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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
- Local-first: All features work completely offline on your device, right now
- Transparent mesh: Nodes find each other automatically and advertise capabilities (expertise, speed, capacity)
- Intelligent routing: Requests automatically go to the best node for the job—local, LAN, or internet relay
- No single authority: No server you must trust, no account required, no central gatekeeper
- Emergency-ready: When connectivity degrades, the UI and routing degrade gracefully; no sudden failures
- 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:
- Ask — LLM routing + Agent Mode
- Chat — Direct messages + QR invites
- Mesh — Live topology graph
- Marketplace — Community coordination
- Files — BLAKE3 blob store
- Emergency — Degraded mode + connectivity probe
- Settings — Node config, peer list, RAG ingest
- 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 6.18.0requiringhuggingface-hub>=1.2.0transformers 4.38+requiringhuggingface-hub<1.0- These ranges never overlap → unsolvable conflict
Every attempt to downgrade or workaround failed:
- Pinning
transformers<4.38.0still requiredhuggingface-hub<1.0 - Downgrading to
transformers 4.30.xhad 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:
- Removed the explicit transformers pin from
requirements.txt - Let pip resolve the entire dependency graph transitively
- 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:
# 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:
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 — live node, no download needed. Try the Ask tab, toggle Agent Mode, explore the mesh.
💻 Desktop (3 min)
# 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)
# 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)
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 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:
{
"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
- Formal spec before code: The 13-module + 4 cross-cutting spec meant every developer knew exactly what success looked like
- Event sourcing for offline-first: Lamport clocks + immutable logs made sync automatic and correct
- Content addressing for dedup: BLAKE3 made re-ingestion idempotent and fast
- Gradio for rapid UI iteration: Deployed UI changes in minutes, not days
- HF Spaces for deployment: One-click deployment, ZeroGPU support, built-in community features
What Was Hard
- Dependency hell in Docker: transformers + gradio version conflict took 6 hours to solve (see June 2026 section)
- Mobile responsiveness: SVG topology + mobile layout required multiple iterations
- Local LLM inference latency: 4B models on CPU can be slow; users expect instant results
- Mesh discovery on WiFi networks: mDNS not available on all networks; fallback to relay required
What We'd Do Differently
- Ship async-first from day 1: Early prototype was sync; refactor to async took weeks
- Pin dependencies aggressively: Would have pinned transformers + gradio versions sooner to avoid conflicts
- 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
- HF Spaces: main + Nemotron companion
- Docs: 17 formal spec documents
- 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
- Try it: https://huggingface.co/spaces/build-small-hackathon/HearthNet
- Read the spec: docs/00-OVERVIEW.md
- Fork & modify: https://github.com/ckal/HearthNet
- Deploy locally:
pip install -e . && python app.py - 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.