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Applied Compute - A 35B open-weight model trained to search a precomputed index answers

Applied Compute · Sep 2026 · 0:14 · AI-annotated

Length
0:14
Scenes
2 · 7.4 s avg
Product on screen
0:00
Voiceover
No
00:00:00 / 00:14:45

Applied Compute’s 0:14 launch film runs 2 scenes, about 7.4 s each. The product is on screen from the first frame. There is no explicit call to action. A single cut-free UI animation on a flat off-white canvas, split into a corpus grid and an agent panel, walks through a three-tab code search pipeline at a steady, methodical pace. Its signature moves are the row-by-row red sweep, the blue lines tying grid squares to the results list, and color-coded squares that mirror the live counters.

No filming indicatedEstimated needs: 1 capture group — your screens or recordings
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Applied Compute - A 35B open-weight model trained to search a precomputed index answers, scene by scene

· 2 scenes as text

  1. 01 · Demo · 0:00–0:07

    A split UI screen: a red outline sweeps a 300-square grid row by row, squares turning gray as a counter climbs to 300 and three repo names fill a results list. The tab switches to Index lookup, the grid resets, and a blue arrow points to a turbopuffer search box.

    Techniques: Split screen

    Camera: Static

  2. 02 · Demo · 0:07–0:14

    A flat two-panel interface walks through a code search. Blue lines tie ten highlighted squares in the corpus grid to a search box, the candidate list fills with scores, then the rerank step reorders the list and turns three squares green.

    On-screen text: “one query, ten candidates with filepaths and line ranges”, “ten candidates, relevance still unranked”, “three repositories opened, the other 297 never touched”

    Camera: Static