Here is a polar projection of Saturn from August 8th to show how the spokes are spread out within the B-ring.
This is done via software to re-project the image from this night into an overhead polar view looking directly down on the south pole of the planet.
The main spoke features are indicated with the red arrows.
Captured in this remarkable image, NGC 1232 spans 200,000 light-years, boasting a reddish core, bright blue spiral arms, and a distorted companion galaxy, all positioned 60 million light-years from Earth in the Eridanus constellation.
(Credit: ESO)
137 AI workflows run on one home server and kill the entire SaaS bill.
Seven departments. Zero monthly seats.
Sales. Deals. Marketing. Operations. Intelligence. Customer. Back office.
Every node is a live agent. Click it. The skill runs locally.
No OpenAI invoice. No $60 - 80k SDR. No $2 - 4k agency retainer.
The company brain sits in the center - a local knowledge base every agent reads and writes to.
Context compounds. Nothing starts from zero. No cloud tax.
Each job ships a real skill file with build order and autonomy grade: human-led, human-assisted, fully autonomous.
You hand off exactly what the ladder allows.
The server stays on.
The map stays live.
The subscriptions stay dead.
Someone bought a used Mac Pro for $800 and spent a few weeks turning it into a local AI workstation.
Total build cost:
~$2,000.
Comparable new workstation:
Many times more expensive.
More RAM.
More storage.
Dedicated graphics.
Local models running without monthly bills.
The interesting part isn't the exact hardware.
It's the economics.
Most people assume serious AI infrastructure requires expensive cloud servers.
A small group is buying older hardware and repurposing it into capable local machines.
One-time cost.
No hourly billing.
No API meter running in the background.
The used hardware market is becoming one of the cheapest ways to build private AI infrastructure.
Bookmark this before everyone starts looking at old workstations differently.