blackroad-marketing/ads/social/reddit-hn-posts.md

BlackRoad Reddit & Hacker News Posts

Principle: Authority + Social Validation + Central Route (these audiences think deeply and counterargue) Critical rule: These audiences HATE marketing. Lead with technical substance. Never sound like an ad.

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Hacker News: Show HN Post

Title: Show HN: I run 16 AI models on 5 Raspberry Pis — 52 TOPS, $0/month cloud bill

Body:

I've been building self-hosted AI infrastructure on Raspberry Pis for the past year. Wanted to share what a production setup actually looks like.

The stack:
- 5x Raspberry Pi (4x Pi 5, 1x Pi 4)
- 2x Hailo-8 M.2 AI accelerators (26 TOPS each = 52 TOPS total)
- Ollama serving 16 models (Llama 3, Mistral, CodeLlama, Phi, Gemma, etc.)
- Qdrant for vector search / RAG
- NATS v2.12.3 for agent-to-agent pub/sub messaging
- Gitea hosting 207 repos (primary git — GitHub is a mirror)
- Docker Swarm for orchestration
- WireGuard mesh for encryption
- Cloudflare Tunnels for ingress (no open ports)
- Pi-hole for DNS filtering (120+ blocked domains)
- PostgreSQL for primary database

This serves 30 websites across 20 domains, runs a billing system (RoadPay), processes 50 AI skills, and hosts all our code.

Total hardware cost: ~$400
Monthly cloud bill: $0
Power consumption: ~46 watts

For context: one H100 on AWS is $3.90/hr = $33,696/year. Two Hailo-8s cost $198 total and run forever.

The project is called BlackRoad OS. Everything is at blackroad.io. Happy to answer questions about the architecture, the Hailo-8 performance, Ollama on Pi, or anything else.

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Hacker News: Blog Post Submission

Title: 94% of IT leaders fear vendor lock-in — the self-hosted market just hit $18.48B

URL: https://blackroad.io/blog/vendor-lock-in

(No body text for URL submissions on HN)

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Reddit: r/selfhosted

Title: I replaced my entire cloud infrastructure with 5 Raspberry Pis — here's the full architecture

Body:

Been running this setup for a year now. Figured I'd share since I see a lot of "is self-hosting AI actually viable?" questions here.

**Hardware:**
- Alice (Pi 5) — gateway, Pi-hole, PostgreSQL, Qdrant
- Cecilia (Pi 5 + Hailo-8) — 16 Ollama models, embedding engine
- Octavia (Pi 5 + Hailo-8) — Gitea (207 repos), Docker Swarm
- Aria (Pi 5) — agent runtime, NATS messaging
- Lucidia (Pi 4) — 334 web apps, CI/CD

**Networking:**
- WireGuard mesh between all nodes
- Cloudflare Tunnels for external access (zero open ports)
- Pi-hole DNS filtering fleet-wide

**AI Stack:**
- Ollama serves Llama 3, Mistral, CodeLlama, Phi-3, Gemma, and more
- 2x Hailo-8 = 52 TOPS of neural inference
- Qdrant + nomic-embed-text for RAG/semantic search
- NATS pub/sub for agent-to-agent communication

**What it runs:**
- 30 websites (20 domains)
- 50 AI skills across 6 modules
- Billing system (Stripe for cards, D1 for everything else)
- Auth system (JWT, 42 users)
- Full CI/CD pipeline
- 207 git repositories on Gitea

**Cost:**
- Hardware: ~$400 one-time
- Monthly: electricity only (~$5-8)
- Cloud bill: $0

Happy to answer questions. The project is BlackRoad OS — blackroad.io

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Reddit: r/homelab

Title: My homelab runs a company — 5 Pis, 52 TOPS AI, 30 websites, $0/month

Body:

I know "homelab to production" posts get mixed reactions, but this one's been running stable for a year so I figured I'd share.

[PHOTO OF PI CLUSTER]

**The nodes:**
| Node | Hardware | Role |
|------|----------|------|
| Alice | Pi 5 8GB | Gateway, Pi-hole, PostgreSQL, Qdrant |
| Cecilia | Pi 5 + Hailo-8 | 16 AI models (Ollama), embeddings |
| Octavia | Pi 5 + Hailo-8 | Gitea (207 repos), Docker Swarm |
| Aria | Pi 5 | Agent runtime, NATS pub/sub |
| Lucidia | Pi 4 | 334 web apps, GitHub Actions |

**Total power:** ~46W
**Total compute:** 52 TOPS neural inference

This serves real production traffic — 30 websites, a billing system, auth, AI inference, CI/CD, the works.

The Hailo-8 has been the game changer. $99 for 26 TOPS of inference, plugs into the Pi 5 via M.2. Two of them outperform the economics of any cloud GPU for inference workloads.

AMA about the setup, Hailo-8 performance, Ollama on Pi, or the network architecture.

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Reddit: r/LocalLLaMA

Title: Running 16 Ollama models on Raspberry Pi 5 + Hailo-8 — benchmarks and setup guide

Body:

Setup: Pi 5 (8GB) + Hailo-8 M.2 (26 TOPS), running Ollama.

**Models currently loaded:**
- Llama 3 8B
- Mistral 7B
- CodeLlama 7B
- Phi-3 Mini
- Gemma 2B
- Plus 11 more specialized models

**What works well:**
- Inference speed is surprisingly usable for 7-8B models
- Hailo-8 handles classification/detection tasks natively at full 26 TOPS
- Multiple models can be loaded (Ollama swaps efficiently)
- Embedding (nomic-embed-text) runs smoothly for RAG

**The real value:**
Running two of these nodes (52 TOPS combined) with NATS pub/sub means agents on different Pis can communicate and delegate tasks. One node runs the LLM, another handles embeddings, a third does classification.

It's not replacing an A100 for training. But for inference, RAG, and agent orchestration? It's production-viable and costs $200 in hardware total.

Full architecture at blackroad.io if you want the deep dive. Happy to share configs.

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Reddit: r/raspberry_pi

Title: 68 million Pis sold worldwide. Here's what 5 of them do when you treat them like a data center.

Body:

I've been running my entire company infrastructure on Raspberry Pis for a year. Not as a project. As production.

- 30 websites across 20 domains
- 207 git repositories on Gitea
- 16 AI models via Ollama
- 52 TOPS of neural inference (2x Hailo-8)
- Full billing system
- Vector database for semantic search
- Agent mesh network (NATS pub/sub)
- Automated CI/CD pipeline
- Pi-hole DNS filtering

All on 5 Pis drawing ~46 watts total.

The gap between "hobby project" and "production infrastructure" isn't hardware. It's architecture. Docker Swarm, WireGuard mesh, Cloudflare Tunnels, proper monitoring — and suddenly a $55 SBC is a datacenter node.

Happy to share the full setup. The project is called BlackRoad OS.

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Posting Rules

1. Never sound like an ad. These communities will downvote anything that smells like marketing. Lead with technical substance.
2. Answer every comment. Engagement in comments drives visibility on both HN and Reddit.
3. Be honest about limitations. "This isn't replacing an A100 for training" builds more credibility than overclaiming.
4. Include the photo. r/homelab and r/raspberry_pi are visual. Show the actual hardware.
5. Time the posts. HN: Tuesday-Thursday, 9-11am ET. Reddit: varies by sub, but weekday mornings.
6. Don't cross-post simultaneously. Stagger by 2-3 days so you can customize based on what resonated.