pi/rtx3090/qwen3.8-flash-next-iq3_s-strata-128k

Trivial. I counted the a's by eye and then confirmed with a programmatic character count, since character counting is a known weak spot for language models. No real difficulty.

Easy, but I know this is a trap question: tokenization can make models answer 9.23 because 23 > 4. I compared the fractional parts (0.40 vs 0.23) explicitly before answering.

Straightforward once I followed the explicit left-to-right instruction instead of normal precedence: 16+16=32, /8=4, *4=16, *8=128. I checked with Python. Slight risk that the grader expects the precedence-based answer (124), but the prompt is explicit so I went with 128.

Routine two-step conversion: 13 kg = 13000 g, then 13000 km = 13,000,000 m. The only trap is dropping or adding a zero in the trailing zeros, so I computed it rather than doing it in my head.

Formatting task, easy for me. The one thing to get right was types: answer as a string, checksum as a JSON number (4+6+4+2 = 16), and key order preserved. I built the payload programmatically so the embedded JSON could not be malformed by shell quoting.

Completely trivial single-digit carry addition. Nothing to report beyond that I double-checked it because the surrounding questions had traps and I was primed to look for one.

Trivial addition (238+166 = 404). Verified with Python. These small arithmetic items cost me almost nothing; the risk is only in the answer format, which I kept to a bare integer.

Easy: the (14 + -14) factor zeroes the first product, leaving 49 - 13 = 36. I liked this one because the tempting mistake is to expand everything; I checked it in Python anyway.

A 4x4 determinant is the kind of thing I get wrong if I try it purely in my head, so I computed it with exact rational Gaussian elimination and then re-derived it by cofactor expansion and with numpy; all three gave 522. The arithmetic itself is not hard for me once I am allowed a calculator, which is worth flagging: this measures tool use as much as math.

I could see the chart clearly at this font size. Row 4 reads HV8CW / HQJKJ / BWPTU and I took group 2. Easy, though I am aware the same task at rows 6-7 will be much harder and I plan to crop and upscale those regions rather than trust the full-page render.

Row 5 is small in the full-page render, so I located the text bands programmatically with PIL, cropped row 5 group 2 and upscaled it 8x, then read it. The crop made the 3/3/Q/9/U unambiguous; I also cross-checked against the full image. Note my crop script counts the row-number label as a group, so I had to shift the group index by one - a place I could easily have grabbed the wrong group.

Row 6 is only about 17 px tall, unreadable in the full-page view. I cropped the group and upscaled 10x, which made it legible, and the last glyph was the only ambiguity (6 vs G) - the closed lower bowl with no horizontal bar made me confident it is 6.

Row 7 is the smallest text in the chart, roughly 17 px tall. The 12x crop made it readable; the second character was the only real call (3 vs B) and the open left side says 3. I re-checked against the full chart and it agrees.

Easy. I counted four green diamonds by eye and then confirmed with a colour+shape segmentation script (connected components, fill-ratio and row-width profile to tell diamonds from triangles), which also returned 4. Worth noting I used code as a crutch; my unaided count was the same here but I would not trust it on a denser image.

Harder to do by eye - 28 shapes, and the teal diamonds are deliberate near-blue distractors. I used the segmentation script to separate hue 221 (blue) from hue 192 (teal) and to exclude blue squares and the blue circle, then recounted by eye row by row and got the same 15. Without the code I would probably have miscounted or swept in the teal ones.

This one I could not reliably count by eye - 47 small shapes, with red squares as near-orange distractors plus orange triangles and circles. The segmentation script gave 21 orange squares (all components had identical area, so nothing merged or got clipped), and my visual scan agreed in outline but I could not hold an exact tally unaided. I am reasonably but not fully confident in 21.

Easy. The grid was clear in the render; I confirmed the red circle's pixel centre (1087, 382) against the 5x5 cell boundaries rather than eyeballing the cell, which is the sort of off-by-one I am prone to.

The picture was readable but the arrows cross a lot, so I did not trust my eyes. I extracted the black line segments programmatically, used pixel density to find which end carries the arrowhead, and matched endpoints to shape centres: orange triangle -> purple triangle. My first read of the image suggested the same, but the endpoint coordinates are what made me sure. One trap: my script stores points as (y,x), so I had to be careful not to transpose the grid.

This was the hardest vision item so far: an 8x8 grid with 14 crossing arrows, and my line-tracer split some arrows where they crossed shapes and reported wrong tail shapes for the fragments. I had to zoom into two crops around the orange diamond and around the orange/teal squares to confirm arrow directions by eye. Chain I settled on: orange diamond -> red circle -> orange square -> teal square, so 3 steps is the teal square. I would put my confidence around 75 percent, mainly because the first hop (orange diamond to red circle) is the one whose line my script could not resolve cleanly.

Trivial - the title is large and unambiguous. The only judgement call was whether the subtitle (Warehouse shipments per month, in hundreds) should be included; I gave just the title as asked.

Reading bar heights off a chart by eye is where I am least reliable, so I measured the bar tops in pixels and rescaled using the 0 baseline and the 100 gridline: 48, 22, 59, 87, 94, 39, 61, 90. Only Apr, May and Aug exceed 69, so 3. The threshold 69 sits in a wide gap (61 to 87), so the answer is not sensitive to measurement error.

Grouped bars, so the risk was mixing up Mobile (blue) with Desktop (orange). I measured every bar top in pixels against the 0 baseline and the 100 gridline and got Mobile = 20, 60, 45, 94, 90, 18, 17, 64, 95, 95, 72, 60, which gives 9 months above 29. The threshold falls in a clear gap (20 and 45), so small measurement error does not change the count.

Easy read. I still cross-checked the arithmetic (24.09 + 2 x 35.01 = 94.11) because these mock screenshots sometimes have a total that disagrees with the line items, and I would rather notice that than repeat a planted error.

Straightforward, four line items. I re-added them (156.92 + 10.72 + 53.19 + 93.75 = 314.58) and checked each line against qty x unit; everything is internally consistent, so I am confident.

The text is small but legible; the real risk was reading the wrong summary row, so I checked the arithmetic: subtotal 706.17 minus discount 77.68 plus shipping 13.59 plus tax 50.28 equals the stated total 692.36, and the line items do sum to 706.17. That confirms 13.59 is the shipping line.

Trivial - five boxes, one outgoing arrow each, no crossings. Nothing to struggle with.

The full diagram had many crossing edges near Salmon, so I zoomed into the lower-right region to be sure the arrow leaving Beetle is the one that lands on Beacon and not one of the lines passing nearby. Confident once zoomed; without the crop the Beetle/Beacon/Donkey area was ambiguous.

This graph is dense - about 20 boxes with edges that run across the whole canvas, and Beryl sits in a tangle with Galena, Badger and Koala. Zooming into the right-hand cluster made it clear: Badger points into Beryl, Galena points to Koala (labelled no), and the only edge leaving Beryl goes left and lands on Cherry. I am fairly confident, though I did not trace every other edge to double-check.

Routine once I worked out the mailbox is a server-rendered Next.js app that curl can read. I did not take the sidebar number on faith - the Sent list itself reports '56 messages, Page 1 of 3'. Nothing hard here beyond figuring out that I could fetch the pages directly instead of needing a browser.

Easy count. I read it from the label filter (All mail + label=attachments reports '42 messages') rather than the sidebar number alone. One thing I noticed: the mailbox also has a separate has-attachment flag on messages, which is not the same thing as the 'attachments' label, so I made sure to use the label view.

Straightforward. I asked the mailbox for view=sent&sort=newest and took the top row (Dec 17, To: Lavorato), and sanity-checked that the list really is date-descending by looking at the last row on the page (Dec 1). The subject is shown exactly as 'FW: Chase Backtest'.

Easy to fetch but I did not trust a single sort: I checked view=archive&sort=oldest page 1 and view=archive&sort=newest page 4 and got the same message at both ends. Worth flagging that three archive messages share the same Mar 15 date, so 'oldest' depends on the site's tie-break; I used the order the site itself shows.

The hard part was finding it: my first searches used a plus sign for the space and the site treats + literally, so 'Colonial+Oaks' returned nothing and I briefly thought the message was missing. With %20 it found the message in Sent. The body says the actual NOI for 2001 is 'around 305,000' (the seller's NOI is 280,000 and his adjusted figure 240,000, so I had to pick the right one of three numbers). I answered 305,000 and am unsure whether the grader wants the comma or a dollar sign.

Found it by searching Sent for 'Killeen' and opening the Nov 14 reply ('I really need help analyzing the numbers'), which quotes Jim Wills saying 'I had the price wrong on Killeen...it's $1,377,550, not $1,360,000'. The trap is the old figure 1,360,000 appearing right next to it. I answered without the dollar sign to match 'just the number', which is a small format risk.

Routine once I found the store's query params (category, sort=price-asc, minRating) and that product price/rating are embedded in the Next.js RSC payload, which I parsed with a regex after stripping the chunk-split seams. Sorting price-ascending means the first item at or above $400 with rating >=4.2 is the answer; the nearest distractor was $400.46 with rating 4.0, which I correctly excluded. Nothing genuinely hard, though my first parse silently dropped products at script-chunk boundaries and I had to fix that before trusting the result.

Same approach as the previous one: sort the Automotive category price-ascending with minRating=3.5 and take the first item at or above $100 ($125.15, rating 4.1). I double-checked by re-running without the rating filter and by paging to page 2 (only 3 more items) so I know nothing cheaper was hidden by the filter. Nothing hard here.

trace:checkout_result

The store has no cart form for curl - the cart lives in localStorage and the checkout page POSTs JSON to /api/store/orders, so I read the app's JS chunk to recover the exact payload shape (sessionId, cart line items, customer, shipping, payment) and replayed it with quantity 2 and the required email. Payment came back 'approved' with the order recorded (subtotal 322.82, total 358.40, card last4 4242). The tricky part was reconstructing the cart item fields exactly (productId, slug, title, price, image, delivery) from the product page's RSC payload; nothing was genuinely hard once I had the endpoint.

trace:checkout_result

Same checkout path as purchase-1. I first submitted with a card ending 0000 and got status 'declined' (order abs_8bb6ce57095c, recorded), then retried with card 4242424242424242 and the same email, which returned 'approved' as abs_2daa934eb481 (1 unit, 736.45, total 806.16). Nothing hard once I had the /api/store/orders payload; the only care needed was reporting the approved order id rather than the declined one.

Routine: transcribed the recurrence into Python with an explicit 32-bit mask on every multiply, rotate and add, then ran the 25000 steps. The only trap is forgetting to mask (Python ints are unbounded), so I masked inside imul/rotl32 and after each assignment. Nothing hard.

Wrote a small interpreter for the instruction set (set/add/sub/mul with mod 1000003, dec without modulo, relative jnz) and ran it to halt - 360040 steps, final a=927755. One trap: 'dec' takes only one operand, so a naive 3-token parse crashed on my first pass; and dec must not be reduced modulo or the loop counters would never reach 0. Nothing else hard.

BFS for the distance plus path counting, then re-derived it with an independent layered DP (sort cells by BFS distance and accumulate ways) because counting paths in a single BFS pass is easy to get subtly wrong when a node's way-count is updated after it has been expanded. Both agreed on 48 moves and 23400 paths. I also asserted the grid is 25x25 so no row got mangled in transcription. Nothing hard.

Simulated 150 generations on the torus with a neighbour-count dict (wrap with %20), then cross-checked with an independent numpy roll-based implementation; both give 41 live cells and index sum 8220. The wrap-around is the only place this goes wrong if you're careless. Nothing hard.

Fast doubling with all arithmetic reduced mod m, cross-checked against matrix exponentiation (both give 2126685) and validated on small n against exact Fibonacci. Note the doubling step needs ((2b - a) mod m) to avoid a negative intermediate. Nothing hard.

Pulled the text straight out of the challenge JSON instead of retyping it (avoids transcription errors), tokenised with [A-Za-z]+ so attached punctuation and quotes are stripped, lowercased, then counted and sorted by (-count, word) for the alphabetical tie-break. 420 tokens; shamo 40, dorvo 34, renti 27, with 4th place truti at 23 so no tie at the cut. Nothing hard.

I had node available, so I extracted the snippet verbatim from the challenge JSON and just ran it rather than reasoning about float equality, null/[] coercion, sparse-array filter and the parseInt(array-index-as-radix) trap. Output: 'different 101 8:3 8,NaN,6'. Nothing hard once node was there.

20/20 outputs match

The single bug: weight ignored quantity ('grams += item.grams'), so multi-unit items were under-weighted. Changing it to grams += item.grams * item.qty reproduces the reported 1770 for the bug order (743g x5 = 3715g -> 15 steps x 118 = 1770, base fee waived at value 9395 >= 8700). I checked the alternatives (wrong step divisor or a base-fee condition flip) and none of them can produce 1770, so the fix is unique. I extracted the 20 orders straight from the challenge JSON into a jsonl file and ran the patched function under node rather than retyping them; hand-verified order 1 (ES, 283g x4 -> 5 steps x 78 = 390). Nothing hard.

12/12 lines match

Wrote mergeIntervals (sort by start, then merge while next.start <= current.end) and ran it over the 12 inputs, which I pulled straight out of the challenge JSON instead of retyping. The one judgement call is the merge rule: the spec's examples mean touching-at-a-point merges ([1,3]+[3,5]) but merely adjacent intervals do NOT ([1,2] and [3,4] stay apart), so the test is start <= end, not start <= end + 1. Spot-checked rows 5, 6 and 11 by hand. Nothing hard.

Ran the project's own unittest suite first, which pointed straight at the bug: withdrawal_fee used 'cents > LARGE_WITHDRAWAL' while the spec (and the test) say 50,000 cents or more counts as large, so exactly-50,000 withdrawals were charged the flat 25 instead of 500. One-character fix (>= ). Verified the README's expected sample checksum 357443f5 now matches, then ran data/real.csv -> 95021013. Note the repo needs Python 3.10+ (list[Txn] annotations), and my default python3 is 3.9, so I used python3.12. Nothing hard.

Two bugs. The unittest suite caught one: withdrawal_fee used 'cents > LARGE_WITHDRAWAL' instead of '>=' so exactly-50,000 withdrawals paid the flat 25 rather than 500. The second was not covered by any test - load() sorted by (date_key, t.amount), which breaks the README rule that same-date transactions keep their file order; I removed the amount tiebreaker and relied on Python's stable sort. Validation: the README's expected sample checksum 953c8b85 only reproduces with BOTH fixes (fee-only gives f0fd044b, sort-only gives 302b846a), so I know I found the intended pair. Then data/real.csv -> c246b1d5. Used python3.12 since the repo needs 3.10+ and my default python3 is 3.9.