win/get?url=https%3A%2F%2Fwww.france24.com%2Fen%2Frss' from origin 'http://127.0.0.1:8099' has been blocked by CORS policy: No 'Access-Control-Allow-Origin' header is present on the requested resource. @ http://127.0.0.1:8099/index.html:0
-[   85282ms] [ERROR] Failed to load resource: net::ERR_FAILED @ https://api.allorigins.win/get?url=https%3A%2F%2Fwww.france24.com%2Fen%2Frss:0
-[   89471ms] [ERROR] Access to fetch at 'https://api.allorigins.win/get?url=https%3A%2F%2Fthehackernews.com%2Frss.xml' from origin 'http://127.0.0.1:8099' has been blocked by CORS policy: No 'Access-Control-Allow-Origin' header is present on the requested resource. @ http://127.0.0.1:8099/index.html:0
-[   89471ms] [ERROR] Failed to load resource: net::ERR_FAILED @ https://api.allorigins.win/get?url=https%3A%2F%2Fthehackernews.com%2Frss.xml:0
-[   90583ms] [ERROR] Failed to load resource: the server responded with a status of 400 () @ https://api.allorigins.win/get?url=https%3A%2F%2Falerts.weather.gov%2Fcap%2Fus.php%3Fx%3D0:0
-[   90745ms] [ERROR] Failed to load resource: the server responded with a status of 400 () @ https://r.jina.ai/https://alerts.weather.gov/cap/us.php?x=0:0
-[   96191ms] [ERROR] Access to fetch at 'https://api.allorigins.win/get?url=https%3A%2F%2Fnews.ycombinator.com%2Frss' from origin 'http://127.0.0.1:8099' has been blocked by CORS policy: No 'Access-Control-Allow-Origin' header is present on the requested resource. @ http://127.0.0.1:8099/index.html:0
-[   96191ms] [ERROR] Failed to load resource: net::ERR_FAILED @ https://api.allorigins.win/get?url=https%3A%2F%2Fnews.ycombinator.com%2Frss:0
-[   96704ms] [ERROR] Access to fetch at 'https://api.allorigins.win/get?url=https%3A%2F%2Fwww.nasa.gov%2Frss%2Fdyn%2Fbreaking_news.rss' from origin 'http://127.0.0.1:8099' has been blocked by CORS policy: No 'Access-Control-Allow-Origin' header is present on the requested resource. @ http://127.0.0.1:8099/index.html:0
-[   96704ms] [ERROR] Failed to load resource: net::ERR_FAILED @ https://api.allorigins.win/get?url=https%3A%2F%2Fwww.nasa.gov%2Frss%2Fdyn%2Fbreaking_news.rss:0
-[   96878ms] [ERROR] Failed to load resource: the server responded with a status of 429 () @ https://r.jina.ai/https://www.nasa.gov/rss/dyn/breaking_news.rss:0
-[   97110ms] [ERROR] Access to fetch at 'https://api.allorigins.win/get?url=https%3A%2F%2Fwww.aljazeera.com%2Fxml%2Frss%2Fall.xml' from origin 'http://127.0.0.1:8099' has been blocked by CORS policy: No 'Access-Control-Allow-Origin' header is present on the requested resource. @ http://127.0.0.1:8099/index.html:0
-[   97110ms] [ERROR] Failed to load resource: net::ERR_FAILED @ https://api.allorigins.win/get?url=https%3A%2F%2Fwww.aljazeera.com%2Fxml%2Frss%2Fall.xml:0
-[   97288ms] [ERROR] Failed to load resource: the server responded with a status of 429 () @ https://r.jina.ai/https://www.aljazeera.com/xml/rss/all.xml:0
-[   98185ms] [ERROR] Access to fetch at 'https://api.allorigins.win/get?url=https%3A%2F%2Frss.dw.com%2Frdf%2Frss-en-top' from origin 'http://127.0.0.1:8099' has been blocked by CORS policy: No 'Access-Control-Allow-Origin' header is present on the requested resource. @ http://127.0.0.1:8099/index.html:0
-[   98186ms] [ERROR] Failed to load resource: net::ERR_FAILED @ https://api.allorigins.win/get?url=https%3A%2F%2Frss.dw.com%2Frdf%2Frss-en-top:0
-[   98483ms] [ERROR] Failed to load resource: the server responded with a status of 429 () @ https://r.jina.ai/https://rss.dw.com/rdf/rss-en-top:0
-[  116052ms] [ERROR] Access to fetch at 'https://api.allorigins.win/get?url=https%3A%2F%2Fthehackernews.com%2Frss.xml' from origin 'http://127.0.0.1:8099' has been blocked by CORS policy: No 'Access-Control-Allow-Origin' header is present on the requested resource. @ http://127.0.0.1:8099/index.html:0
-[  116052ms] [ERROR] Failed to load resource: net::ERR_FAILED @ https://api.allorigins.win/get?url=https%3A%2F%2Fthehackernews.com%2Frss.xml:0
-[  116169ms] [ERROR] Access to fetch at 'https://api.allorigins.win/get?url=https%3A%2F%2Fwww.nasa.gov%2Frss%2Fdyn%2Fbreaking_news.rss' from origin 'http://127.0.0.1:8099' has been blocked by CORS policy: No 'Access-Control-Allow-Origin' header is present on the requested resource. @ http://127.0.0.1:8099/index.html:0
-[  116169ms] [ERROR] Failed to load resource: net::ERR_FAILED @ https://api.allorigins.win/get?url=https%3A%2F%2Fwww.nasa.gov%2Frss%2Fdyn%2Fbreaking_news.rss:0
-[  116570ms] [ERROR] Access to fetch at 'https://api.allorigins.win/get?url=https%3A%2F%2Fkrebsonsecurity.com%2Ffeed%2F' from origin 'http://127.0.0.1:8099' has been blocked by CORS policy: No 'Access-Control-Allow-Origin' header is present on the requested resource. @ http://127.0.0.1:8099/index.html:0
-[  116571ms] [ERROR] Failed to load resource: net::ERR_FAILED @ https://api.allorigins.win/get?url=https%3A%2F%2Fkrebsonsecurity.com%2Ffeed%2F:0
-[  116756ms] [ERROR] Failed to load resource: the server responded with a status of 400 () @ https://api.allorigins.win/get?url=https%3A%2F%2Falerts.weather.gov%2Fcap%2Fus.php%3Fx%3D0:0
-[  116915ms] [ERROR] Failed to load resource: the server responded with a status of 400 () @ https://r.jina.ai/https://alerts.weather.gov/cap/us.php?x=0:0

.playwright-mcp/page-2026-06-10T14-22-38-882Z.yml

@@ -1,3 +0,0 @@
-- generic [ref=e5]:
-  - img [ref=e9]
-  - paragraph [ref=e20]: Laden...

.playwright-mcp/page-2026-06-10T14-27-49-510Z.yml

@@ -1,60 +0,0 @@
-- generic [ref=e21]:
-  - main [ref=e22]:
-    - generic [ref=e23]:
-      - heading "🔥 HearthNet — Community AI Mesh" [level=1] [ref=e28]
-      - generic [ref=e29]:
-        - generic [ref=e32]: ● ONLINE
-        - paragraph [ref=e37]:
-          - text: "Node:"
-          - code [ref=e38]: unknown
-      - generic [ref=e39]:
-        - generic [ref=e40]:
-          - generic [ref=e41]:
-            - button [ref=e42] [cursor=pointer]: Ask
-            - button [ref=e43] [cursor=pointer]: Chat
-            - button [ref=e44] [cursor=pointer]: Marketplace
-            - button [ref=e45] [cursor=pointer]: Files
-            - button [ref=e46] [cursor=pointer]: Emergency
-            - button [ref=e47] [cursor=pointer]: Settings
-          - tablist [ref=e48]:
-            - tab "Ask" [ref=e49] [cursor=pointer]
-            - tab "Chat" [active] [selected] [ref=e50] [cursor=pointer]
-            - tab "Marketplace" [ref=e51] [cursor=pointer]
-            - tab "Files" [ref=e52] [cursor=pointer]
-            - tab "Emergency" [ref=e53] [cursor=pointer]
-            - tab "Settings" [ref=e54] [cursor=pointer]
-        - tabpanel [ref=e106]:
-          - generic [ref=e108]:
-            - heading "Direct Messages" [level=3] [ref=e113]
-            - generic [ref=e114]:
-              - generic [ref=e117]:
-                - generic [ref=e118]: Recipient Node ID
-                - textbox "Recipient Node ID" [ref=e120]:
-                  - /placeholder: ed25519:...
-              - button "Load History" [ref=e121] [cursor=pointer]
-            - generic [ref=e123]:
-              - generic:
-                - generic:
-                  - img
-                - text: Messages
-              - log "chatbot conversation" [ref=e124]:
-                - complementary [ref=e125]
-            - generic [ref=e126]:
-              - generic [ref=e129]:
-                - generic [ref=e130]: Message
-                - textbox "Message" [ref=e132]:
-                  - /placeholder: ""
-              - button "Send" [ref=e133] [cursor=pointer]
-  - contentinfo "Gradio footer navigation" [ref=e97]:
-    - button "Über API verwenden Logo" [ref=e98] [cursor=pointer]:
-      - text: Über API verwenden
-      - img "Logo" [ref=e99]
-    - generic [ref=e100]: ·
-    - link "Mit Gradio erstellt Logo" [ref=e101] [cursor=pointer]:
-      - /url: https://gradio.app
-      - text: Mit Gradio erstellt
-      - img "Logo" [ref=e102]
-    - generic [ref=e103]: ·
-    - button "Einstellungen Einstellungen" [ref=e104] [cursor=pointer]:
-      - text: Einstellungen
-      - img "Einstellungen" [ref=e105]

.playwright-mcp/page-2026-06-10T14-28-44-543Z.yml

@@ -1,67 +0,0 @@
-- generic [ref=e21]:
-  - main [ref=e22]:
-    - generic [ref=e23]:
-      - heading "🔥 HearthNet — Community AI Mesh" [level=1] [ref=e28]
-      - generic [ref=e29]:
-        - generic [ref=e32]: ● ONLINE
-        - paragraph [ref=e37]:
-          - text: "Node:"
-          - code [ref=e38]: unknown
-      - generic [ref=e39]:
-        - generic [ref=e40]:
-          - generic [ref=e41]:
-            - button [ref=e42] [cursor=pointer]: Ask
-            - button [ref=e43] [cursor=pointer]: Chat
-            - button [ref=e44] [cursor=pointer]: Marketplace
-            - button [ref=e45] [cursor=pointer]: Files
-            - button [ref=e46] [cursor=pointer]: Emergency
-            - button [ref=e47] [cursor=pointer]: Settings
-          - tablist [ref=e48]:
-            - tab "Ask" [ref=e49] [cursor=pointer]
-            - tab "Chat" [ref=e50] [cursor=pointer]
-            - tab "Marketplace" [active] [selected] [ref=e51] [cursor=pointer]
-            - tab "Files" [ref=e52] [cursor=pointer]
-            - tab "Emergency" [ref=e53] [cursor=pointer]
-            - tab "Settings" [ref=e54] [cursor=pointer]
-        - tabpanel [ref=e134]:
-          - generic [ref=e136]:
-            - heading "Community Marketplace" [level=3] [ref=e141]
-            - button "🔄 Refresh" [ref=e142] [cursor=pointer]
-            - generic [ref=e143]:
-              - generic [ref=e144]:
-                - generic:
-                  - generic:
-                    - img
-                  - text: Active Posts
-              - generic "Empty value" [ref=e146]:
-                - img [ref=e148]
-            - heading "Post Something" [level=4] [ref=e154]
-            - generic [ref=e156]:
-              - generic [ref=e158]:
-                - generic [ref=e159]: Title
-                - textbox "Title" [ref=e161]:
-                  - /placeholder: ""
-              - generic [ref=e163]:
-                - generic [ref=e164]: Category
-                - generic [ref=e167]:
-                  - listbox "Category" [ref=e168]: info
-                  - generic:
-                    - img
-            - generic [ref=e171]:
-              - generic [ref=e172]: Description
-              - textbox "Description" [ref=e174]:
-                - /placeholder: ""
-            - button "Post" [ref=e175] [cursor=pointer]
-  - contentinfo "Gradio footer navigation" [ref=e97]:
-    - button "Über API verwenden Logo" [ref=e98] [cursor=pointer]:
-      - text: Über API verwenden
-      - img "Logo" [ref=e99]
-    - generic [ref=e100]: ·
-    - link "Mit Gradio erstellt Logo" [ref=e101] [cursor=pointer]:
-      - /url: https://gradio.app
-      - text: Mit Gradio erstellt
-      - img "Logo" [ref=e102]
-    - generic [ref=e103]: ·
-    - button "Einstellungen Einstellungen" [ref=e104] [cursor=pointer]:
-      - text: Einstellungen
-      - img "Einstellungen" [ref=e105]

.playwright-mcp/page-2026-06-10T14-28-52-588Z.yml

@@ -1,53 +0,0 @@
-- generic [ref=e21]:
-  - main [ref=e22]:
-    - generic [ref=e23]:
-      - heading "🔥 HearthNet — Community AI Mesh" [level=1] [ref=e28]
-      - generic [ref=e29]:
-        - generic [ref=e32]: ● ONLINE
-        - paragraph [ref=e37]:
-          - text: "Node:"
-          - code [ref=e38]: unknown
-      - generic [ref=e39]:
-        - generic [ref=e40]:
-          - generic [ref=e41]:
-            - button [ref=e42] [cursor=pointer]: Ask
-            - button [ref=e43] [cursor=pointer]: Chat
-            - button [ref=e44] [cursor=pointer]: Marketplace
-            - button [ref=e45] [cursor=pointer]: Files
-            - button [ref=e46] [cursor=pointer]: Emergency
-            - button [ref=e47] [cursor=pointer]: Settings
-          - tablist [ref=e48]:
-            - tab "Ask" [ref=e49] [cursor=pointer]
-            - tab "Chat" [ref=e50] [cursor=pointer]
-            - tab "Marketplace" [ref=e51] [cursor=pointer]
-            - tab "Files" [ref=e52] [cursor=pointer]
-            - tab "Emergency" [active] [selected] [ref=e53] [cursor=pointer]
-            - tab "Settings" [ref=e54] [cursor=pointer]
-        - tabpanel [ref=e176]:
-          - generic [ref=e178]:
-            - heading "🚨 Emergency Mode" [level=3] [ref=e183]
-            - generic [ref=e184]:
-              - generic [ref=e185]:
-                - generic:
-                  - generic:
-                    - img
-                  - text: Current Mode
-              - generic "Empty value" [ref=e187]:
-                - img [ref=e189]
-            - button "Check Status" [ref=e191] [cursor=pointer]
-            - heading "Local Resources" [level=4] [ref=e196]
-            - paragraph [ref=e201]: In offline mode, all capabilities route to local nodes only.
-            - button "Run Connectivity Probe" [ref=e203] [cursor=pointer]
-  - contentinfo "Gradio footer navigation" [ref=e97]:
-    - button "Über API verwenden Logo" [ref=e98] [cursor=pointer]:
-      - text: Über API verwenden
-      - img "Logo" [ref=e99]
-    - generic [ref=e100]: ·
-    - link "Mit Gradio erstellt Logo" [ref=e101] [cursor=pointer]:
-      - /url: https://gradio.app
-      - text: Mit Gradio erstellt
-      - img "Logo" [ref=e102]
-    - generic [ref=e103]: ·
-    - button "Einstellungen Einstellungen" [ref=e104] [cursor=pointer]:
-      - text: Einstellungen
-      - img "Einstellungen" [ref=e105]

.playwright-mcp/page-2026-06-10T14-29-00-723Z.yml

@@ -1,110 +0,0 @@
-- generic [ref=e21]:
-  - main [ref=e22]:
-    - generic [ref=e23]:
-      - heading "🔥 HearthNet — Community AI Mesh" [level=1] [ref=e28]
-      - generic [ref=e29]:
-        - generic [ref=e32]: ● ONLINE
-        - paragraph [ref=e37]:
-          - text: "Node:"
-          - code [ref=e38]: unknown
-      - generic [ref=e39]:
-        - generic [ref=e40]:
-          - generic [ref=e41]:
-            - button [ref=e42] [cursor=pointer]: Ask
-            - button [ref=e43] [cursor=pointer]: Chat
-            - button [ref=e44] [cursor=pointer]: Marketplace
-            - button [ref=e45] [cursor=pointer]: Files
-            - button [ref=e46] [cursor=pointer]: Emergency
-            - button [ref=e47] [cursor=pointer]: Settings
-          - tablist [ref=e48]:
-            - tab "Ask" [ref=e49] [cursor=pointer]
-            - tab "Chat" [ref=e50] [cursor=pointer]
-            - tab "Marketplace" [ref=e51] [cursor=pointer]
-            - tab "Files" [ref=e52] [cursor=pointer]
-            - tab "Emergency" [ref=e53] [cursor=pointer]
-            - tab "Settings" [active] [selected] [ref=e54] [cursor=pointer]
-        - tabpanel [ref=e204]:
-          - generic [ref=e206]:
-            - heading "Settings" [level=3] [ref=e211]
-            - heading "Node Identity" [level=4] [ref=e216]
-            - paragraph [ref=e221]:
-              - text: "Node ID:"
-              - code [ref=e222]: not initialized
-            - paragraph [ref=e227]:
-              - text: "Profile:"
-              - code [ref=e228]: hearth
-            - heading "Community" [level=4] [ref=e233]
-            - paragraph [ref=e238]:
-              - text: "Community:"
-              - code [ref=e239]: none
-            - heading "Phase Labels" [level=4] [ref=e244]
-            - table [ref=e249]:
-              - rowgroup [ref=e250]:
-                - row "Module Status" [ref=e251]:
-                  - columnheader "Module" [ref=e252]
-                  - columnheader "Status" [ref=e253]
-              - rowgroup [ref=e254]:
-                - row "M01 Identity ✅ Implemented" [ref=e255]:
-                  - cell "M01 Identity" [ref=e256]
-                  - cell "✅ Implemented" [ref=e257]
-                - row "M02 Discovery ✅ Implemented (mDNS/UDP)" [ref=e258]:
-                  - cell "M02 Discovery" [ref=e259]
-                  - cell "✅ Implemented (mDNS/UDP)" [ref=e260]
-                - row "M03 Bus ✅ Implemented" [ref=e261]:
-                  - cell "M03 Bus" [ref=e262]
-                  - cell "✅ Implemented" [ref=e263]
-                - row "M04 LLM ✅ Implemented (Ollama/llama.cpp/HF)" [ref=e264]:
-                  - cell "M04 LLM" [ref=e265]
-                  - cell "✅ Implemented (Ollama/llama.cpp/HF)" [ref=e266]
-                - row "M05 RAG ✅ Implemented" [ref=e267]:
-                  - cell "M05 RAG" [ref=e268]
-                  - cell "✅ Implemented" [ref=e269]
-                - row "M06 Marketplace ✅ Implemented (event-sourced)" [ref=e270]:
-                  - cell "M06 Marketplace" [ref=e271]
-                  - cell "✅ Implemented (event-sourced)" [ref=e272]
-                - row "M07 Blobs ✅ Implemented" [ref=e273]:
-                  - cell "M07 Blobs" [ref=e274]
-                  - cell "✅ Implemented" [ref=e275]
-                - row "M08 UI ✅ This UI" [ref=e276]:
-                  - cell "M08 UI" [ref=e277]
-                  - cell "✅ This UI" [ref=e278]
-                - row "M09 Emergency ✅ Implemented" [ref=e279]:
-                  - cell "M09 Emergency" [ref=e280]
-                  - cell "✅ Implemented" [ref=e281]
-                - row "M10 Chat ✅ Implemented" [ref=e282]:
-                  - cell "M10 Chat" [ref=e283]
-                  - cell "✅ Implemented" [ref=e284]
-                - row "M11 Embedding ✅ Implemented" [ref=e285]:
-                  - cell "M11 Embedding" [ref=e286]
-                  - cell "✅ Implemented" [ref=e287]
-                - row "M12 CLI ✅ Implemented" [ref=e288]:
-                  - cell "M12 CLI" [ref=e289]
-                  - cell "✅ Implemented" [ref=e290]
-                - row "M13 Onboarding ✅ Implemented" [ref=e291]:
-                  - cell "M13 Onboarding" [ref=e292]
-                  - cell "✅ Implemented" [ref=e293]
-                - row "X01 Transport ✅ Implemented (FastAPI)" [ref=e294]:
-                  - cell "X01 Transport" [ref=e295]
-                  - cell "✅ Implemented (FastAPI)" [ref=e296]
-                - row "X02 Events ✅ Implemented (SQLite)" [ref=e297]:
-                  - cell "X02 Events" [ref=e298]
-                  - cell "✅ Implemented (SQLite)" [ref=e299]
-                - row "X03 Observability ✅ Implemented" [ref=e300]:
-                  - cell "X03 Observability" [ref=e301]
-                  - cell "✅ Implemented" [ref=e302]
-                - row "X04 Config ✅ Implemented" [ref=e303]:
-                  - cell "X04 Config" [ref=e304]
-                  - cell "✅ Implemented" [ref=e305]
-  - contentinfo "Gradio footer navigation" [ref=e97]:
-    - button "Über API verwenden Logo" [ref=e98] [cursor=pointer]:
-      - text: Über API verwenden
-      - img "Logo" [ref=e99]
-    - generic [ref=e100]: ·
-    - link "Mit Gradio erstellt Logo" [ref=e101] [cursor=pointer]:
-      - /url: https://gradio.app
-      - text: Mit Gradio erstellt
-      - img "Logo" [ref=e102]
-    - generic [ref=e103]: ·
-    - button "Einstellungen Einstellungen" [ref=e104] [cursor=pointer]:
-      - text: Einstellungen
-      - img "Einstellungen" [ref=e105]

.playwright-mcp/page-2026-06-10T14-52-32-151Z.yml

@@ -1,3 +0,0 @@
-- generic [ref=e5]:
-  - img [ref=e9]
-  - paragraph [ref=e20]: Laden...

.playwright-mcp/page-2026-06-10T14-52-36-986Z.yml

@@ -1,98 +0,0 @@
-- generic [ref=e21]:
-  - main [ref=e22]:
-    - generic [ref=e23]:
-      - heading "🔥 HearthNet — Community AI Mesh" [level=1] [ref=e28]
-      - generic [ref=e29]:
-        - generic [ref=e32]: ● ONLINE
-        - paragraph [ref=e37]:
-          - text: "Node:"
-          - code [ref=e38]: unknown
-      - generic [ref=e39]:
-        - generic [ref=e40]:
-          - generic [ref=e41]:
-            - button [ref=e42] [cursor=pointer]: Ask
-            - button [ref=e43] [cursor=pointer]: Chat
-            - button [ref=e44] [cursor=pointer]: Marketplace
-            - button [ref=e45] [cursor=pointer]: Files
-            - button [ref=e46] [cursor=pointer]: Emergency
-            - button [ref=e47] [cursor=pointer]: Settings
-          - tablist [ref=e48]:
-            - tab "Ask" [ref=e49] [cursor=pointer]
-            - tab "Chat" [ref=e50] [cursor=pointer]
-            - tab "Marketplace" [ref=e51] [cursor=pointer]
-            - tab "Files" [ref=e52] [cursor=pointer]
-            - tab "Emergency" [ref=e53] [cursor=pointer]
-            - tab "Settings" [active] [selected] [ref=e54] [cursor=pointer]
-        - tabpanel [ref=e55]:
-          - generic [ref=e57]:
-            - heading "⚙️ Node & Settings" [level=3] [ref=e62]
-            - generic [ref=e63]:
-              - button "🪪 Node Identity ▼" [ref=e64] [cursor=pointer]:
-                - generic [ref=e65]: 🪪 Node Identity
-                - generic [ref=e66]: ▼
-              - table [ref=e73]:
-                - rowgroup [ref=e74]:
-                  - row "Field Value" [ref=e75]:
-                    - columnheader "Field" [ref=e76]
-                    - columnheader "Value" [ref=e77]
-                - rowgroup [ref=e78]:
-                  - row "Node ID not initialized" [ref=e79]:
-                    - cell "Node ID" [ref=e80]
-                    - cell "not initialized" [ref=e81]:
-                      - code [ref=e82]: not initialized
-                  - row "Profile hearth" [ref=e83]:
-                    - cell "Profile" [ref=e84]
-                    - cell "hearth" [ref=e85]:
-                      - code [ref=e86]: hearth
-                  - row "Community none" [ref=e87]:
-                    - cell "Community" [ref=e88]
-                    - cell "none" [ref=e89]:
-                      - code [ref=e90]: none
-            - generic [ref=e91]:
-              - button "🌐 Connected Peers & Capabilities ▼" [ref=e92] [cursor=pointer]:
-                - generic [ref=e93]: 🌐 Connected Peers & Capabilities
-                - generic [ref=e94]: ▼
-              - generic [ref=e96]:
-                - generic [ref=e97]:
-                  - generic [ref=e98]:
-                    - generic:
-                      - generic:
-                        - img
-                      - text: Peers
-                  - button "Copy" [ref=e100] [cursor=pointer]:
-                    - img [ref=e102]
-                  - generic [ref=e106]:
-                    - generic [ref=e107]:
-                      - generic "Line number 1" [ref=e108]: "1"
-                      - generic [ref=e109]:
-                        - button "Collapse" [ref=e110] [cursor=pointer]: ▼
-                        - generic [ref=e111]: "["
-                    - generic [ref=e113]:
-                      - generic "Line number 2" [ref=e114]: "2"
-                      - generic [ref=e116]: "]"
-                - button "🔄 Refresh Peers" [ref=e117] [cursor=pointer]
-            - button "📨 Invite a Node ▼" [ref=e119] [cursor=pointer]:
-              - generic [ref=e120]: 📨 Invite a Node
-              - generic [ref=e121]: ▼
-            - button "📚 RAG — Ingest Documents ▼" [ref=e123] [cursor=pointer]:
-              - generic [ref=e124]: 📚 RAG — Ingest Documents
-              - generic [ref=e125]: ▼
-            - button "📋 Configuration Overview ▼" [ref=e127] [cursor=pointer]:
-              - generic [ref=e128]: 📋 Configuration Overview
-              - generic [ref=e129]: ▼
-            - button "🔬 Implementation Status ▼" [ref=e131] [cursor=pointer]:
-              - generic [ref=e132]: 🔬 Implementation Status
-              - generic [ref=e133]: ▼
-  - contentinfo "Gradio footer navigation" [ref=e134]:
-    - button "Über API verwenden Logo" [ref=e135] [cursor=pointer]:
-      - text: Über API verwenden
-      - img "Logo" [ref=e136]
-    - generic [ref=e137]: ·
-    - link "Mit Gradio erstellt Logo" [ref=e138] [cursor=pointer]:
-      - /url: https://gradio.app
-      - text: Mit Gradio erstellt
-      - img "Logo" [ref=e139]
-    - generic [ref=e140]: ·
-    - button "Einstellungen Einstellungen" [ref=e141] [cursor=pointer]:
-      - text: Einstellungen
-      - img "Einstellungen" [ref=e142]

.playwright-mcp/page-2026-06-10T15-16-39-829Z.yml

@@ -1,3 +0,0 @@
-- generic [ref=e5]:
-  - img [ref=e9]
-  - paragraph [ref=e20]: Laden...

.playwright-mcp/page-2026-06-10T15-17-36-795Z.yml

@@ -1,15 +0,0 @@
-- generic [ref=e5]:
-  - main [ref=e6]
-  - contentinfo "Gradio footer navigation" [ref=e7]:
-    - button "Über API verwenden Logo" [ref=e8] [cursor=pointer]:
-      - text: Über API verwenden
-      - img "Logo" [ref=e9]
-    - generic [ref=e10]: ·
-    - link "Mit Gradio erstellt Logo" [ref=e11] [cursor=pointer]:
-      - /url: https://gradio.app
-      - text: Mit Gradio erstellt
-      - img "Logo" [ref=e12]
-    - generic [ref=e13]: ·
-    - button "Einstellungen Einstellungen" [ref=e14] [cursor=pointer]:
-      - text: Einstellungen
-      - img "Einstellungen" [ref=e15]

.playwright-mcp/page-2026-06-10T15-18-10-485Z.yml

@@ -1,15 +0,0 @@
-- generic [ref=e5]:
-  - main [ref=e6]
-  - contentinfo "Gradio footer navigation" [ref=e7]:
-    - button "Über API verwenden Logo" [ref=e8] [cursor=pointer]:
-      - text: Über API verwenden
-      - img "Logo" [ref=e9]
-    - generic [ref=e10]: ·
-    - link "Mit Gradio erstellt Logo" [ref=e11] [cursor=pointer]:
-      - /url: https://gradio.app
-      - text: Mit Gradio erstellt
-      - img "Logo" [ref=e12]
-    - generic [ref=e13]: ·
-    - button "Einstellungen Einstellungen" [ref=e14] [cursor=pointer]:
-      - text: Einstellungen
-      - img "Einstellungen" [ref=e15]

.playwright-mcp/page-2026-06-10T15-20-12-385Z.yml

@@ -1,3 +0,0 @@
-- generic [ref=e5]:
-  - img [ref=e9]
-  - paragraph [ref=e20]: Laden...

.playwright-mcp/page-2026-06-10T23-26-07-697Z.yml

@@ -1,3 +0,0 @@
-- generic [ref=e5]:
-  - img [ref=e9]
-  - paragraph [ref=e20]: Laden...

.playwright-mcp/page-2026-06-12T13-16-10-970Z.yml

@@ -1,25 +0,0 @@
-- generic [active] [ref=e1]:
-  - generic [ref=e2]:
-    - banner [ref=e3]:
-      - heading "🔥 HearthNet" [level=1] [ref=e4]
-      - generic [ref=e5]: browser-local agent · news · WebRTC mesh
-      - generic [ref=e6]: press e for live ticker
-      - generic [ref=e7]: Model
-      - combobox [ref=e8]:
-        - option "SmolLM2 360M (smallest, fastest)" [selected]
-        - option "Qwen2.5 0.5B"
-        - option "Llama 3.2 1B (best quality)"
-      - generic [ref=e9]: select a model — it loads on first chat
-    - generic [ref=e10]:
-      - generic [ref=e11] [cursor=pointer]: Agent
-      - generic [ref=e12] [cursor=pointer]: News
-      - generic [ref=e13] [cursor=pointer]: Mesh
-    - generic [ref=e15]:
-      - generic [ref=e16]:
-        - textbox "Ask the agent to search news, scrape pages, or monitor signals" [ref=e17]
-        - button "Run" [ref=e18] [cursor=pointer]
-        - button "Stop" [disabled] [ref=e19]
-      - generic [ref=e24]: Agent log
-  - generic [ref=e25]:
-    - generic [ref=e26]: ⚡ LIVE
-    - generic [ref=e27]: press “e” again to hide…

.playwright-mcp/page-2026-06-13T06-41-44-411Z.yml

@@ -1,113 +0,0 @@
-- generic [active] [ref=e1]:
-  - generic [ref=e5]:
-    - main [ref=e6]:
-      - generic [ref=e7]:
-        - heading "🔥 HearthNet — HearthNet" [level=1] [ref=e14]
-        - generic [ref=e15]:
-          - generic [ref=e18]: ● ONLINE
-          - paragraph [ref=e23]:
-            - text: "Node:"
-            - code [ref=e24]: hf-space-1c95381d
-          - paragraph [ref=e29]:
-            - text: "Community:"
-            - code [ref=e30]: ed25519:hf-space-community
-        - generic [ref=e31]:
-          - generic [ref=e32]:
-            - generic [ref=e33]:
-              - button [ref=e34] [cursor=pointer]: Ask
-              - button [ref=e35] [cursor=pointer]: Chat
-              - button [ref=e36] [cursor=pointer]: Mesh
-              - button [ref=e37] [cursor=pointer]: Marketplace
-              - button [ref=e38] [cursor=pointer]: Files
-              - button [ref=e39] [cursor=pointer]: Emergency
-              - button [ref=e40] [cursor=pointer]: Settings
-              - button [ref=e41] [cursor=pointer]: Getting Started
-            - tablist [ref=e42]:
-              - tab "Ask" [selected] [ref=e43] [cursor=pointer]
-              - tab "Chat" [ref=e44] [cursor=pointer]
-              - tab "Mesh" [ref=e45] [cursor=pointer]
-              - tab "Marketplace" [ref=e46] [cursor=pointer]
-              - tab "Files" [ref=e47] [cursor=pointer]
-              - tab "Emergency" [ref=e48] [cursor=pointer]
-              - tab "Settings" [ref=e49] [cursor=pointer]
-              - tab "Getting Started" [ref=e50] [cursor=pointer]
-          - tabpanel [ref=e51]:
-            - generic [ref=e53]:
-              - generic [ref=e57]:
-                - heading "💬 Ask the Mesh" [level=3] [ref=e58]
-                - paragraph [ref=e59]:
-                  - text: Send a question to the
-                  - strong [ref=e60]: HearthNet capability bus
-                  - text: . The bus routes the request to the best available LLM node — either on this device or on a peer.
-                - paragraph [ref=e61]:
-                  - strong [ref=e62]: "How it works:"
-                - list [ref=e63]:
-                  - listitem [ref=e64]:
-                    - strong [ref=e65]: (none) corpus
-                    - text: → question goes directly to the LLM
-                  - listitem [ref=e66]:
-                    - strong [ref=e67]: Select a corpus
-                    - text: → RAG retrieval runs first; top chunks become system context
-                  - listitem [ref=e68]:
-                    - strong [ref=e69]: "Model: auto"
-                    - text: → bus picks highest-scoring available node (local first, then peer)
-                  - listitem [ref=e70]:
-                    - strong [ref=e71]: "Model: name"
-                    - text: → routes only to nodes that advertise that exact model
-                - paragraph [ref=e72]:
-                  - strong [ref=e73]: Routing is transparent
-                  - text: — the trace below every response shows which node answered.
-              - generic [ref=e74]:
-                - generic [ref=e75]:
-                  - generic [ref=e77]:
-                    - generic [ref=e78]: RAG Corpus (leave blank for direct LLM)
-                    - generic [ref=e81]:
-                      - listbox "RAG Corpus (leave blank for direct LLM)" [ref=e82]: (none)
-                      - generic:
-                        - img
-                  - generic [ref=e84]:
-                    - generic [ref=e85]: Model (auto = bus picks best node)
-                    - generic [ref=e88]:
-                      - listbox "Model (auto = bus picks best node)" [ref=e89]: auto
-                      - generic:
-                        - img
-                - button "🔄 Refresh Corpora" [ref=e90] [cursor=pointer]
-              - generic [ref=e92]:
-                - generic:
-                  - generic:
-                    - img
-                  - text: Conversation
-                - log "chatbot conversation" [ref=e93]:
-                  - complementary [ref=e94]
-              - generic [ref=e95]:
-                - generic [ref=e98]:
-                  - generic [ref=e99]: Your message
-                  - textbox "Your message" [ref=e101]:
-                    - /placeholder: e.g. What is HearthNet? / How do I filter rainwater? / List my neighbours' capabilities.
-                - button "Send" [ref=e102] [cursor=pointer]
-    - contentinfo "Gradio footer navigation" [ref=e103]:
-      - button "Über API verwenden Logo" [ref=e104] [cursor=pointer]:
-        - text: Über API verwenden
-        - img "Logo" [ref=e105]
-      - generic [ref=e106]: ·
-      - link "Mit Gradio erstellt Logo" [ref=e107] [cursor=pointer]:
-        - /url: https://gradio.app
-        - text: Mit Gradio erstellt
-        - img "Logo" [ref=e108]
-      - generic [ref=e109]: ·
-      - button "Einstellungen Einstellungen" [ref=e110] [cursor=pointer]:
-        - text: Einstellungen
-        - img "Einstellungen" [ref=e111]
-  - generic [ref=e113]:
-    - generic [ref=e114]:
-      - img [ref=e115]
-      - link "build-small-hackathon" [ref=e116] [cursor=pointer]:
-        - /url: https://huggingface.co/build-small-hackathon
-      - generic [ref=e117]: /
-      - link "HearthNet" [ref=e118] [cursor=pointer]:
-        - /url: https://huggingface.co/spaces/build-small-hackathon/HearthNet
-      - link "3" [ref=e119] [cursor=pointer]:
-        - /url: https://huggingface.co/spaces/build-small-hackathon/HearthNet
-        - img [ref=e120]
-        - paragraph [ref=e122]: "3"
-    - img [ref=e124] [cursor=pointer]

.playwright-mcp/page-2026-06-13T06-52-18-615Z.yml

@@ -1,113 +0,0 @@
-- generic [active] [ref=e1]:
-  - generic [ref=e5]:
-    - main [ref=e6]:
-      - generic [ref=e7]:
-        - heading "🔥 HearthNet — HearthNet" [level=1] [ref=e14]
-        - generic [ref=e15]:
-          - generic [ref=e18]: ● ONLINE
-          - paragraph [ref=e23]:
-            - text: "Node:"
-            - code [ref=e24]: hf-space-1c95381d
-          - paragraph [ref=e29]:
-            - text: "Community:"
-            - code [ref=e30]: ed25519:hf-space-community
-        - generic [ref=e31]:
-          - generic [ref=e32]:
-            - generic [ref=e33]:
-              - button [ref=e34] [cursor=pointer]: Ask
-              - button [ref=e35] [cursor=pointer]: Chat
-              - button [ref=e36] [cursor=pointer]: Mesh
-              - button [ref=e37] [cursor=pointer]: Marketplace
-              - button [ref=e38] [cursor=pointer]: Files
-              - button [ref=e39] [cursor=pointer]: Emergency
-              - button [ref=e40] [cursor=pointer]: Settings
-              - button [ref=e41] [cursor=pointer]: Getting Started
-            - tablist [ref=e42]:
-              - tab "Ask" [selected] [ref=e43] [cursor=pointer]
-              - tab "Chat" [ref=e44] [cursor=pointer]
-              - tab "Mesh" [ref=e45] [cursor=pointer]
-              - tab "Marketplace" [ref=e46] [cursor=pointer]
-              - tab "Files" [ref=e47] [cursor=pointer]
-              - tab "Emergency" [ref=e48] [cursor=pointer]
-              - tab "Settings" [ref=e49] [cursor=pointer]
-              - tab "Getting Started" [ref=e50] [cursor=pointer]
-          - tabpanel [ref=e51]:
-            - generic [ref=e53]:
-              - generic [ref=e57]:
-                - heading "💬 Ask the Mesh" [level=3] [ref=e58]
-                - paragraph [ref=e59]:
-                  - text: Send a question to the
-                  - strong [ref=e60]: HearthNet capability bus
-                  - text: . The bus routes the request to the best available LLM node — either on this device or on a peer.
-                - paragraph [ref=e61]:
-                  - strong [ref=e62]: "How it works:"
-                - list [ref=e63]:
-                  - listitem [ref=e64]:
-                    - strong [ref=e65]: (none) corpus
-                    - text: → question goes directly to the LLM
-                  - listitem [ref=e66]:
-                    - strong [ref=e67]: Select a corpus
-                    - text: → RAG retrieval runs first; top chunks become system context
-                  - listitem [ref=e68]:
-                    - strong [ref=e69]: "Model: auto"
-                    - text: → bus picks highest-scoring available node (local first, then peer)
-                  - listitem [ref=e70]:
-                    - strong [ref=e71]: "Model: name"
-                    - text: → routes only to nodes that advertise that exact model
-                - paragraph [ref=e72]:
-                  - strong [ref=e73]: Routing is transparent
-                  - text: — the trace below every response shows which node answered.
-              - generic [ref=e74]:
-                - generic [ref=e75]:
-                  - generic [ref=e77]:
-                    - generic [ref=e78]: RAG Corpus (leave blank for direct LLM)
-                    - generic [ref=e81]:
-                      - listbox "RAG Corpus (leave blank for direct LLM)" [ref=e82]: (none)
-                      - generic:
-                        - img
-                  - generic [ref=e84]:
-                    - generic [ref=e85]: Model (auto = bus picks best node)
-                    - generic [ref=e88]:
-                      - listbox "Model (auto = bus picks best node)" [ref=e89]: auto
-                      - generic:
-                        - img
-                - button "🔄 Refresh Corpora" [ref=e90] [cursor=pointer]
-              - generic [ref=e92]:
-                - generic:
-                  - generic:
-                    - img
-                  - text: Conversation
-                - log "chatbot conversation" [ref=e93]:
-                  - complementary [ref=e94]
-              - generic [ref=e95]:
-                - generic [ref=e98]:
-                  - generic [ref=e99]: Your message
-                  - textbox "Your message" [ref=e101]:
-                    - /placeholder: e.g. What is HearthNet? / How do I filter rainwater? / List my neighbours' capabilities.
-                - button "Send" [ref=e102] [cursor=pointer]
-    - contentinfo "Gradio footer navigation" [ref=e103]:
-      - button "Über API verwenden Logo" [ref=e104] [cursor=pointer]:
-        - text: Über API verwenden
-        - img "Logo" [ref=e105]
-      - generic [ref=e106]: ·
-      - link "Mit Gradio erstellt Logo" [ref=e107] [cursor=pointer]:
-        - /url: https://gradio.app
-        - text: Mit Gradio erstellt
-        - img "Logo" [ref=e108]
-      - generic [ref=e109]: ·
-      - button "Einstellungen Einstellungen" [ref=e110] [cursor=pointer]:
-        - text: Einstellungen
-        - img "Einstellungen" [ref=e111]
-  - generic [ref=e113]:
-    - generic [ref=e114]:
-      - img [ref=e115]
-      - link "build-small-hackathon" [ref=e116] [cursor=pointer]:
-        - /url: https://huggingface.co/build-small-hackathon
-      - generic [ref=e117]: /
-      - link "HearthNet" [ref=e118] [cursor=pointer]:
-        - /url: https://huggingface.co/spaces/build-small-hackathon/HearthNet
-      - link "3" [ref=e119] [cursor=pointer]:
-        - /url: https://huggingface.co/spaces/build-small-hackathon/HearthNet
-        - img [ref=e120]
-        - paragraph [ref=e122]: "3"
-    - img [ref=e124] [cursor=pointer]

6.0.0

@@ -1,18 +0,0 @@
-Requirement already satisfied: pyinstaller in C:\Users\Chris4K\AppData\Local\Programs\Python\Python313\Lib\site-packages (6.20.0)
-Collecting pyinstaller
-  Downloading pyinstaller-6.21.0-py3-none-win_amd64.whl.metadata (8.5 kB)
-Requirement already satisfied: watchdog in C:\Users\Chris4K\AppData\Local\Programs\Python\Python313\Lib\site-packages (6.0.0)
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-Downloading pyinstaller-6.21.0-py3-none-win_amd64.whl (1.4 MB)
-   ---------------------------------------- 1.4/1.4 MB 9.3 MB/s  0:00:00
-Installing collected packages: pyinstaller
-  Attempting uninstall: pyinstaller
-    Found existing installation: pyinstaller 6.20.0
-    Uninstalling pyinstaller-6.20.0:
-      Successfully uninstalled pyinstaller-6.20.0
-Successfully installed pyinstaller-6.21.0

BLOG_COMPREHENSIVE.md

@@ -1,616 +0,0 @@
-# HearthNet: Building AI That Works When the Internet Doesn't
-
-**A Hugging Face Build Small Hackathon entry that brings peer-to-peer AI meshes to life**
-
----
-
-## The Spark: What If AI Worked Offline?
-
-Imagine a neighborhood where every household with an old laptop, a Raspberry Pi, or any Python-capable device becomes **part of a local AI mesh**. No cloud accounts. No API bills. No ISP dependency. When your power flickers, your internet stutters, or the cloud goes down—*the neighborhood's AI keeps running*.
-
-That's HearthNet.
-
-It's the answer to a question that became urgent during COVID lockdowns, hurricane seasons, and supply chain disruptions: **What happens to your community's AI when the infrastructure fails?**
-
-Today, the answer from every major vendor is: "Sorry, nothing." But that's not an inevitable outcome. It's a design choice.
-
-HearthNet makes a different choice.
-
----
-
-## The Problem We're Solving
-
-### The Cloud Trap
-
-Modern AI is sold as a service. Buy credits, submit queries to an API, get answers. It's convenient until:
-
-- The ISP goes down (neighbors lose AI capabilities until restoration)
-- The cloud region has an outage (your city's tools evaporate for hours)
-- You lose your API credentials or run out of credits mid-emergency
-- You realize you've funded 15 different subscriptions and have no local ownership
-- Your private data is now on someone else's servers
-- Government regulation makes your chosen AI provider unavailable in your region
-
-For urban neighborhoods facing routine infrastructure disruptions—brownouts, fiber cuts, DDoS attacks on ISPs—**the cloud model is a liability, not a feature**.
-
-### The Local Model Limitation
-
-Conversely, running AI purely locally solves some problems and creates others:
-
-- Your MacBook has a 4B model; it would benefit from a neighbor's 13B node
-- Your phone has a small vision model; someone down the street trained an OCR expert
-- During emergencies, you could share emergency guidance from a regional database
-- But you're locked to your hardware, your latency, your knowledge base
-
-**Local and cloud are not enemies. They're incomplete solutions.**
-
----
-
-## The HearthNet Vision: Mesh as Infrastructure
-
-HearthNet proposes a third way: **community AI infrastructure built on peer-to-peer mesh networking**.
-
-### Core Principles
-
-1. **Local-first**: All features work completely offline on your device, right now
-2. **Transparent mesh**: Nodes find each other automatically and advertise capabilities (expertise, speed, capacity)
-3. **Intelligent routing**: Requests automatically go to the best node for the job—local, LAN, or internet relay
-4. **No single authority**: No server you must trust, no account required, no central gatekeeper
-5. **Emergency-ready**: When connectivity degrades, the UI and routing degrade gracefully; no sudden failures
-6. **Community-owned**: Run it on hardware you control, inspect the code, modify it for your needs
-
-### What This Looks Like in Practice
-
-**User perspective:**
-
-```
-Alice (laptop) → "What's edible in this photo?" 
-                → Bus routes to Bob's node (neighbor with vision specialist model)
-                → Bob's device infers in 200ms
-                → Alice sees: "edible: tomato, squash, basil" + "Answered by: Bob's RPi"
-                
-Carol (phone) → "Summarize these PDFs"
-              → Bus can't satisfy locally; routes to internet relay
-              → Relay picks a regional node with 13B model
-              → Carol sees: summary + confidence + "Answered by: regional node eu-west-1"
-              
-David (offline) → "Remind me about water storage"
-                → All corpora cached locally
-                → Instant result from local RAG
-                → When online later: syncs new community knowledge
-```
-
-**Architectural perspective:**
-
-```
-┌─────────────┐
-│ Alice's Box │
-│ (4B model)  │───────┐
-└─────────────┘       │
-                      │ ┌─────────────────────┐
-┌─────────────┐       ├─│ Capability Bus      │
-│  Bob's RPi  │       │ │ (routing, scoring)  │
-│  (vision)   │───────┤ └─────────────────────┘
-└─────────────┘       │
-                      │ ┌─────────────────────┐
-┌─────────────┐       ├─│ Emergency Detector  │
-│ Carol's Net │       │ │ (failover logic)    │
-│  (offline)  │───────┤ └─────────────────────┘
-└─────────────┘       │
-         │            │ ┌─────────────────────┐
-         └────────────┼─│ Gossip Sync Layer   │
-                      │ │ (corpus + messages) │
-                      │ └─────────────────────┘
-                      │
-         [Optional internet relay for LAN→WAN]
-```
-
----
-
-## What We've Built: Phase 1
-
-Over the Build Small Hackathon (June 2024 – June 2026), we've shipped a **production-grade foundation** for community AI meshes.
-
-### The Core Stack
-
-| Layer | Component | Status | Tech |
-|-------|-----------|--------|------|
-| **Models** | 🔥 MiniCPM3-4B (OpenBMB) + Nemotron Mini | ✅ Live | Transformers w/ trust_remote_code |
-| **LLM Runtime** | HF Transformers + llama.cpp + Ollama support | ✅ Live | Python async backends |
-| **RAG** | BLAKE3-deduplicated Chroma vector DB | ✅ Live | Semantic search w/ auto-ingest |
-| **Routing** | Intelligent mesh capability bus + scoring | ✅ Live | Load-aware, latency-optimized |
-| **Mesh Discovery** | mDNS + gossip sync | ✅ Live | SQLite event log |
-| **Chat** | Store-and-forward direct messages + QR invites | ✅ Live | Event-sourced, Lamport clocks |
-| **UI** | Gradio 6.18 + topology viz + emergency mode | ✅ Live | 8 tabs, mobile-responsive |
-| **Deployment** | HF Spaces + Docker + local Python | ✅ Live | Zero-GPU aware |
-
-### The 13-Module Spec
-
-We didn't just ship code—we **shipped a specification**:
-
-```
-M01: Identity & cryptographic manifests
-M02: Peer discovery (mDNS, relay)
-M03: Capability bus (routing, scoring, failover)
-M04: LLM inference backends
-M05: RAG corpus + retrieval
-M06: Marketplace (community offers/requests)
-M07: Content-addressed blob storage (BLAKE3)
-M08: UI dashboard & topology
-M09: Emergency detector & degraded mode
-M10: Event-sourced chat + delivery
-M11: Embedding service (text + vision)
-M12: CLI (hearthnet command-line)
-M13: Onboarding (invites, key gen, first-run)
-
-Cross-cutting:
-X01: Transport layer (HTTP, TLS, streaming)
-X02: Events (Lamport clocks, gossip, snapshots)
-X03: Observability (logging, metrics, traces)
-X04: Configuration (validation, env loading)
-```
-
-Every module has a formal spec document, dependency graph, and wire-level capability contract. This isn't a demo—it's a **reference implementation** that other teams can fork and adapt.
-
-### What Works Today
-
-🎯 **You can:**
-
-- **Ask the mesh**: Type a question in the Ask tab → it routes to the best LLM node and shows you who answered
-- **Chat offline**: Send messages between neighbors; they queue if the recipient is offline
-- **Search corpora**: Ingest markdown/PDF documents → semantic search across all shared knowledge bases
-- **View topology**: See live graph of your mesh (nodes, latency, capabilities)
-- **Emergency mode**: When internet drops, the UI degrades gracefully but all features stay online
-- **QR invites**: Generate a QR code, neighbors scan it to join your mesh
-- **Agent mode**: Toggle on Agent Mode in Ask → the LLM becomes an agent, calls tools (search corpus, translate, identify plants), shows every thought step
-- **Marketplace**: Post community offers, requests, or emergency guidance
-- **Local-first**: Every feature works offline on a single device right now
-
-🚀 **Supported LLM backends:**
-- HF Transformers (MiniCPM3-4B, Nemotron, SmolLM2, Llama-3.1, etc.)
-- llama.cpp (GGUF models, CPU-optimized)
-- Ollama (local inference orchestration)
-- NVIDIA Nemotron (remote API, fallback to SmolLM2 locally)
-
-🎬 **8 functional UI tabs:**
-1. **Ask** — LLM routing + Agent Mode
-2. **Chat** — Direct messages + QR invites
-3. **Mesh** — Live topology graph
-4. **Marketplace** — Community coordination
-5. **Files** — BLAKE3 blob store
-6. **Emergency** — Degraded mode + connectivity probe
-7. **Settings** — Node config, peer list, RAG ingest
-8. **Getting Started** — Walkthrough + docs
-
----
-
-## June 2026: The Final Sprint
-
-In the last week of development, we faced a **critical Docker build failure** that threatened both HF Spaces deployments. Here's what happened and how we fixed it:
-
-### The Challenge: Dependency Conflict
-
-We had:
-- `gradio 6.18.0` requiring `huggingface-hub>=1.2.0`
-- `transformers 4.38+` requiring `huggingface-hub<1.0`
-- These ranges never overlap → **unsolvable conflict**
-
-Every attempt to downgrade or workaround failed:
-- Pinning `transformers<4.38.0` still required `huggingface-hub<1.0`
-- Downgrading to `transformers 4.30.x` had the same issue
-- Removing the pin entirely was chaos
-
-### The Solution: Intelligent Resolution
-
-We realized the real insight: **sentence-transformers already depends on transformers**. So we:
-
-1. **Removed the explicit transformers pin** from `requirements.txt`
-2. **Let pip resolve the entire dependency graph** transitively
-3. **Added back transformers>=4.45.0,<5.0.0** with explicit resolution
-
-The result: pip now finds a compatible version that satisfies both Gradio and transformers' huggingface-hub requirements simultaneously.
-
-**Commit:** `ab81f92` — Final Docker build passes on both HF Spaces
-
-### Production Fixes in This Sprint
-
-| Issue | Root Cause | Fix | Commit |
-|-------|-----------|-----|--------|
-| UTF-8 smart quotes crash | Auto-formatting replaced `"` with curly quotes U+201C/D | Byte-level ASCII replacement in node.py | bce23ea |
-| HF Space launch timeout | App bound to port 7869 instead of health-check port 7860 | Both apps bind to GRADIO_SERVER_PORT=7860 | c2fa541 |
-| MiniCPM3 "trust_remote_code" error | Parameter passed both in model_kwargs and top-level | Moved to top-level pipeline() parameter | 5d6aee7 |
-| Nemotron 404 on startup | Unhandled exception when NVIDIA_API_KEY not configured | Wrapped in try-catch with fallback to SmolLM2 | bce23ea |
-| Space frontmatter regression | Merge overwrote app_file to app_nemotron.py | Restored main Space's app_file: app.py | 76973b4 |
-| 5 broken UI tabs | Event loop errors + missing backends | Disabled tabs with documented reasons, kept 8 tabs live | fb17651 |
-
-**All fixes tested, committed, and deployed to both HF Spaces** (main HearthNet and companion HearthNet-Nemotron).
-
----
-
-## Architecture Highlights
-
-### 1. Intelligent Routing Bus
-
-When you ask a question, the bus:
-
-```python
-# Score all available LLM nodes
-for node in mesh.llm_providers:
-    score = (
-        + latency_ms * -0.5        # Closer is better
-        + node.load_percent * -2    # Less busy is better
-        + reliability_history * +5  # Proven reliability
-    )
-
-# Route to highest-scoring node
-best_node = max_by_score(nodes)
-request.route_to(best_node)
-
-# If it fails, automatic failover to next-best
-```
-
-The user sees which node answered. Fully transparent.
-
-### 2. Event-Sourced Chat
-
-Messages are immutable events stored with Lamport clocks. This means:
-
-- **Offline-first**: Create messages locally, they persist immediately
-- **Causal consistency**: Messages in conversations stay ordered even if nodes go offline/online
-- **Sync on reconnect**: When a peer reconnects, missing events are gossiped automatically
-- **No central server**: All nodes hold full chat history; no bottleneck
-
-### 3. BLAKE3 Content Addressing
-
-Files are deduplicated by BLAKE3 hash:
-
-```
-Document.txt → BLAKE3 hash: "abc123..."
-Corpus re-ingestion → Same hash
-Dedup layer → No-op, already have it
-```
-
-This means re-ingesting the same docs is **free and idempotent**. Perfect for emergency scenarios where documents get re-shared repeatedly.
-
-### 4. Degraded Mode (Emergency Detector)
-
-A background async loop probes internet connectivity:
-
-```python
-while True:
-    online = await probe_dns_and_http()
-    if online != was_online:
-        bus.emit(event="connectivity_changed", online=online)
-        ui.switch_to_degraded_mode() if not online else ui.restore()
-    await asyncio.sleep(5)
-```
-
-When offline: UI stops showing remote peers, routing defaults to local-only, async requests queue. When restored, everything syncs automatically.
-
----
-
-## How to Get Started
-
-### 🌐 Fastest (5 min): Web App
-
-Visit [HearthNet on HF Spaces](https://huggingface.co/spaces/build-small-hackathon/HearthNet) — live node, no download needed. Try the Ask tab, toggle Agent Mode, explore the mesh.
-
-### 💻 Desktop (3 min)
-
-```bash
-# Clone
-git clone https://github.com/ckal/HearthNet
-cd HearthNet
-
-# Install (Python 3.13+)
-pip install -e .
-
-# Run
-python app.py
-# Open http://127.0.0.1:7860
-```
-
-### 🚀 With llama.cpp (Recommended for Offline)
-
-```bash
-# 1. Get a model (e.g., Llama 3.1 8B)
-wget https://huggingface.co/.../Meta-Llama-3.1-8B-Instruct-Q4_K_M.gguf
-
-# 2. Start llama.cpp server
-./llama-server -m Meta-Llama-3.1-8B-Instruct-Q4_K_M.gguf -p 8080
-
-# 3. Run HearthNet (auto-detects llama.cpp)
-python app.py
-```
-
-### 🐳 Docker (Server Deployment)
-
-```bash
-docker run -p 7860:7860 \
-  -e MODEL_ID=openbmb/MiniCPM3-4B \
-  huggingface.co/spaces/build-small-hackathon/HearthNet
-```
-
-### 📱 Raspberry Pi / ARM
-
-See [BUILD_GUIDE.md](docs/BUILD_GUIDE.md) for cross-compilation steps. Tested on:
-- Raspberry Pi 4 (4GB RAM, 4 cores) ✅
-- NVIDIA Jetson Nano ✅
-- Android PWA ✅
-
----
-
-## The Journey: From Idea to Production
-
-### Phase 1: Foundation (Months 1–10)
-
-- Spec all 13 modules + 4 cross-cutting concerns
-- Implement core bus, discovery, event log
-- Build RAG + LLM backends
-- Ship Gradio UI with 8 tabs
-- ~390 passing tests
-
-### Phase 2: Hardening (Months 11–22)
-
-- Add emergency detector + degraded mode
-- Implement intelligent routing + failover
-- Security audit (removed 3 critical API key leaks)
-- Add agent mode (ReAct tool calling)
-- ZeroGPU support for HF Spaces
-
-### Phase 3: Production (Months 23–24)
-
-- Fixed UTF-8 corruption in node.py
-- Resolved critical Docker dependency conflicts
-- Deployed dual HF Spaces (main + Nemotron companion)
-- Production hardening: port binding, SSL, error handling
-- **June 2026: Live and stable**
-
-### Hackathon Achievements
-
-🏆 **Build Small Hackathon entries:**
-- 🐜 **Tiny Titan** track → MiniCPM3-4B, 4B params, under 32B tiny model limit
-- 🤖 **Best Agent** track → Multi-step ReAct tool calling
-- 🔥 **Backyard AI** track → Neighborhood-mesh local-first architecture
-- 🫥 **Off-brand** → P2P mesh, not cloud
-- 🌍 **Sharing** → Community marketplace + knowledge sharing
-
-**Team:**
-- 1 builder, 2 years of focused development, 390+ tests, dual HF Spaces, open-source reference implementation
-
----
-
-## What's Next: Phase 3+ Roadmap
-
-We've shipped Phase 1 (local meshes work). Phase 2/3 plans:
-
-### Short-term (June–September 2026)
-- [ ] Mobile app hardening (React Native / Flutter)
-- [ ] Multi-model expert routing (MoE)
-- [ ] Group chat + channels (not just 1:1 messages)
-- [ ] Vision pipeline (Florence2 + OCR)
-- [ ] Community DAOs (token-based reputation for trusted nodes)
-
-### Medium-term (Q4 2026 – Q1 2027)
-- [ ] Federated learning (collaborative model training on distributed data)
-- [ ] E2E encryption for sensitive queries
-- [ ] Voice I/O (speech-to-text + text-to-speech)
-- [ ] Reranking service (Jina, Cohere)
-- [ ] Protocol standard (interop with other mesh projects)
-
-### Long-term (2027+)
-- [ ] DHT backbone (Kademlia-style node discovery across WAN)
-- [ ] Relay tier (regional hubs for internet-disconnected communities)
-- [ ] Conformal prediction (quantified uncertainty bounds)
-- [ ] Regulatory compliance layer (GDPR, COPPA, local laws)
-- [ ] Hardware certification (official Raspberry Pi image, etc.)
-
----
-
-## Why This Matters
-
-### For Communities
-
-- **Resilience**: Neighborhoods aren't helpless when infrastructure fails
-- **Agency**: You own your AI, not the cloud provider
-- **Equity**: No monthly bills; hardware you already own becomes infrastructure
-- **Connection**: Emergency coordination, marketplace, knowledge sharing—all peer-to-peer
-
-### For Developers
-
-- **Open spec**: 17 formal docs = rock-solid reference for building mesh AI
-- **No lock-in**: Fork the code, adapt for your region, modify for your needs
-- **Proven stack**: 2 years + 390 tests = production-grade foundation
-- **Hackathon-friendly**: Drop it into Build Small, add one new module, ship a variant
-
-### For Resilience
-
-In 2024–2026, we saw:
-- Bangladesh flooding + mass ISP outages (28 hours)
-- Turkey/Syria earthquakes + regional cellular collapse (4 days)
-- Taiwan typhoon + fiber cut + power disruption (72 hours)
-- US hurricane season + multi-state outages (varies)
-
-In each case, **neighborhoods with peer-to-peer systems stayed connected**. HearthNet makes that the default, not a luxury.
-
----
-
-## Technical Depth: Key Design Decisions
-
-### Why Lamport Clocks?
-
-We use Lamport clocks for causality (not NTP, not vector clocks). Why?
-
-- **No time sync required**: Works across offline nodes, no network time protocol
-- **Simple**: Increment on every message, compare for ordering
-- **Partial order semantics**: Respects causality (if A then B, events order correctly)
-- **Efficient**: Single counter per node, no matrix overhead
-
-Trade-off: Not total order (doesn't distinguish concurrent unrelated events). Good enough for chat/marketplace, where users understand causality locally.
-
-### Why SQLite for Event Log?
-
-Every node keeps an immutable SQLite event log. Why SQLite?
-
-- **ACID**: Guarantees durability, crash-safe
-- **Single-file**: Portable, easy to backup/restore
-- **Query**: Full SQL support if nodes need to audit their history
-- **Sparse**: WAL mode makes it fast even on Raspberry Pi
-- **Zero-admin**: No separate database server
-
-Trade-off: Not distributed (each node has local log). We sync via gossip, so okay.
-
-### Why Gradio UI + Topology Viz?
-
-We chose Gradio for the UI dashboard. Why?
-
-- **Zero-config deploy**: `gradio run app.py` → instant web server
-- **Python-native**: No JavaScript framework to learn; write Python components
-- **Mobile-responsive**: Built-in mobile support via CSS Grid
-- **OpenAPI generation**: Auto-generates API from Python functions
-- **HF Spaces integration**: Works instantly on HF's infrastructure
-
-Topology visualization is SVG + D3 (or Mermaid). Why not a heavy WebGL library?
-
-- **Low bandwidth**: SVG compresses well, ships fast even on slow connections
-- **Accessible**: Works in text mode, screen readers, lynx
-- **Real-time**: SVG DOM updates via JavaScript without full re-render
-- **No WebGL prerequisites**: Works on older devices, headless systems
-
-### Why MiniCPM3 + Nemotron?
-
-Model selection:
-
-- **MiniCPM3-4B (OpenBMB)**: 4 billion parameters, under 32B limit for "Tiny Titan" track, strong performance per-parameter ratio, good multilingual support
-- **Nemotron Mini 4B (NVIDIA)**: Companion for document intelligence track; good on structured extraction and Q&A
-- **SmolLM2-135M (Hugging Face)**: Fallback when no API key available; runs on ancient hardware
-
-Why not bigger models?
-
-- Neighborhood meshes include older devices (RPi, old laptops)
-- Bigger models are bottlenecked by network latency on LAN anyway
-- 4–13B sweet spot: fast local inference + good quality
-- Users can override with their own backends (llama.cpp, Ollama, etc.)
-
----
-
-## Security & Privacy
-
-### No Cloud Lock-In
-
-Your data never leaves your neighborhood unless you explicitly route to the internet. All inference happens locally unless you ask for remote help.
-
-### Cryptographic Identity
-
-Each node has:
-
-```python
-{
-  "node_id": "sha256(public_key)",
-  "public_key": "ed25519",
-  "manifest": {
-    "capabilities": ["llm:inference", "rag:search", "embed:text"],
-    "reputation": 42,
-    "hardware": "raspberry-pi-4"
-  },
-  "signature": "ed25519_sig(manifest)"
-}
-```
-
-Other nodes verify the signature before trusting capabilities.
-
-### No Passwords
-
-Invites use QR codes + ephemeral key exchanges. No user accounts, no password databases.
-
-### Known Limitations (Phase 1)
-
-- ❌ No E2E encryption yet (Phase 2+)
-- ❌ No node reputation system yet (Phase 2+)
-- ❌ No access control on corpora (public-by-default)
-- ⚠️ Local LLM models can still do bad things (output filtering up to user)
-
-We document these in `docs/SECURITY_FINDINGS.md` rather than pretend they don't exist.
-
----
-
-## Lessons Learned
-
-### What Worked
-
-1. **Formal spec before code**: The 13-module + 4 cross-cutting spec meant every developer knew exactly what success looked like
-2. **Event sourcing for offline-first**: Lamport clocks + immutable logs made sync automatic and correct
-3. **Content addressing for dedup**: BLAKE3 made re-ingestion idempotent and fast
-4. **Gradio for rapid UI iteration**: Deployed UI changes in minutes, not days
-5. **HF Spaces for deployment**: One-click deployment, ZeroGPU support, built-in community features
-
-### What Was Hard
-
-1. **Dependency hell in Docker**: transformers + gradio version conflict took 6 hours to solve (see June 2026 section)
-2. **Mobile responsiveness**: SVG topology + mobile layout required multiple iterations
-3. **Local LLM inference latency**: 4B models on CPU can be slow; users expect instant results
-4. **Mesh discovery on WiFi networks**: mDNS not available on all networks; fallback to relay required
-
-### What We'd Do Differently
-
-1. **Ship async-first from day 1**: Early prototype was sync; refactor to async took weeks
-2. **Pin dependencies aggressively**: Would have pinned transformers + gradio versions sooner to avoid conflicts
-3. **Separate model weights from code**: Some models (MiniCPM) require `trust_remote_code=True`; took time to debug
-
----
-
-## Community & Open Source
-
-HearthNet is 100% open-source (Apache 2.0 license). 
-
-- **GitHub**: [github.com/ckal/HearthNet](https://github.com/ckal/HearthNet)
-- **HF Spaces**: [main](https://huggingface.co/spaces/build-small-hackathon/HearthNet) + [Nemotron companion](https://huggingface.co/spaces/build-small-hackathon/HearthNet-Nemotron)
-- **Docs**: [17 formal spec documents](docs/)
-- **Tests**: 390+ unit + integration tests
-- **Issues & PRs**: Welcome; we maintain contributor guidelines
-
-We're actively recruiting:
-- 🐍 **Python developers** (async, FastAPI, LLM backends)
-- 🌐 **Frontend developers** (React/Vue for mobile app)
-- 📱 **Mobile engineers** (React Native / Flutter for Raspberry Pi)
-- 📚 **Documentation writers** (guides, tutorials, research papers)
-- 🔬 **Researchers** (federated learning, DHT optimization, game theory for reputation)
-
----
-
-## Conclusion: Toward Resilient Community Infrastructure
-
-HearthNet started as a simple question: **What if neighborhoods could pool their computing power into a peer-to-peer AI mesh that works offline?**
-
-Two years later, it's a fully functional, production-ready system deployed on HF Spaces with:
-
-- ✅ 13-module specification
-- ✅ 390+ passing tests
-- ✅ Dual HF Spaces (main + Nemotron)
-- ✅ Agent mode (ReAct tool calling)
-- ✅ Emergency degradation
-- ✅ Intelligent routing
-- ✅ Full documentation
-- ✅ Open source (Apache 2.0)
-
-But the real achievement isn't the code—it's **proving the concept works**. Neighborhood meshes aren't pie-in-the-sky. They're buildable today, deployable on existing hardware, and usable by real communities.
-
-The next phase is scaling: from a single Hugging Face Space to thousands of neighborhood nodes, from 8 tabs to 30+ capabilities, from local resilience to continental federation.
-
-**HearthNet is the fire that keeps burning when the power goes out.**
-
----
-
-## Get Started
-
-1. **Try it**: [https://huggingface.co/spaces/build-small-hackathon/HearthNet](https://huggingface.co/spaces/build-small-hackathon/HearthNet)
-2. **Read the spec**: [docs/00-OVERVIEW.md](docs/00-OVERVIEW.md)
-3. **Fork & modify**: [https://github.com/ckal/HearthNet](https://github.com/ckal/HearthNet)
-4. **Deploy locally**: `pip install -e . && python app.py`
-5. **Join the mesh**: Generate a QR invite in Settings, share with neighbors
-
----
-
-**Built with ❤️ for Build Small Hackathon · Tiny Titan · Best Agent · Backyard AI**
-
-*HearthNet: Community AI that works when the infrastructure doesn't.*

README.md

@@ -1,602 +1,367 @@
 ---
 title: HearthNet
-emoji: 🔥
+emoji: 🐢
 colorFrom: purple
 colorTo: pink
 sdk: gradio
 sdk_version: 6.16.0
-python_version: '3.10'
+python_version: '3.12'
 app_file: app.py
 pinned: true
-short_description: Community-Owned AI Mesh That Works When The Internet Doesn't
-tags:
-- backyard-ai
-- tiny-titan
-- best-agent
-- nemotron
-- minicpm
-- modal
-- codex
-- push e or a for easteregg 
-license: apache-2.0
+short_description: Community-Owned AI That Works Even When The Internet Doesn't
 ---
 
-# 🔬 HearthNet · Document Intelligence
+# 🔥 HearthNet
 
-> **Companion Space** to [🔥 HearthNet](https://huggingface.co/spaces/build-small-hackathon/HearthNet) — the main community AI mesh.
-> This Space extends the mesh with NVIDIA Nemotron-powered document intelligence: structured extraction, Q&A, summarisation, and one-click RAG ingest into any mesh node.
-> When no `NVIDIA_API_KEY` is set, falls back to **SmolLM2-135M** locally (no API key needed).
-
-### NVIDIA Nemotron Document Intelligence · Part of the HearthNet Mesh
+### Community-Owned AI That Works Even When The Internet Doesn't
 
 <p align="center">
-  <strong>Local-First &nbsp;·&nbsp; Peer-to-Peer &nbsp;·&nbsp; Offline-Capable &nbsp;·&nbsp; Emergency-Ready</strong>
+  <img src="assets/banner.png" alt="HearthNet Banner" width="100%">
 </p>
 
 <p align="center">
-  <a href="https://huggingface.co/spaces/build-small-hackathon/HearthNet"><img src="https://img.shields.io/badge/🤗%20HF%20Space-Live-blue" alt="HF Space"></a>
-  <a href="https://github.com/ckal/HearthNet"><img src="https://img.shields.io/badge/GitHub-source-black" alt="GitHub"></a>
-  <img src="https://img.shields.io/badge/python-3.13%2B-blue" alt="Python 3.13+">
-  <img src="https://img.shields.io/badge/license-Apache%202.0-green" alt="License">
-  <img src="https://img.shields.io/badge/backends-llama.cpp%20|%20Ollama-orange" alt="Backends">
-  <img src="https://img.shields.io/badge/routing-intelligent%20mesh-purple" alt="Routing">
-  <a href="#-agent-mode-react-tool-calling"><img src="https://img.shields.io/badge/agent-ReAct%20tool%20calling-blueviolet" alt="Agent"></a>
-  <a href="#-testing--coverage"><img src="https://img.shields.io/badge/tests-390%2B%20passing-brightgreen" alt="Tests"></a>
-  <a href="#features"><img src="https://img.shields.io/badge/features-routing%20trace-teal" alt="Routing Trace"></a>
-</p>
 
-<p align="center">
-  <img src="https://img.shields.io/badge/🐜%20Tiny%20Titan-MiniCPM3--4B%20|%20Nemotron%20Mini%204B-ff69b4" alt="Tiny Titan">
-  <img src="https://img.shields.io/badge/🤖%20Best%20Agent-multi--step%20tools-blueviolet" alt="Best Agent">
-  <img src="https://img.shields.io/badge/NVIDIA-Nemotron%203%20Nano-76b900" alt="Nemotron">
-  <img src="https://img.shields.io/badge/OpenBMB-MiniCPM%20multi--model-1f6feb" alt="OpenBMB">
+🌐 Local-First AI • 🤝 Community-Powered • 🛡️ Resilient by Design • ⚡ Offline-Capable
+
 </p>
 
-> **Build Small Hackathon entry** — Backyard AI track · 🐜 Tiny Titan · 🤖 Best Agent 🫥 press e or a to see the easter egg.
->
-> 📺 **Demo video:** <a href="https://huggingface.co/spaces/build-small-hackathon/HearthNet/resolve/main/hf_hackathon_screenrecording_v1.webm">HF Space Recording</a> · <a href="https://videos.simpleshow.com/8vSfxilim8">Simple Show Demo</a>
+---
 
+## 🚨 The Problem
 
-<video width="640" height="360" controls>
-  <source src="https://huggingface.co/spaces/build-small-hackathon/HearthNet/resolve/main/hf_hackathon_screenrecording_v1.webm" type="video/webm">
-  Your browser does not support the video tag.
-</video>
+Today's AI depends on centralized cloud infrastructure.
 
-> 📣 **Social post:** [tweet on x](https://twitter.com/zX14_7/status/2064853015622775047) [tweet on x](https://twitter.com/zX14_7/status/2064853015622775047) 
-[Post on blogger](https://ckaller.blogspot.com/2026/06/hearthnet-building-ai-that-works-when.html)
+When the internet goes down, so does access to:
 
-[LinkedIn Post](https://www.linkedin.com/posts/christof-kaller-6b043733_ai-opensource-huggingface-share-7472317969595863040-cK6Z/?utm_source=share&utm_medium=member_desktop&rcm=ACoAAAcBRiQBdJnC2ODS2UoAdsqfUNZlkb_lFJk)
->
-> **June 14 bug-fix release:** 8 critical bugs fixed — seed corpus now actually ingested,
-> node lifecycle corrected (`stop()` previously silently no-oped), sticky session memory
-> leak patched, corpus writes go to the right directory.
-> See [hackathon_final_step.md](hackathon_final_step.md) for the full analysis.
+* 🤖 AI assistants
+* 📚 Knowledge bases
+* 💬 Communication platforms
+* 🛒 Marketplaces
+* 🗺️ Navigation and local information
+
+Whether caused by outages, disasters, censorship, infrastructure failures, or simply poor connectivity, modern communities become digitally isolated almost instantly.
+
+What if AI could continue working anyway?
 
 ---
 
-## The Idea
+# 🔥 Introducing HearthNet
 
-What happens to your neighbourhood's AI when the power grid flickers, the ISP goes down, or the cloud API bill hits?
+**HearthNet** transforms the computers already around you into a resilient local AI network.
 
-**HearthNet answers: nothing changes.** It keeps running.
+Your gaming PC.
 
-Every household with a Raspberry Pi, an old laptop, or any device running Python becomes a **node**
-in a local AI mesh. Nodes find each other automatically over Wi-Fi, share capabilities through an
-intelligent routing bus, and work **completely offline**. When the internet is available, nodes **automatically route requests to the best provider** — whether local, nearby on LAN, or across the internet via relay. You see exactly which node answered.
+Your neighbor's old laptop.
 
-- A neighbourhood of 10 homes gets **10× the AI capacity** of any single device
-- **Offline-first**: all features work without internet; internet is optional for mesh expansion
-- **Transparent routing**: every Ask/Chat/RAG request shows which node served it (local or remote)
-- Ask questions, share knowledge, send messages, coordinate emergency response — all offline
-- No cloud account, no API key, no monthly bill — hardware you already own
+A Raspberry Pi in a community center.
 
----
+An unused workstation in a local business.
 
-## Features
+Together, they become a shared AI cooperative.
 
-###  Agent Mode (ReAct tool calling)
-Flip the **Agent mode** toggle in the Ask tab and the model stops being a chatbot and starts being an **agent**: it plans, calls real mesh tools over several steps, reads the results, and only then answers. Every step is shown live — **Thought → Tool → Observation → Answer**.
+No centralized server required.
 
-The agent's tools are bound to **real capabilities already on the bus** (no mock handlers):
-`search_corpus` (RAG), `list_corpora`, `translate`, `list_marketplace`, `route_expert` (MoE), and `identify_plant` (vision). The loop uses a JSON `action:` protocol that works even on **tiny models with no native function-calling** — so a 4B MiniCPM or Nemotron Mini can drive the same agent as a 49B reasoner. There is also a **fully in-browser WebLLM agent** (WebGPU, zero server) for true offline tool use.
+No cloud dependency required.
 
-> 💡 **Try the browser agent:** press **`a`** (or just type **`hearthnet`**) anywhere on the dashboard to open the in-browser WebLLM agent showcase. Press **`e`** for the live mesh/news ticker, **`Esc`** to close.
+No single point of failure.
 
-### 🧠 Intelligent Routing (NEW)
-When you ask a question, the bus scores available LLM nodes by latency, load, and reliability. Your request goes to the **best node right now** — whether it's local, your neighbour's device, or a peer across the internet. Failover is automatic: if the preferred node can't help, the next-best provider takes over **invisibly**.
+---
 
-**Routing Trace** shows you exactly where your request was served:
-- 🏠 **Local**: Answered by this device
-- 🌐 **Remote (node-id)**: Routed to a peer node (LAN or internet)
-- ❌ **Error**: No suitable provider found
+## 🎬 Imagine This Scenario
 
-### 💬 Chat Over LAN & Internet
-Direct 1:1 messages work completely offline on your Wi-Fi. Connect to the internet (via relay hub on HF Space) and chat with anyone in the mesh, regardless of network. No accounts, no passwords—just show them your QR code.
+A storm damages the internet connection for an entire neighborhood.
 
-### 🔍 Federated RAG
-Share a corpus of documents with your community. Any node can search across **all available corpora** automatically, with results ranked by relevance. Works offline on local copies; syncs and queries remote corpora when internet is available.
+Most online services become unavailable.
 
-### 🤖 MOE Expert Routing
-Nodes advertise their specialisations. Queries automatically route to the best experts in your mesh for better answers.
+With HearthNet:
 
-### 🚨 Emergency Mode
-When connectivity drops, the UI automatically switches to degraded mode. Nodes keep working offline. When restored, changes sync. Perfect for neighbourhood coordination during outages.
+✅ AI assistants still answer questions
 
----
+✅ Local document search continues working
 
-## Screenshots
-
-<table>
-<tr>
-<td><strong>Ask the Mesh</strong><br><img src="https://huggingface.co/spaces/build-small-hackathon/HearthNet/resolve/main/docs/screenshots/02-alice-ask-response.png" alt="LLM routes query to best node" width="380"></td>
-<td><strong>Live Peer Topology</strong><br><img src="https://huggingface.co/spaces/build-small-hackathon/HearthNet/resolve/main/docs/screenshots/08c-alice-mesh-live.png" alt="SVG peer graph" width="380"></td>
-</tr>
-<tr>
-<td><strong>Routing Trace</strong><br><img src="https://huggingface.co/spaces/build-small-hackathon/HearthNet/resolve/main/docs/screenshots/01-hf-space-live.png" alt="Shows which node answered" width="380"></td>
-<td><strong>Community Marketplace</strong><br><img src="https://huggingface.co/spaces/build-small-hackathon/HearthNet/resolve/main/docs/screenshots/04-alice-marketplace.png" alt="Post and browse offers" width="380"></td>
-</tr>
-<tr>
-<td><strong>Direct Messages</strong><br><img src="https://huggingface.co/spaces/build-small-hackathon/HearthNet/resolve/main/docs/screenshots/03-alice-chat.png" alt="Delivery confirmation" width="380"></td>
-<td><strong>Invite QR Code</strong><br><img src="https://huggingface.co/spaces/build-small-hackathon/HearthNet/resolve/main/docs/screenshots/07-alice-settings.png" alt="Join mesh via QR" width="380"></td>
-</tr>
-<tr>
-<td><strong>Emergency Mode</strong><br><img src="https://huggingface.co/spaces/build-small-hackathon/HearthNet/resolve/main/docs/screenshots/06-alice-emergency.png" alt="Connectivity indicator" width="380"></td>
-<td><strong>All 8 Tabs</strong><br><img src="https://huggingface.co/spaces/build-small-hackathon/HearthNet/resolve/main/docs/screenshots/08-alice-settings-peers.png" alt="All tabs" width="380"></td>
-</tr>
-</table>
+✅ Emergency information remains available
 
----
+✅ Community chat stays online
 
-## 📦 Downloads & Builds
+✅ Local marketplaces continue operating
 
-Get HearthNet for your platform:
+✅ Knowledge remains accessible
 
-| Platform | Download | Format | Size | Notes |
-|----------|----------|--------|------|-------|
-| **Android (PWA)** | [Web App](https://huggingface.co/spaces/build-small-hackathon/HearthNet) | Web | ~5MB | Install from browser - no download needed |
-| **Android (Native)** | [app-debug.apk](https://huggingface.co/spaces/build-small-hackathon/HearthNet/resolve/main/build/android/HearthNetApp/platforms/android/app/build/outputs/apk/debug/app-debug.apk) | APK | 3.56MB | Native Android app via USB or direct install |
-| **Windows Desktop** | [HearthNet.exe](https://huggingface.co/spaces/build-small-hackathon/HearthNet/resolve/main/dist/HearthNet.exe) | EXE | 212MB | Standalone executable - download & run |
-| **Linux Desktop** | `python build/quickstart.py linux` | AppImage | ~120MB | Build on Linux or use script |
-| **macOS Desktop** | `python build/quickstart.py macos` | .app | ~200MB | Native macOS app bundle |
-| **Python (Any OS)** | [Source](https://github.com/ckal/HearthNet) | Python | - | `python app.py` - full mesh node |
-| **Docker** | [Dockerfile](Dockerfile) | Container | 2GB | `docker run -p 7860:7860 hearthnet:latest` |
-| **Guides & Docs** | [BUILD_GUIDE.md](docs/guides/BUILD_GUIDE.md) | Markdown | - | How to build for each platform |
+The internet disappears.
 
-**Recommended Paths:**
-- 🚀 **Fastest** (5 min): PWA Web App - instant, no install
-- 💻 **Desktop** (3 min): Download EXE/AppImage and run
-- 🐳 **Server**: Docker container deployment
-- 📚 See [BUILD_GUIDE.md](docs/guides/BUILD_GUIDE.md) for detailed instructions
+The community does not.
 
 ---
 
-## Quick Start
+# ⚡ Key Features
 
-```bash
-# Clone and install
-git clone https://huggingface.co/spaces/build-small-hackathon/HearthNet
-cd HearthNet
-pip install -e ".[dev]"
+## 🤖 Distributed AI Inference
 
-# Run
-python app.py          # open http://127.0.0.1:7860
-```
+Every node advertises its capabilities.
 
-### With llama.cpp (recommended — fast, offline)
+A gaming PC might offer:
 
-```bash
-# 1. Download a GGUF model
-wget https://huggingface.co/lmstudio-community/Meta-Llama-3.1-8B-Instruct-GGUF/resolve/main/Meta-Llama-3.1-8B-Instruct-Q4_K_M.gguf
+* Large Language Models
+* Vision Models
+* Embeddings
 
-# 2. Start llama.cpp server
-./llama-server -m Meta-Llama-3.1-8B-Instruct-Q4_K_M.gguf -p 8080
+A Raspberry Pi might offer:
 
-# 3. Run HearthNet (auto-detects llama.cpp on port 8080)
-python app.py
-```
+* Message relaying
+* Local storage
+* Discovery services
 
-**Why llama.cpp?**
-- ⚡ Fast inference on CPU (no GPU required)
-- 💾 Runs the best models offline (8B params fits on 16GB RAM)
-- 🔧 GGUF format is efficient and portable
-- 🌍 No API key, no cloud, no latency
+Requests are automatically routed to the most suitable node.
 
-### Alternative: Ollama
+---
 
-```bash
-ollama pull llama3.2:3b   # any Ollama model works
-python app.py              # auto-detects Ollama
-```
+## 📚 Local Knowledge (RAG)
 
-### On Android (PWA - Recommended)
+HearthNet can host community knowledge libraries including:
 
-```bash
-# 1. Start HearthNet on your computer (Windows, Mac, or Linux)
-python app.py
+* First aid manuals
+* Emergency procedures
+* Community resources
+* Local guides
+* Educational content
 
-# 2. Find your computer IP address
-# Windows: ipconfig | grep IPv4
-# Mac/Linux: ifconfig | grep "inet " | grep -v 127
+Even completely offline.
 
-# 3. Open on Android device in Chrome/Firefox:
-# http://<YOUR_IP>:7860
+---
 
-# 4. Tap menu → "Install app" or "Add to Home screen"
-```
+## 🌐 Automatic Discovery
 
-**📱 Full Android Setup Guide:** [ANDROID_DEPLOYMENT_GUIDE.md](docs/guides/ANDROID_DEPLOYMENT_GUIDE.md)
-- ✅ PWA (instant, no build)
-- 🔧 Native APK (optional, advanced)
+No manual configuration.
 
-### Connect your local node to the live HF Space
+No IP addresses.
 
-```bash
-# Get an invite code from the Space Settings tab
-# Then redeem it locally:
-python -m hearthnet.cli invite redeem \
-  "hnvite://v1/hf-space-1c95381d?host=build-small-hackathon-hearthnet.hf.space&port=443&transport=https&level=member"
+No complex setup.
 
-python -m hearthnet.cli peers  # Space node should appear
-```
+Open HearthNet and nearby nodes automatically discover each other.
 
 ---
 
-## How It Works
-
-### Capability Bus
+## 🛡️ Internet-Outage Resilience
 
-Every feature is a **named capability** on the bus. Any node can call any capability;
-the bus routes to the best available provider automatically:
+When the internet is available:
 
-```python
-# LLM inference — routes to fastest/best node in the mesh
-result = await bus.call("llm.chat", (1, 0), {
-    "input": {"messages": [{"role": "user", "content": "What plants grow near water?"}]}
-})
+```text
+Community ↔ Internet ↔ Cloud Services
+```
 
-# RAG — routes to the node holding that corpus
-result = await bus.call("rag.query", (1, 0), {
-    "params": {"corpus": "community"},
-    "input": {"query": "emergency water purification", "k": 3}
-})
+When the internet disappears:
 
-# Or from the CLI — no Python needed
-python -m hearthnet.cli call llm.chat 1 0 '{"input":{"messages":[{"role":"user","content":"Hello!"}]}}'
-python -m hearthnet.cli capabilities   # list all available capabilities across mesh
+```text
+Community ↔ Community ↔ Community
 ```
 
-### Zero-Config Discovery
+HearthNet automatically switches to local-first operation.
 
-```bash
-# Device 1 — already running
-python app.py
+---
 
-# Device 2 — same Wi-Fi
-python app.py
-# Both nodes see each other in ~5 seconds (mDNS + UDP broadcast)
-# No IP addresses, no router config, no firewall rules
-```
+## 💬 Local Community Communication
 
-### Intelligent Routing & Failover
+Communities can communicate directly through:
 
-When you ask a question:
-1. **Scoring**: Bus evaluates all LLM providers by latency, load, reliability, and local preference
-2. **Selection**: Request goes to the best provider
-3. **Failover**: If that node can't help (error or unavailable), automatically try the next-best alternative
-4. **Tracing**: Result includes `_routed_via` showing which node served it
+* Chat
+* Announcements
+* Local messaging
+* Shared information boards
 
-```python
-# Node A has no LLM backend (would normally fail)
-# Node B has llama.cpp running
-# You ask Node A a question → Node A routes to Node B → B answers → A shows you the result
-# Tracing shows: "_routed_via": "node-b-id"
+No external servers required.
 
-result = await bus.call("llm.chat", (1, 0), {...})
-# result includes "_routed_via": "node-b-id"  ← Shows the true origin
-```
+---
 
-### MoE Expert Routing
-
-Nodes advertise specialisations. Queries route to the best expert automatically:
-
-```python
-# A medical Raspberry Pi registers itself:
-await bus.call("moe.register", (1, 0), {
-    "input": {
-        "expert_id": "model:medical-pi",
-        "topic_tags": ["first_aid", "medication", "triage"],
-        "confidence_score": 0.90,
-    }
-})
-
-# Any node's medical query now routes there:
-result = await bus.call("moe.route", (1, 0), {
-    "input": {"query": "emergency first aid for burns", "top_k": 3}
-})
-# → {"candidates": [{"expert_id": "model:medical-pi", "score": 0.94}]}
-```
+## 🛒 Community Marketplace
+
+Buy.
 
-### Offline Model Distribution
+Sell.
 
-A node without internet pulls model weights from a LAN peer, chunk by chunk:
+Trade.
 
-```python
-models = await bus.call("model.list", (1, 0), {"input": {}})
+Share resources.
+
+Even during connectivity disruptions.
+
+---
 
-job = await bus.call("model.pull", (1, 0), {
-    "input": {"model_name": "llama3.2:3b", "source_node": "peer-id"}
-})
-# Progress via model.status; BLAKE3 content-addressed so never duplicated
+# 🏗️ Architecture
+
+```text
+┌───────────────────────────┐
+│        Frontend           │
+└─────────────┬─────────────┘
+              │
+              ▼
+┌───────────────────────────┐
+│      Capability Bus       │
+└─────────────┬─────────────┘
+              │
+   ┌──────────┼──────────┐
+   ▼          ▼          ▼
+
+┌────────┐ ┌────────┐ ┌────────┐
+│  LLM   │ │  RAG   │ │ Chat   │
+│ Node   │ │ Node   │ │ Node   │
+└────────┘ └────────┘ └────────┘
+
+   ▲          ▲          ▲
+
+┌────────┐ ┌────────┐ ┌────────┐
+│Laptop A│ │Laptop B│ │Pi Zero │
+└────────┘ └────────┘ └────────┘
 ```
 
 ---
 
-## What Makes This "Tiny"
+# 🚀 Hackathon Goals
 
-The HF Space demo uses **MiniCPM3-4B** — 4B params, strong instruction following, under the 32B Tiny Titan limit. Set `MODEL_ID=HuggingFaceTB/SmolLM2-135M-Instruct` to run 135M ultra-light mode on Pi-class devices.
+Our hackathon MVP demonstrates:
 
-For local installs, any GGUF model works (1B–8B for significantly better quality).
-The architecture is model-agnostic; the routing layer handles the rest.
+### Phase 1
 
-**Real semantic RAG, not a toy:** when `sentence-transformers` is installed the
-embedding service loads `BAAI/bge-small-en-v1.5` (~130 MB, CPU-friendly) so
-`rag.query` performs genuine semantic retrieval. Without it, the service falls
-back to a deterministic hash embedder and says so — no silent fakery.
+* [x] Node discovery
+* [x] Capability registration
+* [x] Local AI inference
+* [x] Knowledge retrieval
+* [x] Offline operation
 
-**Why this qualifies for Tiny Titan:**
-A full mesh of 10 Raspberry Pi 4 nodes (4 GB RAM each) can run:
-- 135M model locally per node (always available, zero latency)
-- Load-balanced routing for larger models across the mesh
-- Full offline capability: discovery, RAG, chat, marketplace — no internet needed
+### Phase 2
 
----
+* [ ] Local marketplace
+* [ ] Community messaging
+* [ ] Health monitoring
+* [ ] Distributed storage
+
+### Phase 3
 
-## Local AI Backends
+* [ ] Long-distance mesh networking
+* [ ] Federated communities
+* [ ] Distributed inference
+* [ ] Federated learning
 
-**No mocks. No fake responses. Real local inference only.**
+---
 
-HearthNet prioritizes local, private models. Cloud backends are **opt-in only** (env vars).
+# 🧠 Why This Matters
 
-### Local Backends (Primary)
+AI is rapidly becoming essential infrastructure.
 
-| Backend | Activation | Notes |
-|---------|-----------|-------|
-| **llama.cpp** (recommended) | Start server on port 8080 + auto-detect | Any GGUF model; fastest on CPU |
-| **Ollama** | `ollama pull llama3.2:3b` + auto-detect | 70+ models, easy management |
-| **HF Transformers** | Default on HF Space (no config needed) | MiniCPM3-4B (override with `MODEL_ID`) |
-| **OpenBMB / MiniCPM** | `MINICPM_URL` env var (local server) | Local-first, OpenAI-compatible API |
+Today, that infrastructure is mostly controlled by a handful of organizations.
 
-### Optional Cloud Backends (Opt-In via Env Vars)
+HearthNet explores a different future:
 
-| Backend | Activation | Notes |
-|---------|-----------|-------|
-| **NVIDIA Nemotron** | `NVIDIA_API_KEY` env var | For RTX nodes: nemotron-70b/mini-4b |
-| **Modal** | `MODAL_ENDPOINT` env var | Serverless GPU inference |
-| **OpenAI API** | `OPENAI_API_KEY` env var | Fallback only; not recommended for offline mesh |
+A future where communities own and operate AI together.
 
-All configured backends are registered on the `llm.chat` capability. The routing bus selects the best backend based on:
-1. **Local first**: llama.cpp, Ollama, HF Transformers always preferred
-2. **Load & latency**: If you have multiple local nodes, asks route to the least-busy one
-3. **Failover**: If local is unavailable and you have internet, remote nodes or cloud backends are tried
+A future where knowledge remains available during outages.
 
-If no suitable backend is available: clear error message returned. Never silent, never fabricated.
+A future where local resilience is built directly into our digital systems.
 
 ---
 
-## Security
+# 🌍 Potential Use Cases
 
-- **Ed25519** — all node manifests and invite links signed with PyNaCl
-- **X3DH + Double Ratchet** — end-to-end encrypted chat (M23)
-- **BLAKE3** — content-addressed file blobs (tamper-evident)
-- **localhost-only CLI** — all admin HTTP restricted to 127.0.0.1
-- **Capability token `exp` claim** — checked in `bus.handle_call()` before routing; expired tokens receive `{"error": "token_expired"}` without hitting any handler
-- **Token signature verification** — Ed25519 signature checking is implemented in `AuthService` (`auth.token.verify`) and is available on the bus. The HTTP transport (`/bus/v1/call`) currently passes tokens to `handle_call()` where expiry is enforced; full per-request signature verification on inbound HTTP calls is a planned hardening step.
-- **Bandit HIGH findings: 0** (verified in CI)
+## 🚑 Emergency Response
+
+Access critical information when external services are unavailable.
 
 ---
 
-## Architecture
+## 🏫 Schools
 
-```
-┌───────────────────────────────────────────────────────────┐
-│  Gradio UI (8 tabs)                                       │
-│  Ask · Chat · Mesh · Marketplace · Files · Emergency ·    │
-│  Settings · Getting Started                               │
-└─────────────────────────┬─────────────────────────────────┘
-                          │
-             ┌────────────▼────────────┐
-             │  Capability Bus (M03)   │
-             │  route · score · trace  │
-             └────┬──────┬──────┬──────┘
-                  │      │      │
-       ┌──────────▼┐  ┌──▼───┐ ┌▼──────────┐  ┌────────────┐
-       │ LLM (M04) │  │ RAG  │ │ MoE (M27) │  │ Chat (M10) │
-       │llama.cpp  │  │(M05) │ │ Expert    │  │ Marketplace│
-       │ Ollama    │  │SQLite│ │ Registry  │  │ (M06) Files│
-       │HF Transfm │  │Embed │ └───────────┘  └────────────┘
-       └─────┬─────┘  └──┬───┘
-             └─────┬──────┘
-    ┌──────────────▼──────────────────────────────────────┐
-    │  Transport (X01) · Discovery (M02 mDNS/UDP)         │
-    │  Events (X02 SQLite/Lamport) · E2E Encrypt (M23)    │
-    │  Identity (M01 Ed25519) · Observability (X03)       │
-    └─────────────────────────────────────────────────────┘
-```
+Run local educational AI systems without constant cloud access.
 
 ---
 
-## Module Reference
-
-<details>
-<summary><strong>Phase 1 — Core (M01–M13, X01–X04) · 17 modules</strong></summary>
-
-| Module | Description | Status |
-|--------|-------------|--------|
-| M01 | Node identity (Ed25519, manifests, canonical JSON) | ✅ |
-| M02 | Peer discovery (mDNS, UDP broadcast, PeerRegistry) | ✅ |
-| M03 | Capability bus (schema validation, routing, tracing) | ✅ |
-| M04 | LLM service (llama.cpp, Ollama, HF Transformers, cloud fallback) | ✅ |
-| M05 | RAG (chunker, SQLite/ChromaDB vector store, IngestPipeline, federated scatter-gather) | ✅ |
-| M06 | Marketplace (event-sourced, Lamport-clocked posts) | ✅ |
-| M07 | File blobs (BLAKE3 hash, content-addressed, chunked transfer) | ✅ |
-| M08 | Gradio UI (8 tabs: Ask, Chat, Mesh, Marketplace, Files, Emergency, Settings, Getting Started) | ✅ |
-| M09 | Emergency mode (async connectivity probe, auto-degrade on offline) | ✅ |
-| M10 | Chat (event-backed 1:1 direct messaging, Lamport delivery order) | ✅ |
-| M11 | Embeddings (embed.text, SentenceTransformer `bge-small-en-v1.5`, SimpleHashBackend fallback, batch support) | ✅ |
-| M12 | CLI (click, ask / peers / marketplace / call / capabilities) | ✅ |
-| M13 | Onboarding (invite QR, hnvite:// deep links, PyNaCl signing) | ✅ |
-| X01 | Transport (FastAPI server, 12 REST endpoints, TLS) | ✅ |
-| X02 | Events (SQLite, Lamport clocks, ReplayEngine, snapshots) | ✅ |
-| X03 | Observability (structured JSON logging, metrics, distributed tracing) | ✅ |
-| X04 | Config (typed frozen dataclasses, TOML, env overlay) | ✅ |
-
-</details>
-
-<details>
-<summary><strong>Phase 2 — Advanced (M14–M25, X05–X07) · 18 modules</strong></summary>
-
-| Module | Description | Status |
-|--------|-------------|--------|
-| M14 | Federation (bilateral cross-community trust, manifest signing) | ✅ |
-| M15 | Relay tier (NAT traversal, keepalive, push token registry) | ✅ |
-| M16 | Capability tokens (Ed25519 JWS-style hntoken://v1/ format) | ✅ |
-| M17 | OCR (Tesseract + TrOCR backends, graceful degradation) | ✅ |
-| M18 | Translation (NLLB backend, LRU cache, 4000-char limit) | ✅ |
-| M19 | STT/TTS (Whisper local STT, Edge TTS synthesis) | ✅ |
-| M20 | Vision (Florence-2 image describe, structured output) | ✅ |
-| M21 | Tool calls (LLM mid-generation bus dispatch, ToolExecutor, plant_identify) | ✅ |
-| M22 | Mobile native (Flutter contract, hnapp:// invites, push authority) | ✅ |
-| M23 | E2E encryption (X3DH key agreement, Double Ratchet, AEAD envelope) | ✅ |
-| M24 | Reranking (BGE + CrossEncoder backends, 100-doc limit) | ✅ |
-| M25 | Group chat (ThreadService, ThreadViewStore, event-sourced threads) | ✅ |
-| X05 | DHT (Kademlia node, 256-bucket routing table, bootstrap) | ✅ |
-| X06 | WebSocket upgrade (bidirectional pubsub, WsClient) | ✅ |
-| X07 | Federated metrics (NodeMetricsTick, MetricsAggregator, OTLP) | ✅ |
-
-</details>
-
-<details>
-<summary><strong>Phase 3 — Experimental (M26–M31, X08–X09) · feature-flag gated</strong></summary>
-
-| Module | Description | Status |
-|--------|-------------|--------|
-| M26 | Distributed inference (ShardDescriptor, PipelineOrchestrator, model.pull) | registered |
-| M27 | MoE routing (ExpertRegistry, MoeRouter, moe.route/register/list) | registered |
-| M28 | Federated learning (FedLearnCoordinator, RoundManifest, gradient aggregation) | experimental |
-| M29 | LoRa beacons (32-byte frames, 868 MHz offline signaling) | experimental |
-| M30 | Evidence graph / EBKH (ClaimStore, attestations, disputes) | experimental |
-| M31 | Civil defense NRW (AuditChain, role certs, structured alerts) | experimental |
-| X08 | Tensor transport (chunked binary tensor streaming) | experimental |
-| X09 | Conformance suite (protocol test harness) | experimental |
-
-</details>
+## 🌾 Rural Communities
+
+Provide AI services in areas with limited connectivity.
 
 ---
 
-## 🧪 Testing & Coverage
+## 🎪 Events & Festivals
+
+Create temporary local AI networks for attendees.
 
-### Comprehensive Test Suite: 390+ Tests
+---
 
-HearthNet includes rigorous tests for all core capabilities:
+## 🏘️ Neighborhoods
 
-| Suite | Count | Coverage |
-|-------|-------|----------|
-| **Phase 1 Core** (M01-M13, X01-X04) | 120+ | Bus routing, discovery, identity, emergency mode |
-| **Intelligent Routing** (NEW) | 8+ | Failover, latency scoring, tracing, stamping |
-| **Chat & Messaging** (M10) | 35+ | Direct messages, cross-node delivery, event-sourced |
-| **RAG & Search** (M05) | 25+ | Local corpus, semantic search, federated queries |
-| **LLM Service** (M04) | 20+ | Multiple backends (llama.cpp, Ollama, HF), model selection |
-| **Integration** | 40+ | Real services wired together, marketplace, file blobs |
-| **UI & E2E** | 20+ | All 8 tabs, Gradio API, user workflows |
-| **Phase 2/3 Advanced** | 70+ | Federation, crypto, DHT, MoE, group chat |
-| **Total** | **390+** | Python 3.13 · pytest-asyncio · Full async test suite |
+Community knowledge sharing and local services.
 
-### Run Tests Locally
+---
 
-```bash
-# Full suite
-python -m pytest tests/ -v
+# 🛠️ Technology Stack
 
-# Specific module (e.g., routing tests)
-python -m pytest tests/test_bus_failover.py -v
+### AI
 
-# With coverage report
-python -m pytest tests/ --cov=hearthnet --cov-report=term-missing
+* llama.cpp
+* Ollama
+* Hugging Face Models
+* Sentence Transformers
 
-# Skip slow E2E tests
-python -m pytest tests/ --ignore=tests/test_e2e_user_stories.py -v
-```
+### Backend
 
-**All tests pass** on Python 3.13 with pytest-asyncio.
+* Python
+* FastAPI
+* WebSockets
 
-**Focus areas:**
-- ✅ Well-covered: Bus routing, identity, chat, discovery, emergency mode
-- 🎯 Strong: LLM service, RAG pipeline, marketplace, event system
-- 📈 Expanding: Transport layer, UI advanced features, observability metrics
+### Knowledge Layer
 
----
+* ChromaDB
+* FAISS
+* Local Embeddings
 
-## 🔗 Deployment & Source
+### Infrastructure
 
-| Resource | Purpose |
-|----------|---------|
-| **HF Space** (Primary) | [https://huggingface.co/spaces/build-small-hackathon/HearthNet](https://huggingface.co/spaces/build-small-hackathon/HearthNet) | Live demo + Downloads |
-| **GitHub** (Mirror/CI) | [https://github.com/ckal/HearthNet](https://github.com/ckal/HearthNet) | Source control + Issue tracking |
+* Docker
+* Docker Compose
 
-**Deployment Architecture:**
-- 📡 **HF Space**: Live demo, PWA app, binary downloads (exe, apk, etc.)
-- 🐙 **GitHub**: Source repository, CI/CD, releases, issue tracking
-- 🔄 **Sync**: Changes push to both simultaneously
+### Frontend
 
-**Build Artifacts Available:**
-- Windows EXE: [dist/HearthNet.exe](https://huggingface.co/spaces/build-small-hackathon/HearthNet/resolve/main/dist/HearthNet.exe) (212 MB)
-- Android APK: [build/android/.../app-debug.apk](https://huggingface.co/spaces/build-small-hackathon/HearthNet/resolve/main/build/android/HearthNetApp/platforms/android/app/build/outputs/apk/debug/app-debug.apk) (3.56 MB)
-- Build scripts: [BUILD_GUIDE.md](docs/guides/BUILD_GUIDE.md) for EXE, AppImage, .app, Docker
+* Gradio / Web UI
 
 ---
 
-## Contributing & Docs
+# 👥 Team
 
-| Resource | Link |
-|----------|------|
-| Architecture | [ARCHITECTURE.md](docs/ARCHITECTURE.md) |
-| System overview | [docs/00-OVERVIEW.md](docs/00-OVERVIEW.md) |
-| Capability contract | [docs/CAPABILITY_CONTRACT.md](docs/CAPABILITY_CONTRACT.md) |
-| Roadmap | [docs/roadmap.md](docs/roadmap.md) |
-| Task tracker | [tasks.md](tasks.md) |
-| Phase 2+3 specs | [docs/p2_p3/](docs/p2_p3/) |
+Built during the Hugging Face Hackathon ❤️
 
----
+We believe that the future of AI should be:
+
+✅ Open
+
+✅ Local
 
-## Hackathon Entry
+✅ Resilient
 
-**Track:** 🏕️ Backyard AI (Practical)
+✅ Community-Owned
 
-**Why HearthNet wins:**
+---
+
+# 🤝 Join Us
 
-🐜 **Tiny Titan:** Runs on MiniCPM3-4B (4B params, under 32B limit). Ultra-light mode with SmolLM2-135M (135M) via `MODEL_ID` env var for Raspberry Pi and edge devices.
+We're actively looking for:
 
-🤖 **Best Agent:** Capability bus + intelligent routing = distributed agentic system. Nodes score, select, and failover to the best provider autonomously. MOE expert routing means each specialist node attracts the right queries.
+* AI Engineers
+* Distributed Systems Developers
+* Frontend Developers
+* DevOps Engineers
+* Designers
+* Community Builders
 
-**Optional integrations:**
-- NVIDIA Nemotron: Document intelligence for RAG (`NVIDIA_API_KEY` env var)
-- OpenBMB MiniCPM: Local-first models via `MINICPM_URL` (llama.cpp-compatible)
-- Modal: Serverless GPU as remote node (`MODAL_ENDPOINT` env var)
+Ideas, feedback, contributions, and discussions are always welcome.
 
 ---
 
-## Links
+# 🌟 Vision
 
-| | |
-|--|--|
-| 🤗 HF Space (Live) | https://huggingface.co/spaces/build-small-hackathon/HearthNet |
-| 🐙 GitHub (Source) | https://github.com/ckal/HearthNet |
-| 📚 Architecture | [docs/ARCHITECTURE.md](docs/ARCHITECTURE.md) |
-| 🧪 Tests | `python -m pytest tests/ -v` |
+> "The cloud owns AI today.
+>
+> HearthNet is our bet that communities will own it tomorrow."
 
 ---
 
 <p align="center">
-  Built with open source models and the belief that communities should own their AI.<br>
-  <em>Small model. Big mesh. Real resilience.</em>
+  Built with ❤️, ☕, open source, and a slightly unreasonable belief that neighborhoods should have their own AI.
 </p>

agents.md

@@ -1,45 +0,0 @@
-# HearthNet Agent Coordination
-
-## Active Roles
-
-| Agent | Role | Ownership |
-| --- | --- | --- |
-| Codex lead | Integration, docs, final verification, deployment | Whole repo coordination |
-| Planck | Phase 1 doc synthesis | Read-only docs review |
-| Avicenna | Prototype assessment | Read-only HTML/README review |
-| Kepler | HF Space and quality checklist | Read-only deployment/tooling review |
-| Mill | Python Phase 1 core | `hearthnet/`, `tests/` |
-| Anscombe | Gradio Space UI | `app.py`, optional `static/` |
-| Hypatia | Quality/tool config | `pyproject.toml`, requirements, config files |
-| Darwin | M01-M07 + contract/glossary audit | Read-only coverage review |
-| Gibbs | M08-M13 audit | Read-only coverage review |
-| Franklin | X01-X04 + overview/PRD audit | Read-only coverage review |
-| Mendel | No-mock local-first model architecture audit | Read-only implementation review |
-| Pascal | Gradio/server/UI audit | Read-only UI/runtime review |
-| Erdos | Coordination docs policy audit | Read-only tasks/agents review |
-
-## Collaboration Rules
-
-- Workers have disjoint write scopes.
-- No worker may revert another worker's edits.
-- Services do not import each other directly; they communicate through the bus.
-- UI talks to the controller/facades, not directly to services.
-- No mocks or fake AI paths in implementation-facing code. Phase 1 may keep clearly labeled prototype/demo surfaces, but shipped services must use real local-first components or explicit unavailable/degraded states.
-- Local AI must be local-first: prefer Ollama, llama.cpp, or local Hugging Face Transformers backends. OpenAI may be used only as an opt-in online fallback when local models are unavailable or explicitly disabled.
-- Do not add security-tool suppression pragmas, broad ignores, or Bandit/Ruff/Pylint bypasses to pass checks. Fix the finding or document a narrow, reviewed exception in `tasks.md`.
-- Quality gates must be run before deployment.
-- UI must follow the spec architecture: UI talks through controller/facades/bus snapshots only, with no direct service imports.
-- Spec adherence is a quality gate: changes must map to the relevant M/X docs, capability contract, and glossary terms.
-
-## Integration Checklist
-
-- [ ] Merge worker changes.
-- [ ] Resolve conflicts without losing existing prototypes.
-- [ ] Update `tasks.md` statuses.
-- [ ] Verify no mocks, fake model responses, or unlabeled simulations remain in implementation paths.
-- [ ] Verify local-first model backends are real and OpenAI is only an opt-in online fallback.
-- [ ] Verify no new security pragmas, blanket ignores, or quality bypasses were introduced.
-- [ ] Verify UI behavior and wording do not overclaim missing spec features.
-- [ ] Verify implemented behavior is traceable to M01-M13, X01-X04, `CAPABILITY_CONTRACT.md`, and `GLOSSARY.md`.
-- [ ] Run all requested checks.
-- [ ] Commit and push to the Hugging Face Space.

app.py

@@ -1,681 +0,0 @@
-"""HearthNet — Hugging Face Space entry point.
-
-This Space runs a **real** HearthNet node using HuggingFace Transformers as the
-LLM backend. All 8 tabs are live:
-
-  Ask        — LLM + RAG queries routed via capability bus
-  Chat       — Event-sourced direct messages between nodes
-  Mesh       — Live topology graph of discovered peers
-  Marketplace — Community offers / requests / emergency posts
-  Files      — BLAKE3 content-addressed blob store
-  Emergency  — Offline-mode probe and connectivity status
-  Settings   — Node identity, peer list, QR invite, RAG ingest
-
-Difference between this Space and a local install
-──────────────────────────────────────────────────
-  HF Space     → single node, no real peer mesh, MiniCPM5-1B for LLM
-  Local node   → full peer mesh, any LLM backend (Ollama / llama.cpp / HF),
-                 file sharing, multi-node chat, hardware acceleration
-
-Quick start (local, full features):
-  git clone https://huggingface.co/spaces/build-small-hackathon/HearthNet
-  cd HearthNet
-  pip install -e .
-  python -m hearthnet.cli run
-  # Open http://localhost:7860 in your browser
-
-See docs/HOWTO.md for Raspberry Pi, Docker, and multi-node mesh setup.
-"""
-
-from __future__ import annotations
-
-import contextlib
-import os
-
-# ─────────────────────────────────────────────────────────────────────────────
-# Optional HF Spaces GPU decorator
-# ─────────────────────────────────────────────────────────────────────────────
-try:
-    import spaces as _spaces  # type: ignore[import]
-
-    HF_SPACES = True
-except ImportError:
-    HF_SPACES = False
-
-# ─────────────────────────────────────────────────────────────────────────────
-# Bootstrap a real HearthNet node
-# ─────────────────────────────────────────────────────────────────────────────
-
-MODEL_ID = os.getenv("MODEL_ID", "HuggingFaceTB/SmolLM2-135M-Instruct")
-MODEL_REVISION = os.getenv("MODEL_REVISION", "12fd25f77366fa6b3b4b768ec3050bf629380bac")
-
-#MODEL_ID = os.getenv("MODEL_ID", "openbmb/MiniCPM3-4B")
-# Pin the exact commit so trust_remote_code never re-execs modeling_minicpm.py
-# mid-session and creates a second class object (the "not the same object as itself"
-# pickle error). Override via env var to test a newer revision.
-#MODEL_REVISION = os.getenv("MODEL_REVISION") or "d6b14ddaefdb11c624dd75c3c779549bc90b08cb"
-
-SEED_CORPUS = [
-    {
-        "id": "water.001",
-        "title": "Water Safety",
-        "text": (
-            "If the mains supply is disrupted, use stored clean water first. "
-            "Rainwater should be filtered through clean cloth, brought to a rolling "
-            "boil for at least one minute, and stored in a clean covered container. "
-            "Adult daily minimum: 3 litres for drinking and sanitation."
-        ),
-    },
-    {
-        "id": "power.001",
-        "title": "Power Outage",
-        "text": (
-            "Keep refrigerators closed to preserve food up to 4 hours. "
-            "Disconnect sensitive electronics. Reserve battery banks for communication. "
-            "Share verified charging points through the local marketplace. "
-            "Candles are a fire risk — use battery or wind-up torches."
-        ),
-    },
-    {
-        "id": "mesh.001",
-        "title": "HearthNet Routing",
-        "text": (
-            "A HearthNet UI sends requests to a capability bus. The bus scores local "
-            "capabilities higher than remote ones and routes to the best available "
-            "provider. If a node is quarantined the bus fails over automatically. "
-            "RAG corpus routing uses the 'corpus' parameter to match the right node."
-        ),
-    },
-    {
-        "id": "firstaid.001",
-        "title": "First Aid — Bleeding",
-        "text": (
-            "Apply direct firm pressure to the wound with a clean cloth. "
-            "Maintain pressure for at least 10 minutes. Do not remove the cloth — "
-            "add more on top if it soaks through. Elevate the limb above heart level "
-            "if possible. Seek emergency care if bleeding is severe or arterial."
-        ),
-    },
-    {
-        "id": "firstaid.002",
-        "title": "CPR Basics",
-        "text": (
-            "If a person is unresponsive and not breathing normally: call emergency services, "
-            "then give 30 chest compressions (hard, fast, centre of chest) followed by "
-            "2 rescue breaths. Continue the 30:2 cycle until help arrives or the person "
-            "recovers. Hands-only CPR (compressions without rescue breaths) is acceptable "
-            "for untrained bystanders."
-        ),
-    },
-    {
-        "id": "setup.001",
-        "title": "Node Setup — Quick Start",
-        "text": (
-            "Install HearthNet with: pip install hearthnet. "
-            "Run: python -m hearthnet.cli run "
-            "to start a node. Open http://localhost:7860 in your browser. "
-            "Other devices on the same LAN discover your node automatically via mDNS. "
-            "Use the Settings tab to generate an invite QR for devices on other networks."
-        ),
-    },
-    {
-        "id": "setup.002",
-        "title": "Node Setup — Specialized Nodes",
-        "text": (
-            "Register only the capabilities your hardware supports. "
-            "An OCR Raspberry Pi: register OcrService. "
-            "A medical knowledge node: register RagService with a medical corpus. "
-            "A thin client (phone): register no services — all bus calls route to peers. "
-            "The bus auto-discovers and routes to the best provider in the mesh."
-        ),
-    },
-    {
-        "id": "emergency.001",
-        "title": "Emergency Communication Plan",
-        "text": (
-            "Before a disaster: exchange node IDs with neighbours. "
-            "During internet outage: HearthNet switches to offline mode automatically. "
-            "All routing stays local. Use the mesh to share offers and requests. "
-            "For emergency alerts, post to the Marketplace with category=emergency. "
-            "Battery-powered device with HearthNet can serve the whole neighbourhood."
-        ),
-    },
-    {
-        "id": "food.001",
-        "title": "Emergency Food Safety",
-        "text": (
-            "In a power outage, refrigerated food is safe for up to 4 hours. "
-            "Frozen food stays safe for 24-48 hours if the freezer stays closed. "
-            "Discard meat, poultry, seafood, dairy, or cooked food left above 4°C "
-            "for more than 2 hours. When in doubt, throw it out."
-        ),
-    },
-    {
-        "id": "shelter.001",
-        "title": "Shelter in Place",
-        "text": (
-            "During chemical or biological hazards, stay indoors. "
-            "Close all windows and doors. Turn off HVAC. "
-            "Seal gaps with wet towels or tape. "
-            "Monitor emergency broadcasts on battery radio. "
-            "Do not leave until authorities give the all-clear."
-        ),
-    },
-]
-
-
-def _build_node():
-    """Bootstrap the HearthNet node for this Space.
-
-    Uses HfLocalBackend (MiniCPM5-1B by default) so inference works without Ollama.
-    Falls back to _UnavailableBackend if transformers is not installed.
-    """
-    import hashlib
-    import os
-    import socket
-
-    from hearthnet.node import HearthNode
-    from hearthnet.services.chat.service import ChatService
-    from hearthnet.services.files.service import FileService
-    from hearthnet.services.llm.backends.hf_local import HfLocalBackend
-    from hearthnet.services.llm.service import LlmService
-    from hearthnet.services.marketplace.service import MarketplaceService
-
-    # Generate a stable node_id from the HF Space hostname (so it doesn't change on restart).
-    # Use SPACE_HOST env var (set only on HF Spaces) to differentiate: local nodes get
-    # "local-*" prefix so they never collide with live "hf-space-*" peers in the relay.
-    _space_host = os.getenv("SPACE_HOST", "")
-    _host = _space_host or socket.gethostname()
-    _suffix = hashlib.sha256(_host.encode()).hexdigest()[:8]
-    if _space_host:
-        _node_id = f"hf-space-{_suffix}"
-        _display = os.getenv("SPACE_TITLE", f"HearthNet Space ({_suffix})")
-    else:
-        _node_id = f"local-{_suffix}"
-        _display = os.getenv("SPACE_TITLE", f"HearthNet Local ({_suffix})")
-
-    node = HearthNode(
-        node_id=_node_id,
-        display_name=_display,
-        community_id="ed25519:hf-space-community",
-    )
-
-    # LLM — HF Transformers backend (MiniCPM5-1B by default)
-    try:
-        backend = HfLocalBackend(model=MODEL_ID, revision=MODEL_REVISION)
-        # On ZeroGPU Spaces, wrap generation with @spaces.GPU so CUDA is
-        # allocated for exactly the duration of one generation call.
-        # Free-function form (no `self` argument) avoids any risk of ZeroGPU
-        # touching the model object across a serialisation boundary.
-        if HF_SPACES:
-            from hearthnet.services.llm.backends.hf_local import HfLocalBackend as _HfLocalBackend
-
-            # Module-level singleton: populated on first warm() call.
-            _hf_backend: list = []  # list-as-mutable-cell so the closure can write
-
-            _orig_generate_sync = _HfLocalBackend._generate_sync
-
-            @_spaces.GPU(duration=120)
-            def _free_gpu_generate(messages, max_tokens=256, temperature=0.7):
-                """Free function — no self, no model object crossing the GPU boundary."""
-                b = _hf_backend[0]
-                return _orig_generate_sync(b, messages, max_tokens=max_tokens, temperature=temperature)
-
-            def _patched_generate_sync(self, messages, max_tokens=256, temperature=0.7):
-                if not _hf_backend:
-                    _hf_backend.append(self)
-                else:
-                    _hf_backend[0] = self
-                return _free_gpu_generate(messages, max_tokens=max_tokens, temperature=temperature)
-
-            _HfLocalBackend._generate_sync = _patched_generate_sync  # type: ignore[method-assign]
-
-        backends: list = [backend]
-        # ── Sponsor cloud backends (opt-in via env) ───────────────────────
-        # NVIDIA Nemotron (prize track) — cloud NIM, no local availability check.
-        if os.getenv("NVIDIA_API_KEY"):
-            try:
-                from hearthnet.services.llm.backends.nemotron import NemotronBackend
-
-                backends.append(NemotronBackend(api_key_env="NVIDIA_API_KEY"))
-            except Exception:
-                pass
-        # Modal serverless GPU (prize track).
-        if os.getenv("MODAL_ENDPOINT"):
-            try:
-                from hearthnet.services.llm.backends.modal_backend import ModalBackend
-
-                modal_b = ModalBackend()
-                if modal_b.is_available():
-                    backends.append(modal_b)
-            except Exception:
-                pass
-        # MiniCPM local server (OpenBMB prize track).
-        # MINICPM_URL          → OpenAI-compatible vLLM/SGLang/llama.cpp endpoint
-        # MINICPM_MODELS       → comma-separated model ids to advertise (multi-model
-        #                        serving from one server). Omit → full MiniCPM catalogue.
-        # MINICPM_LIGHTWEIGHT  → "1" to also advertise Pi-friendly small models.
-        _minicpm_url = os.getenv("MINICPM_URL")
-        if _minicpm_url:
-            try:
-                from hearthnet.services.llm.backends.openbmb import OpenBmbBackend
-
-                _models_env = os.getenv("MINICPM_MODELS", "")
-                _models = [m.strip() for m in _models_env.split(",") if m.strip()] or None
-                _lightweight = os.getenv("MINICPM_LIGHTWEIGHT", "") in ("1", "true", "yes")
-                minicpm = OpenBmbBackend(
-                    base_url=_minicpm_url,
-                    models=_models,
-                    include_lightweight=_lightweight,
-                )
-                if minicpm.is_available():
-                    backends.append(minicpm)
-            except Exception:
-                pass
-
-        llm = LlmService(backends=backends)
-    except Exception:
-        llm = LlmService()  # _UnavailableBackend — shows clear error
-
-    node.bus.register_service(llm)
-
-    # ── Durable event log (ZeroGPU-safe; no mDNS/transport on a single Space) ──
-    event_log = None
-    try:
-        import tempfile
-        from pathlib import Path
-
-        from hearthnet.events import EventLog
-
-        _data_dir = Path(os.getenv("HEARTHNET_DATA_DIR", tempfile.gettempdir())) / "hearthnet-space"
-        _data_dir.mkdir(parents=True, exist_ok=True)
-        event_log = EventLog(_data_dir / "events.db", node.community_id, node.node_id)
-        node._event_log = event_log
-    except Exception:
-        event_log = None
-
-    # ── Blob store for content-addressed RAG documents ────────────────────
-    blob_store = None
-    try:
-        import tempfile
-        from pathlib import Path
-
-        from hearthnet.blobs.store import BlobStore
-
-        blob_store = BlobStore(
-            Path(os.getenv("HEARTHNET_DATA_DIR", tempfile.gettempdir()))
-            / "hearthnet-space"
-            / "blobs"
-        )
-    except Exception:
-        blob_store = None
-
-    # ── Real semantic RAG (replaces the in-memory demo corpus) ────────────
-    from hearthnet.bus.capability import RouteRequest
-    from hearthnet.services.rag.federated import FederatedRagService
-    from hearthnet.services.rag.service import RagService
-
-    # Register the embedding backend first so rag.query routes through embed.text.
-    node.install_extended_services(research=True)
-
-    import tempfile
-
-    _data_env = os.getenv("HEARTHNET_DATA_DIR", "")
-    _data_base = Path(_data_env) if _data_env else Path(tempfile.gettempdir())
-    # Verify the base path (or its first existing ancestor) is writable.
-    # Falls back to tempdir if e.g. /data persistent storage isn't mounted.
-    _check = _data_base
-    while not _check.exists():
-        _check = _check.parent
-    if not os.access(_check, os.W_OK):
-        _data_base = Path(tempfile.gettempdir())
-        print(f"[hearthnet] HEARTHNET_DATA_DIR {_data_env!r} not writable, using tmpdir")
-    _corpora_dir = _data_base / "hearthnet-space" / "corpora"
-    rag = RagService(
-        corpus="community",
-        corpora_dir=_corpora_dir,
-        bus=node.bus,
-        event_log=event_log,
-        blob_store=blob_store,
-    )
-    node.bus.register_service(rag)
-    node.bus.register_service(FederatedRagService(node.bus, corpus="community"))
-
-    # Seed the corpus through the real ingest path (content-addressed + logged).
-    async def _seed_corpus() -> None:
-        import pathlib
-
-        # 1. Fixed emergency seed documents (water, first aid, CPR, etc.)
-        for doc in SEED_CORPUS:
-            with contextlib.suppress(Exception):
-                await rag.handle_ingest(
-                    RouteRequest(
-                        capability="rag.ingest",
-                        version_req=(1, 0),
-                        body={
-                            "input": {
-                                "corpus": "community",
-                                "documents": [
-                                    {
-                                        "id": doc["id"],
-                                        "title": doc["title"],
-                                        "text": doc["text"],
-                                    }
-                                ],
-                            }
-                        },
-                        caller=node.node_id,
-                        trace_id="seed",
-                        deadline_ms=0,
-                    )
-                )
-
-        # 2. Ingest all .md / .txt files from docs/ (main), docs/guides/, assets/initial_docs/.
-        # Files are content-addressed (BLAKE3), so re-ingesting the same file is a no-op.
-        _app_root = pathlib.Path(__file__).parent
-        _doc_dirs = [
-            _app_root / "docs",  # Main docs: CAPABILITY_CONTRACT, GLOSSARY, M01-M13, X01-X04, etc.
-            _app_root / "docs" / "guides",
-            _app_root / "assets" / "initial_docs",
-        ]
-        _text_suffixes = {".md", ".txt", ".rst"}
-        _ingested = 0
-        _failed = 0
-        for _doc_dir in _doc_dirs:
-            if not _doc_dir.exists():
-                continue
-            for _doc_file in sorted(_doc_dir.rglob("*")):
-                if _doc_file.suffix.lower() not in _text_suffixes:
-                    continue
-                try:
-                    _text = _doc_file.read_text(encoding="utf-8", errors="replace")
-                    if len(_text.strip()) < 80:
-                        continue
-                    _title = _doc_file.stem.replace("-", " ").replace("_", " ").title()
-                    _doc_id = f"file:{_doc_file.relative_to(_app_root).as_posix()}"
-                    await rag.handle_ingest(
-                        RouteRequest(
-                            capability="rag.ingest",
-                            version_req=(1, 0),
-                            body={
-                                "input": {
-                                    "text": _text,
-                                    "title": _title,
-                                    "doc_cid": _doc_id,
-                                }
-                            },
-                            caller=node.node_id,
-                            trace_id="seed-docs",
-                            deadline_ms=0,
-                        )
-                    )
-                    _ingested += 1
-                except Exception as _ie:
-                    _failed += 1
-                    print(f"[hearthnet] seed ingest failed {_doc_file.name}: {_ie}")
-        print(f"[hearthnet] seed corpus done: {_ingested} docs ingested, {_failed} failed")
-
-    # Run seed corpus in a dedicated thread with its own event loop to avoid
-    # conflicts with any loop already running (e.g. Gradio's internal loop).
-    import asyncio
-    import threading
-
-    def _seed_in_thread() -> None:
-        loop = asyncio.new_event_loop()
-        asyncio.set_event_loop(loop)
-        try:
-            loop.run_until_complete(_seed_corpus())
-        except Exception as _e:
-            print(f"[hearthnet] seed corpus failed: {_e}")
-        finally:
-            loop.close()
-
-    # Run seeding as a pure background daemon — do NOT join() with a short
-    # timeout. 78+ docs × embedding time >> 60 s, so a join would kill the
-    # thread before most docs are stored. Gradio starts serving immediately;
-    # docs become available for RAG queries as they are ingested.
-    _seed_thread = threading.Thread(target=_seed_in_thread, daemon=True, name="hearthnet-seed")
-    _seed_thread.start()
-
-    # Register this node's LLM model as an expert in the MoE registry so
-    # route_expert tool calls return meaningful results instead of an empty list.
-    try:
-        _moe_tags = list({
-            doc.get("id", "").split(".")[0]
-            for doc in SEED_CORPUS
-            if doc.get("id")
-        } | {"emergency", "mesh", "community"})
-        loop_moe = asyncio.new_event_loop()
-        loop_moe.run_until_complete(
-            node.bus.call(
-                "moe.register",
-                (1, 0),
-                {
-                    "input": {
-                        "expert_id": f"model:{MODEL_ID}",
-                        "expert_type": "model",
-                        "topic_tags": _moe_tags,
-                        "confidence_score": 0.6,
-                        "community_id": node.community_id,
-                        "name": MODEL_ID.split("/")[-1],
-                        "ttl_seconds": 0,
-                    }
-                },
-            )
-        )
-        loop_moe.close()
-    except Exception:
-        pass
-
-    # Marketplace, Chat, Files — now durably event-sourced where supported.
-    node.bus.register_service(MarketplaceService(event_log=event_log, node_id=node.node_id))
-    node.bus.register_service(ChatService(node.node_id, event_log=event_log, bus=node.bus))
-    node.bus.register_service(FileService())
-
-    return node
-
-
-# Build node and Gradio app at import time (HF Spaces requires module-level `demo`)
-_node = _build_node()
-
-# ── Local-only: start mDNS peer discovery + HTTP bus transport ────────────────
-# On HF Space (SPACE_HOST set): port 7080 is not exposed to the internet and mDNS
-# doesn't cross network boundaries — the relay hub handles internet peering instead.
-# Locally: node.start() activates zero-config LAN discovery and makes this node's
-# bus callable by other nodes over HTTP so RAG, chat, and LLM route across devices.
-if not os.getenv("SPACE_HOST"):
-    import asyncio as _asyncio
-    import threading as _threading
-
-    def _run_local_networking() -> None:
-        _loop = _asyncio.new_event_loop()
-        _asyncio.set_event_loop(_loop)
-        try:
-            # node._event_log is already set by _build_node(); start() reuses it
-            # (see the "already set" guard added to node.start()).
-            _loop.run_until_complete(_node.start(port=7080))
-            _loop.run_forever()
-        except Exception as _exc:
-            print(f"[hearthnet] local networking start failed: {_exc}")
-
-    _threading.Thread(
-        target=_run_local_networking, daemon=True, name="hearthnet-local-node"
-    ).start()
-
-# Relay hub: pull-based mailbox router so NAT-bound nodes mesh all-to-all through
-# this public Space (see hearthnet/transport/relay_hub.py). Members poll their
-# mailbox over HTTPS; the Space never needs to reach back into a home network.
-from hearthnet.transport.relay_hub import RelayHub as _RelayHub  # noqa: E402
-from hearthnet.transport.relay_hub import mount_relay_endpoints as _mount_relay_endpoints  # noqa: E402
-
-import tempfile as _tempfile
-from pathlib import Path as _Path2
-
-_relay_db_path = (
-    _Path2(os.getenv("HEARTHNET_DATA_DIR", _tempfile.gettempdir()))
-    / "hearthnet-space"
-    / "relay.db"
-)
-_relay_db_path.parent.mkdir(parents=True, exist_ok=True)
-_relay_hub = _RelayHub(db_path=_relay_db_path)
-
-from hearthnet.ui.app import build_ui as _build_ui  # noqa: E402
-
-_ui = _build_ui(
-    bus=_node.bus,
-    state_bus=_node.state_bus,
-    node=_node,
-    display_name=_node.display_name,
-    node_id=_node.node_id,
-    community_id=_node.community_id,
-)
-
-demo = _ui.build()
-
-# Gradio 6 moved theme/css from gr.Blocks() to launch(). Set them directly on the
-# demo object so HF Spaces' auto-launch (which we don't control) picks them up.
-if _ui.theme is not None and hasattr(demo, "theme"):
-    demo.theme = _ui.theme
-if _ui.css is not None and hasattr(demo, "css"):
-    demo.css = _ui.css
-
-# ── Serve webagent at /webagent/ ──────────────────────────────────────────────
-# HF Space enables Gradio SSR mode (GRADIO_SSR_MODE=true), where a Node.js layer
-# intercepts ALL requests before Python/FastAPI sees them, making StaticFiles
-# mounts invisible. Fix: force SSR off so Python handles all requests directly.
-from pathlib import Path as _Path
-
-import gradio as _gr
-
-_webagent_dir = _Path(__file__).parent / "webagent"
-
-# 1) Override the env var that launch() reads when ssr_mode param is None
-os.environ["GRADIO_SSR_MODE"] = "false"
-
-# 2) Also patch _resolve_ssr_mode in case HF passes ssr_mode=True explicitly
-_gr.Blocks._resolve_ssr_mode = lambda self, ssr_mode=None, **kw: False
-
-
-def _mount_bus_endpoints(app) -> None:
-    """Expose the node's capability bus on the Space's public port.
-
-    On HF Spaces only the Gradio port is reachable from the internet — the
-    node's internal HttpServer (port 7080) is not. Mounting the bus RPC
-    endpoints directly into the Gradio FastAPI app lets a remote/local node
-    peer with this Space via ``discovery.peer.add`` and route real
-    ``llm.chat`` / ``rag.query`` / ``moe.*`` calls to it over HTTPS.
-    """
-    try:
-        from fastapi import Body
-        from fastapi.responses import JSONResponse
-    except Exception as exc:  # pragma: no cover
-        print(f"[hearthnet] bus endpoint mount skipped: {exc}")
-        return
-
-    if any(getattr(r, "path", "") == "/bus/v1/call" for r in app.routes):
-        return
-
-    def _parse_version(v) -> tuple[int, int]:
-        parts = str(v).split(".")
-        if len(parts) < 2:
-            parts.append("0")
-        return (int(parts[0]), int(parts[1]))
-
-    @app.get("/manifest")
-    async def _hn_manifest():
-        return JSONResponse(_node.manifest().as_dict())
-
-    @app.get("/health")
-    async def _hn_health():
-        return JSONResponse({"status": "ok", "node_id": _node.node_id})
-
-    @app.get("/bus/v1/capabilities")
-    async def _hn_capabilities():
-        return JSONResponse([e.descriptor.name for e in _node.bus.registry.all_local()])
-
-    @app.post("/bus/v1/call")
-    async def _hn_bus_call(payload: dict = Body(...)):
-        capability = payload.get("capability")
-        if not capability:
-            return JSONResponse(
-                {"error": "bad_request", "message": "capability required"}, status_code=400
-            )
-        version = _parse_version(payload.get("version", "1.0"))
-        call_body = {
-            "params": payload.get("params", {}),
-            "input": payload.get("input", {}),
-        }
-        try:
-            result = await _node.bus.call(capability, version, call_body)
-            return JSONResponse(result)
-        except Exception as exc:
-            code = getattr(exc, "code", "call_error")
-            return JSONResponse({"error": code, "message": str(exc)}, status_code=500)
-
-    # New routes are appended last; move them ahead of Gradio's SPA catch-all.
-    for _path in ("/bus/v1/call", "/bus/v1/capabilities", "/manifest", "/health"):
-        for _i in range(len(app.routes) - 1, -1, -1):
-            if getattr(app.routes[_i], "path", "") == _path:
-                app.routes.insert(0, app.routes.pop(_i))
-                break
-
-
-# 3) Patch App.create_app to inject the StaticFiles mount after Gradio routes
-if _webagent_dir.exists():
-    try:
-        import gradio.routes as _gr_routes
-        from fastapi.staticfiles import StaticFiles as _SF
-
-        _orig_create_app = _gr_routes.App.__dict__["create_app"].__func__
-
-        def _patched_create_app(blocks, app=None, **kwargs):
-            result = _orig_create_app(blocks, app=app, **kwargs)
-            try:
-                if not any(getattr(r, "name", "") == "webagent" for r in result.routes):
-                    result.mount("/webagent", _SF(directory=str(_webagent_dir)), name="webagent")
-                    _wrt = result.routes.pop()
-                    result.routes.insert(0, _wrt)
-            except Exception as _me:
-                print(f"[hearthnet] webagent mount: {_me}")
-            _mount_bus_endpoints(result)
-            _mount_relay_endpoints(result, _relay_hub)
-
-            # Auto-join: the Space node registers itself in its own relay hub
-            # so remote nodes that connect see it in the roster immediately.
-            try:
-                _caps = [
-                    f"{e.descriptor.name}@{e.descriptor.version[0]}.{e.descriptor.version[1]}"
-                    for e in _node.bus.registry.all_local()
-                ]
-                _nid = getattr(_node, "node_id_full", _node.node_id)
-                _relay_hub.join(
-                    _nid,
-                    display_name=_node.display_name,
-                    community_id=_node.community_id,
-                    capabilities=_caps,
-                    endpoint="",
-                )
-                _relay_hub.set_local_handler(_nid, _node.bus)
-                print(f"[hearthnet] Space node '{_node.display_name}' joined local relay hub")
-            except Exception as _je:
-                print(f"[hearthnet] self-join relay failed: {_je}")
-            return result
-
-        _gr_routes.App.create_app = staticmethod(_patched_create_app)
-    except Exception as _pe:
-        print(f"[hearthnet] create_app patch failed: {_pe}")
-
-if __name__ == "__main__":
-    import os
-
-    # HF Spaces health-checks port 7860. Bind explicitly and disable Gradio
-    # SSR mode (Node proxy on a different port crashes on HF and the health
-    # check on :7860 then times out -> "workload was not healthy").
-    _port = int(os.environ.get("GRADIO_SERVER_PORT", "7860"))
-    demo.launch(
-        server_name="0.0.0.0",
-        server_port=_port,
-        ssr_mode=False,
-        theme=_ui.theme,
-        css=_ui.css,
-    )

app_nemotron.py

@@ -1,558 +0,0 @@
-"""HearthNet Document Intelligence — Nemotron-powered second Space.
-
-A standalone Gradio app focused entirely on document intelligence using
-NVIDIA Nemotron models. Can run independently OR as part of a HearthNet mesh.
-
-Deploy as a second HF Space alongside the main HearthNet mesh Space.
-
-Prize targets:
-  - NVIDIA Nemotron Hardware Prize (RTX 5080): Build with Nemotron models ✅
-  - 🐜 Tiny Titan: Nemotron-nano-8B is 8B params (under 32B) ✅
-  - 🎨 Off Brand: Custom-styled beyond default Gradio look ✅
-
-Usage:
-  python app_nemotron.py
-
-Environment:
-  NVIDIA_API_KEY   — NVIDIA NIM API key (get free at build.nvidia.com)
-  NEMOTRON_URL     — local NIM endpoint (optional, for offline use)
-  HEARTHNET_NODE   — URL of a HearthNet mesh node to push results into
-"""
-
-from __future__ import annotations
-
-import asyncio
-import os
-
-import gradio as gr
-
-# HF Spaces GPU support
-try:
-    import spaces
-    HAS_SPACES = True
-except ImportError:
-    HAS_SPACES = False
-
-# ── Optional mesh connection ──────────────────────────────────────────────────
-_MESH_NODE = os.getenv("HEARTHNET_NODE", "")
-_NVIDIA_KEY = os.getenv("NVIDIA_API_KEY", "")
-_NEMOTRON_URL = os.getenv("NEMOTRON_URL", "")
-
-# ── Nemotron model catalogue ──────────────────────────────────────────────────
-_MODELS = {
-    "Nemotron Nano 8B (fast)": "nvidia/llama-3.1-nemotron-nano-8b-instruct",
-    "Nemotron Super 49B (deep)": "nvidia/llama-3.3-nemotron-super-49b-v1",
-    "Nemotron 70B (balanced)": "nvidia/llama-3.1-nemotron-70b-instruct",
-}
-
-_SCHEMAS = {
-    "Invoice / Receipt": """{
-  "vendor": "string",
-  "date": "string",
-  "total_amount": "number",
-  "currency": "string",
-  "line_items": [{"description": "string", "amount": "number"}],
-  "tax": "number"
-}""",
-    "Medical Form": """{
-  "patient_name": "string",
-  "date_of_birth": "string",
-  "diagnosis": ["string"],
-  "medications": ["string"],
-  "doctor": "string",
-  "date": "string"
-}""",
-    "Legal Document": """{
-  "document_type": "string",
-  "parties": ["string"],
-  "effective_date": "string",
-  "key_obligations": ["string"],
-  "governing_law": "string"
-}""",
-    "Meeting Notes": """{
-  "date": "string",
-  "attendees": ["string"],
-  "decisions": ["string"],
-  "action_items": [{"owner": "string", "task": "string", "due": "string"}]
-}""",
-    "Custom (edit below)": "{}",
-}
-
-# ── Custom HearthNet theme ────────────────────────────────────────────────────
-_theme = gr.themes.Soft(
-    primary_hue=gr.themes.colors.orange,
-    secondary_hue=gr.themes.colors.purple,
-    neutral_hue=gr.themes.colors.gray,
-    font=[gr.themes.GoogleFont("Inter"), "ui-sans-serif", "sans-serif"],
-).set(
-    button_primary_background_fill="*primary_500",
-    button_primary_background_fill_hover="*primary_600",
-    block_title_text_weight="600",
-    block_border_width="1px",
-)
-
-
-# ── Core functions ────────────────────────────────────────────────────────────
-
-def _get_endpoint(api_key: str) -> str:
-    return _NEMOTRON_URL.rstrip("/") + "/v1" if _NEMOTRON_URL else "https://integrate.api.nvidia.com/v1"
-
-
-def _run_async(coro):
-    """Run a coroutine safely whether or not a loop is already running."""
-    try:
-        loop = asyncio.get_running_loop()
-    except RuntimeError:
-        loop = None
-    if loop and loop.is_running():
-        import concurrent.futures
-        with concurrent.futures.ThreadPoolExecutor(max_workers=1) as pool:
-            fut = pool.submit(asyncio.run, coro)
-            return fut.result()
-    return asyncio.run(coro)
-
-
-def _local_smol_chat(messages: list, max_tokens: int = 512) -> str:
-    """SmolLM2-135M local fallback — no API key required."""
-    try:
-        from transformers import pipeline as _pipeline  # type: ignore[import-untyped]
-
-        _smol_id = "HuggingFaceTB/SmolLM2-135M-Instruct"
-        pipe = _pipeline("text-generation", model=_smol_id, device_map="auto", torch_dtype="auto")
-        prompt = ""
-        for m in messages:
-            role, content = m.get("role", "user"), m.get("content", "")
-            if role == "system":
-                prompt += f"<|im_start|>system\n{content}<|im_end|>\n"
-            elif role == "user":
-                prompt += f"<|im_start|>user\n{content}<|im_end|>\n"
-            elif role == "assistant":
-                prompt += f"<|im_start|>assistant\n{content}<|im_end|>\n"
-        prompt += "<|im_start|>assistant\n"
-        result = pipe(prompt, max_new_tokens=max_tokens, return_full_text=False, do_sample=False)
-        return result[0]["generated_text"].strip()
-    except Exception as exc:
-        return f"[SmolLM2 unavailable: {exc}]"
-
-
-async def _nemotron_chat(messages: list, model: str, api_key: str, temperature: float = 0.1) -> str:
-    import httpx
-
-    endpoint = _get_endpoint(api_key)
-    headers = {"Content-Type": "application/json"}
-    if api_key:
-        headers["Authorization"] = f"Bearer {api_key}"
-
-    payload = {
-        "model": model,
-        "messages": messages,
-        "temperature": temperature,
-        "max_tokens": 2048,
-    }
-    async with httpx.AsyncClient(timeout=60.0) as c:
-        r = await c.post(f"{endpoint}/chat/completions", json=payload, headers=headers)
-        r.raise_for_status()
-        return r.json()["choices"][0]["message"]["content"]
-
-
-@spaces.GPU if HAS_SPACES else lambda f: f
-def extract_structured(
-    doc_text: str,
-    schema_preset: str,
-    custom_schema: str,
-    model_label: str,
-    api_key: str,
-) -> tuple[str, str]:
-    """Extract structured data from documents using Nemotron.
-    
-    Wrapped with @spaces.GPU to signal GPU usage to HF Spaces.
-    Falls back gracefully if GPU unavailable (e.g., local testing).
-    """
-    import json
-
-    if not doc_text.strip():
-        return '{"error": "No document text provided"}', "⚠ Provide document text"
-
-    key = api_key.strip() or _NVIDIA_KEY
-    schema = custom_schema.strip() if schema_preset == "Custom (edit below)" else _SCHEMAS[schema_preset]
-    model = _MODELS.get(model_label, list(_MODELS.values())[0])
-
-    system = (
-        "You are a precise structured data extraction engine. "
-        "Extract information from the document and return ONLY valid JSON "
-        f"matching this exact schema:\n{schema}\n"
-        "If a field is not found, use null. Never add fields not in the schema."
-    )
-    messages = [
-        {"role": "system", "content": system},
-        {"role": "user", "content": f"Document:\n\n{doc_text[:5000]}"},
-    ]
-
-    try:
-        if key or _NEMOTRON_URL:
-            raw = _run_async(_nemotron_chat(messages, model, key, temperature=0.05))
-            label = f"✓ Extracted with {model_label}"
-        else:
-            raw = _local_smol_chat(messages, max_tokens=512)
-            label = "✓ Extracted with SmolLM2-135M (local fallback)"
-        try:
-            parsed = json.loads(raw)
-            return json.dumps(parsed, indent=2), label
-        except json.JSONDecodeError:
-            return raw, f"⚠ Model returned non-JSON (shown as-is)"
-    except Exception as exc:
-        return f'{{"error": "{exc}"}}', f"⚠ Error: {exc}"
-
-
-def ask_document(doc_text: str, question: str, model_label: str, api_key: str) -> str:
-    if not doc_text.strip():
-        return "Provide a document first."
-    if not question.strip():
-        return "Ask a question."
-
-    key = api_key.strip() or _NVIDIA_KEY
-    model = _MODELS.get(model_label, list(_MODELS.values())[0])
-    messages = [
-        {
-            "role": "system",
-            "content": "Answer questions about the document concisely and accurately. "
-            "Cite specific parts of the document when relevant.",
-        },
-        {
-            "role": "user",
-            "content": f"Document:\n\n{doc_text[:4000]}\n\nQuestion: {question}",
-        },
-    ]
-    try:
-        if key or _NEMOTRON_URL:
-            return _run_async(_nemotron_chat(messages, model, key, temperature=0.3))
-        return _local_smol_chat(messages, max_tokens=512)
-    except Exception as exc:
-        return f"Error: {exc}"
-
-
-def summarise_document(doc_text: str, style: str, model_label: str, api_key: str) -> str:
-    if not doc_text.strip():
-        return "Provide a document first."
-
-    key = api_key.strip() or _NVIDIA_KEY
-    model = _MODELS.get(model_label, list(_MODELS.values())[0])
-    style_prompts = {
-        "Executive (3 bullets)": "Summarise in exactly 3 bullet points for an executive audience.",
-        "Detailed (paragraph)": "Write a thorough 2-paragraph summary covering all key points.",
-        "ELI5 (simple)": "Explain this document as simply as possible, as if to a 10-year-old.",
-        "Action items only": "List only the action items, decisions, and next steps.",
-    }
-    prompt = style_prompts.get(style, "Summarise the document.")
-    messages = [
-        {"role": "system", "content": prompt},
-        {"role": "user", "content": f"Document:\n\n{doc_text[:5000]}"},
-    ]
-    try:
-        if key or _NEMOTRON_URL:
-            return _run_async(_nemotron_chat(messages, model, key, temperature=0.4))
-        return _local_smol_chat(messages, max_tokens=512)
-    except Exception as exc:
-        return f"Error: {exc}"
-
-
-def push_to_mesh(doc_text: str, doc_title: str, corpus: str, mesh_url: str) -> str:
-    import httpx
-
-    url = (mesh_url.strip() or _MESH_NODE).rstrip("/")
-    if not url:
-        return "⚠ Set HEARTHNET_NODE env var or enter mesh URL to push to mesh."
-    if not doc_text.strip():
-        return "⚠ No document to push."
-
-    async def _push():
-        payload = {
-            "capability": "rag.ingest",
-            "version": "1.0",
-            "params": {"corpus": corpus or "documents"},
-            "input": {
-                "documents": [
-                    {
-                        "id": f"doc-{hash(doc_text) % 100000}",
-                        "title": doc_title or "Untitled",
-                        "text": doc_text,
-                    }
-                ]
-            },
-        }
-        async with httpx.AsyncClient(timeout=15.0) as c:
-            r = await c.post(f"{url}/bus/v1/call", json=payload)
-            r.raise_for_status()
-            return r.json()
-
-    try:
-        _run_async(_push())
-        return f"✓ Document pushed to mesh at {url}\nCorpus: {corpus}\nNow searchable via Ask tab on any mesh node."
-    except Exception as exc:
-        return f"⚠ Push failed: {exc}"
-
-
-# ── Build UI ──────────────────────────────────────────────────────────────────
-
-def build_app() -> gr.Blocks:
-    with gr.Blocks(
-        title="HearthNet · Document Intelligence",
-    ) as demo:
-        # ── Header ────────────────────────────────────────────────────────────
-        gr.HTML("""
-<div class="grad-banner">
-  <h1>🔬 HearthNet · Document Intelligence</h1>
-  <p>Structured extraction &amp; Q&amp;A powered by NVIDIA Nemotron · Part of the HearthNet mesh</p>
-</div>
-<p>
-  <span class="feature-badge" style="background:#7c3aed;color:white">NVIDIA Nemotron</span>
-  <span class="feature-badge" style="background:#f97316;color:white">Structured Extraction</span>
-  <span class="feature-badge" style="background:#0ea5e9;color:white">Offline Capable</span>
-  <span class="feature-badge" style="background:#10b981;color:white">Mesh RAG Ingest</span>
-</p>
-""")
-
-        # ── Shared controls (sidebar-style top row) ────────────────────────────
-        with gr.Row():
-            model_selector = gr.Dropdown(
-                label="🤖 Nemotron Model",
-                choices=list(_MODELS.keys()),
-                value=list(_MODELS.keys())[0],
-                scale=2,
-            )
-            api_key_box = gr.Textbox(
-                label="🔑 NVIDIA API Key",
-                value="",
-                type="password",
-                placeholder="nvapi-... leave blank if NVIDIA_API_KEY env var is set",
-                scale=3,
-            )
-
-        # ── Main tabs ──────────────────────────────────────────────────────────
-        with gr.Tabs():
-
-            # ── Tab 1: Structured Extraction ──────────────────────────────────
-            with gr.Tab("📊 Extract"):
-                with gr.Row():
-                    with gr.Column(scale=2):
-                        extract_doc = gr.Textbox(
-                            label="Document",
-                            placeholder="Paste text, or upload a file below...",
-                            lines=12,
-                        )
-                        extract_file = gr.File(
-                            label="Upload file",
-                            type="filepath",
-                            file_types=[".txt", ".md", ".csv"],
-                        )
-                        schema_preset = gr.Dropdown(
-                            label="Schema preset",
-                            choices=list(_SCHEMAS.keys()),
-                            value="Invoice / Receipt",
-                        )
-                        custom_schema = gr.Code(
-                            label="Schema (JSON)",
-                            language="json",
-                            value=_SCHEMAS["Invoice / Receipt"],
-                            lines=8,
-                        )
-
-                    with gr.Column(scale=3):
-                        extract_btn = gr.Button("⚡ Extract with Nemotron", variant="primary", size="lg")
-                        extract_out = gr.Code(label="Extracted JSON", language="json", lines=16)
-                        extract_status = gr.Textbox(label="Status", lines=1, interactive=False)
-
-                def on_preset_change(preset):
-                    return _SCHEMAS.get(preset, "{}")
-
-                schema_preset.change(on_preset_change, inputs=[schema_preset], outputs=[custom_schema])
-
-                def load_extract_file(fp):
-                    if not fp:
-                        return ""
-                    try:
-                        with open(fp, encoding="utf-8", errors="replace") as f:
-                            return f.read(8000)
-                    except Exception as e:
-                        return f"Error: {e}"
-
-                extract_file.change(load_extract_file, inputs=[extract_file], outputs=[extract_doc])
-                extract_btn.click(
-                    extract_structured,
-                    inputs=[extract_doc, schema_preset, custom_schema, model_selector, api_key_box],
-                    outputs=[extract_out, extract_status],
-                )
-
-            # ── Tab 2: Document Q&A ───────────────────────────────────────────
-            with gr.Tab("💬 Ask"):
-                with gr.Row():
-                    with gr.Column(scale=2):
-                        ask_doc = gr.Textbox(
-                            label="Document",
-                            placeholder="Paste the document to query...",
-                            lines=14,
-                        )
-
-                    with gr.Column(scale=3):
-                        ask_question_box = gr.Textbox(
-                            label="Question",
-                            placeholder="What is the total? Who are the parties? What are the obligations?",
-                            lines=2,
-                        )
-                        ask_btn = gr.Button("🔍 Ask Nemotron", variant="primary")
-                        ask_out = gr.Textbox(label="Answer", lines=8)
-
-                ask_btn.click(
-                    ask_document,
-                    inputs=[ask_doc, ask_question_box, model_selector, api_key_box],
-                    outputs=[ask_out],
-                )
-
-            # ── Tab 3: Summarise ──────────────────────────────────────────────
-            with gr.Tab("✂ Summarise"):
-                with gr.Row():
-                    with gr.Column(scale=2):
-                        sum_doc = gr.Textbox(
-                            label="Document",
-                            placeholder="Paste document text...",
-                            lines=14,
-                        )
-
-                    with gr.Column(scale=3):
-                        sum_style = gr.Dropdown(
-                            label="Summary style",
-                            choices=[
-                                "Executive (3 bullets)",
-                                "Detailed (paragraph)",
-                                "ELI5 (simple)",
-                                "Action items only",
-                            ],
-                            value="Executive (3 bullets)",
-                        )
-                        sum_btn = gr.Button("✂ Summarise with Nemotron", variant="primary")
-                        sum_out = gr.Textbox(label="Summary", lines=10)
-
-                sum_btn.click(
-                    summarise_document,
-                    inputs=[sum_doc, sum_style, model_selector, api_key_box],
-                    outputs=[sum_out],
-                )
-
-            # ── Tab 4: Push to Mesh ───────────────────────────────────────────
-            with gr.Tab("🕸 Push to Mesh"):
-                gr.Markdown(
-                    "Send extracted/processed documents into a HearthNet mesh node's RAG corpus. "
-                    "After ingesting, documents become searchable from any mesh node's **Ask** tab."
-                )
-                with gr.Row():
-                    with gr.Column():
-                        mesh_doc = gr.Textbox(
-                            label="Document text",
-                            placeholder="Paste processed document...",
-                            lines=10,
-                        )
-                        mesh_title = gr.Textbox(label="Document title", placeholder="Invoice #123")
-                        mesh_corpus = gr.Textbox(label="Corpus name", value="documents")
-                        mesh_url = gr.Textbox(
-                            label="HearthNet mesh node URL",
-                            value=_MESH_NODE,
-                            placeholder="http://localhost:7860 or https://your-space.hf.space",
-                        )
-                        mesh_push_btn = gr.Button("🚀 Push to mesh", variant="primary")
-
-                    with gr.Column():
-                        mesh_status = gr.Textbox(label="Status", lines=5)
-                        gr.Markdown(
-                            """
-**How to use with the HearthNet main Space:**
-1. Set `HEARTHNET_NODE = https://build-small-hackathon-hearthnet.hf.space`
-2. Or run locally: `python app.py` → `http://localhost:7860`
-3. Documents ingested here appear in the **Ask** tab on all mesh nodes
-
-**Local multi-node example:**
-```bash
-# Node 1 (main mesh)
-python app.py --port 7860
-
-# Node 2 (this document intelligence app)
-python app_nemotron.py --port 7861
-HEARTHNET_NODE=http://localhost:7860
-```
-"""
-                        )
-
-                mesh_push_btn.click(
-                    push_to_mesh,
-                    inputs=[mesh_doc, mesh_title, mesh_corpus, mesh_url],
-                    outputs=[mesh_status],
-                )
-
-            # ── Tab 5: About ──────────────────────────────────────────────────
-            with gr.Tab("ℹ About"):
-                gr.Markdown(
-                    f"""
-## HearthNet Document Intelligence
-
-A companion app to the [HearthNet mesh](https://huggingface.co/spaces/build-small-hackathon/HearthNet)
-that adds NVIDIA Nemotron-powered document processing.
-
-### Models
-| Model | Size | Best for |
-|-------|------|---------|
-| Nemotron Nano 8B | 8B | Fast extraction, Pi-friendly |
-| Nemotron 70B | 70B | Deep reasoning, complex docs |
-| Nemotron Super 49B | 49B | Balanced quality/speed |
-
-All models are under 32B parameters individually ✅
-
-### Architecture
-```
-Document Input ──► Nemotron Parse ──► Structured JSON
-                                   ──► Q&A Answers  
-                                   ──► Summary
-                                        │
-                                        ▼
-                              HearthNet RAG Corpus
-                              (searchable on all mesh nodes)
-```
-
-### Prize Targets
-- 🏆 **NVIDIA Nemotron Hardware Prize** (RTX 5080) — builds with Nemotron ✅
-- 🐜 **Tiny Titan** — Nano 8B model ✅
-- 🎨 **Off Brand** — Custom purple-to-orange UI ✅
-
-### Links
-- [Main HearthNet Space](https://huggingface.co/spaces/build-small-hackathon/HearthNet)
-- [HF Profile](https://huggingface.co/Chris4K)
-- [X / Twitter](https://x.com/zX14_7)
-- [GitHub](https://github.com/ckal)
-- [NVIDIA NIM API](https://build.nvidia.com) — free tier available
-
-**Current status:** API key: {'✓ configured' if _NVIDIA_KEY else '✗ not set (add NVIDIA_API_KEY)'}
-**Mesh node:** {_MESH_NODE or '✗ not set (add HEARTHNET_NODE)'}
-"""
-                )
-
-    return demo
-
-
-if __name__ == "__main__":
-    demo = build_app()
-    # HF Spaces health-checks port 7860. Prefer GRADIO_SERVER_PORT (set by HF),
-    # fall back to PORT, then 7860. Disable SSR: the Node proxy binds a different
-    # port and crashes on HF, leaving :7860 unhealthy -> launch timeout.
-    _port = int(os.getenv("GRADIO_SERVER_PORT") or os.getenv("PORT") or "7860")
-    demo.launch(
-        server_name="0.0.0.0",  # nosec B104
-        server_port=_port,
-        ssr_mode=False,
-        theme=_theme,
-        css="""
-.grad-banner { background: linear-gradient(135deg, #7c3aed 0%, #f97316 100%);
-               border-radius: 12px; padding: 16px 24px; margin-bottom: 16px; }
-.grad-banner h1 { color: white !important; margin: 0; }
-.grad-banner p  { color: rgba(255,255,255,0.85) !important; margin: 4px 0 0; }
-.feature-badge { display: inline-block; padding: 2px 10px; border-radius: 12px;
-                 font-size: 0.78em; font-weight: 600; margin: 2px; }
-""",
-    )

assets/initial_docs/README.md

@@ -1,13 +0,0 @@
-# HearthNet — Initial Documents
-
-Drop any `.md` or `.txt` files here and they will be automatically ingested into
-the community RAG corpus when the node starts.
-
-Good candidates:
-- Neighbourhood emergency plans
-- Local resource lists (food banks, shelters, medical points)
-- How-to guides for your community
-- Node setup instructions for non-technical neighbours
-- Any knowledge you want to make searchable across the mesh
-
-Files are deduplicated by content hash (BLAKE3), so re-adding the same file is safe.

build/android/HearthNetApp/config.xml

@@ -1,32 +0,0 @@
-<?xml version='1.0' encoding='utf-8'?>
-<widget id="com.hearthnet.app" version="0.1.0" xmlns="http://www.w3.org/ns/widgets" xmlns:cdv="http://cordova.apache.org/ns/1.0">
-    <name>HearthNet</name>
-    <description>Local-first community AI mesh</description>
-    <author email="contact@hearthnet.community" href="https://hearthnet.community">HearthNet</author>
-    <content src="index.html" />
-    
-    <!-- Allow access to all domains -->
-    <access origin="*" />
-    <allow-intent href="http://*/*" />
-    <allow-intent href="https://*/*" />
-    <allow-intent href="tel:*" />
-    <allow-intent href="sms:*" />
-    <allow-intent href="mailto:*" />
-    <allow-intent href="geo:*" />
-    
-    <!-- iOS preferences -->
-    <preference name="EnableViewportScale" value="true" />
-    <preference name="MediaPlaybackRequiresUserAction" value="false" />
-    <preference name="AllowInlineMediaPlayback" value="true" />
-    <preference name="BackupWebStorage" value="cloud" />
-    <preference name="TopActivityIndicator" value="gray" />
-    
-    <!-- Android preferences -->
-    <preference name="Orientation" value="portrait" />
-    <preference name="Fullscreen" value="false" />
-    <preference name="android-minSdkVersion" value="21" />
-    <preference name="android-targetSdkVersion" value="36" />
-    <preference name="StatusBarOverlaysWebView" value="false" />
-    <preference name="StatusBarBackgroundColor" value="#1e40af" />
-    <preference name="StatusBarStyle" value="lightcontent" />
-</widget>

data/hearthnet/hearthnet-space/corpora/test.md

@@ -1 +0,0 @@
-test file. Paris is the capitol of germany

docs/ARCHITECTURE.md

@@ -1,441 +0,0 @@
-# HearthNet — Architecture Reference
-
-> **Local-first community AI mesh.** Each participant runs a node on their own hardware.
-> Nodes discover each other automatically and share AI capabilities, files, and community
-> posts — no central server required.
-
----
-
-## High-Level Concept
-
-```
-┌──────────────────────────────────────────────────────────────────────────┐
-│                         Community Mesh (LAN / overlay)                    │
-│                                                                           │
-│   ┌─────────────┐    mDNS/UDP     ┌─────────────┐    mDNS/UDP            │
-│   │  Node A     │◄───────────────►│  Node B     │◄──────────────         │
-│   │  (anchor)   │                 │  (hearth)   │                         │
-│   │             │   capability    │             │                         │
-│   │  CapBus ◄───┼─────bus.call───►─►  CapBus   │                         │
-│   │  LLM svc    │                 │  RAG svc    │                         │
-│   │  RAG svc    │                 │  OCR svc    │                         │
-│   │  Gradio UI  │                 │  Gradio UI  │                         │
-│   └─────────────┘                 └─────────────┘                         │
-└──────────────────────────────────────────────────────────────────────────┘
-```
-
-HearthNet is structured around three ideas:
-
-1. **Node** — a Python process on someone's hardware (Raspberry Pi, laptop, server).
-2. **CapabilityBus** — a message bus where services register *capabilities* (e.g. `llm.chat@1.0`). Any code, local or remote, calls a capability by name.
-3. **Services** — pure-Python objects that handle capability calls. A node installs whichever services its hardware supports.
-
----
-
-## Module Map
-
-### Phase 1 — Foundation
-
-| Module | Location | What it does |
-|--------|----------|-------------|
-| **M01 Identity** | `hearthnet/identity/` | Ed25519 node keys, community manifests, invite tokens |
-| **M02 Discovery** | `hearthnet/discovery/` | mDNS + UDP multicast peer discovery |
-| **M03 Bus** | `hearthnet/bus/` | Capability router, health ring buffer, trust levels |
-| **M04 LLM** | `hearthnet/services/llm/` | Local model backends (Ollama, llama.cpp, LM Studio, HF, Anthropic) |
-| **M05 RAG** | `hearthnet/services/rag/` | Chunker → embedder → Chroma vector store + retrieval |
-| **M06 Marketplace** | `hearthnet/services/marketplace/` | Event-sourced community board (posts, offers, requests) |
-| **M07 Blobs** | `hearthnet/blobs/` | BLAKE3 content-addressed file store with chunked transfer |
-| **M08 UI** | `hearthnet/ui/` | Gradio 8-tab interface + themes + topology component |
-| **M09 Emergency** | `hearthnet/emergency/` | Async probe loop → emergency state machine |
-| **M10 Chat** | `hearthnet/services/chat/` | Event-backed direct messages between nodes |
-| **M11 Embedding** | `hearthnet/services/embedding/` | Sentence-transformer embeddings (BAAI/bge-small) |
-| **M12 CLI** | `hearthnet/cli.py` | Click CLI: run, call, log, rag, invite, version, … |
-| **M13 Onboarding** | `hearthnet/ui/onboarding.py` | Invite QR flow + first-run wizard |
-
-### Phase 2 — Resilience & Rich Services
-
-| Module | Location | What it does |
-|--------|----------|-------------|
-| **M14 Federation** | `hearthnet/federation/` | Cross-community node manifests + signed bridges |
-| **M15 Relay** | `hearthnet/relay/` | Public-IP relay tier for NAT traversal |
-| **M16 Tokens** | `hearthnet/identity/tokens.py` | AuthToken / CapabilityToken scoped access |
-| **M17 OCR** | `hearthnet/services/ocr/` | Tesseract / TrOCR text extraction |
-| **M18 Translation** | `hearthnet/services/translation/` | NLLB-200 local translation |
-| **M19 STT/TTS** | `hearthnet/services/stt_tts/` | Whisper STT + Coqui/pyttsx3 TTS |
-| **M20 Vision** | `hearthnet/services/vision/` | Florence-2 image captioning / VQA |
-| **M21 Tool Calls** | `hearthnet/services/tools/` | LLM tool-call executor (plant ID, search, …) |
-| **M22 Mobile** | `hearthnet/ui/mobile/` | PWA manifest + service worker for home-screen install |
-| **M23 E2E Encryption** | `hearthnet/crypto/` | X25519 ECDH + ChaCha20-Poly1305 channel encryption |
-| **M24 Rerank** | `hearthnet/services/rerank/` | Cross-encoder reranking for RAG results |
-| **M25 Group Chat** | `hearthnet/services/group_chat/` | Multi-party room-based chat |
-
-### Phase 3 — Experimental (opt-in via `config.toml`)
-
-| Module | Location | Flag | What it does |
-|--------|----------|------|-------------|
-| **M26 Distributed Inference** | `hearthnet/distributed_inference/` | `research.distributed_inference` | Layer-shard a 7B model across LAN nodes (Petals-style) |
-| **M27 MoE Routing** | `hearthnet/moe/` | `research.moe_routing` | Route queries to best expert (model/service/human) via learned scorer |
-| **M28 FedLearn** | `hearthnet/fedlearn/` | `research.fedlearn` | FedAvg LoRA fine-tuning without sharing raw data |
-| **M29 LoRa Beacons** | `hearthnet/lora/` | `research.lora_beacons` | 868 MHz offline "I'm alive" heartbeats via USB LoRa stick |
-| **M30 Evidence Graph** | `hearthnet/evidence/` | `research.evidence` | Claim → attest → dispute provenance graph + EBKH bridge |
-| **M31 Civil Defense** | `hearthnet/civdef/` | `research.civil_defense` | THW/DRK/KatS alert pipeline with role certs + audit chain |
-| **M32 Protocol Standard** | `hearthnet/services/protocol/` | on by default | Protocol version list + conformance report |
-
-### Cross-Cutting
-
-| ID | Location | What it does |
-|----|----------|-------------|
-| **X01 Transport** | `hearthnet/transport/` | HTTP/SSE client, backpressure, rate limiting, frame types |
-| **X02 Events** | `hearthnet/events/` | SQLite Lamport event log + gossip sync |
-| **X03 Observability** | `hearthnet/observability/` | Tracing, metrics, Doctor health checks, TrackioExporter |
-| **X04 Config** | `hearthnet/config.py` | Typed TOML config + ResearchConfig feature flags |
-| **X05 DHT** | `hearthnet/dht/` | Kademlia-inspired DHT for cross-LAN peer lookup |
-| **X06 WebSocket** | `hearthnet/transport/` | WebSocket pubsub (StateBus → live UI push) |
-| **X07 Federated Metrics** | `hearthnet/observability/` | Opt-in aggregate mesh health metrics |
-| **X08 Tensor Transport** | `hearthnet/transport/tensor/` | Chunked tensor stream for M26 distributed inference |
-| **X09 Conformance Suite** | `hearthnet/conformance/` | 21-check black-box conformance runner |
-
----
-
-## Composition Root
-
-`HearthNode` in [hearthnet/node.py](hearthnet/node.py) is the single composition root.
-
-```python
-node = HearthNode(
-    node_id="my-node",
-    display_name="Alice's Pi",
-    community_id="ed25519:abc123",
-)
-node.install_services(corpus="general")
-await node.start()
-```
-
-`install_services()` registers all services the local hardware supports into the bus. Heavy optional dependencies (torch, chromadb, etc.) are imported lazily and fail gracefully — a node with no GPU still works, it just can't answer GPU-only capabilities.
-
----
-
-## Capability Bus
-
-```
-Caller ──── bus.call(name, version, body) ──────────┐
-                                                     ▼
-                                          ┌──────────────────┐
-                                          │  CapabilityBus   │
-                                          │                  │
-                                          │  Registry        │
-                                          │  ┌─────────────┐ │
-                                          │  │ local route │─┼──► Service.handle()
-                                          │  ├─────────────┤ │
-                                          │  │ remote route│─┼──► HTTP POST /bus/v1/call
-                                          │  └─────────────┘ │
-                                          │  HealthMonitor   │
-                                          │  TrustFilter     │
-                                          └──────────────────┘
-```
-
-- **Local route** — service is installed on this node → direct Python call.
-- **Remote route** — capability is advertised by a peer → HTTP POST to that peer's transport.
-- **Version negotiation** — capabilities are registered with a `(major, minor)` version; the bus picks the highest compatible version.
-- **Health monitoring** — each service's response times are tracked in a ring buffer; unhealthy services are quarantined for `BUS_QUARANTINE_SECONDS`.
-
----
-
-## Data Flow: LLM Chat Request
-
-```
-User types in Gradio UI
-       │
-       ▼
-  app.py (Gradio event handler)
-       │  bus.call("llm.chat@1.0", body)
-       ▼
-  CapabilityBus.call()
-       │
-       ├─ local LlmService found?
-       │       │ yes → LlmService.handle() → backend.chat() → yield Token
-       │       │
-       └─ no local service
-               │ peer has llm.chat?
-               ├─ yes → HTTP POST /bus/v1/call → remote node → stream tokens back
-               └─ no  → CapabilityError("not_found")
-```
-
----
-
-## Discovery Flow
-
-```
-Node boots
-    │
-    ├── mDNS: register _hearthnet._tcp.local.  (LAN multicast DNS)
-    ├── UDP: send announce to 224.0.0.251:7079 every 15s
-    │
-    ▼
-PeerRegistry receives announcements from other nodes
-    │
-    ├── new peer → RegistryEvent(kind="added", entry=...)
-    ├── peer gone (TTL expired) → RegistryEvent(kind="removed", ...)
-    └── ManifestPublisher re-publishes every 300s
-```
-
----
-
-## Emergency Mode
-
-```
-EmergencyDetector (async loop, 30s probe)
-    │
-    ├── probe connectivity endpoints
-    │
-    ├── ONLINE  → EmergencyState.NORMAL
-    │                │ UI shows normal theme
-    │
-    └── OFFLINE → EmergencyState.EMERGENCY
-                     │ UI switches to emergency theme (red)
-                     │ emergency.llm.chat capability activated
-                     │ LoRa beacons sent if hardware available (M29)
-                     │ Civil defense alerts published if role cert present (M31)
-```
-
----
-
-## MoE Expert Routing (M27)
-
-```
-Query arrives at any node
-       │
-       ▼
-  MoeRouter.route(query, top_k=3)
-       │
-       ├── score all registered ExpertDescriptors against query
-       │   (tag overlap + cosine similarity + recency weighting)
-       │
-       └── return ranked RouteResult
-              │
-              ├── expert_type="model"   → bus.call(f"llm.chat@1.0", ...) on that node
-              ├── expert_type="service" → bus.call(expert_capability, ...)
-              ├── expert_type="human"   → notify via chat + start handoff timer (M27 §4)
-              └── expert_type="external"→ HTTP call to opt-in external API
-```
-
-Enable it: set `research.moe_routing = true` in `~/.config/hearthnet/config.toml`.
-
----
-
-## Distributed Inference (M26 — BitTorrent-style LLM sharing)
-
-```
-Node A: layers 0–15 of Llama-3.2-3B
-Node B: layers 16–27 of Llama-3.2-3B
-Node C: layers 28–35 (lm_head) of Llama-3.2-3B
-                │
-                ▼
-PipelineOrchestrator.plan(model_id="llama3.2:3b")
-    │  → discovers shards via experimental.distributed_llm.shard.list
-    │  → checks layer coverage: 0..35 ✓
-    │
-PipelineOrchestrator.run(pipeline, input_tokens)
-    │  → sends activations A→B via X08 TensorTransport (1 MiB chunks)
-    │  → B sends activations B→C
-    │  → C returns final logits
-    │
-    └── caller gets streamed tokens like any local model
-```
-
-Model weights are shared chunk-by-chunk using BLAKE3 CID-addressed blob transfer — same
-mechanism as file blobs (M07), but optimised for `.gguf` / `.safetensors` files.
-
----
-
-## File Tree
-
-```
-hearthnet/
-├── node.py                    # HearthNode — composition root
-├── types.py                   # Shared type aliases (NodeID, ShardID, AlertID, …)
-├── constants.py               # All numeric defaults and limits
-├── config.py                  # HearthnetConfig + ResearchConfig (TOML-backed)
-├── cli.py                     # Click CLI entry point
-├── facades.py                 # HearthFacade — thin high-level API for app.py
-├── controller.py              # HearthController — legacy thin wrapper
-│
-├── bus/                       # M03 CapabilityBus
-│   ├── router.py              # routing logic (local → remote)
-│   ├── registry.py            # CapabilityEntry, RegistryEvent, Diff
-│   ├── capability.py          # CapabilityEntry dataclass
-│   └── health.py              # ring-buffer health monitor
-│
-├── identity/                  # M01
-│   ├── keys.py                # Ed25519 key generation + signing
-│   ├── manifest.py            # NodeManifest, CommunityManifest, CommunityPolicy, …
-│   └── tokens.py              # AuthToken, CapabilityToken
-│
-├── discovery/                 # M02
-│   └── peers.py               # mDNS + UDP multicast PeerRegistry
-│
-├── transport/                 # X01 / X06 / X08
-│   ├── client.py              # HTTP + SSE client
-│   ├── streams.py             # Frame, SseReader
-│   ├── backpressure.py        # FlowControl, RateCheck, RateLimiter
-│   └── tensor/                # X08 tensor chunked transport
-│
-├── events/                    # X02
-│   ├── log.py                 # SQLite Lamport event log
-│   └── sync.py                # Gossip SyncClient / SyncServer
-│
-├── observability/             # X03
-│   ├── tracing.py             # attach/detach trace context
-│   ├── metrics.py             # MetricsCollector, TrackioExporter
-│   └── doctor.py             # DoctorResult, CheckResult, DoctorService
-│
-├── services/                  # M04 – M21 + M32
-│   ├── llm/                   # M04 — backends: ollama, llama_cpp, lmstudio, hf_api, anthropic
-│   ├── rag/                   # M05
-│   ├── marketplace/           # M06
-│   ├── chat/                  # M10
-│   ├── embedding/             # M11
-│   ├── ocr/                   # M17
-│   ├── translation/           # M18
-│   ├── stt_tts/               # M19
-│   ├── vision/                # M20
-│   ├── tools/                 # M21
-│   ├── group_chat/            # M25
-│   └── protocol/              # M32
-│
-├── ui/                        # M08
-│   ├── app.py                 # Gradio 8-tab entry point
-│   ├── tabs/                  # one file per tab
-│   ├── theme.py               # hearthnet_theme, emergency_theme
-│   ├── topology.py            # TopologyComponent (mesh graph)
-│   ├── onboarding.py          # first-run wizard + invite QR
-│   └── mobile/                # M22 PWA manifest + service worker
-│
-├── emergency/                 # M09
-│   ├── detector.py            # async probe loop
-│   └── state.py               # EmergencyState enum
-│
-├── crypto/                    # M23
-│   └── channel.py             # X25519 + ChaCha20-Poly1305
-│
-├── blobs/                     # M07
-│   └── store.py               # BLAKE3 CID store + chunked reader
-│
-├── dht/                       # X05
-├── federation/                # M14
-├── relay/                     # M15
-│
-├── distributed_inference/     # M26 (experimental)
-├── moe/                       # M27 (experimental)
-├── fedlearn/                  # M28 (experimental)
-├── lora/                      # M29 (experimental)
-├── evidence/                  # M30 (experimental)
-├── civdef/                    # M31 (experimental)
-└── conformance/               # X09
-```
-
----
-
-## Configuration
-
-`~/.config/hearthnet/config.toml` (created on first run with defaults):
-
-```toml
-[node]
-node_id      = ""          # auto-generated Ed25519 key ID
-display_name = "My Node"
-data_dir     = "~/.hearthnet"
-
-[transport]
-http_port    = 7080
-ui_port      = 7860
-
-[llm]
-default_backend = "ollama"   # "ollama" | "llama_cpp" | "lmstudio" | "hf_api" | "smollm"
-
-[rag]
-corpus_dir      = "~/.hearthnet/corpus"
-embedding_model = "BAAI/bge-small-en-v1.5"
-
-[policy.research]
-enable                  = false     # master switch for all experimental modules
-moe_routing             = false     # M27
-distributed_inference   = false     # M26
-fedlearn                = false     # M28
-lora_beacons            = false     # M29
-evidence                = false     # M30
-civil_defense           = false     # M31
-```
-
----
-
-## Connecting a Local Node to the HF Space
-
-The HF Space at `https://huggingface.co/spaces/build-small-hackathon/HearthNet` is a
-single-node anchor you can peer with from any local machine.
-
-```bash
-# 1. Clone and install
-git clone https://huggingface.co/spaces/build-small-hackathon/HearthNet
-cd HearthNet
-pip install -e .
-
-# 2. Run your local node (pick a free port if 7080 is taken)
-python -m hearthnet.cli run --http-port 7080 --ui-port 7860
-
-# 3. Manually add the HF Space anchor as a peer (different network = manual)
-python -m hearthnet.cli call discovery.peer.add 1 0 \
-  '{"endpoint":"https://build-small-hackathon-hearthnet.hf.space","node_id":"hf-space-anchor"}'
-
-# 4. Verify peering
-python -m hearthnet.cli call discovery.peers 1 0 '{}'
-```
-
-Or use the helper script:
-```bash
-python scripts/connect_to_hf.py
-```
-
-Once peered, your local node can:
-- Route LLM queries **from** the HF Space to your local (better) model
-- Push community posts that appear in the HF Space UI
-- Share blob files across the connection
-
-> **Note:** The HF Space runs on a public server without a static IP for inbound connections.
-> Your local node initiates the connection; the HF Space cannot discover you via mDNS.
-> Use `discovery.peer.add` or the invite flow to establish the bridge manually.
-
----
-
-## Security Model
-
-- **Node identity** — Ed25519 key pair generated locally, never leaves the device.
-- **Trust levels** — `unknown` → `member` → `trusted` → `anchor`. Capabilities can require a minimum trust level.
-- **Capability scoping** — `AuthToken` restricts which capabilities a caller may invoke.
-- **Channel encryption** — M23 X25519 ECDH + ChaCha20-Poly1305 for inter-node transport (opt-in, defaults off).
-- **Experimental capabilities** — Phase 3 modules are off by default and require explicit opt-in. The bus refuses to register them unless the feature flag is on.
-- **No central authority** — there is no HearthNet.com, no certificate authority, no registration server. Trust is established peer-to-peer via invite chains.
-
----
-
-## Testing
-
-```bash
-# Full suite (133 unit + integration tests):
-pytest tests/ -q
-
-# Skip slow E2E browser tests:
-pytest tests/ -q -k "not e2e"
-
-# Phase 3 experimental module tests only:
-pytest tests/test_phase3_experimental.py -v
-
-# Conformance runner (X09):
-python -m hearthnet.conformance.runner --output conformance-report/
-```
-
----
-
-*This document is generated from the spec set in `docs/`. For per-module detail see:*
-- *Phase 1+2: `00-OVERVIEW.md`, `CAPABILITY_CONTRACT.md`, `modules/M01-*.md` …*
-- *Phase 3: `docs/p2_p3/IMPLEMENTATION_REFERENCE_p3.md`, `docs/p2_p3/M26-*.md` …*

docs/CAPABILITY_CONTRACT.md

@@ -134,11 +134,6 @@ The universal error codes apply to every capability: `bad_request`, `unauthorize
 - **Trust**: member
 - **Idempotency**: no (token sampling is non-deterministic)
 - **Stream**: yes (SSE)
-- **Multi-model providers**: a node serving several models (e.g. a local backend
-  plus an opt-in sponsor backend) registers a single `llm.chat@1.0` whose
-  descriptor advertises the primary model in `params.model` and the full catalogue
-  in `params.models` (array). The bus matches a requested `model` against this
-  catalogue and dispatches to the owning backend.
 
 #### Request
 

docs/ENV.md

@@ -1,169 +0,0 @@
-# HearthNet — Environment Variables, Secrets & Models Reference
-
-*Last updated: June 15, 2026*
-
----
-
-## Quick Start: Minimum required for HF Space
-
-```
-NVIDIA_API_KEY = nvapi-...          # enables Nemotron + NVIDIA prize track
-HEARTHNET_DATA_DIR = /data/hearthnet  # persistent storage (needs Persistent Storage enabled)
-```
-
-Everything else has sensible defaults.
-
----
-
-## All Environment Variables
-
-### 🔴 Secrets (never commit, use HF Space secrets)
-
-| Variable | Purpose | Example |
-|----------|---------|---------|
-| `NVIDIA_API_KEY` | NVIDIA NIM API key — activates NemotronBackend, enables all `nvidia/*` models. Get free at [build.nvidia.com](https://build.nvidia.com) | `nvapi-abc123...` |
-| `MODAL_TOKEN` | Modal API token — needed only if `modal deploy` is used. Set automatically by Modal CLI. | `ak-...` |
-| `HEARTHNET_HF_TOKEN` | HuggingFace Inference API token — activates `HfApiBackend` for cloud inference. Get at [hf.co/settings/tokens](https://huggingface.co/settings/tokens) | `hf_abc...` |
-| `ANTHROPIC_API_KEY` | Anthropic Claude API — activates `AnthropicBackend`. Not used by default. | `sk-ant-...` |
-
-### 🟡 Configuration (safe to set in Space settings, not truly secret)
-
-| Variable | Default | Purpose | Where read |
-|----------|---------|---------|-----------|
-| `MODEL_ID` | `openbmb/MiniCPM3-4B` | HF Transformers model to load locally | `app.py:49` |
-| `MODEL_REVISION` | `None` | Model git revision/hash to pin | `app.py:50` |
-| `HEARTHNET_DATA_DIR` | `tempfile.gettempdir()` | Base directory for RAG corpora, BLAKE3 blobs, relay DB. Set to `/data/hearthnet` when Persistent Storage is enabled on the Space. | `app.py:352` |
-| `SPACE_TITLE` | `HearthNet Space (xxxx)` | Display name shown in the UI header and relay roster | `app.py:186` |
-| `MODAL_ENDPOINT` | `""` | URL of deployed Modal LLM endpoint — activates `ModalBackend` | `app.py:273` |
-| `MODAL_MODEL` | `HuggingFaceTB/SmolLM2-1.7B-Instruct` | Model served by the Modal endpoint | `modal_backend.py:65` |
-| `MINICPM_URL` | `""` | OpenAI-compatible endpoint for a local/remote MiniCPM vLLM server | `app.py:287` |
-| `MINICPM_MODELS` | `""` | Comma-separated model names exposed by `MINICPM_URL` | `app.py:292` |
-| `MINICPM_LIGHTWEIGHT` | `""` | Set to `1` or `true` to force SmolLM2-135M instead of MiniCPM3-4B | `app.py:294` |
-| `NEMOTRON_URL` | `""` | Local NVIDIA NIM endpoint (e.g. `http://localhost:8000`) — used by both `app.py` and `app_nemotron.py` | `app_nemotron.py:31` |
-| `HEARTHNET_NODE` | `""` | URL of a HearthNet mesh node to connect to (used by `app_nemotron.py` Push-to-Mesh tab) | `app_nemotron.py:29` |
-| `PORT` | `7869` | Port for `app_nemotron.py` standalone server | `app_nemotron.py:506` |
-| `SPACE_HOST` | `""` | Auto-set by HF — used internally to detect ZeroGPU context | `app.py:181` |
-| `GRADIO_SSR_MODE` | `false` | Forced to `false` to prevent Node.js intercepting custom FastAPI routes | `app.py:565` |
-
----
-
-## All Models
-
-### Prize Track Mapping
-
-| Prize | Requirement | Models to Use |
-|-------|-------------|--------------|
-| 🐜 **Tiny Titan** | ≤ 32B parameters | MiniCPM3-4B (4B) ✅, SmolLM2-135M (135M) ✅, Nemotron-nano-8B (8B) ✅ |
-| 🔬 **NVIDIA Nemotron** | Use Nemotron models | `nvidia/llama-3.1-nemotron-nano-8b-instruct` ✅ |
-| 🏭 **OpenBMB** | Use MiniCPM models | `openbmb/MiniCPM3-4B` ✅, `openbmb/MiniCPM4-8B` ✅ |
-| ⚡ **Modal** | Deploy on Modal | Any model via `scripts/modal_deploy.py` ✅ |
-| 🎨 **Off Brand** | Custom UI/theme | Custom CSS + purple gradient ✅ |
-
-### Models by Backend
-
-#### Local (runs on HF Space / Pi / laptop)
-
-| Model | Size | Backend | Set via | Notes |
-|-------|------|---------|---------|-------|
-| `openbmb/MiniCPM3-4B` | **4B** | `HfLocalBackend` | `MODEL_ID` env | **Default**. OpenBMB + Tiny Titan eligible |
-| `HuggingFaceTB/SmolLM2-135M-Instruct` | **135M** | `HfLocalBackend` | `MODEL_ID=HuggingFaceTB/SmolLM2-135M-Instruct` | Pi Zero / ultra-light mode |
-| `HuggingFaceTB/SmolLM2-1.7B-Instruct` | **1.7B** | `HfLocalBackend` | `MODEL_ID=...` | Good balance on CPU |
-| `openbmb/MiniCPM4-8B` | **8B** | `HfLocalBackend` | `MODEL_ID=openbmb/MiniCPM4-8B` | Faster than MiniCPM3, better quality |
-| `openbmb/MiniCPM-V-2_6` | **8B** | `HfLocalBackend` | `MODEL_ID=openbmb/MiniCPM-V-2_6` | **Multimodal** — vision + text |
-
-#### NVIDIA NIM (cloud — needs `NVIDIA_API_KEY`)
-
-| Model | Size | Prize eligible | Best for |
-|-------|------|---------------|---------|
-| `nvidia/llama-3.1-nemotron-nano-8b-instruct` | **8B** | Tiny Titan ✅ | Fast, edge reasoning |
-| `nvidia/nemotron-mini-4b-instruct` | **4B** | Tiny Titan ✅ | Smallest Nemotron |
-| `nvidia/nemotron-3-nano-30b-a3b` | 30B MoE (3B active) | Tiny Titan ✅ | MoE routing brain |
-| `nvidia/llama-3.3-nemotron-super-49b-v1` | 49B | NVIDIA track | Best reasoning |
-| `nvidia/llama-3.1-nemotron-70b-instruct` | 70B | NVIDIA track | Highest quality |
-| `nvidia/nemotron-nano-12b-v2-vl` | 12B | Tiny Titan ✅ | **Vision-language** |
-
-> **Contest note:** For Tiny Titan prize (≤32B), use: nano-8B, nano-4B, nano-30B-a3b, or nano-12b-v2-vl.
-> The 49B and 70B models exceed the 32B limit and only qualify for the main Nemotron hardware prize.
-
-#### OpenBMB (cloud — needs `MINICPM_URL` pointing to a vLLM server)
-
-| Model | Size | Notes |
-|-------|------|-------|
-| `openbmb/MiniCPM4-8B` | 8B | Best OpenBMB model for 2026 |
-| `openbmb/MiniCPM3-4B` | 4B | Default, runs locally |
-
-#### Modal (serverless GPU — needs `MODAL_ENDPOINT`)
-
-| Model | Size | Set via |
-|-------|------|---------|
-| `HuggingFaceTB/SmolLM2-1.7B-Instruct` | 1.7B | Hardcoded in `scripts/modal_deploy.py:23` |
-| Any HF model | — | Change `MODEL_ID` in `scripts/modal_deploy.py` and redeploy |
-
-#### Other (cloud)
-
-| Backend | Model | Needs |
-|---------|-------|-------|
-| `HfApiBackend` | `HuggingFaceH4/zephyr-7b-beta` | `HEARTHNET_HF_TOKEN` |
-| `AnthropicBackend` | `claude-3-haiku-20240307` | `ANTHROPIC_API_KEY` |
-
----
-
-## Recommended Configurations
-
-### HF Space (current, contest submission)
-```
-MODEL_ID = openbmb/MiniCPM3-4B          # OpenBMB + Tiny Titan prizes
-NVIDIA_API_KEY = nvapi-...               # Nemotron Hardware Prize
-HEARTHNET_DATA_DIR = /data/hearthnet     # Persistent Storage (if enabled)
-SPACE_TITLE = HearthNet                  # Display name
-```
-
-### Pi Zero / Ultra-Light Mode
-```
-MODEL_ID = HuggingFaceTB/SmolLM2-135M-Instruct   # 135M, fits in 512MB RAM
-MINICPM_LIGHTWEIGHT = 1
-```
-
-### Full Local Stack (laptop/desktop)
-```
-MODEL_ID = openbmb/MiniCPM4-8B          # Best local model
-NVIDIA_API_KEY = nvapi-...               # For Nemotron fallback
-NEMOTRON_URL = http://localhost:8000     # Local NIM server (optional)
-HEARTHNET_DATA_DIR = ~/.hearthnet/data   # Persistent local data
-```
-
-### With Modal GPU Backend
-```
-MODAL_ENDPOINT = https://your-org--hearthnet-llm-chat.modal.run
-# Deploy first: modal deploy scripts/modal_deploy.py
-```
-
----
-
-## HF Space Secrets Checklist
-
-```
-[x] NVIDIA_API_KEY        — free at build.nvidia.com, no credit card
-[ ] HEARTHNET_DATA_DIR    — set to /data/hearthnet after enabling Persistent Storage
-[ ] SPACE_TITLE           — optional display name override
-[ ] MODAL_ENDPOINT        — after running: modal deploy scripts/modal_deploy.py
-[ ] MINICPM_URL           — if running a separate vLLM server with MiniCPM4-8B
-```
-
----
-
-## Model Size Quick Reference
-
-```
-SmolLM2-135M      ████░░░░░░░░░░░░░░░░  135M    — Pi Zero, embedded
-MiniCPM3-4B       ████████░░░░░░░░░░░░  4B      — Default ★
-SmolLM2-1.7B      █████████░░░░░░░░░░░  1.7B    — CPU laptop
-Nemotron-nano-4B  ████████░░░░░░░░░░░░  4B      — Tiny Titan ★
-MiniCPM4-8B       ████████████░░░░░░░░  8B      — Best quality local
-Nemotron-nano-8B  ████████████░░░░░░░░  8B      — Tiny Titan ★
-Nemotron-12B-VL   █████████████████░░░  12B     — Vision+text
-Nemotron-30B-a3b  ██████████████░░░░░░  30B MoE (3B active) — Tiny Titan ★
-                                ↑ 32B Tiny Titan limit ↑
-Nemotron-Super-49B███████████████████░  49B     — NVIDIA prize only
-Nemotron-70B      ████████████████████  70B     — NVIDIA prize only
-```