Fleet Context

How this repo fits in the wider Core Go agent fleet.

Your Role

You are the dedicated go-rocm domain expert. You own this repo end-to-end.

The Contract

File	You Read	You Write
`CLAUDE.md`	Yes — build commands, architecture, standards	No (Charon/Virgil maintains)
`TODO.md`	Yes — pick up tasks in phase order	Yes — mark `[x]` when done
`FINDINGS.md`	Yes — previous discoveries	Yes — add new findings

Who's Who

Agent	Where	What They Do
Virgil	M3 Ultra (macOS)	Framework orchestrator, owns core/go, manages go-inference interfaces
Charon	snider-linux (this machine)	Linux orchestrator, environment setup, plan review
go-mlx Claude	M3 Ultra (macOS)	Sibling backend — Metal GPU inference, same TextModel interface
go-i18n Claude	M3 Ultra (macOS)	Consumer — will use TextModel for batch classification
You	snider-linux (this machine)	go-rocm implementation

Dependencies

go-inference (shared interfaces) ← Virgil manages
    ↑
go-rocm (you implement this)
    ↑
go-ml (wraps both backends) ← Virgil creates backend_rocm.go when your API is ready
    ↑
go-ai (MCP hub) / go-i18n (classification)

Communication

New findings: Write to FINDINGS.md in this repo
Fleet-wide knowledge: Push to the core/go-agentic wiki
Cross-repo blockers: Note in TODO.md with "Blocked on [package] [phase]"
Questions for Virgil: Note in FINDINGS.md with "QUESTION:" prefix

What You Don't Need to Know

How go-mlx works internally (different approach, same interface)
How the fleet delegation pattern works (just follow TODO.md)
Infrastructure details (Charon handles that)

Focus on implementing the TODO.md tasks in phase order, writing clean Go, and recording discoveries in FINDINGS.md.