DeepSeek V4.1 Flash (Venice) (thinking on) on AIS collision risk: closest point of approach and alert levels
DDeepSeek V4.1 Flash (Venice)thinking on · venice-mini-agent v1.3.1 · cloud · 2026-10-27 · by @bobsiopener
- Estimated cost
- $0.0073
- Wall time
- 23s
- Human step-ins
- 0
- Outcome
- Pass
Compare with other models on this task →
Summary
Exact prompt
# AIS collision risk: CPA, TCPA and alert levels `input.json` (synthetic; not for collision avoidance): `own` ship (`lat`, `lon`, `cog` degrees true, `sog` knots), `targets` (each `mmsi`, `lat`, `lon`, `cog`, `sog`), thresholds `cpa_danger_nm`, `cpa_caution_nm`, `tcpa_max_min`. Write `solve.py` (Python 3 standard library only); `python solve.py input.json answer.json` writes `answer.json` and `radar.svg`. Fresh inputs are used too. ## Rules 1. Skip a target (list its mmsi in `skipped`, ascending) if lat, lon, cog or sog is missing/null, sog >= 102.3 (AIS "not available"), or cog >= 360 (not available). Keep the rest in input order. 2. Local flat frame around own ship in nm: x = (lon - own lon) * 60 * cos(own lat), y = (lat - own lat) * 60. Velocity of a ship = (sog * sin(cog), sog * cos(cog)) knots. 3. Relative position r = target - own, relative velocity v = v_target - v_own. If |v|^2 < 1e-9, TCPA = 0. Otherwise TCPA (hours) = max(0, -(r . v) / |v|^2). CPA = |r + v * TCPA|. 4. `range_nm` = |r|, `bearing_deg` = atan2(x, y) mod 360 (true bearing from own ship), `tcpa_min` = TCPA * 60. 5. Level: `danger` if CPA <= cpa_danger and tcpa_min <= tcpa_max; else `caution` if CPA <= cpa_caution and tcpa_min <= tcpa_max; else `none`. 6. `alerts`: mmsi of danger then caution targets, each group sorted by tcpa_min then mmsi. Round range/CPA to 3 decimals, bearing to 1, tcpa_min to 2. ## Visual output: radar.svg Static, self-contained SVG (root `<svg>`, no scripts, images, links or external assets; renders headless) with an element `id="own-ship"` and one element `id="target-<mmsi>"` for every non-skipped target. The drawing itself is not graded. ## answer.json `{"targets": [{"mmsi", "range_nm", "bearing_deg", "cpa_nm", "tcpa_min", "level"}, ...], "skipped": [...], "alerts": [...]}` ## Scoring Hidden checks (partial credit; all must pass): `filtering`, `range_bearing`, `cpa_tcpa`, `levels_and_alerts`, `radar_svg_static` (radar.svg exists, follows the static rules, has `own-ship` and every `target-<mmsi>`), `fresh_run_1`, `fresh_run_2`. Tolerances: distances 0.002 nm, bearings 0.11, minutes 0.011 (fresh runs 0.011 for all numbers). 60-second limit per run.
AIS not-available sentinels (sog 102.3, cog 360), a stationary target, diverging targets with TCPA clamped to 0, longitude scaling, near-threshold crossing targets, and a two-key alert sort. Inspired by https://github.com/fpugliano/oroboro-dashboard Headless, offline run on a reproducible fixture. Cost shown is a list-price estimate (measured tokens x the site's price table).
Benchmark code for comparing AI models, not navigation or safety software.
Success criteria: 1. filtering 2. range_bearing 3. cpa_tcpa 4. levels_and_alerts 5. radar_svg_static 6. fresh_run_1 7. fresh_run_2
Setup notes: Use Case Scout batch 10 case k08-ais-cpa-tcpa (offline fixture). Prepared with the case's setup script; hidden key/tests kept outside the agent's work dir. Prompt = TASK.md verbatim. Scored automatically with the scout's scorer. Tools: list_files/read_file/write_file/run_shell (venice-mini-agent v1.3), web disabled. Single run, no retries. Visual output (radar.svg) rendered offline to PNG with google-chrome headless, network blocked, 1280x800 at ~3 s after load. Harness: venice-mini-agent v1.3.1 via the Venice OpenAI-compatible API: same tool loop, system prompt and limits as the cases 02-25 Venice runs (max 25 turns, max 16,000 output tokens per call, temperature 0.2, Venice system prompt and web search off, per-run cost cap $0.05, thinking on (model default)). Same task text, work-dir layout and scorer as the Claude runs of this case. Cost = measured per-call tokens x Venice list price; paid from the Venice DIEM daily allowance. Harness: venice-mini-agent v1.3.1 via Venice OpenAI-compatible API (16000 max tokens/call, $0.05 run cap). Score: 7/7. Cost is an ESTIMATE: measured per-call tokens x the site's list-price table (paid from Venice DIEM allowance; Venice billing not itemized). input tokens include cached reads. Single run.
Estimated cost List-price estimate; not actually billed.
Output
Metrics
- Estimated cost
- $0.0073
- Wall time
- 23s
- Human step-ins
- 0
- Turns
- 6
| Model | Input tok | Output tok | Cost |
|---|---|---|---|
| DDeepSeek V4.1 Flash (Venice)venice/deepseek-v4-1-flashthinking on | 22,833 | 3,477 | $0.0073 |
Reproduce this run
- Use the use case prompt on the use case page.
- Same setup: DeepSeek V4.1 Flash (Venice) (venice/deepseek-v4-1-flash, thinking on) via
venice-mini-agent v1.3.1, cloud. - It counts as a success only if it meets every success criterion: 1. filtering 2. range_bearing 3. cpa_tcpa 4. levels_and_alerts 5. radar_svg_static 6. fresh_run_1 7. fresh_run_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.
Was this useful?
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
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"final_message": "All checks pass. Summary:\n\n**`solve.py`** (Python 3 stdlib only) implements the spec:\n\n- **Filtering**: skips targets with missing/null `lat`/`lon`/`cog`/`sog`, `sog >= 102.3`, or `cog >= 360`; skipped MMSIs sorted ascending, valid targets kept in input order.\n- **Geometry**: local flat nm frame (`x = \u0394lon\u00b760\u00b7cos(lat)`, `y = \u0394lat\u00b760`), velocity `(sog\u00b7sin(cog), sog\u00b7cos(cog))`, relative `r`/`v`, `TCPA = max(0, -(r\u00b7v)/|v|\u00b2)` (0 if `|v|\u00b2 < 1e-9`), `CPA = |r + v\u00b7TCPA|`, plus `range_nm`, `bearing_deg = atan2(x,y) mod 360`, `tcpa_min = TCPA\u00b760`.\n- **Levels/alerts**: `danger`/`caution`/`none` by CPA and TCPA thresholds; alerts list danger then caution, each sorted by `tcpa_min` then `mmsi`.\n- **Rounding**: range/CPA 3 dp, bearing 1 dp, tcpa_min 2 dp.\n- **`radar.svg`**: static self-contained SVG (root `<svg>`, no scripts/images/links/external assets) with `id=\"own-ship\"` and `id=\"target-<mmsi>\"` for every non-skipped target.\n\nVerified on the provided input (2 skipped: `340778672` cog=360, `660159254` sog=102.3; alerts `[795070736, 613463677, 265918776, 562375419]`) and on a synthetic edge-case input (null field, zero-speed target, head-on collision). SVG parses as valid XML with all required IDs.",
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"arguments": "{\"path\":\"solve.py\",\"content\":\"#!/usr/bin/env python3\\n\\\"\\\"\\\"AIS collision risk: CPA, TCPA and alert levels.\\n\\nUsage: python solve.py input.json answer.json\\nWrites answer.json and radar.svg (static, self-contained).\\n\\\"\\\"\\\"\\nimport json\\nimport math\\nimport sys\\n\\n\\ndef is_missing(v):\\n return v is None\\n\\n\\ndef valid_target(t):\\n for key in (\\\"lat\\\", \\\"lon\\\", \\\"cog\\\", \\\"sog\\\"):\\n if key not in t or is_missing(t[key]):\\n return False\\n try:\\n sog = float(t[\\\"sog\\\"])\\n cog = float(t[\\\"cog\\\"])\\n except (TypeError, ValueError):\\n return False\\n if sog >= 102.3:\\n return False\\n if cog >= 360:\\n return False\\n return True\\n\\n\\ndef compute(own, t):\\n lat0 = float(own[\\\"lat\\\"])\\n lon0 = float(own[\\\"lon\\\"])\\n coslat = math.cos(math.radians(lat0))\\n\\n # Local flat frame (nm)\\n x = (float(t[\\\"lon\\\"]) - lon0) * 60.0 * coslat\\n y = (float(t[\\\"lat\\\"]) - lat0) * 60.0\\n\\n # Velocities (knots)\\n cog_o = math.radians(float(own[\\\"cog\\\"]))\\n sog_o = float(own[\\\"sog\\\"])\\n vox = sog_o * math.sin(cog_o)\\n voy = sog_o * math.cos(cog_o)\\n\\n cog_t = math.radians(float(t[\\\"cog\\\"]))\\n sog_t = float(t[\\\"sog\\\"])\\n vtx = sog_t * math.sin(cog_t)\\n vty = sog_t * math.cos(cog_t)\\n\\n rx, ry = x, y\\n vx = vtx - vox\\n vy = vty - voy\\n\\n v2 = vx * vx + vy * vy\\n if v2 < 1e-9:\\n tcpa = 0.0\\n else:\\n tcpa = max(0.0, -(rx * vx + ry * vy) / v2)\\n\\n cpx = rx + vx * tcpa\\n cpy = ry + vy * tcpa\\n cpa = math.hypot(cpx, cpy)\\n\\n rng = math.hypot(rx, ry)\\n bearing = math.degrees(math.atan2(rx, ry)) % 360.0\\n tcpa_min = tcpa * 60.0\\n\\n return rng, bearing, cpa, tcpa_min\\n\\n\\ndef level_for(cpa, tcpa_min, danger, caution, tcpa_max):\\n if cpa <= danger and tcpa_min <= tcpa_max:\\n return \\\"danger\\\"\\n if cpa <= caution and tcpa_min <= tcpa_max:\\n return \\\"caution\\\"\\n return \\\"none\\\"\\n\\n\\ndef make_svg(own, results):\\n \\\"\\\"\\\"Static self-contained radar SVG.\\\"\\\"\\\"\\n size = 600\\n cx = cy = size / 2.0\\n max_range = 12.0 # nm displayed radius\\n scale = (size / 2.0 - 30.0) / max_range\\n\\n parts = []\\n parts.append(\\n '<svg xmlns=\\\"http://www.w3.org/2000/svg\\\" width=\\\"%d\\\" height=\\\"%d\\\" '\\n 'viewBox=\\\"0 0 %d %d\\\">' % (size, size, size, size)\\n )\\n parts.append('<rect width=\\\"%d\\\" height=\\\"%d\\\" fill=\\\"#0b1a0b\\\"/>' % (size, size))\\n # range rings\\n for i in range(1, 5):\\n r = (size / 2.0 - 30.0) * i / 4.0\\n parts.append(\\n '<circle cx=\\\"%.1f\\\" cy=\\\"%.1f\\\" r=\\\"%.1f\\\" fill=\\\"none\\\" '\\n 'stroke=\\\"#1f5f1f\\\" stroke-width=\\\"1\\\"/>' % (cx, cy, r)\\n )\\n # cross hairs\\n parts.append(\\n '<line x1=\\\"%.1f\\\" y1=\\\"30\\\" x2=\\\"%.1f\\\" y2=\\\"%d\\\" stroke=\\\"#1f5f1f\\\" '\\n 'stroke-width=\\\"1\\\"/>' % (cx, cx, size - 30)\\n )\\n parts.append(\\n '<line x1=\\\"30\\\" y1=\\\"%.1f\\\" x2=\\\"%d\\\" y2=\\\"%.1f\\\" stroke=\\\"#1f5f1f\\\" '\\n 'stroke-width=\\\"1\\\"/>' % (cy, size - 30, cy)\\n )\\n\\n # own ship at center\\n parts.append(\\n '<polygon id=\\\"own-ship\\\" points=\\\"%.1f,%.1f %.1f,%.1f %.1f,%.1f\\\" '\\n 'fill=\\\"#33ff33\\\"/>' % (\\n cx, cy - 8, cx - 5, cy + 6, cx + 5, cy + 6)\\n )\\n\\n colors = {\\\"danger\\\": \\\"#ff3333\\\", \\\"caution\\\": \\\"#ffcc00\\\", \\\"none\\\": \\\"#33ccff\\\"}\\n for res in results:\\n mmsi = res[\\\"mmsi\\\"]\\n rng = res[\\\"range_nm\\\"]\\n brg = math.radians(res[\\\"bearing_deg\\\"])\\n px = cx + rng * math.sin(brg) * scale\\n py = cy - rng * math.cos(brg) * scale\\n col = colors.get(res[\\\"level\\\"], \\\"#33ccff\\\")\\n parts.append(\\n '<circle id=\\\"target-%s\\\" cx=\\\"%.1f\\\" cy=\\\"%.1f\\\" r=\\\"5\\\" '\\n 'fill=\\\"%s\\\"/>' % (mmsi, px, py, col)\\n )\\n\\n parts.append(\\\"</svg>\\\")\\n return \\\"\\\\n\\\".join(parts)\\n\\n\\ndef main():\\n if len(sys.argv) < 3:\\n print(\\\"usage: python solve.py input.json answer.json\\\", file=sys.stderr)\\n return 1\\n in_path = sys.argv[1]\\n out_path = sys.argv[2]\\n\\n with open(in_path, \\\"r\\\") as f:\\n data = json.load(f)\\n\\n own = data[\\\"own\\\"]\\n targets = data.get(\\\"targets\\\", [])\\n danger = float(data[\\\"cpa_danger_nm\\\"])\\n caution = float(data[\\\"cpa_caution_nm\\\"])\\n tcpa_max = float(data[\\\"tcpa_max_min\\\"])\\n\\n skipped = []\\n results = []\\n for t in targets:\\n if not valid_target(t):\\n skipped.append(t.get(\\\"mmsi\\\"))\\n continue\\n rng, bearing, cpa, tcpa_min = compute(own, t)\\n lvl = level_for(cpa, tcpa_min, danger, caution, tcpa_max)\\n results.append({\\n \\\"mmsi\\\": t[\\\"mmsi\\\"],\\n \\\"range_nm\\\": round(rng, 3),\\n \\\"bearing_deg\\\": round(bearing, 1),\\n \\\"cpa_nm\\\": round(cpa, 3),\\n \\\"tcpa_min\\\": round(tcpa_min, 2),\\n \\\"level\\\": lvl,\\n })\\n\\n skipped.sort()\\n\\n danger_list = [r for r in results if r[\\\"level\\\"] == \\\"danger\\\"]\\n caution_list = [r for r in results if r[\\\"level\\\"] == \\\"caution\\\"]\\n danger_list.sort(key=lambda r: (r[\\\"tcpa_min\\\"], r[\\\"mmsi\\\"]))\\n caution_list.sort(key=lambda r: (r[\\\"tcpa_min\\\"], r[\\\"mmsi\\\"]))\\n alerts = [r[\\\"mmsi\\\"] for r in danger_list] + [r[\\\"mmsi\\\"] for r in caution_list]\\n\\n answer = {\\n \\\"targets\\\": results,\\n \\\"skipped\\\": skipped,\\n \\\"alerts\\\": alerts,\\n }\\n\\n with open(out_path, \\\"w\\\") as f:\\n json.dump(answer, f, indent=1)\\n\\n svg = make_svg(own, results)\\n with open(\\\"radar.svg\\\", \\\"w\\\") as f:\\n f.write(svg)\\n\\n return 0\\n\\n\\nif __name__ == \\\"__main__\\\":\\n sys.exit(main())\\n\"}"
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"content": "#!/usr/bin/env python3\n\"\"\"AIS collision risk: CPA, TCPA and alert levels.\n\nUsage: python solve.py input.json answer.json\nWrites answer.json and radar.svg (static, self-contained).\n\"\"\"\nimport json\nimport math\nimport sys\n\n\ndef is_missing(v):\n return v is None\n\n\ndef valid_target(t):\n for key in (\"lat\", \"lon\", \"cog\", \"sog\"):\n if key not in t or is_missing(t[key]):\n return False\n try:\n sog = float(t[\"sog\"])\n cog = float(t[\"cog\"])\n except (TypeError, ValueError):\n return False\n if sog >= 102.3:\n return False\n if cog >= 360:\n return False\n return True\n\n\ndef compute(own, t):\n lat0 = float(own[\"lat\"])\n lon0 = float(own[\"lon\"])\n coslat = math.cos(math.radians(lat0))\n\n # Local flat frame (nm)\n x = (float(t[\"lon\"]) - lon0) * 60.0 * coslat\n y = (float(t[\"lat\"]) - lat0) * 60.0\n\n # Velocities (knots)\n cog_o = math.radians(float(own[\"cog\"]))\n sog_o = float(own[\"sog\"])\n vox = sog_o * math.sin(cog_o)\n voy = sog_o * math.cos(cog_o)\n\n cog_t = math.radians(float(t[\"cog\"]))\n sog_t = float(t[\"sog\"])\n vtx = sog_t * math.sin(cog_t)\n vty = sog_t * math.cos(cog_t)\n\n rx, ry = x, y\n vx = vtx - vox\n vy = vty - voy\n\n v2 = vx * vx + vy * vy\n if v2 < 1e-9:\n tcpa = 0.0\n else:\n tcpa = max(0.0, -(rx * vx + ry * vy) / v2)\n\n cpx = rx + vx * tcpa\n cpy = ry + vy * tcpa\n cpa = math.hypot(cpx, cpy)\n\n rng = math.hypot(rx, ry)\n bearing = math.degrees(math.atan2(rx, ry)) % 360.0\n tcpa_min = tcpa * 60.0\n\n return rng, bearing, cpa, tcpa_min\n\n\ndef level_for(cpa, tcpa_min, danger, caution, tcpa_max):\n if cpa <= danger and tcpa_min <= tcpa_max:\n return \"danger\"\n if cpa <= caution and tcpa_min <= tcpa_max:\n return \"caution\"\n return \"none\"\n\n\ndef make_svg(own, results):\n \"\"\"Static self-contained radar SVG.\"\"\"\n size = 600\n cx = cy = size / 2.0\n max_range = 12.0 # nm displayed radius\n scale = (size / 2.0 - 30.0) / max_range\n\n parts = []\n parts.append(\n '<svg xmlns=\"http://www.w3.org/2000/svg\" width=\"%d\" height=\"%d\" '\n 'viewBox=\"0 0 %d %d\">' % (size, size, size, size)\n )\n parts.append('<rect width=\"%d\" height=\"%d\" fill=\"#0b1a0b\"/>' % (size, size))\n # range rings\n for i in range(1, 5):\n r = (size / 2.0 - 30.0) * i / 4.0\n parts.append(\n '<circle cx=\"%.1f\" cy=\"%.1f\" r=\"%.1f\" fill=\"none\" '\n 'stroke=\"#1f5f1f\" stroke-width=\"1\"/>' % (cx, cy, r)\n )\n # cross hairs\n parts.append(\n '<line x1=\"%.1f\" y1=\"30\" x2=\"%.1f\" y2=\"%d\" stroke=\"#1f5f1f\" '\n 'stroke-width=\"1\"/>' % (cx, cx, size - 30)\n )\n parts.append(\n '<line x1=\"30\" y1=\"%.1f\" x2=\"%d\" y2=\"%.1f\" stroke=\"#1f5f1f\" '\n 'stroke-width=\"1\"/>' % (cy, size - 30, cy)\n )\n\n # own ship at center\n parts.append(\n '<polygon id=\"own-ship\" points=\"%.1f,%.1f %.1f,%.1f %.1f,%.1f\" '\n 'fill=\"#33ff33\"/>' % (\n cx, cy - 8, cx - 5, cy + 6, cx + 5, cy + 6)\n )\n\n colors = {\"danger\": \"#ff3333\", \"caution\": \"#ffcc00\", \"none\": \"#33ccff\"}\n for res in results:\n mmsi = res[\"mmsi\"]\n rng = res[\"range_nm\"]\n brg = math.radians(res[\"bearing_deg\"])\n px = cx + rng * math.sin(brg) * scale\n py = cy - rng * math.cos(brg) * scale\n col = colors.get(res[\"level\"], \"#33ccff\")\n parts.append(\n '<circle id=\"target-%s\" cx=\"%.1f\" cy=\"%.1f\" r=\"5\" '\n 'fill=\"%s\"/>' % (mmsi, px, py, col)\n )\n\n parts.append(\"</svg>\")\n return \"\\n\".join(parts)\n\n\ndef main():\n if len(sys.argv) < 3:\n print(\"usage: python solve.py input.json answer.json\", file=sys.stderr)\n return 1\n in_path = sys.argv[1]\n out_path = sys.argv[2]\n\n with open(in_path, \"r\") as f:\n data = json.load(f)\n\n own = data[\"own\"]\n targets = data.get(\"targets\", [])\n danger = float(data[\"cpa_danger_nm\"])\n caution = float(data[\"cpa_caution_nm\"])\n tcpa_max = float(data[\"tcpa_max_min\"])\n\n skipped = []\n results = []\n for t in targets:\n if not valid_target(t):\n skipped.append(t.get(\"mmsi\"))\n continue\n rng, bearing, cpa, tcpa_min = compute(own, t)\n lvl = level_for(cpa, tcpa_min, danger, caution, tcpa_max)\n results.append({\n \"mmsi\": t[\"mmsi\"],\n \"range_nm\": round(rng, 3),\n \"bearing_deg\": round(bearing, 1),\n \"cpa_nm\": round(cpa, 3),\n \"tcpa_min\": round(tcpa_min, 2),\n \"level\": lvl,\n })\n\n skipped.sort()\n\n danger_list = [r for r in results if r[\"level\"] == \"danger\"]\n caution_list = [r for r in results if r[\"level\"] == \"caution\"]\n danger_list.sort(key=lambda r: (r[\"tcpa_min\"], r[\"mmsi\"]))\n caution_list.sort(key=lambda r: (r[\"tcpa_min\"], r[\"mmsi\"]))\n alerts = [r[\"mmsi\"] for r in danger_list] + [r[\"mmsi\"] for r in caution_list]\n\n answer = {\n \"targets\": results,\n \"skipped\": skipped,\n \"alerts\": alerts,\n }\n\n with open(out_path, \"w\") as f:\n json.dump(answer, f, indent=1)\n\n svg = make_svg(own, results)\n with open(\"radar.svg\", \"w\") as f:\n f.write(svg)\n\n return 0\n\n\nif __name__ == \"__main__\":\n sys.exit(main())\n"
},
"result": "wrote 5248 chars"
},
{
"turn": 3,
"response": {
"id": "chatcmpl-2b0c7475",
"choices": [
{
"index": 0,
"message": {
"content": null,
"tool_calls": [
{
"index": 0,
"id": "call_de239c0d2bc04e03a31d74fd",
"type": "function",
"function": {
"name": "run_shell",
"arguments": "{\"command\":\"cd . && python3 solve.py input.json answer.json && cat answer.json && echo \\\"---\\\" && head -c 400 radar.svg\"}"
}
}
],
"role": "assistant"
},
"finish_reason": "tool_calls",
"logprobs": null,
"raw_output": null
}
],
"created": 1793060805,
"model": "deepseek-v4-1-flash",
"service_tier": null,
"object": "chat.completion",
"usage": {
"prompt_tokens": 3927,
"completion_tokens": 67,
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"prompt_tokens_details": {
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},
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"completion_tokens_details": {
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"reasoning_tokens": 0,
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}
},
"cost": {
"usd": 0,
"diem": 0.0006969000000000001
},
"venice_parameters": {
"strip_thinking_response": false,
"disable_thinking": false,
"enable_e2ee": true,
"enable_web_search": "off",
"enable_web_scraping": false,
"enable_web_citations": false,
"include_search_results_in_stream": false,
"include_venice_system_prompt": false,
"enable_x_search": false,
"web_search_citations": [],
"return_search_results_as_documents": false
}
}
},
{
"turn": 3,
"tool": "run_shell",
"args": {
"command": "cd . && python3 solve.py input.json answer.json && cat answer.json && echo \"---\" && head -c 400 radar.svg"
},
"result": "exit=0\nstdout:\n{\n \"targets\": [\n {\n \"mmsi\": 795070736,\n \"range_nm\": 7.981,\n \"bearing_deg\": 202.0,\n \"cpa_nm\": 0.309,\n \"tcpa_min\": 11.82,\n \"level\": \"danger\"\n },\n {\n \"mmsi\": 276139249,\n \"range_nm\": 5.193,\n \"bearing_deg\": 355.7,\n \"cpa_nm\": 5.192,\n \"tcpa_min\": 0.17,\n \"level\": \"none\"\n },\n {\n \"mmsi\": 265918776,\n \"range_nm\": 7.18,\n \"bearing_deg\": 293.1,\n \"cpa_nm\": 0.66,\n \"tcpa_min\": 6.2,\n \"level\": \"caution\"\n },\n {\n \"mmsi\": 743277030,\n \"range_nm\": 5.986,\n \"bearing_deg\": 85.4,\n \"cpa_nm\": 0.318,\n \"tcpa_min\": 32.02,\n \"level\": \"none\"\n },\n {\n \"mmsi\": 562375419,\n \"range_nm\": 3.312,\n \"bearing_deg\": 105.8,\n \"cpa_nm\": 0.724,\n \"tcpa_min\": 25.19,\n \"level\": \"caution\"\n },\n {\n \"mmsi\": 613463677,\n \"range_nm\": 6.126,\n \"bearing_deg\": 359.0,\n \"cpa_nm\": 0.013,\n \"tcpa_min\": 13.23,\n \"level\": \"danger\"\n },\n {\n \"mmsi\": 286629533,\n \"range_nm\": 3.941,\n \"bearing_deg\": 316.4,\n \"cpa_nm\": 1.245,\n \"tcpa_min\": 53.42,\n \"level\": \"none\"\n },\n {\n \"mmsi\": 637679438,\n \"range_nm\": 5.672,\n \"bearing_deg\": 124.2,\n \"cpa_nm\": 5.672,\n \"tcpa_min\": 0.0,\n \"level\": \"none\"\n },\n {\n \"mmsi\": 327721356,\n \"range_nm\": 4.489,\n \"bearing_deg\": 138.5,\n \"cpa_nm\": 4.489,\n \"tcpa_min\": 0.0,\n \"level\": \"none\"\n },\n {\n \"mmsi\": 777617410,\n \"range_nm\": 7.368,\n \"bearing_deg\": 162.4,\n \"cpa_nm\": 4.584,\n \"tcpa_min\": 50.26,\n \"level\": \"none\"\n }\n ],\n \"skipped\": [\n 340778672,\n 660159254\n ],\n \"alerts\": [\n 795070736,\n 613463677,\n 265918776,\n 562375419\n ]\n}---\n<svg xmlns=\"http://www.w3.org/2000/svg\" width=\"600\" height=\"600\" viewBox=\"0 0 600 600\">\n<rect width=\"600\" height=\"600\" fill=\"#0b1a0b\"/>\n<circle cx=\"300.0\" cy=\"300.0\" r=\"67.5\" fill=\"none\" stroke=\"#1f5f1f\" stroke-width=\"1\"/>\n<circle cx=\"300.0\" cy=\"300.0\" r=\"135.0\" fill=\"none\" stroke=\"#1f5f1f\" stroke-width=\"1\"/>\n<circle cx=\"300.0\" cy=\"300.0\" r=\"202.5\" fill=\"none\" stroke=\"#1f5f1f\" stroke-width=\"1\"/>\n<\nstderr:\n"
},
{
"turn": 4,
"response": {
"id": "chatcmpl-e61965d6",
"choices": [
{
"index": 0,
"message": {
"content": null,
"tool_calls": [
{
"index": 0,
"id": "call_b0ea561c00d44a88957e9805",
"type": "function",
"function": {
"name": "run_shell",
"arguments": "{\"command\":\"cd . && python3 -c \\\"\\nimport xml.dom.minidom,re\\ns=open('radar.svg').read()\\nd=xml.dom.minidom.parseString(s)\\nids=[e.getAttribute('id') for e in d.getElementsByTagName('*') if e.getAttribute('id')]\\nprint('ids:',ids)\\nprint('script' in s.lower(), 'http' in s.lower(), '<image' in s.lower(), '<a ' in s.lower())\\n\\\"\"}"
}
}
],
"role": "assistant",
"reasoning_content": "Let me verify the logic. Target 660159254 has sog 102.3 \u2192 skipped. 340778672 cog 360 \u2192 skipped. Good.\n\nLet me double check the \"none\" targets with tcpa 0.0 \u2014 those are targets moving away (TCPA clamped to 0). Fine.\n\nLet me verify the SVG has all target ids and own-ship. Also check no scripts/links. Good.\n\nLet me verify a couple computations manually. Target 795070736: lat 46.83549, lon -127.20639. own lat 46.95882, lon -127.13341.\n\nx = (-127.20639 - (-127.13341)) * 60 * cos(46.95882) = (-0.07298)*60*cos(46.95882). cos(46.95882\u00b0)=0.6826. = -4.3788*0.6826 = -2.989. y = (46.83549-46.95882)*60 = -0.12333*60 = -7.3998. range = sqrt(2.989^2+7.3998^2)=sqrt(8.934+54.76)=sqrt(63.69)=7.98. Good.\n\nBearing atan2(x,y)=atan2(-2.989,-7.3998). That's in third quadrant \u2192 180+atan(2.989/7.3998)=180+22.0=202.0. Good.\n\nEverything looks consistent. Let me verify the SVG validity with a quick parse and check ids."
},
"finish_reason": "tool_calls",
"logprobs": null,
"raw_output": null
}
],
"created": 1793060806,
"model": "deepseek-v4-1-flash",
"service_tier": null,
"object": "chat.completion",
"usage": {
"prompt_tokens": 4907,
"completion_tokens": 482,
"total_tokens": 5389,
"prompt_tokens_details": {
"cached_tokens": 1920
},
"cache_read_input_tokens": 1920,
"completion_tokens_details": {
"accepted_prediction_tokens": null,
"reasoning_tokens": 339,
"rejected_prediction_tokens": null
}
},
"cost": {
"usd": 0,
"diem": 0.0014889
},
"venice_parameters": {
"strip_thinking_response": false,
"disable_thinking": false,
"enable_e2ee": true,
"enable_web_search": "off",
"enable_web_scraping": false,
"enable_web_citations": false,
"include_search_results_in_stream": false,
"include_venice_system_prompt": false,
"enable_x_search": false,
"web_search_citations": [],
"return_search_results_as_documents": false
}
}
},
{
"turn": 4,
"tool": "run_shell",
"args": {
"command": "cd . && python3 -c \"\nimport xml.dom.minidom,re\ns=open('radar.svg').read()\nd=xml.dom.minidom.parseString(s)\nids=[e.getAttribute('id') for e in d.getElementsByTagName('*') if e.getAttribute('id')]\nprint('ids:',ids)\nprint('script' in s.lower(), 'http' in s.lower(), '<image' in s.lower(), '<a ' in s.lower())\n\""
},
"result": "exit=0\nstdout:\nids: ['own-ship', 'target-795070736', 'target-276139249', 'target-265918776', 'target-743277030', 'target-562375419', 'target-613463677', 'target-286629533', 'target-637679438', 'target-327721356', 'target-777617410']\nFalse True False False\n\nstderr:\n"
},
{
"turn": 5,
"response": {
"id": "chatcmpl-f48fda5c",
"choices": [
{
"index": 0,
"message": {
"content": null,
"tool_calls": [
{
"index": 0,
"id": "call_d706012d4cd649b380ae136c",
"type": "function",
"function": {
"name": "run_shell",
"arguments": "{\"command\":\"cd . && cat > /tmp/in2.json <<'EOF'\\n{\\\"own\\\":{\\\"lat\\\":0,\\\"lon\\\":0,\\\"cog\\\":0,\\\"sog\\\":10},\\n\\\"targets\\\":[{\\\"mmsi\\\":1,\\\"lat\\\":0.1,\\\"lon\\\":0,\\\"cog\\\":180,\\\"sog\\\":10},\\n{\\\"mmsi\\\":2,\\\"lat\\\":null,\\\"lon\\\":0,\\\"cog\\\":0,\\\"sog\\\":5},\\n{\\\"mmsi\\\":3,\\\"lat\\\":0.2,\\\"lon\\\":0.2,\\\"cog\\\":0,\\\"sog\\\":0}],\\n\\\"cpa_danger_nm\\\":0.5,\\\"cpa_caution_nm\\\":1.0,\\\"tcpa_max_min\\\":30}\\nEOF\\npython3 solve.py /tmp/in2.json /tmp/out2.json && cat /tmp/out2.json\"}"
}
}
],
"role": "assistant",
"reasoning_content": "The \"http\" appears in xmlns=\"http://www.w3.org/2000/svg\" which is fine (namespace, not external asset). That's required for SVG. Good.\n\nLet me verify the skipped logic: 660159254 has sog 102.3 \u2192 skipped. 340778672 cog 360 \u2192 skipped. Correct.\n\nEverything looks good. Let me double check the \"http\" concern \u2014 the static rules say no external assets. The xmlns namespace URI is standard and not fetched. Fine.\n\nLet me verify a fresh run works with a different input to be safe."
},
"finish_reason": "tool_calls",
"logprobs": null,
"raw_output": null
}
],
"created": 1793060810,
"model": "deepseek-v4-1-flash",
"service_tier": null,
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"usage": {
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},
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}
},
"cost": {
"usd": 0,
"diem": 0.00078606
},
"venice_parameters": {
"strip_thinking_response": false,
"disable_thinking": false,
"enable_e2ee": true,
"enable_web_search": "off",
"enable_web_scraping": false,
"enable_web_citations": false,
"include_search_results_in_stream": false,
"include_venice_system_prompt": false,
"enable_x_search": false,
"web_search_citations": [],
"return_search_results_as_documents": false
}
}
},
{
"turn": 5,
"tool": "run_shell",
"args": {
"command": "cd . && cat > /tmp/in2.json <<'EOF'\n{\"own\":{\"lat\":0,\"lon\":0,\"cog\":0,\"sog\":10},\n\"targets\":[{\"mmsi\":1,\"lat\":0.1,\"lon\":0,\"cog\":180,\"sog\":10},\n{\"mmsi\":2,\"lat\":null,\"lon\":0,\"cog\":0,\"sog\":5},\n{\"mmsi\":3,\"lat\":0.2,\"lon\":0.2,\"cog\":0,\"sog\":0}],\n\"cpa_danger_nm\":0.5,\"cpa_caution_nm\":1.0,\"tcpa_max_min\":30}\nEOF\npython3 solve.py /tmp/in2.json /tmp/out2.json && cat /tmp/out2.json"
},
"result": "exit=0\nstdout:\n{\n \"targets\": [\n {\n \"mmsi\": 1,\n \"range_nm\": 6.0,\n \"bearing_deg\": 0.0,\n \"cpa_nm\": 0.0,\n \"tcpa_min\": 18.0,\n \"level\": \"danger\"\n },\n {\n \"mmsi\": 3,\n \"range_nm\": 16.971,\n \"bearing_deg\": 45.0,\n \"cpa_nm\": 12.0,\n \"tcpa_min\": 72.0,\n \"level\": \"none\"\n }\n ],\n \"skipped\": [\n 2\n ],\n \"alerts\": [\n 1\n ]\n}\nstderr:\n"
},
{
"turn": 6,
"response": {
"id": "chatcmpl-3a45f8c9",
"choices": [
{
"index": 0,
"message": {
"content": "All checks pass. Summary:\n\n**`solve.py`** (Python 3 stdlib only) implements the spec:\n\n- **Filtering**: skips targets with missing/null `lat`/`lon`/`cog`/`sog`, `sog >= 102.3`, or `cog >= 360`; skipped MMSIs sorted ascending, valid targets kept in input order.\n- **Geometry**: local flat nm frame (`x = \u0394lon\u00b760\u00b7cos(lat)`, `y = \u0394lat\u00b760`), velocity `(sog\u00b7sin(cog), sog\u00b7cos(cog))`, relative `r`/`v`, `TCPA = max(0, -(r\u00b7v)/|v|\u00b2)` (0 if `|v|\u00b2 < 1e-9`), `CPA = |r + v\u00b7TCPA|`, plus `range_nm`, `bearing_deg = atan2(x,y) mod 360`, `tcpa_min = TCPA\u00b760`.\n- **Levels/alerts**: `danger`/`caution`/`none` by CPA and TCPA thresholds; alerts list danger then caution, each sorted by `tcpa_min` then `mmsi`.\n- **Rounding**: range/CPA 3 dp, bearing 1 dp, tcpa_min 2 dp.\n- **`radar.svg`**: static self-contained SVG (root `<svg>`, no scripts/images/links/external assets) with `id=\"own-ship\"` and `id=\"target-<mmsi>\"` for every non-skipped target.\n\nVerified on the provided input (2 skipped: `340778672` cog=360, `660159254` sog=102.3; alerts `[795070736, 613463677, 265918776, 562375419]`) and on a synthetic edge-case input (null field, zero-speed target, head-on collision). SVG parses as valid XML with all required IDs.",
"role": "assistant",
"reasoning_content": "Wait, target 3: sog 0, own sog 10 cog 0. r=(0.2*60, 0.2*60)=(12,12). v = (0,0)-(0,10)=(0,-10). r.v = 12*0+12*(-10)=-120. |v|^2=100. tcpa=1.2h=72min. cpa = |r+v*tcpa| = |(12, 12-12)|=(12,0)=12. Correct.\n\nEverything works. The \"http\" match was from xmlns namespace which is fine (not an external asset). Let me verify no external references \u2014 xmlns is required for SVG. Good.\n\nDone."
},
"finish_reason": "stop",
"logprobs": null,
"raw_output": null
}
],
"created": 1793060812,
"model": "deepseek-v4-1-flash",
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}
},
"cost": {
"usd": 0,
"diem": 0.000958335
},
"venice_parameters": {
"strip_thinking_response": false,
"disable_thinking": false,
"enable_e2ee": true,
"enable_web_search": "off",
"enable_web_scraping": false,
"enable_web_citations": false,
"include_search_results_in_stream": false,
"include_venice_system_prompt": false,
"enable_x_search": false,
"web_search_citations": [],
"return_search_results_as_documents": false
}
}
}
]
}
]