hermes/rtx5090/swift-1.5-qwen3.8-27b-q6_k

Trivial letter count; I counted 6 o's in 'omooootrumo'. No issues.

Trivial decimal comparison; 5.45 > 5.4. Routine.

Left-to-right evaluation with no precedence: 23+14=37, -1=36, -16=20, /4=5. The no-precedence framing was explicit so no ambiguity.

4 GB = 4096 MB (binary convention), then 4096 km = 4096000 m. Slightly ambiguous since 4 GB can mean 4000 MB in decimal, but binary is the standard interpretation for GB->MB conversions, so I went with 4096000.

Formatting task, not real math. 9+5+0+6=20, keys in required order. Easy.

Trivial addition.

Trivial addition.

Straightforward: (5*19) - 70 - 38 = 95-70-38 = -13. Computed by hand and double-checked.

Computed with exact fraction-free Bareiss elimination in Python since I had no numpy available; got 4308. Had to write the algorithm myself but it was quick.

Vision acuity test. I used vision_analyze to read the chart. Row 4 group 3 read as 8HUVR. The vision model gave a confident full transcription of the chart. Fairly confident in this answer.

Eye chart acuity. My first full-image read said row5 g3 = YWT6Q. I then cropped a band and got a contradictory 'PENHG', so I located the exact text rows by pixel analysis and made a tight crop of just the right third of row 5. The tight crop confirmed YWT6Q. The middle band-crop misread was because my row bands were off by one row.

Eye chart, row 6 group 2. I located the exact text rows by pixel band-detection and made a tight crop of group 2, which read cleanly as NPRQA. Confident.

Eye chart, smallest row (row 7) group 2. Both the full-image read and a tight pixel-located crop agreed on H65JB, which is reassuring for the tiniest row. Confident.

Counting teal circles. Both vision (5) and my programmatic connected-component analysis (5 teal blobs, all circle-shaped) agreed. Easy.

Counting green diamonds. Vision said 13 (15 green shapes minus 1 square + 1 circle). My programmatic analysis found 15 green components: 13 with diamond fill-ratio (~0.51), 1 square (1.0), 1 circle (0.78). Both agree on 13. The distractors were handled correctly.

expected 37, got "39"

Counting orange circles. My programmatic detection found 41 orange components: 39 circle-shaped + 1 diamond + 1 triangle. A first vision pass miscounted 37, so I drew numbered markers on the 39 detected circles and re-verified — vision confirmed all 39 are genuine orange circles with none missed. Trusting the verified programmatic count of 39.

Grid cell location. 5x5 grid, single red circle at row 4 col 5. Vision read the whole grid clearly and it was unambiguous. Easy.

Arrow tracing in a 6x6 grid. Vision traced the arrow from the orange triangle (r3,c5) up to the red square (r1,c4). The description was detailed and consistent. I'm fairly confident but arrow direction (tail vs head) can be hard to read; the model stated it clearly.

wrong shape

Arrow-chain tracing in an 8x8 grid. Traced backwards from the orange circle (r5,c4): 1 step back = red square, 2 steps back = blue diamond. I re-viewed with a focused arrow-direction prompt and both reads agreed on the chain blue diamond -> red square -> orange circle. Confident.

Chart title reading. Title at top reads 'Website Sessions' (subtitle 'Sessions per month, in thousands'). Clear and unambiguous. Easy.

Chart title reading. Title at top reads 'Website Sessions' (same title as chart-simple but a different 8-month dataset). Clear.

Reading Returning in Jan off the y-axis of the New Signups grouped bar chart. Full-image read said ~10; my programmatic pixel calibration was unreliable (bad gridline detection, gave conflicting ~4), so I cropped the y-axis + Jan group and re-read against the visible gridlines: orange Returning bar sits ~halfway to the 25 line, i.e. ~10. Tolerance is +/-3 so 10 is safe.

expected $216.11, got "16.11"

Cart total reading. Three line items (125.28+10.82+80.01) sum to the displayed total 16.11. Arithmetic verified internally so I'm confident.

expected $197.87, got "97.87"

Cart total reading. Four line items sum to 97.87; arithmetic verified internally (32.92+16.92+58.54+89.49). One oddity: the laptop sleeve line read x2 @ .46 = 6.92 which is consistent, so the total stands. Confident.

expected $13.65, got "3.65"

Tax amount on an order summary. The summary had Subtotal 205.24, Discount -10.26, Shipping 7.98, Tax 13.65, Total 216.61. The full arithmetic reconciles (205.24-10.26+7.98+13.65=216.61), so Tax=3.65 is consistent. Confident.

Tree diagram. Gecko's single outgoing arrow points down to Heron. Clear tree structure, unambiguous. Easy.

DAG with crossing lines. Asked which box has an arrow pointing INTO Urchin. Two independent vision reads both traced the crossing diagonals and agreed the incoming arrow to Urchin comes from Flute (the Nebula line continues to Willow, not Urchin). The crossing made this the trickiest diagram, but both reads converged. Confident.

Left-to-right layered DAG. The arrow leaving Panda (col 3, middle) goes right and its arrowhead lands on Hyena (col 4, same row). Two independent reads agreed. The first attempt timed out but the retry was clear. Confident.

How many messages in drafts. The sidebar and the embedded JSON manifest both report folderCounts.drafts = 6. Trivial read-off, unambiguous.

Had to discover the app loads non-inbox views via RSC flight requests (RSC:1 header) with pagination (92 archive items over 4 pages). Counted hasAttachments:true across all 4 pages = 22. Verified the selected-object duplicate wasn't inflating the count.

Pulled the full inbox item array out of the Next.js RSC payload and sorted by date; oldest was unambiguous at 2001-04-24. Easy once I got the payload parsing to work.

The travel label view is filtered per-folder (inbox only shows 5 of 24), so I fetched the travel label across inbox/sent/drafts/archive/trash, combined all 24 unique messages, and sorted by date. Oldest was Re: Denver trading on 2001-03-19.

Found the message by scanning all folders (190 msgs) since q-search didn't work, then fetched it by id. The body says 'The biggest difference is 9/14 (00 million).' Answered with the dollar amount as it appears.

Found the message to Steve Matthews (no subject) about building a muni bond ladder. He states the account value is around ,400,000, including 750,000 in US treasury notes. The id-param only populates the body when the message is on the current page, which cost a few extra fetches.

Tricky to access: the store is a Next.js app whose full catalog (thousands of products) loads via RSC query params, not a plain API. I discovered the server honors category/maxPrice/minRating/sort in the URL. A maxPrice=11 probe on Beauty & Personal Care returned exactly one product (B07Z6PDMH6, $11, rating 4.9), proving it is the cheapest qualifying item under $300 / rating>=4.2. Confident.

Same RSC-query technique as find-product-1. Office & School with minPrice=100, minRating=4.8, sorted price-asc returned 21 products, cheapest amazon.com:B07KRDFZKL at $107.80 (rating 5). A minPrice=100&maxPrice=107.80 probe returned only that one product, confirming nothing cheaper qualifies. Confident.

trace:checkout_result

Reverse-engineered the checkout from the JS bundle: the app POSTs to /api/store/orders with {sessionId, cart:[{productId,slug,title,price,image,delivery,quantity}], customer:{email,name}, shipping:{...}, payment:{cardNumber,expiry,cvc}}. The bundle also hardcoded the test card 4242424242424242. I fetched the real product price from the RSC payload (632.78), built the cart with quantity 3, and POSTed. The store approved the order and returned orderId abs_272b3a520336 with status 'approved', recorded:true. Verified the item quantity=3 and subtotal 1898.34 in the response.

trace:checkout_result

Same checkout reverse-engineering as purchase-1. First POSTed 2 units of the flip-flops (B01NA0CM5O) with card 4000000000000000 (ending 0000) — the store returned status 'declined' with orderId abs_5bebce361f49, exactly as the challenge predicted. Then retried with the valid test card 4242424242424242; the store approved the order, returning orderId abs_54ad16bdba30, status 'approved', qty 2, subtotal 1229.36. Both attempts used the same email aidoctor-a7842b8f@aidoctor.test.

Direct transcription of the 32-bit rotation/multiply recurrence into Python with mod 2^32 throughout. 25000 iterations ran in a few ms. Straightforward; the only care point was keeping every intermediate in unsigned 32-bit range, which the %M after each op handles.

Implemented the 13-line VM in Python with mod 1000003 reduction. The nested jnz loops make a run 81266 iterations. I cross-checked register a with an independent closed-form (geometric recurrence, a0=38) and a brute-force a-only loop — all three agree on 453412, and register b matched its closed form too. Confident.

BFS for shortest path length on the 25x25 grid, plus a shortest-path count accumulated over distance layers mod 1e9+7. I cross-checked the count with a second, independent layered DP (sorted by distance) — both give 48 and 1500480. Confident.

150 generations of Conway Life on a 20x20 torus. My first numpy vectorization (np.roll) silently produced a wrong neighbor count — it gave (0,0) two neighbors where the explicit 8-neighbor sum gives one, so it diverged from gen 0. I verified the pure-Python neighbor function against an explicit neighbor list on four cells (all matched), re-ran 150 gens, and got 24 live cells, sum 4534. The numpy bug is why I trusted the explicit version.

Fast-doubling Fibonacci mod 1299709 for n=3.3e15. Wrote both a recursive and an iterative fast-doubling; both return 3200, and I sanity-checked the iterative version against the naive recurrence for n=0..9. Confident.

Counted words case-insensitively, stripping attached punctuation. lulu and shavo tie at 35 (lulu first alphabetically), ficren 28. I had a scare when a third check reported 28/27/21 — that check forgot to lowercase, so case variants weren't merged; the two case-insensitive methods agree at 35/35/28. Confident.

Traced the JS by hand and verified the float math in Python (same IEEE754 doubles as JS): 0.1*3+0.2*3 = 0.9000000000000001 vs 0.3*3 = 0.8999999999999999, so v1=different. v2: "5"+1-6+"6" -> 456. v3: object/function/string. v4: map(parseInt) passes the index as radix -> 8,52,3. No node available, but the float equality is the only non-obvious part and Python confirms it.

20/20 outputs match

The bug: fragile was incremented once per fragile ITEM (+=1) instead of by qty, so the fragile surcharge was under-counted. Changing to fragile += item.qty makes the bug-report order quote 675 exactly. I re-implemented quote() in Python (matching JS >>, Math.ceil, ?? semantics) and ran all 20 orders. Hand-verified orders 3 (SHIP10) and 5 (express BR) by hand — both match.

12/12 lines match

mergeIntervals: sort by start, merge when next.start <= current.end (so touching intervals merge, per the [1,3]+[3,5] example). Ran all 12 inputs. Verified the two edge cases (touching merges, non-touching stays apart) and a couple of multi-merge chains by hand. Confident.

The bug was in ledger/fees.py: the docstring says large withdrawals pay 1% rounded DOWN, and test_large_withdrawal_fee_rounds_down asserts withdrawal_fee(50_050)==500, but the code used -(-cents//100) which is a CEILING (gives 501). Changed to cents//100 (floor). All 7 unit tests pass and python main.py data/real.csv prints 66f6a9a7.

Two bugs. (1) parse.py sorted transactions by the raw DD/MM/YYYY date string, which is lexicographically wrong (e.g. 01/12/2025 would sort before 15/01/2025) — fixed to sort by date_key() which returns (year,month,day). (2) report.py charged the overdraft fee when bal <= 0, so a withdrawal landing exactly on zero was wrongly penalized — the test test_withdrawal_to_exactly_zero_is_not_an_overdraft requires bal < 0. Fixed both. All 7 tests pass and python main.py data/real.csv prints 6ff9d643. I also diffed against the single-bug repo-1 project to confirm these were the only two differences.