ops-dashboard · pi-gpt-5.5
pi · started 2026-10-08T14:20:04.101Z · horizon 420s · judge ai (openai:gpt-5.5), 4 frames scored
Area under curve
0.907
headline, 0-1
Tab title/icon
27.5s
browser chrome, not the page
First render
27.5s
anything on screen
First reviewable
38.1s
worth feedback
Final score
1.00
no regression
Correctness over time
Where the time went
99%
Model thinking 56.1sTool round trips 0.1sUnaccounted 0.8s
| Phase | Time | Share | Side |
|---|---|---|---|
| Model thinking | 56.1s | 98.6% | agent |
| Tool round trips | 0.1s | 0.1% | agent |
| Dependency install | 0.0s | 0.0% | toolchain |
| Build | 0.0s | 0.0% | toolchain |
| Dev server boot | 0.0s | 0.0% | toolchain |
| First paint | 0.0s | 0.0% | toolchain |
| Unaccounted | 0.8s | 1.3% | unknown |
| Wall clock (cold start) | 56.9s | 100% |
Agent or toolchain
99%
Agent (model + tool round trips) 56.1s
Toolchain (install + build + boot + paint) 0.0s
Unaccounted 0.8s
Accounted for: 98.7% of wall clock.
- Excludes 15.0s of deliberate idle observation (settle window); the curve still covers it.
Iteration: prompt to visible change
Follow-ups were injected into the same live session.
| Edit | First visible change | Correct change | Agent done | Tool calls | After agent | Broken for |
|---|---|---|---|---|---|---|
| Make the page heading blue. | 2.6s | 2.6s | 3.2s | 1 (edit) | -0.6s | -- |
| Add an eleventh depot row for Porto: 6,480 shipments, 93.1% on-time, $19.95 cost per shipment. | 3.7s | 3.7s | 4.5s | 1 (edit) | -0.8s | -- |
Judged frames
Early dashboard shell with correct heading, but required operational content is missing.
Visible header and KPI row are well designed, but table, bars, and footer are missing from view.
Strong dashboard with accurate table and KPIs, but footer is missing and volume bars are not clearly proportional.
All requested dashboard elements are visible, accurate, aligned, and deliberately styled.
Caveats
- 4 poll ticks were skipped because a capture overran the interval.
- judge: openai:gpt-5.5 does not accept a sampling temperature, so these frames were scored at the provider's default rather than at 0. Two runs of the same page can be scored differently, and that variance is inside the AUC. result.json records the temperature as null rather than as the value that was asked for, so p2p compare will not pool this run with one that was scored at a fixed temperature.