Ask questions over your own trades, then preserve the useful insights

The app stores typed and voice turns in the same durable chats, allows renaming and deleting them, and supports both normal chat and streaming progress updates. Ask “Give me a summary of what happened in this chat” to recap the full chat, including older turns outside the normal recent-message window.

Each completed exchange contributes to a cumulative chat summary and a searchable conversation episode. Relevant earlier exchanges are recalled with bounded semantic and text search, so phrases such as “go back to what I said earlier” work in both Tutor chat and Live voice. Useful preferences, goals, corrections, strategies, decisions, and unresolved topics may also carry across conversations.

Typed chat and Live voice can use the same guarded read tools for trades, positions, tracking, analytics, insights, notes, emotions, goals, accounts, playbooks, backtests, reports, achievements, Community, Mentor Mode, notifications, workspace metadata, Economic Calendar, Market News, Market Monitor analysis, product documentation, and current web facts. The assistant must complete required lookups before making user-specific or time-sensitive claims.

Open Settings → Intelligence to turn automatic saving or proactive recall on or off, search memories, correct their wording, pin important context, forget one item, or clear everything. You can also say “Remember that…” or “Forget…” in either chat or voice. Security credentials, payment secrets, and incidental chatter are excluded from automatic memory.

Saved memories retain a link to their source conversation. Forgetting a memory leaves its transcript intact; deleting a chat removes its transcript, chat summary, searchable episodes, queued indexing work, and any memory supported only by that chat.

Intelligence learns from your trades and the feedback you give its answers, so its coaching adapts to you over time.

Insights surface recurring themes and can be marked read individually or in bulk.

Message feedback is tracked so the system can measure what worked and what failed in the AI responses.