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A single binary turnsa machine into a node
Tick one box and your machine arrives with it running.Installed, licensed, free — and yours a minute after you ask.
Whichever one your machine runs, dxflow drives it — nothing to install.
Four surfaces, one API behind them
All on the machine, all through the same endpoints.
Containerized apps with lifecycle control, limits, ports, volumes and live logs.
File storage on the engine volume — upload, download, zip and signed links.
Terminal sessions on the host, several at once, no SSH client on your side.
A conversation that builds and operates workflows through the same API.
Ask for the workflowand watch it run
It finds or writes the workflow, then starts it, watches it and reworks it — through the same API the console uses. Every move is a call you could have made yourself.
Run jupyter here with my project files, and give me a link.
hub://jupyter, mounted and running on 4 cores.
workflow createworkflow startworkflow logsAdd the sample data, and more memory.
Uploaded data/, raised it to 16 GB, restarted.
artifact uploadworkflow updatePublished over HTTPS, certificate already issued.
Compute should accelerate the work,not become the work
One project rarely lives on one machine.Each arrives with its own tools, its own way in, its own upkeep.