machine commands #

/good [note] #

Save the latest user/assistant turn as a good example.

/good
/good --as "Go to Settings → Security → Reset password."
/good --trace

Default destination: the project's train.jsonl in the Machine store. Run /machine data to see the path.

/good --trace #

Reconstruct the last user turn as a trajectory:

• user
• assistant prose
• assistant tool calls
• tool results

Default destination: the project's traces.jsonl in the Machine store. These rows are explicitly judged good. Reasoning is omitted unless --reasoning is set.

The JSONL row contains:

• messages — Tinker chat JSONL, including OpenAI-style tool_calls[].function
• trajectory — Letta-style records
• kind: "trace"
• tag, source, entryIds

/bad [note] #

Same as /good, written to the project's rejected.jsonl in the Machine store.

/tagged #

List recent rows from train.jsonl, traces.jsonl, observed.jsonl, and rejected.jsonl in the Machine store.

/machine auto #

Save the span Pi is about to compact as an unlabeled full trajectory.

/machine auto          # toggle
/machine auto on
/machine auto off
/machine auto status

Off by default. /machine auto on persists the setting for the current project. Two automatic writes go to observed.jsonl in the Machine store:

• Compaction: session_before_compact writes the span before firstKeptEntryId with kind: "auto-compact".
• Headless exit: when pi -p or --mode json finishes a real prompt (session_shutdown reason quit), the last user turn is written with kind: "auto-headless". Slash-command-only runs (/train, /good, …) are skipped.

Reasoning is omitted. /machine auto off disables both.

Automatic rows use tag: "observed". Completion and compaction are evidence that work happened, not judgments that the behavior was good. /train does not select observed.jsonl by default.

/machine fork [note] #

Start a new headless pi -p run that reviews the latest observed turn.

/machine fork
/machine fork use Julia, not Python
machine fork please don't delete the file

Machine reads the latest auto-headless (else latest observed) row in this project's observed.jsonl, builds a follow-up prompt from that user turn, the previous assistant answer, and your leftover args as the correction, then spawns pi -p with MACHINE_FORK=1 if pi is on PATH. If the host cannot run a program, it prints the prompt and the command so you can rerun headless yourself.

This does not copy the observation into train.jsonl. Observed rows stay unlabeled; only /good creates positive training examples.

The same flow is available outside the TUI as the machine CLI (machine help, machine data, machine observed, machine fork, machine correction --id).

/machine correction --id <id> prompt #

Replay a specific saved row instead of the latest observation.

/machine correction --id c0ffee123456 use Julia, not Python
machine correction --id c0ffee123456 "use Julia, not Python"
machine correct --id c0ffee123456 "use Julia, not Python"

Machine looks up that id in observed.jsonl, then traces.jsonl, then train.jsonl. It spawns pi -p with the original user turn plus the correction, setting MACHINE_FORK=1 and MACHINE_ROW_ID=<id>. If pi is not on PATH, it prints the composed prompt.

New train / traces / observed / rejected rows get a short hex id. Older rows keep working; machine observed prints (no id) when a row has none. Observed rows stay unlabeled; this does not copy them into train.jsonl.

/machine data #

Show the current project's Machine store, automatic-capture setting, dataset paths, and row counts.

Machine stores data outside the working tree by default:

• All platforms: ~/.machine/<project>-<hash>

Set MACHINE_DATA_DIR to override the base directory. Full trajectories can contain tool output and private source material; inspect them before training or copying them.

Versions before 0.2.0 used a project-local data/ directory. Machine leaves existing files in place; pass one explicitly to /train or move reviewed rows yourself.

/machine work and /machine student #

Flip between the daily driver and the student (Tinker) lane.

/machine student                    # Inkling-Small 256K, or latest /tinker use checkpoint
/machine student Qwen/Qwen3.8-27B:peft:262144
/machine student thinkingmachines/Inkling-Small:peft:262144
/machine work                       # back to Grok / last non-Tinker model

/machine student remembers the current non-Tinker model. /machine work switches back to it. Checkpoints registered with /tinker use are preferred over the Inkling-Small 256K base when no id is passed. HuggingFace-style Tinker ids (Qwen/…, thinkingmachines/…, tinker://…) do not need a tinker/ prefix. /machine ft is an alias for /machine student.

Machine registers the Tinker chat catalog itself (Inkling, Qwen3.8-27B 64K/256K, and /tinker use checkpoints). pi-tinker is no longer required.

Train on Qwen 3.8-27B with /train sft --model Qwen/Qwen3.8-27B. Chat the 256K peft id after /reload:

/machine student Qwen/Qwen3.8-27B:peft:262144

See tinker/commands.md for /tinker.

/train validate [jsonl] #

Lightweight JSONL checks, plus Python renderer/token-mask validation when tinker-cookbook is installed.

/train validate
/train validate /path/to/reviewed-traces.jsonl --model thinkingmachines/Inkling-Small --quick

The default file is the explicitly judged train.jsonl in the current project's Machine store.

/train prefers .venv/bin/python in the current project, then the machine package venv, then python3.

/train sft [jsonl] #

Write train_sft.py and tinker.yaml for Tinker Cookbook SFT.

/train sft
/train sft /path/to/reviewed-traces.jsonl --model thinkingmachines/Inkling-Small --force
/train sft --epochs 10 --steps 60

Training runs at most one epoch by default; small datasets often need --epochs N for the format to stick before --steps can take effect.

/train smoke [--yes] #

Run python3 train_sft.py max_steps=2. This can use the Tinker API.

/train eval #

/train eval init --force
/train eval baseline --yes
/train eval checkpoint tinker://.../sampler_weights/... --yes
/train eval compare eval_results/baseline.json eval_results/step-20.json

Status line (Pi) #

Pi paints a compact Machine footer next to other extension statuses:

• last turn untagged after a reply that has not been /good or /bad
• last: good, last: good trace, or last: rejected just after a judgment
• student when the Tinker lane is active
• auto when automatic observation is on
• quiet N train and N obs counts from this project's store

The line hides when there is nothing to report. Letta Code has no footer.