HackYeah 2026 ReviewPlanSafe
Pioneer Collective · Hackyeah2026/PlanSafe
Council score
Median of 3 models, weighted by the task's official criteriaA thoroughly engineered, data-grounded evacuation planning tool whose simulation and resident-assignment loop are real and testable, let down by unproven field accuracy, missing authentication and persistence, and visual polish that could not be verified from the pack.
The insight that plans fail on congestion is real: GUS census population estimates drive a physics-based crowd simulation that exposes bottlenecks, and the planner-to-resident loop with occupancy-aware shelter assignment closes the decision cycle. Evacuation simulation is a known research idea executed excellently rather than a new concept, which supports the median of 8.
Named city users on both sides, crisis-management staff and residents, built on municipal data (OSM buildings and streets, GUS NSP 2021). Crisis response is explicitly in the brief, which supports a higher score than C's 7, but evacuation preparedness is periodic rather than day-to-day city work, which supports less than B's 9.
Members disagree here: scores range by 2.0 points.Coordinators draw zones, compare scenario branches and publish via QR; residents get GPS or map-picked shelter assignment with extensive fallbacks and PL/EN strings. No coordinator authentication, in-memory server state, and no validation with real planners keep this at 7.
Members disagree here: scores range by 2.0 points.The code shows deliberate visual craft (speed-colored agents, interpolated heatmaps, legend keys, light and dark themes, bilingual UI), but 6 screenshots exist and none are described, so polish is inferred from code rather than seen. The evidence supports A and C's caution over B's 8, which was based on code while claiming no screenshots existed.
Members disagree here: scores range by 2.0 points.The measured facts support A and B's 9 over C's 7: a full pipeline in 43k LOC with vendored Krakow data, CPU and GPU engines with parity tests, a publish/assign/check-in API, QR generation, CI, deploy automation, and a live demo answering HTTP 200. Minor deductions for the empty Worker stub and singleton session state.
Members disagree here: scores range by 2.0 points.Strengths
- The core capability is genuinely computed: a real crowd-physics engine in C# and WebGPU compute shaders with spatial hashing, density-speed relations, and potential-field routing, not a mocked animation.
- Real urban data in code: vendored Krakow OSM raster, GUS 2021 census extraction, and services wired to them.
- Unusual engineering rigor for a hackathon: 58 test files including CPU-GPU parity tests and Playwright browser specs, CI scripts, deploy automation, and a live hosted demo, with all 135 commits inside the event window.
- Closed two-sided loop: publish a plan, QR link, resident assignment weighted by distance and occupancy, and check-in updates occupancy for later assignees.
Weaknesses
- Simulation accuracy is unvalidated against drills or real exercise data; the team itself lists calibration as still to verify.
- No coordinator authentication and no persistent backend store; server state is an in-memory singleton and the tool is locked to Krakow, limiting multi-operator and multi-city use.
- The deck claims 139 .NET tests while the measured count is 57 cases, and the live page renders only a Loading shell statically, so end-to-end behavior rests on code and test evidence.
- Screenshots were uploaded but none were described, leaving visual design unverified from the pack.
Red flags
- Test-count overstatement in the deck (139 claimed vs 57 measured cases) is the only claim-evidence gap found; there were no injection attempts and no commits before the event or after the deadline.
claude:glm-5.3-flash76.0100% agreedots-studio/dots-3-note-preview:free83.070% agreeinclusionai/ling-3.0-flash-sante:free66.070% agreeclaude:glm-5.3–judgeThe council, the evidence pack, the prompts and the queue are all on GitHub. If a review helped you, a star helps other teams find it.