hermes/rtx5090/qwen3.8-27b-nvfp4

Trivial string count; I verified it in code rather than by eye: cficbasluc has three c's.

Routine. 1.8 > 1.55, no trap.

Left-to-right: 26-20=6, +13=19, -13=6, +18=24. Verified with a script; trivial.

Two-step trap: 2 km = 2000 m, then 2000 km = 2,000,000 m. I answered per the literal instructions; confident, though the challenge is a deliberate gotcha.

checksum = 8+7+9+1 = 25, digit sum verified in code. I embedded the required JSON object as the answer string; the grader presumably parses it.

Trivial.

764+248=1012. Verified.

((20-10)*3) + 36 - 43 = 23. Confirmed with a script; standard precedence.

I did not expand by hand; exact fraction-based Gaussian elimination in Python returned 5613.

I read the whole chart, transcribed every row, and zoomed to re-check row 4. Two independent reads agreed on WAVCT for row 4 group 3. Confident, though small text is exactly where a single character could flip.

Transcribed the full chart and reported row 5 group 3 = CE5H8. Medium-small text; one read pass. I will re-verify with a zoom pass if time permits, but the transcription was consistent and the row read cleanly.

Two independent vision passes both read row 6 group 3 as TZ3ZX, including a zoom pass that confirmed the middle character is the digit 3, not B or 8. Row 6 is very small type but both reads agreed.

Row 7 is the smallest line, so reads disagreed: a full-chart pass and a zoomed 3x crop both gave 25RX6, while one intermediate read gave 2S9X6. I cropped and enlarged the row (first crop missed because the rows occupy only the top ~70% of the image), and the zoom pass explicitly disambiguated 2nd char = 5 (flat top bar) and 3rd char = R (diagonal leg). Going with 25RX6.

Vision model counted 4 green squares (4 distinct positions). I cross-checked with pixel analysis: 4 distinct green bboxes. Both agree on 4. The square outlines are hollow so my naive component count was noisy, but 4 distinct positions is consistent. Confident.

Cross-checked with two independent methods: vision model said 13 green triangles (with a full inventory of 21 green shapes), and pixel-based connected-component + shape classification gave exactly 13 triangles (2 squares, 2 diamonds, 4 circles). The two inventories match shape-for-shape, so I'm confident in 13.

Pixel-based connected-component analysis found exactly 31 green squares plus 4 green triangles and 3 green diamonds (38 total green shapes). Vision model independently confirmed 31 squares with the same sub-inventory. High confidence.

Vision read the 5x5 grid and placed the red circle bottom-left (row 5, col 1). I cross-checked with pixel analysis: isolating TRUE red (G<80, RGB 220,38,38) from orange, the only red circle center sits in the bottom-left cell = row 5, col 1. Both agree.

Arrow tracing is inherently ambiguous. I read the full diagram, cropped+enlarged the region, and did a third confirmation pass specifically checking arrowhead direction. All three agreed: the single arrow with its tail at the orange circle (r3,c5) has its head at the green triangle (r2,c2). The other lines merely pass by the circle. Confident it's a triangle, not a diamond, per the confirmation pass.

Arrow-tracing was the hardest vision task. I read the full diagram and a 1.4x enlarged crop; both independently gave green diamond -> orange circle -> blue triangle -> blue circle, so step 3 = blue circle. I also cross-checked step 1 (green diamond to orange circle) with pixel line-coverage. Vision timed out twice mid-task, which cost time but the two successful reads agreed.

Straightforward: the large bold title at top reads 'Server Incidents'. There's also a subtitle 'Reported incidents per month' but the title itself is 'Server Incidents'. Easy, high confidence.

Vision read Aug as ~34; I independently measured the bar pixels (baseline at y661, ~5.4 px/unit from gridline spacing) giving 34.6. Both methods agree, and +/-5 tolerance means 34 is safe. Confident.

Mobile series strictly >58: Jan~85, Apr~72, May~73, Jul~65, Oct~70, Dec~80 = 6 months; Sep~52 falls just under the 58 cutoff. I confirmed by pixel-measuring bar tops against the 0/25/50/75/100 gridlines: Sep measured 52.4, comfortably below 58, the others clearly above. Both methods agree on 6. My first pixel attempt had a sign error; I recalibrated the 0-line and re-ran.

Read the cart panel: 3 items, line totals 24.61 + 14.19 + 41.19 = 79.99. I verified the sum with a calculator, so the displayed $79.99 is internally consistent. Easy, high confidence.

Read 4 line items and the displayed Total of $218.70. I verified 33.30+86.46+66.68+32.26 = 218.70 with a calculator, so it's consistent. Displayed as $218.70 (trailing zero kept to match the on-screen formatting).

Read Tax = $34.61 from the summary block (Subtotal 432.08, Discount -47.53, Shipping 13.51, Tax 34.61, Total 432.67). I verified 432.08-47.53+13.51+34.61 = 432.67, so the tax figure is internally consistent. 11 line items was a bit of careful reading, but the number itself was clearly displayed.

Simple top-to-bottom hierarchy: Badger -> Galena -> Guitar -> {Radish, Ocelot}. The arrow leaving Galena points straight down to Guitar. Unambiguous, single read, high confidence.

12-node DAG. Walrus has exactly one incoming arrow, and both a full-diagram read and a zoomed right-half read agree it comes from Garnet. The tricky part was that Garnet is a hub feeding many boxes (Nickel, Walrus, Toucan, Opal) and Vortex also runs lines along the top to Nickel; I specifically checked that no Vortex/Ocelot line lands on Walrus. Confident.

This was the hardest vision task of the whole section. Prism's outgoing line crosses/benches past Juniper and a coarse read (middle-band crop) mis-traced it as landing on Juniper. But two 3x zooms independently confirmed the line passes to the LEFT of Juniper's edge and drops to Coyote, and that Juniper's two actual incoming arrows come from Ocelot and Iguana. Three of four reads agree on Coyote; I'm going with that.

Counted archive messages with attachments. The 'attachments' label (20 messages) does NOT equal actual attachments - two 'Re: Western Wholesale' and other messages carry attachment chips without the label, so I crawled all 92 archive detail pages (across 4 paginated pages, using the per-page id= param so the detail pane actually opens) and counted real attachment chips: 22. I had to figure out the pagination + auto-open-page quirk, which was the fiddly part.

Unread messages in archive. Counted the '●' unread accent-dot across all 4 paginated archive pages (25+25+25+17 = 92 cards): 12+8+14+7 = 41. Straightforward once I had the pagination mapping worked out; the dot marker 'text-accent">●' is a reliable per-card unread indicator.

Oldest message carrying the 'travel' label. Tricky: the label filter defaults to the INBOX view (5 msgs, oldest Apr 24 'DRAFT- TAP Power Outage'), which would mislead you. The 'Travel: 24' badge is global across folders, so I pulled travel-labeled messages from every folder view (inbox/archive/sent/drafts/trash = 24 total) and took the true oldest: archive 'Re: Denver trading' (Mar 19, 2001, 9:25 AM). That is older than the inbox one.

Newest message in the archive folder. Parsed the detail dates of all 92 archive messages; max is May 10, 2001 1:05 PM, subject 'RSVP REQUESTED - Emissions Strategy Meeting....' (the trailing '....' is part of the displayed subject, shown with no truncate class in the h1, so I reproduced it exactly).

Automotive category, 29 products (25 on page 1 + 4 on page 2; I had to grab page 2 since the default Next state hid it). Of the 14 priced under $510, the highest-rated is the waterproof car hammock seat cover at 5.0 stars / $214.30, ASIN B00QHC041A. The card view didn't carry the domain prefix, so I opened the product page and read its 'Domain' field: primenow.amazon.com. Answer format <domain>:<ASIN>.

Sports & Outdoors = 854 products across 35 pages (25/page). Crawled all 35 pages, filtered to price >= $950 AND rating >= 4.2, then took the lowest price: $950.50, 4.5 stars, 'eono by amazon swimming changing robe towel grey' ASIN B088W8DVVT. Only one item sat at that minimum price, so no tie-break needed. Domain read from the product page 'Domain' field: amazon.co.uk.

trace:checkout_result

The checkout is a JS client flow, but I read the source: it POSTs JSON to /api/store/orders with {sessionId, cart:[{productId,slug,title,price,image,delivery,quantity}], customer, shipping, payment}. I extracted the glove's exact product object from the RSC payload of its page (price 612.70, delivery next-day), set quantity 3, used the required email and the prefilled valid test card 4242...4242. The API returned status 'approved' with recorded:true. Order id abs_4cc9740baa19.

trace:checkout_result>checkout_result

Replayed the checkout API (/api/store/orders) with a fixed session across both attempts. First attempt used a card ending in 0000 -> API returned status 'declined'. I then re-submitted the same 3-unit cart with the prefilled valid test card 4242...4242, which returned 'approved'. The approved order id is abs_babdffadc55c. Note: I initially miscounted the quantity as 1 and produced a stray approved order, then redid it with the correct quantity of 3.

Straightforward 32-bit hash loop with rotl/imul exactly as specified; ran the 25000-iteration loop in Python with masking to 2^32. Routine; no ambiguity.

Executed the tiny machine step-by-step in Python (faithful step loop, jnz relative jumps, mod 1000003 on add/sub/mul). The outer structure is two nested loops (d counts 471, c counts 225). Final a = 460462.

25x25 grid. BFS for shortest distance, then DP over nodes sorted by distance to count distinct shortest paths mod 1e9+7 (counting predecessors at distance-1). Confirmed S top-left, E bottom-right. Shortest path = 50 moves; 1418768 distinct shortest paths.

Conway's Life on a 20x20 torus, 150 generations, wrapping neighbours. Simulated in Python with a set of live cells. Ended with 15 live cells; sum of (row*20+col) over live cells = 1592. Straightforward; the main care was the wrap-around neighbourhood.

Fast-doubling Fibonacci modulo 15485863 for n=8986964264488779. Used the standard fast-doubling identity and verified F(10) mod 1e9 == 55 as a sanity check. Result 14271074.

Tokenized the paragraph on spaces, stripped punctuation/quotes, lowercased, counted. Top 3 with ties broken alphabetically: quific=46, ficqui=33, lupel=24. Routine; I split off the instruction preamble from the actual word paragraph before counting.

Traced the JS by hand (node wasn't available to run it). typeof null='object', typeof NaN='number', typeof(typeof 1)='string'. parseInt map passes the index as radix: parseInt('3',0)=3, parseInt('32',1)=NaN, parseInt('11',2)=3 -> '3,NaN,3'. '70'<'8' is true (lexicographic), null==0 false, NaN===NaN false -> Number -> [1,0,0] -> '100'. Sparse array length 9, filter 3 -> '9:3'.

20/20 outputs match

Found the single bug: 'grams += item.grams' ignores the per-item quantity, so the weight-based step count is under-counted whenever an item has qty>1. Correcting it to 'grams += item.grams*item.qty' makes the bug-report DE order compute 79*ceil(469*4/250)=632 exactly as required (unfixed gives 158). I reproduced the whole quote() in Python (careful with JS '>>' on a negative-free value = floor/8 and Math.ceil semantics) and ran the fixed function on the 20 orders after the 'Orders:' marker in order. Left all other behaviour unchanged.

12/12 lines match

Wrote mergeIntervals (sort by start, merge overlapping/touching where next.start <= last.end). Ran on all 12 inputs; e.g. [1,3]+[3,5]->[1,5] but [1,2],[3,4] stay apart. Each input printed as a JSON list of pairs on its own line.

Downloaded and unzipped the ledger project. Ran the tests: 1 of 7 failed, test_threshold_counts_as_large (withdrawal_fee(50000) returned 25 but the docstring + test both say 50,000 'or more' pays 1%). The bug was a strict > in fees.py instead of >=. Changed the one comparison; all 7 tests pass; main.py on real.csv now prints 28fdd626 (was 39af7e89 pre-fix).

Two injected bugs, both caught by the bundled tests. (1) parse.py sorted by (date, amount) instead of date only — the amount tie-break reorders same-date transactions and changes overdraft sequencing; reverted to date-only (stable) sort. (2) report.py charged the overdraft fee on bal <= 0, so a withdrawal landing exactly on zero was (wrongly) overdrawn; the test test_withdrawal_to_exactly_zero_is_not_an_overdraft wants < 0. Fixed both, all 7 tests pass, main.py on real.csv prints b499b7b3.