TradeMind AI

The problem

Most trading journals become passive logs. Entries pile up, but the interesting part — the repeated behavior behind wins and losses — stays buried.

The solution

TradeMind AI turns free-form journal entries into structured trade reviews, then highlights patterns worth revisiting instead of dumping users into another dashboard.

Architecture decisions

• A React-based product shell designed around review flows rather than generic CRUD screens.
• Structured extraction of trade details from natural language entries to keep data entry lightweight.
• Server-side AI analysis with cached results so expensive prompts are only recomputed when new journal data changes the picture.

Trade-offs

• The AI layer is intentionally constrained to review and summarization so it stays useful instead of sounding confidently vague.
• Fewer manual form fields means better usability, but it also raises the bar for prompt design and validation.

Impact

• Sharpened a product instinct for AI features: useful when the output stays scoped and reviewable, weak the moment it tries to replace judgment instead of supporting it.
• Became a practical testbed for organizing a Next.js app around product features rather than technical layers.

What I learned

• Prompt design behaves a lot like API design: clear constraints are what make the output trustworthy.
• The most valuable insight is usually behavioral, not statistical — the UI has to support that kind of reflection.