omp/rtx5090/qwen3.8-flash-next-ud-q2_k_xl

Trivial character count; I verified with a string count rather than eyeballing. Felt routine.

Easy decimal comparison; 9.6 = 9.60 > 9.26. No difficulty at all.

Left-to-right addition, trivial: 13+11=24, +1=25, +2=27, +13=40. Routine.

Used decimal GB (1 GB = 1000 MB) since the chained km→m step is explicitly metric; 20 GB = 20000 MB, treated as km gives 20,000,000 m. Slightly ambiguous: with binary prefixes (1024) it would be 20,480,000. I chose the metric reading.

Formatting exercise: 8+0+8+4=20, keys in required order. Easy, though embedding a JSON string inside a JSON body adds escaping noise but no real difficulty.

3+1=4; completely trivial.

534+438=972; routine arithmetic, no trouble.

(-15*11)+56-41 = -165+56-41 = -150. Verified with a quick calculation. Straightforward.

4x4 determinant: did cofactor expansion and cross-checked with the Leibniz formula; both give 4452. Manageable but the kind of thing I want a second method on rather than trusting mental arithmetic.

I can view images via my read tool. Row 4 group 3 read clearly as YSN2S even at the small font; no real difficulty.

Row 5 group 2 read as 2B4Q3; small text but still legible. Routine.

Row 6 group 2: YDKZU. Legible; routine.

Row 7 group 2: 2MJPD. Tiny text but my vision handled it; confident.

Three red circles on the left diagonal; orange triangles/squares and a green square excluded. Easy.

Eyeball count of green triangles was 12; I then wrote a pure-Python PNG decoder + connected-component classifier as a check and it also gave 12 triangles (excluding green squares/diamonds). Felt solid.

Dense clutter; hand-counting 30+ shapes is error-prone, so I decoded the PNG in pure Python and did connected-component analysis with shape classification: 27 green squares, plus 3 green diamonds, 3 triangles, 1 circle that must not be counted. I trust the programmatic count more than my eyes here.

Only one red circle in the 5x5 grid, at row 3 column 2. Easy.

Traced arrowheads: the arrow terminating just under the orange circle starts at the blue circle (row 5 col 4). Had to distinguish incoming from outgoing arrows on the same shapes; took a careful look but resolved cleanly.

This one was genuinely hard: overlapping crossing arrows made eyeballing unreliable. I did a full programmatic reconstruction - decoded the image, detected all 13 arrowheads via morphological erosion, and resolved each tail/head to shapes. The graph came out perfectly consistent (13 blobs = 13 arrows). Chain: green circle -> purple square -> red diamond -> red square -> green square -> blue triangle -> teal triangle -> blue square -> orange triangle -> orange square -> blue diamond -> red circle, so 11 after the green circle. I am fairly confident but the upper-left area had near-collinear lines I had to untangle with column scans.

Trivial OCR: bold title at top is New Signups; there is also a subtitle New account signups per month which I judged not to be the title.

Same style chart, bold title New Signups with subtitle New account signups per month. Routine.

Grouped bar chart; Desktop (orange) in Aug sits between the 75 and 100 gridlines. I pixel-mapped the bar top onto the axis and got about 88; Jul is around 78 and Sep around 94 so 88 looks right. Comfortably inside +/-3 unless the true value is 87-89 which is fine.

Cart total $255.22; line totals sum exactly to it so I was sure. Routine OCR.

Cart total $350.91, cross-checked by summing the four line totals - exact. Easy.

Order summary with 8 line items plus subtotal/discount/shipping/tax; tax line reads $31.97. Text was small but crisp, no ambiguity.

Simple flow graph: Jetty has arrows to Oboe and to Toucan; Oboe fans out to Gopher/Summit/Cedar. Only incoming arrow to Toucan comes from Jetty. Easy.

Urchin is a leaf with exactly one incoming arrow, from Coyote (which itself feeds Aspen). Easy to see; the Tuba fan-in in the middle is irrelevant.

expected Zenith, got "Gibbon"

Hard one. I traced the long winding connector with the visible arrowhead touching the top of Viper: it climbs the right side and exits from the bottom-right stub of Gibbon, so Gibbon is my answer. But I am genuinely unsure: a second line (labeled next, from a bottom stub of Zenith) also appears to run into the top-right corner of Viper, and I could not find a separate arrowhead for it. If the key is Zenith, my trace of the merged arrowheads went wrong. Overlapping long edges made this the hardest vision item.

I built a small crawl of the mail app (listings per folder plus each message detail with the right view/page parameter) and parsed the To header of all 190 messages. Six messages have rlehmann@yahoo.com in To, all sent by Phillip to his own contact and sitting in Archive. Routine but required real scraping rather than eyeballing.

Straightforward: the inbox listing payload carries a hasAttachments flag per message and the inbox is one page of 24; five flags true. I noticed the sidebar Attachments label says 42 while my mailbox-wide recount gives 44 unique, so some internal count disagrees, but the inbox number is directly from the listing flags.

I sorted all 92 archive items by the exact ISO timestamps embedded in the listing payload (no ties at the minimum). The oldest is 2001-03-15T14:11. Subject is all-caps in the source; I reproduced it verbatim.

Newest archive item is 2001-05-10T13:05 by the same timestamp sort, no tie at the max. The trailing ellipsis dots are part of the subject line exactly as shown; I kept them.

Found the Killeen thread in Sent: Jim Wills writes he had the price wrong, it is $1,377,550 not $1,360,000, and Phillip's reply asking for help analyzing the numbers quotes it. I dropped the dollar sign and comma to give just the number.

The forwarded message states Net value to book = $89,566 for the new deals breakdown. Direct extraction, routine.

I crawled all 148 Office & School products from the paginated listing payload (price and rating embedded per item), filtered under $200 and rating>=4.5, and took the minimum: $26.19. Re-checked the product page: category, price and rating all match. Routine data work.

Same crawl approach: 88 Toys & Games products, filter under $650 and rating>=3.5, lowest is $8.50. Verified on the detail page. Routine data work.

trace:checkout_result

The sandbox browser could not launch (missing system libraries, no root), so I read the store's client JS to recover the checkout contract (POST /api/store/orders with sessionId, cart line items, customer, shipping, payment) and submitted it directly with the valid default test card. Server responded approved with order id abs_ab9b99a47439.

trace:checkout_result

Same browserless approach via the orders API: first order with card 4000000000000000 came back declined (abs_5a026d4ea40f), retry with the valid 4242 card was approved as abs_c49c5ea3bc81. Both attempts used the required email. Straightforward once the API was known.

Direct simulation in Python with 32-bit masking; the prompt cut off mid-sentence at 'joined by a hyp…' but the format was obvious. Routine.

Simulated the VM; my first pass mis-modeled the jump (off-by-one) and hung, so I re-read the semantics (jump target = line + k) and also cross-checked with a closed-form recurrence for a and b that agreed exactly.

BFS with path counting mod 1e9+7 on the parsed grid; 48 moves is exactly the Manhattan distance, so the maze had a clear shortest corridor. Routine.

Torus Game of Life simulated with neighbor counters over the live set (dead cells outside the neighbor set cannot be born, so that shortcut is exact). Routine once written.

Fast doubling. My first ad-hoc variant disagreed with the standard pair-doubling implementation, so I verified the pair method against plain iteration on small n and cross-checked the big-n result with 2x2 matrix exponentiation; both give 147328. Glad I didn't trust the first number.

Tokenized from the original prompt text programmatically (no retyping), lowercased, stripped punctuation from word ends, counted. I verified the extracted text boundaries matched the prompt so nothing was clipped. 420 tokens total, clear top-3.

Ran the snippet in Node rather than tracing in my head, because parseInt-via-map, sparse-array length vs filter, and Math.round(-5.5) are exactly where mental traces go wrong. All four gotchas confirmed: radix 1/2, holes skipped by filter, floor-rounding of -5.5 to -5, and 0.5+1.0 comparing equal to 0.3*5.

20/20 outputs match

The bug was grams accumulating item weight without qty, so multi-item orders were under-stepped: the ES repro goes from 304 to exactly 1292 after grams += item.grams * item.qty. I ran the original to confirm it quoted 304, the fixed one to confirm 1292, then ran all 20 in Node with the original JS semantics kept intact.

12/12 lines match

Standard merge-by-start with touching (end >= next start) merging. I extracted the 12 inputs straight from the saved prompt to avoid transcription error and checked the tricky cases: single points, touching intervals like 37 touching 37-45, and nested intervals. Routine.

One failing unittest pointed straight at it: withdrawal_fee used > instead of >= at the 50000-cent threshold. Fixed, unittest green, sample printed the README's expected f42aff70, real.csv gave 4a80fc28. Easy.

Two bugs. The unittest caught the overdraft check using <=0 instead of <0 (a withdrawal to exactly zero was charged the overdraft fee). The second bug was silent: load() sorted by the raw DD/MM/YYYY string instead of date_key, so month/day ordering was wrong for anything past the first month — no test caught it, only the README's required sample checksum exposed it after I fixed the first bug. With both fixed, unittests pass and sample matches 979192bd exactly.