I designed a methodology for (autonomously) training transformer language models on a single consumer GPU.

​ Six stages, each with a verification gate (concrete pass criteria), a failure-mode catalog, and per-card hardware profiles. The part I think this community will care about: each stage has its own AGENTS.md file. machine-readable specs, explicit gates, clean handoffs between stages. The methodology was structured so an orchestration framework could execute it stage-by-stage, with the AGENTS.md serving as the per-stage spec each agent reads before doing the work. which means an OpenClaw setup could plausibly execute the whole thing autonomously. one agent per stage, no human in the loop after kickoff.. The cheap demo target is Stage 0 (tokenizer training). CPU-only, finishes in hours not days, has a clean verification gate (round-trip fidelity, fertility, coverage), produces a real artifact. If anyone wants to try running it through OpenClaw and document the trace, I'd cite the operator in any followups (HN, blog posts, future iterations of the methodology). The goal is to see what an agent harness actually does when given a methodology designed to be co-executed, not just co-read. Success teaches us the AGENTS.md format works for orchestration. Failure teaches us where the spec needs to be tighter. let me know if you're interested.