Gemini 3.8 Flash (effort medium · thinking on) on Sailboat instrument dashboard from an NMEA 0183 log
GGemini 3.8 Flashmedium · thinking on · Antigravity CLI 1.4.5 (agy -p) · cloud · 2026-10-22 · by @bobsiopener
- Estimated cost
- $0.57
- Wall time
- 5m
- Human step-ins
- 0
- Outcome
- Pass
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Summary
Exact prompt
# Sailboat instrument dashboard from an NMEA 0183 log `log.nmea` is a **synthetic** instrument log from a sailboat; `input.json` names it and gives alarm thresholds. Write `solve.py` (Python 3 standard library only) so that `python solve.py input.json answer.json` parses the log, computes true wind and VMG, finds alarm periods, writes `answer.json`, **and** writes a static display `dashboard.html` in the working directory. We re-run it on fresh logs, so do not hard-code anything. ## Log format Each line is `<t>,<sentence>` where `<t>` is an integer second and `<sentence>` is an NMEA 0183 sentence `$<body>*<hh>`. The checksum hh is two hex digits (either case) equal to the XOR of all characters of `<body>` (between `$` and `*`). ## Rules 1. Blank lines are ignored and not counted. A line is **malformed** if it has no comma before the sentence, `<t>` is not an integer, the sentence does not start with `$`, has no `*`, the checksum is not exactly 2 hex digits, or (after the checksum passes) the sentence type is not one of those below or a needed field is missing or not a number. A line whose checksum does not match is **bad_checksum**. Every other line is **valid** (this includes MWV lines that are skipped by rule 3). 2. The sentence type is the last 3 characters of the first field. Fields are split on commas (field 0 is the talker+type): - `HDT`: field 1 = true heading (degrees). - `VHW`: field 5 = boat speed through water (knots). - `DBT`: field 3 = depth (metres). - `VTG`: field 5 = speed over ground (knots). - `MWV`: field 1 = apparent wind angle (degrees clockwise from the bow, 0-360), field 2 = reference, field 3 = apparent wind speed (knots), field 5 = status. 3. Keep the latest heading and boat speed. For an MWV with reference `R` and status `A`, if a heading and a boat speed have already been seen, make a **true wind sample** at its time: x = AWS*cos(AWA) - BSP, y = AWS*sin(AWA); TWS = hypot(x, y); TWA = atan2(y, x) in degrees mod 360; TWD = (heading + TWA) mod 360; VMG = BSP*cos(TWA). MWV lines with another reference or status are valid but make no sample. 4. Alarms: depth alarm on DBT readings with depth < `depth_min`; wind alarm on true wind samples with TWS > `tws_max`; speed alarm on VTG readings with SOG > `sog_max`. For each type, an alarm is a maximal run of consecutive readings of that type (in file order) that break the limit; `start`/`end` are the times of its first and last breaking reading. Sort all alarms by start, then type name. 5. `last` holds the last sample's tws, twa, twd, vmg (2 decimals) and the last boat speed and depth values as read. ## answer.json `{"counts": {"valid", "bad_checksum", "malformed"}, "true_wind_samples": n, "last": {"tws", "twa", "twd", "vmg", "bsp", "depth"}, "tws_mean": .., "tws_max": .., "alarms": [{"type": "depth"|"wind"|"speed", "start", "end"}, ...]}` (mean and max of TWS over all samples, 2 decimals). ## dashboard.html A self-contained static page (no scripts or network needed) with these element ids: `tws`, `twa`, `twd`, `vmg`, `bsp`, `depth` (each element's text is only the value from `last` with 1 decimal), an `<svg id="wind-rose">` containing a `<line id="twd-needle">` pointing toward TWD, and `<ul id="alarm-list">` with exactly one `<li>` per alarm. Styling is up to you. It must be static and self-contained so it can be screenshotted headless: no `<script>`, no `<img>`/`<link>`/`<iframe>`/`<object>`/`<embed>`, no `on*` handlers, no `href`/`src` except `#...` or `data:`, no CSS `url(http...)` or `@import`, no fetches. ## Scoring Hidden checks (partial credit = fraction passed; all must pass): `sentence_counts` (exact), `true_wind_and_vmg` (within 0.011, sample count exact), `alarm_intervals` (exact), `dashboard_elements` (file exists, static rules above, ids present, `wind-rose` is `<svg>`, `twd-needle` is `<line>`, `alarm-list` is `<ul>`), `dashboard_values` (each value within 0.051 of the answer, li count) on the given log; `fresh_log_1` (all answer keys plus dashboard values) and `fresh_log_2` (all answer keys) on fresh logs. Each run has a 60-second limit.
Checksum XOR with lowercase hex, malformed vs bad-checksum vs skipped MWV, true wind from apparent wind with the latest heading and speed, alarm runs per reading type, and a static HTML display whose values are cross-checked. Original benchmark task written for usecaserank.com. Headless, offline run on a reproducible fixture. Cost shown is a list-price API-equivalent estimate (run on a Google account via Antigravity CLI, not billed per token).
Benchmark code for comparing AI models, not navigation or safety software.
Success criteria: 1. sentence_counts 2. true_wind_and_vmg 3. alarm_intervals 4. dashboard_elements 5. dashboard_values 6. fresh_log_1 7. fresh_log_2
Setup notes: Use Case Scout batch10 case k01-nmea-instrument-dashboard (offline fixture). Prepared with batch10/prepare.sh on the shared box; only TASK.md + starting files went to the agent machine, hidden keys stayed on the box. Prompt = TASK.md verbatim. Flags: -p --model gemini-3.8-flash-medium --sandbox --dangerously-skip-permissions --disable-slash-commands --output-format stream-json, cwd=workdir. Scored on the box with `python score.py WORKDIR KEYDIR`. Single run, no retries, no human input. Visual output (dashboard.html) rendered offline to PNG with google-chrome headless, network blocked, 1280x800 at ~3 s after load. Requested model gemini-3.8-flash-medium (effort medium); priced at the base model row gemini-3.8-flash (pricing.json, https://ai.google.dev/gemini-api/docs/pricing). Score: 7/7. Cost is an ESTIMATE: measured tokens x pricing.json list price (Google account, nothing billed per token); output_tokens include thinking tokens. Tool calls 40 (0 failed). Single run.
Estimated cost List-price estimate; not actually billed.
Output
Metrics
- Estimated cost
- $0.57
- Wall time
- 5m
- Human step-ins
- 0
- Turns
- 41
| Model | Input tok | Output tok | Cost |
|---|---|---|---|
| GGemini 3.8 Flashgoogle/gemini-3.8-flasheffort medium · thinking on | 1,929,910 | 64,911 | $0.57 |
Reproduce this run
- Use the use case prompt on the use case page.
- Same setup: Gemini 3.8 Flash (google/gemini-3.8-flash, effort medium · thinking on) via
Antigravity CLI 1.4.5 (agy -p), cloud. - It counts as a success only if it meets every success criterion: 1. sentence_counts 2. true_wind_and_vmg 3. alarm_intervals 4. dashboard_elements 5. dashboard_values 6. fresh_log_1 7. fresh_log_2
- Log model, tokens, wall time, human step-ins and the evidence log, then submit it as a reproduction. A different model? Submit a head-to-head rerun.
How runs are scored: Methodology.
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Automated checks
Trust 80% comes from these checks (how Trust % works).
- Run date is not in the future : passed
- Model is in the dated pricing table : passed
- Cost recomputed from tokens matches : passed
- Cached tokens are consistent : passed
- Output throughput is plausible : passed
- Autonomy matches step-ins : passed
- Step-ins do not exceed turns : passed
- Retries agree with first-try result : passed
- Evidence log SHA-256 matches : passed
- Evidence log is substantial : passed
Evidence log
{"event": "init", "conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "init": {"model": "gemini-3.8-flash-medium", "cwd": "/home/bob/usecase-runs/agy-batch/out/gemini-3.8-flash-medium/k01/work", "tools": ["ask_custom_permission", "ask_permission", "ask_question", "browser_click_element", "browser_drag_pixel_to_pixel", "browser_get_dom", "browser_get_network_request", "browser_input", "browser_list_network_requests", "browser_mouse_down", "browser_mouse_up", "browser_move_mouse", "browser_press_key", "browser_refresh_page", "browser_resize_window", "browser_scroll", "browser_scroll_dom", "browser_select_option", "browser_subagent", "call_mcp_tool", "capture_browser_console_logs", "capture_browser_screenshot", "click_browser_pixel", "command_status", "define_subagent", "delete_knowledge", "execute_browser_javascript", "find_by_name", "finish", "generate_image", "grep_search", "invoke_subagent", "list_browser_pages", "list_dir", "list_permissions", "list_plugin_accounts", "list_resources", "manage_inbox", "manage_subagents", "manage_task", "multi_replace_file_content", "notebook_edit", "notebook_execution", "open_browser_url", "read_browser_page", "read_resource", "read_url_content", "replace_file_content", "run_command", "run_workflow", "schedule", "search_marketplace", "search_web", "sed_file", "send_command_input", "send_message", "view_file", "wait", "wait_5_seconds", "write_to_file"], "permission_mode": "always-proceed"}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 0, "state": "DONE", "step_type": "user_input"}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 1, "state": "DONE", "step_type": "agent_response", "duration_seconds": 1.564190293, "usage": {"input_tokens": 9038, "output_tokens": 64, "thinking_tokens": 27, "cache_read_tokens": 0, "total_tokens": 9102}}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 2, "state": "ACTIVE", "step_type": "tool", "tool_name": "run_command", "tool_info": {"name": "run_command", "parameters": {"CommandLine": "ls -la"}}}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 2, "state": "DONE", "step_type": "tool", "tool_name": "run_command", "duration_seconds": 0.288549743, "tool_info": {"name": "run_command", "parameters": {"CommandLine": "ls -la"}, "output": "Command exited with code 0.\n\nCommand output:\ntotal 40\r\ndrwxr-xr-x 2 bob bob 4096 Oct 22 10:56 .\r\ndrwxrwxr-x 3 bob bob 4096 Oct 22 10:56 ..\r\n-rw-r--r-- 1 bob bob 4114 Oct 22 10:56 TASK.md\r\n-rw-r--r-- 1 bob bob 961 Oct 22 10:56 dashboard.html\r\n-rw-r--r-- 1 bob bob 95 Oct 22 10:56 input.json\r\n-rw-r--r-- 1 bob bob 14141 Oct 22 10:56 log.nmea\r\n"}}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 3, "state": "DONE", "step_type": "agent_response", "duration_seconds": 3.239603866, "usage": {"input_tokens": 9363, "output_tokens": 86, "thinking_tokens": 35, "cache_read_tokens": 0, "total_tokens": 9449}}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 4, "state": "ACTIVE", "step_type": "tool", "tool_name": "read_file", "tool_info": {"name": "read_file", "parameters": {"Path": "/home/bob/usecase-runs/agy-batch/out/gemini-3.8-flash-medium/k01/work/input.json"}}}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 4, "state": "DONE", "step_type": "tool", "tool_name": "read_file", "duration_seconds": 0.145382108, "tool_info": {"name": "read_file", "parameters": {"Path": "/home/bob/usecase-runs/agy-batch/out/gemini-3.8-flash-medium/k01/work/input.json"}, "output": "8 lines, 95 bytes"}}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 5, "state": "DONE", "step_type": "agent_response", "duration_seconds": 9.275624411, "usage": {"input_tokens": 9618, "output_tokens": 76, "thinking_tokens": 25, "cache_read_tokens": 0, "total_tokens": 9694}}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 6, "state": "ACTIVE", "step_type": "tool", "tool_name": "read_file", "tool_info": {"name": "read_file", "parameters": {"Path": "/home/bob/usecase-runs/agy-batch/out/gemini-3.8-flash-medium/k01/work/TASK.md"}}}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 6, "state": "DONE", "step_type": "tool", "tool_name": "read_file", "duration_seconds": 0.650540875, "tool_info": {"name": "read_file", "parameters": {"Path": "/home/bob/usecase-runs/agy-batch/out/gemini-3.8-flash-medium/k01/work/TASK.md"}, "output": "27 lines, 4114 bytes"}}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 7, "state": "DONE", "step_type": "agent_response", "duration_seconds": 1.778662659, "usage": {"input_tokens": 11096, "output_tokens": 66, "thinking_tokens": 15, "cache_read_tokens": 0, "total_tokens": 11162}}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 8, "state": "ACTIVE", "step_type": "tool", "tool_name": "read_file", "tool_info": {"name": "read_file", "parameters": {"Path": "/home/bob/usecase-runs/agy-batch/out/gemini-3.8-flash-medium/k01/work/dashboard.html"}}}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 8, "state": "DONE", "step_type": "tool", "tool_name": "read_file", "duration_seconds": 0.034450067, "tool_info": {"name": "read_file", "parameters": {"Path": "/home/bob/usecase-runs/agy-batch/out/gemini-3.8-flash-medium/k01/work/dashboard.html"}, "output": "6 lines, 961 bytes"}}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 9, "state": "DONE", "step_type": "agent_response", "duration_seconds": 2.408394732, "usage": {"input_tokens": 11709, "output_tokens": 355, "thinking_tokens": 293, "cache_read_tokens": 0, "total_tokens": 12064}}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 10, "state": "ACTIVE", "step_type": "tool", "tool_name": "read_file", "tool_info": {"name": "read_file", "parameters": {"EndLine": 50, "Path": "/home/bob/usecase-runs/agy-batch/out/gemini-3.8-flash-medium/k01/work/log.nmea", "StartLine": 1}}}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 10, "state": "DONE", "step_type": "tool", "tool_name": "read_file", "duration_seconds": 0.029366995, "tool_info": {"name": "read_file", "parameters": {"EndLine": 50, "Path": "/home/bob/usecase-runs/agy-batch/out/gemini-3.8-flash-medium/k01/work/log.nmea", "StartLine": 1}, "output": "502 lines, 14141 bytes"}}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 11, "state": "DONE", "step_type": "agent_response", "duration_seconds": 4.000429171, "usage": {"input_tokens": 13480, "output_tokens": 86, "thinking_tokens": 43, "cache_read_tokens": 0, "total_tokens": 13566}}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 12, "state": "ACTIVE", "step_type": "tool", "tool_name": "run_command", "tool_info": {"name": "run_command", "parameters": {"CommandLine": "wc -l log.nmea"}}}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 12, "state": "DONE", "step_type": "tool", "tool_name": "run_command", "duration_seconds": 0.387362531, "tool_info": {"name": "run_command", "parameters": {"CommandLine": "wc -l log.nmea"}, "output": "Command exited with code 0.\n\nCommand output:\n502 log.nmea\r\n"}}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 13, "state": "DONE", "step_type": "agent_response", "duration_seconds": 4.005912714, "usage": {"input_tokens": 13658, "output_tokens": 73, "thinking_tokens": 37, "cache_read_tokens": 0, "total_tokens": 13731}}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 14, "state": "ACTIVE", "step_type": "tool", "tool_name": "run_command", "tool_info": {"name": "run_command", "parameters": {"CommandLine": "git status"}}}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 14, "state": "DONE", "step_type": "tool", "tool_name": "run_command", "duration_seconds": 0.332603829, "tool_info": {"name": "run_command", "parameters": {"CommandLine": "git status"}, "output": "Command exited with code 128.\n\nCommand output:\nfatal: not a git repository (or any parent up to mount point /)\r\nStopping at filesystem boundary (GIT_DISCOVERY_ACROSS_FILESYSTEM not set).\r\n"}}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 15, "state": "DONE", "step_type": "agent_response", "duration_seconds": 6.732638148, "usage": {"input_tokens": 13855, "output_tokens": 1434, "thinking_tokens": 1083, "cache_read_tokens": 0, "total_tokens": 15289}}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 16, "state": "ACTIVE", "step_type": "tool", "tool_name": "run_command", "tool_info": {"name": "run_command", "parameters": {"CommandLine": "python3 -c \"\nwith open('log.nmea') as f:\n for i, line in enumerate(f, 1):\n line = line.strip()\n if not line: continue\n parts = line.split(',', 1)\n if len(parts) < 2:\n print(f'Line {i}: no comma: {line}')\n continue\n t_str, sentence = parts\n try:\n int(t_str)\n except ValueError:\n print(f'Line {i}: t not int: {line}')\n if not sentence.startswith('$') or '*' not in sentence:\n print(f'Line {i}: bad st\u2026"}}}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 16, "state": "DONE", "step_type": "tool", "tool_name": "run_command", "duration_seconds": 0.279727859, "tool_info": {"name": "run_command", "parameters": {"CommandLine": "python3 -c \"\nwith open('log.nmea') as f:\n for i, line in enumerate(f, 1):\n line = line.strip()\n if not line: continue\n parts = line.split(',', 1)\n if len(parts) < 2:\n print(f'Line {i}: no comma: {line}')\n continue\n t_str, sentence = parts\n try:\n int(t_str)\n except ValueError:\n print(f'Line {i}: t not int: {line}')\n if not sentence.startswith('$') or '*' not in sentence:\n print(f'Line {i}: bad st\u2026"}, "output": "Command exited with code 0.\n\nCommand output:\nLine 25: bad checksum: 3B != 1a\r\nLine 147: bad checksum: 43 != 62\r\nLine 182: bad checksum: 48 != 69\r\nLine 209: bad checksum: 75 != 54\r\nLine 247: bad checksum: 4C != 6D\r\nLine 262: bad checksum: 4E != 6F\r\nLine 300: bad start or no star: $IIDBT,,f,7.4,M,,F\r\nLine 325: bad checksum: 1E != 3F\r\nLine 333: bad checksum: 25 != 04\r\nLine 339: bad checksum: 4F != 6e\r\nLine 344: bad checksum: 42 != 63\r\nLine 376: bad checksum: 71 != 50\r\n"}}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 17, "state": "DONE", "step_type": "agent_response", "duration_seconds": 2.22495624, "usage": {"input_tokens": 15587, "output_tokens": 438, "thinking_tokens": 43, "cache_read_tokens": 0, "total_tokens": 16025}}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 18, "state": "ACTIVE", "step_type": "tool", "tool_name": "run_command", "tool_info": {"name": "run_command", "parameters": {"CommandLine": "python3 -c \"\nwith open('log.nmea') as f:\n for i, raw in enumerate(f, 1):\n line = raw.strip()\n if not line:\n print(f'Line {i}: blank')\n continue\n if ',' not in line:\n print(f'Line {i}: no comma')\n continue\n t_str, sentence = line.split(',', 1)\n try:\n int(t_str)\n except ValueError:\n print(f'Line {i}: t not int: {t_str}')\n continue\n if not sentence.startswith('$') or '*' not in sente\u2026"}}}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 18, "state": "DONE", "step_type": "tool", "tool_name": "run_command", "duration_seconds": 0.387663321, "tool_info": {"name": "run_command", "parameters": {"CommandLine": "python3 -c \"\nwith open('log.nmea') as f:\n for i, raw in enumerate(f, 1):\n line = raw.strip()\n if not line:\n print(f'Line {i}: blank')\n continue\n if ',' not in line:\n print(f'Line {i}: no comma')\n continue\n t_str, sentence = line.split(',', 1)\n try:\n int(t_str)\n except ValueError:\n print(f'Line {i}: t not int: {t_str}')\n continue\n if not sentence.startswith('$') or '*' not in sente\u2026"}, "output": "Command exited with code 0.\n\nCommand output:\nValid line 1: stype=HDT, fields=['IIHDT', '322.1', 'T']\r\nValid line 2: stype=VHW, fields=['IIVHW', '', 'T', '', 'M', '4.95', 'N', '', 'K']\r\nValid line 3: stype=DBT, fields=['IIDBT', '', 'f', '7.4', 'M', '', 'F']\r\nValid line 4: stype=VTG, fields=['GPVTG', '322.1', 'T', '', 'M', '5.15', 'N', '', 'K']\r\nValid line 5: stype=MWV, fields=['IIMWV', '77.2', 'R', '17.7', 'N', 'A']\r\nValid line 6: stype=HDT, fields=['IIHDT', '324.5', 'T']\r\nValid line 7: stype=VHW, fields=['IIVHW', '', 'T', '', 'M', '4.82', 'N', '', 'K']\r\nValid line 8: stype=DBT, fields=['IIDBT', '', 'f', '7.5', 'M', '', 'F']\r\nValid line 9: stype=VTG, fields=['GPVTG', '324.5', 'T', '', 'M', '5.29', 'N', '', 'K']\r\nValid line 10: stype=MWV, fields=['IIMWV', '75.0', 'R', '18.4', 'N', 'A']\r\nValid line 11: stype=HDT, fields=['IIHDT', '325.5', 'T']\r\nValid line 12: stype=VHW, fields=['IIVHW', '', 'T', '', 'M', '5.07', 'N', '', 'K']\r\nValid line 13: stype=DBT, fields=['IIDBT', '', 'f', '7.8', 'M', '', 'F']\r\nValid line 14: stype=VTG, fields=['GPVTG', '325.5', 'T', '', 'M', '5.12', 'N', '', 'K']\r\nValid line 15: stype=MWV, fields=['IIMWV', '71.1', 'R', '17.8', 'N', 'A']\r\nValid line 16: stype=HDT, fields=['IIHDT', '320.8', 'T']\r\nValid line 17: stype=VHW, fields=['IIVHW', '', 'T', '', 'M', '4.91', 'N', '', 'K']\r\nValid line 18: stype=DBT, fields=['IIDBT', '', 'f', '8.0', 'M', '', 'F']\r\nValid line 19: stype=VTG, fields=['GPVTG', '320.8', 'T', '', 'M', '5.25', 'N', '', 'K']\r\nValid line 20: stype=MWV, fields=['IIMWV', '77.4', 'R', '18.5', 'N', 'A']\r\nValid line 21: stype=HDT, fields=['IIHDT', '322.1', 'T']\r\nValid line 22: stype=VHW, fields=['IIVHW', '', 'T', '', 'M', '4.43', 'N', '', 'K']\r\nValid line 23: stype=DBT, fields=['IIDBT', '', 'f', '8.3', 'M', '', 'F']\r\nValid line 24: stype=VTG, fields=['GPVTG', '322.1', 'T', '', 'M', '4.20', 'N', '', 'K']\r\nValid line 26: stype=HDT, fields=['IIHDT', '323.0', 'T']\r\nValid line 27: stype=VHW, fields=['IIVHW', '', 'T', '', 'M', '4.76', 'N', '', 'K']\r\nValid line 28: stype=DBT, fields=['IIDBT', '', 'f', '8.7', 'M', '', 'F']\r\nValid line 29: stype=VTG, fields=['GPVTG', '323.0', 'T', '', 'M', '5.01', 'N', '', 'K']\r\nValid line 30: stype=MWV, fields=['IIMWV', '73.4', 'R', '17.1', 'N', 'A']\r\nValid line 31: stype=HDT, fields=['IIHDT', '320.6', 'T']\r\nTraceback (most recent call last):\r\n File \"<string>\", line 37, in <module>\r\n print(f'Valid line {i}: stype={stype}, fields={fields}')\r\n ~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\nBrokenPipeError: [Errno 32] Broken pipe\r\n"}}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 19, "state": "DONE", "step_type": "agent_response", "duration_seconds": 3.303621359, "usage": {"input_tokens": 17307, "output_tokens": 403, "thinking_tokens": 19, "cache_read_tokens": 0, "total_tokens": 17710}}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 20, "state": "ACTIVE", "step_type": "tool", "tool_name": "run_command", "tool_info": {"name": "run_command", "parameters": {"CommandLine": "python3 -c \"\nfrom collections import Counter\ncounts = Counter()\nwith open('log.nmea') as f:\n for i, raw in enumerate(f, 1):\n line = raw.strip()\n if not line:\n counts['blank'] += 1\n continue\n if ',' not in line:\n counts['malformed_no_comma'] += 1\n continue\n t_str, sentence = line.split(',', 1)\n try:\n int(t_str)\n except ValueError:\n counts['malformed_t'] += 1\n continue\n if not sente\u2026"}}}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 20, "state": "DONE", "step_type": "tool", "tool_name": "run_command", "duration_seconds": 0.818972126, "tool_info": {"name": "run_command", "parameters": {"CommandLine": "python3 -c \"\nfrom collections import Counter\ncounts = Counter()\nwith open('log.nmea') as f:\n for i, raw in enumerate(f, 1):\n line = raw.strip()\n if not line:\n counts['blank'] += 1\n continue\n if ',' not in line:\n counts['malformed_no_comma'] += 1\n continue\n t_str, sentence = line.split(',', 1)\n try:\n int(t_str)\n except ValueError:\n counts['malformed_t'] += 1\n continue\n if not sente\u2026"}, "output": "Command exited with code 0.\n\nCommand output:\nCounter({'valid_HDT': 99, 'valid_MWV': 99, 'valid_DBT': 98, 'valid_VTG': 98, 'valid_VHW': 94, 'bad_checksum': 11, 'blank': 1, 'valid_XDR': 1, 'malformed_format': 1})\r\n"}}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 21, "state": "DONE", "step_type": "agent_response", "duration_seconds": 6.57766769, "usage": {"input_tokens": 5718, "output_tokens": 481, "thinking_tokens": 396, "cache_read_tokens": 12162, "total_tokens": 6199}}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 22, "state": "ACTIVE", "step_type": "tool", "tool_name": "run_command", "tool_info": {"name": "run_command", "parameters": {"CommandLine": "python3 -c \"\nwith open('log.nmea') as f:\n for i, line in enumerate(f, 1):\n if 'XDR' in line:\n print(i, line)\n\""}}}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 22, "state": "DONE", "step_type": "tool", "tool_name": "run_command", "duration_seconds": 0.287364649, "tool_info": {"name": "run_command", "parameters": {"CommandLine": "python3 -c \"\nwith open('log.nmea') as f:\n for i, line in enumerate(f, 1):\n if 'XDR' in line:\n print(i, line)\n\""}, "output": "Command exited with code 0.\n\nCommand output:\n277 162,$IIXDR,C,19.5,C,AIR*07\r\n\r\n"}}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 23, "state": "DONE", "step_type": "agent_response", "duration_seconds": 11.693237736, "usage": {"input_tokens": 2259, "output_tokens": 3785, "thinking_tokens": 3686, "cache_read_tokens": 16213, "total_tokens": 6044}}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 24, "state": "ACTIVE", "step_type": "tool", "tool_name": "run_command", "tool_info": {"name": "run_command", "parameters": {"CommandLine": "python3 -c \"\nwith open('log.nmea') as f:\n for i, line in enumerate(f, 1):\n if 'MWV' in line:\n print(i, line.strip())\n\" | head -n 30"}}}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 24, "state": "DONE", "step_type": "tool", "tool_name": "run_command", "duration_seconds": 0.294896863, "tool_info": {"name": "run_command", "parameters": {"CommandLine": "python3 -c \"\nwith open('log.nmea') as f:\n for i, line in enumerate(f, 1):\n if 'MWV' in line:\n print(i, line.strip())\n\" | head -n 30"}, "output": "Command exited with code 0.\n\nCommand output:\n5 0,$IIMWV,77.2,R,17.7,N,A*3E\r\n10 3,$IIMWV,75.0,R,18.4,N,A*32\r\n15 6,$IIMWV,71.1,R,17.8,N,A*34\r\n20 9,$IIMWV,77.4,R,18.5,N,A*35\r\n25 12,$IIMWV,77.7,R,17.7,N,A*1a\r\n30 15,$IIMWV,73.4,R,17.1,N,A*3a\r\n35 18,$IIMWV,76.1,R,15.9,N,A*30\r\n40 21,$IIMWV,76.2,R,17.5,N,A*3D\r\n45 24,$IIMWV,72.3,R,18.7,N,A*35\r\n50 27,$IIMWV,72.0,R,19.6,N,A*36\r\n55 30,$IIMWV,71.4,R,19.6,N,A*31\r\n60 33,$IIMWV,70.4,R,17.9,N,A*31\r\n65 36,$IIMWV,67.3,R,17.3,N,A*3A\r\n70 39,$IIMWV,67.6,R,17.9,N,A*35\r\n75 42,$IIMWV,64.5,R,16.8,N,A*35\r\n80 45,$IIMWV,60.8,R,17.6,N,A*33\r\n85 48,$IIMWV,60.5,R,20.4,N,A*38\r\n90 51,$IIMWV,60.5,R,19.3,N,A*35\r\n95 54,$IIMWV,x62.3,R,17.5,N,A*41\r\n100 57,$IIMWV,61.5,R,17.9,N,A*30\r\n105 60,$IIMWV,58.9,R,17.9,N,A*36\r\n110 63,$IIMWV,58.8,R,18.1,N,A*30\r\n115 66,$IIMWV,56.5,R,16.7,N,A*3B\r\n120 69,$IIMWV,54.2,R,17.3,N,A*3B\r\n125 72,$IIMWV,52.8,R,18.3,N,A*38\r\n130 75,$IIMWV,49.7,R,18.7,N,A*39\r\n135 78,$IIMWV,48.1,R,21.0,N,A*33\r\n140 81,$IIMWV,47.3,R,21.5,N,A*3B\r\n145 84,$IIMWV,49.7,R,22.4,N,A*33\r\n150 87,$IIMWV,46.5,R,22.0,N,A*3A\r\n"}}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 25, "state": "DONE", "step_type": "agent_response", "duration_seconds": 1.962585609, "usage": {"input_tokens": 6978, "output_tokens": 208, "thinking_tokens": 115, "cache_read_tokens": 16241, "total_tokens": 7186}}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 26, "state": "ACTIVE", "step_type": "tool", "tool_name": "run_command", "tool_info": {"name": "run_command", "parameters": {"CommandLine": "python3 -c \"\nbody = 'IIMWV,x62.3,R,17.5,N,A'\nc = 0\nfor ch in body: c ^= ord(ch)\nprint(hex(c))\n\""}}}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 26, "state": "DONE", "step_type": "tool", "tool_name": "run_command", "duration_seconds": 0.299266218, "tool_info": {"name": "run_command", "parameters": {"CommandLine": "python3 -c \"\nbody = 'IIMWV,x62.3,R,17.5,N,A'\nc = 0\nfor ch in body: c ^= ord(ch)\nprint(hex(c))\n\""}, "output": "Command exited with code 0.\n\nCommand output:\n0x41\r\n"}}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 27, "state": "DONE", "step_type": "agent_response", "duration_seconds": 5.683151677, "usage": {"input_tokens": 3222, "output_tokens": 1115, "thinking_tokens": 85, "cache_read_tokens": 20295, "total_tokens": 4337}}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 28, "state": "ACTIVE", "step_type": "tool", "tool_name": "run_command", "tool_info": {"name": "run_command", "parameters": {"CommandLine": "python3 -c \"\nwith open('log.nmea') as f:\n for i, raw in enumerate(f, 1):\n line = raw.strip()\n if not line:\n print(f'Line {i}: BLANK')\n continue\n if ',' not in line:\n print(f'Line {i}: NO COMMA')\n continue\n t_str, sentence = line.split(',', 1)\n try:\n t = int(t_str)\n except ValueError:\n print(f'Line {i}: T NOT INT ({t_str})')\n continue\n if not sentence.startswith('$') or '*' not in \u2026"}}}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 28, "state": "DONE", "step_type": "tool", "tool_name": "run_command", "duration_seconds": 0.436317362, "tool_info": {"name": "run_command", "parameters": {"CommandLine": "python3 -c \"\nwith open('log.nmea') as f:\n for i, raw in enumerate(f, 1):\n line = raw.strip()\n if not line:\n print(f'Line {i}: BLANK')\n continue\n if ',' not in line:\n print(f'Line {i}: NO COMMA')\n continue\n t_str, sentence = line.split(',', 1)\n try:\n t = int(t_str)\n except ValueError:\n print(f'Line {i}: T NOT INT ({t_str})')\n continue\n if not sentence.startswith('$') or '*' not in \u2026"}, "output": "Command exited with code 0.\n\nCommand output:\nLine 25: BAD CHECKSUM\r\nLine 95: MWV field 1 not float: x62.3\r\nLine 147: BAD CHECKSUM\r\nLine 182: BAD CHECKSUM\r\nLine 209: BAD CHECKSUM\r\nLine 247: BAD CHECKSUM\r\nLine 262: BAD CHECKSUM\r\nLine 266: BLANK\r\nLine 277: UNKNOWN TYPE (XDR)\r\nLine 300: NOT $ OR NO * ($IIDBT,,f,7.4,M,,F)\r\nLine 325: BAD CHECKSUM\r\nLine 333: BAD CHECKSUM\r\nLine 339: BAD CHECKSUM\r\nLine 344: BAD CHECKSUM\r\nLine 376: BAD CHECKSUM\r\n"}}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 29, "state": "DONE", "step_type": "agent_response", "duration_seconds": 14.291840481, "usage": {"input_tokens": 4620, "output_tokens": 2316, "thinking_tokens": 873, "cache_read_tokens": 20297, "total_tokens": 6936}}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 30, "state": "ACTIVE", "step_type": "tool", "tool_name": "run_command", "tool_info": {"name": "run_command", "parameters": {"CommandLine": "python3 -c \"\nimport math, json\n\nwith open('input.json') as f:\n config = json.load(f)\n\nlatest_heading = None\nlatest_bsp = None\nlatest_depth = None\n\nvalid_count = 0\nbad_cs_count = 0\nmalformed_count = 0\n\nsamples = []\nvtg_readings = []\ndbt_readings = []\n\nwith open(config['log']) as f:\n for line in f:\n line = line.strip()\n if not line:\n continue\n if ',' not in line:\n malformed_count += 1\n continue\n t_str, sentence = line.split(',', 1)\n try\u2026"}}}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 30, "state": "DONE", "step_type": "tool", "tool_name": "run_command", "duration_seconds": 0.295966041, "tool_info": {"name": "run_command", "parameters": {"CommandLine": "python3 -c \"\nimport math, json\n\nwith open('input.json') as f:\n config = json.load(f)\n\nlatest_heading = None\nlatest_bsp = None\nlatest_depth = None\n\nvalid_count = 0\nbad_cs_count = 0\nmalformed_count = 0\n\nsamples = []\nvtg_readings = []\ndbt_readings = []\n\nwith open(config['log']) as f:\n for line in f:\n line = line.strip()\n if not line:\n continue\n if ',' not in line:\n malformed_count += 1\n continue\n t_str, sentence = line.split(',', 1)\n try\u2026"}, "output": "Command exited with code 0.\n\nCommand output:\ncounts: {'valid': 487, 'bad_checksum': 11, 'malformed': 3}\r\ntrue_wind_samples: 95\r\nlast sample raw: {'t': 297, 'tws': 17.35106677784368, 'twa': 69.52334235467946, 'twd': 47.72334235467946, 'vmg': 1.7701183004277277, 'bsp': 5.06, 'heading': 338.2}\r\nlast bsp: 5.06 depth: 5.6\r\nlast formatted: tws=17.4, twa=69.5, twd=47.7, vmg=1.8, bsp=5.1, depth=5.6\r\n"}}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 31, "state": "DONE", "step_type": "agent_response", "duration_seconds": 16.172606572, "usage": {"input_tokens": 3176, "output_tokens": 5507, "thinking_tokens": 5420, "cache_read_tokens": 24368, "total_tokens": 8683}}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 32, "state": "ACTIVE", "step_type": "tool", "tool_name": "run_command", "tool_info": {"name": "run_command", "parameters": {"CommandLine": "python3 -c \"\nimport json\n\nwith open('input.json') as f:\n config = json.load(f)\n\n# Let's run our alarm detection\nfrom solve_pilot import get_alarms # or inline\n\""}}}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 32, "state": "DONE", "step_type": "tool", "tool_name": "run_command", "duration_seconds": 0.391629392, "tool_info": {"name": "run_command", "parameters": {"CommandLine": "python3 -c \"\nimport json\n\nwith open('input.json') as f:\n config = json.load(f)\n\n# Let's run our alarm detection\nfrom solve_pilot import get_alarms # or inline\n\""}, "output": "Command exited with code 1.\n\nCommand output:\nTraceback (most recent call last):\r\n File \"<string>\", line 8, in <module>\r\n from solve_pilot import get_alarms # or inline\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\nModuleNotFoundError: No module named 'solve_pilot'\r\n"}}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 33, "state": "DONE", "step_type": "agent_response", "duration_seconds": 2.254919367, "usage": {"input_tokens": 8800, "output_tokens": 354, "thinking_tokens": 27, "cache_read_tokens": 24396, "total_tokens": 9154}}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 34, "state": "ACTIVE", "step_type": "tool", "tool_name": "run_command", "tool_info": {"name": "run_command", "parameters": {"CommandLine": "python3 -c \"\nimport math, json\n\nwith open('input.json') as f:\n config = json.load(f)\n\ndepth_min = config['alarms']['depth_min']\ntws_max = config['alarms']['tws_max']\nsog_max = config['alarms']['sog_max']\n\nlatest_heading = None\nlatest_bsp = None\n\nwind_samples = []\nvtg_readings = []\ndbt_readings = []\n\nwith open(config['log']) as f:\n for line in f:\n line = line.strip()\n if not line or ',' not in line: continue\n t_str, sentence = line.split(',', 1)\n try: t = int(t_str)\n \u2026"}}}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 34, "state": "DONE", "step_type": "tool", "tool_name": "run_command", "duration_seconds": 0.329844604, "tool_info": {"name": "run_command", "parameters": {"CommandLine": "python3 -c \"\nimport math, json\n\nwith open('input.json') as f:\n config = json.load(f)\n\ndepth_min = config['alarms']['depth_min']\ntws_max = config['alarms']['tws_max']\nsog_max = config['alarms']['sog_max']\n\nlatest_heading = None\nlatest_bsp = None\n\nwind_samples = []\nvtg_readings = []\ndbt_readings = []\n\nwith open(config['log']) as f:\n for line in f:\n line = line.strip()\n if not line or ',' not in line: continue\n t_str, sentence = line.split(',', 1)\n try: t = int(t_str)\n \u2026"}, "output": "Command exited with code 0.\n\nNo output from the command."}}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 35, "state": "DONE", "step_type": "agent_response", "duration_seconds": 4.000280998, "usage": {"input_tokens": 5180, "output_tokens": 1189, "thinking_tokens": 16, "cache_read_tokens": 28457, "total_tokens": 6369}}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 36, "state": "ACTIVE", "step_type": "tool", "tool_name": "run_command", "tool_info": {"name": "run_command", "parameters": {"CommandLine": "python3 -c \"\nimport math, json\n\nwith open('input.json') as f:\n config = json.load(f)\n\ndepth_min = config['alarms']['depth_min']\ntws_max = config['alarms']['tws_max']\nsog_max = config['alarms']['sog_max']\n\nlatest_heading = None\nlatest_bsp = None\n\nwind_samples = []\nvtg_readings = []\ndbt_readings = []\n\nwith open(config['log']) as f:\n for line in f:\n line = line.strip()\n if not line or ',' not in line: continue\n t_str, sentence = line.split(',', 1)\n try: t = int(t_str)\n \u2026"}}}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 36, "state": "DONE", "step_type": "tool", "tool_name": "run_command", "duration_seconds": 0.294955276, "tool_info": {"name": "run_command", "parameters": {"CommandLine": "python3 -c \"\nimport math, json\n\nwith open('input.json') as f:\n config = json.load(f)\n\ndepth_min = config['alarms']['depth_min']\ntws_max = config['alarms']['tws_max']\nsog_max = config['alarms']['sog_max']\n\nlatest_heading = None\nlatest_bsp = None\n\nwind_samples = []\nvtg_readings = []\ndbt_readings = []\n\nwith open(config['log']) as f:\n for line in f:\n line = line.strip()\n if not line or ',' not in line: continue\n t_str, sentence = line.split(',', 1)\n try: t = int(t_str)\n \u2026"}, "output": "Command exited with code 0.\n\nCommand output:\nAlarms count: 23\r\n<li>speed 3-3</li>\r\n<li>speed 9-9</li>\r\n<li>speed 21-21</li>\r\n<li>speed 27-27</li>\r\n<li>speed 36-36</li>\r\n<li>speed 42-48</li>\r\n<li>speed 54-54</li>\r\n<li>speed 60-111</li>\r\n<li>wind 84-84</li>\r\n<li>wind 93-93</li>\r\n<li>wind 99-99</li>\r\n<li>speed 117-132</li>\r\n<li>speed 144-156</li>\r\n<li>speed 171-186</li>\r\n<li>speed 195-228</li>\r\n<li>depth 210-216</li>\r\n<li>speed 234-240</li>\r\n<li>speed 246-267</li>\r\n<li>speed 273-273</li>\r\n<li>speed 285-285</li>\r\n<li>speed 291-291</li>\r\n<li>depth 294-297</li>\r\n<li>speed 297-297</li>\r\n"}}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 37, "state": "DONE", "step_type": "agent_response", "duration_seconds": 6.548404486, "usage": {"input_tokens": 2647, "output_tokens": 1926, "thinking_tokens": 1793, "cache_read_tokens": 32523, "total_tokens": 4573}}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 38, "state": "ACTIVE", "step_type": "tool", "tool_name": "run_command", "tool_info": {"name": "run_command", "parameters": {"CommandLine": "python3 -c \"\nwith open('log.nmea') as f:\n vhw = [l for l in f if 'VHW' in l]\n print('last VHW:', vhw[-5:])\nwith open('log.nmea') as f:\n dbt = [l for l in f if 'DBT' in l]\n print('last DBT:', dbt[-5:])\n\""}}}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 38, "state": "DONE", "step_type": "tool", "tool_name": "run_command", "duration_seconds": 0.30696001, "tool_info": {"name": "run_command", "parameters": {"CommandLine": "python3 -c \"\nwith open('log.nmea') as f:\n vhw = [l for l in f if 'VHW' in l]\n print('last VHW:', vhw[-5:])\nwith open('log.nmea') as f:\n dbt = [l for l in f if 'DBT' in l]\n print('last DBT:', dbt[-5:])\n\""}, "output": "Command exited with code 0.\n\nCommand output:\nlast VHW: ['285,$IIVHW,,T,,M,4.34,N,,K*48\\n', '288,$IIVHW,,T,,M,4.65,N,,K*4C\\n', '291,$IIVHW,,T,,M,4.95,N,,K*43\\n', '294,$IIVHW,,T,,M,5.01,N,,K*4F\\n', '297,$IIVHW,,T,,M,5.06,N,,K*48\\n']\r\nlast DBT: ['285,$IIDBT,,f,7.0,M,,F*16\\n', '288,$IIDBT,,f,7.0,M,,F*16\\n', '291,$IIDBT,,f,6.2,M,,F*15\\n', '294,$IIDBT,,f,5.7,M,,F*13\\n', '297,$IIDBT,,f,5.6,M,,F*12\\n']\r\n"}}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 39, "state": "DONE", "step_type": "agent_response", "duration_seconds": 22.090389364, "usage": {"input_tokens": 4916, "output_tokens": 7571, "thinking_tokens": 6865, "cache_read_tokens": 32529, "total_tokens": 12487}}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 40, "state": "ACTIVE", "step_type": "tool", "tool_name": "run_command", "tool_info": {"name": "run_command", "parameters": {"CommandLine": "python3 -c \"\nimport math, json\n\nwith open('input.json') as f:\n config = json.load(f)\n\nlatest_heading = None\nlatest_bsp = None\nsamples = []\n\nwith open(config['log']) as f:\n for line in f:\n line = line.strip()\n if not line or ',' not in line: continue\n t_str, sentence = line.split(',', 1)\n try: t = int(t_str)\n except ValueError: continue\n if not sentence.startswith('$') or '*' not in sentence: continue\n body, star, cs = sentence[1:].rpartition('*')\n \u2026"}}}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 40, "state": "DONE", "step_type": "tool", "tool_name": "run_command", "duration_seconds": 0.310696438, "tool_info": {"name": "run_command", "parameters": {"CommandLine": "python3 -c \"\nimport math, json\n\nwith open('input.json') as f:\n config = json.load(f)\n\nlatest_heading = None\nlatest_bsp = None\nsamples = []\n\nwith open(config['log']) as f:\n for line in f:\n line = line.strip()\n if not line or ',' not in line: continue\n t_str, sentence = line.split(',', 1)\n try: t = int(t_str)\n except ValueError: continue\n if not sentence.startswith('$') or '*' not in sentence: continue\n body, star, cs = sentence[1:].rpartition('*')\n \u2026"}, "output": "Command exited with code 0.\n\nCommand output:\nsamples count: 95\r\nmean=17.3885, round(mean, 2)=17.39\r\nmax=19.7890, round(max, 2)=19.79\r\n"}}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 41, "state": "DONE", "step_type": "agent_response", "duration_seconds": 11.989724682, "usage": {"input_tokens": 8521, "output_tokens": 4004, "thinking_tokens": 3874, "cache_read_tokens": 36633, "total_tokens": 12525}}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 42, "state": "ACTIVE", "step_type": "tool", "tool_name": "run_command", "tool_info": {"name": "run_command", "parameters": {"CommandLine": "python3 -c \"\nwith open('log.nmea') as f:\n times = []\n for line in f:\n line = line.strip()\n if line and ',' in line:\n times.append(int(line.split(',', 1)[0]))\nprint('Min time:', min(times), 'Max time:', max(times), 'Is sorted:', times == sorted(times))\n\""}}}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 42, "state": "DONE", "step_type": "tool", "tool_name": "run_command", "duration_seconds": 0.324554439, "tool_info": {"name": "run_command", "parameters": {"CommandLine": "python3 -c \"\nwith open('log.nmea') as f:\n times = []\n for line in f:\n line = line.strip()\n if line and ',' in line:\n times.append(int(line.split(',', 1)[0]))\nprint('Min time:', min(times), 'Max time:', max(times), 'Is sorted:', times == sorted(times))\n\""}, "output": "Command exited with code 0.\n\nCommand output:\nMin time: 0 Max time: 297 Is sorted: True\r\n"}}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 43, "state": "DONE", "step_type": "agent_response", "duration_seconds": 14.910270996, "usage": {"input_tokens": 4472, "output_tokens": 3274, "thinking_tokens": 3177, "cache_read_tokens": 44788, "total_tokens": 7746}}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 44, "state": "ACTIVE", "step_type": "tool", "tool_name": "run_command", "tool_info": {"name": "run_command", "parameters": {"CommandLine": "python3 -c \"\nprint(-45.0 % 360)\nprint(-0.0 % 360)\nprint(360.0 % 360)\nprint(365.2 % 360)\n\""}}}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 44, "state": "DONE", "step_type": "tool", "tool_name": "run_command", "duration_seconds": 0.303793999, "tool_info": {"name": "run_command", "parameters": {"CommandLine": "python3 -c \"\nprint(-45.0 % 360)\nprint(-0.0 % 360)\nprint(360.0 % 360)\nprint(365.2 % 360)\n\""}, "output": "Command exited with code 0.\n\nCommand output:\n315.0\r\n0.0\r\n0.0\r\n5.199999999999989\r\n"}}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 45, "state": "DONE", "step_type": "agent_response", "duration_seconds": 4.321174701, "usage": {"input_tokens": 3782, "output_tokens": 1276, "thinking_tokens": 1144, "cache_read_tokens": 48869, "total_tokens": 5058}}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 46, "state": "ACTIVE", "step_type": "tool", "tool_name": "run_command", "tool_info": {"name": "run_command", "parameters": {"CommandLine": "python3 -c \"\nwith open('log.nmea') as f:\n for line in f:\n if 'VTG' in line:\n parts = line.strip().split(',')\n t = int(parts[0])\n sog = float(parts[5])\n if t >= 290:\n print(t, sog, sog > 5.2)\n\""}}}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 46, "state": "DONE", "step_type": "tool", "tool_name": "run_command", "duration_seconds": 0.373827991, "tool_info": {"name": "run_command", "parameters": {"CommandLine": "python3 -c \"\nwith open('log.nmea') as f:\n for line in f:\n if 'VTG' in line:\n parts = line.strip().split(',')\n t = int(parts[0])\n sog = float(parts[5])\n if t >= 290:\n print(t, sog, sog > 5.2)\n\""}, "output": "Command exited with code 1.\n\nCommand output:\nTraceback (most recent call last):\r\n File \"<string>\", line 7, in <module>\r\n sog = float(parts[5])\r\nValueError: could not convert string to float: 'M'\r\n"}}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 47, "state": "DONE", "step_type": "agent_response", "duration_seconds": 3.621727124, "usage": {"input_tokens": 5191, "output_tokens": 807, "thinking_tokens": 654, "cache_read_tokens": 48868, "total_tokens": 5998}}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 48, "state": "ACTIVE", "step_type": "tool", "tool_name": "run_command", "tool_info": {"name": "run_command", "parameters": {"CommandLine": "python3 -c \"\nwith open('log.nmea') as f:\n for line in f:\n if 'VTG' in line:\n t, sentence = line.strip().split(',', 1)\n body = sentence[1:].rpartition('*')[0]\n fields = body.split(',')\n sog = float(fields[5])\n if int(t) >= 290:\n print(t, sog, sog > 5.2)\n\""}}}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 48, "state": "DONE", "step_type": "tool", "tool_name": "run_command", "duration_seconds": 0.345709884, "tool_info": {"name": "run_command", "parameters": {"CommandLine": "python3 -c \"\nwith open('log.nmea') as f:\n for line in f:\n if 'VTG' in line:\n t, sentence = line.strip().split(',', 1)\n body = sentence[1:].rpartition('*')[0]\n fields = body.split(',')\n sog = float(fields[5])\n if int(t) >= 290:\n print(t, sog, sog > 5.2)\n\""}, "output": "Command exited with code 0.\n\nCommand output:\n291 5.59 True\r\n294 5.17 False\r\n297 5.22 True\r\n"}}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 49, "state": "DONE", "step_type": "agent_response", "duration_seconds": 1.972687144, "usage": {"input_tokens": 2045, "output_tokens": 145, "thinking_tokens": 53, "cache_read_tokens": 52936, "total_tokens": 2190}}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 50, "state": "ACTIVE", "step_type": "tool", "tool_name": "run_command", "tool_info": {"name": "run_command", "parameters": {"CommandLine": "python3 -c \"\nwith open('log.nmea') as f:\n for i, line in enumerate(f, 1):\n if '297,' in line:\n print(i, line.strip())\n\""}}}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 50, "state": "DONE", "step_type": "tool", "tool_name": "run_command", "duration_seconds": 0.301090379, "tool_info": {"name": "run_command", "parameters": {"CommandLine": "python3 -c \"\nwith open('log.nmea') as f:\n for i, line in enumerate(f, 1):\n if '297,' in line:\n print(i, line.strip())\n\""}, "output": "Command exited with code 0.\n\nCommand output:\n498 297,$IIHDT,338.2,T*28\r\n499 297,$IIVHW,,T,,M,5.06,N,,K*48\r\n500 297,$IIDBT,,f,5.6,M,,F*12\r\n501 297,$GPVTG,338.2,T,,M,5.22,N,,K*71\r\n502 297,$IIMWV,55.6,R,19.7,N,A*34\r\n"}}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 51, "state": "DONE", "step_type": "agent_response", "duration_seconds": 14.272469426, "usage": {"input_tokens": 2424, "output_tokens": 5401, "thinking_tokens": 5219, "cache_read_tokens": 52928, "total_tokens": 7825}}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 52, "state": "ACTIVE", "step_type": "tool", "tool_name": "run_command", "tool_info": {"name": "run_command", "parameters": {"CommandLine": "python3 -c \"\nfrom collections import defaultdict\nwith open('log.nmea') as f:\n seen = defaultdict(list)\n for line in f:\n line = line.strip()\n if not line or ',' not in line: continue\n t, sentence = line.split(',', 1)\n stype = sentence.split(',')[0][-3:]\n seen[stype].append(int(t))\n\nfor stype, ts in seen.items():\n dups = len(ts) - len(set(ts))\n print(stype, 'count:', len(ts), 'duplicates:', dups)\n\""}}}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 52, "state": "DONE", "step_type": "tool", "tool_name": "run_command", "duration_seconds": 0.302427409, "tool_info": {"name": "run_command", "parameters": {"CommandLine": "python3 -c \"\nfrom collections import defaultdict\nwith open('log.nmea') as f:\n seen = defaultdict(list)\n for line in f:\n line = line.strip()\n if not line or ',' not in line: continue\n t, sentence = line.split(',', 1)\n stype = sentence.split(',')[0][-3:]\n seen[stype].append(int(t))\n\nfor stype, ts in seen.items():\n dups = len(ts) - len(set(ts))\n print(stype, 'count:', len(ts), 'duplicates:', dups)\n\""}, "output": "Command exited with code 0.\n\nCommand output:\nHDT count: 100 duplicates: 0\r\nVHW count: 100 duplicates: 0\r\nDBT count: 100 duplicates: 0\r\nVTG count: 100 duplicates: 0\r\nMWV count: 100 duplicates: 0\r\nXDR count: 1 duplicates: 0\r\n"}}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 53, "state": "DONE", "step_type": "agent_response", "duration_seconds": 37.662558264, "usage": {"input_tokens": 7966, "output_tokens": 15769, "thinking_tokens": 11739, "cache_read_tokens": 52948, "total_tokens": 23735}}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 54, "state": "ACTIVE", "step_type": "tool", "tool_name": "write_file", "tool_info": {"name": "write_file", "parameters": {"Path": "/home/bob/usecase-runs/agy-batch/out/gemini-3.8-flash-medium/k01/work/solve.py"}}}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 54, "state": "DONE", "step_type": "tool", "tool_name": "write_file", "duration_seconds": 0.092274448, "tool_info": {"name": "write_file", "parameters": {"Path": "/home/bob/usecase-runs/agy-batch/out/gemini-3.8-flash-medium/k01/work/solve.py"}}}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 55, "state": "DONE", "step_type": "agent_response", "duration_seconds": 3.390198115, "usage": {"input_tokens": 19679, "output_tokens": 73, "thinking_tokens": 23, "cache_read_tokens": 57080, "total_tokens": 19752}}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 56, "state": "ACTIVE", "step_type": "tool", "tool_name": "run_command", "tool_info": {"name": "run_command", "parameters": {"CommandLine": "python3 solve.py input.json answer.json"}}}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 56, "state": "DONE", "step_type": "tool", "tool_name": "run_command", "duration_seconds": 0.41293411, "tool_info": {"name": "run_command", "parameters": {"CommandLine": "python3 solve.py input.json answer.json"}, "output": "Command exited with code 1.\n\nThere were sandbox errors that may or may not be related to the failure. If you think the failure is because of running in the sandbox, you can run the command again with `BypassSandbox` set to true to request explicit user permission.\n\nCommand output:\nTraceback (most recent call last):\r\n File \"/home/bob/usecase-runs/agy-batch/out/gemini-3.8-flash-medium/k01/work/solve.py\", line 368, in <module>\r\n main()\r\n ~~~~^^\r\n File \"/home/bob/usecase-runs/agy-batch/out/gemini-3.8-flash-medium/k01/work/solve.py\", line 233, in main\r\n with open(answer_path, 'w', encoding='utf-8') as f:\r\n ~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\nOSError: [Errno 30] Read-only file system: 'answer.json'\r\n"}}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 57, "state": "DONE", "step_type": "agent_response", "duration_seconds": 2.615783064, "usage": {"input_tokens": 3756, "output_tokens": 160, "thinking_tokens": 119, "cache_read_tokens": 73381, "total_tokens": 3916}}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 58, "state": "ACTIVE", "step_type": "tool", "tool_name": "run_command", "tool_info": {"name": "run_command", "parameters": {"CommandLine": "pwd && ls -ld ."}}}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 58, "state": "DONE", "step_type": "tool", "tool_name": "run_command", "duration_seconds": 0.314642883, "tool_info": {"name": "run_command", "parameters": {"CommandLine": "pwd && ls -ld ."}, "output": "Command exited with code 0.\n\nCommand output:\n/home/bob/usecase-runs/agy-batch/out/gemini-3.8-flash-medium/k01/work\r\ndrwxr-xr-x 2 bob bob 4096 Oct 22 11:00 .\r\n"}}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 59, "state": "DONE", "step_type": "agent_response", "duration_seconds": 3.68121992, "usage": {"input_tokens": 4069, "output_tokens": 191, "thinking_tokens": 151, "cache_read_tokens": 73373, "total_tokens": 4260}}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 60, "state": "ACTIVE", "step_type": "tool", "tool_name": "run_command", "tool_info": {"name": "run_command", "parameters": {"CommandLine": "touch test.txt"}}}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 60, "state": "DONE", "step_type": "tool", "tool_name": "run_command", "duration_seconds": 0.299857926, "tool_info": {"name": "run_command", "parameters": {"CommandLine": "touch test.txt"}, "output": "Command exited with code 1.\n\nThere were sandbox errors that may or may not be related to the failure. If you think the failure is because of running in the sandbox, you can run the command again with `BypassSandbox` set to true to request explicit user permission.\n\nCommand output:\ntouch: cannot touch 'test.txt': Read-only file system\r\n"}}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 61, "state": "DONE", "step_type": "agent_response", "duration_seconds": 3.394891276, "usage": {"input_tokens": 4418, "output_tokens": 179, "thinking_tokens": 123, "cache_read_tokens": 73365, "total_tokens": 4597}}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 62, "state": "ACTIVE", "step_type": "tool", "tool_name": "run_command", "tool_info": {"name": "run_command", "parameters": {"CommandLine": "python3 solve.py input.json answer.json"}}}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 62, "state": "DONE", "step_type": "tool", "tool_name": "run_command", "duration_seconds": 0.06310797, "tool_info": {"name": "run_command", "parameters": {"CommandLine": "python3 solve.py input.json answer.json"}, "output": "Command exited with code 0.\n\nNo output from the command."}}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 63, "state": "DONE", "step_type": "agent_response", "duration_seconds": 8.021370836, "usage": {"input_tokens": 4692, "output_tokens": 87, "thinking_tokens": 36, "cache_read_tokens": 73357, "total_tokens": 4779}}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 64, "state": "ACTIVE", "step_type": "tool", "tool_name": "read_file", "tool_info": {"name": "read_file", "parameters": {"Path": "/home/bob/usecase-runs/agy-batch/out/gemini-3.8-flash-medium/k01/work/answer.json"}}}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 64, "state": "DONE", "step_type": "tool", "tool_name": "read_file", "duration_seconds": 0.076006616, "tool_info": {"name": "read_file", "parameters": {"Path": "/home/bob/usecase-runs/agy-batch/out/gemini-3.8-flash-medium/k01/work/answer.json"}, "output": "135 lines, 1941 bytes"}}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 65, "state": "DONE", "step_type": "agent_response", "duration_seconds": 4.180079587, "usage": {"input_tokens": 2143, "output_tokens": 61, "thinking_tokens": 10, "cache_read_tokens": 77430, "total_tokens": 2204}}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 66, "state": "ACTIVE", "step_type": "tool", "tool_name": "read_file", "tool_info": {"name": "read_file", "parameters": {"Path": "/home/bob/usecase-runs/agy-batch/out/gemini-3.8-flash-medium/k01/work/dashboard.html"}}}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 66, "state": "DONE", "step_type": "tool", "tool_name": "read_file", "duration_seconds": 0.057825094, "tool_info": {"name": "read_file", "parameters": {"Path": "/home/bob/usecase-runs/agy-batch/out/gemini-3.8-flash-medium/k01/work/dashboard.html"}, "output": "138 lines, 3831 bytes"}}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 67, "state": "DONE", "step_type": "agent_response", "duration_seconds": 7.738416973, "usage": {"input_tokens": 4371, "output_tokens": 1939, "thinking_tokens": 1563, "cache_read_tokens": 77432, "total_tokens": 6310}}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 68, "state": "ACTIVE", "step_type": "tool", "tool_name": "run_command", "tool_info": {"name": "run_command", "parameters": {"CommandLine": "python3 -c \"\nimport solve\nimport json, tempfile, os\n\ntest_nmea = '''\n# Just testing various sentences\n0,$IIHDT,100.0,T*20\n\n0,$IIVHW,,T,,M,5.0,N,,K*49\n0,$GPVTG,100.0,T,,M,6.0,N,,K*79\n0,$IIDBT,,f,4.0,M,,F*13\n0,$IIMWV,45.0,R,10.0,N,A*12\n'''\n# Let's compute valid checksums for test sentences:\ndef make_line(t, body):\n cs = 0\n for c in body: cs ^= ord(c)\n return f'{t},${body}*{cs:02X}'\n\nprint(make_line(0, 'IIHDT,100.0,T'))\nprint(make_line(0, 'IIVHW,,T,,M,5.0,N,,K'))\nprint(make_line(0, 'GPVTG,100.0,T,,M,6\u2026"}}}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 68, "state": "DONE", "step_type": "tool", "tool_name": "run_command", "duration_seconds": 0.085179591, "tool_info": {"name": "run_command", "parameters": {"CommandLine": "python3 -c \"\nimport solve\nimport json, tempfile, os\n\ntest_nmea = '''\n# Just testing various sentences\n0,$IIHDT,100.0,T*20\n\n0,$IIVHW,,T,,M,5.0,N,,K*49\n0,$GPVTG,100.0,T,,M,6.0,N,,K*79\n0,$IIDBT,,f,4.0,M,,F*13\n0,$IIMWV,45.0,R,10.0,N,A*12\n'''\n# Let's compute valid checksums for test sentences:\ndef make_line(t, body):\n cs = 0\n for c in body: cs ^= ord(c)\n return f'{t},${body}*{cs:02X}'\n\nprint(make_line(0, 'IIHDT,100.0,T'))\nprint(make_line(0, 'IIVHW,,T,,M,5.0,N,,K'))\nprint(make_line(0, 'GPVTG,100.0,T,,M,6\u2026"}, "output": "Command exited with code 0.\n\nCommand output:\n0,*23\r\n0,*7E\r\n0,*49\r\n0,*15\r\n0,*3D\r\n"}}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 69, "state": "DONE", "step_type": "agent_response", "duration_seconds": 8.735877463, "usage": {"input_tokens": 2345, "output_tokens": 2790, "thinking_tokens": 103, "cache_read_tokens": 81507, "total_tokens": 5135}}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 70, "state": "ACTIVE", "step_type": "tool", "tool_name": "write_file", "tool_info": {"name": "write_file", "parameters": {"Path": "/home/bob/usecase-runs/agy-batch/out/gemini-3.8-flash-medium/k01/work/test_solve.py"}}}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 70, "state": "DONE", "step_type": "tool", "tool_name": "write_file", "duration_seconds": 0.25093624, "tool_info": {"name": "write_file", "parameters": {"Path": "/home/bob/usecase-runs/agy-batch/out/gemini-3.8-flash-medium/k01/work/test_solve.py"}}}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 71, "state": "DONE", "step_type": "agent_response", "duration_seconds": 7.708518818, "usage": {"input_tokens": 5206, "output_tokens": 73, "thinking_tokens": 23, "cache_read_tokens": 81512, "total_tokens": 5279}}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 72, "state": "ACTIVE", "step_type": "tool", "tool_name": "run_command", "tool_info": {"name": "run_command", "parameters": {"CommandLine": "python3 test_solve.py"}}}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 72, "state": "DONE", "step_type": "tool", "tool_name": "run_command", "duration_seconds": 0.558963388, "tool_info": {"name": "run_command", "parameters": {"CommandLine": "python3 test_solve.py"}, "output": "Command exited with code 0.\n\nCommand output:\nTest Answer: {\r\n \"counts\": {\r\n \"valid\": 14,\r\n \"bad_checksum\": 2,\r\n \"malformed\": 24\r\n },\r\n \"true_wind_samples\": 3,\r\n \"last\": {\r\n \"tws\": 10.0,\r\n \"twa\": 0.0,\r\n \"twd\": 90.0,\r\n \"vmg\": 5.0,\r\n \"bsp\": 5.0,\r\n \"depth\": 8.0\r\n },\r\n \"tws_mean\": 15.0,\r\n \"tws_max\": 20.0,\r\n \"alarms\": [\r\n {\r\n \"type\": \"depth\",\r\n \"start\": 10,\r\n \"end\": 20\r\n },\r\n {\r\n \"type\": \"speed\",\r\n \"start\": 10,\r\n \"end\": 20\r\n },\r\n {\r\n \"type\": \"wind\",\r\n \"start\": 20,\r\n \"end\": 20\r\n }\r\n ]\r\n}\r\nExpected malformed: 24, Actual: 24\r\nExpected bad_checksum: 2, Actual: 2\r\nExpected valid: 14, Actual: 14\r\nExpected true_wind_samples: 3, Actual: 3\r\nAlarms: [{'type': 'depth', 'start': 10, 'end': 20}, {'type': 'speed', 'start': 10, 'end': 20}, {'type': 'wind', 'start': 20, 'end': 20}]\r\nALL TEST CHECKS PASSED!\r\n"}}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 73, "state": "DONE", "step_type": "agent_response", "duration_seconds": 7.681857571, "usage": {"input_tokens": 5761, "output_tokens": 144, "thinking_tokens": 80, "cache_read_tokens": 81505, "total_tokens": 5905}}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 74, "state": "ACTIVE", "step_type": "tool", "tool_name": "run_command", "tool_info": {"name": "run_command", "parameters": {"CommandLine": "rm -f test_log.nmea test_input.json test_answer.json test_solve.py"}}}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 74, "state": "DONE", "step_type": "tool", "tool_name": "run_command", "duration_seconds": 0.063806717, "tool_info": {"name": "run_command", "parameters": {"CommandLine": "rm -f test_log.nmea test_input.json test_answer.json test_solve.py"}, "output": "Command exited with code 0.\n\nNo output from the command."}}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 75, "state": "DONE", "step_type": "agent_response", "duration_seconds": 6.5726854249999995, "usage": {"input_tokens": 6000, "output_tokens": 74, "thinking_tokens": 18, "cache_read_tokens": 81497, "total_tokens": 6074}}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 76, "state": "ACTIVE", "step_type": "tool", "tool_name": "run_command", "tool_info": {"name": "run_command", "parameters": {"CommandLine": "python3 solve.py input.json answer.json"}}}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 76, "state": "DONE", "step_type": "tool", "tool_name": "run_command", "duration_seconds": 0.112798431, "tool_info": {"name": "run_command", "parameters": {"CommandLine": "python3 solve.py input.json answer.json"}, "output": "Command exited with code 0.\n\nNo output from the command."}}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 77, "state": "DONE", "step_type": "agent_response", "duration_seconds": 2.956313013, "usage": {"input_tokens": 2095, "output_tokens": 62, "thinking_tokens": 24, "cache_read_tokens": 85563, "total_tokens": 2157}}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 78, "state": "ACTIVE", "step_type": "tool", "tool_name": "run_command", "tool_info": {"name": "run_command", "parameters": {"CommandLine": "ls -la"}}}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 78, "state": "DONE", "step_type": "tool", "tool_name": "run_command", "duration_seconds": 0.284757114, "tool_info": {"name": "run_command", "parameters": {"CommandLine": "ls -la"}, "output": "Command exited with code 0.\n\nCommand output:\ntotal 60\r\ndrwxr-xr-x 3 bob bob 4096 Oct 22 11:01 .\r\ndrwxrwxr-x 3 bob bob 4096 Oct 22 10:56 ..\r\n-rw-r--r-- 1 bob bob 4114 Oct 22 10:56 TASK.md\r\ndrwxrwxr-x 2 bob bob 4096 Oct 22 11:01 __pycache__\r\n-rw-rw-r-- 1 bob bob 1941 Oct 22 11:01 answer.json\r\n-rw-r--r-- 1 bob bob 3831 Oct 22 11:01 dashboard.html\r\n-rw-r--r-- 1 bob bob 95 Oct 22 10:56 input.json\r\n-rw-r--r-- 1 bob bob 14141 Oct 22 10:56 log.nmea\r\n-rw-r--r-- 1 bob bob 11853 Oct 22 11:00 solve.py\r\n"}}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 79, "state": "DONE", "step_type": "agent_response", "duration_seconds": 2.505416292, "usage": {"input_tokens": 2517, "output_tokens": 63, "thinking_tokens": 16, "cache_read_tokens": 85555, "total_tokens": 2580}}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 80, "state": "ACTIVE", "step_type": "tool", "tool_name": "run_command", "tool_info": {"name": "run_command", "parameters": {"CommandLine": "rm -rf __pycache__"}}}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 80, "state": "DONE", "step_type": "tool", "tool_name": "run_command", "duration_seconds": 0.067424928, "tool_info": {"name": "run_command", "parameters": {"CommandLine": "rm -rf __pycache__"}, "output": "Command exited with code 0.\n\nNo output from the command."}}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 81, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "`solve.py` h"}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 81, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "as been implemented usin"}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 81, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "g the Python 3 standard "}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 81, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "library to parse the NMEA 0183 log s"}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 81, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "pecified in `input.json`"}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 81, "state": "ACTIVE", "step_type": "agent_response", "text_delta": ", perform al"}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 81, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "l validation and instrum"}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 81, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "ent calculations, find a"}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 81, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "larm periods, and output both `answe"}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 81, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "r.json` and `dashboard.h"}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 81, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "tml`.\n\n### I"}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 81, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "mplementation Summary\n- "}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 81, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "**Sentence Validation & Checksums**: Enforces fo"}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 81, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "rmat rules ("}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 81, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "comma presence, integer timestamp, `$` prefix, `"}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 81, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "*` delimiter"}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 81, "state": "ACTIVE", "step_type": "agent_response", "text_delta": ", 2-hex-digit checksum)."}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 81, "state": "ACTIVE", "step_type": "agent_response", "text_delta": " Lines with mismatched X"}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 81, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "OR checksums"}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 81, "state": "ACTIVE", "step_type": "agent_response", "text_delta": " are classified as `bad_checksum`. Lines failing"}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 81, "state": "ACTIVE", "step_type": "agent_response", "text_delta": " syntax, con"}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 81, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "taining unknown sentence types, or missing numer"}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 81, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "ic fields after passing "}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 81, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "checksums are classified as `malform"}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 81, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "ed`. Blank lines are ign"}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 81, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "ored.\n- **In"}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 81, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "strument State & True Wi"}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 81, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "nd Samples**"}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 81, "state": "ACTIVE", "step_type": "agent_response", "text_delta": ": Tracks latest true heading (`HDT`) and water s"}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 81, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "peed (`VHW`). On valid `"}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 81, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "MWV` sentences with reference `R` and status `A`, computes a"}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 81, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "pparent-to-true wind vec"}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 81, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "tor decompos"}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 81, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "ition ($x = \\text{AWS}\\cos(\\text{AWA}) - \\text{B"}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 81, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "SP}$, $y = \\text{AWS}\\si"}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 81, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "n(\\text{AWA}"}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 81, "state": "ACTIVE", "step_type": "agent_response", "text_delta": ")$), true wind speed ($\\text{TWS} = \\operatornam"}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 81, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "e{hypot}(x, "}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 81, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "y)$), true wind angle ($\\text{TWA} = \\operatorna"}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 81, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "me{atan2}(y, x) \\pmod{36"}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 81, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "0}$), true w"}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 81, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "ind direction ($\\text{TW"}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 81, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "D} = (\\text{heading} + \\text{TWA}) \\"}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 81, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "pmod{360}$), and $\\text{"}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 81, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "VMG} = \\text{BSP}\\cos(\\t"}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 81, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "ext{TWA})$.\n- **Alarms**: Detects maximal consecutive runs o"}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 81, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "f limit-breaking reading"}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 81, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "s for depth (`DBT` $< \\text{depth\\_m"}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 81, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "in}$), wind (`TWS` $> \\t"}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 81, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "ext{tws\\_max}$), and spe"}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 81, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "ed (`VTG` SOG $> \\text{sog\\_max}$), "}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 81, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "sorted by start time and"}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 81, "state": "ACTIVE", "step_type": "agent_response", "text_delta": " type name.\n- **Output Files**:\n - "}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 81, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "`answer.json`: Formats sentence counts, sample c"}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 81, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "ounts, statistics (`tws_"}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 81, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "mean`, `tws_max`), `last` reading va"}}
{"event": "step_update", "step_update": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "step_index": 81, "state": "DONE", "step_type": "agent_response", "text_delta": "lues, and sorted alarm intervals.\n - `dashboard.html`: Self-contained static display containing instrument values with 1 decimal place (`#tws`, `#twa`, `#twd`, `#vmg`, `#bsp`, `#depth`), an SVG `#wind-rose` with `#twd-needle` vector pointing toward TWD, and `#alarm-list` `<ul>` with an `<li>` per alarm.\n", "duration_seconds": 4.107491569, "usage": {"input_tokens": 2676, "output_tokens": 806, "thinking_tokens": 277, "cache_read_tokens": 85546, "total_tokens": 3482}}}
{"event": "result", "result": {"conversation_id": "5beeb527-dbbd-406b-94e4-0f29f482b899", "status": "SUCCESS", "response": "`solve.py` has been implemented using the Python 3 standard library to parse the NMEA 0183 log specified in `input.json`, perform all validation and instrument calculations, find alarm periods, and output both `answer.json` and `dashboard.html`.\n\n### Implementation Summary\n- **Sentence Validation & Checksums**: Enforces format rules (comma presence, integer timestamp, `$` prefix, `*` delimiter, 2-hex-digit checksum). Lines with mismatched XOR checksums are classified as `bad_checksum`. Lines failing syntax, containing unknown sentence types, or missing numeric fields after passing checksums are classified as `malformed`. Blank lines are ignored.\n- **Instrument State & True Wind Samples**: Tracks latest true heading (`HDT`) and water speed (`VHW`). On valid `MWV` sentences with reference `R` and status `A`, computes apparent-to-true wind vector decomposition ($x = \\text{AWS}\\cos(\\text{AWA}) - \\text{BSP}$, $y = \\text{AWS}\\sin(\\text{AWA})$), true wind speed ($\\text{TWS} = \\operatorname{hypot}(x, y)$), true wind angle ($\\text{TWA} = \\operatorname{atan2}(y, x) \\pmod{360}$), true wind direction ($\\text{TWD} = (\\text{heading} + \\text{TWA}) \\pmod{360}$), and $\\text{VMG} = \\text{BSP}\\cos(\\text{TWA})$.\n- **Alarms**: Detects maximal consecutive runs of limit-breaking readings for depth (`DBT` $< \\text{depth\\_min}$), wind (`TWS` $> \\text{tws\\_max}$), and speed (`VTG` SOG $> \\text{sog\\_max}$), sorted by start time and type name.\n- **Output Files**:\n - `answer.json`: Formats sentence counts, sample counts, statistics (`tws_mean`, `tws_max`), `last` reading values, and sorted alarm intervals.\n - `dashboard.html`: Self-contained static display containing instrument values with 1 decimal place (`#tws`, `#twa`, `#twd`, `#vmg`, `#bsp`, `#depth`), an SVG `#wind-rose` with `#twd-needle` vector pointing toward TWD, and `#alarm-list` `<ul>` with an `<li>` per alarm.\n", "duration_seconds": 299.335121748, "num_turns": 1, "usage": {"input_tokens": 276356, "output_tokens": 64911, "thinking_tokens": 49322, "cache_read_tokens": 1653554, "total_tokens": 341267}}}