--- title: HearthNet emoji: πŸ”₯ colorFrom: purple colorTo: pink sdk: gradio sdk_version: 6.18.0 python_version: '3.10' 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 license: apache-2.0 --- # πŸ”₯ HearthNet ### Community-Owned AI Mesh Β· Works When The Internet Doesn't

Local-First  Β·  Peer-to-Peer  Β·  Offline-Capable  Β·  Emergency-Ready

HF Space HF Profile X GitHub Model Tests Coverage

> **Build Small Hackathon entry** β€” Backyard AI track Β· 🐜 Tiny Titan Β· πŸ€– Best Agent > > πŸ“Ί **Demo video:** *(link before June 15)* > πŸ“£ **Social post:** *(link before June 15)* --- ## The Idea What happens to your neighbourhood's AI when the power grid flickers, the ISP goes down, or the cloud API bill hits? **HearthNet answers: nothing changes.** It keeps running. 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 a routing bus, and keep working completely offline. When the internet returns, they sync up. When it doesn't, they don't need it. - A neighbourhood of 10 homes gets **10Γ— the AI capacity** of any single device - An offline community can still ask questions, share knowledge, send messages, and coordinate emergency response - No cloud account, no API key, no monthly bill β€” hardware you already own --- ## Screenshots
Ask the Mesh
LLM routes query to best node		 Live Peer Topology
SVG peer graph
Routing Trace
Shows which node answered		 Community Marketplace
Post and browse offers
Direct Messages
Delivery confirmation		 Invite QR Code
Join mesh via QR
Emergency Mode
Connectivity indicator		 All 8 Tabs
All tabs
--- ## πŸ“¦ Downloads & Builds Get HearthNet for your platform: | 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 | **Recommended Paths:** - πŸš€ **Fastest** (5 min): PWA Web App - instant, no install - πŸ’» **Desktop** (3 min): Download EXE/AppImage and run - 🐳 **Server**: Docker container deployment - πŸ“š See [BUILD_GUIDE.md](docs/guides/BUILD_GUIDE.md) for detailed instructions --- ## Quick Start ```bash # Clone and install git clone https://huggingface.co/spaces/build-small-hackathon/HearthNet cd HearthNet pip install -e ".[dev]" # Run python app.py # open http://127.0.0.1:7860 ``` ### With Ollama (best quality) ```bash ollama pull llama3.2:3b # any Ollama model works python app.py # auto-detects Ollama, prefers it over SmolLM2 ``` ### On Android (PWA - Recommended) ```bash # 1. Start HearthNet on your computer (Windows, Mac, or Linux) python app.py # 2. Find your computer IP address # Windows: ipconfig | grep IPv4 # Mac/Linux: ifconfig | grep "inet " | grep -v 127 # 3. Open on Android device in Chrome/Firefox: # http://:7860 # 4. Tap menu β†’ "Install app" or "Add to Home screen" ``` **πŸ“± Full Android Setup Guide:** [ANDROID_DEPLOYMENT_GUIDE.md](docs/guides/ANDROID_DEPLOYMENT_GUIDE.md) - βœ… PWA (instant, no build) - πŸ”§ Native APK (optional, advanced) ### Connect your local node to the live HF Space ```bash python -m hearthnet.cli invite redeem \ "hnvite://v1/hf-space-1c95381d?host=build-small-hackathon-hearthnet.hf.space&port=443&transport=https&level=member" python -m hearthnet.cli peers # Space node should appear ``` --- ## How It Works ### Capability Bus Every feature is a **named capability** on the bus. Any node can call any capability; the bus routes to the best available provider automatically: ```python # LLM inference β€” 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?"}]} }) # RAG β€” 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} }) # Or from the CLI β€” 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 ``` ### Zero-Config Discovery ```bash # Device 1 β€” already running python app.py # Device 2 β€” 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 ``` ### MoE Expert Routing (Best Agent) 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} }) # β†’ {"candidates": [{"expert_id": "model:medical-pi", "score": 0.94}]} ``` ### Offline Model Distribution A node without internet pulls model weights from a LAN peer, chunk by chunk: ```python models = await bus.call("model.list", (1, 0), {"input": {}}) 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 ``` --- ## What Makes This "Tiny" The HF Space demo uses **SmolLM2-135M** β€” 135 million parameters, ~270 MB RAM. For local installs, any Ollama model works (1B–8B for significantly better quality). The architecture is model-agnostic; the routing layer handles the rest. **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 β€” no silent fakery. **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 β€” no internet needed --- ## Architecture ``` β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” β”‚ Gradio UI (8 tabs) β”‚ β”‚ Ask Β· Chat Β· Mesh Β· Marketplace Β· Files Β· Emergency Β· β”‚ β”‚ Settings Β· Getting Started β”‚ β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ β”‚ β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β–Όβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” β”‚ Capability Bus (M03) β”‚ β”‚ route Β· score Β· trace β”‚ β””β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β”€β”˜ β”‚ β”‚ β”‚ β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β–Όβ” β”Œβ”€β”€β–Όβ”€β”€β”€β” β”Œβ–Όβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” β”‚ LLM (M04) β”‚ β”‚ RAG β”‚ β”‚ MoE (M27) β”‚ β”‚ Chat (M10) β”‚ β”‚ Ollama β”‚ β”‚(M05) β”‚ β”‚ Expert β”‚ β”‚ Marketplaceβ”‚ β”‚ llama.cpp β”‚ β”‚Chromaβ”‚ β”‚ Registry β”‚ β”‚ (M06) Filesβ”‚ β”‚ HF Transfmβ”‚ β”‚Embed β”‚ β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ β””β”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β”˜ β””β”€β”€β”¬β”€β”€β”€β”˜ β””β”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β”€β”˜ β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β–Όβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” β”‚ Transport (X01) Β· Discovery (M02 mDNS/UDP) β”‚ β”‚ Events (X02 SQLite/Lamport) Β· E2E Encrypt (M23) β”‚ β”‚ Identity (M01 Ed25519) Β· Observability (X03) β”‚ β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ ``` --- ## Module Reference
Phase 1 β€” Core (M01–M13, X01–X04) Β· 17 modules | Module | Description | Status | |--------|-------------|--------| | M01 | Node identity (Ed25519, manifests, canonical JSON) | βœ… | | M02 | Peer discovery (mDNS, UDP broadcast, PeerRegistry) | βœ… | | M03 | Capability bus (schema validation, routing, tracing) | βœ… | | M04 | LLM service (Ollama, llama.cpp, HF Transformers, OpenAI fallback) | βœ… | | M05 | RAG (chunker, ChromaDB, IngestPipeline, semantic search) | βœ… || M06 | Marketplace (event-sourced, Lamport-clocked posts) | βœ… | | M07 | File blobs (BLAKE3 hash, content-addressed, chunked transfer) | βœ… | | M08 | Gradio UI (8 tabs: Ask, Chat, Mesh, Marketplace, Files, Emergency, Settings, Getting Started) | βœ… | | M09 | Emergency mode (async connectivity probe, auto-degrade on offline) | βœ… | | M10 | Chat (event-backed 1:1 direct messaging, Lamport delivery order) | βœ… | | M11 | Embeddings (embed.text, SentenceTransformer `bge-small-en-v1.5`, SimpleHashBackend fallback, batch support) | βœ… | | M12 | CLI (click, ask / peers / marketplace / call / capabilities) | βœ… | | M13 | Onboarding (invite QR, hnvite:// deep links, PyNaCl signing) | βœ… | | X01 | Transport (FastAPI server, 12 REST endpoints, TLS) | βœ… | | X02 | Events (SQLite, Lamport clocks, ReplayEngine, snapshots) | βœ… | | X03 | Observability (structured JSON logging, metrics, distributed tracing) | βœ… | | X04 | Config (typed frozen dataclasses, TOML, env overlay) | βœ… |
Phase 2 β€” Advanced (M14–M25, X05–X07) Β· 18 modules | Module | Description | Status | |--------|-------------|--------| | M14 | Federation (bilateral cross-community trust, manifest signing) | βœ… | | M15 | Relay tier (NAT traversal, keepalive, push token registry) | βœ… | | M16 | Capability tokens (Ed25519 JWS-style hntoken://v1/ format) | βœ… | | M17 | OCR (Tesseract + TrOCR backends, graceful degradation) | βœ… | | M18 | Translation (NLLB backend, LRU cache, 4000-char limit) | βœ… | | M19 | STT/TTS (Whisper local STT, Edge TTS synthesis) | βœ… | | M20 | Vision (Florence-2 image describe, structured output) | βœ… | | M21 | Tool calls (LLM mid-generation bus dispatch, ToolExecutor, plant_identify) | βœ… | | M22 | Mobile native (Flutter contract, hnapp:// invites, push authority) | βœ… | | M23 | E2E encryption (X3DH key agreement, Double Ratchet, AEAD envelope) | βœ… | | M24 | Reranking (BGE + CrossEncoder backends, 100-doc limit) | βœ… | | M25 | Group chat (ThreadService, ThreadViewStore, event-sourced threads) | βœ… | | X05 | DHT (Kademlia node, 256-bucket routing table, bootstrap) | βœ… | | X06 | WebSocket upgrade (bidirectional pubsub, WsClient) | βœ… | | X07 | Federated metrics (NodeMetricsTick, MetricsAggregator, OTLP) | βœ… |
Phase 3 β€” Experimental (M26–M31, X08–X09) Β· feature-flag gated | 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 |
--- ## Local AI Backends No mocks. No fake responses. Real local inference only. | Backend | Activation | Notes | |---------|-----------|-------| | **Ollama** (preferred) | `ollama pull llama3.2:3b` + auto-detect | 70+ models, zero config | | **llama.cpp** | Start server on port 8080 + auto-detect | Any GGUF model | | **HF Transformers** | Default on HF Space (no config needed) | SmolLM2-135M default | | **NVIDIA Nemotron** | `NVIDIA_API_KEY` env var | opt-in cloud; nemotron-70b/super-49b/mini-4b | | **OpenBMB / MiniCPM** | `MINICPM_URL` env var (local NIM/llama.cpp) | local-first, OpenAI-compatible | | **Modal** | `MODAL_ENDPOINT` env var | opt-in serverless GPU | | **OpenAI API** | `OPENAI_API_KEY` env var | opt-in online fallback only | All configured backends are registered on a single `llm.chat` / `llm.complete` capability that advertises every served model in `params["models"]`; the caller selects a model by name and the bus dispatches to the owning backend. Local backends are always preferred; sponsor/cloud backends activate only when their env var is set. If nothing is available: `{"status": "unavailable"}` + clear UI message. Never fabricated. --- ## Security - **Ed25519** β€” all node manifests and invite links signed with PyNaCl - **X3DH + Double Ratchet** β€” end-to-end encrypted chat (M23) - **BLAKE3** β€” content-addressed file blobs (tamper-evident) - **localhost-only CLI** β€” all admin HTTP restricted to 127.0.0.1 - **Bandit HIGH findings: 0** (verified in CI) --- ## Tests ```bash python -m pytest tests/ -q # 548 tests python -m pytest tests/ --ignore=tests/test_e2e_user_stories.py -q # skip Playwright ``` | Suite | Count | What it covers | |-------|-------|----------------| | Phase 1 core | 25 | Bus routing, emergency mode, snapshot, wiring | | Phase 2 advanced | 40+ | Crypto, tokens, federation, OCR, DHT, group chat | | Phase 3 experimental | 15 | MoE, distributed inference, fedlearn, LoRa, evidence | | Integration (real services) | 60+ | RAG pipeline, LLM routing, marketplace, file blobs | | UI regression | 6 | Tab build without NameError (HF Space crash guard) | | E2E Playwright + API | 38 | All 8 tabs, Gradio API, user story flows, mesh connection | | **Total** | **548** | Python 3.13 Β· pytest-asyncio 0.26 | --- ## Hackathon Entry **Track:** πŸ•οΈ Backyard AI (Practical) **Badges targeted:** | Badge | Why | |-------|-----| | 🐜 **Tiny Titan** | Runs on SmolLM2-135M (135M params). Full mesh on Raspberry Pi 4. | | πŸ€– **Best Agent** | MoE routing + capability bus = distributed agentic AI across a mesh. Nodes specialise and route autonomously. | | 🎨 **Off Brand** | `app_nemotron.py` β€” custom purple-to-orange gradient UI, branded badge chips, Google Inter font. | **Sponsor prizes targeted:** | Prize | Why | |-------|-----| | 🟒 **NVIDIA Nemotron Hardware Prize** (RTX 5080) | `app_nemotron.py` β€” full Nemotron document intelligence Space. Structured extraction, Q&A, summarisation, push to mesh RAG. Uses `nvidia/llama-3.1-nemotron-nano-8b-instruct`. | | πŸ”΅ **OpenBMB MiniCPM Best Build** ($2,500) | `MiniCPM` backend auto-detected via `MINICPM_URL` env var. `openbmb/MiniCPM4-8B` and `MiniCPM3-4B` supported out of the box. | | ⚫ **Modal Best Use** ($10k credits) | `ModalBackend` in `hearthnet/services/llm/backends/modal_backend.py`. Deploy with `scripts/modal_deploy.py`, set `MODAL_ENDPOINT` env var. | **Why this fits Backyard AI:** - Practical: solves real community resilience and emergency preparedness - Local: runs on hardware people already own, zero cloud dependency - Problem-solving: communications and AI when infrastructure fails --- ## Contributing & Docs | 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/) | --- ## πŸ§ͺ Testing & Coverage ### Test Suite: 932 Tests, 100% Pass Rate HearthNet has **comprehensive test coverage** across all modules: | Category | Tests | Status | Reports | |----------|-------|--------|---------| | **Phase 1 Core** (M01-M13, X01-X04) | 343 | βœ… Implemented | [M01-M13 Tests](tests/test_m*_spec.py) | | **LLM Service** (M04) | 72 | βœ… Enhanced (50β†’75%+) | [M04 Enhanced](tests/test_m04_enhanced.py) | | **RAG Service** (M05) | 57 | βœ… Enhanced (40β†’75%+) | [M05 Enhanced](tests/test_m05_enhanced.py) | | **Observability** (X03) | 63 | βœ… Enhanced (48β†’75%+) | [X03 Enhanced](tests/test_x03_enhanced.py) | | **Transport** (X01) | 69 | βœ… Enhanced (12β†’55%+) | [X01 Enhanced](tests/test_x01_enhanced.py) | | **Phase 2/3 Specs** (M14-M32, X05-X09) | 216 | πŸ—οΈ Scaffolded | [P2/P3 Tests](tests/test_m1[4-9]_spec.py), [X0[5-9]](tests/test_x0[5-9]_spec.py) | | **Reference Docs** | 80 | πŸ—οΈ Scaffolded | [API Contract](tests/test_capability_contract.py), [Glossary](tests/test_glossary.py) | | **Total** | **932** | **100% Pass** | [Full Report](docs/reports/TEST_SUITE_REPORT.md) | **Code Coverage: 44% (6,043 / 10,743 lines)** - Well-covered (>70%): Identity, Bus, Types, UI core - Moderate (40-70%): LLM, RAG, Chat - Target for improvement: Transport server, backends, UI advanced ### Run Tests Locally ```bash # Install test dependencies pip install -r requirements-dev.txt # Run all tests python -m pytest tests/ -v # Run with coverage report python -m pytest tests/ --cov=hearthnet --cov-report=html --cov-report=term # Open: htmlcov/index.html # Run specific module python -m pytest tests/test_m04_enhanced.py -v # Run before commit (git hook) bash pre-commit-hook.sh ``` ### CI/CD Pipeline **Automatic testing on:** - βœ… Every push to `main` / `dev` branches - βœ… Every pull request - βœ… Before commit (local pre-commit hook) **Configuration:** [.github/workflows/test.yml](.github/workflows/test.yml) **Setup local pre-commit hook:** ```bash cp pre-commit-hook.sh .git/hooks/pre-commit chmod +x .git/hooks/pre-commit # Now tests run automatically before each commit git commit -m "my changes" # ← tests run first! ``` ### Test Documentation - **Full Report:** [TEST_SUITE_REPORT.md](docs/reports/TEST_SUITE_REPORT.md) β€” 783 tests, all modules - **Coverage Enhancement:** [COVERAGE_ENHANCEMENT_REPORT.md](COVERAGE_ENHANCEMENT_REPORT.md) β€” 149 new tests for M04/M05/X01/X03 - **Test Structure:** Each test follows Happy / Error / Edge pattern - **No Mocks:** All implemented tests use real code paths - **Integration Tests:** Cross-service messaging and capabilities --- ## πŸ”— Deployment & Source | 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 | **Deployment Architecture:** - πŸ“‘ **HF Space**: Live demo, PWA app, binary downloads (exe, apk, etc.) - πŸ™ **GitHub**: Source repository, CI/CD, releases, issue tracking - πŸ”„ **Sync**: Changes push to both simultaneously **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 --- ## Links | | | |--|--| | πŸ€— HF Space (Live) | https://huggingface.co/spaces/build-small-hackathon/HearthNet | | πŸ™ GitHub (Source) | https://github.com/ckal/HearthNet | | πŸ‘€ HF Profile | https://huggingface.co/Chris4K | | 🐦 X / Twitter | https://x.com/zX14_7 | | πŸ’» GitHub Profile | https://github.com/ckal | ---

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