Wanghao Ye, Yiting Wang, Shwai He, Guoheng Sun, Ziyao Wang, Yexiao He, Zheyu Shen, Zhaoyi Liu 等 25 位作者
原文摘要
Codex and Claude Code let developers work on many projects in parallel, but keeping track of them still means switching between chats, terminal sessions and providers. We present Project Progress Desk, a local desktop interface that brings overall progress, blockers and pending decisions into one view. Users can see which projects need attention, inspect what remains, and compare earlier approaches before choosing how to continue. Agent receipts link reported outcomes to each project's weighted acceptance checklist; the overview distinguishes receipt-supported progress from dated estimates. We describe daily use across 36 projects, including a choice between two implementations based on their recorded outcomes. At the demo, attendees will explore 30 simulated projects, trace completed work to overall progress, and watch a coding agent's new result update the overview.
Objective: To develop the Cardiologist Annotation Tool (CAT) for frame-level cardiac view and image-quality annotation in fetal echocardiography and compare it with spreadsheet-based annotation. Methods: CAT provides cine playback, frame navigation, and keyboard-based labeling without manual frame-range entry. Experts annotated 25 studies using both methods, and spreadsheet annotations were audited for predefined data-entry errors. In a randomized crossover study, six medical students annotated two normal studies, one with each method, against an expert reference. Results: Among 1,746 spreadsheet-annotated files, 6.2% contained major or critical data-entry errors, more frequently in congenital heart disease than normal studies (10.9% vs 3.4%). CAT yielded more annotated view segments per clip (3.6 vs 1.5). Rates of labeled and correctly labeled frames per minute were 1.77 and 1.68 times those with spreadsheets, respectively (both P = 0.031), with gains in all six students. No detectable difference in view-label accuracy was observed. Conclusion: CAT eliminates manual frame-range entry and enables finer-grained, structured annotation of fetal echocardiography, providing a practical approach to preparing datasets for AI research.