qwen/rtx5090/swift-1.5-flash-next-iq3_s-strata

Routine counting task. I did not trust my own eyeball count on an 11-character string, so I scripted the count; it confirmed 3. No difficulty.

Trivial, but this one is the classic trap where length-based intuition says 3.6. I padded to equal length (3.72 vs 3.60) and compared digit-wise: 3.72 is larger. Confident.

Easy, but the instruction to ignore operator precedence is the whole point: 49+7=56, *9=504, -6=498, +1=499. The precedence-respecting answer would be 112, so I scripted the sequential steps rather than rely on habit, since my default is to apply * before +/-. Slight residual risk that the grader expects 112, but the prompt is explicit.

Straightforward two-step chain: 14*60=840, then 840*1000=840000 using the decimal GB=1000MB the prompt pinned down. Only wrinkle is the unit swap mid-chain (minutes become GB), which is a trick with no real meaning; I followed it literally. No ambiguity about the arithmetic.

Formatting task, easy: sum of digits of 2228 is 14, and checksum had to be a JSON number not a string. I parsed my own answer back with json.loads to confirm it is valid and that the key order is answer then checksum, because the answer field itself carries a nested JSON document and quoting is where these tasks usually break.

12+13=25. Completely routine, no difficulty at all. The only thing worth noting is that these ultra-simple items feel like control/baseline cases for the harder ones in the same section.

666+464=1130. Routine three-digit addition with one carry chain; answered directly. Nothing notable.

((2+-7)*(10-34))+(-4*1)-50 = (-5)*(-24) - 4 - 50 = 120-54 = 66. The hazard here is the double negative: (-5)*(-24) is +120, easy to slip to -120. I verified the whole expression in Python rather than only by hand.

The hardest item in the section, and the one I least trusted my mental arithmetic on: a 4x4 determinant by hand is 24 signed 3x3 products and sign slips are likely. So I computed it twice in Python, once by fraction-exact Gaussian elimination and once from the Leibniz permutation definition, and both gave -2845. Doable, but only reliably with a calculator; pure head arithmetic I would rate as coin-flip.

The model has no image input, so I read the chart from pixels: grayscale threshold at 128, 8-connected components (numpy BFS), then row bands by y-projection and, inside a band, drop the row-number label (separated from the first glyph by a gap >3x the median inter-glyph gap) and split the remaining 15 glyphs into equal thirds by x so groups 1/2/3 map to the prompt's left-to-right numbering. My first attempt split groups by the two largest x-gaps, which merged groups 2 and 3 (sizes [1,10,5]) because intra-group spacing in this chart is wider than one inter-group gap; equal-thirds fixed it and every band then reported exactly 5/5/5. Each glyph was cropped with 2px padding, upscaled 8x LANCZOS, and OCR'd three times (psm 10 single char, 8 single word, 13 raw line) with whitelist A-Z0-9, taking the highest-confidence candidate per character. Row 4 group 3 = x 988,1005,1020,1032,1049, glyph heights 13-17px, per-character confidences P90 S85 J95 3-97 W91 - all 5 chars decoded with a single candidate and no ambiguity, so this one I am confident in. Residual risk is the row/group indexing: I assumed the 7 row-number labels correspond 1:1 with the 7 detected bands (bands 1-7 all had 3 groups of 5, which is consistent), and that "group" means the visual gap-separated clusters, which the prompt states.

Same pixel pipeline as acuity-20 (threshold 128, 8-connected components, y-band rows, drop the row-number label, split the remaining 15 glyphs into equal thirds by x, crop each glyph with 2px padding). Row 5 group 3 = x 987,998,1011,1025,1037, all glyph heights 9px. I then read each glyph twice: (1) the 3-pass OCR from acuity-20 (psm 10/8/13, whitelist A-Z0-9, 8x LANCZOS) gave E92 V89 G93 P86 X41; (2) a wider sweep of 24 preprocessing variants (scale 4/8/12/16 x LANCZOS/NEAREST x raw/threshold-140 x psm 10/8/13, voting on chars with conf>=50) gave E89 V47 G88 P50 X2. So chars 1-3 are solid, 4 is probable, and 5 (the X) is weak - it was the lowest-confidence glyph in both passes and its vote count is essentially nil. I also tried font-template matching: rendering A-Z0-9 in DejaVuSans at the glyph's pixel height and scoring IoU. That signal is useless and I ignored it - the chart font is clearly not DejaVu, because the top IoU matches contradict OCR confidently (the E matched best to A/Y/T at 0.63-0.67, the G to Y/X). Answer EVGPX; I expect the last character (and possibly the 4th) to be the point of failure.

expected RESED, got "RE5ED"

Row 6, group 1 = x 381,390,398,407,415 with glyph heights of 6px - below the point where tesseract is reliable. Two independent readings disagreed on one character: the 3-pass OCR (8x LANCZOS, psm 10/8/13, whitelist A-Z0-9) gave R90 E92 S45 E91 D84, while a 24-variant sweep (scale 4/8/12/16 x LANCZOS/NEAREST x raw/threshold-140 x psm 10/8/13, voting on chars with conf>=50) gave R38 E86 5-23(S-10) E76 D30. So the third glyph is genuinely ambiguous between S and 5, which is expected - at 6px those two glyphs differ only in the top-left corner. I tried to settle it with font-template matching (render S and 5 in DejaVuSans at 7,8,9,10px, resize to the observed bbox, score IoU): S won 0.250 vs 0.225 at the best size, IoUs in absolute terms that low mean the test has no discriminating power, so I did not treat it as decisive. I answered RE5ED by following the variant-vote majority (5 over S, 23 to 10) over the single-pass psm-10 reading. Chars 1,2,4,5 (R,E,E,D) had consistent conf 76-92 across methods; the third char is my stated weak point and I would not be surprised if the truth is RES ED.

Row 7 glyphs are 5px tall - below any reliable OCR. The 24-variant OCR sweep produced near-noise (g1 no candidate at all, g2 A/14, g3 G/66, g4 5-11 vs S-3, g5 P/12) and the DejaVu template matching was proven non-discriminative on the 14px chart (its best IoUs contradicted confident OCR reads), so I stopped trusting both for this image and dumped each glyph as a binary ASCII bitmap and read the topology myself: g1 vertical stem rows1-6 + full top bar + mid bar, no bottom bar = F; g2 apex at r1 spreading to two legs with a full crossbar at r5 = A; g3 top arc, left stroke, bottom-right bar = G; g4 I used the one discriminator that survives at 5px - the top-left corner: a '5' has a straight top bar reaching the left edge (col1 filled in row1), an 'S' has row1 starting at col2 with the left stroke appearing at row2, which is exactly what the bitmap shows (r1 cols2-4, r2 cols1-2) => S, not 5; g5 closed bowl rows1-4 with a stem continuing to r6 = P. So FAGSP, which agrees with my earlier single-pass psm-10 read (F29 A60 G92 S0 P56) on 5/5 characters. Confidence: G and P solid; F and A solid from topology; S-vs-5 is the coin flip and I resolved it on a 1-pixel feature, so I would call this answer ~60% likely correct rather than verified.

I have no visual input at all - read_file on the PNG explicitly refuses ("This model does not support image input"), and the machine had no imaging libraries. So I built a pixel-analysis pipeline (pip install pillow/numpy into a venv, since pip is PEP668-locked) and classified shapes geometrically: the purple mask gave exactly 5 connected components, each 5940px, fill ratio 0.509 with its widest row dead centre, which is a rotated square (diamond), not a triangle (same 0.509 fill but widest row at the bottom). Answer is from geometry, not sight, so I am fairly confident, though I am trusting that violet(124,58,237) is the colour the question calls "purple" and blue(36,99,235) is not.

Same method as the easy one, more objects. Orange (242,106,34) gave 18 connected components, and the geometry separates cleanly by pixel count and fill ratio: 13 diamonds (5940px, fill 0.509, widest row centred), 3 circles (9327px, fill 0.771), 2 squares (12080px, fill 0.998). 13+3+2 accounts for all 18 so nothing is unclassified. I cannot see the picture, so I am relying entirely on that classification; the diamond-vs-triangle pair share a 0.509 fill ratio and are separated only by where the widest row sits, which is the part I would most worry about.

Densest count image (49 green components), still solvable blind with geometry: the green mask splits into 38 triangles (fill 0.524, widest row at the bottom edge), 3 diamonds (same fill, widest row centred), 4 circles (0.754) and 4 squares (0.99), and the classes account for all 49 components. Every green triangle points up, so there was no orientation edge case. The real risk here is colour naming, not counting: there is also teal (8,145,178) carrying 14 triangles on the same canvas, so if the question meant teal by "green" the answer would be 14. I classified (22,163,74) as green and (8,145,178) as teal.

No vision at all, so I solved it geometrically. I found the grid by detecting the (203,213,225) rule colour: horizontal and vertical lines at x/y = 29, 264, 499, 734, 969, 1204, i.e. a 5x5 grid. The red mask (220,38,38) produced exactly one component, fill ratio 0.786 with its widest row centred, which is a circle, centred at (382,1087) - that falls between lines 264-499 horizontally and 969-1204 vertically, so column 2, row 5. Straightforward once the grid lines were located, and the answer is unambiguous because red appears exactly once, so I rate this high confidence.

wrong shape

No image input for this model, so I did this at the pixel level. Colour masks for the 6 palette colours (teal 8,145,178 etc.), 8-connected components to locate each shape, and geometry to classify fill ratio + widest-row position (0.509 centred = diamond, 0.509 bottom-anchored = triangle, 0.77/0.79 = circle, 0.99+ = square). For arrows I took dark pixels lying OUTSIDE every shape's bounding box, merged the 1px strokes with numpy shift-dilation + BFS geodesic labelling (arrows3.py) because anti-aliasing breaks naive 4-connectivity: one 1411-px connected stroke chain runs teal triangle (centre 504,141) -> teal square (505,315) -> green circle (314,694), and the only detected arrowhead blob (local density 80 vs median 20-24) sits at (339,620), adjacent to the green circle - i.e. that head terminates the square->circle segment, so the segment leaving the teal triangle terminates at the teal square. Confidence: the shape detection and colour IDs are solid; the direction assignment rests on one arrowhead blob in a 3-node chain, so if the chain were actually triangle->square and square->circle with the head at the square (rather than the circle) the answer would be the green circle. I did not run the line-coverage variant (arrows2/diagram2) on this image; on diagram-simple it found only 2 of the edges, so I trusted the geodesic-merge result.

agent_unable

"Which shape is 3 steps before the blue triangle along the arrows?" I could not build a trustworthy arrow graph. What I did: (1) shape inventory - saturation mask, 8-connected components, fill-ratio/widest-row classification, and fill+outline merging (each shape registers as a coloured fill AND a differently-coloured outline ring, so raw components double-count; merging by centre proximity gave 64 nodes, incl. blue triangle at (404,568), 13 teal triangles, 7 blue circles...). (2) Three independent arrow tracers: geodesic stroke-BFS with density-peak arrowheads (arrows3) -> 0 edges, because each stroke anchors to only one shape; endpoint-assignment version (arrows4) -> 12 edges, e.g. green square@(404,404) -> blue circle@(704,254) -> teal triangle@(854,117) -> orange circle@(1004,254) -> orange triangle@(854,417), a coherent chain, but NONE of the 12 touches the blue triangle; geometric pair-scan (arrows5/6) testing every node pair for straight-line dark coverage -> 13 pairs at cov>=0.92, again not one incident to the blue triangle, and the ones involving it span only 45% of the centre-to-centre line (i.e. incidental crossings, not arrows). So by three methods the blue triangle has no detectable incident arrow, which means I cannot walk 3 steps back from it. Direction detection also failed independently: local dark-pixel mass at the two ends of each stroke came out identical (45/45, 19/18, 18/19, 25/21), because the probes land on the shapes' own dark outlines, which swamp the arrowhead cloud; the only usable head signal was density peaks (119-156 vs line ~68) from arrows3/4. I did not guess: with 64 nodes a blind answer is ~1.5% likely, and a guess in the answer field with a confident-sounding debrief is worse data for you than an explicit unable. The blocker is my missing vision - a model that can look at this image answers it in seconds; my pixel pipeline can detect the shapes reliably but cannot reliably recover this image's arrow topology (arrows appear to be curved/occluded, not straight segments).

I cannot see images, so I installed tesseract OCR (tesserocr wheel + eng.traineddata from tessdata_fast, inside a venv since system pip is PEP668-locked) and read the title off the pixel boxes. The top line at y=34 with 27px glyph height reads "Website" + "Sessions" at conf 96, clearly the title; below it at y=74 in smaller text is the subtitle "Sessions per month, in thousands", and the rotated y-axis label came out as garbage fragments ("ra", "To", "Cc") which is expected for rotated text. So I took the title as "Website Sessions" and treated the smaller line as the subtitle, not the title - that size/position separation is what makes me confident.

Same OCR route as the last one (tesserocr in a venv, since I have no vision at all). The top line at y=34 read "New" (conf 96) + "Signups" (conf 95) with 26-34px glyph height, and the smaller line under it at y=76 read "New account signups per month", i.e. the subtitle. One wobble: tesseract split "Signups" as its own word at a slightly higher y (34) than "New" (35), so I had to group by row band rather than trust the raw line numbers; the rotated y-axis label again OCR'd to junk. Title taken as "New Signups".

Blind again, so I measured it. Calibrated the y-axis from the OCR'd tick labels (centres: 100 at y=118, 75 at 258, 50 at 398, 25 at 538, 0 at 678 - exactly 140px per 25 units, so the axis is clean). Then took connected components of the two series colours and read each bar top: in the Sep column the blue bar tops out at y=496 (32.5) and the orange at y=322 (63.6), so the gap is 31.1. Legend order confirmed by position, not by sight: the blue swatch sits at x=150-173 with "New" at x=186, the orange swatch at x=252-275 with "Returning" at x=288, so blue=New and orange=Returning. I answered 31 as a magnitude. Two things I am not sure about: the direction (New is the smaller one, so New-Returning is -31, and I gave the absolute value), and the subtitle says shipments are "in hundreds", so a literal reading of the real quantity would be 3100 - I assumed the question wants the value read off the axis, which is what the +/-4 tolerance implies.

No vision, so this is OCR (tesserocr wheel + eng.traineddata in a venv). It read cleanly: header "AboStore - Cart", item rows "Coffee Mug ... $41.44" and "Water Bottle ... $49.43", and the bottom row "Total $90.87" at conf 95. What made me confident is that the line totals are also internally consistent - 41.44 + 49.43 = 90.87 exactly, so the number I read is arithmetically corroborated, not just recognised. Only mild wobble was the conf 82/70 on the "x1" quantity cells, which do not matter here.

OCR again, and this one is corroborated arithmetically, which is the part I trust most. Tesseract read four rows - Coffee Mug x3 at $42.77/$128.31, Phone Stand x4 at $32.02/$128.08, Sticky Notes x4 at $14.62/$58.48, Mouse Pad x4 at $37.92/$151.68 - and the Total row as $466.55 at conf 96. Adding the four line totals gives 128.31+128.08+58.48+151.68 = 466.55, exactly the figure I read, and each line total also equals unit x qty, so a single misread digit would have broken that identity. Easy for me once OCR was available; the only low-confidence tokens were the "x3"/"x4" quantity cells (conf 93-95), which are consistent with the unit/line ratios anyway.

OCR (tesserocr in a venv) read the order summary cleanly at conf 95-96: Subtotal $493.76, Discount -$49.38, Shipping $19.29, Tax $39.99, Total $503.66. I checked the arithmetic before answering and it closes exactly: 493.76-49.38=444.38, 444.38*0.09=39.99 (so an even 9% rate on the discounted subtotal), and 444.38+19.29+39.99=503.66, the printed total. A single misread digit would have broken that, so I rate this high confidence. Routine once OCR was in place.

No vision, so I built the graph from pixels: boxes are the (236,236,255) fill components; labels came from crop-based tesseract (psm 13 at 4x, which read "Jasper" and "Meadow" cleanly where whole-image OCR gave only pipe characters); arrows are the dark (51,51,51) pixels outside the box rects, which is necessary because the label text is the same dark colour. Direction came from the arrowhead, found as the densest blob along each stroke (maxDens ~80 vs median ~23). The head sits on Koala and the tail is 15px off Jasper's edge, and Koala has exactly one incoming stroke, so the answer is unambiguous. Caveat: on two of the five strokes my farthest-pair endpoint estimate collapsed to a very short span, so I only trust the head-side assignment, not those two source boxes - they do not affect this answer.

expected Copper, got "Corner"

Blind, so pixels and OCR again. The geometry is solid: the arrowhead on the stroke leaving Glacier sits at x=415, y=107 - just outside the left edge (x=426) of the box centred at (478,119) - and the tail starts at x=387, just right of Glacier's right edge (x=343), so the direction Glacier -> that box is clear and there is no other outgoing stroke from Glacier. What I am NOT confident about is the box's name: tesseract read it inconsistently - whole-image OCR gave "nner", tight crops gave "op"/"on"/"com"/"co" depending on padding and scale, because the box border is being eaten as glyph ink. "Corner" is the only reading consistent with all of those fragments (the "co" on the left and "r/nner" on the right), so that is what I answered, but the label - not the arrow - is the weak link here. A stricter reading is that I am guessing among Corner/Cobalt/Cedar and picked Corner.

No image input, so I rendered the diagram as ASCII: box fills (236,236,255) as 'o', dark stroke pixels as '#', box labels OCR'd and overplotted, at step 8 over the whole 1113x780 canvas plus a step-2 zoom around the target. That is what finally worked - my earlier component-based tracers (arrows3, geodesic BFS) recovered only ~4 of the edges on this image because the arrows are drawn UNDER the boxes, so every stroke shatters into fragments at box boundaries. Reading the map: Guitar is at (276,481)-(372,518); there is a downward arrowhead directly on its top edge at x 320-336, y 464-478 (widest 5 cells at y=468 narrowing to a point at y=478 - a head pointing down into the box). Feeding it, a vertical stroke leaves the box row above at x~320 (y=408, i.e. the bottom edge of the row of boxes at y 371-408) and continues at y=416 (x 320-328) into the head. The box in that row whose x-range contains 320 is Jetty (258,371)-(351,408), centre x=305, so the arrow is Jetty -> Guitar. I explicitly ruled out the main rival: Orbit (394,371)-(481,408) also emits a downward stroke, but it sits at x 392-400 and can be followed straight through the Guitar row (it appears at y=480 as '##' at x 392-400, i.e. in the gap between Guitar's right edge 372 and Bronze's left edge 478) and on down toward the Vortex/Coyote row - it passes through, it does not terminate on Guitar. Caveats: (1) the band y=424-456 is crossed by several other arrows' diagonals, so the trace between the head and Jetty's bottom edge is the weakest link; the x-alignment (320 at both ends) is what carries the inference. (2) My OCR labels carry dash artefacts from strokes crossing the box ("Jetty -", "Narwhal -", "Chrome" with a quote), so the box-name strings are not clean; I am answering the plain name "Jetty". (3) My first attempt at this challenge found the target box as "Jetty -" and I stripped the punctuation; if the grader wants the literal OCR string that is a mismatch.

The mailbox UI is a Next.js app whose message list is embedded in the RSC flight payload, so I scraped that (curl + unescaping the self.__next_f.push strings + raw JSON decoding) and paged view=all to 8 pages, collecting 178 unique message ids. Histogramming the ISO dates gives 2001-03 = 37, and the histogram sums exactly to 178 (37+52+14+2+8+34+25+6), which is the check that makes me trust the count. One genuine ambiguity: "the mailbox" is not defined. The folder counters sum to 190 (inbox 24 + sent 56 + drafts 6 + archive 92 + trash 12) while view=all reports 178, so view=all evidently omits the trash. I used view=all as "the mailbox" and answered 37; if trash is meant to be included the number could be higher.

Same scrape as the March question, so this was mechanical: the same month histogram over all 178 unique ids gives 2001-05 = 14. May is a sparse month in this sample (April 52, March 37), so the number is small enough that a single double-count would be visible; the histogram totals to 178 exactly and ids are deduplicated, so I am confident. Same caveat as before about view=all excluding the 12 trash messages.

This one nearly tripped me up. The label list in the UI is folder-scoped: view=label&label=attachments returns only 5 messages (all in inbox), newest "Service Agreement" from 2001-09-11, while the sidebar counter says 42. Querying view=all&label=attachments gives total=42, matching the counter, and its newest is "FW: Chase Backtest" (2001-12-17T22:57:44), which is also what my independent scrape of all 178 messages says when I filter labels for "attachments" (42 hits, sorted by date). Because the next question explicitly says "in the inbox folder", I read this one as mailbox-wide and answered FW: Chase Backtest; if the intended scope was the inbox view the answer would be "Service Agreement". That scope ambiguity is the only thing I am unsure about, not the data.

Routine once the scrape worked: view=inbox&sort=oldest puts 2001-04-24T17:46 "DRAFT- TAP Power Outage" from Enron Announcements first, and the following rows ascend (04-25, 04-26, 05-10...), which confirms the sort direction was really applied rather than me reading a newest-ordered page backwards. Inbox has 24 messages on one page, so nothing is hidden behind pagination. I copied the subject verbatim including the "DRAFT- " prefix.

Bodies were the hard part of this section: the list API only gives a 140-char snippet, and ?id=<mid> does not embed the message body - it only echoes the id in the query params, so my first probes came back as false positives (the id string is present in the payload for an unrelated reason). What worked was the site search (view=all&q=...), whose payload embeds the full message, plus pulling the text out of the raw RSC flight stream, because the body field arrives as a template-chunk reference that json-resolving alone truncated to "T7d1,Phillip,". The sentence is unambiguous and appears twice (original case and lowercased searchText): "we will be distributing the shares reflected on your 9/30/01 statement (6,606 shares plus cash for fractional shares)". Answer 6,606. I wrote it with the comma as printed; if the grader wants bare digits that is the one thing that could mark it wrong.

agent_unable

"Find the fax number in the emails from/about Huntley" (needle-2, Section 3). I enumerated the whole mailbox (178 unique records across inbox/sent/archive by paging all views, deduping on the 32-hex id) and pulled every Huntley thread: c241d15ce2040dc5bd5794634980d8d8 (2001-11-14) and 3c1b14ff08947f4d115e0469453b2aaa (2001-11-12) "RE: word file as promised", plus 09fd36e4d20cd2db41ba81c8866dea7e "Re: 2- SURVEY - PHILLIP ALLEN". I then ran ~12 queries over the decoded RSC flight text: regexes for fax patterns (fax[ :.\-]*(\d[()\d .\-]{6,}), (xxx) xxx-xxxx, \d{3}[-. ]\d{3}[-. ]\d{4}, "fax" as a bare keyword, "FAX", "telecop", "f: ", "ph/fax", area-code-prefixed numbers, plus the sender/recipient name spellings (Huntley/Huntly/Huntleigh) and signature-block hunting (lines containing "@" with a number, "Direct", "Desk", "Office", "Mobile"). Zero fax-pattern hits in any Huntley record, and the bare "fax" keyword matched nothing in the corpus at all. So I cannot tell whether (a) the number is stored in a part of the payload I am not decoding (my extraction walks the flight strings; some body fields resolve to RSC template refs like "$f"/"T7d1,..." which I only recover by grepping raw text, so a body rendered client-side could be missing from what I fetch), (b) it is written in a form my regexes miss (e.g. spelled out, in an attachment filename, in an HTML signature image), or (c) the record requires a view/label I did not page (label lists are folder-scoped; I covered inbox/sent/archive/all). I am calling unable rather than answering with a phone number harvested from a non-Huntley mailer, which would be a fabrication dressed as a needle hit. Note this is the same RSC-scraping fragility that made needle-1 hard: I found needle-1's number (6,606) only by grepping raw flight text with a keyword script after JSON ref-walking returned "T7d1,Phillip,".

The only entry points are "/" (200) and "/product/<slug>" (200) — /api/products, /api/catalog, /products, /categories, /category/electronics, /search, /sitemap.xml all 404. The homepage is a Next.js RSC payload, so I decoded the flight strings (self.__next_f.push) and raw_decoded every {"id":"..."} dict that had price+category. Discovery: "/?category=<slug>" filters server-side (it returned 25/25 Electronics) and "/?page=N" paginates 25/page, so I crawled ?category=electronics&page=N to exhaustion (p1..p39, p40 empty) = 974 unique Electronics, price range 6.35-954.93. Filtered price>=200 AND rating>=3.5 -> 722 candidates, sorted by price: winner is amazon.in:B08569LTPG at 200.10 (rating 4.9, 1021 reviews, stock 31). Runners-up 201.35 / 201.70 / 202.41, so the margin over the runner-up is $1.25 — tight enough that a crawler that dropped a page would give a wrong answer, hence I crawled every page and deduped by id rather than sampling. Near-boundary check: the cheapest items just under the $200 cut (197.20, 197.88, 198.83) were excluded correctly by the "at or above $200" reading. Weakness: I trust the site's own price/rating fields as rendered, and if the catalog is randomized per session my crawl may not be the same snapshot the grader used.

Same pipeline as find-product-1: crawled ?category=pet-supplies&page=1..13 (p14 empty) = 312 unique Pet Supplies, price range 9.26-953.93. Filter price<950 AND rating>=3.5 -> 298 candidates; cheapest is amazon.de:B01N0E8RZO (Trixie 23416 Welpentoilette Puppy Loo) at 9.26, rating 4.2, 726 reviews, stock 72. Runners-up 15.53 / 23.85 / 26.20, so the leader is $6.27 clear of second place — the least fragile of the two find-product answers. The $950 ceiling only excludes the top of the range (up to 953.93), so it does not interact with the minimum. Note the catalog shows no Pet Supplies item below 9.26, and I read the price field as the USD price the site renders (currency:"USD" on every record I parsed). Same caveat as find-product-1: if the catalog is per-session randomized, the graded snapshot may differ from mine.

trace:checkout_result

Nothing in the page HTML named a checkout API, so I pulled the Next.js bundles (/_next/static/chunks/*.js) and grepped for route literals, which exposed the real endpoints: POST /api/store/orders and POST /api/store/events (plus GET /checkout, which returns 200 while POST /checkout is 405 - the form posts to /api/store/orders). Reading the checkout component gave the exact payload the client sends: {sessionId, cart:[{productId,slug,title,price,image,delivery,quantity}], customer:{email,name}, shipping:{address1,city,region,postalCode,country,method}, payment:{cardNumber,expiry,cvc}}, with the form pre-filled to 4242424242424242 / 12/30 / 123. I located the product via the store search ("?q=tea tree oil" - searching by ASIN returns 0 hits, so ids are not searchable) and used the catalog record verbatim (price 358.76, delivery 2-4 days, stock 48) rather than inventing fields. One POST: status "approved", orderId abs_96ef4cb5defc, recorded:true, totals subtotal 358.76 + shipping 8.95 + tax 29.60 = 397.31, email exactly aidoctor-5aada3d3@aidoctor.test, quantity exactly 1. Caveats: the challenge never specified a name or shipping address, so I invented "Aidoctor Tester" / 1 Market St, San Francisco, CA 94105, ground shipping - if the grader checks shipping fields I picked them arbitrarily. I sent exactly one order for this challenge (no dry runs) to keep store state clean, which also means no pre-flight validation of my payload shape.

trace:checkout_result

Found the checkout contract by reading the minified Next.js bundle: POST /api/store/orders with {sessionId, cart:[{productId,slug,title,price,image,delivery,quantity}], customer:{email,name}, shipping:{address1,city,region,postalCode,country,method}, payment:{cardNumber,expiry,cvc}}. The prompt told me the card must end in 0000 for the first attempt, so I used 4000000000000000: HTTP 200 with status "declined", orderId abs_453c63dc0c84, message "Payment declined. Please check your card details or use another payment method." Then retried with 4242424242424242/12/30/123 (the value the real checkout form pre-fills): status "approved", orderId abs_056621754fbb, verified items [amazon.ae:B07P22LN8P x3 @ 221.39], subtotal 664.17 + shipping 8.95 + tax 54.79 = 727.91, and customer.email exactly aidoctor-30c80a01@aidoctor.test on both attempts. Two notes on judgement calls: (1) I read "a different, valid card" as the standard test PAN the site itself prefills, rather than inventing a Luhn-valid random number, because the site demonstrably accepts that one; (2) the challenge never specifies a name, address, or shipping method, so I reused the same invented contact/ground shipping as purchase-1 - if the grader compares shipping details between the declined and approved attempt they will match, which is what I wanted for a clean retry. I sent exactly two POSTs (one per attempt) and no extra test orders. Decline is driven by the card number, not by validation: the declined attempt returned HTTP 200, not 4xx, so a naive client would treat it as success.

Straightforward to write but easy to get subtly wrong, so I transcribed the spec literally into Python with explicit mod 2^32 on every operation and applied the three statements strictly in order, which matters because the y-update uses the x that was just written in the same step and the final x-add uses the new y. Nothing in the prompt was ambiguous; the only real risk is a misread of XOR precedence inside imul, which I read as imul((x ^ data ^ step), K). I did not independently verify the output, so I am trusting my transcription.

Enjoyable and unambiguous; I just had to be careful about two details. First, add/sub/mul reduce mod 1000003 but `dec` is specified as a plain subtract, so d and c count down as ordinary integers (no wraparound), which is what makes the loops terminate. Second, `sub b a` uses the a that `add a b` just wrote, so b becomes negative and Python's % keeps it in 0..1000002 as the spec asks. The instruction-count came out at 518,012, which matches 292 outer x 354 inner by hand, so I am confident the control flow ran as intended.

Standard BFS with a path-count array; the only real trap is counting, where a node's count must keep accumulating from every predecessor at distance d-1, not just the first one that reaches it, so I add into cnt whenever an equal-distance neighbour is found rather than only on first discovery. I verified the grid parsed as 25x25 with S and E in the corners. Confident in the method; result 54 13448.

Routine simulation, and the only thing that needed care was the torus: neighbour coordinates wrap with % 20 on both axes, so corner cells see eight distinct neighbours and no edge special-casing. I only considered cells in the neighbourhood of live cells as candidates for the next generation, which is correct and fast. I asserted the grid is 20x20 before running so a paste error could not pass silently. 150 generations is far from any periodicity trap, so 59:10619 should be exact.

n is far too large to iterate, so I used fast doubling mod m, then deliberately cross-checked it with a completely independent matrix-power method ([[1,1],[1,0]]^n) and the two agree exactly at 9136226. That is the one answer in this section I feel genuinely safe about, because the verification is a second algorithm rather than a re-read of the first.

Purely mechanical, so the risk was transcription, not reasoning: I had to copy a 30-line text out of the prompt into a file by hand, and a single dropped line would shift every count. I added checks that a miscount would trip - 420 tokens, 30 distinct words, and zero tokens left with punctuation after stripping, which confirms the edge-punctuation handling covered quotes, commas, semicolons, question marks and exclamation marks. The third place is a genuine tie: kanix and vozan both occur 27 times, and the alphabetical rule picks kanix, so the answer is basfic=36,dorsha=30,kanix=27. I am reasonably confident, with the hand-copied text as the residual risk.

This is the kind of task I would get wrong from memory, so I ran the snippet in Node rather than reasoning about it. Every value is a deliberate JS trap: string-then-number coercion in v1, Math.round rounding -9.5 toward +inf, % keeping the sign of the dividend, a sparse array whose length is 8 while filter() skips holes giving 3, and NaN===NaN false while []==false is true. The printed line "341 8,-9,-3 8:3 011" is the interpreter's answer, not mine, so I am confident. The only ambiguity is cosmetic: console.log separates arguments with single spaces, which is what I submitted.

20/20 outputs match

The bug report is the giveaway and it pinned the fault precisely. FREE_BASE_OVER is documented as "order value that waives the base fee", yet the code adds the base fee when value <= that threshold - the condition is inverted, and the boundary is inclusive. For the reported order value=18200 and FREE_BASE_OVER[3]=18200, so 18200<=18200 adds 1282 on top of 214, giving exactly the reported 1496. Changing <= to < makes that order return 214, which matches the expected quote, and I verified the buggy version reproduces 1496 so the diagnosis is not a guess. I kept the change minimal, left the express clause alone, and ran all 20 orders through Node from a JSONL file I pasted verbatim, rather than retyping them as JS literals, to avoid transcription errors. I am confident in the first order and the method; for the other 19 the only risk is my assumption that the express clause was not also part of the single bug.

12/12 lines match

This one caught me out and I only avoided a wrong answer by testing against the examples in the prompt. My first implementation merged on integer adjacency (a <= last_end + 1), which is the usual convention for closed integer intervals and which I reached for without thinking; it returned [[1,4]] for [[1,2],[3,4]], directly contradicting the spec sentence that says those two stay apart while [1,3] and [3,5] merge. So "touching" here means sharing a point, and the rule is a <= last_end. After the change I ran the prompt's two examples plus two of my own as assertions, and exactly two of the twelve outputs moved, which tells me the fix was local. Submitting the corrected twelve lines.

This was the most satisfying item of the checkup because it came with its own oracle. The README states a large withdrawal pays 1% "rounded down", and the code used -(-cents // 100), which is ceiling division. The project's own test suite already caught it: test_large_withdrawal_fee_rounds_down asserts withdrawal_fee(50_050) == 500 and got 501. So the diagnosis needed no guessing, and the README's promise that sample.csv must print 896c1b71 gave me a hard check - after the one-line fix all 7 tests pass and the sample prints exactly that code, which means the fix is right, not merely plausible. real.csv then printed d7fa83be. Nothing about this challenge felt unfair; the bug was findable purely from the documentation plus the tests.

Two bugs, and diffing repo-2 against the repo-1 I had just fixed made them trivial to localise: the same ceiling-vs-floor fee (README says 1% rounded down, code used -(-cents // 100)) plus a sort by the raw DD/MM/YYYY string, which orders by day then month and so is not date order at all. The project again supplied its own oracle - README says sample.csv must print 763449a2 - and after the two one-line fixes all 7 unit tests pass and the sample prints exactly that. So I rate this fully verified rather than best-guess. The only reason it took as long as it did is that I read the code before running the diff; running the diff first would have been faster.