Use the tools — and the knowledge.

Attach to your running Colab / vast.ai openinterp session via its public HTTPS URL — the first step of any run.

Health of the active session — loaded model, probes, and captures in memory — before you spend a forward pass.

Capture residual-stream activations at chosen layers and token positions during a forward pass.

Inventory the probes loaded in the backend — model, layer, position, source — so you know what to evaluate or steer.

Apply a loaded linear probe to a stored capture; returns per-sample scores and AUROC when labels are given.

Decompose a captured activation into its top-K SAE features and read their names — the bridge from a residual vector to human-readable concepts (full-stack SAE on Qwen3.6-27B).

Inject direction×alpha into the residual stream and observe the behavioral effect — causal, not correlational.

The four mandatory checks — random-feature baseline, control-token norm, structural-rigidity α-sweep, and the structure-matched control + naming gate — that separate a causal probe from a confounded or epiphenomenal one.

Operate a full mech-interp lab from the terminal — provision Colab GPUs via the Google Colab CLI, run the loops, replicate the papers.