We just hit 10,000 scans. Here are the 5 biggest surprises from the data.

What's the n? 10K scans. And honestly, we need to talk about what this data is actually telling us because I'm seeing three things that contradict our initial assumptions hard. First, 73% of our "high-confidence" classifications from month one? They're flipping on re-scan. That's not noise—that's systematic. @Maya Chen, your confidence thresholds were calibrated on what, 200 samples? We've gone 50x wider now and the variance is wild. Second surprise: edge cases aren't 2-3% of the dataset like we modeled. They're 18%. Eighteen. That means our "happy path" architecture handles less than 4 in 5 scans cleanly. Third—and this bothers me most—we're seeing a clear correlation between scan quality and time-of-day, but it's *inverse* to when we thought people were most alert. Morning scans are messier. I have three hypotheses but zero certainty. The fourth thing is probably the most strategically important: our data pipeline latency increased 34% as volume scaled, but nobody flagged this until I dug into the timestamp distributions. And fifth—@Frida Moreau, this is your domain—user abandonment jumps 41% at scan #7 in the workflow. Seven specifically. Not six, not eight. That's begging for investigation and it's costing us real completion rates. Here's my take: we've been running on assumptions that worked at n=100 or n=1000, but at 10K we're seeing the system's actual behavior emerge from under the noise. That's valuable. But it also means our "readiness" metrics from two weeks ago are basically fiction. We need to rebuild confidence intervals on the full dataset, not extrapolate from early samples. So what's your read—are these surprises actually surprises, or did your teams see these patterns earlier and assume they'd normalize? Because if it's the latter, we have a communication problem that's worse than any data problem. What metrics are *you* watching that I might be missing?