Small changes. Better testing.

Filters that silently matched nothing were hard to notice and easy to reintroduce. Alongside the fix, we tightened how filters are built and tested.

• Rebuilt the filter search syntax so quoted values survive intact, covered by an exhaustive test suite
• Made a misconfigured filter fail immediately instead of quietly rendering nothing
• Fixed long filter names pushing their counts out of column, and shortened them with the full name on hover

Filtering on a name that contains a space — a phase called "Contact Voltage Drop", a batch called "Lot 2024 rev B" — matched only the first word and returned nothing.

• Fixed filters on values containing spaces or commas, everywhere filters are used
• Added export to the Phase Pareto and Measurement Control toolbars, matching runs and units
• Added the search bar to Measurement Control

Offboarding a team member is now a single action with nothing left to clean up by hand.

• Removing a member now automatically deactivates their organization API keys and MCP connections in one step
• Improved the remove and re-invite flow so returning members get a fresh start while their test history stays intact
• Updated the member management docs to walk through the new offboarding flow

Phase Pareto and Measurement Control are the two analytics tables that could not be exported, so getting their data into a spreadsheet meant copying rows by hand.

• Added CSV and JSON export to the phase and measurement lists, available from the selection bar once rows are selected.
• Added a copy-to-clipboard option so selected rows can be pasted straight into a spreadsheet.
• Kept numeric, boolean and text measurement values in separate columns so numbers stay sortable in the exported file.

Managing who has access to your organization is now simpler and safer.

• Replaced account suspension with a clear "Remove from organization" action that revokes access without touching the person's account elsewhere
• Removed members keep their history: runs and records they created stay fully visible for traceability
• Restoring access is now a simple re-invite

A yield alert could close itself while the line was still failing. The drop that raised it slowly became part of the baseline it was being compared against, so a single quality incident produced a string of alerts opening and closing on their own — one incident on a production line generated nine.

• Fixed yield alerts closing on the very failures that raised them, so one incident now stays one alert until someone resolves it
• Locked each alert to the baseline measured when it was raised, so the bar to close it no longer drifts while the incident is still open
• Applied the same rule to custom yield rules, which recalculated their reference window on every check

Every alert can now launch an AI root-cause investigation on demand — testing hypotheses against production data and reporting what actually changed.

• Added a root-cause investigation to every alert, started with one click: it builds a live hypothesis tree scoped to the incident window, tests each lead against baseline data, and shows the supporting evidence as charts.
• Added a written conclusion that highlights the most likely cause(s), with recommended next steps split into immediate fixes and longer-term improvements grounded in the findings.
• Added drift attribution that pinpoints whether a measurement shifted in level, spread, or ranking, plus incident-vs-baseline checks for outcome shifts and retest churn.
• Added stop and re-run controls for investigations, with results visible to everyone in the organization.

One production incident — a failing fixture, a drifting environment — used to raise a separate drift alert for every affected measurement, and brief wobbles could alert and resolve within minutes.

• Grouped automatic drift alerts into one event per procedure and part: every drifting measurement attaches to the same alert with its own timeline, and same-day recurrences reopen it instead of opening a duplicate. Custom alert rules are unchanged.
• Added persistence gates: a drift now has to hold across consecutive runs before it alerts — and before it auto-resolves — ending one-off wobble alerts and resolve/reopen flapping. Sudden critical shifts still fire immediately.
• Added an instrument-resolution floor: a shift smaller than the measurement device's own step no longer alarms, silencing last-digit noise on quantized sensors.

On locked-down factory networks where the realtime streaming server is unreachable (missing DNS record, firewalled WebSockets), test runs and the station service could freeze forever before starting, with no error and no log.

• Fixed runs and the station service to start immediately in every case: the dashboard live view now connects in the background and can never block a run, the operator UI, or result uploads. If it cannot connect within 10 seconds, a clear warning explains what to check and the station keeps working locally
• Fixed stations where a user login coexisted with the station identity: deployment runs now always use the station identity, restoring the dashboard live view that was silently disabled
• Added self-identifying diagnostics: every run prints its CLI version and writes a per-run log from the very start, so support screenshots and log files always show which binary ran and what happened

TofuPilot now watches your production test data and tells you when quality slips — automatically, with no dashboards to babysit.

• Added automatic detection for measurement drift, yield drops, and streaks of failed runs — each judged against the procedure's own history, with nothing to configure.
• Added checks for units that get retested too often and for reference (golden) samples that stop behaving — early signs of a drifting fixture or test.
• Added custom alert rules with your own thresholds and scope, plus an in-app inbox and email notifications when something fires.
• Added full Alerts documentation: alert types, automatic detection, rules, and the alert lifecycle.