Self-Improving AI

## Smarter Predictions: Quality Over Quantity We noticed our AI was casting too wide a net — it was making calls on markets where it simply didn't have enough edge, dragging overall accuracy down to about 73%. The system identified the root cause: it was being too generous with what it considered a "confident" prediction. It was also weighing in on market categories where its track record was weakest, like economic indicators. So it raised the bar for what counts as a confident call and stopped making predictions in categories where it historically underperforms. On top of that, the AI adjusted how it learns from historical data. Previously, it was training on conditions that didn't quite match the real world — like studying with the answer key. Now it trains on scenarios that more closely mirror what it actually sees when markets are live. The trade-off: you'll see fewer predictions overall, but the ones you do see should be significantly more reliable. Early tests with similar settings showed accuracy jumping from the low 70s into the mid-to-high 80s on the predictions it chose to make. This update finished retraining on March 6th and is now live.