Skip to content

Gemini 4 Argon (effort medium · thinking on) on Life-like automaton from RLE with a Klein-bottle grid and a playable animated page

GGemini 4 Argonmedium · thinking on · Antigravity CLI 1.4.8 (agy -p) · cloud · 2026-10-28 · by @bobsiopener

Trust 80%Fully autonomoussuccess · first try
Estimated cost
$0.11
Wall time
1m
Human step-ins
0
Outcome
Pass

Compare with other models on this task →

Summary

Exact prompt

# Life-like cellular automaton from an RLE file, with a playable page `input.json`: `width`, `height`, `topology`, `rule`, `pattern` (an RLE file name), `offset` [ox, oy], `generations` G, `checkpoints` (list of generation numbers). Write `solve.py` (Python 3 standard library only); `python solve.py input.json answer.json` must write `answer.json` and `life.html` in the working directory. Fresh inputs are generated. ## RLE grammar (full) 1. Lines whose first non-blank character is `#` are comments. Blank lines are ignored. 2. The first non-comment line is the header `x = m, y = n[, rule = ...]`. Ignore its values; the rule always comes from `input.json`. 3. The remaining lines are concatenated with line breaks removed. Everything after the first `!` is ignored, even if it looks like more pattern. 4. Tokens are an optional decimal run count (default 1) followed by a tag: `o` (or any other letter except `b`/`B`) = live cells, `b`/`B` or `.` = dead cells, `$` = end of row. A count on `$` skips that many rows: `3$` moves down 3 rows and resets x to 0. Missing trailing dead cells in a row are implied. 5. Pattern cell (x, y) is placed at grid cell (x+ox, y+oy). For `torus`/`klein`, wrap that position with modulo width/height. For `bounded`, drop cells that fall outside the grid. ## Rule string 6. `Bdigits/Sdigits` (case-insensitive; spaces around `/` allowed) gives the birth and survival neighbour counts. A string with no letters, `digits/digits`, is the legacy **S/B** order: survival first, then birth (`23/3` = B3/S23). Either digit list may be empty. ## Topology (8-neighbour Moore neighbourhood) 7. `bounded`: cells outside the grid are always dead. `torus`: both axes wrap. `klein`: x wraps normally; when a neighbour crosses the top or bottom edge (ny < 0 or ny >= height), its x is mirrored to width-1-x before both axes wrap. A neighbour position that appears more than once counts more than once. (This happens only on tiny grids, never in the tests.) 8. A dead cell with a neighbour count in B is born; a live cell with a count in S survives; all others die. Updates are simultaneous. ## answer.json `{"birth": sorted B, "survive": sorted S, "initial_population": n, "populations": [pop at gen 0..G], "cells_at": {"<g>": [[x, y], ...] for each checkpoint, sorted by y then x}}` ## life.html (checked in a headless browser with network blocked) - A single self-contained file: no external scripts, styles, images, fonts or network calls; under 300 KB. Embedding the initial cells is fine. - It contains `<canvas id="board">` drawing the grid, plus elements `#gen` and `#pop` whose text is the current generation number and live-cell count. - On load it starts animating at least 4 generations per second. - It exposes `window.life` with `reset()` (back to generation 0, stays paused or playing as before), `step()` (exactly one generation, updates #gen/#pop), `play()`, `pause()` and `cells()` (live cells as [[x, y], ...] sorted by y then x). It must use the same rules, topology and placement as above. ## Scoring Hidden checks (partial credit; all must pass; exact): - `rule_and_rle_parse`, `populations_klein`, `checkpoint_cells`; - `life_html_structure`; - `browser_step_exact`: pause, reset, 37 steps; then `cells()`, #gen = `37` and #pop must match; - `browser_autoplay_no_network`: reset, play, wait 2 s; #gen >= 4, #pop = cells().length, no network requests; - `fresh_run_1`, `fresh_run_2` (new pattern, rule and topology; the second also re-runs the browser step check). 40-second limit per run.

Creative follow-up to batch 11, but the animation is checked by stepping it in a headless browser and comparing cell sets against the Python reference. Inspired by https://hnshowcase.com/post/49791296/conway-s-game-of-life-running-from-a-512-byte-x86-boot-secto 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).

Success criteria: 1. rule_and_rle_parse 2. populations_klein 3. checkpoint_cells 4. life_html_structure 5. browser_step_exact 6. browser_autoplay_no_network 7. fresh_run_1 8. fresh_run_2

Setup notes: batch12 case m12-life-rle-animation (offline fixture). Prepared with 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-4-argon-medium --sandbox --dangerously-skip-permissions --disable-slash-commands --output-format stream-json, cwd=workdir. Scored on the box with the case's scorer. Single run, no retries, no human input. Timeout: 10-minute inactivity limit, no total time cap. Visual output (life.html) rendered offline with google-chrome headless (CDP pipe, network blocked): PNG 1280x800 at ~3 s after load; MP4 ~6 s captured at 1280x720, H.264 1280x720. Requested model gemini-4-argon-medium (effort medium); priced at the base model row gemini-4-argon (pricing.json, https://ai.google.dev/gemini-api/docs/pricing). Score: 8/8. Cost is an ESTIMATE: measured tokens x pricing.json list price (Google account, nothing billed per token); output_tokens include thinking tokens. Tool calls 6 (0 failed). Single run.

Estimated cost List-price estimate; not actually billed.

Output

Metrics

Estimated cost
$0.11
Wall time
1m
Human step-ins
0
Turns
6
Model usage
ModelInput tokOutput tokCost
GGemini 4 Argongoogle/gemini-4-argoneffort medium · thinking on70,6225,597$0.11

Reproduce this run

  1. Use the use case prompt on the use case page.
  2. Same setup: Gemini 4 Argon (google/gemini-4-argon, effort medium · thinking on) via Antigravity CLI 1.4.8 (agy -p), cloud.
  3. It counts as a success only if it meets every success criterion: 1. rule_and_rle_parse 2. populations_klein 3. checkpoint_cells 4. life_html_structure 5. browser_step_exact 6. browser_autoplay_no_network 7. fresh_run_1 8. fresh_run_2
  4. 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?

⇄ Rerun on another model

Automated checks

Trust 80% comes from these checks (how Trust % works).

Evidence log
{"event": "init", "conversation_id": "24a3e5c4-0b67-4807-81c7-19b5c3ef2fd6", "init": {"model": "gemini-4-argon-medium", "cwd": "/home/bob/usecase-runs/b2930/out-poll-2026-10-27/agy/gemini-4-argon-medium/m12/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": "24a3e5c4-0b67-4807-81c7-19b5c3ef2fd6", "step_index": 0, "state": "DONE", "step_type": "user_input"}}
{"event": "step_update", "step_update": {"conversation_id": "24a3e5c4-0b67-4807-81c7-19b5c3ef2fd6", "step_index": 1, "state": "DONE", "step_type": "agent_response", "duration_seconds": 4.872430881, "usage": {"input_tokens": 8127, "output_tokens": 62, "thinking_tokens": 0, "cache_read_tokens": 0, "total_tokens": 8189}}}
{"event": "step_update", "step_update": {"conversation_id": "24a3e5c4-0b67-4807-81c7-19b5c3ef2fd6", "step_index": 2, "state": "ACTIVE", "step_type": "tool", "tool_name": "run_command", "tool_info": {"name": "run_command", "parameters": {"CommandLine": "ls -la && cat input.json 2>/dev/null || true"}}}}
{"event": "step_update", "step_update": {"conversation_id": "24a3e5c4-0b67-4807-81c7-19b5c3ef2fd6", "step_index": 2, "state": "DONE", "step_type": "tool", "tool_name": "run_command", "duration_seconds": 1.206112652, "tool_info": {"name": "run_command", "parameters": {"CommandLine": "ls -la && cat input.json 2>/dev/null || true"}, "output": "Command exited with code 0.\n\nCommand output:\ntotal 20\r\ndrwxr-xr-x 2 bob bob 4096 Oct 27 20:35 .\r\ndrwxrwxr-x 3 bob bob 4096 Oct 27 20:39 ..\r\n-rw-r--r-- 1 bob bob 3542 Oct 27 20:35 TASK.md\r\n-rw-r--r-- 1 bob bob  183 Oct 27 20:35 input.json\r\n-rw-r--r-- 1 bob bob  346 Oct 27 20:35 pattern.rle\r\n{\r\n \"width\": 54,\r\n \"height\": 48,\r\n \"topology\": \"klein\",\r\n \"rule\": \"B36/S23\",\r\n \"pattern\": \"pattern.rle\",\r\n \"offset\": [\r\n  44,\r\n  39\r\n ],\r\n \"generations\": 160,\r\n \"checkpoints\": [\r\n  37,\r\n  160\r\n ]\r\n}"}}}
{"event": "step_update", "step_update": {"conversation_id": "24a3e5c4-0b67-4807-81c7-19b5c3ef2fd6", "step_index": 3, "state": "DONE", "step_type": "agent_response", "duration_seconds": 7.479305451, "usage": {"input_tokens": 2442, "output_tokens": 139, "thinking_tokens": 0, "cache_read_tokens": 6073, "total_tokens": 2581}}}
{"event": "step_update", "step_update": {"conversation_id": "24a3e5c4-0b67-4807-81c7-19b5c3ef2fd6", "step_index": 4, "state": "ACTIVE", "step_type": "tool", "tool_name": "read_file", "tool_info": {"name": "read_file", "parameters": {"Path": "/home/bob/usecase-runs/b2930/out-poll-2026-10-27/agy/gemini-4-argon-medium/m12/work/pattern.rle"}}}}
{"event": "step_update", "step_update": {"conversation_id": "24a3e5c4-0b67-4807-81c7-19b5c3ef2fd6", "step_index": 4, "state": "DONE", "step_type": "tool", "tool_name": "read_file", "duration_seconds": 0.0822314, "tool_info": {"name": "read_file", "parameters": {"Path": "/home/bob/usecase-runs/b2930/out-poll-2026-10-27/agy/gemini-4-argon-medium/m12/work/pattern.rle"}, "output": "9 lines, 346 bytes"}}}
{"event": "step_update", "step_update": {"conversation_id": "24a3e5c4-0b67-4807-81c7-19b5c3ef2fd6", "step_index": 5, "state": "ACTIVE", "step_type": "tool", "tool_name": "read_file", "tool_info": {"name": "read_file", "parameters": {"Path": "/home/bob/usecase-runs/b2930/out-poll-2026-10-27/agy/gemini-4-argon-medium/m12/work/TASK.md"}}}}
{"event": "step_update", "step_update": {"conversation_id": "24a3e5c4-0b67-4807-81c7-19b5c3ef2fd6", "step_index": 5, "state": "DONE", "step_type": "tool", "tool_name": "read_file", "duration_seconds": 0.067139447, "tool_info": {"name": "read_file", "parameters": {"Path": "/home/bob/usecase-runs/b2930/out-poll-2026-10-27/agy/gemini-4-argon-medium/m12/work/TASK.md"}, "output": "36 lines, 3542 bytes"}}}
{"event": "step_update", "step_update": {"conversation_id": "24a3e5c4-0b67-4807-81c7-19b5c3ef2fd6", "step_index": 6, "state": "DONE", "step_type": "agent_response", "duration_seconds": 32.920062882, "usage": {"input_tokens": 3161, "output_tokens": 3848, "thinking_tokens": 0, "cache_read_tokens": 7007, "total_tokens": 7009}}}
{"event": "step_update", "step_update": {"conversation_id": "24a3e5c4-0b67-4807-81c7-19b5c3ef2fd6", "step_index": 7, "state": "ACTIVE", "step_type": "tool", "tool_name": "write_file", "tool_info": {"name": "write_file", "parameters": {"Path": "/home/bob/usecase-runs/b2930/out-poll-2026-10-27/agy/gemini-4-argon-medium/m12/work/solve.py"}}}}
{"event": "step_update", "step_update": {"conversation_id": "24a3e5c4-0b67-4807-81c7-19b5c3ef2fd6", "step_index": 7, "state": "DONE", "step_type": "tool", "tool_name": "write_file", "duration_seconds": 1.9091469060000001, "tool_info": {"name": "write_file", "parameters": {"Path": "/home/bob/usecase-runs/b2930/out-poll-2026-10-27/agy/gemini-4-argon-medium/m12/work/solve.py"}}}}
{"event": "step_update", "step_update": {"conversation_id": "24a3e5c4-0b67-4807-81c7-19b5c3ef2fd6", "step_index": 8, "state": "DONE", "step_type": "agent_response", "duration_seconds": 6.175132737, "usage": {"input_tokens": 3927, "output_tokens": 77, "thinking_tokens": 0, "cache_read_tokens": 10169, "total_tokens": 4004}}}
{"event": "step_update", "step_update": {"conversation_id": "24a3e5c4-0b67-4807-81c7-19b5c3ef2fd6", "step_index": 9, "state": "ACTIVE", "step_type": "tool", "tool_name": "run_command", "tool_info": {"name": "run_command", "parameters": {"CommandLine": "python3 solve.py input.json answer.json && ls -lh answer.json life.html && head -n 30 answer.json"}}}}
{"event": "step_update", "step_update": {"conversation_id": "24a3e5c4-0b67-4807-81c7-19b5c3ef2fd6", "step_index": 9, "state": "DONE", "step_type": "tool", "tool_name": "run_command", "duration_seconds": 1.978395823, "tool_info": {"name": "run_command", "parameters": {"CommandLine": "python3 solve.py input.json answer.json && ls -lh answer.json life.html && head -n 30 answer.json"}, "output": "Command exited with code 0.\n\nCommand output:\n-rw-rw-r-- 1 bob bob 6.8K Oct 27 20:40 answer.json\r\n-rw-rw-r-- 1 bob bob 5.8K Oct 27 20:40 life.html\r\n{\r\n  \"birth\": [\r\n    3,\r\n    6\r\n  ],\r\n  \"survive\": [\r\n    2,\r\n    3\r\n  ],\r\n  \"initial_population\": 94,\r\n  \"populations\": [\r\n    94,\r\n    105,\r\n    92,\r\n    114,\r\n    110,\r\n    94,\r\n    96,\r\n    84,\r\n    87,\r\n    88,\r\n    98,\r\n    94,\r\n    92,\r\n    97,\r\n    93,\r\n    104,\r\n    92,\r\n    88,\r\n    93,\r\n"}}}
{"event": "step_update", "step_update": {"conversation_id": "24a3e5c4-0b67-4807-81c7-19b5c3ef2fd6", "step_index": 10, "state": "DONE", "step_type": "agent_response", "duration_seconds": 9.219269945, "usage": {"input_tokens": 2426, "output_tokens": 668, "thinking_tokens": 0, "cache_read_tokens": 12047, "total_tokens": 3094}}}
{"event": "step_update", "step_update": {"conversation_id": "24a3e5c4-0b67-4807-81c7-19b5c3ef2fd6", "step_index": 11, "state": "ACTIVE", "step_type": "tool", "tool_name": "run_command", "tool_info": {"name": "run_command", "parameters": {"CommandLine": "python3 -c '\nimport json, subprocess\n\n# Check rule parser edge cases\nfrom solve import parse_rule\nassert parse_rule(\"B36/S23\") == ([3, 6], [2, 3])\nassert parse_rule(\"b3/s23\") == ([3], [2, 3])\nassert parse_rule(\"B3 / S23\") == ([3], [2, 3])\nassert parse_rule(\"23/3\") == ([3], [2, 3])\nassert parse_rule(\"/3\") == ([3], [])\nassert parse_rule(\"23/\") == ([], [2, 3])\nassert parse_rule(\"B/S\") == ([], [])\nassert parse_rule(\"S23/B3\") == ([3], [2, 3])\nprint(\"Rule tests passed!\")\n' && node -e '\nconst fs = require(\"fs\");\nc\u2026"}}}}
{"event": "step_update", "step_update": {"conversation_id": "24a3e5c4-0b67-4807-81c7-19b5c3ef2fd6", "step_index": 11, "state": "DONE", "step_type": "tool", "tool_name": "run_command", "duration_seconds": 4.45264134, "tool_info": {"name": "run_command", "parameters": {"CommandLine": "python3 -c '\nimport json, subprocess\n\n# Check rule parser edge cases\nfrom solve import parse_rule\nassert parse_rule(\"B36/S23\") == ([3, 6], [2, 3])\nassert parse_rule(\"b3/s23\") == ([3], [2, 3])\nassert parse_rule(\"B3 / S23\") == ([3], [2, 3])\nassert parse_rule(\"23/3\") == ([3], [2, 3])\nassert parse_rule(\"/3\") == ([3], [])\nassert parse_rule(\"23/\") == ([], [2, 3])\nassert parse_rule(\"B/S\") == ([], [])\nassert parse_rule(\"S23/B3\") == ([3], [2, 3])\nprint(\"Rule tests passed!\")\n' && node -e '\nconst fs = require(\"fs\");\nc\u2026"}, "output": "Command exited with code 0.\n\nCommand output:\nRule tests passed!\r\nJS simulation and DOM checks passed!\r\n"}}}
{"event": "step_update", "step_update": {"conversation_id": "24a3e5c4-0b67-4807-81c7-19b5c3ef2fd6", "step_index": 12, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "Implemented [solve.py](file:///home/bob/usecase-"}}
{"event": "step_update", "step_update": {"conversation_id": "24a3e5c4-0b67-4807-81c7-19b5c3ef2fd6", "step_index": 12, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "runs/"}}
{"event": "step_update", "step_update": {"conversation_id": "24a3e5c4-0b67-4807-81c7-19b5c3ef2fd6", "step_index": 12, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "b2930/out-poll-2026-10-27/agy/gemini"}}
{"event": "step_update", "step_update": {"conversation_id": "24a3e5c4-0b67-4807-81c7-19b5c3ef2fd6", "step_index": 12, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "-4-argon-medium/m12/work/solve.py) a"}}
{"event": "step_update", "step_update": {"conversation_id": "24a3e5c4-0b67-4807-81c7-19b5c3ef2fd6", "step_index": 12, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "nd generated [answer.jso"}}
{"event": "step_update", "step_update": {"conversation_id": "24a3e5c4-0b67-4807-81c7-19b5c3ef2fd6", "step_index": 12, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "n](file:///"}}
{"event": "step_update", "step_update": {"conversation_id": "24a3e5c4-0b67-4807-81c7-19b5c3ef2fd6", "step_index": 12, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "home/bob/usecase-runs/b2930/out-poll-2026-"}}
{"event": "step_update", "step_update": {"conversation_id": "24a3e5c4-0b67-4807-81c7-19b5c3ef2fd6", "step_index": 12, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "10-27/agy/gemini-4-argon-medium/m12/work/answer."}}
{"event": "step_update", "step_update": {"conversation_id": "24a3e5c4-0b67-4807-81c7-19b5c3ef2fd6", "step_index": 12, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "json) and [life.html](file:///home/bob/usecase-r"}}
{"event": "step_update", "step_update": {"conversation_id": "24a3e5c4-0b67-4807-81c7-19b5c3ef2fd6", "step_index": 12, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "uns/b2930"}}
{"event": "step_update", "step_update": {"conversation_id": "24a3e5c4-0b67-4807-81c7-19b5c3ef2fd6", "step_index": 12, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "/out-poll-2026-10-27/agy/gemini-4-argon-"}}
{"event": "step_update", "step_update": {"conversation_id": "24a3e5c4-0b67-4807-81c7-19b5c3ef2fd6", "step_index": 12, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "medium/m12/work/life.html).\n\n### Sum"}}
{"event": "step_update", "step_update": {"conversation_id": "24a3e5c4-0b67-4807-81c7-19b5c3ef2fd6", "step_index": 12, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "mary\n- **[`p"}}
{"event": "step_update", "step_update": {"conversation_id": "24a3e5c4-0b67-4807-81c7-19b5c3ef2fd6", "step_index": 12, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "arse_rule`](file:///home"}}
{"event": "step_update", "step_update": {"conversation_id": "24a3e5c4-0b67-4807-81c7-19b5c3ef2fd6", "step_index": 12, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "/bob/usecase"}}
{"event": "step_update", "step_update": {"conversation_id": "24a3e5c4-0b67-4807-81c7-19b5c3ef2fd6", "step_index": 12, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "-runs/b2930/out-poll-2026-10-27/"}}
{"event": "step_update", "step_update": {"conversation_id": "24a3e5c4-0b67-4807-81c7-19b5c3ef2fd6", "step_index": 12, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "agy/gemini-4-argon-medium/m12/work/solve.py#L7-L"}}
{"event": "step_update", "step_update": {"conversation_id": "24a3e5c4-0b67-4807-81c7-19b5c3ef2fd6", "step_index": 12, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "29"}}
{"event": "step_update", "step_update": {"conversation_id": "24a3e5c4-0b67-4807-81c7-19b5c3ef2fd6", "step_index": 12, "state": "ACTIVE", "step_type": "agent_response", "text_delta": ")**: Parses both `B.../S...` (case-insensitive, whitespace-t"}}
{"event": "step_update", "step_update": {"conversation_id": "24a3e5c4-0b67-4807-81c7-19b5c3ef2fd6", "step_index": 12, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "olerant) and"}}
{"event": "step_update", "step_update": {"conversation_id": "24a3e5c4-0b67-4807-81c7-19b5c3ef2fd6", "step_index": 12, "state": "ACTIVE", "step_type": "agent_response", "text_delta": " legacy `S/B` ("}}
{"event": "step_update", "step_update": {"conversation_id": "24a3e5c4-0b67-4807-81c7-19b5c3ef2fd6", "step_index": 12, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "`digits/digits`) rule formats into s"}}
{"event": "step_update", "step_update": {"conversation_id": "24a3e5c4-0b67-4807-81c7-19b5c3ef2fd6", "step_index": 12, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "orted birth and survival"}}
{"event": "step_update", "step_update": {"conversation_id": "24a3e5c4-0b67-4807-81c7-19b5c3ef2fd6", "step_index": 12, "state": "ACTIVE", "step_type": "agent_response", "text_delta": " neighbor count lists.\n-"}}
{"event": "step_update", "step_update": {"conversation_id": "24a3e5c4-0b67-4807-81c7-19b5c3ef2fd6", "step_index": 12, "state": "ACTIVE", "step_type": "agent_response", "text_delta": " **[`parse_r"}}
{"event": "step_update", "step_update": {"conversation_id": "24a3e5c4-0b67-4807-81c7-19b5c3ef2fd6", "step_index": 12, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "le`]("}}
{"event": "step_update", "step_update": {"conversation_id": "24a3e5c4-0b67-4807-81c7-19b5c3ef2fd6", "step_index": 12, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "file:///home/bob/usecase-runs/b2930/out-poll-202"}}
{"event": "step_update", "step_update": {"conversation_id": "24a3e5c4-0b67-4807-81c7-19b5c3ef2fd6", "step_index": 12, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "6-10-27/agy/gemini-4-argon-medium/m12/work/"}}
{"event": "step_update", "step_update": {"conversation_id": "24a3e5c4-0b67-4807-81c7-19b5c3ef2fd6", "step_index": 12, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "solve.py#L32-L73)**: Strips `#` comm"}}
{"event": "step_update", "step_update": {"conversation_id": "24a3e5c4-0b67-4807-81c7-19b5c3ef2fd6", "step_index": 12, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "ents and bla"}}
{"event": "step_update", "step_update": {"conversation_id": "24a3e5c4-0b67-4807-81c7-19b5c3ef2fd6", "step_index": 12, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "nk lines, ignores the he"}}
{"event": "step_update", "step_update": {"conversation_id": "24a3e5c4-0b67-4807-81c7-19b5c3ef2fd6", "step_index": 12, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "ader line and"}}
{"event": "step_update", "step_update": {"conversation_id": "24a3e5c4-0b67-4807-81c7-19b5c3ef2fd6", "step_index": 12, "state": "ACTIVE", "step_type": "agent_response", "text_delta": " everything after `!`, decodes run-length tokens"}}
{"event": "step_update", "step_update": {"conversation_id": "24a3e5c4-0b67-4807-81c7-19b5c3ef2fd6", "step_index": 12, "state": "ACTIVE", "step_type": "agent_response", "text_delta": " (`b`/`B`/`."}}
{"event": "step_update", "step_update": {"conversation_id": "24a3e5c4-0b67-4807-81c7-19b5c3ef2fd6", "step_index": 12, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "` dead, other letters li"}}
{"event": "step_update", "step_update": {"conversation_id": "24a3e5c4-0b67-4807-81c7-19b5c3ef2fd6", "step_index": 12, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "ve, `$` row "}}
{"event": "step_update", "step_update": {"conversation_id": "24a3e5c4-0b67-4807-81c7-19b5c3ef2fd6", "step_index": 12, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "skips),"}}
{"event": "step_update", "step_update": {"conversation_id": "24a3e5c4-0b67-4807-81c7-19b5c3ef2fd6", "step_index": 12, "state": "ACTIVE", "step_type": "agent_response", "text_delta": " and applies `[ox, oy]` offset placement accordi"}}
{"event": "step_update", "step_update": {"conversation_id": "24a3e5c4-0b67-4807-81c7-19b5c3ef2fd6", "step_index": 12, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "ng to the grid topology."}}
{"event": "step_update", "step_update": {"conversation_id": "24a3e5c4-0b67-4807-81c7-19b5c3ef2fd6", "step_index": 12, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "\n- **[`build_neighbors`]"}}
{"event": "step_update", "step_update": {"conversation_id": "24a3e5c4-0b67-4807-81c7-19b5c3ef2fd6", "step_index": 12, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "(file:///hom"}}
{"event": "step_update", "step_update": {"conversation_id": "24a3e5c4-0b67-4807-81c7-19b5c3ef2fd6", "step_index": 12, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "e/bob/"}}
{"event": "step_update", "step_update": {"conversation_id": "24a3e5c4-0b67-4807-81c7-19b5c3ef2fd6", "step_index": 12, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "usecase-runs/b2930/out-poll-2026-10-"}}
{"event": "step_update", "step_update": {"conversation_id": "24a3e5c4-0b67-4807-81c7-19b5c3ef2fd6", "step_index": 12, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "2"}}
{"event": "step_update", "step_update": {"conversation_id": "24a3e5c4-0b67-4807-81c7-19b5c3ef2fd6", "step_index": 12, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "7/agy/gemini-4-argon-medium/m12/work/solve.py#L7"}}
{"event": "step_update", "step_update": {"conversation_id": "24a3e5c4-0b67-4807-81c7-19b5c3ef2fd6", "step_index": 12, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "6-L100) & [`simulate`](file:///home/"}}
{"event": "step_update", "step_update": {"conversation_id": "24a3e5c4-0b67-4807-81c7-19b5c3ef2fd6", "step_index": 12, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "bob/usecase-runs/b2930/"}}
{"event": "step_update", "step_update": {"conversation_id": "24a3e5c4-0b67-4807-81c7-19b5c3ef2fd6", "step_index": 12, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "out-poll-2026-10-27/agy/gemini-4-arg"}}
{"event": "step_update", "step_update": {"conversation_id": "24a3e5c4-0b67-4807-81c7-19b5c3ef2fd6", "step_index": 12, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "on-medium"}}
{"event": "step_update", "step_update": {"conversation_id": "24a3e5c4-0b67-4807-81c7-19b5c3ef2fd6", "step_index": 12, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "/m12/work/solve.py#L112-L144)**: Precomputes 8-n"}}
{"event": "step_update", "step_update": {"conversation_id": "24a3e5c4-0b67-4807-81c7-19b5c3ef2fd6", "step_index": 12, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "eighbor Moore indices"}}
{"event": "step_update", "step_update": {"conversation_id": "24a3e5c4-0b67-4807-81c7-19b5c3ef2fd6", "step_index": 12, "state": "ACTIVE", "step_type": "agent_response", "text_delta": " for `bounded`, `torus`, and `klein` (mirroring `x` to `widt"}}
{"event": "step_update", "step_update": {"conversation_id": "24a3e5c4-0b67-4807-81c7-19b5c3ef2fd6", "step_index": 12, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "h - 1 - x"}}
{"event": "step_update", "step_update": {"conversation_id": "24a3e5c4-0b67-4807-81c7-19b5c3ef2fd6", "step_index": 12, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "` on top/bottom crossings before wrapping) and records per-g"}}
{"event": "step_update", "step_update": {"conversation_id": "24a3e5c4-0b67-4807-81c7-19b5c3ef2fd6", "step_index": 12, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "eneration po"}}
{"event": "step_update", "step_update": {"conversation_id": "24a3e5c4-0b67-4807-81c7-19b5c3ef2fd6", "step_index": 12, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "pulations and sorted che"}}
{"event": "step_update", "step_update": {"conversation_id": "24a3e5c4-0b67-4807-81c7-19b5c3ef2fd6", "step_index": 12, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "ckpoint coor"}}
{"event": "step_update", "step_update": {"conversation_id": "24a3e5c4-0b67-4807-81c7-19b5c3ef2fd6", "step_index": 12, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "dinates.\n- **[`generate_"}}
{"event": "step_update", "step_update": {"conversation_id": "24a3e5c4-0b67-4807-81c7-19b5c3ef2fd6", "step_index": 12, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "html"}}
{"event": "step_update", "step_update": {"conversation_id": "24a3e5c4-0b67-4807-81c7-19b5c3ef2fd6", "step_index": 12, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "`](file:///home/bob/usecase-runs/b2930/out-poll-"}}
{"event": "step_update", "step_update": {"conversation_id": "24a3e5c4-0b67-4807-81c7-19b5c3ef2fd6", "step_index": 12, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "20"}}
{"event": "step_update", "step_update": {"conversation_id": "24a3e5c4-0b67-4807-81c7-19b5c3ef2fd6", "step_index": 12, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "26-10-27/agy/gemini-4-argon-medium/m12/work"}}
{"event": "step_update", "step_update": {"conversation_id": "24a3e5c4-0b67-4807-81c7-19b5c3ef2fd6", "step_index": 12, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "/solve.py#L147-L343)**: Emits a self-contained ["}}
{"event": "step_update", "step_update": {"conversation_id": "24a3e5c4-0b67-4807-81c7-19b5c3ef2fd6", "step_index": 12, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "life.html](f"}}
{"event": "step_update", "step_update": {"conversation_id": "24a3e5c4-0b67-4807-81c7-19b5c3ef2fd6", "step_index": 12, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "ile:///home/"}}
{"event": "step_update", "step_update": {"conversation_id": "24a3e5c4-0b67-4807-81c7-19b5c3ef2fd6", "step_index": 12, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "bob/usecase-runs/b2930/out-poll-2026"}}
{"event": "step_update", "step_update": {"conversation_id": "24a3e5c4-0b67-4807-81c7-19b5c3ef2fd6", "step_index": 12, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "-10"}}
{"event": "step_update", "step_update": {"conversation_id": "24a3e5c4-0b67-4807-81c7-19b5c3ef2fd6", "step_index": 12, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "-27/agy/gemini-4-argon-medium/m12/work/life.html"}}
{"event": "step_update", "step_update": {"conversation_id": "24a3e5c4-0b67-4807-81c7-19b5c3ef2fd6", "step_index": 12, "state": "ACTIVE", "step_type": "agent_response", "text_delta": ")"}}
{"event": "step_update", "step_update": {"conversation_id": "24a3e5c4-0b67-4807-81c7-19b5c3ef2fd6", "step_index": 12, "state": "ACTIVE", "step_type": "agent_response", "text_delta": " (~5.8 KB, zero external assets/requ"}}
{"event": "step_update", "step_update": {"conversation_id": "24a3e5c4-0b67-4807-81c7-19b5c3ef2fd6", "step_index": 12, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "ests) with `<canvas id=\""}}
{"event": "step_update", "step_update": {"conversation_id": "24a3e5c4-0b67-4807-81c7-19b5c3ef2fd6", "step_index": 12, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "board\">`, `#"}}
{"event": "step_update", "step_update": {"conversation_id": "24a3e5c4-0b67-4807-81c7-19b5c3ef2fd6", "step_index": 12, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "gen`, `#pop`,"}}
{"event": "step_update", "step_update": {"conversation_id": "24a3e5c4-0b67-4807-81c7-19b5c3ef2fd6", "step_index": 12, "state": "ACTIVE", "step_type": "agent_response", "text_delta": " auto-play at 20 generations/sec on load, and `window.life` (`reset`, `step`, `play`, `pause`, `cells`) matching the Pyt"}}
{"event": "step_update", "step_update": {"conversation_id": "24a3e5c4-0b67-4807-81c7-19b5c3ef2fd6", "step_index": 12, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "hon simulation exactly."}}
{"event": "step_update", "step_update": {"conversation_id": "24a3e5c4-0b67-4807-81c7-19b5c3ef2fd6", "step_index": 12, "state": "DONE", "step_type": "agent_response", "text_delta": "\n", "duration_seconds": 10.579513369, "usage": {"input_tokens": 2819, "output_tokens": 803, "thinking_tokens": 0, "cache_read_tokens": 12424, "total_tokens": 3622}}}
{"event": "result", "result": {"conversation_id": "24a3e5c4-0b67-4807-81c7-19b5c3ef2fd6", "status": "SUCCESS", "response": "Implemented [solve.py](file:///home/bob/usecase-runs/b2930/out-poll-2026-10-27/agy/gemini-4-argon-medium/m12/work/solve.py) and generated [answer.json](file:///home/bob/usecase-runs/b2930/out-poll-2026-10-27/agy/gemini-4-argon-medium/m12/work/answer.json) and [life.html](file:///home/bob/usecase-runs/b2930/out-poll-2026-10-27/agy/gemini-4-argon-medium/m12/work/life.html).\n\n### Summary\n- **[`parse_rule`](file:///home/bob/usecase-runs/b2930/out-poll-2026-10-27/agy/gemini-4-argon-medium/m12/work/solve.py#L7-L29)**: Parses both `B.../S...` (case-insensitive, whitespace-tolerant) and legacy `S/B` (`digits/digits`) rule formats into sorted birth and survival neighbor count lists.\n- **[`parse_rle`](file:///home/bob/usecase-runs/b2930/out-poll-2026-10-27/agy/gemini-4-argon-medium/m12/work/solve.py#L32-L73)**: Strips `#` comments and blank lines, ignores the header line and everything after `!`, decodes run-length tokens (`b`/`B`/`.` dead, other letters live, `$` row skips), and applies `[ox, oy]` offset placement according to the grid topology.\n- **[`build_neighbors`](file:///home/bob/usecase-runs/b2930/out-poll-2026-10-27/agy/gemini-4-argon-medium/m12/work/solve.py#L76-L100) & [`simulate`](file:///home/bob/usecase-runs/b2930/out-poll-2026-10-27/agy/gemini-4-argon-medium/m12/work/solve.py#L112-L144)**: Precomputes 8-neighbor Moore indices for `bounded`, `torus`, and `klein` (mirroring `x` to `width - 1 - x` on top/bottom crossings before wrapping) and records per-generation populations and sorted checkpoint coordinates.\n- **[`generate_html`](file:///home/bob/usecase-runs/b2930/out-poll-2026-10-27/agy/gemini-4-argon-medium/m12/work/solve.py#L147-L343)**: Emits a self-contained [life.html](file:///home/bob/usecase-runs/b2930/out-poll-2026-10-27/agy/gemini-4-argon-medium/m12/work/life.html) (~5.8 KB, zero external assets/requests) with `<canvas id=\"board\">`, `#gen`, `#pop`, auto-play at 20 generations/sec on load, and `window.life` (`reset`, `step`, `play`, `pause`, `cells`) matching the Python simulation exactly.\n", "duration_seconds": 80.493643286, "num_turns": 1, "usage": {"input_tokens": 22902, "output_tokens": 5597, "thinking_tokens": 0, "cache_read_tokens": 47720, "total_tokens": 28499}}}

Get new runs and ranking changes weekly