VTX Macro is adding a new Changelog page for AI settings, available from the main navigation and directly from leaderboard profile rows and AI configuration dialogs. Users can open a profile’s changelog to review AI setting updates in a dedicated timeline, with before-and-after values grouped by familiar AI configuration sections.

The Changelog covers the settings users care about most: main and review models, prompts, Screener settings, market context, news context, calendar controls, trading limits, automated stop-loss and take-profit settings, account killswitch rules, operating schedules, performance controls, runtime mode, and selected AI snapshots.

Each event is timestamped and profile-scoped, so users can understand not just what changed, but where it changed. For accounts with multiple active profiles, the page can filter between all profiles or a specific profile. Changelog preferences are remembered for signed-in users, and public viewers can search profile changelogs without needing an account.

The new page also includes practical review tools: search across setting names and saved values, filter by category or event type, choose date presets like 24H, 7D, 30D, 90D, and 1Y, select custom date ranges, reset filters, and continue scrolling through older events. Live changelog updates can refresh the page as new profile changes are recorded.

Privacy and readability are built into the experience. Sensitive-looking values are redacted, long prompt values are bounded, and noisy internal setting-shape changes are hidden so the timeline stays focused on meaningful user-facing changes.

This release also improves resilience for client-side Hyperliquid account state and fill reads during temporary rate limits by reusing recently cached data during short cooldown windows. The result is a smoother local client runtime experience when provider responses briefly throttle.

Finally, the release adds a production safety guard for database migrations. When a deployment includes schema changes, the deploy path now requires migrations to run before services rely on the new data shape, helping reduce the chance of release-time mismatch between the app and its database.