AI Infrastructure Upgrades

Module: running dispatched agents on a Linux server for maximum execution efficiency

Four steps. You study a fleet that runs, design yours around what your machine has, build it, and dispatch one real task from start to finish.

Updated

Prompt 1

Study a fleet that runs, and measure mine against it

Studies a dispatch fleet that runs and measures your setup against it, into FLEET-GAP.md. Writes nothing else.

Prompt 2

Design my fleet, then stop for approval

Designs your fleet from your intake and gap report, with every number carrying its measurement, then stops for approval. Writes two files and no code.

Prompt 3

Build every file in the manifest

Builds every file in the approved manifest and nothing that is not in it, pasting real output for each check. Asks before touching a file it did not create.

Prompt 4

Dispatch one real task, end to end

Dispatches one real task end to end without helping the run, then grades the artifact rather than the exit code.

The four steps

ends with
1Study a fleet that runs, and measure mine against itFLEET-GAP.md
2Design mine, then stop for approvalFLEET-DESIGN.md + a manifest
3Build every file in the manifesta running fleet, with the verification output pasted
4Dispatch one real task from start to finishFLEET-VERIFIED.md

Each step names the next one. Paste a step, run it, then paste the next.

What the steps read

  • kit/fleet-architecture.md has twenty design decisions from a working ~7,000-line runtime. Each one comes with the failure that produced it.
  • kit/dispatch-anatomy.md has the run path, every pre-flight refusal in order, and the ~250-line minimum viable version.

Resources

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