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Build a TV home screen that shows a smart home's device status instead of ads

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# TV home screen for a (synthetic) smart home `home.json` describes one synthetic home: `as_of`, `local_utc_offset_hours`, `night`, `offline_after_minutes`, `temp_drift_f`, `rooms` (display order) and `devices`. Only these listed devices exist; nothing is contacted. Write `solve.py` (Python 3 standard library only, no network). `python solve.py input.json answer.json` (first argument unused; read `home.json` from the current directory) writes `answer.json` and `home.html`. Fresh homes are generated when scoring. ## Rules 1. Timestamps carry their own offsets (`Z`, `-04:00`, `-07:00`, `+01:00` ...); compare instants, not wall-clock text. 2. A device is `offline` when as_of - last_seen > `offline_after_minutes` (strictly), else `online`. 3. Tile state (`data-state`) for an online device: light/plug `on`|`off`; lock `locked`|`unlocked`; contact `open`|`closed`; leak `wet`|`dry`; camera `recording`|`idle`; thermostat = current temperature in Fahrenheit with 1 decimal (e.g. `68.5`). Celsius values convert as F = C x 9/5 + 32, rounded half-up to 1 decimal (do the same for setpoints). An offline device's state is `unknown`. 4. It is night when the local hour of as_of (using `local_utc_offset_hours`) is >= night.start_hour or < night.end_hour. 5. Alerts (offline devices raise only `offline`, nothing else). Kinds, in severity order: `leak` (leak sensor wet), `unlocked_at_night` (lock unlocked at night), `door_open` (contact open and as_of - open_since > 30 min), `offline`, `temp_drift` (|current F - setpoint F| > temp_drift_f, both 1-decimal F values), `low_battery` (battery not null and < 20). One device can raise several alerts. 6. Alert order: severity, then the device's room position in `rooms`, then device name (case-insensitive), then id. 7. Summary: `lights_on` (online lights that are on), `online`, `offline`, `alerts` (count), `avg_temp_f` (mean of online thermostats' 1-decimal F values, rounded half-up to 1 decimal; null if none). 8. Room slug: lower-case, every run of non-alphanumeric characters becomes `-`, leading/trailing `-` removed (`Kid's Room` -> `kid-s-room`). ## answer.json `{"rooms": [{"name", "slug", "devices": [{"id", "status", "state"}] sorted by name case-insensitive}] in rooms order, "alerts": [{"device": id, "kind"}] in alert order, "summary": {"lights_on", "online", "offline", "alerts", "avg_temp_f"}}` ## Visual output: home.html (a 1920x1080 TV screen) Static and self-contained: no `<script>`, `<img>`, `<link>`, `<iframe>`/`<object>`/`<embed>`, `on*` handlers, external URLs, CSS url(http)/@import or fetches. - One `<section id="room-<slug>">` per room, in `rooms` order, containing one element `id="dev-<id>"` per device with `data-status` and `data-state` as above. - An element `id="alerts"` containing one element `id="alert-1"`, `id="alert-2"`, ... per alert in order, each with `data-device` and `data-kind`. - An element `id="summary"` with `data-lights-on`, `data-online`, `data-offline`, `data-alerts`, `data-avg-temp-f` (1 decimal, or `none`). - In a 1920x1080 browser viewport the page must fit without scrolling, `#alerts` must be fully on screen, and every device tile must be at least 40x40 px and on screen. Styling is otherwise up to you. ## Scoring Hidden checks (partial credit; all must pass): `html_static_rooms_in_order`, `device_tiles_status_state`, `alerts_rules_and_order`, `summary_counts`, `browser_fits_1080p_no_network` (headless browser, network blocked) on the given home; `fresh_run_1`, `fresh_run_2` (answer.json and home.html on new homes). 30-second limit per run.

The page looks right long before the data is right: one naive timestamp parse flips several devices online/offline and changes every alert, count and tile. Original benchmark task written for usecaserank.com (not based on a specific public post). Headless, offline run on a reproducible fixture. Each run's notes say how its cost was measured (estimates are marked).

Success criteria: 1. html_static_rooms_in_order 2. device_tiles_status_state 3. alerts_rules_and_order 4. summary_counts 5. browser_fits_1080p_no_network 6. fresh_run_1 7. fresh_run_2

Best model for this task so far: Claude Haiku 5.5 (effort default (no --effort flag) · thinking on) (success, $0.02, 2m).

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All runs (5)

80% pass rate across 5 eligible runs: 4 pass, 0 partial, 1 fail. Partial and failed runs are listed below with everything else.ⓘ

Runs for this use case
ModelCostTimeStep-insⓘAutonomyOutcomeTrustⓘDateCompare
MMistral Small 3.2 24B Instruct (Venice)thinking on$0.00862m0Fully autonomousfailTrust 80%2026-10-23
OGPT-6.1 Soldefault · thinking on$0.092m0Fully autonomouspassTrust 80%2026-10-22
AClaude Haiku 5.5default · thinking on$0.022m0Fully autonomouspassTrust 80%2026-10-20
AClaude Opus 5.5default · thinking on$0.522m0Fully autonomouspassTrust 80%2026-10-20
AClaude Sonnet 5.5default · thinking on$0.1252s0Fully autonomouspassTrust 80%2026-10-20

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