u/Weird_Night_2176

Spent 2 months building a local AI agent stack on 4 SBCs. Orange Pi 5 Plus for Ollama inference, Odroid XU4 for PostgreSQL with pgvector, Jetson Orin Nano for CrewAI orchestration.

Just published a complete setup guide covering:

- Hardware selection under $650 total

- Ollama model server with fan control

- pgvector for semantic agent memory

- CrewAI wired to local inference

- Full automation on cron schedule

Full build on YouTube free if you want to verify before buying.

Been documenting building a local AI company from scratch since March. Product 1 was the concept and strategy. Product 2 just dropped: the actual setup guide.

Every command to go from bare hardware to 14 autonomous agents running on schedule. 4 nodes, PostgreSQL with vector embeddings, CrewAI, Tailscale mesh, Command Center PWA.

$8/month total running cost. No cloud dependency.

Happy to answer questions about the build.

Sharing this week's update on my ongoing public build, an AI agent cluster I'm running at home that I document weekly.

The numbers this week:
- Portfolio value: $98,808
- Total Anthropic API spend since day 1: $1.11
- Stock P&L: -$598 (the agent had a rough week)
- Open trades: 7

What I built this week: replaced WhatsApp with a fully custom command center app (I call it BlackBox CC) so I can message my AI agents from my iPhone via Tailscale without touching any third-party messaging platform.

Also added a 4th node to the cluster and did a post-mortem on the $492 trading bug, turned out to be a logic error in the CrewAI agent, not a model issue.

The $1.11 API bill is real. Most of the heavy lifting runs on local qwen models via Ollama.

Quick build log from this week's episode of my ongoing homelab AI project:

- Added 4th node to the cluster
- Ollama running qwen2.5:3b and qwen3.5:4b across the stack
- Discovered and fixed a $592 bug my trading agent introduced (always fun)
- Total external API spend: $1.11 since the project started

The big thing this week: I built a custom web UI I'm calling BlackBox CC, basically a replacement for WhatsApp that lets me communicate with my AI agents from anywhere, secured via Tailscale so I can hit it from my iPhone when I'm out.

Happy to go into the node networking setup, how I'm distributing Ollama workloads, or the Tailscale config if anyone's curious.

After my last post here about running a 4-node AI stack, a lot of people asked for more detail on the setup and how to actually make money with it. So I wrote it up.

The Orange Pi 5 Plus is the Ollama model server in my stack, runs qwen3.5:4b (4.7B parameters) comfortably under 7GB RAM. It handles all the AI inference for 14 agents across 6 crews. Cost to run: about $3/month in electricity.

The guide covers:

→ Single node budget path — Orange Pi only, under $125 total

→ Full 4-node distributed stack → The $8/month cost breakdown

→ 14 AI agents and what each one does

→3 services you can offer immediately to make money with it

Wrote it up as a guide, link in my profile if you want the full breakdown!

Happy to answer questions here, this community gave great feedback last time.

What models are you currently running on your Orange Pi?

Been running OpenClaw as the gateway for my AI CEO "Bosefus" for a few months now. WhatsApp worked but I kept hitting the Twilio sandbox 50 message/day limit and wanted something purpose-built.

So I built my own app.

It's a React PWA served from the Jetson itself, accessible from anywhere via Tailscale. WebSocket connects through a lightweight Express proxy to the OpenClaw gateway on localhost.

What it does:

-Live chat interface with Bosefus

-Agent dashboard - 14 agents, last task, status

-Trading desk - live Alpaca positions + crypt P&L

-Crew run history - every automated job logged by name

-Ollama model status from the Orange Pi model server

-Build Fund tracker - saving toward the next hardware upgrade

-Works on iPhone via Tailscale

The WebSocket proxy pattern was the key piece, since OpenClaw binds to loopback only the Express server bridges the connection so the React frontend can talk to it from any device on the Tailscale mesh.

Full build walkthrough dropping on YouTube this weekend.

Anyone else building custom interfaces on top of OpenClaw?

Been running a distributed AI agent stack for 2 months with PostgreSQL as the persistent memory layer. Wanted to share the schema design since I haven't seen many examples of this pattern.

The setup: PostgreSQL 14 running on an Odroid XU4, accessed by 14 CrewAI agents running on a separate Jetson Orin Nano Super node. All connections go over the local network.

The schema:

conversations: stores all WhatsApp messages between me and the AI CEO. 54+ rows and growing.

agent memory: structured summaries the agents write after each session. 75+ entries. This is how agents remember context between runs without embedding search.

crew runs: logs every crew session with start time, status, and output summary.

paper trades: full trade ledger. symbol, entry/exit price, P&L, reasoning, agent that made the call.

treasury: tracks paper capital across USD, BTC, ETH, SOL.

seo campaigns, seo content, seo competitors: the content pipeline for a local business SEO service.

approvals, ventures, daily briefings: operational tables for the AI company structure.

The interesting design decision: agents write summaries to memory after each run rather than storing raw outputs. Keeps the table lean and queryable without needing a vector store. Works well for structured recall.

Anyone else using PostgreSQL as agent memory? Curious how others are handling the structured vs semantic tradeoff.

Full build documented on Youtube and build document in my bio!

After my last post here about running a 4-node AI stack, a lot of people asked for more detail on the setup and how to actually make money with it. So I wrote it up.

The Orange Pi 5 Plus is the Ollama model server in my stack, runs qwen3.5:4b (4.7B parameters) comfortably under 7GB RAM. It handles all the AI inference for 14 agents across 6 crews. Cost to run: about $3/month in electricity.

The guide covers:

→ Single node budget path — Orange Pi only, under $125 total

→ Full 4-node distributed stack → The $8/month cost breakdown

→ 14 AI agents and what each one does

→3 services you can offer immediately to make money with it

Wrote it up as a guide, link in my profile if you want the full breakdown!

Happy to answer questions here, this community gave great feedback last time.

What models are you currently running on your Orange Pi?

I've been building an AI company on edge hardware for the past few months (documenting it on YouTube) and one of the tools I built generates SEO-optimized Amazon product descriptions using Claude AI.

I've been using it internally but wanted to test it with real Amazon sellers before I officially launch.

Here's what it does:

- Writes 150-200 word product descriptions optimized for Amazon search

- Hooks that address customer pain points, not just feature lists

- Natural keyword integration (no keyword stuffing)

- Matches your brand voice (professional, casual, premium, etc.)

- 24 hour turnaround

I'm offering 3 free product descriptions to anyone who wants to test it. No pitch, no pressure, I just want real feedback from actual sellers before I set my pricing.

Drop a comment if you want the free trial.